From 75ea2faf9978eece3da83d07f37479c92ed0871b Mon Sep 17 00:00:00 2001 From: Ishaan Datta Date: Sat, 5 Oct 2024 23:19:51 -0700 Subject: [PATCH] uploaded examples again --- .../Notebook Tutorials/1. Introduction.ipynb | 724 +++++++ .../2. TF-TRT Detection.ipynb | 585 ++++++ ... the Tensorflow TensorRT Integration.ipynb | 664 +++++++ .../3. Using Tensorflow 2 through ONNX.ipynb | 1275 ++++++++++++ .../4. Using PyTorch through ONNX.ipynb | 992 ++++++++++ .../5. Understanding TensorRT Runtimes.ipynb | 107 + .../EfficientDet-TensorRT8.ipynb | 665 +++++++ .../Notebook Tutorials/object_counting.ipynb | 210 ++ .../Notebook Tutorials/object_tracking.ipynb | 245 +++ .../Notebook Tutorials/qat-ptq-workflow.ipynb | 1732 ++++++++++++++++ examples/Notebook Tutorials/tutorial.ipynb | 658 +++++++ examples/Nvidia TRT Clean/classify-trt.py | 78 + .../convert_te_onnx_to_trt_onnx.py | 261 +++ examples/Nvidia TRT Clean/helper.py | 111 ++ examples/Nvidia TRT Clean/onnx_helper.py | 89 + examples/Nvidia TRT Clean/trt_classify.py | 49 + examples/Nvidia TRT Clean/trt_engine.py | 20 + .../ONNX Runtime/YOLOv8-ONNXRuntime/README.md | 43 + .../ONNX Runtime/YOLOv8-ONNXRuntime/main.py | 229 +++ .../YOLOv8-OpenCV-ONNX-Python/README.md | 19 + .../YOLOv8-OpenCV-ONNX-Python/main.py | 130 ++ .../README.md | 63 + .../main.py | 338 ++++ examples/Old Example Conversion/README.md | 483 +++++ examples/Old Example Conversion/common.py | 143 ++ .../Old Example Conversion/onnx_export.py | 61 + examples/Old Example Conversion/run_trt.py | 164 ++ examples/Old Example Conversion/yolo.py | 238 +++ examples/ROS2 Yolo Nodes/README.md | 5 + .../efficientdet/config/cam2image.yaml | 11 + .../efficientdet/efficientdet/__init__.py | 0 .../efficientdet/efficientdet_node.py | 61 + .../efficientdet/scripts/__init__.py | 0 .../efficientdet/scripts/infer.py | 106 + .../efficientdet/scripts/utils.py | 281 +++ .../launch/efficientdet_launch.py | 25 + .../ROS2 Yolo Nodes/efficientdet/package.xml | 20 + .../efficientdet/resource/efficientdet | 0 .../ROS2 Yolo Nodes/efficientdet/setup.cfg | 4 + .../ROS2 Yolo Nodes/efficientdet/setup.py | 32 + .../efficientdet/test/test_copyright.py | 23 + .../efficientdet/test/test_flake8.py | 23 + .../efficientdet/test/test_pep257.py | 23 + .../yolov4/config/cam2image.yaml | 11 + .../yolov4/launch/yolov4_launch.py | 25 + examples/ROS2 Yolo Nodes/yolov4/package.xml | 20 + .../ROS2 Yolo Nodes/yolov4/resource/yolov4 | 0 examples/ROS2 Yolo Nodes/yolov4/setup.cfg | 4 + examples/ROS2 Yolo Nodes/yolov4/setup.py | 32 + .../yolov4/test/test_copyright.py | 23 + .../yolov4/test/test_flake8.py | 23 + .../yolov4/test/test_pep257.py | 23 + .../ROS2 Yolo Nodes/yolov4/yolov4/__init__.py | 0 .../yolov4/yolov4/scripts/__init__.py | 0 .../yolov4/yolov4/scripts/infer.py | 90 + .../yolov4/yolov4/scripts/utils.py | 287 +++ .../yolov4/yolov4/yolov4_node.py | 62 + .../yolov5/config/cam2image.yaml | 11 + .../yolov5/launch/yolov5_launch.py | 25 + examples/ROS2 Yolo Nodes/yolov5/package.xml | 20 + .../ROS2 Yolo Nodes/yolov5/resource/yolov5 | 0 examples/ROS2 Yolo Nodes/yolov5/setup.cfg | 4 + examples/ROS2 Yolo Nodes/yolov5/setup.py | 32 + .../yolov5/test/test_copyright.py | 23 + .../yolov5/test/test_flake8.py | 23 + .../yolov5/test/test_pep257.py | 23 + .../ROS2 Yolo Nodes/yolov5/yolov5/__init__.py | 0 .../yolov5/yolov5/scripts/__init__.py | 0 .../yolov5/yolov5/scripts/infer.py | 90 + .../yolov5/yolov5/scripts/utils.py | 287 +++ .../yolov5/yolov5/yolov5_node.py | 62 + examples/Ultralytics Module/autobackend.py | 671 +++++++ examples/Ultralytics Module/exporter.py | 1196 +++++++++++ examples/Ultralytics Module/main.py | 130 ++ examples/Ultralytics Module/model.py | 1129 +++++++++++ examples/Ultralytics Module/predictor.py | 403 ++++ examples/Ultralytics Module/results.py | 1741 +++++++++++++++++ examples/Ultralytics Module/validator.py | 338 ++++ examples/onnx2trt.sh | 18 + 79 files changed, 17816 insertions(+) create mode 100644 examples/Notebook Tutorials/1. Introduction.ipynb create mode 100644 examples/Notebook Tutorials/2. TF-TRT Detection.ipynb create mode 100644 examples/Notebook Tutorials/2. Using the Tensorflow TensorRT Integration.ipynb create mode 100644 examples/Notebook Tutorials/3. Using Tensorflow 2 through ONNX.ipynb create mode 100644 examples/Notebook Tutorials/4. Using PyTorch through ONNX.ipynb create mode 100644 examples/Notebook Tutorials/5. Understanding TensorRT Runtimes.ipynb create mode 100644 examples/Notebook Tutorials/EfficientDet-TensorRT8.ipynb create mode 100644 examples/Notebook Tutorials/object_counting.ipynb create mode 100644 examples/Notebook Tutorials/object_tracking.ipynb create mode 100644 examples/Notebook Tutorials/qat-ptq-workflow.ipynb create mode 100644 examples/Notebook Tutorials/tutorial.ipynb create mode 100644 examples/Nvidia TRT Clean/classify-trt.py create mode 100644 examples/Nvidia TRT Clean/convert_te_onnx_to_trt_onnx.py create mode 100644 examples/Nvidia TRT Clean/helper.py create mode 100644 examples/Nvidia TRT Clean/onnx_helper.py create mode 100644 examples/Nvidia TRT Clean/trt_classify.py create mode 100644 examples/Nvidia TRT Clean/trt_engine.py create mode 100644 examples/ONNX Runtime/YOLOv8-ONNXRuntime/README.md create mode 100644 examples/ONNX Runtime/YOLOv8-ONNXRuntime/main.py create mode 100644 examples/ONNX Runtime/YOLOv8-OpenCV-ONNX-Python/README.md create mode 100644 examples/ONNX Runtime/YOLOv8-OpenCV-ONNX-Python/main.py create mode 100644 examples/ONNX Runtime/YOLOv8-Segmentation-ONNXRuntime-Python/README.md create mode 100644 examples/ONNX Runtime/YOLOv8-Segmentation-ONNXRuntime-Python/main.py create mode 100644 examples/Old Example Conversion/README.md create mode 100644 examples/Old Example Conversion/common.py create mode 100644 examples/Old Example Conversion/onnx_export.py create mode 100644 examples/Old Example Conversion/run_trt.py create mode 100644 examples/Old Example Conversion/yolo.py create mode 100644 examples/ROS2 Yolo Nodes/README.md create mode 100644 examples/ROS2 Yolo Nodes/efficientdet/config/cam2image.yaml create mode 100644 examples/ROS2 Yolo Nodes/efficientdet/efficientdet/__init__.py create mode 100644 examples/ROS2 Yolo Nodes/efficientdet/efficientdet/efficientdet_node.py create mode 100644 examples/ROS2 Yolo Nodes/efficientdet/efficientdet/scripts/__init__.py create mode 100644 examples/ROS2 Yolo Nodes/efficientdet/efficientdet/scripts/infer.py create mode 100644 examples/ROS2 Yolo Nodes/efficientdet/efficientdet/scripts/utils.py create mode 100644 examples/ROS2 Yolo Nodes/efficientdet/launch/efficientdet_launch.py create mode 100644 examples/ROS2 Yolo Nodes/efficientdet/package.xml create mode 100644 examples/ROS2 Yolo Nodes/efficientdet/resource/efficientdet create mode 100644 examples/ROS2 Yolo Nodes/efficientdet/setup.cfg create mode 100644 examples/ROS2 Yolo Nodes/efficientdet/setup.py create mode 100644 examples/ROS2 Yolo Nodes/efficientdet/test/test_copyright.py create mode 100644 examples/ROS2 Yolo Nodes/efficientdet/test/test_flake8.py create mode 100644 examples/ROS2 Yolo Nodes/efficientdet/test/test_pep257.py create mode 100644 examples/ROS2 Yolo Nodes/yolov4/config/cam2image.yaml create mode 100644 examples/ROS2 Yolo Nodes/yolov4/launch/yolov4_launch.py create mode 100644 examples/ROS2 Yolo Nodes/yolov4/package.xml create mode 100644 examples/ROS2 Yolo Nodes/yolov4/resource/yolov4 create mode 100644 examples/ROS2 Yolo Nodes/yolov4/setup.cfg create mode 100644 examples/ROS2 Yolo Nodes/yolov4/setup.py create mode 100644 examples/ROS2 Yolo Nodes/yolov4/test/test_copyright.py create mode 100644 examples/ROS2 Yolo Nodes/yolov4/test/test_flake8.py create mode 100644 examples/ROS2 Yolo Nodes/yolov4/test/test_pep257.py create mode 100644 examples/ROS2 Yolo Nodes/yolov4/yolov4/__init__.py create mode 100644 examples/ROS2 Yolo Nodes/yolov4/yolov4/scripts/__init__.py create mode 100644 examples/ROS2 Yolo Nodes/yolov4/yolov4/scripts/infer.py create mode 100644 examples/ROS2 Yolo Nodes/yolov4/yolov4/scripts/utils.py create mode 100644 examples/ROS2 Yolo Nodes/yolov4/yolov4/yolov4_node.py create mode 100644 examples/ROS2 Yolo Nodes/yolov5/config/cam2image.yaml create mode 100644 examples/ROS2 Yolo Nodes/yolov5/launch/yolov5_launch.py create mode 100644 examples/ROS2 Yolo Nodes/yolov5/package.xml create mode 100644 examples/ROS2 Yolo Nodes/yolov5/resource/yolov5 create mode 100644 examples/ROS2 Yolo Nodes/yolov5/setup.cfg create mode 100644 examples/ROS2 Yolo Nodes/yolov5/setup.py create mode 100644 examples/ROS2 Yolo Nodes/yolov5/test/test_copyright.py create mode 100644 examples/ROS2 Yolo Nodes/yolov5/test/test_flake8.py create mode 100644 examples/ROS2 Yolo Nodes/yolov5/test/test_pep257.py create mode 100644 examples/ROS2 Yolo Nodes/yolov5/yolov5/__init__.py create mode 100644 examples/ROS2 Yolo Nodes/yolov5/yolov5/scripts/__init__.py create mode 100644 examples/ROS2 Yolo Nodes/yolov5/yolov5/scripts/infer.py create mode 100644 examples/ROS2 Yolo Nodes/yolov5/yolov5/scripts/utils.py create mode 100644 examples/ROS2 Yolo Nodes/yolov5/yolov5/yolov5_node.py create mode 100644 examples/Ultralytics Module/autobackend.py create mode 100644 examples/Ultralytics Module/exporter.py create mode 100644 examples/Ultralytics Module/main.py create mode 100644 examples/Ultralytics Module/model.py create mode 100644 examples/Ultralytics Module/predictor.py create mode 100644 examples/Ultralytics Module/results.py create mode 100644 examples/Ultralytics Module/validator.py create mode 100644 examples/onnx2trt.sh diff --git a/examples/Notebook Tutorials/1. Introduction.ipynb b/examples/Notebook Tutorials/1. Introduction.ipynb new file mode 100644 index 0000000..23c5f8f --- /dev/null +++ b/examples/Notebook Tutorials/1. Introduction.ipynb @@ -0,0 +1,724 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Getting Started with TensorRT" + ] + }, + { + "attachments": { + "tensorrt_landscape.png": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAABdoAAALsCAYAAAD09gUAAAAMTWlDQ1BJQ0MgUHJvZmlsZQAASImV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\nAQTsKfA/PrWWJX/i+nsAAAAASUVORK5CYII=\n" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "TensorRT is an SDK for optimizing trained deep learning models to enable high-performance inference. TensorRT contains a deep learning inference __optimizer__ for trained deep learning models and an optimized __runtime__ for execution. After you have trained your deep learning model in a framework of your choice, TensorRT enables you to run it with higher throughput and lower latency. \n", + "\n", + "The TensorRT ecosystem breaks broadly down into two parts:\n", + "


\n", + "![TensorRT Landscape](./images/tensorrt_landscape.png)\n", + "


\n", + "Essentially,\n", + "\n", + "1. The various paths users can follow to convert their models to optimized TensorRT engines\n", + "2. The various runtimes users can target with TensorRT when deploying their optimized TensorRT engines\n", + "\n", + "If you have a model in Tensorflow or PyTorch and want to run inference as efficiently as possible - with low latency, high throughput, and less memory consumption - this guide will help you achieve just that!!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## How Do I Use TensorRT:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "TensorRT is a large and flexible project. It can handle a variety of workflows, and which workflow is best for you will depend on your specific use-case and problem setting. Abstractly, the process for deploying a model from a deep learning framework to TensorRT looks like this:\n", + "\n", + "![TensorRT Workflow](./images/tensorrt_workflow.png)\n", + "\n", + "To help you get there, this guide will help you answer five key questions:\n", + "\n", + "1. __What format should I save my model in?__\n", + "2. __What batch size(s) am I running inference at?__\n", + "3. __What precision am I running inference at?__\n", + "4. __What TensorRT path am I using to convert my model?__\n", + "5. __What runtime am I targeting?__\n", + "\n", + "This guide will walk you broadly through all of these decision points while giving you an overview of your options at each step.\n", + "\n", + "We could talk about these points in isolation, but they are best understood in the context of an actual end-to-end workflow. Let's get started on a simple one here, using a TensorRT API wrapper written for this guide. Once you understand the basic workflow, you can dive into the more in depth notebooks on the TF-TRT and ONNX converters!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Simple TensorRT Demonstration through ONNX:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are several ways of approaching TensorRT conversion and deployment. Here, we will take a pretrained ResNet50 model, convert it to an optimized TensorRT engine, and run it in the TensorRT runtime.\n", + "\n", + "For this simple demonstration we will focus the ONNX path - one of the two main automatic approaches for TensorRT conversion. We will then run the model in the TensorRT Python API using a simplified wrapper written for this guide. Essentially, we will follow this path to convert and deploy our model:\n", + "\n", + "![ONNX Conversion](./images/onnx_onnx.png)\n", + "\n", + "We will follow the five questions above. For a more in depth discussion, the section following this demonstration will cover options available at these steps in more detail." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "__IMPORTANT NOTE:__ Please __shutdown all other notebooks and Tensorflow/PyTorch processes__ before running these steps. TensorRT and Tensorflow/PyTorch can not be loaded into your Python processes at the same time." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 1. What format should I save my model in?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The two main automatic conversion paths for TensorRT require different model formats to successfully convert a model. TF-TRT uses Tensorflow SavedModels, and the ONNX path requires models be saved in ONNX. Here, we will use ONNX.\n", + "\n", + "We are going to use ResNet50 - a basic backbone vision model that can be used for a variety of purposes. For the sake of demonstration, here we will perform classification using a __pretrained ResNet50 ONNX__ model included with the [ONNX model zoo](https://github.com/onnx/models).\n", + "\n", + "We can download a pretrained ResNet50 from the ONNX model zoo and untar it by doing the following:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2021-01-30 00:56:52-- https://s3.amazonaws.com/download.onnx/models/opset_8/resnet50.tar.gz\n", + "Resolving s3.amazonaws.com (s3.amazonaws.com)... 52.217.17.118\n", + "Connecting to s3.amazonaws.com (s3.amazonaws.com)|52.217.17.118|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 101706397 (97M) [binary/octet-stream]\n", + "Saving to: ‘resnet50.tar.gz’\n", + "\n", + "resnet50.tar.gz 100%[===================>] 96.99M 17.3MB/s in 17s \n", + "\n", + "2021-01-30 00:57:10 (5.55 MB/s) - ‘resnet50.tar.gz’ saved [101706397/101706397]\n", + "\n" + ] + } + ], + "source": [ + "!wget https://s3.amazonaws.com/download.onnx/models/opset_8/resnet50.tar.gz -O resnet50.tar.gz\n", + "!tar xzf resnet50.tar.gz" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "See how to export ONNX models that will work with this same trtexec command in the [Tensorflow through ONNX notebook](./3.%20Using%20Tensorflow%202%20through%20ONNX.ipynb), and in the [PyTorch through ONNX notebook](./4.%20Using%20PyTorch%20through%20ONNX.ipynb)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2. Which batch size(s) will I use?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Batch size can have a large effect on the optimizations TensorRT performs on our model. When using ONNX, we need to tell TensorRT what batch size to expect. Additionally, we need to tell TensorRT whether to expect a fixed batch size, or a range of batch sizes.\n", + "\n", + "TensorRT is capable of handling the batch size dynamically if you don’t know until runtime what exact batch size you will need. That said, a fixed batch size allows TensorRT to make additional optimizations. For this example workflow, we use a fixed batch size of 32. \n", + "\n", + "We set the batch size when we save our model (see [the Tensorflow through ONNX notebook](./3.%20Using%20Tensorflow%202%20through%20ONNX.ipynb)), and we tell TensorRT to expect a fixed batch size by setting the _--explicitBatch_ flag in our __trtexec__ command when converting our model below." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "BATCH_SIZE=32" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3. What precision will I use?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Inference typically requires less numeric precision than training. With some care, lower precision can give you faster computation and lower memory consumption without sacrificing any meaningful accuracy. TensorRT supports TF32, FP32, FP16, and INT8 precisions.\n", + "\n", + "FP32 is the default training precision of most frameworks, so we will start by using FP32 for inference here. Let's create a \"dummy\" batch to work with in order to test our model. TensorRT will use the precision of the input batch throughout the rest of the network by default." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "PRECISION = np.float32\n", + "\n", + "dummy_input_batch = np.zeros((BATCH_SIZE, 224, 224, 3), dtype=PRECISION)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 4. What TensorRT path am I using to convert my model?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The ONNX conversion path is one of the most universal and performant paths for automatic TensorRT conversion. It works for Tensorflow, PyTorch, and many other frameworks. There are several tools to help users convert models from ONNX to a TensorRT engine. \n", + "\n", + "One common approach is to use trtexec - a command line tool included with TensorRT that can, among other things, convert ONNX models to TensorRT engines and profile them." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "&&&& RUNNING TensorRT.trtexec # trtexec --onnx=resnet50/model.onnx --saveEngine=resnet_engine_intro.trt --explicitBatch\n", + "[01/30/2021-00:57:12] [I] === Model Options ===\n", + "[01/30/2021-00:57:12] [I] Format: ONNX\n", + "[01/30/2021-00:57:12] [I] Model: resnet50/model.onnx\n", + "[01/30/2021-00:57:12] [I] Output:\n", + "[01/30/2021-00:57:12] [I] === Build Options ===\n", + "[01/30/2021-00:57:12] [I] Max batch: explicit\n", + "[01/30/2021-00:57:12] [I] Workspace: 16 MiB\n", + "[01/30/2021-00:57:12] [I] minTiming: 1\n", + "[01/30/2021-00:57:12] [I] avgTiming: 8\n", + "[01/30/2021-00:57:12] [I] Precision: FP32\n", + "[01/30/2021-00:57:12] [I] Calibration: \n", + "[01/30/2021-00:57:12] [I] Refit: Disabled\n", + "[01/30/2021-00:57:12] [I] Safe mode: Disabled\n", + "[01/30/2021-00:57:12] [I] Save engine: resnet_engine_intro.trt\n", + "[01/30/2021-00:57:12] [I] Load engine: \n", + "[01/30/2021-00:57:12] [I] Builder Cache: Enabled\n", + "[01/30/2021-00:57:12] [I] NVTX verbosity: 0\n", + "[01/30/2021-00:57:12] [I] Tactic sources: Using default tactic sources\n", + "[01/30/2021-00:57:12] [I] Input(s)s format: fp32:CHW\n", + "[01/30/2021-00:57:12] [I] Output(s)s format: fp32:CHW\n", + "[01/30/2021-00:57:12] [I] Input build shapes: model\n", + "[01/30/2021-00:57:12] [I] Input calibration shapes: model\n", + "[01/30/2021-00:57:12] [I] === System Options ===\n", + "[01/30/2021-00:57:12] [I] Device: 0\n", + "[01/30/2021-00:57:12] [I] DLACore: \n", + "[01/30/2021-00:57:12] [I] Plugins:\n", + "[01/30/2021-00:57:12] [I] === Inference Options ===\n", + "[01/30/2021-00:57:12] [I] Batch: Explicit\n", + "[01/30/2021-00:57:12] [I] Input inference shapes: model\n", + "[01/30/2021-00:57:12] [I] Iterations: 10\n", + "[01/30/2021-00:57:12] [I] Duration: 3s (+ 200ms warm up)\n", + "[01/30/2021-00:57:12] [I] Sleep time: 0ms\n", + "[01/30/2021-00:57:12] [I] Streams: 1\n", + "[01/30/2021-00:57:12] [I] ExposeDMA: Disabled\n", + "[01/30/2021-00:57:12] [I] Data transfers: Enabled\n", + "[01/30/2021-00:57:12] [I] Spin-wait: Disabled\n", + "[01/30/2021-00:57:12] [I] Multithreading: Disabled\n", + "[01/30/2021-00:57:12] [I] CUDA Graph: Disabled\n", + "[01/30/2021-00:57:12] [I] Separate profiling: Disabled\n", + "[01/30/2021-00:57:12] [I] Skip inference: Disabled\n", + "[01/30/2021-00:57:12] [I] Inputs:\n", + "[01/30/2021-00:57:12] [I] === Reporting Options ===\n", + "[01/30/2021-00:57:12] [I] Verbose: Disabled\n", + "[01/30/2021-00:57:12] [I] Averages: 10 inferences\n", + "[01/30/2021-00:57:12] [I] Percentile: 99\n", + "[01/30/2021-00:57:12] [I] Dump refittable layers:Disabled\n", + "[01/30/2021-00:57:12] [I] Dump output: Disabled\n", + "[01/30/2021-00:57:12] [I] Profile: Disabled\n", + "[01/30/2021-00:57:12] [I] Export timing to JSON file: \n", + "[01/30/2021-00:57:12] [I] Export output to JSON file: \n", + "[01/30/2021-00:57:12] [I] Export profile to JSON file: \n", + "[01/30/2021-00:57:12] [I] \n", + "[01/30/2021-00:57:13] [I] === Device Information ===\n", + "[01/30/2021-00:57:13] [I] Selected Device: Tesla V100-DGXS-16GB\n", + "[01/30/2021-00:57:13] [I] Compute Capability: 7.0\n", + "[01/30/2021-00:57:13] [I] SMs: 80\n", + "[01/30/2021-00:57:13] [I] Compute Clock Rate: 1.53 GHz\n", + "[01/30/2021-00:57:13] [I] Device Global Memory: 16155 MiB\n", + "[01/30/2021-00:57:13] [I] Shared Memory per SM: 96 KiB\n", + "[01/30/2021-00:57:13] [I] Memory Bus Width: 4096 bits (ECC enabled)\n", + "[01/30/2021-00:57:13] [I] Memory Clock Rate: 0.877 GHz\n", + "[01/30/2021-00:57:13] [I] \n", + "----------------------------------------------------------------\n", + "Input filename: resnet50/model.onnx\n", + "ONNX IR version: 0.0.3\n", + "Opset version: 8\n", + "Producer name: onnx-caffe2\n", + "Producer version: \n", + "Domain: \n", + "Model version: 0\n", + "Doc string: \n", + "----------------------------------------------------------------\n", + "[01/30/2021-00:57:28] [W] [TRT] /workspace/TensorRT/parsers/onnx/onnx2trt_utils.cpp:218: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32.\n", + "[01/30/2021-00:57:33] [I] [TRT] Some tactics do not have sufficient workspace memory to run. Increasing workspace size may increase performance, please check verbose output.\n", + "[01/30/2021-00:58:00] [I] [TRT] Detected 1 inputs and 1 output network tensors.\n", + "[01/30/2021-00:58:01] [I] Engine built in 48.3635 sec.\n", + "[01/30/2021-00:58:01] [I] Starting inference\n", + "[01/30/2021-00:58:04] [I] Warmup completed 0 queries over 200 ms\n", + "[01/30/2021-00:58:04] [I] Timing trace has 0 queries over 3.00755 s\n", + "[01/30/2021-00:58:04] [I] Trace averages of 10 runs:\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11589 ms - Host latency: 2.17926 ms (end to end 4.1571 ms, enqueue 0.555537 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11435 ms - Host latency: 2.17651 ms (end to end 4.15565 ms, enqueue 0.558661 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11344 ms - Host latency: 2.1762 ms (end to end 4.15384 ms, enqueue 0.55706 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11272 ms - Host latency: 2.17611 ms (end to end 4.15171 ms, enqueue 0.558392 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11353 ms - Host latency: 2.17744 ms (end to end 4.1518 ms, enqueue 0.560349 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11425 ms - Host latency: 2.17675 ms (end to end 4.15527 ms, enqueue 0.555679 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11343 ms - Host latency: 2.17668 ms (end to end 4.15175 ms, enqueue 0.581152 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.1118 ms - Host latency: 2.17372 ms (end to end 4.15207 ms, enqueue 0.524023 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11344 ms - Host latency: 2.17588 ms (end to end 4.15279 ms, enqueue 0.540732 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11282 ms - Host latency: 2.17574 ms (end to end 4.15146 ms, enqueue 0.577774 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11476 ms - Host latency: 2.1772 ms (end to end 4.15719 ms, enqueue 0.520935 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.1123 ms - Host latency: 2.17551 ms (end to end 4.1503 ms, enqueue 0.561722 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11477 ms - Host latency: 2.17788 ms (end to end 4.15459 ms, enqueue 0.558096 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.112 ms - Host latency: 2.17459 ms (end to end 4.15193 ms, enqueue 0.544376 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.1121 ms - Host latency: 2.17496 ms (end to end 4.14929 ms, enqueue 0.557077 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11109 ms - Host latency: 2.17415 ms (end to end 4.14834 ms, enqueue 0.556427 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11354 ms - Host latency: 2.17654 ms (end to end 4.15099 ms, enqueue 0.55946 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11252 ms - Host latency: 2.17513 ms (end to end 4.15128 ms, enqueue 0.550018 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11353 ms - Host latency: 2.17624 ms (end to end 4.15395 ms, enqueue 0.548749 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11119 ms - Host latency: 2.1734 ms (end to end 4.14968 ms, enqueue 0.555878 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.1116 ms - Host latency: 2.17479 ms (end to end 4.14795 ms, enqueue 0.558081 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11241 ms - Host latency: 2.17573 ms (end to end 4.15023 ms, enqueue 0.558087 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11302 ms - Host latency: 2.1759 ms (end to end 4.15216 ms, enqueue 0.556372 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11303 ms - Host latency: 2.17605 ms (end to end 4.15298 ms, enqueue 0.559839 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11364 ms - Host latency: 2.17699 ms (end to end 4.15289 ms, enqueue 0.558508 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11139 ms - Host latency: 2.17415 ms (end to end 4.15061 ms, enqueue 0.544489 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11447 ms - Host latency: 2.17653 ms (end to end 4.15565 ms, enqueue 0.556574 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11271 ms - Host latency: 2.17557 ms (end to end 4.15234 ms, enqueue 0.55708 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11097 ms - Host latency: 2.17423 ms (end to end 4.14752 ms, enqueue 0.558173 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11241 ms - Host latency: 2.17522 ms (end to end 4.15159 ms, enqueue 0.556244 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11332 ms - Host latency: 2.1765 ms (end to end 4.15158 ms, enqueue 0.558722 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11271 ms - Host latency: 2.17546 ms (end to end 4.15314 ms, enqueue 0.555103 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11219 ms - Host latency: 2.1749 ms (end to end 4.14993 ms, enqueue 0.560773 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11271 ms - Host latency: 2.17522 ms (end to end 4.15272 ms, enqueue 0.557971 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11303 ms - Host latency: 2.17612 ms (end to end 4.15154 ms, enqueue 0.555768 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11324 ms - Host latency: 2.17654 ms (end to end 4.15027 ms, enqueue 0.552136 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11302 ms - Host latency: 2.17607 ms (end to end 4.15239 ms, enqueue 0.554169 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11107 ms - Host latency: 2.17465 ms (end to end 4.14894 ms, enqueue 0.546442 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11313 ms - Host latency: 2.17551 ms (end to end 4.15522 ms, enqueue 0.523438 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11049 ms - Host latency: 2.17324 ms (end to end 4.14769 ms, enqueue 0.540741 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11405 ms - Host latency: 2.17726 ms (end to end 4.15367 ms, enqueue 0.559326 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11097 ms - Host latency: 2.17369 ms (end to end 4.15007 ms, enqueue 0.556116 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11149 ms - Host latency: 2.17452 ms (end to end 4.14658 ms, enqueue 0.559424 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11427 ms - Host latency: 2.17723 ms (end to end 4.15461 ms, enqueue 0.555127 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.1131 ms - Host latency: 2.17538 ms (end to end 4.15183 ms, enqueue 0.560632 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11249 ms - Host latency: 2.17482 ms (end to end 4.15189 ms, enqueue 0.557959 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11375 ms - Host latency: 2.17643 ms (end to end 4.15238 ms, enqueue 0.562488 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.1116 ms - Host latency: 2.1741 ms (end to end 4.14967 ms, enqueue 0.547644 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11403 ms - Host latency: 2.17742 ms (end to end 4.15244 ms, enqueue 0.555774 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11252 ms - Host latency: 2.17515 ms (end to end 4.1512 ms, enqueue 0.558606 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.1114 ms - Host latency: 2.17408 ms (end to end 4.14801 ms, enqueue 0.554724 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11332 ms - Host latency: 2.17617 ms (end to end 4.15319 ms, enqueue 0.547461 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11351 ms - Host latency: 2.17595 ms (end to end 4.15333 ms, enqueue 0.558728 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11222 ms - Host latency: 2.17472 ms (end to end 4.15006 ms, enqueue 0.556201 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11324 ms - Host latency: 2.17637 ms (end to end 4.15137 ms, enqueue 0.559521 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11014 ms - Host latency: 2.17297 ms (end to end 4.14584 ms, enqueue 0.555689 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11836 ms - Host latency: 2.18071 ms (end to end 3.91808 ms, enqueue 0.570044 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.12634 ms - Host latency: 2.18938 ms (end to end 4.18035 ms, enqueue 0.521484 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.12777 ms - Host latency: 2.19177 ms (end to end 4.17981 ms, enqueue 0.551685 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.12837 ms - Host latency: 2.19091 ms (end to end 4.18446 ms, enqueue 0.538281 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.12396 ms - Host latency: 2.18756 ms (end to end 4.17269 ms, enqueue 0.559168 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.12452 ms - Host latency: 2.18771 ms (end to end 4.17476 ms, enqueue 0.554163 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11993 ms - Host latency: 2.18292 ms (end to end 4.16759 ms, enqueue 0.556616 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.12032 ms - Host latency: 2.18378 ms (end to end 4.16458 ms, enqueue 0.5573 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11735 ms - Host latency: 2.18063 ms (end to end 4.16068 ms, enqueue 0.559265 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.1175 ms - Host latency: 2.18032 ms (end to end 4.16119 ms, enqueue 0.555395 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11755 ms - Host latency: 2.18091 ms (end to end 4.1616 ms, enqueue 0.559143 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11436 ms - Host latency: 2.17661 ms (end to end 4.15618 ms, enqueue 0.556836 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11536 ms - Host latency: 2.17844 ms (end to end 4.15696 ms, enqueue 0.544946 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11466 ms - Host latency: 2.17747 ms (end to end 4.15542 ms, enqueue 0.55824 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11517 ms - Host latency: 2.17788 ms (end to end 4.15695 ms, enqueue 0.555554 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.116 ms - Host latency: 2.17919 ms (end to end 4.15634 ms, enqueue 0.580286 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11315 ms - Host latency: 2.17648 ms (end to end 4.15194 ms, enqueue 0.599573 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11315 ms - Host latency: 2.1769 ms (end to end 4.15255 ms, enqueue 0.536169 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11353 ms - Host latency: 2.17578 ms (end to end 4.15463 ms, enqueue 0.558142 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11212 ms - Host latency: 2.17516 ms (end to end 4.15062 ms, enqueue 0.543506 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11364 ms - Host latency: 2.17599 ms (end to end 4.15186 ms, enqueue 0.570325 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11353 ms - Host latency: 2.17643 ms (end to end 4.15061 ms, enqueue 0.59751 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11312 ms - Host latency: 2.17607 ms (end to end 4.15355 ms, enqueue 0.543774 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11362 ms - Host latency: 2.17668 ms (end to end 4.15172 ms, enqueue 0.5672 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11373 ms - Host latency: 2.17716 ms (end to end 4.15122 ms, enqueue 0.55531 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11373 ms - Host latency: 2.17621 ms (end to end 4.15096 ms, enqueue 0.573486 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11293 ms - Host latency: 2.17657 ms (end to end 4.14648 ms, enqueue 0.623621 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11317 ms - Host latency: 2.1764 ms (end to end 4.14712 ms, enqueue 0.558459 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11262 ms - Host latency: 2.17612 ms (end to end 4.14435 ms, enqueue 0.5927 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.1124 ms - Host latency: 2.17574 ms (end to end 4.14446 ms, enqueue 0.585632 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11322 ms - Host latency: 2.17765 ms (end to end 4.14637 ms, enqueue 0.586939 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11245 ms - Host latency: 2.17583 ms (end to end 4.14529 ms, enqueue 0.586645 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11262 ms - Host latency: 2.17637 ms (end to end 4.14568 ms, enqueue 0.58977 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11357 ms - Host latency: 2.17761 ms (end to end 4.14595 ms, enqueue 0.586621 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11194 ms - Host latency: 2.17544 ms (end to end 4.14165 ms, enqueue 0.590649 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11379 ms - Host latency: 2.17812 ms (end to end 4.1459 ms, enqueue 0.588574 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.1127 ms - Host latency: 2.17659 ms (end to end 4.14382 ms, enqueue 0.588721 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11211 ms - Host latency: 2.176 ms (end to end 4.13708 ms, enqueue 0.595557 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11335 ms - Host latency: 2.17754 ms (end to end 4.14412 ms, enqueue 0.59043 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11245 ms - Host latency: 2.17676 ms (end to end 4.1438 ms, enqueue 0.591821 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11279 ms - Host latency: 2.17673 ms (end to end 4.14336 ms, enqueue 0.584399 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.1115 ms - Host latency: 2.17537 ms (end to end 4.14294 ms, enqueue 0.59458 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11104 ms - Host latency: 2.17471 ms (end to end 4.14072 ms, enqueue 0.597974 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11108 ms - Host latency: 2.17488 ms (end to end 4.14187 ms, enqueue 0.625659 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11348 ms - Host latency: 2.17795 ms (end to end 4.14451 ms, enqueue 0.586597 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11323 ms - Host latency: 2.17646 ms (end to end 4.14539 ms, enqueue 0.590649 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11301 ms - Host latency: 2.17666 ms (end to end 4.146 ms, enqueue 0.595654 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11292 ms - Host latency: 2.17634 ms (end to end 4.14446 ms, enqueue 0.593213 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11011 ms - Host latency: 2.17397 ms (end to end 4.13892 ms, enqueue 0.622974 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11248 ms - Host latency: 2.17588 ms (end to end 4.14495 ms, enqueue 0.596289 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11477 ms - Host latency: 2.17993 ms (end to end 4.14653 ms, enqueue 0.582837 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11292 ms - Host latency: 2.17661 ms (end to end 4.14492 ms, enqueue 0.603027 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11355 ms - Host latency: 2.17661 ms (end to end 4.14651 ms, enqueue 0.566821 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11223 ms - Host latency: 2.17651 ms (end to end 4.14209 ms, enqueue 0.625342 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11147 ms - Host latency: 2.17524 ms (end to end 4.14187 ms, enqueue 0.615601 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11353 ms - Host latency: 2.17744 ms (end to end 4.14546 ms, enqueue 0.575049 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11223 ms - Host latency: 2.17561 ms (end to end 4.1418 ms, enqueue 0.597363 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11243 ms - Host latency: 2.17581 ms (end to end 4.14287 ms, enqueue 0.589453 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11177 ms - Host latency: 2.17585 ms (end to end 4.1418 ms, enqueue 0.612402 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11296 ms - Host latency: 2.17734 ms (end to end 4.14319 ms, enqueue 0.619409 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11218 ms - Host latency: 2.17603 ms (end to end 4.1449 ms, enqueue 0.565015 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11343 ms - Host latency: 2.17698 ms (end to end 4.14553 ms, enqueue 0.587769 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11301 ms - Host latency: 2.17622 ms (end to end 4.14519 ms, enqueue 0.591113 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11316 ms - Host latency: 2.17698 ms (end to end 4.14565 ms, enqueue 0.608716 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11514 ms - Host latency: 2.17805 ms (end to end 4.14985 ms, enqueue 0.553271 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11558 ms - Host latency: 2.17832 ms (end to end 4.15251 ms, enqueue 0.582935 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.12288 ms - Host latency: 2.18635 ms (end to end 3.91743 ms, enqueue 0.62688 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.12126 ms - Host latency: 2.18442 ms (end to end 4.16807 ms, enqueue 0.494141 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11946 ms - Host latency: 2.18298 ms (end to end 4.16011 ms, enqueue 0.591016 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.12029 ms - Host latency: 2.18364 ms (end to end 4.16406 ms, enqueue 0.564893 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.12004 ms - Host latency: 2.18499 ms (end to end 4.16604 ms, enqueue 0.557935 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.1198 ms - Host latency: 2.18325 ms (end to end 4.16367 ms, enqueue 0.554346 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.1158 ms - Host latency: 2.17839 ms (end to end 4.15647 ms, enqueue 0.565063 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.1156 ms - Host latency: 2.179 ms (end to end 4.15845 ms, enqueue 0.550684 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11472 ms - Host latency: 2.1781 ms (end to end 4.15522 ms, enqueue 0.566064 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.1186 ms - Host latency: 2.18154 ms (end to end 4.1626 ms, enqueue 0.552197 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11624 ms - Host latency: 2.17861 ms (end to end 4.15891 ms, enqueue 0.560767 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11423 ms - Host latency: 2.17747 ms (end to end 4.15547 ms, enqueue 0.565674 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11482 ms - Host latency: 2.17715 ms (end to end 4.15476 ms, enqueue 0.544629 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11367 ms - Host latency: 2.17717 ms (end to end 4.15237 ms, enqueue 0.560815 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.1157 ms - Host latency: 2.17856 ms (end to end 4.15601 ms, enqueue 0.560864 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11323 ms - Host latency: 2.17678 ms (end to end 4.15149 ms, enqueue 0.593677 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11255 ms - Host latency: 2.17583 ms (end to end 4.14907 ms, enqueue 0.570288 ms)\n", + "[01/30/2021-00:58:04] [I] Average on 10 runs - GPU latency: 2.11191 ms - Host latency: 2.17317 ms (end to end 4.15513 ms, enqueue 0.491797 ms)\n", + "[01/30/2021-00:58:04] [I] Host Latency\n", + "[01/30/2021-00:58:04] [I] min: 2.16614 ms (end to end 2.19995 ms)\n", + "[01/30/2021-00:58:04] [I] max: 2.20166 ms (end to end 4.19275 ms)\n", + "[01/30/2021-00:58:04] [I] mean: 2.17729 ms (end to end 4.14859 ms)\n", + "[01/30/2021-00:58:04] [I] median: 2.17676 ms (end to end 4.15125 ms)\n", + "[01/30/2021-00:58:04] [I] percentile: 2.19336 ms at 99% (end to end 4.18213 ms at 99%)\n", + "[01/30/2021-00:58:04] [I] throughput: 0 qps\n", + "[01/30/2021-00:58:04] [I] walltime: 3.00755 s\n", + "[01/30/2021-00:58:04] [I] Enqueue Time\n", + "[01/30/2021-00:58:04] [I] min: 0.447021 ms\n", + "[01/30/2021-00:58:04] [I] max: 0.669434 ms\n", + "[01/30/2021-00:58:04] [I] median: 0.559113 ms\n", + "[01/30/2021-00:58:04] [I] GPU Compute\n", + "[01/30/2021-00:58:04] [I] min: 2.10223 ms\n", + "[01/30/2021-00:58:04] [I] max: 2.13513 ms\n", + "[01/30/2021-00:58:04] [I] mean: 2.11412 ms\n", + "[01/30/2021-00:58:04] [I] median: 2.11353 ms\n", + "[01/30/2021-00:58:04] [I] percentile: 2.12988 ms at 99%\n", + "[01/30/2021-00:58:04] [I] total compute time: 2.97245 s\n", + "&&&& PASSED TensorRT.trtexec # trtexec --onnx=resnet50/model.onnx --saveEngine=resnet_engine_intro.trt --explicitBatch\n" + ] + } + ], + "source": [ + "!trtexec --onnx=resnet50/model.onnx --saveEngine=resnet_engine_intro.trt --explicitBatch" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "__Notes on the flags above:__\n", + " \n", + "Tell trtexec where to find our ONNX model:\n", + "\n", + " --onnx=resnet50/model.onnx \n", + "\n", + "Tell trtexec where to save our optimized TensorRT engine:\n", + "\n", + " --saveEngine=resnet_engine_intro.trt\n", + "\n", + "Tell trtexec to expect a fixed batch size when optimizing (the exact value of this batch size will be inferred from the ONNX file)\n", + "\n", + " --explicitBatch" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 5. What runtime will I use?\n", + "\n", + "After we have our TensorRT engine created successfully, we need to decide how to run it with TensorRT.\n", + "\n", + "There are two types of TensorRT runtimes: a standalone runtime which has C++ and Python bindings, and a native integration into TensorFlow. In this section, we will use a simplified wrapper (ONNXClassifierWrapper) which calls the standalone runtime. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# If you get an error in this cell, restart your notebook (possibly your whole machine) and do not run anything that imports/uses Tensorflow/PyTorch\n", + "\n", + "from onnx_helper import ONNXClassifierWrapper\n", + "trt_model = ONNXClassifierWrapper(\"resnet_engine_intro.trt\", [BATCH_SIZE, 1000], target_dtype = PRECISION)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "__Note__: If this conversion fails, please restart your Jupyter notebook kernel (in menu bar Kernel->Restart Kernel) and run steps 3 to 5 again. If you get an error like 'TypeError: pybind11::init(): factory function returned nullptr' there is likely some dangling process on the GPU - restart your machine and try again.\n", + "\n", + "We will feed our batch of randomized dummy data into our ONNXClassifierWrapper to run inference on that batch:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1.6954490e-04, 6.5457245e-04, 7.4289841e-05, 5.2106294e-05,\n", + " 1.2014447e-04, 2.3334271e-04, 1.8507861e-05, 1.9884911e-04,\n", + " 5.1907176e-05, 4.5095466e-04], dtype=float32)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Warm up:\n", + "trt_model.predict(dummy_input_batch)[0][:10] # softmax probability predictions for the first 10 classes of the first sample" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can get a rough sense of performance using %%timeit:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3.91 ms ± 533 ns per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" + ] + } + ], + "source": [ + "%%timeit\n", + "trt_model.predict(dummy_input_batch)[0][:10]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Applying TensorRT to Your Model:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is a simple example applied to a single model, but how should you go about answering these questions for your workload?\n", + "\n", + "First and foremost, it is a good idea to get an understanding of what your options are, and where you can learn more about them! \n", + "\n", + "### __Compatible Models:__ MLP/CNN/RNN/Transformer/Embedding/Etc\n", + "\n", + "TensorRT is compatible with models consisting of [these layers](https://docs.nvidia.com/deeplearning/tensorrt/support-matrix/index.html#layers-matrix). Using only supported layers ensures optimal performance without having to write any custom plugin code.\n", + "\n", + "In terms of framework, TensorRT is integrated directly with Tensorflow - and most other major deep learning frameworks, such as PyTorch, are supported by first converting to ONNX format.\n", + "\n", + "### __Conversion Methods:__ ONNX/TF-TRT/TensorRT API\n", + "\n", + "The __ONNX__ path is the most performant and framework-agnostic automatic way of converting models. It's main disadvantage is that it must convert networks completely - if a network has an unsupported layer ONNX can't convert it unless you write a custom plugin.\n", + "\n", + "You can see an example of how to use TensorRT with ONNX:\n", + "- [Here](./3.%20Using%20Tensorflow%202%20through%20ONNX.ipynb) in this guide for Tensorflow\n", + "- [Here](./4.%20Using%20PyTorch%20through%20ONNX.ipynb) in this guide for PyTorch\n", + "\n", + "__TF-TRT__ is a high level API for automatically converting Tensorflow models. It contains a parser, and runs inside the default Tensorflow runtime. Its ease of use and flexibility are its biggest advantages. TF-TRT can convert Tensorflow networks with unsupported layers in them - it will optimize whatever operations it can, and will leave the rest of the network alone.\n", + "\n", + "You can find an example included with this guide of using TF-TRT to convert and run a model [here]((./2.%20Using%20the%20Tensorflow%20TensorRT%20Integration.ipynb)).\n", + "\n", + "Last, there is the __TensorRT API__. The TensorRT ONNX path and TF-TRT integration both automatically convert models to TensorRT engines for you. Sometimes, however, we want to convert something complex, or have the maximum amount of control in how our TensorRT engine is created. This let's us do things like using dynamic batch dimensions outside of TF-TRT, or create custom plugins for layers that TensorRT doesn't support. \n", + "\n", + "When using this approach, we create TensorRT engine manually operation-by-operation using the TensorRT API's available in Python and C++. This process involves building a network identical in structure to your target network using the TensorRT API, and then loading in the weights directly in proper format. You can find more details on this [in the TensorRT documentation](https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html#c_topics).\n", + "\n", + "### __Batch Size:__ Prioritize Latency/Prioritize Throughput, Fixed Batch Size/Dynamic Batch Size\n", + "\n", + "Batch size determination is usually based on the tradeoff between throughput and latency. If you need low latency, use a low batch size. If you prefer high throughput and can accept higher latency, you can use a large batch size instead.\n", + "\n", + "TensorRT has two batch size modes: __explicit__ and __dynamic__. \n", + "\n", + "__Explicit batch networks__ accept a fixed predetermined batch size. Explicit batch mode is useful if you know exactly what batch size you expect - as it lets you skip the added step of specifying an optimization profile. This mode is required when converting networks through the ONNX path, as opposed to TF-TRT and the TensorRT API.\n", + "\n", + "You can see an example of setting an explicit batch size in either of the ONNX notebooks listed above.\n", + "\n", + "__Dynamic shape networks__ can accept a range of batch sizes. You must provide an '__optimization profile__' when using dynamic shapes in order to specify the possible range of batch sizes you expect to recieve. This is required because TensorRT does a lot of batch-size specific optimizations.\n", + "\n", + "For more information on best practices regarding batching, see the [TensorRT best practices guide](https://docs.nvidia.com/deeplearning/tensorrt/best-practices/index.html#batching).\n", + "\n", + "### __Precision:__ TF32/FP32/FP16/INT8\n", + "\n", + "TensorRT feature support - such as precision - for NVIDIA GPUs is determined by their __compute capability__. You can check the compute cabapility of your card on the [NVIDIA website](https://developer.nvidia.com/cuda-gpus).\n", + "\n", + "TensorRT supports different precisions depending on said compute capability. You can check what features are supported by your compute capability in the [TensorRT documentation](https://docs.nvidia.com/deeplearning/tensorrt/support-matrix/index.html#hardware-precision-matrix).\n", + "\n", + "__TF32__ is the default training precision on cards with compute cabapilities 8.0 and higher (e.g. NVIDIA A100 and later) - use when you want to replicate your original model performance as closely as possible on cards with compute capability of 8.0 or higher. \n", + "\n", + "TF32 is a precision designed to preserve the range of FP32 with the precision of FP16. In practice, this means that TF32 models train faster than FP32 models while still converging to the same accuracy. This feature is only available on newer GPUs.\n", + "\n", + "__FP32__ is the default training precision on cards with compute cabapilities of less than 8.0 (e.g. pre-NVIDIA A100) - use when you want to replicate your original model performance as closely as possible on cards with compute capability of less than 8.0\n", + "\n", + "__FP16__ is an inference focused reduced precision. It gives up some accuracy for faster models with lower latency and lower memory footprint. In practice, the accuracy loss is generally negligible in FP16 - so FP16 is a fairly safe bet in most cases for inference. Cards that are focused on deep learning training often have strong FP16 capabilities, making FP16 a great choice for GPUs that are expected to be used for both training and inference.\n", + "\n", + "__INT8__ is an inference focused reduced precision. It further reduces memory requirements and latency compared to FP16. INT8 has the potential to lose more accuracy than FP16 - but TensorRT provides tools to help you quantize your network's INT8 weights to avoid this as much as possible. INT8 requires the extra step of calibrating how TensorRT should quantize your weights to integers - requiring some sample data. With careful tuning and a good calibration dataset, accuracy loss from INT8 is often minimal. This makes INT8 a great precision for lower-power environments such as those using T4 GPUs or AGX Jetson modules - both of which have strong INT8 capabilities.\n", + "\n", + "### __Runtime:__ TF-TRT/Python API/C++ API/TRITON\n", + "\n", + "For a more in depth discussion of these options and how they compare see [this notebook on TensorRT Runtimes!](./Intro_Notebooks/3.Runtimes.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What do I do if I run into issues with conversion?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here are several steps you can try if your model is not converting to TensorRT properly:\n", + "\n", + "1. Check the logs - if you are using a tool such as trtexec to convert your model, it will tell you which layer is problematic\n", + "2. Write a custom plugin - you can find more information on it [here]().\n", + "3. Alternatively, if you are using ONNX and Tensorflow try switching to TF-TRT - it can support partial Tensorflow graph optimizations\n", + "4. Use alternative implementations of the layers or operations in question in your network definition - for example, it can be easier to use the padding argument in your convolutional layers instead of adding an explicit padding layer to the network. \n", + "5. TF-TRT can be harder to debug, but tools like graph surgeon https://docs.nvidia.com/deeplearning/tensorrt/api/python_api/graphsurgeon/graphsurgeon.html can help you fix specific nodes in your graph as well as pull it apart for analysis or patch specific nodes in your graph\n", + "6. Ask on the [NVIDIA developer forums](https://forums.developer.nvidia.com/c/ai-data-science/deep-learning/tensorrt) - we have many active TensorRT experts at NVIDIA who who browse the forums and can help\n", + "7. Post an issue on the [TensorRT OSS Github](https://github.com/NVIDIA/TensorRT)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Next Steps:\n", + "\n", + "You have now taken a model saved in ONNX format, converted it to an optimized TensorRT engine, and deployed it using the Python runtime. This is a great first step towards getting better performance out of your deep learning models at inference time!\n", + "\n", + "Now, you can check out the remaining notebooks in this guide. See:\n", + "\n", + "- [2. Using the TF-TRT Tensorflow Integration](./2.%20Using%20the%20Tensorflow%20TensorRT%20Integration.ipynb)\n", + "- [3. Using Tensorflow 2 through ONNX.ipynb](./3.%20Using%20Tensorflow%202%20through%20ONNX.ipynb)\n", + "- [4. Using PyTorch through ONNX.ipynb](./4.%20Using%20PyTorch%20through%20ONNX.ipynb)\n", + "- [5. Understanding TensorRT Runtimes.ipynb](./5.%20Understanding%20TensorRT%20Runtimes.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Profiling

\n", + "\n", + "This is a great next step for further optimizing and debugging models you are working on productionizing\n", + "\n", + "You can find it here: https://docs.nvidia.com/deeplearning/tensorrt/best-practices/index.html\n", + "\n", + "

TRT Dev Docs

\n", + "\n", + "Main documentation page for the ONNX, layer builder, C++, and legacy APIs\n", + "\n", + "You can find it here: https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html\n", + "\n", + "

TRT OSS GitHub

\n", + "\n", + "Contains OSS TRT components, sample applications, and plugin examples\n", + "\n", + "You can find it here: https://github.com/NVIDIA/TensorRT" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/Notebook Tutorials/2. TF-TRT Detection.ipynb b/examples/Notebook Tutorials/2. TF-TRT Detection.ipynb new file mode 100644 index 0000000..ed694d7 --- /dev/null +++ b/examples/Notebook Tutorials/2. TF-TRT Detection.ipynb @@ -0,0 +1,585 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# TF-TRT Keras Retinanet Detection Example:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this notebook, we are going to optimize a Retinanet detection model from the official Keras examples! \n", + "\n", + "You can find the implementation here: https://keras.io/examples/vision/retinanet/\n", + "\n", + "In general, detection models can be tricky to optimize because they tend to require a lot of custom logic for sub-tasks such as region proposal, output decoding, or non-maximum suppression. This makes them a good demonstration of TF-TRT's capabilities - It does a great job of optimizing a large part of the network while leaving the custom logic untouched." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's make sure our GPUs are properly configured and visible:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fri Jan 29 23:17:01 2021 \n", + "+-----------------------------------------------------------------------------+\n", + "| NVIDIA-SMI 450.80.02 Driver Version: 450.80.02 CUDA Version: 11.1 |\n", + "|-------------------------------+----------------------+----------------------+\n", + "| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n", + "| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n", + "| | | MIG M. |\n", + "|===============================+======================+======================|\n", + "| 0 Tesla V100-DGXS... On | 00000000:07:00.0 Off | 0 |\n", + "| N/A 42C P0 37W / 300W | 125MiB / 16155MiB | 0% Default |\n", + "| | | N/A |\n", + "+-------------------------------+----------------------+----------------------+\n", + "| 1 Tesla V100-DGXS... On | 00000000:08:00.0 Off | 0 |\n", + "| N/A 43C P0 38W / 300W | 6MiB / 16158MiB | 0% Default |\n", + "| | | N/A |\n", + "+-------------------------------+----------------------+----------------------+\n", + "| 2 Tesla V100-DGXS... On | 00000000:0E:00.0 Off | 0 |\n", + "| N/A 42C P0 38W / 300W | 6MiB / 16158MiB | 0% Default |\n", + "| | | N/A |\n", + "+-------------------------------+----------------------+----------------------+\n", + "| 3 Tesla V100-DGXS... On | 00000000:0F:00.0 Off | 0 |\n", + "| N/A 43C P0 37W / 300W | 6MiB / 16158MiB | 0% Default |\n", + "| | | N/A |\n", + "+-------------------------------+----------------------+----------------------+\n", + " \n", + "+-----------------------------------------------------------------------------+\n", + "| Processes: |\n", + "| GPU GI CI PID Type Process name GPU Memory |\n", + "| ID ID Usage |\n", + "|=============================================================================|\n", + "+-----------------------------------------------------------------------------+\n" + ] + } + ], + "source": [ + "!nvidia-smi" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will also need matplotlib to run the model. If you do not have it, run:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (3.3.4)\n", + "Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.6/dist-packages (from matplotlib) (1.17.3)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib) (0.10.0)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib) (1.3.1)\n", + "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in /usr/local/lib/python3.6/dist-packages (from matplotlib) (2.4.7)\n", + "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib) (2.8.1)\n", + "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.6/dist-packages (from matplotlib) (8.1.0)\n", + "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from cycler>=0.10->matplotlib) (1.15.0)\n", + "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.0 is available.\n", + "You should consider upgrading via the '/usr/bin/python -m pip install --upgrade pip' command.\u001b[0m\n" + ] + } + ], + "source": [ + "!pip install matplotlib" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Remember to sucessfully deploy a TensorRT model, you have to make __five key decisions__:\n", + "\n", + "1. __What format should I save my model in?__\n", + "2. __What batch size(s) am I running inference at?__\n", + "3. __What precision am I running inference at?__\n", + "4. __What TensorRT path am I using to convert my model?__\n", + "5. __What runtime am I targeting?__\n", + "\n", + "Let's give it a shot!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. What format should I save my model in?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will work with one of the Keras example RetinaNet implementations. We can download the implementation code for the specific version of it required here:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2021-01-29 23:17:05-- https://raw.githubusercontent.com/keras-team/keras-io/cd6201c1bfa37625f503f51e8fd3c572666770e4/examples/vision/retinanet.py\n", + "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.40.133\n", + "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.40.133|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 35046 (34K) [text/plain]\n", + "Saving to: ‘retinanet.py’\n", + "\n", + "retinanet.py 100%[===================>] 34.22K --.-KB/s in 0.002s \n", + "\n", + "2021-01-29 23:17:05 (20.1 MB/s) - ‘retinanet.py’ saved [35046/35046]\n", + "\n" + ] + } + ], + "source": [ + "!wget -O retinanet.py https://raw.githubusercontent.com/keras-team/keras-io/cd6201c1bfa37625f503f51e8fd3c572666770e4/examples/vision/retinanet.py" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The code has some unnecessary setup steps, so we will pull out just the model implementation itself using sed (you can check the end result in the [retinanet_model.py](./retinanet_model.py) file)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "!sed -n '1,40 p; 71,820 p' retinanet.py > retinanet_model.py" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "!mkdir -p tmp_savedmodels" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We perform some imports and setup:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "import tensorflow as tf\n", + "from tensorflow import keras\n", + "from tensorflow.keras import layers" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "img_size = (224, 224)\n", + "num_classes = 10" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can import our necessary RetinaNet functions from the example and initialize our detection model:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n", + "94773248/94765736 [==============================] - 2s 0us/step\n" + ] + } + ], + "source": [ + "from retinanet_model import RetinaNet, DecodePredictions, get_backbone\n", + "\n", + "resnet50_backbone = get_backbone()\n", + "model = RetinaNet(num_classes, resnet50_backbone)\n", + "\n", + "image = tf.keras.Input(shape=[None, None, 3], name=\"image\")\n", + "predictions = model(image, training=False)\n", + "detections = DecodePredictions(confidence_threshold=0.5)(image, predictions)\n", + "inference_model = tf.keras.Model(inputs=image, outputs=detections)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we save our model in SavedModel format!" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/tracking/tracking.py:111: Model.state_updates (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n", + "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/tracking/tracking.py:111: Layer.updates (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n", + "INFO:tensorflow:Assets written to: tmp_savedmodels/detect_model/assets\n" + ] + } + ], + "source": [ + "model_dir = \"tmp_savedmodels/detect_model\"\n", + "model.save(model_dir) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. What batch size(s) am I running inference at?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will create a dummy batch of size 32:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "dummy_input = np.zeros((32, img_size[0], img_size[1], 3))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "CombinedNonMaxSuppression(nmsed_boxes=array([[[0., 0., 0., 0.],\n", + " [0., 0., 0., 0.],\n", + " [0., 0., 0., 0.],\n", + " ...,\n", + " [0., 0., 0., 0.],\n", + " [0., 0., 0., 0.],\n", + " [0., 0., 0., 0.]],\n", + "\n", + " [[0., 0., 0., 0.],\n", + " [0., 0., 0., 0.],\n", + " [0., 0., 0., 0.],\n", + " ...,\n", + " [0., 0., 0., 0.],\n", + " [0., 0., 0., 0.],\n", + " [0., 0., 0., 0.]],\n", + "\n", + " [[0., 0., 0., 0.],\n", + " [0., 0., 0., 0.],\n", + " [0., 0., 0., 0.],\n", + " ...,\n", + " [0., 0., 0., 0.],\n", + " [0., 0., 0., 0.],\n", + " [0., 0., 0., 0.]],\n", + "\n", + " ...,\n", + "\n", + " [[0., 0., 0., 0.],\n", + " [0., 0., 0., 0.],\n", + " [0., 0., 0., 0.],\n", + " ...,\n", + " [0., 0., 0., 0.],\n", + " [0., 0., 0., 0.],\n", + " [0., 0., 0., 0.]],\n", + "\n", + " [[0., 0., 0., 0.],\n", + " [0., 0., 0., 0.],\n", + " [0., 0., 0., 0.],\n", + " ...,\n", + " [0., 0., 0., 0.],\n", + " [0., 0., 0., 0.],\n", + " [0., 0., 0., 0.]],\n", + "\n", + " [[0., 0., 0., 0.],\n", + " [0., 0., 0., 0.],\n", + " [0., 0., 0., 0.],\n", + " ...,\n", + " [0., 0., 0., 0.],\n", + " [0., 0., 0., 0.],\n", + " [0., 0., 0., 0.]]], dtype=float32), nmsed_scores=array([[0., 0., 0., ..., 0., 0., 0.],\n", + " [0., 0., 0., ..., 0., 0., 0.],\n", + " [0., 0., 0., ..., 0., 0., 0.],\n", + " ...,\n", + " [0., 0., 0., ..., 0., 0., 0.],\n", + " [0., 0., 0., ..., 0., 0., 0.],\n", + " [0., 0., 0., ..., 0., 0., 0.]], dtype=float32), nmsed_classes=array([[0., 0., 0., ..., 0., 0., 0.],\n", + " [0., 0., 0., ..., 0., 0., 0.],\n", + " [0., 0., 0., ..., 0., 0., 0.],\n", + " ...,\n", + " [0., 0., 0., ..., 0., 0., 0.],\n", + " [0., 0., 0., ..., 0., 0., 0.],\n", + " [0., 0., 0., ..., 0., 0., 0.]], dtype=float32), valid_detections=array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int32))" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "inference_model.predict(dummy_input)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. What precision am I running inference at?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will stick with the same FP32 precision used during training:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "PRECISION = \"FP32\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. What TensorRT path am I using to convert my model?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will use our example TF-TRT based ModelOptimizer wrapper:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "from helper import ModelOptimizer\n", + "\n", + "model_opt = ModelOptimizer(model_dir)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Convert to our target precision, saving the result in a new SavedModel:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Linked TensorRT version: (7, 2, 1)\n", + "INFO:tensorflow:Loaded TensorRT version: (7, 2, 2)\n", + "INFO:tensorflow:Loaded TensorRT 7.2.2 and linked TensorFlow against TensorRT 7.2.1. This is supported because TensorRT minor/patch upgrades are backward compatible\n", + "INFO:tensorflow:Could not find TRTEngineOp_0_2 in TF-TRT cache. This can happen if build() is not called, which means TensorRT engines will be built and cached at runtime.\n", + "INFO:tensorflow:Could not find TRTEngineOp_0_0 in TF-TRT cache. This can happen if build() is not called, which means TensorRT engines will be built and cached at runtime.\n", + "INFO:tensorflow:Could not find TRTEngineOp_0_1 in TF-TRT cache. This can happen if build() is not called, which means TensorRT engines will be built and cached at runtime.\n", + "INFO:tensorflow:Could not find TRTEngineOp_0_3 in TF-TRT cache. This can happen if build() is not called, which means TensorRT engines will be built and cached at runtime.\n", + "INFO:tensorflow:Assets written to: tmp_savedmodels/detect_model_FP32/assets\n", + "conversion complete! prediction shape: (32, 9441, 14)\n" + ] + } + ], + "source": [ + "opt_trt = model_opt.convert(model_dir+'_'+PRECISION, precision=PRECISION)\n", + "print(\"conversion complete! prediction shape:\", opt_trt.predict(dummy_input).shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. What TensorRT runtime am I targeting?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will stick to our TF-TRT/Tensorflow runtime:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warming up...\n", + "(32, 9441, 14)\n", + "(32, 9441, 14)\n", + "Done warming up!\n" + ] + } + ], + "source": [ + "print(\"Warming up...\")\n", + "\n", + "print(model.predict(dummy_input).shape)\n", + "print(opt_trt.predict(dummy_input).shape)\n", + "\n", + "print(\"Done warming up!\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Performance Comparisons:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "109 ms ± 5.53 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" + ] + } + ], + "source": [ + "%%timeit\n", + "\n", + "preds = model.predict(dummy_input)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "45.1 ms ± 106 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" + ] + } + ], + "source": [ + "%%timeit\n", + "\n", + "preds = opt_trt.predict(dummy_input)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": true + } + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/Notebook Tutorials/2. Using the Tensorflow TensorRT Integration.ipynb b/examples/Notebook Tutorials/2. Using the Tensorflow TensorRT Integration.ipynb new file mode 100644 index 0000000..7eda93b --- /dev/null +++ b/examples/Notebook Tutorials/2. Using the Tensorflow TensorRT Integration.ipynb @@ -0,0 +1,664 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Using TF-TRT With Tensorflow 2:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Tensorflow/TensorRT integration (TF-TRT) is a high level Python interface for TensorRT that works directly with Tensorflow models. In Tensorflow 2, TF-TRT allows you to convert Tensorflow SavedModels to TensorRT optimized models and run them within Python. This is a simple and flexible way to get started with TensorRT when using Tensorflow.\n", + "\n", + "This notebook provides a basic introduction and wrapper that makes it easy to work with basic Keras/TF2 models. We will take a pretrained Resnet-50 model from the keras.applications model zoo, convert it using TF-TRT, and run it in the TF-TRT Python runtime!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Use this when:\n", + "- You want the API with the least dependencies\n", + "- You are willing to give up some optimizations in exchange for more flexibility\n", + "- You have a network which contains operations unsupported by the ONNX parser but still want to use an automatic parser\n", + "- You do not want to write custom C++ plugins/optimizations if your network has unsupported operations\n", + "- You are okay with being limited to the Tensorflow or TRITON runtimes in most cases" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the TF-TRT portion of this guide, we will be using a wrapper included with the notebooks in the [TensorRT OSS examples](https://github.com/NVIDIA/TensorRT).\n", + "\n", + "You can clone the entire repository and work inside it, or you can grab just the wrapper by:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2021-01-29 23:37:25-- https://raw.githubusercontent.com/NVIDIA/TensorRT/main/quickstart/IntroNotebooks/helper.py\n", + "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.40.133\n", + "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.40.133|:443... connected.\n", + "HTTP request sent, awaiting response... 404 Not Found\n", + "2021-01-29 23:37:25 ERROR 404: Not Found.\n", + "\n" + ] + } + ], + "source": [ + "!wget \"https://raw.githubusercontent.com/NVIDIA/TensorRT/main/quickstart/IntroNotebooks/helper.py\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "__Checking your GPU status:__\n", + "\n", + "Lets see what GPU hardware we are working with. Our hardware can matter a lot because different cards have different performance profiles and precisions they tend to operate best in. For example, a V100 is relatively strong as FP16 processing vs a T4, which tends to operate best in the INT8 mode." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fri Jan 29 23:37:26 2021 \n", + "+-----------------------------------------------------------------------------+\n", + "| NVIDIA-SMI 450.80.02 Driver Version: 450.80.02 CUDA Version: 11.1 |\n", + "|-------------------------------+----------------------+----------------------+\n", + "| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n", + "| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n", + "| | | MIG M. |\n", + "|===============================+======================+======================|\n", + "| 0 Tesla V100-DGXS... On | 00000000:07:00.0 Off | 0 |\n", + "| N/A 42C P0 37W / 300W | 125MiB / 16155MiB | 0% Default |\n", + "| | | N/A |\n", + "+-------------------------------+----------------------+----------------------+\n", + "| 1 Tesla V100-DGXS... On | 00000000:08:00.0 Off | 0 |\n", + "| N/A 42C P0 38W / 300W | 6MiB / 16158MiB | 0% Default |\n", + "| | | N/A |\n", + "+-------------------------------+----------------------+----------------------+\n", + "| 2 Tesla V100-DGXS... On | 00000000:0E:00.0 Off | 0 |\n", + "| N/A 41C P0 38W / 300W | 6MiB / 16158MiB | 0% Default |\n", + "| | | N/A |\n", + "+-------------------------------+----------------------+----------------------+\n", + "| 3 Tesla V100-DGXS... On | 00000000:0F:00.0 Off | 0 |\n", + "| N/A 42C P0 37W / 300W | 6MiB / 16158MiB | 0% Default |\n", + "| | | N/A |\n", + "+-------------------------------+----------------------+----------------------+\n", + " \n", + "+-----------------------------------------------------------------------------+\n", + "| Processes: |\n", + "| GPU GI CI PID Type Process name GPU Memory |\n", + "| ID ID Usage |\n", + "|=============================================================================|\n", + "+-----------------------------------------------------------------------------+\n" + ] + } + ], + "source": [ + "!nvidia-smi" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Basic usage: Optimizing a TF2/Keras model with TensorRT in FP32:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Remember to sucessfully deploy a TensorRT model, you have to answer __five important questions__:\n", + "\n", + "1. __What format should I save my model in?__\n", + "2. __What batch size(s) am I running inference at?__\n", + "3. __What precision am I running inference at?__\n", + "4. __What TensorRT path am I using to convert my model?__\n", + "5. __What runtime am I targeting?__\n", + "\n", + "We will be following this path to convert and deploy our model:\n", + "\n", + "![TF-TRT](./images/tf_trt.png)\n", + "\n", + "Lets address these five questions here!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. What format should I save my model in?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For TF-TRT, we need our models to be in [SavedModel format](https://www.tensorflow.org/guide/saved_model). We can load up, for example, a Keras model and save it appropriately as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "!mkdir -p tmp_savedmodels" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow.keras.applications import ResNet50\n", + "\n", + "model_dir = 'tmp_savedmodels/resnet50_saved_model'\n", + "model = ResNet50(include_top=True, weights='imagenet')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/tracking/tracking.py:111: Model.state_updates (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n", + "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/tracking/tracking.py:111: Layer.updates (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n", + "INFO:tensorflow:Assets written to: tmp_savedmodels/resnet50_saved_model/assets\n" + ] + } + ], + "source": [ + "model.save(model_dir) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. What batch size(s) am I running inference at?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we generate a dummy batch of data to pass into the network just to get an understanding of its performance. This is normally where you would supply a numpy batch of images." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "BATCH_SIZE = 32\n", + "\n", + "dummy_input_batch = np.zeros((BATCH_SIZE, 224, 224, 3))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. What precision am I running inference at?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will start with FP32 precision as a baseline! Later in this notebook, we will go through and look at how we can reduce our precision from the default." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "PRECISION = \"FP32\" # Options are \"FP32\", \"FP16\", or \"INT8\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. What TensorRT path am I using to convert my model?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will be using a simplified wrapper (ModelOptimizer) around TF-TRT to handle our conversions for this notebook. The wrapper is bare bones, meant as a springboard for further develoment - not a finished product. It can help us easily and quickly convert a TF-TRT model to a number of precisions." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "from helper import ModelOptimizer # using the helper from \n", + "\n", + "model_dir = 'tmp_savedmodels/resnet50_saved_model'\n", + "\n", + "opt_model = ModelOptimizer(model_dir)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Linked TensorRT version: (7, 2, 1)\n", + "INFO:tensorflow:Loaded TensorRT version: (7, 2, 2)\n", + "INFO:tensorflow:Loaded TensorRT 7.2.2 and linked TensorFlow against TensorRT 7.2.1. This is supported because TensorRT minor/patch upgrades are backward compatible\n", + "INFO:tensorflow:Could not find TRTEngineOp_0_0 in TF-TRT cache. This can happen if build() is not called, which means TensorRT engines will be built and cached at runtime.\n", + "INFO:tensorflow:Assets written to: tmp_savedmodels/resnet50_saved_model_FP32/assets\n" + ] + } + ], + "source": [ + "model_fp32 = opt_model.convert(model_dir+'_FP32', precision=PRECISION)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. What TensorRT runtime am I targeting?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "TF-TRT essentially yields a Tensorflow graph with some optimized TensorRT operations included in it. We can run this graph with .predict() like we would any other Tensorflow model." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1.6964252e-04, 3.3007402e-04, 6.1350249e-05, ..., 1.4622317e-05,\n", + " 1.4449877e-04, 6.6086568e-04],\n", + " [1.6964252e-04, 3.3007402e-04, 6.1350249e-05, ..., 1.4622317e-05,\n", + " 1.4449877e-04, 6.6086568e-04],\n", + " [1.6964252e-04, 3.3007402e-04, 6.1350249e-05, ..., 1.4622317e-05,\n", + " 1.4449877e-04, 6.6086568e-04],\n", + " ...,\n", + " [1.6964252e-04, 3.3007402e-04, 6.1350249e-05, ..., 1.4622317e-05,\n", + " 1.4449877e-04, 6.6086568e-04],\n", + " [1.6964252e-04, 3.3007402e-04, 6.1350249e-05, ..., 1.4622317e-05,\n", + " 1.4449877e-04, 6.6086568e-04],\n", + " [1.6964252e-04, 3.3007402e-04, 6.1350249e-05, ..., 1.4622317e-05,\n", + " 1.4449877e-04, 6.6086568e-04]], dtype=float32)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model_fp32.predict(dummy_input_batch)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now have a finished TF-TRT optimized Tensorflow graph!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "__We can now compare the TensorRT optimized model with the original:__" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1.6964252e-04, 3.3007402e-04, 6.1350249e-05, ..., 1.4622317e-05,\n", + " 1.4449877e-04, 6.6086568e-04],\n", + " [1.6964252e-04, 3.3007402e-04, 6.1350249e-05, ..., 1.4622317e-05,\n", + " 1.4449877e-04, 6.6086568e-04],\n", + " [1.6964252e-04, 3.3007402e-04, 6.1350249e-05, ..., 1.4622317e-05,\n", + " 1.4449877e-04, 6.6086568e-04],\n", + " ...,\n", + " [1.6964252e-04, 3.3007402e-04, 6.1350249e-05, ..., 1.4622317e-05,\n", + " 1.4449877e-04, 6.6086568e-04],\n", + " [1.6964252e-04, 3.3007402e-04, 6.1350249e-05, ..., 1.4622317e-05,\n", + " 1.4449877e-04, 6.6086568e-04],\n", + " [1.6964252e-04, 3.3007402e-04, 6.1350249e-05, ..., 1.4622317e-05,\n", + " 1.4449877e-04, 6.6086568e-04]], dtype=float32)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Warm up - the first batch through a model generally takes longer\n", + "model.predict(dummy_input_batch)\n", + "model_fp32.predict(dummy_input_batch)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "53.5 ms ± 423 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit\n", + "\n", + "model.predict_on_batch(dummy_input_batch)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "29.5 ms ± 117 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" + ] + } + ], + "source": [ + "%%timeit\n", + "\n", + "model_fp32.predict(dummy_input_batch)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Reducing Precision:\n", + "\n", + "Inference typically requires less numeric precision than training. With some care, lower precision can give you faster computation and lower memory consumption without sacrificing any meaningful accuracy. TensorRT supports TF32, FP32, FP16, and INT8 precisions." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "__Reducing precision to FP16:__" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "FP16 \"mixed precision\" inference gives up some accuracy in exchange for faster models with lower latency and lower memory footprint. In practice, the accuracy loss is generally negligible in FP16 - so FP16 is a fairly safe bet in most cases for inference. Cards that are focused on deep learning training often have strong FP16 capabilities, making FP16 a great choice for GPUs that are expected to be used for both training and inference - such as the NVIDIA V100\n", + "\n", + "Let's convert our model to FP16 and see how it performs:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Linked TensorRT version: (7, 2, 1)\n", + "INFO:tensorflow:Loaded TensorRT version: (7, 2, 2)\n", + "INFO:tensorflow:Loaded TensorRT 7.2.2 and linked TensorFlow against TensorRT 7.2.1. This is supported because TensorRT minor/patch upgrades are backward compatible\n", + "INFO:tensorflow:Could not find TRTEngineOp_1_0 in TF-TRT cache. This can happen if build() is not called, which means TensorRT engines will be built and cached at runtime.\n", + "INFO:tensorflow:Assets written to: tmp_savedmodels/resnet50_saved_model_FP16/assets\n" + ] + }, + { + "data": { + "text/plain": [ + "array([[1.7182514e-04, 3.3864001e-04, 6.3493084e-05, ..., 1.5010530e-05,\n", + " 1.4759685e-04, 6.7664997e-04],\n", + " [1.7182514e-04, 3.3864001e-04, 6.3493084e-05, ..., 1.5010530e-05,\n", + " 1.4759685e-04, 6.7664997e-04],\n", + " [1.7182514e-04, 3.3864001e-04, 6.3493084e-05, ..., 1.5010530e-05,\n", + " 1.4759685e-04, 6.7664997e-04],\n", + " ...,\n", + " [1.7182514e-04, 3.3864001e-04, 6.3493084e-05, ..., 1.5010530e-05,\n", + " 1.4759685e-04, 6.7664997e-04],\n", + " [1.7182514e-04, 3.3864001e-04, 6.3493084e-05, ..., 1.5010530e-05,\n", + " 1.4759685e-04, 6.7664997e-04],\n", + " [1.7182514e-04, 3.3864001e-04, 6.3493084e-05, ..., 1.5010530e-05,\n", + " 1.4759685e-04, 6.7664997e-04]], dtype=float32)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model_fp16 = opt_model.convert(model_dir+'_FP16', precision=\"FP16\")\n", + "\n", + "model_fp16.predict(dummy_input_batch)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "13.5 ms ± 20.2 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" + ] + } + ], + "source": [ + "%%timeit\n", + "\n", + "model_fp16.predict(dummy_input_batch)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "__Reducing precision to INT8:__\n", + "\n", + "Whether you want to further reduce to INT8 precision depends on hardware - Turing cards and later INT8 is often better. Inference focused cards such as the NVIDIA T4 or systems-on-module such as Jetson AGX Xavier do well with INT8. In contrast, on a training-focused GPU like V100, INT8 often isn't any faster than FP16." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To perform INT8 inference, we need to see what the normal range of activations are in the network so we can quantize our INT8 representations based on a normal set of values for our dataset. It is important that this dataset is representative of the testing samples in order to maintain accuracy levels.\n", + "\n", + "Here, we just want to see how our network performs in TensorRT from a runtime standpoint - so we will just feed dummy data and dummy calibration data into TensorRT." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "dummy_calibration_batch = np.zeros((8, 224, 224, 3))\n", + "\n", + "opt_model.set_calibration_data(dummy_calibration_batch)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then, we convert our model to INT8 as before:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Linked TensorRT version: (7, 2, 1)\n", + "INFO:tensorflow:Loaded TensorRT version: (7, 2, 2)\n", + "INFO:tensorflow:Loaded TensorRT 7.2.2 and linked TensorFlow against TensorRT 7.2.1. This is supported because TensorRT minor/patch upgrades are backward compatible\n", + "INFO:tensorflow:Assets written to: tmp_savedmodels/resnet50_saved_model_INT8/assets\n" + ] + }, + { + "data": { + "text/plain": [ + "array([[1.61497956e-04, 3.58211488e-04, 7.12977999e-05, ...,\n", + " 1.43723055e-05, 1.47045619e-04, 7.21490127e-04],\n", + " [1.61497956e-04, 3.58211488e-04, 7.12977999e-05, ...,\n", + " 1.43723055e-05, 1.47045619e-04, 7.21490127e-04],\n", + " [1.61497956e-04, 3.58211488e-04, 7.12977999e-05, ...,\n", + " 1.43723055e-05, 1.47045619e-04, 7.21490127e-04],\n", + " ...,\n", + " [1.61497956e-04, 3.58211488e-04, 7.12977999e-05, ...,\n", + " 1.43723055e-05, 1.47045619e-04, 7.21490127e-04],\n", + " [1.61497956e-04, 3.58211488e-04, 7.12977999e-05, ...,\n", + " 1.43723055e-05, 1.47045619e-04, 7.21490127e-04],\n", + " [1.61497956e-04, 3.58211488e-04, 7.12977999e-05, ...,\n", + " 1.43723055e-05, 1.47045619e-04, 7.21490127e-04]], dtype=float32)" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model_int8 = opt_model.convert(model_dir+'_INT8', precision=\"INT8\")\n", + "\n", + "model_int8.predict(dummy_input_batch)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "13.1 ms ± 29.5 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" + ] + } + ], + "source": [ + "%%timeit\n", + "\n", + "model_int8.predict(dummy_input_batch)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Next Steps:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can find other Jupyter Notebooks demonstrating TF-TRT conversions and end to end workflows for many other Keras applications and models, including detection models and segmentation models, in other example TF-TRT notebooks!\n", + "\n", + "Here are links to those notebooks:\n", + "\n", + "[__Classification Examples__](./Additional%20Examples/1.%20TF-TRT%20Classification.ipynb)\n", + "\n", + "[__Detection Example__](./Additional%20Examples/2.%20TF-TRT%20Detection.ipynb)\n", + "\n", + "[__Segmentation Example__](./Additional%20Examples/3.%20TF-TRT%20Segmentation.ipynb)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/Notebook Tutorials/3. Using Tensorflow 2 through ONNX.ipynb b/examples/Notebook Tutorials/3. Using Tensorflow 2 through ONNX.ipynb new file mode 100644 index 0000000..aa8f632 --- /dev/null +++ b/examples/Notebook Tutorials/3. Using Tensorflow 2 through ONNX.ipynb @@ -0,0 +1,1275 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Using Tensorflow through ONNX:\n", + "\n", + "The ONNX path to getting a TensorRT engine is a high-performance approach to TensorRT conversion that works with a variety of frameworks - including Tensorflow and Tensorflow 2.\n", + "\n", + "TensorRT's ONNX parser is an all-or-nothing parser for ONNX models that ensures an optimal, single TensorRT engine and is great for exporting to the TensorRT API runtimes. ONNX models can be easily generated from Tensorflow models using the ONNX project's tf2onnx tool.\n", + "\n", + "In this notebook we will take a look at how ONNX models can be generated from a Keras/TF2 ResNet50 model, how we can convert those ONNX models to TensorRT engines using trtexec, and finally how we can use the native Python TensorRT runtime to feed a batch of data into the TRT engine at inference time.\n", + "\n", + "Essentially, we will follow this path to convert and deploy our model:\n", + "\n", + "![Tensorflow+ONNX](./images/tf_onnx.png)\n", + "\n", + "__Use this when:__\n", + "- You want the most efficient runtime performance possible out of an automatic parser\n", + "- You have a network consisting of mostly supported operations - including operations and layers that the ONNX parser uniquely supports (Such as RNNs/LSTMs/GRUs)\n", + "- You are willing to write custom C++ plugins for any unsupported operations (if your network has any)\n", + "- You do not want to use the manual layer builder API" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "__Checking your GPU status:__\n", + "\n", + "Lets see what GPU hardware we are working with. Our hardware can matter a lot because different cards have different performance profiles and precisions they tend to operate best in. For example, a V100 is relatively strong as FP16 processing vs a T4, which tends to operate best in the INT8 mode." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 377 + }, + "id": "IJBfZsGo8yaV", + "outputId": "f4c4e20d-fcfd-43a2-b10d-c6978c25c91f" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Wed Jun 9 19:47:48 2021 \n", + "+-----------------------------------------------------------------------------+\n", + "| NVIDIA-SMI 450.80.02 Driver Version: 450.80.02 CUDA Version: 11.3 |\n", + "|-------------------------------+----------------------+----------------------+\n", + "| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n", + "| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n", + "| | | MIG M. |\n", + "|===============================+======================+======================|\n", + "| 0 Tesla V100-DGXS... On | 00000000:07:00.0 Off | 0 |\n", + "| N/A 45C P0 63W / 300W | 5572MiB / 16155MiB | 0% Default |\n", + "| | | N/A |\n", + "+-------------------------------+----------------------+----------------------+\n", + "| 1 Tesla V100-DGXS... On | 00000000:08:00.0 Off | 0 |\n", + "| N/A 44C P0 41W / 300W | 9MiB / 16158MiB | 0% Default |\n", + "| | | N/A |\n", + "+-------------------------------+----------------------+----------------------+\n", + "| 2 Tesla V100-DGXS... On | 00000000:0E:00.0 Off | 0 |\n", + "| N/A 43C P0 41W / 300W | 9MiB / 16158MiB | 0% Default |\n", + "| | | N/A |\n", + "+-------------------------------+----------------------+----------------------+\n", + "| 3 Tesla V100-DGXS... On | 00000000:0F:00.0 Off | 0 |\n", + "| N/A 44C P0 39W / 300W | 9MiB / 16158MiB | 0% Default |\n", + "| | | N/A |\n", + "+-------------------------------+----------------------+----------------------+\n", + " \n", + "+-----------------------------------------------------------------------------+\n", + "| Processes: |\n", + "| GPU GI CI PID Type Process name GPU Memory |\n", + "| ID ID Usage |\n", + "|=============================================================================|\n", + "+-----------------------------------------------------------------------------+\n" + ] + } + ], + "source": [ + "!nvidia-smi" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Remember to sucessfully deploy a TensorRT model, you have to make __five key decisions__:\n", + "\n", + "1. __What format should I save my model in?__\n", + "2. __What batch size(s) am I running inference at?__\n", + "3. __What precision am I running inference at?__\n", + "4. __What TensorRT path am I using to convert my model?__\n", + "5. __What runtime am I targeting?__" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. What format should I save my model in?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our first step is to load up a pretrained ResNet50 model. This can be done easily using keras.applications - a collection of pretrained image model classifiers that can additionally be used as backbones for detection and other deep learning problems.\n", + "\n", + "We can load up a pretrained classifier with batch size 32 as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "iVRVItvR8quS" + }, + "outputs": [], + "source": [ + "from tensorflow.keras.applications import ResNet50\n", + "\n", + "BATCH_SIZE = 32" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "cKT07xPV8qua" + }, + "outputs": [], + "source": [ + "model = ResNet50(weights='imagenet')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the purposes of checking our non-optimized model, we can use a dummy batch of data to verify our performance and the consistency of our results across precisions. 224x224 RGB images are a common format, so lets generate a batch of them.\n", + "\n", + "Once we generate a batch of them, we will feed it through the model using .predict() to \"warm up\" the model. The first batch you feed through a deep learning model often takes a lot longer as just-in-time compilation and other runtime optimizations are performed. Once you get that first batch through, further performance tends to be more consistent.\n", + "\n", + "To create a test batch, we will simply repeat one open-source dog image from http://www.dog.ceo" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(32, 224, 224, 3)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "from skimage import io\n", + "from skimage.transform import resize\n", + "from matplotlib import pyplot as plt\n", + "\n", + "url='https://images.dog.ceo/breeds/retriever-golden/n02099601_3004.jpg'\n", + "img = resize(io.imread(url), (224, 224))\n", + "input_batch = 255*np.array(np.repeat(np.expand_dims(np.array(img, dtype=np.float32), axis=0), BATCH_SIZE, axis=0), dtype=np.float32)\n", + "\n", + "input_batch.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQEAAAD8CAYAAAB3lxGOAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAEAAElEQVR4nOz9WYxtWZrfh/3WsMczxhx3zLmGrDG7qrrJbplkAyRNGjYEw4ApGbAeDFh+0YMBP5jQkwG9+MED/GSbhgDDpiHLgA3TosmmRLYotbrZVd1dVV2VWZmV053vjenEGfe4Jj+sfaOachVFil1gAp0LCOSNyIgz7LPXt77hP4gQAp+vz9fn68/ukv+qX8Dn6/P1+fpXuz4PAp+vz9ef8fV5EPh8fb7+jK/Pg8Dn6/P1Z3x9HgQ+X5+vP+Pr8yDw+fp8/Rlfv7QgIIT4a0KInwohPhZC/M1f1vN8vj5fn69/uSV+GTgBIYQCPgT+CvAU+APg3wwh/ORP/ck+X5+vz9e/1PplZQK/CnwcQvg0hNAD/3fgX/8lPdfn6/P1+fqXWPqX9Lh3gCd/4vunwK/9whehZdAKRmWJQGCsxXmPdQ6pFEIIgg+IAAiBDx7nLFIKhAQlJd57QACCEEAKifcBrRTee0IIeO9IkoQ0TbHW0vcdUim8d2RphkQSBHSmx4f4s+A94uZxA23XgxBkWYIUAu88goB3HqUUWmt6a3DOgRAIpSCAcx6tFEJACJ4sTTHGYKxFaY1Sir7vQQSyLKVrW7RW6ESSppKubzk8PEDLlIuLa0wHzkGaAMqQFwLnJW0NSTLC+x7T10gJwidIkRNEwPke7+N1lUqQpglCCJqmxXlPlmWMRmPq3Y6u7ZBS4kMABEKI4RMTKCWRQmKtRWuFsQYpBEWRIxAIqTC9wRgDAtJUgQg4Z/E+oJSOn5EL8b13PW74fJx38fMUkKYpQgicdTjrIATKskBKycHBQXxu03F1dYX3EucC1nmss4QQyPOc/YN9lstr2rZhOh2T5xnT6ZjO1iyuV9R1T1nkCCHpe0vwYE18vuADPgSElGRZRp6nOG+x1mKtwYeA9wEhBePxlKZpQARCCAQf4jVyDq00UsQ7SSrJ4eEBm/WaqqoBCYKfXV8hEAK8j1m6khIhBNYN1wWQUkCAsizoe0PXdvF3dUKRF3RtS6olSivatmd/f48nz86uQghH/3/777/2Nv+XXEKIfxv4twGSRPL2lw956+5rSCRt33N2vWCx3SLzjFxn7E9muLbH+kBrGozt2FVrxpOE/f05zjnW6x2CBCkypEjoq44izciyjLquqOsdt26f8s473+C9n7xL27ZcXl6ytzfj3u27CCPopWexvaYyNV98403q9Q7XGvrOYoLk6fklJIq7p/uYpqGrGgqdkkrFq6+8wmgy5ve++z0u1tdMDvdIixxjFM22ZX9vSpYqunrH7dunXFyec7VYsHd4iFSa88tLmn7Dt7/zOrazNE3D3XuHCLljPJX8jb/x3+PO6Rv81n/0Pf72//m36OuEu7cyiv1r5rccXZ+jxBd58NGGansG/gw6Q2JuM8lfxRc9Vb/gen1FMcoYTQoOjw+w3nF+eUmaZnzhS1/i3t37/Od//z/h4uKSICRKpSR5wWa75Vfe+TYEuHjxIm5K50lzjRMd1nbcv3uHu6d3cUby4JOn7KqK+f6E6X7C+eIBL86eE1zCbLoHTjIppuRJydnZOVVdcXh8iA2GXbPD+Rggx+WE2XhKrlNs0/Hnv/Md7pwec+v2EUfHB/znv/vbnF+84N33zrlcVBjnWW13bOuKk1un3L5zTFXP+erXvsB/86/+JfbmI14sHrD1T7m82mD6hO3G8v57D7m+rLg4W7M8X9JsHUVaoPOSq/WWv/7X/zL37p/w6OEHPH76gOvVgqazzA/22DYdt+/e5/LqmnysSXSCaQzbTU2iUn7zL/wmP/7hD1lfX/HX/tpfIUkS/v2/9bd55dUJzms8HkfAhYAQgiRJ0EqRKI0Wkq5paawlLUqc62mbHWVZ8tf+6l/h+3/0Qz756FMSXXByfIff+I2/wMcfvIdZn/Ptb3+Lf/Ld32cym/Hk2dmjn7cXf1lB4Blw7098f3f42c0KIfwt4G8B7B9Mw2/8+r+GtoHHD5/QNA3b7Tae1FKgEMzmE84ePccLwd7ePuUo5ZNPP8B7S9d17O3tEYJkvaoQMtycKt578jzHmJ48z9Fa8+DBQ6RQOOfI85x79+4RbMD0PctmS+d6qrphs92SCInSGlu1eBSj0QiZJYggcMaT6ZRUJ3hjaaqGqm7IsowvfenL1LajMQYhwDpDCAEBWBs3uDUOrRK0VGyrit4YkkQQfEOWFbx4vo0X7+4xtgt8+JMz/sP/63/M9//oQ3aVQjMiyyd0bUpf5ayrlm98/RbOaN770VNm49usqzVajtGpYtc3VHVF27ToRNA2grMXZ7zy2qscH8HZ+SWLqwVlMSZJUpx3SCVwePp6S5InHJ0ccnVxhfEOEQI+eDbVhje+fJe+azi/fEaWJOzNjijKFKUlOlFsthuauiXRKfloGk/b3hAKqOs6ZlJS0fcGh6XvepIsIUkTjDU0TcNkf8TJwQldb5ju7fOTDz6k+1HF4dE+u13FrvqEtq0RWqOSQF5q0hxsaPlv/Xf+Kl/8wn32D0co6chzwdnVFu8dk8keDz55wHvv/pTNyrO6ahHeM8pTbt++jUoKrjctT58+4/mzB7Tdhu22ouss870p9++9wicPH7HbVkzGU2yoCM6hlKJtW2ZHc9566y1+/MOf8M1f+TZf/fo3+Fv/x/8DxVjSGwcIrHdY73DBEwDvPbKIGY9znrpp6J0nzXP8kLHdvn0LKRXG9CitkFJgTE+aJYzGYx4/+4jnl+e0pufq0cNfuFl/WUHgD4C3hBCvETf/vwH8D37RL3vn+fCnHzHLR4QQUyhnLZPpBCsCiZYURUbAkqYFeZEjREBJjXGGtu0pipI8H7NePYgpqlIkiSZYj7V2SHtjWg5gjMUYy+HhIY8ePeHV+68wGiVcbK5BBpq65npxzaQYUagUayyti+mfEiBcQAVQQuCtw/Q95+fn9NYw29/j6PSUj588BALeOvquwzuHdzGFbtsW7xxJmpAkKdZuCM7jFewfltQ7gfPw/MmaPNnj9Tdu84//0Q8pi5JJOaerKhIFSa7Q8phf/dZf5Gpzwfd//IcsrhakhUTrGUUxQvkMmViCjyVQkiTMZjN8iAF0tV5xcXlJmuY0TcuTJ0/pnYmllwgURUZelqRpxvf+8LuMRiNu3TlFenj08CHT+YiDkxkXFzVXq0vSVJPnOS706ESz227Z1JfIJDCdzpAU1LuGPC9xxmM6R1EUjCcThJa0piVNc7I8ZVNvSVRCcAHbG+pNhe06kkTzwx/8Id/5zq/w1a99k+9+73s0piFIi9AK3xnKUcLewZjpLMeFik11yf6RpCg0R9mUnzyq+OTjp9S7B+y2kpPju7S7CwQtUii0SvDBgbd8652vUxQZjx9/RFEqRmVGbyreeuMtsmKEM57a1oymmjxPqesG72P637Ytn3zyCavNgmL0Fr//vT/g2fMz0rygbw1KxtNfaYUUGiEEUkoIEEIsaYsixzYdddMgROC1V+/z9a9/nfPzC4yxZFkGXmFsR13t6ExL5w2Pnz7CuJ5yXPzCzfpLaQyGECzw7wD/AHgf+H+EEN77Z/2NsZ4sK9hstjjnef31N9BK432s/5u2BgKj8QhrLI8ePaFpO6RQKKWw1iGlQusEKeUQQV2spaxhNBpRliVd13FwcMhsNqOu6hhIjGE8HlOOSqSUZGlGmmX4AF3bEUKs1bquo+9b+m4oyK2jq1v6piXVKVmaMh5PcN7z6cMHrDcbnHPUTY3zDiFjXyHWuBYfAlmaoqSEIHDOMx4Hvv2dbyJEYLep6VpBvRUk4ZCL5y3vfO03+Df+xr/Fwf4B682CIFt2254//IMPefz4jIurR8hsjc4bNtsKqUbUXUVtFhSFpiwKDg72uX3rNuVohJCSq4tLkiRjvrfParnmwYMHnJ2fo7QiEDg+PeJLX/4SAU9vO6azKdYbrlcL6rbi9v0TrKhp+g0yDbRmR9WsafuKXbWl72PAI4AUiqbpyLKcLMtp25aqqui6HmMsTdNirUPrDO9jf8g4g1ASISUeaHvDw4ePsNbjUXzve9/ngw8/pWladKoYjTOk8hjXYlzL1775RS6vn/GDH/0Tvv/Hv8fj5x/y/OwBVbXl8OAAYxyPHj3n0cMz1uuWJMnxHoRUSClp6pr7d+/yza9/nXe+8Q7Hh8e43nL75Dbf/Oo32J8fcDA/wPaWro59FCkkIsSeRlVV/MH3/wjnPT9+7z3+4W//Z+TjCZ31+BB7RDEIaNI0JU3iPWydxdgYjNM0BTx93+Fc3PTz+Zzz87Ob3kdRFLH0bXZst2tkIlGZJi0zVPaLz/tfWk8ghPD3gL/3z/XLQrC/d8jdO/dYXi3ZP9zj9qv3+fD/+3fIJiMmZYm1PVKCVoreBapdRV7kJKkgeFhcXSOEvmniGWMRNjAqCrI8Yzyd0DQNvTV0xnDvlVe4vLpitVlzfHLKaDTG1fGEScqcY3nMdDymWseUXGmNAALgrMV1BokgVZrgPQzNszzPebG4pLId6aggCInpOoosoyxynOlItCKEgJKSIi+GJpPFGcf+fESik9gos4GutohQYPsU02f8p7/9+3znO+8QvKLrLElhmKg5L86uyKZb3v76MSe3cn7ywwXvP14wTlKc3zBOEpJsRqI1idYoJXHWUe0qDo8O+cY77/Dpw0fsdhVN3VIkCqSImcd0wnq74sXFc77yla+yP9/no59+yNXFFc5YVtsl67Dl6NYebbdld71F54L9wzlnT68QAsq8RKeO+XwPO0nY3ztgebViu6xo25Y8y0nSlDTN0GmC0IK2a5FSUdUNZTainIyYjqe0bYOzCePpnB//+D2urhd4L+n6nnIyZjKdstyu2awXPH3+iB++WzCdKerqEuuvcWHNwcGMpq55/OiaD396xYtnPfVOYo3GG4+1kGY5ewd7TC3YvkdLwav37pMlgsvzFxzsHaJQJDLhYO+As7MFwXm2mw1SKISQSCEw3nJ5eUmRj7hcXGOMI5MpMsmQ0qF8QGgFOjYAAyCGBqBAEIKPt9fQkA0hcH5+zuPHT+i6Dq01AolEoZOEq6tLttUWlac4EfBSoLX6hdvvX1lj8E8ugcD0lqpqAMFicc3TyzO0Tjg+OmJU5OgQKEc5velo+ljnz2ZjpILNZsVqtUMgSZIcQsA5RyJjRzrNMtq2xRhDmqa0TYtzjldefZWHDx+SpjlKaVTmKcoClaWkZUGRZexWWwgxPdOJJhcC6z3eOnBQ5CWm62nbls5aUmupmxaVpxRZwa5p8N4zn48ZlSNWyxqIaV6WZUwmU65XK0xv8D5wfV3xD/7+f4YzKQf7B5y3FbY35FmJ6QKPHpxxfHjG5fmGk8PbJBnstlu2u5bZacrBUcJsP3BwkqLLhiIPHOwfM59JLs9a+q6n7RuW1zmm7ynynL29faxxtHXLZDzFO8F0UlDXNWVZstqsePz0KcW4YFutmUzG7B/sUVcVfWd49OQxxVHP/funnNzeZ1LkTGYjMjHh+nJFU7VkecJkOubOnXsIWUIQrK63CCEZjUYUeUFelOwfHtI7Q9VUZDns7e9R72rSNMEYw/VqSTCOUTmi7zqsNfS9YzrZY121eC/oh1LP+zjNaduGNM85u7jE+Q3zecqrr93hYP+AP/zep6yWHbYXVDtHqjOSJCdLM4QQ7KodRTbi8aOHrJcLxuMC61q0TKh3Nd//ox+CUiyvrmOgSFJ0KhAagvNYY8AHrLWxtECg0oS67XDOMSsLpI0lDEoMkxhQSYJSCjHcyyHEe94GR9+1LJdLPvroo2HSohAIpNAoKVgsLrHOkBaaTV1hraVMy1+4/z4TQaBtO+qmpeu62ARpK86uLxgfzlFa0tQVWIuQkt1ux67uSdM4yhqNC9q2xbkGrdKhGehQQZIohTEG5yxXV0u898xmM9brDSEErHW88spr7O3t0fcWaS3G9HTeIVON6XrqqmFeTAghkKYJeZ6xrWqEF3R9T5okOO9x3oOF0HWAYLfdEaSgGXoBo7Ikz9MYPAg3QWBUllxeLbDGQhCcPTMsLp/y9pfv0HU9+3sT0kzQ9juyNOPenXu0dWCz6ijzCU+fXpHqfdI8cPvOASE8YblcgQgcHKbcPTrki1+4R3ANFy9+grWWznRsNjvSLOHNN96kHE/46KOPqXaxsUkA6zxpnmGt4+zsBbt6w1/4zf8GH330ETpV7B/s8eDTBzGjCJ6ub3j67DG3T0752he/yuLFjnbbkGhN7QNSKPKsJFEpnQm8eP6czWZDkiSc3rnHvbt3OTg84u4rr/Di4pwXZy8oxyWzozGXlxdcPD9js1ihhWK73CBEHFnudjXzeZwOJWlJ21jqbsHyeotMdUyPq5bXXr/FF976i1ycfwpI1ust1oA1kCY5e3sFfddhWolSCXvzOc5seXF2xv78gFE+x/SW1fWSNFUcHxxTtRVPHj0iLUvOzi9IEkWWKO7eOY2Nyu0O0xuCDySpGk50H0vWQiGAvqlJRTzHCeC8v2lmJ1pj+h5rDFoqyjKnahvCkHUulyvKLI+jxRAQEooyZ71ek+UJInFYPCDZNtUv3H+fiSAgBJjesFwuKYqc1WZJksT59XazwVtDJhUJKs7bdcKbb77BV7/2Nlme8A9+67domx4pFUqlpKmndR3OO6wxrDdrnPf0pqceLmKaZWR5hpCK58/PmE3GTFLBbrejmE7p+562bgkh0PcW7wKj8ZiTu3d49/2f0DYdcpjrChnrOaQkzTKO51N2bUPV1jjn0DrejCHEOXlRlAQXm4XVrsL0Buc8wQe6VpIoyeq6JUsTyjxDyIa+3zCfj8mznE8+fkxwKX0nCS4nLQre+dZXmR0FHr14wv7REUo0INbMD0aMJhPOnm0JQcZmKfG58yLl8OCI69WKtu7QOsG7aqjdNUIJmram7Vq0Uvylv/QXWCyu+M53vsXf+4/+Pkorml1NOhaUZUnb9jjrkUJS7XasFy1pmjEejxHSs1lvabtHeFKMscznc+588Ta/8s13+PKXv8xs/4BiNKbuWjabNTrRlPOMtq05e/qMj977KRfPz1hdLxEePvjJ+0wnE4IHJRQEBQgWl1e0jSVBIULCelWxWu6QwnF5sUFLiTUPObu4Zn++By7Q1Dmr6wVt3ZEXOa+8+grnZ49YXm+xxnDr1VvcOj6mbSvG44zRuGBbb3ny7AkfP3iAEB4hBbduHfOFN97i2fPnmM4QnENISZpolIDGxGYpztM0LZkSGGvIEh3vD2tpux49lG3OudgkROC9J3mZIQxjRB9CzBYGHMU3vvE1fvjDH+JxEUugBKCGcvXnr89EEAAI+FjfSHkD5KHVeNOiAF0UJDLWWHmeIaXC+0C1a+haAyjEUIcpGdN3Z3qcczRtS1mWFGWJUgpjLJPpDKUUT548Zb1a8613vo51BmsNIXiUTuLr8rG5Y41FCslsNiVNE+ymRQuJCwEPBCnRaUJWFMwP90mqHd1FjwgtWkqSRNHUFcYaZnqM8R5jYi+AALZ3JEmKFJpUKe7eeo3t9hyE4eCwYDpL6NsM23e8eHEGQWJa2J++ScBy9/Y9anPJxVPPfHRCSst2fcn19QoZCp4/WeCcpMhzptMxNkQAy3q95vnz57RtS5aVEGJDq8zH7OotXdujdYJONKvlitu3TpmMxzR1TfAeZz1pkjMpx4yKnN2mjmPeuqfMx8i0QMuMpq3YVTvazoIqODo85Ctvv82vfOObvPWFL3Kwt49OUlCKPaU5dbcI3mNEg/dTDmd73Dk6ZbNY0lQVTx89YbNa8eUvv81qteXDjz5kMppifeDSXyPQCK9QImN9veWD9x5gjEXKFuED5y8WeBSpHrNZX7JeVfR9h04kUga22xVKCXSi6fqOPMvYPzigqVKKMmU8KSjGBUmmObt8wWoXMN5wfLJHolKuL5dsVhskguA9fdcgBaQKvO0JHrI0YZSmbNfLCDwS4EMgSeK2tC6WD0miEQGcADGk/+ElAE1rrOljc5nAdrthPClZrK6RWuNDIACoXzwD+EwEAaUUSgmM7cnLEZPxiMX2GmEMxngSKaEo4SWa0ASePn3GdrvFOcN6s8M7j8EhMISh4/qyEx8nDA6lNCA4OjpCIOi7nq7rqZuG7XaHLmPDrq4qyumUNM3oiOOquqppmprl8hoICKXwHmwI2BBwDOhEYLer6Lue4ALBBhKtGZUF1XaDVoo0SWibBq3iZCIEKPIcGyBTY6bTKXdv3+fd959RVSsOD6ccH8/pasvZ8zUhGCaTAiWhryaotOGPf/A+UlsunwlCu6PpGuqt48XzBaYes1kFbAQ7kqYp0hm89zx/9pzLiyvSrEBrj5TxNK3rlr6z5FlBkim8sPze7/4u89mMn7z7HtVuh7MRi2H7QLv1KGPYmYom6ZjkexwcHGI7RZ44lJQY05KXJftHt3jl/n2+8+1v8+W3v8y4LJEhELwltl4FUkiCEgQDpreoINibzRglmr6dcry3R5nn3L1zjxcvzsmyhMfPL3l+domWGiUUIigUCfvzIxLpCaIiTXLqrcWYjiQrMK5hcbmmaTRpmjOdTkiThMX1FUdHUwJT6k1sUAqpSJIUEPHAyhRKS3ywjGcFvTME0bPd7Hj27AW73WY4xQPe9nStJ0tzskRxsH/EbDbn+uqK3WY14CMCeZEzGo/p+z5OoULAMaT7iUKIMIwQI4q2qRvqpqYscgiOn/70fb705S9StTtWTR0zVCD4z3gmkCQaKUESSFPNZDphtBnjtaI33ZDJSLwXsYE1nPCr1ZKXBChjPM6Zm3Ge9x7vbIT2hthpTdOU3a5ib38/jqLcANZRKdvtjoPJHlmWsqp26KJglI9pk/ZmbltXOx49ehSxDInG9obe+wj0cA7lPb5tuVou40gnCEQQpEnKeDyib2uKIgKWnLEUkwIzlBqT8ZTNbkeiMk6Ob9N1ESDTNC3n52dU1Q7rLBcXL0iTwGgEXddwdV4z24c/+O4POD05oV3nfHq5pek2CJGDTxmPjuh2KevtmuBD7D8I6LuezW4bN/M4GcoACUh26wqpBXmW4qwhK1M+/fgTXrl/nw/f/5C6qsnSgiRL2DYb6pWllz04QyNa1H5JeWdMYx2dqxmPpty+c8rR6TH3XnuDWycnvPraq0wmY7zp8SFmrt4FjDEonSKkRCIRHpyx2C7OwOvtjtlozBffeiOOdvOMo8N9Hj2/4o9+8CPquuLy+prOGqRQHO2fMJ1lmH5D12/Q0iF1xnpds91VpElKno9RuiBJS6RU7LaGvYM55Uhz3l+h0wwQyEQjZKCqKzI0dbNDqMCrr92lanc03YZmI2mqBmcsKkvQiYLgybKEr7z9JaSIB8jR0SGL8/MbwI/DI5ViOp2yvL6mNSZmWxhwAV2mqFQjBCil0ToGK2t7Ei0Bx3bXs7c3Z7wYcbHZkKu4xZ39jAeBWFMLyrSg61uapqEoCjrhkc4AcQzoh3mqQGCtjThz54dN6m9O/ZeBobeWNFEopTk4OuTo8Jh33/sJZ2fnjEYjlEpiHZ9n1E2DUgfMplOWux3eB8pyRFe2ZGnGeDzhYrmgXi6ZH+5juljfCxmwHiye1vTooOlNj5QSJQSZ1qRaoVUc0UghMdYCERvedz193+M8JDrhYG+PNJVcXp4jhSDLMjarhmdPLhgV+3RtRV4EvFiDMti+wVuNDJLgJF0VaBtH7yAtU06Ob/HWW2/huo/YLGIdWRQ5PniaTYM1ljRNSVRCayzWxFqyby3lKEehadqaLFOs1itOj08iGEmnFGnOKJ9gjMU1lsm0QMie7WqFHwtsF/AuNt7u3b/N21//ErfunDI/OGQ0HkUOQCzowXuC8LiXtXIBaZqhpCJRCabv8cZie4PpWpZd7NdoCQTH8eE+hye3yfOUk9NDLq6uefDkMZeLK4ILJDKjswItMsZF5G0sFi3eSg4PD8mKKZ1xbDY10iuSFKSCsizJsjzW4VKCl7hgsd5RLdd4Ybn/6h2mB2POrzydaXj+rL7B+EMg1QpnPHvTGf/6f/uvc352zm/9/f8YYQ3L60XkwIh4j3dtN/At4mEWfKA3BgL4HjI94CWG7FYIgVKx5pdKkucJi+tL6qbCBxAiZrc+2F+4/z4TQUArTZomKC3ZrDYsrhd4LbEi4D3xNPfgvCBLc7yw7HY7xuNJHAM1zZCmxc3vfXzDSisQgvFkwv7BAWKYsV5cXjI3hul0jk5S0jSnqmqM6SlHJYlOCCFG2ywrKMoRSaJwD128YUWsz9Khx+DaFoKPUFovyYsSCfiXxBEp6bo21n3e4V1EyKVJQtN0KBk/qPnBHvfunnJ+8RwhW4q8RAjYrhuW1x13v3LEeDxitdnS9hvSDLSumEzmGJdjuwbTVQQvSDQkSjAeZUxmGpX0COFIk5TZdErV1Fhrh9FnSgixOWudRwWPRCOCAi/QUmN7S111BOfRUpHIFNtbRCaZFHM22y0ne/eQ9NitZW96RNdYymLG7Tt3+MrXvsT91+4wmpaoLEUpicATvEXgbpq4bWfYVi06zZCqRCBBOWwIeGcRwSPxERXX9lTbDVIm5HlBUk6YTQt+9TvfpLeBjz79lPd/+lOulwskglE+oiimSOHY2ooi36MoBSrLyIqSbV3z9NkTrPWURcF6fU2R5zCQdwIR0CVlIMszlptzklJw+94pdbeh6bfs6prFlSMEgdYJiZKkicJ4w+nxPvfv3qKrdnHacb2krRt0lscpC4F2GP8x3M1DRT+Q1SIy9SXRqaHl5OSEzWZF3zeoINBJzieffMRqt8OT4l0cR3v3Gc8ElJakaUyRvXf0fU8ICkMgEBBSxYYVEYeeCoHWMR0iCPrODAysgA+OEGIgzbOMEDxJkrLb7Viv1rRtOzQeY4YhEIiB6dV1XXwcEfH9xhikkHRth1Lx8ZMkpelarIfJfI4A6q7FhhA7wUimo4KDvT36tuPs7AwI7HZbnHOkaUpZ5KjRiDTN6I0jCM1oPGPv4AAhHLvdJVmmODreQ20TpJ9S7zymi4Gt6bbM9hWHxylm2zKeBtbbjs16RVFqxuM5nakRiWdXLXj67H1Wmyf40KPUCOc8bdsSfEQsIiLTUrysxUMgTVKc9dRVQ5IrvLNMxmPapkEEkEHQdD2NrCnyKVYLsJosTyjzKfPpAePRjDde/wJvvPkmr7x+j2KcojOJ0BKCJ3hPcBbbd1gT8RXrbU3TWSbzfYQUCC9QQqJkzKa0FDhrqHdbqqrCGMt0usdms6Lzz+iMZe/gmCQree3V+8zmU54+e0rfN4xHKVI6Ls+fkyYjpJ7T9i3bZocQgTRTBOEwtsU6Sd3scM7hQ6Dre7oBlzAaZ6RZEkFU1RX5VLGtVtTtlrPzDcaW5FlOliucbSB4ppMRb7x2j7NnTzh//hRnehrbc7h/SNU7jHekKh5a1tqbrDbuD4VEYIPDGIPWKQLQWnP79h36vqOpdygV+QPb3QZrPagc50LskXnxC/ffZyIIxM47JGkCQiCVRChNZw2JToabQOKtx/aRaDGbx+6+dyGezD6OUoQQSCVumidSSNq24cWLlrpu6PouEoKcQ4iA1prgXcQZytgZDz7Qty2m73Hecn5xzmhUkCQRh9D2FtCRhuwcfW+o6wYRAqrUSK354pe+TFPXvDg/xzvYrnakqWY8mTAeFWid0HUdPnisNRweHZGkGR998hOC6hE6YzqboVTByeGM1bJmu9silOPo1oSvfP2E01slH7/rcFRM9wsaU3HrcJ833/oCH378Iav1il215MWZYFcvEMojtWS727DbbkmSBKk03VB7ZmkCWHpjiKxdB85RjEY4AQcHM/q+IxDQSWyKGW9IvCXPC87OLzjYG1OWEybjCa/cu8vbX/4Ch8cn5EWOShOklghhYxBwsZwxTY1znmqzYXG1xAYdiTQhILwYGnIZNknj9XKG3rR0XUO9q6h3O4SQOK2xztP1PaCZ7R8wH4+YfvELOG/IMkVvWqaTCW1v+OTRCx4+fkBVV/QYgggURYJSkGiFlGD6DiE1LjjqrkYIDzKj7Rtme1POl49RlRsg4AVts0JqwWQ6phwlrBYd3luydESepTx7+pS6rrh9eoQUgte+8CX+4I9/Qtd7RqOS8XTKdlfFA4l4b2sVpwPxwAAVuBm9ZnmK8wYfYnaptUBrgfUChEIGInJRJb9w/30mgkDfdWRSM5nMeP7iHIsgSxK0jyevMxYlAnXf0FlHkAqVpFgX66Ku7xFCoEWs46QQIALexL4BzsUbnbgJnAsY29N1LbdPTrFtw7rdEVRKniq00mx3Fd4YwLNrtvS2i131oCiUZnwwo6mrCBn2Fte3MXUdyoKQKrQs6QSUIaGvHPuTfebTMQiHx3F5fcnZ5TnWgQuWy7NrNu2SkzszJIradtRdQzmaoLIGr9dkU8e3vvEr7B1p2q4lKXq21YZ8XrCfTLn/xjH7pwX9w5plvaGYTNBygpY7OrnBqzhPdsFBiNcpWIeQnmJU4ENL19d4GUFXzhlQGVJ4yjKlqnfko4Ss0NT9jqTI6OyGyXifbmvY9TW3Tw65ff+Et966zdFBRlF4lAaVlQgZCH0Tv0xN6HaEtsIbS7dasjq/ROVzgg84OwQBoZBK4wVUfUvjOpwMuGAwpqVabijyAjmeEaTG1g3eBTZdR16OmO7NGI1HJEUOespoNme5XvLg2VPqdkvb1+zMLiJB8UglCd5h+o5E6pgFKoMJDc5aqsslu+qKO/cOqPotdtsynk4Z1yDDApn2HJyMEMIj5RRrOpbbHT/+yQd842tf4/4rr7G/f0Jwlr29Oe/++Ad0sudgOmfvaMZuu8K7HjE0tKUI8YCzmhAUIcSDRsjAcnNF0+1AWdICVOJQ2qGtR3uB7OMYUYh/BdyBf5HlrUX4OPronSPIl8IgCukDRVEQgsOYlt56ZJJTVTuyrAAEzvsb8YsowCDxzsRmCQJEFG0IAtI8ZbvZkSYZm82aLNUcHx6wXl5Rdz0nJ6cUWcFyscRZE5uPIQYRIRR9azk8OuLeq6/w3nvvonVMleUguiGkoO07fvrxR7gAje0pVU6RlJyenGJcg/OGbbUaPsCKvYMjetdycXnG/umc8d6U3WrHarPFGc9yfYVzLcvNC5I8cOvebZ6+eMj3f/Axpd9HJoA1JKOUdJzw+PwRV+slq23HndMMZxOcTfCAlx6VKpTWVNsqCnsET5ol9H1N3+8wtiEvZhjjMI1BJQzXMACe+XxGQID2ONmTFjn5RJGUc7TSnN69xf1X7nDr9gFJYpDSoFOF1AneG3zfY+odod8R2i1dvaFtWqrlmmp1TTrRBGMjq9IGpAqRUQeY4GmNwXhHb3ps3yHwCO9wvcELh7AOLSX9rsU2Fc12xXh/j8nhIeV8jkoKgtxSjlKyQuNWsRQxzmOtv/ks+64lyUsQlrpbo7ZQ1TvqZoNQPXde22f/aI73AdOD6QTBSvJCcHxrj+ViwSuv3eX6asGzp5d8/OkT3nrjTWbzff613/iLCODdH/8hiewoEk+RCVItsKan71tARhCUAKUFwugBEBUFc+qmout3uNCT5Yqs0OhUojqBUgJvLM4HZPADAennr89EEAhEhl7YbiMSSic4N7DtnGc2m9I19dBAc1hrb9h/LxVvQoj9g1j/RFWiTL5kngWCj8o/SZJSqZoQArvdjqqq2d/bw4fAbrvj/v0oQpIkMV2P8GJLOiC6IpgoQp2LoiBLU87cixi0ZKxdvXM8fPiQ3jqMNfjEsn8w4+TkkE8efoCnp+0qrO04OJpydLQHwnJ0PCMpJcurDZvljpPDU27dPmG3XnN1cYl1Lad3Zvzk3Z/y6OkjHj8859Ys4fBoD9tbdpsdz59ccrW4ZrfuyJICETQX55c0dY1QAuctUqUEYhmSJmmcXAi4ulpF+m+qBqUkzcHhmOPjE1bra5yNaM08K1httiQ6ZberuXU8R0tFlqS89uqr/Mo3vsbd27co8jyOfrVCJpqAx9keZzr6vsO3LbauqTZbttsd6/WOtmkJaYc1HQSPRyAGZZ8kScmSFBkEwQa89TgXSLIcpKbv49jXKU2RpngXpw2WQNO3dM4yDwGV56gguH16l9V6y3K9paq7m3sJIZEy9qh6F0vVF+fP2FYbvHe03Y5bdw44PT3lzp1jttuK3/md32dxuUP4yCfRQrHd7DjeP6JrDVpognN88skDZuM9vvn1dyjygrPzc3SiyMjojcU6x2Q2oen72BRXkRQkpSCO/FVko4o4NbBNG/UF8gStYkPb+Qj79sJgQ+QdvJxO/bz1mQgCANZZ+u02jveEwBpLANIkIQwXI4SYFtkQhsgmb2b43vuhq29puxbwFEkewRLBx/EOgBBMJlPqXRVhybsth/sHKK1ZXC/Y7WKDpSzLKPwxdNCjPFkUc1gsFjxfXHB0vI8xhizLaOs29iKIGIWm6bA+QmilAp0EdtWKpt3R9Ft6U4NwzOY5eSlR2nH77gEfPXjE4ydnSARH87uM8imXzxfUu55EC5TM+OiDT3DCkmrw1lEWYzb1huvLHc4qjPF4I9mbzkl1wWZ3jfcepaKAR6rtjTxbnmcopai6mt4E0jxhPp+wWNSMJ1OOjw6ZzfYiUce0GONp6pZqWwEC18c+TTHNOTk55ptf/Srf/OpXmJUZioiVV2mKGEA1zvYRz+Etpu+oq4pqV7Hd7thudzRtC3l3Qw6SUg2NMU2aJEyKEdusoBcbEhm1GLxzGGvp+i4iMLVGhxxc5HQYP5BwROwvFNMZSMG0nPLqvde5vt6w3bWsdpvYTcfh8Xjn6IxFSYXdrNizc2bTCVJ7skyzWi0ZjXJEUFydX7NetZRpgRSSq4srNqsNz56+YLPacHpyRL3b8PjRE8pszG//o3/MyfERQkiKyQRXt0QtONjb26PpDF1n4vRGJRFIpUAy9LqUIASB8wHrAyFI2t7hvaVqDKZ3SO1wysVGt/yMjwiliOOU3RDV3KClFoina9O2KBHYVQ06zwewhY/Y6EH7ryxLiiJjtV7inUcnEjPwsaWKvHAXAt450jShQUQu/dWCO7fuMJlMuLh6ymazQSlFnueRMzCQOYKPwSZJEparDdfbJeUoIwyPF0VLYqAxXY/pDUiBVprxqADhePb8MbtqTWt2JJlivj9Gac9kVmCtZ7m+4PzsktV1Q5lnrK93vJAXPH96FsEeXoGT5OmI2/eO2K4/4PDggMlowmq1QbqU3bUhy0tSMWacTRgXY9pRz3q9QGmJ89D13YDNUHR9x2QyplQ5b8znuNATpCPdetI0IyDoe8tkMmNx3bPdVEwne4zLKYvFNVlaIILi1ukpX/vK27z56n3GRYZWsckqk5SkLBBKRg0G3+P7Fryl7zp2u11s2HY9XRsxE4k1mL6jNy06TUhVDs4ivKFQmlle0kpNrxN8lrFarWnaFmsbQnB4Uiw2Vi8IgonlnPGegCAgyMsRtveM8wn3777K9WrNrm6wzseyz3RD03eA7qaB1myRjWM2LREyovOscYzyGV1twAayMuNwb45WioO9QxaXC5SQnJwec2YdXdPx6MFjzp5d8Y2vv82v/vlvko9GNNZTTKZ01lK3LUmaEm7kPgTGxoAWBAjUULIMmW6QOC+o66gf6azABYFMQOpY7vWh/4X77792EBBC3AP+L8AJMaP/WyGE/50Q4n8B/I+By+FX/91BW+AXBwGpyNKMaiABEeII0A+peOQUCKwNCOfQWUZvDFJExpW1loODA6QSdF2LUjFa2t5itSMZgkAIPxOAHE/GtFXDYnlN28fU/uVjFUXBZrOhLMuB9OMYj6ZkWcZ2uyXLUvbSecTbD4Kh3vt44xNLhe2uIgDHpyccHh1SjnOWq0t2uy06g/nelHJcstqsCcGyXK94/vyCtunJ0xyNpq16rlmihCYM9WGiMqbjObdP79B92fHKrS/GjnpvyVTJcrVD+pJxNmM+OWBvtkdbNVxetaSjhLIosZ1lXW+iSk2A0TjyKu69cpfl5pqLq0vK0RhrPUVRonVK3zU448ELDvePOD4+5bf/0X9KUYwZF2MO9vY52t9nOipJhcD2cfJR5gVCJxBcbHT5juB7ZPB4GwUyrXH4QSDUuXgCe2sHGHGcEti6ol4tMNsN2hgS70kIpEJgTBu7+BhkcGA9IUROhhAK23Z0DnzbE4KIUGiZ0LUdvTUcTA94/f4bbHcNV6trHH6Q+RLoTJKmisk0xTnDcn1NklqatmC32VFtO3K9RXhFniTcu3WXg1tTTG/QIuHqxRVHt46Zjmf4w8D14prVck3fLRiPco7vHrLc7PBCkqQZl9drrhZrhNSx70LAB27kx4QSKCEIQg6wGImQCu8FbRuBXlIWSN0jtUSqOOJu+vZPPwgAFvifhRC+L4SYAH8khPhPhv/3vw0h/K/+eR8opvRRIWg0GtHbQF6WjMdTVstr8ixhNplw5/Zd3v/4I3SacHp6irOBqop86TzPI4c6zfC+H2rXOK9FCPrKRKEO5ymKgsl4QqKS+KFs1qy3GyBKQY1GI4QQ7O3tsV6vWa/X7O8dcnh4yGq1QipFrjIAsjSlpR5EQhRKKgSRDWasZzaZcnR0RLtbUVUN1gVUUEBs8BjjODs/48HDa9q2pe8TEqUInoHtZphOxhBSppOcRGkcCYlO+OrbX2Bz1XN29hyBQAmN8BqcJMtzJuWEUZFDsIzKFJ3IG/ZZ11usdRR5hrGWfrfharFgW28x1mBMGNSHc6xxrFcbvBMUxRhnA1/6wtt8/3s/REpNluQUaca0LMi1RAaDdYbOWPJI5ABn4mnuehIJQYIUL9PbCKP1zkdk3MCfT5REix5b1azOX7B48ox6saDb7qi3W/quoesaZLBkaRTVcF0PzgJRycgHAdZgW4cRFlBsshIpU7xMMJ1Bpwl3Tu/Qdj3hk4+4WF6hpEYXySBIkzEaaZ4/e44LPdbWVPWGIi85nN2iqwPtNpLZDvcOuHv3lMePnrDdbOk7S7BwfnYFPlAWY0zv8G7L9fWSv/db/4CL9RWTyRj0jq636CSlabs4FZGxIRuEiNMtEVA6jsxfKlprpUGoQflZo7WGgX2YqITa1fTdLyETCCG8AF4M/94KId4nSo3/i69BXjmEwGQ8wQVJlpccHB5SVxVSCg4PD3nzzTf55PEjVJLyjW98g+AF6/Wa7XZL13V0fcvR0RGLxQWTyQScH5BeITKyBFEqWyryvODVV1+jbztOTm+RZZrZLGc2m5HnOWmaxmxkyA6MMTfBSitobMd4PKKuGmazKYmMqkZpmjKfzsiynLrtONjbY1SO2a1XeC9IkhznLM4KLi+XXC527B/OAEXbJjgrQThSpZnPpwTrKIuMzbrh5PgI8NR1xfJ6ifeGj95/hDWBcrRHte0Y5SXeOLx2bDYrDg7GKGV54417eA3L1Yq+Mdy6dYI3gc1qTW8Mxaig73u22y2rzYpqlzCfzfnkk0/x3jKdjMmyhODgg5/8lL/wG7/J3vyA7bZiVI7Z399nbz5lXOYoLLaLk5zI5QjDl0P6HiUCFo8kTnNeimk65+MIJ4TIuEsU2JpqueDy8Sc8++hTrp4+p68qlBRRaswZlJYkWYpE0ZiANwYhAR+bhzKAMx3GWbxXLILCOpge3EJ5iWl6iiLnlTv3ubi64unzZ/jgGY0mTGZTshzSLOA8ZJlm/2CKMx1eFty9ex/pco7md3j+9BnjcoRCsFou2ay3lOWIvrdsVkvGoxHee8ajCbPpjJNbR3z3h3+ISyyr7Y7OCWbzA4rRjGfPXgzgH0nAR8Ja8IhgUSqP0vXWR/n7vIiEOBuABILCW4mwKjY4u4Btf8mIQSHEq8A7wHeB3wD+HSHEvwX8ITFbWP5X/D15XnD14BGvv3XITCZ89NEnLK6vo1Zbqnn48BFPHj/BWkfdbfjxj3/MyfFp3Gx1zWazoW2jFLkQgjRJWG6WdG1HlmdRwz5JaNt+6CXA6ekpm82GW7dvcXl5xlu332J//4CnT58yHo8HAk/kMUgp2WwiK2w6GhF6SVVV4Byj0QFf/fJXWVwucNZxeus2B4cHXC+XXF1f8+Lsgq4xaJ0TpGA0KSjLESfze5ycdoynY+7eeYOmc1RbQ7ACbzy3T0/56IMPOTza40tfuk+gJ8gOv7I8GDjsQViyPCdNNLPJmNsne5y9uKSqdixCx8F+ThCG6d4B49mcNM05f3HOdDylb2G7a1Ey8iScczRNR9tavFPsdhXGdrz5xuu0TRXRnG0MFL/9D3+b1fWGruu4e/c+t2/fIc8yMi3p6w68i+o+DKexN9iuxjRbUt9FbIUxeOsQXsRywMQu9qgsGY0KlBZsLl5w/uQhV88es3j+mKcPHtDtqoi8nExI8gzvJELF6ZCSEp0kaCni67U9tvfIEMAF6t2OujV0vQORM5qM6dsGYQyjUc6rd+6zWF5zvV0DkrqqcS4Gstu35sz3cqrtNT5EnUjTOUZpyv7+AalOWa+XfPzdD9hstkzGE1575YTZZMZ7774fhW6NAyl451u/wmodRW0D4AO0reE0H+EGfExRRnHQpm3i5ElFRqLWir6LE5bZdE6e5SyuFvSdochHMQMygXpZ43OH2VmmowlL1j93//1LBwEhxBj4fwL/0xDCRgjxvwf+PWLo//eA/zXwP/o5f3fjOzAqM6qqou97tNKUZTrU5VsO9vYIPsTavW2QSYYeBBmbpmW5XPHixQu22w3WWkKwpJliV1V0pqfuWrIiJ00SrHGUZQEBri4v+aM/+iO22x3nl1dcnD0nU3cxxrJcLjHGUNdRkkzKSDLZbDbMZlN0mrG4XA0GEJLF4opMJ3gTx2oX5+fUdUXTdixXK1QAawzGGuq2Ic0L2sYzneeURc6onDCdJRRlQaILZuM5TdXw/PFTpPKc3jrizp1jLi6ekmQ5T88FvTF861vv0FQNP/rjnxD8liSZcOv0mLZuGJUps70ovyZVGOS9Jmyrhq43NFXPbLrP8fExu92WuulIMsne/j7TvTnBT/DWMptNePvtL/P+++/Rtw3lfkFwgo8+/ISDvQPu3LrLdDJDacV8PkNoi2l21LuK1kmEUtGAxTpEVyNdS9/V7DY7NqsldV0TfCxl8rxAIdnf36fIM1xbcfX0AdfPHrM8f8H6+oK22tLWDU1doxJNWmQIPLbrEWrAl0gxGNMolJSEYOOG7cE4j+sDni06u4oZhbN0TRSyvX/7Lr21fP/dP+ZytSBIgSRhcnzIycmE09MJf/C936cYFVTLjuX1Blem7M8PuHt3ynZ7zdOnjwHJdLJHlme88dYXODg85ez5Cz766Kec3DohyRJ+/N67BAmeaMKikwhk8y4wm8zwwWOcGUpdi/NRqr6pdwihojbEZEyWZKwWS6qqRVKzfzDHdAZhDKIPyB6C8v/lLfinEwSEEMkQAP5vIYT/F0AI4fxP/P//E/B3f97f/knfgaP9aWi7FqUUL168QKiUg8NDur6PmIA0jfLPrsYLG8UXfYin3SLW0lonvOSip1lCXVckacpE6+joIiUHB3OKouDZ0+fUTcP60SOElAPlFK6urri6WgwKxTY6AsHNaDBJkhu58O12x3hUEoRnt1rz6e5TZpMZWiVUVRWdk2SczRZZRt92pHkGKJrasFxteHFxRW96dJbQ94ZyVKJ1JBxpBDjQieQnH7zL1eKAXbXk9TdfoSwnPHm24OGjp/RtF3X49wtM63j08FOurhbcunXKrdunLJZnrDdrnj17znrXYQzk+YjWd0wnUySCi4sL6rZm73CPvf0DRpMRJ6dvcLC/z6gsaNuax49K9qZTfu3Xfo3f/73v8vTRc956463IpWgaJvMZ+cE+obrEmpbddgPlnCwr8NbS77bY9QLR71heX3H+4oLF5TVta1Eqw4uEJM1QUnN8fEyRZVTrNVdPnrBdXNJsN7RtDUqQjUqsC/Q+0LQ9Mk3QUiKFIiiJEgGlQKjoYtQZj3MdfefoncdL8KJmu1pQ5AlpnmPqhq0zTOUh909vs9vtaD9o2TUNRwenvPnq6zi/5umTp1ycVRzuK5zVnJ9f0o4EMiTsqsDV9Tku2Mh1kfDeT37CJx8/YjKeMpvP6axhNJkgE83T52ekM40NgUQllOWIpq6j9FuaDbB0DzL2SxKt6PBY0zMqx0gh2a7XbALUdYVpPPlcYZqWIsnoG4NUgkJlhM796QcBERk7/z7wfgjhf/Mnfn5r6BcA/HeBd/85HuxG9ODp06c4JLdv30VIiTGWZNgYLy3G2rbhyZMnKJUQQkQU7nY7mqZlb3/KrVvHeG9v8AQvnj7H9j1f/uKXIcDHH3481J+BoizRSUKW6mGDRyMT59wNVTNJEq6vr9nb2+PDDz+k7fuofDQZIbyIzbM2/qy1Dc56kjQ2bJI0QYhAEBHznmWRJ7/Z7ggC2q5DKEVV12RFRpIIhAhkSYbvHbdPTnj48EnEt/cVXgTyIkHKgh/98Yf0nWM2G7Fe7whOsrhYErxgca04OJ6zWK64vFry4jIq6cxmB+RpwWqxZHG5ZFSMyPKCJEtxLvD87BxxKfjhH38csx4pGI9K2rbBG8dv/8Pf5upyQVcbrq6u2Jvuc3pyi3uvvBLBSE1FtV2yWS/Zmx4ikwTTNLTbLduzF9hmxdnFC85eXLBdN4SgyYspQuc4FCqXTOdzlFZsNxvWV9d0u5rgAsgEpzOSskCjcCLQC00iErTKYtZBQAoXxfSDQ2nIModODaFuaPuOICGRiqbeUm1zErVHsH1UNxaKwzu3eOerX8c6x7vvf8DixZI/bt/j1p0RVbNiPk/4whfe5Ozxjlwf4nrJf/F7v0ugpigj5yDPC775zjf59NMnfPjBJ/S9Zble0/Udl4srdKawwaJDrNuTJCPVOV3T0ewaQgZJluC8offR7yL4ftCfiHDm4H0kwgWB8IH79484Pjzk/PwCrSQyLxiV5T91oP2pBgFi7f8/BH4shPjh8LN/F/g3hRDfJB7LD4H/yX/VAwkEdd1grYtEkc5weXUZacDT2Y1jjxBR7ikoGWfdGPK8YDabAVFkwznPer3GB4cLgdNbt6I01nVMPU3bR3ZhH0U7qroiL0t88LTdzxqAL3UJXuq5XV9fc+dO7Hv2fcQeJElK3e4AbrwPTNvfoAYJgSzL8b5nPI3qvSFInIvINE8kd2itKcoiRv5gMX1PWY6joUXbc3rrDsb0JOmI9brFBUVe7LGrHSk93mlWq4rjg2Om0whQUlpzfnHFbtfiQhy36iRDq5QsLSgKw3q14brdoLWiGBVxqmECwYUBRtyxbWpMH/UOrTVcnJ8jhWZ/b4/rqwX78wO+9PbbjPf28NUlm6tzHnz6CdebhsNXvkTwRFUnAl21ZXXxjMXlOdv1hqa2SJGjVYF3AhskeZKgBbRNxeLqktXlCms6jBcYkbKxFc50SJ1Fwkw2QWlN5yVJECgxKPF4i3Ce4AI6zShKj9oZur7BBk8hFVJ52irDljm+b+naqAOxvS7YPznhW199hywpePTsOVIFvvTWV6iafT765Mfcu32XWSnZrQTPHl+xq7dkhafuG4pxgXOB1WaFCw5HoOk7jLX4YDlfXLBpVoxmBSpPkcQsxhqLs57D/UMm4wlVU7Gro7S9ShR90xJcoKk8ImzJEk2ZF9SVQUvNK/fuIKXgm9/4Kp9+/AlGtRRFxnbTkSS/BBZhCOG/IDqA/pfXP5/XwD/9WAQf0DpBBEkS4kabTKYkSULwnqIcoZSktTbqCCp1Ax2+vLykbduYUQRPVdXM5mPOFwvSVc5kOmW9WvPhhx+iiEIdQmk609M08XSwfYfMNWpI4ZMkYTIZ0zQtu12Fkgm73Y7bt+/gnz+n2kbDUEKg73tSqQfN+HAjBum9j8YkBRyd7JEkJyyuVzx+8jTOcIUkEOmjSsdMJM1S2q5jb37Iwav7fPTBRzgTS6AsTxmLFERO2/aYXuOcJ00KZvMxQSQkaU6WpNx75T4PHj+maSyQoRNFkib0xqFFj04yppN5pGLb4YSRHmMdnoDWUaTy4PAA7wxNXTObTjHK0mxr9Eih05TvfOtbvP3lLxPw+OC4vrrg4aefIPMJk+kUZy279Zaw2yGtIRWBIksZFQWmq24MROO1i5Dduq7xl4GL8zO2mxbnDEalVE6y7ByrqqZ3O0ZFyeE8MB9PmYxG4AJBeqQaBDlEFCpBQJJm6CTFWEfTW1AKpRN86MGbGKTqit5YgogaC8d37vLOV7/JfHZIVqbMJwVp0nDr+JQ8y2k1PPj0Y8bjA9759q/w7MWHLJZPybNJ7Cv88PuU5Zz5/pym7qiaiulsTNO1GN8wnU9Y1zVCJWidxoaedfG5pjNGoxK9UixWUQ9hPBpFHcddFanFxhK8oywSBPD86WNGZcndW7eYT8fYLKGuKpp+R57nv3D/fSYQgyH4YXyn6Dsz+NJJynJ0oxO4v7/PannN5voaLyPgQ0pB25obffyyLPDe0PU1VSUxxrBarTiY76O15mqxokwSkrQbWIiOEALb3Q5vevbyGUIIjDHDRo61+stpwnK55PXXX+fy6gpCoBscil++xkhciDzvZJAib5qa8XzKvXunjCZTqh9t2VVbVJJErPgwuVEv1ZWrijwfMZ7Mme8dUjfv4Y1jOplSVR3G1sz2TxCiRCeGqlqiRUBOc9arNZLYVMqyAtM72tbR24DOJVk+SLIHwW5bYXtLopPBlKUnKzOyrERnUeQlkrQSRuWMF8+eslwuGZdj9vf3cH3PpJzw7W/9CulkjPMtrms5Oz+jqja8evdVpgeHeBvYbrc0Z+f4zZpMK27dOiHPdyi1QZBTjA8QqqBzgWwyiepLpsdZh1I5fe+pjGfZWBatY9laqqaHVcPZouJoPuf+rTvMC0meAZlGaRmFZ2XULhQykqaEkhjX0XUtee7w1uCdpchTUq1o2pZgLKbu2C43eCE4mB5QTsfUmzNEojg5ucWzp895/HBDVfd87Wvf4dnzBzx/fsb+YUkxGSNVx9MnF/igECJjvdugE002KjC2IfgI8c2LgjQdgwfpFcEbdtsdWmjG0zHRMbpBWcV0UjAaFZRZhtYJy+sl2+2ON994E7znhz94wHze8O6P/pg0Tbhz75R3330XE2BSfNa5AyJKfRsXT1GHoDc/k05q22jg8VI4RKsI4qir5mfgl8EiDBHhxF3XkqYpXddzeXlJ13UkOpYT1jqkjlp2SZpCiAKPDAjFzWYzAIbUjVeBNR5jDNPpbFCETWiaGuEjn8EYQxiQb3VdR9qxjDZeo1HGrTsnnF9c8fjJI6TyWBdTz0xr6qYl2Ej53Nvb5/j4lKdPn3F9tcL0DhEk69VuUEvOUConSwVa9aSpwVhHQFEUI9q6AiF49PAxVdVincB6STBExZxcE1zcmMHB3jynKEdol+JF1EYwzkbDjsEMNE5eDBBo25ZURFm3r3zlbV77yttgo+LSZrXixYvnKKU4vXuXtMjpW4eWimq3Y3d+xt5Ycev111EqI0lGzOanHB7dozWw3Fak4zF7+3t44ZnP52yKCW3vqOua5bZm1Rh2JlA78L2laSra2mKN4M5+wXycwSSHXJKIwTYeEXn4Wg8KVD1d39O2lrre0TQVB/vloCZ0xep6RTGa0TcdIdEUecl6taVzO2b7gjIf0eWBPOv5wluvobXi4wef4IXFeMhDVABSKlrdW9OTpPHQWSyuGI1zEJHN+Y13vsXquub5k2eYvicZtAMWiwXrzYqqrWnrlmKcs9tuaat41hweHjCZjFFSMRmVBB/Y20vIMx1l0aeH3L53mx/86IfoPKGclMDq526/z0QQ8MEjM0Vft0iR4LXGO9jUUf1mbzpF64y2XaBVTjmeRF01X5OnCSFEpZxExeah845gIE0iiaVvenASnaZIlbHZrggi5WB+RN00uM6TpRnrXYWUkr0sxweBkhG+XNc1INisVxAcEgemJysyrpZXJEqT5RkdhoCl6lsCGZNyTJHnvHrrNXyT8OG7D7m+qlBpQds0+GDRXpCrJGLBDRRoquWaerOl3mxJ0kiRLouCuq4IxtJ1G9IsQ0mL3V1SFDmhr2jqhlFZst3u2Gwrmqoh1Sm4mDY65fFJLCGUCkgt6Wx0Q9aJZjqdsq12sf8iNSIEGu8Y5Tm+TxAIdFFyvap45bXX+PW//JfJJmO6viO4msXV+2jVkxS3ufPGnyfoBO8vePbpe/zOP/4BT54/5zvvvMn+LdB6xP03XmF++xV0PoFdRV6mjEZjxrMpzvSUWcre8R6N7bDLLX1t0C7BtY7gU7I8Q0pJ1fd8enVNUhvk3phCnVJmY3rbIVxDrhVp8OTSkScJShU4AU56eg+dMTRNjZaePBX0/ZbV6gJVJpTzPbbVNaPJjLCboVzC8sUlm3XPq7e+xNViSb29xlQ1o3SfjJyjyTHUV7z69huY1vDpJw85PTxi7+iQH/zoR3Q7QzEpMUKxt3fCevMpvW+QuaRuarI0Z9Nsca0g0SVaz+lqCGXCarvBmBaZ9RRlQi88P330kIP9KUf39uMhtt5Qm44f/fGPCM6Dd6yXPx8jAJ+VIOAcQQ7sweAATZJluBDou47FteOia/HOkBVF5LkPsFetFFJBlqWkiR6syqPNGESZ8USnECLt2LrYX8iyaDratX20vq4bQugG/EEzjGT04GDkfoZnD548z6JLrDF89StfwRjDRx99FKcJIZCnOmoJWoPpwHSGxeWKF88vYopubKy7kySCXrLoqdDWDV3TDKIY7obr4H2cLCSpAjxdHxmLWgnyXNH3Lc470jzFhUBV1bRth3WOJI2SbEJAoqLrTfAOOcgseB/nzzJEvzqtFUpJsmIUNe6FxhqPd1EIlSApyjFf+erXeO3NNyI7UwlEcOQpzCcl09Et9g9vEYTnyeMP+Ye/9Xf5g9/9KS4b88rrd9jVUVq+nM7JZ3Oc1NTLJXXfsX90QJIq2mpQdgo29ixsBz6gpQQX8MaBzgevP0dvOjZdxVR72mrMLgkoDDq0CBsNOBIVgzqDX0RAEAb9wCjvTdSSdAHron1Z5saURU5wljTJ2G07ri53pGlOUYw5PcnY7rYorWlXPdZ4XrufIVHsT/d4fP2YUZ7zpS9+kawo+Z3f+QEHt0akacF0f59nT5+z3axARpei5bqmqmsEkiCjelWSJpjOMypnGOdouy7iPUyDs9E/c7VdURYF1lmquuXoSHG9XOIhCuzwGdcYdIOWmtYa46O8mFQpUijSkaata0zfRa31ENN4b22kkBqD9EO33Q+a9VLdsBG1Tm7osiGE2GDLykFWLNxAM6N9eXQK6k3sS4QsGzQLYgrWdR2r1QqtNNPJjOVqydHRcTTUzJ9ExR5jSHRKIiIPvGlazs4vCBeXbDYbhOCmzNGDyWmSaIo0w3Q9Td/H7vawXlKlm6ahLMvBpyAMWAZHmuUgXpY13DQXq7qJMm0SMp0hlUAl8oYQhfgTOgwhwqu7vv/ZWFRHGzYhJV0bZdfTLME5x+3bt/nWt77F0fEReIscdPWno5xJkTGZniKl4PryjE8+/ICHn37CZrVgelygpKBzgTzJUHkBw1Rlu9vSNDUyS2NmV+/o6orddhvl1vsecCgRYg8kOFxXgZFI15NrKHS06hIyxJNdGFTo6L1F6wxrMwge5yP12Pvo4ee9x1iDGhyuFX7o+UQH6nEx4nKxpKprLi7O2GxXfPvb34plX56xd3BAWY7w4ZLpbMazF2dsVisOv3PEhx99zMHREX/uz/05LhYLJrOM0XjEK6++SjYa8eDhRyAM49GIvfkek9GE87MLql0bIdcyoFNFCLGwURKyPAHvIUhunZ4iRODFi+cE6xmVI7TUZElGIyt0oqP03j9DT+CXYk3+L7pCCBBExJEP97+SUa+/riu6ponKwfGXSeRgwDi4EFkbxUmbpo2NRQ8gcYPb70sfwWQQZ3BD8Oj7boAs5zene1EUP9MnlJIw9CLKsiCEwIMHD6ib6L4TQuDRo0csFgtmszmTyYQ8LwiDJXWSphyfnBCAx0+e3EwB5vM5ycBLaLtusOO2MNiZ/UnGoxhEVSGO2rquYzQaMR6PabsW5wJZmqNkMgRByWw2I0sTsjxFSNBakqbJzSQjGrHEG97fMCs9XdfeBJm+72/YkdY58jy/IbPcuXuH1994gyzPsNZExfCuoUwViQzkWmCqDcvzc2zf8Pr9O5wczHF9EwPebJ/R3gHJaIoXkr7rcNaSaIHUEmvbCDFud7Fsco5EKzKtKBLJrEwYZxLlWoSpKVTgeFZyenLA0fEBo7JAK0GSDH5/pqNuKpq2xgdPYAiEARgEZ17iQl7K1Xsf6/qujSrR5aigrne8ePGC58+eU9cNSqes1mvG0wnT2YxXX3+dvYMDQDKazhhPpvgAk+ksZrbek5c5fQ+379xlu92yWW/om462brHGcuv0FkVRYKwhCI8PliAsQnuqKsqOTcqCNNEUWcbpySl5muNtYL2qKbIxs8mMpm4jFX+wdP9nBYHPRCYAMRu4IZwNXgOjckyiFflkRFNF8cWsKOKbGkRFIcQGXIi9gKhIFB/TDzezGPzdXzbw+sHyOppaRsUarQVSxpvee0+SDTc9kGUZ49GYtmk5O4t+8D1RbOTF8xc3tamAKIiqouqLRPD1r32d3lje//CnpFmKCbFE8cOJ7JzDWoOxFqUUqZK47mcb9WVAeOnN+PL1FEWBMRaNilzzEEjSSHXOfVSneYluRHo88XnsELxevjc/CK5IFRWXX4qoGGMQRHy8dw6VRmfn/f193nrrTQ4PDyAEvHeRQu0dSZqQKkHf1AjTMRnlvPXm6xyqknFywPm65otf+iJH915lNJuSTKYEIXHek6SaMk9JZMD3Da6vaXYbujb6II5HBZPeI5MoDpsoqGqLEjAe5Rzuz9jPPUWeEJSgSFNGZYLrJd3Ws6ta2q4BUpJUIZ0cSqOXaXJ8Ly+DAj4SnJzp6Zqag+MTdrst19d7GNuz3mzonWWxvEamKR8/eBA1LYCu71Eq4eNPH1C1LYv1mr/9H/wHXC02dMYwLqPFmBSS+WSKty27aoc6khwfHfLJp5/igyPVMROwvidISd81JCryYvzAvbi6uGS1XOGNp60sXWNwJnB5sUBIPxwm4ga9+vPWZyQIxAJVyPiCvA84Y8izlK999StcLxY8ffwYa4d01XMjxyxEVBQWSmJtrJ+FjDgCqeQN6EdKOQQLjxt8CZIkjamg6ZEyJQR3szFfbkAfAqPRKPYc0jSir4zBS8/tu3c5Pz+jaaLJqR4soossw3Y9venprWGz3UZ14xBl0pumJvj4Wl+y6MLgGSeG4KaUoigKlFLUdX1TFmit2Ww2g8NRIEmKqN4j1TANWbCrKiaTcbRL7zukEkPt7yJSEgbSir8xQpFB3gTAACjh0fpn71fKDtP3vPHGG3zlK19hOpsiRMTpe++QgzWb0joyP5VmNJmgOGRkNcKM6XXBm1/9ItPDA3RRIJMUY3oCgiLPYxAIlrZr6Oot9W4dJ0MukGpNmSWoBLJUk6pAX8bsYFRkcXwme6SIwhtZljMa5YQUEhxtF0VJjSOWRnqQEhsIji+zoZdZICFE3r611Lsto/GYw/053f17TKYTirKkalquVxvWux1VXdP2Pb/5zjvsNte89+57fPjxJ6R5gfGOTz56iEehMk1V76h2Fb/6q7/Kd3/vd9iuFgjg1ulpJHLZyIxMswQhJW1rhh6GiaAqE3sXwTkefvKMpmnJs4Q0EVxeXFHvOqTyyJQIroOhR/bz12ckCAyyyINKsJYh+q5L2JtPefr4EUIKppPJwP5i+MC4kRaTf0JqTA26bCHIKAIqJVqrQbNAo1S0JyvLkr7v2e0qwJOm3NTEYvhdgiDRKd6DkpogQclAUaZMJlMW19c0bYdUCuuiSCXZAESxjp9++BG7uo4SUL1BJRo7oAqNsQQRGWTGe5x1N3LpaZqS5/k/RWd+GQQuLi4oyzJiGaSmKHMQis5YkBJjzQ3hJM0TxqMRVVUTeksIg0VbCPHLR4ML4QJprtEpsc8yHJA+RD+CEAK3bt3inXfe4c033yDJM2DoTRgblZaHr13To/KSotQouUN3kuKLJySzA9K9MSLPEUmCD+B8QMhYbo0yjTAdptnR7Nao4AaacZT9zhOJRpKnUCQCyMlTRaYliZaUMo36e0qgk2GcnKgYwLOEQE3bNRirAR8vPGKQMY8iJFJqNArrY38H2ZFIwXazZDqbUxY5xk3YVTWWwJOnz6nbhslsjpSSN996i76t+d3f/UPOz5fs7U1xPjCdzVhuNhhjODo95Ytf/CLvvPMODz76gKP5lMPjQ+7eu8PzsxeD/0V05tKpQtgenEMIh+0NWnpGxZTgJcZEGzlV5KRZwmZd4YynnOQEbCyZ8bS/DD2BP8310v77JTeAEDVVqs2aJ48f0zTRw88Zi1axq25tH8dYQtD1/WDlJHHe4q3FhRDHeQMI6WWNLaW4+X40ihtpu90Opg76RjD0pQd8BA/ZwaMvNuNiOl6yXFzTd2YQpnzZjPQ0bXS+lR4uF9f01qK0xgeig4/S2OH0fmmEIlXEgnvcDSbiZQYzGo1u+gFa6wgeGXQQu6pFiMiahMiVcC5mH85ZJpMxs/kM6xxNGyXU3RAEGPoNznuEkJSjMSEENptNNKsIgSzNKJKMsij41rd+ha997SvsHx5AcENGFcuwRKUEkeCDpnMC0oIgIeiUtJyQ6oJs74AwykAp0FE0FhkDdqoKUunxdUU/fE3GI6azKcascAjKPMXFj4WQScCSaYmWIPEkitgFVwpJiBMVHd9jWZbotMa4GuuBIPDOD05RHiMsSkqUEiig6w1926C8BxFod5I8y5BAU9d89OAh49k8Ctb0hrppuXfvNl3X84ff+yM22xqlBb31mF10e/IB0ixjVI5YrVZ89NMPOTo4xHYNf/7X/jwez4/ffTf2Y6zFOkuqMnQqCdaBhuCiS7EclLnLIkNLOZDqQAQRLfakpHV2uIflTYP7563PRBCIMmKDeUiArmkoihHVbhNlv3VMM5d1PXxQUBZ5VBEOAecdSiusszeDEOcdYVCKfXmSvkQgvmwU+uAwJo4FhYivIU0iECkMIidK6cgKHJRcjDFDk0yw3e5izTw0z14+r28d3kZhECUjLFgoibOOYGx01hleZ/7S5spahJLIof5/iVp8qbc4Ho+BOEF52bTLsox6W9P2kVueFxlZkdN1AWOi23KaJTRdExtiIUTVIOsQMmYbIQSwljTPuX///o1Ii9IJQkBeFBRpyltvvMlbb73JyelJ5PDfNK4CUmms0eikpDaQT/fx6YjedwRdkI5Sgi4QaY5Ic7wIIAUixACN1mAMru9pthuWiwVd05Bqxagcs00b2r4nHWzl5EAOCs6TyJgxKkEkMCEi/fflNR5KqyzLyLIcKRucsxHJN7BRgx/ETIgZJoASkX/gjaCrHS5Y8jwjS3KctVwvrllvK05OTylGJe+//wF13fDhTz/ivfc+IM+z2MPKy8jwcy7KfyvFfD7n4uycjz/8iOkoZ28+GUpVOD+/oKoago9TMGN6pBLIIHAikCSKRKvBcDdqMIQQ9RikkBRF9E40pkMrjXdD2ak00P3c/feZCAIvazIlJM5Z8B4liIafeBAq1urWDLTiyNzr+p7eGJSKzDsRdUfjGjwAvI/69d1AVY6fcRRt6PueZkDGxR6jG6ycBT4wdNujZqAcmI5pmkVfAx+13EO1w/Q9etB1F8Pc2RoTyxuiLwLO32z2YEPMDFwgLTLUULcJJenbniyLH4vWmvV6jRCC8XgcNwwxqL3UQEQJbPBR1DRJUIkmIbrXVk1F03WsNhuCh4DADrZUSioSnUavOyFRSmOMpWk7vA8RpGQd3nn29/Y4OjpkNpsxnowH8cpormpNj1AZNigIml3rmB+f4FSOJSMtJmjhETqHvCBohRjkw0SIiD5JlHI3VU1b10MTuKet6sEPwEbEooBkMHiVSIKUCG9RBJSIsuRSSNygxhszPn8zOtZao5MEpEGEaG92ozsZXh4W0V9CD0EkOIexPdb1dGXJaJaSKs1sMuNytYpmrC5mYG3T8fHHn0SPS51ytbjm+PiEclQyn8/4J9/9LtZafuPXfwNnLX/n7/y/edjs+MbX3ua9995j72Cf6+sVIQiUjpD11DryURYBWcEzGk8o8pKmamnaOFl66T1Z5hlqGKNbH23j0ySN7Mh/xiDwsxEEBn+BJNe0TU2eZ3gXfQBj97yPIpTeQ3CkaWz6tW0UEdFJpBwzBIHoqyeR0RAo4gsINE0TnW6Vupkg3Ci2hICSacRq1y3JWNP3hq7rI0hGSoQIfyI1NyDCcBN41BDI0jSl7yzWB1zM/4mvJo4ijYkjS+883vlYXpQlfqivdeJvRoLxJDCkacp2u70ZYb1UPNrb2xsynzCMguINgIgeizpJEEPvQcqoq3BzkZDDKR6FP4y1vP/+B7FMAKyNAKldVUUSVwjcuXOH0WQc5b9UZEG+3EBCpphNQ9M7bo3neKFxMsEJjRUuGowmGWgQIRCsIQSL8B6cw3Yd2/WaZr3BWU/fGa6ulqzXLW3bDZ37aLKqhYpiIB4CPvoTuFjSBEXsDxFt4QkC0zusCzf8Aa3B+yFTGF5/vM/itVcqjuFFiA1r27f4LtAWJUpnKCE4OjxkXTdcXFxRVVXsF3nPcnGNDIqmaXnjjTf49V//dfb29xBS8vjpU5qm4a3X3+SnH7xPtd1R1xuePnuKUJLsaRTSuXV6m9ZEyrEg0oSDiwY7RVGQ5wXL6w3rVR2DuI/SbS4EgnfD/RV9MYs8J/ifNYB/3vpMBIFoChqtnwVi+IBj469r42nQGz9QbWM5IBUoFcikRiUJq+12aOy8NI8Q4GPqKGTEIMSJQrhh+PnBueglaShISVmWtG1706V/OV3QSg3KNXG0V7VVbMAZO4wb4yZ07mcnjxuAOEIqsiKnLErapom1v/dxO4rYXW+NIQCz8ZhExbr/5TLG0LZt5BpkUVnJDUKSLlhciPgK46KFaz+wI411ERVpLCK4iLS0wxiUKMcltbppEtZ1TZ7nAy3a3jgrd33P8fExd+/dIUk0wZlB/DIgxNBbUAlN1WCMYzKNmolBKBxE1JpSBKUg2BsTUi3jGC4Q6Jqas+cvuD4/o68qtuuKzaaharqh8Rtl+aUAQYi7lJcjMB/LvsHIU6jIqgsv90cQN5nQS5QgA0jIOx9LAhmGeyKOCZ0DJaNasTc91hna3RaVFAOrMwabumlxNrpPR/Kbpd60pFnK/XuvkCY5T588Y7leUWQ5qUr40Y9+xHd/7/fYXK+4de+Y49MTemv58N2f4HxgNtsjNz27qiYZmLVlltP2nqppaDtL3Xa4oXcWBsORIERUSfYWqSRaaUxv8NajxWd8RBiIqaWznixLsX2EQjoXbZS8j9bVOk1IEoXWgjTVCDmmNxbjHFIGhJAgI2mDALbrb+bhSRodWqQcPAjccMMMGYG1UYSkLEc3GoX7+/vREce/ZAnyM1ciY6ibGqSMGASt483cdjcMtjgWhDRPyPIcPwigeBsJMT9TK3JRG94YnCvRMtxMPeII09yAhtI0vRkd9n2PUAEhozJwLG3iWG+z3SGlYKIia9AYE9+jjXBr9bI5aAfkIFAURXw9LmrtCwGj0Yj5bM7R0RHO2RsnYZTABYtUEX4slYybMEA5GkfXICLoxgcXhT+Fx5qWdrPC9g1FkaIzjZIyOjg/f8GjTz/FNh3CQ9u5qOXnAsh4MvvBSOSl4vYNGFZEGTERXuJExIA7EbHpGlwElg2CplJER+vYzH0ZyLkJDN4FhBaARziHdBbfRyVj3xu2yw1920fthSSCpkzXxGDcW+bzParNjv/P3/m7XC8XHB4fMBmPuLy45B/8/d/i/NkLTOt4/bVXefvrX+F3f/e7XFxdURQll5cLfAiRVq8ybt86oap2nHXP2e4a+m6DFJrxZITpLaY3sc80lEFCx2uKg6qpML2JBLlfsD4bQSBEHnuqHXmWcb3dxJmwUHTWRWUhnTIaj5lN8ug3EAyjMkf1Pe16E/HxUmEGSKyxFkRE00WmYUqaZpg+puMvG5FxEhDLAobpQZT4Ukwm0xicgqXvLdZEKXOpYg2dpiku+JtpgrHxJM6yDGssTVdFI5NBMKTZVYyKMgaQuiZLs+H9EzUPNhvqpkaJEZPJBK012+2W6+vrIU2NUOPpdErTNOx2W7JMx1m9eQmMGsatUqCUHv4d1Y2EjH0JHwJayGGs6RCCgWeQ3vRSlNAoAVmes7e/x9XVJYvFgpPTk8HazeGcRaeRaCSDR4tAphVJniGcRUgbxT3wIBwEi2m2bK8vCc6S6TnkGqEVbV2zuLzi/OwS2xmKrIhBW0mMdcPJHzAmxGlAEj836+LpPQwcBtyFwgvFy0AGEufA2RADCvHaJEIgkFjropy3jH8fm4UBj4yqPc6igVRKMqURoaetG/q2xzhPkqWkOiHYOH0p8oK+Mzz45AGPXzxnPBkxHk0wfc9uu+X64gLhQcuU6WzOp48e8umjaFvnBzNV5wPOGWbTebRLW16zXW/Z7Xpa45iMSvK0oG16un4DBHobvTjyLIkBs7Xxv85j+1/idEAI8RDYAg6wIYRvCyH2gf8QeJWoLvTf/2cpDscKlnjji6gYmyYp5XjEcr0GYZFpzmQ6YTxKaesV3sSJgE4UOpEUKo/yUn0fTwAJ0meMRiVSSvK8QAhJr0ycAqTxRuj7Pjb5spREJWidUBQl3nmuF9fx1JAqAm8GTwLpY/BIk5Sm76ODbvA3/QOpNMrH5t+oHHF0dBwputbdlDxSRI+C3jpEEkU2d3WDaVt2ux3T6ZSiKG5MUV5CiLuuuznRje2RqRz0GGJ67kKk7uZFSaITjHH4EANDkqYkSRoDoFJonbyUQCD2HAVFWcb6uI0a9l3bcn5+zna94s/9+q8BIebJzg5gJ4UzLvoJiECZaWSqwHSIZGja4cB2OGdod2u6aoeSgLcwTHGqXcVus8V0BmfB6Ti9QIL2HryJEwGGaZLzUV7Lxoax1oFEiajtKFS0mI8pJj685JVIXprTKqlIlCRRCufiOE7KyJkQAB6CcHgb8NaQpYoiTZCAMxFnoqWmNS3WGIoiJ89y+rYmS1KcdXRdz/7ePLpWLZfszWfMpzPOdxX7szlKZUgpePD4IUIGhITeGJLBBVolBYf7RwQb6JoOJfWA7oz/DSL2fWIjO8QDRAmyXMcSCUmapZFJ+Iv5Q39qmcBvhhCu/sT3fxP4RyGE/6UQ4m8O3//Pf9EfJ0nC4eE+r7/+KrvVNa6vGY0K9g/20UnMBlA61j1DI3A2ncVZqo9KNV4IhNKxLveeVCT0IRqEJmmKUpq2bYcUMcRZ6zCnf+ljOJ/NB1BOPCEvLi4Yj8dYY6iqKjrB1k1k2iUaY1z0IUhSgnMooaPteR9xBaPRmKPDI/YPDjg7O0Moya6qsF0fBS5edi5FnD075wgI6jrOlXe7CqXkDa4BoGlqkiR+bFEpqBvSYzVAiyMMN80iLXez3t7oNMZsKKFt26HUCAOrTsSGU1lw995dltdLLl5cxN7H/4+5P4nRbd3T/KDf2632ayNi96e9bd7KzKrCxkYIBMYWCBDCYmLBgF7AhBkDmgEDPEEIhBggBEgIMwDJIFnYCBDYwkKis8slV1GuvHkz895zT7ebaL9mtW/H4L8i7q10nnSZrJTOkrb23nH2jrPji2+96988z++ZJ/7oF7/gB599Slk4cpJSPAb/VHWk5CHMKBJ1VaCtIoYJ7cBphUqRNHUMc6A7ydpXaU2YZiYyvjsz9iMZjbGPJh+F9wlXGgmUCYoYRtEIsJjOlsNNaYs2Vg7CzDJzQQayefkLi5ZD9B6/pTbVojT1fl7OBwmR0Uv+gcR6zeiqxhrNOPQcj2LLLauCcVm5+vkxX9EzDRO2cPg5sNquGOaR92/f8erFc64uL7l++57f+93flbZOG66urri/OzCOj+EhDq0069WK/X7P+/fXsjoceuZZKFMoQ0YtlabkEIxDTwppaSUWNsdCbRJc+Z99/WW1A/8k8I8tv/5ngH+ZP+cQKArDjz9/zqcf7XmrzmxXr0BF6trhbI2rSmKGu/sHUAG7Knj9as84jDw8HCmNI2b5pjulGH2SmzSt8aPIWYd5ZJj7RSWXiJTkpJlm0Vrv92vW6x13t7dM04w1Gh8jry4vuP5wQ0ZTVDVdN2CTptCZjJaSXmmUtehKJL7TMNM2DevVCmsc53NHPwyEFBhnzzxNVGUpPalOGKU4d4JMr1xNDqCU5XA8UxaO1Vo0AjEFQvTMQXp/lCLMSoamWrFer5Ypd6AsC4xWaLVMjmOisBprNWVZMM+BGCJVWZGAsqhpqoamqDgqRVKZ0Uus1/F0T9v8mO5wR/Iv0FkxDT2uqRB5TiCpQMaw2mxRaiIpJ5WZrglpAh/ox0Q/W0K0dFNg+nAmeM/D3R3fvuvoY4MqtigEd5aTxxhwVmThOWpQiYQMja2xWPsYVGtARXzS6JB4pFWhjBwOLJLynNExYuwCI13aFFk1ZHKIKGckAFdBIuEJJBvwauT21HF3uiNQgIWkMj5F8jQRw0wKgbKWVKeH48Ann79h8jV/+IcfeLi/59nlBcYathd7fuenP+YXX/0xaU7kAFVREWKmaetlNgW39/ccTg/000g/zEyDX1o88XugoF21oh0IXlbTy2Ys5UjX9+I+tH+5M4EM/F+UUhn4ny4o8Re/RRx+h+QV/j3Xb+cObNYVLy8VpTnw4pmladbM80RMHh/OrJpE3dQ8v1jT+RmfEus1XO5WXKyNRC11A8dzRzd4eq9xpWXWKx7CiFKJKXZUDXTTWZ4ABRhVsFptOB0GmnqJsk5JfOxGKgZlRHxiyoKmXtGPEeNKAYK4EmsL2SYohdWGQgvlaImM5ObmlvHdgE+BlCNaKXSp8DoQkpSgWllQkcJBWzfEKbPebCirinkWqtJuv+Hh4Ratk2jIyXR9h1EXGDR+nCl2FaPvCfNM6QzTeAZmCid9c84ZV2Q2puF06lm1K6qy4XQ6MZ570jzzR6cj3k8knclG0nyMAZUG+sMNOfQS/3Y8sCufQU4olUkWsq1ptjugJxUFQa0pzI6gR7LO5KJGhxXD6Wtu7u85nh64vrnj3dsPHO/vmc8zZa6odAF6IKYZnRcxstbowgIzMUdIkiblnEMvk++E2GZJAUMQHYISt2DMkdnPEAI2egoKtNWgPIqIVRmVFSomtJV2IWsICrwJ+MLT64EH3zPknpEErsBUDj8OWFdCUhjn8HnG1Y5tXfPJJ284Hg/8yR8pvvjVLyF5sk78nV/8XfYv9lx/uEFFzT/0e3+dfhwIKXFzd4eyhruHew7DkYT8O2JGqMsmL9J2udGNU7K9ygqdNCZajFUM+Ux0IjYz7V9uJfDvzTl/o5R6DvxflVI//+3/mHPOywHBn/r4U+7AJ28ucl1b9hcbPl69FEiDXjPNI8fTHcbCalWx3m64vn9A24L9ZkuhHfUbiYLu+5Gb21uOXU820i+9/+Y9thgoGstH1ZpkIsc+YJ1jt7tkVW/YtBd88+Vb7q47umPH0N9jrAxlZOAUeP36ghAGnM0Ym7i8XDGMR2KcF6GSyDyrssAYT1kBzHTnmQxYK6q0oigY+p7NZg1IsszFxU62IFHaom1T0p/uKFxgt11xf++ZppGhO9HWtXju0cQ5EUNC2562KZgMhHBGMWPN4+rSYbSnLN1TiZkjMlwzFc+u9pRlDXmQHtJIXLgxitoVBB8Zp5mqbRmnAVcY5nEgo2X19MhvULKitFbAlvI1O+acMdpSFhUxRZpyxRR7TqcT79695du3H/jm7Xs+vL9h6jpUmGksXK0bGqdF3r9ImmWVq8l5sY8vmwpJma5wrpB5jIcUw7L686SsUdpJib846XKSzYWIr9RvTGgLJzIs7YqyFlJEK4Mra2xRUtXQrhRzn1ivNlxctfz8538oA0lkSDr5yGa7fUq4evf+PZvtinEcePvuHdY5rq9v+Bf/xX8RYy2f/eBzfvrT36GqKzb7Hf/s//Z/x5/8+lcydF7eyxlwVtq5sMxirHX4JCvS8/nEOI04bVj6JLz3NE3N3d2Ji4tL/tLwYjnnb5afPyil/jngHwXeP+YPKKVeAR/+vM/hCstf/4f+CrvdjtlPbPuGzXbNhw/vCLFntW65uLgQSKbKuLJFA5fbPRrN6XDm9ZsXXF5dMHqPz4lz17HbRFJu2OxXrPYth/4Bbd/QrlYURU2YMnW55uULxfkA776yzPM9SsH9vXACnz9bs9nsmMYj3Xnk+bMVb95c8O7dif3FczabLe/ff2AcJ9br9ilq2y/46KauKNuakD1t23A6abyfWK1b6qri00+ecXi4Z5zGBXYZKMsJ72+Z55a2UazaFd4HVquWnCJGL16BnCHd42xDXWn8fId1hot1xel0QKmZ16+uhCyTIlVRkpJi6CesUcCZnDxF4cXK21Tk7BjHgckY+i6jG8dm2+DDyO3dDQ8PL9hsd5RlJU8/ZcnZExfZ8/l4JE0zdlVQmpKQFit30xC94/bmlp///Od8+dXXXN/cc/dwpO9HwjShoif5RF1oCluhEOUbLLZvvUzrs6g3fQqLK3Lp9+2iW8hS+aQ4k5Jk+aUgWgOjJMzT2MVluoiFUOpRYsSSnYZKCZUyRVXi6pbRZ7phQhlH3ViyUuz3Oy72e06HB9F2pIitSlwhLcGXf/RHEo7jNMY5DqcTP/nJj6mqii+//DWffPIJN9c3/OLnf8hnP/icoqw4PBw5n860qxaNJkRpbawWSlb20hIYY0k2Ef28zLvk6/fRk9BMY6CpJeW7qdvvvP/+oglELaCXQNIW+A8B/x3gnwf+s8B/d/n5f//nfZ6ydFw921I3FUOfePnyAqUVqJmqFmJsUze0qxXNekXXzzzcPbBaN0LMLTX7/UaMRTlz7nuGcUXxeUvdgLaAy4x+z2q7IgN9N/H+7S3n4y2F1fzuX/khn77eUNeZh4d7/ubfvOWHP/yIH//4Dc6VrFa/zzh4yrLm5uaGfjD8I//Iz3j+/AVffPEFXTdQliXv373nw4cb+n5gGEbqGnb7mn5MkAc2a8vtzYHL/RWr1Z7nzzcY1ZGSIaWAn05stpkQBvr+RFk0XFw8p20uyNlIT6sMQ9dTNzXby8Q4jFxdXYCSHr1tay4vLW3b8OzZFYfDAx8+vCdnjzEWrUeUGhnHAaUaynKmqmtWK7ERhzBQb9ZY7TEriyYSwsiXv/6Cj968Ybe/pK4LtBJoaZJIYXCWeZ6YTh2r54KEn6eOAtFX/PEvv+H/86/8Df7W3/pb3N7e00+e2UdQRnwT2uLDyLE/Y02iMhCTXqTNcmM+bkkee3Z5gj+WunITaKGwEb0cBpOfmL0mxYhG4bTBGiObhpiWfz+gf5OObZSwFEgJV1Rk5bg7nPnm/TXZtNh6w/X1e4Z+RpGZp0EoSwq0Nsyz5/r6hg8fPlBVFcMoZp7Vas1ut8MYw263Zxon3r//wN3dPXf3D8ze8+HdO5y2iykLITdFAZ/IZumR8yDZjWTRqBgrVcE0jxSuICfRrVSlozt333n//UUrgRfAP7fALizwv845/5+VUv8q8M8qpf6LwK+Bf+rP+ySimpvZbq/ouwMQxCNdWdarF4yTyGS9H9luN5RV5M3LFzjrONw/SHqtkkjnwjhinFitd1TFDmdhDhPn8cyq2tCYhuvbW/pO09rn2CJyfz7he2gq+N2ffc71dcv7t3/C7//uD/l3/SO/z+3tPavG4mzJD3/4E/7Vf/VfYdVOfP7ZC3JKXF3UvHi+JYbI2D9AWlNVz7m9u8Nax8efvuHm7pqvv/6azXbFdv2ai4uGnCOn4wfWrebFy5e8ffstQ+9pmx373QXznHi4P/Hq5Qs260vu78/s1iuaesXFdsPFxZ7NVU8MkbatF16Apygsz55fLUj2gd3W4uxE3w2igUBRWMdud8GbNx89zTTmeeL29obLy5LLj17SnwemrufDt++o6h0hzuQs0tqQxFugVJJJukLasBi5/fCB5tNAtCKYHrqeP/jX/w3+b/+Pv80f/OEvefjwFf0wETKSpKuMDCyNIqfIoR+wJuM2rRwAyxVzXNgJmsIWKIXcLLYUE5hRZJ1QtiCoQJhFRelDwnslVu3lJuJRKuy9zAEeLdVakqOUEiehQkxko890Y1iSfyMqJmII/OqXf0LbtIR5ZNXUWKuZs+F0OmKt4Opc4ciz6En2+x3eS/BNWRbc397hvWcaJ5xz3NzeMnQ9RV0t2ggwiM0+BLE7K8QV6PxMURYLjSsTougmjJkpS0e1xMoXzvH+3XcX43+hQyDn/Evgr/0ZH78F/om/38+TUuLu9pa2rQl+BkSmO0/jgpYqmNTE2A+EACjLar/jw/v3HI8n6rrm5uY97WpFVTdAorCObfuSr778lofDifNw5vmLKxqz492vvqWqVkKknUZsKDBpy4cPX8r6T3l+9IOPefFsy6qxvP3myNdf/gnPn72kLhTPLtZcXPyAwgYe7g989PqK9VripC/3LW9ePuP16zd8eH9NjIEpTNxcz9SF5uPXz/j44zfE6OmHM9988yWbiy2ff/IKw7z0uCU/+tGPaeo1X/76G55dvcLamvdvbwFLWTacPn7Jfr9Dlx8Wk0zCOYP3M11/5vNPX2KM5nB4IJNpa808eZq65f7+gRgSH330MT/4wQ95eDgQgudweOCLL8TZePXJ5wQfefv1t1RW8YOPP+f5i2eSlKQNMSRsVmhtyWkWWKmXf//XX3/F678WUFZhtOVXX37NP/8v/Av8v/7GHzJ7hWMkLGKcfvb4mCgLR6oK7CJCmlNEFxZjxaeQVV70EglrzVM0nbMOZwussURELut0gVGBsRMTmoiFhLisFIsZLOJjFrVgVou6UWYFeiFWkcWEo7Th4Txw7AbQFqUNKUWePbtiHEeMilSFpSyEVoRxHI49SmXRnziJmReremQcBzabDePYS/pUVXN/f8/x/oBVmtIVGKUF9PqIBlOK2QMuYczC3ljk0nMMiBxeYYyiKA1l5XC5kNVllorgu67vhWIQRMH3x3/0S8qqpKxqpmni/fv3aKV58+YNztWUpabvB06nB+5vrvn2m28pipJnz55xOBw5HB8Wdv8eZzUpGG4+dDw8dPRDT+0iD9df8s2v7tjuYN1YVDakyXG6H/HTQJgVNx/esdu0GJ3pu9NiWskUVvH226+Yx4711vHt22+4u5O1j8qBoTvx0etXfPzRx7x6+ZrT5x3jOPKHf/SHjMMzPvv4Na9evcCHifvzgdcvnlGYzKtXL/j49Uu64z2b9ZaiKFhVNXVV8uqFyE3rusXqhLMVxjjUmwvatuHt9S0hyF68qQuKbcvtbaA/H7m82LNZ1XjvWTc19cUlz66ecf/wAFmx3WxZ1QVxdjjX8vL5BZd72cxUF3uMdmQ/8uPPPuP5xQs0BRmY/EyIinoJXU0pkYJHzyMKuLuRSPmsND4kvn37Lb/4xS+4vr4BU7JywhEIaMZpZJg8KddYA9qJdyTkSFJIUpOW3X7+rbogLy5PHwI5q8Vg5tEWbOXEOqu0QEtgQW9njFZPKVPxSUIsO/dHKIpCiUjJGOqqwK1XHIaBYVpcq4svojCGZ1c7VIyEyjKNA5DJ2bFqazJZZiUkVq2E5Vxff6As7KJuVDR1CVloSTHM1E1NjB5lNW6xRKOWf13pKKzFR1HDOmexVok+oy7YrkqqoqCwFqMtw+CpVyWHw5HtesNbbv70jQd8Tw4BpTSvXn3ML/7wF8xTQuUHDscDMUb2+z3BQ86aoqqIc8fY9YyTsOeqqpShVyVWXj97op/pTgfm/kvmuSPGke2mwerE6XCkNECYqAp4/uw5P//5HzF0B/b7PdM08OL5c2Y/sWolJbZtaj755GP2u0u++NUvUUrRj3c83D+gUHTnjsPDgc16xZvXH+NcIUEl6xXrVUtT/1V+76/8lK47M449b999i1Wa7CN1UXKx3XF/c4dVBqcdV/vnpJR4uLvj3bt36NcilFFE6qpAqXn5f9wJY2+z4Xg8Mg0DVisu9xfiPYiJoRuFmjvMNEVDoR3PL64AuZHOh4MAK3SkbRvajz7icDgyWcVms8b9+IfsNxeo6MjRSOk6zWgrYS0ZeRrFJI5AoxRdd8YUJWEZ4A39SNf1i+GJJzFLiEHgHd5TRMGlx5TJMRJiXGTYohrUWmOCJgSezFMaMYkFIskHJj+JczQGSqMWJoIEfhqbQXnJuNAyZExZlIIiepKVICiMNcL4qwqqpsTWJa01tKsVPp+xzsoO/nRPUzfsLnbEMHN3myisYcrVk++jH3qK0kLyIkVvaxSBh/sbUvT4eaIsStqmIuWEMdDWpfAWtByIy9wSV4i2ZPKTMBlUpi0LCrPm5fM9bVVTlwVj35GjojvP4q5NAr7le30IoFnVWy52z4gxUtoGpycqZ6nciugVzpWo7ChdxcvnLxa3mF6SXcJCw4lQllgjarSg7ylrzxQ8q1VJzmeuLkvWqwusLbi8bLi8KPjVF6NYR2koi4q2abi9vcYow831Dbvtju1mi3MFCs1uu+Orb255dvWctllRFiXr1Ya2XeN94O7unpvrW9brDc+fP2O/3TGOPeO5w6B5/fwlzy8vOZ8OlBtHd+z4xS9+zk9/+ju09QarS2xh6U4DZEXhjCTROoWxnvP5CAOcTifqesN+e4FCy0Q8JaxxS4ISODPhmgKrSnabHU29JqVE153wIcjwC8l3KFzBdrslNpmqcOx3G3btFrKhP3uaeoWztYA+y2KBe6SFaCwu0Kosif7IPHn63FMXjk8++ZQf/ejHfPXub8mO38lBEFNc6L/gg2eaNIREoRM5l8QQRd9ARhmZ6MekZGPgWfIoxYEaY6YfPSl55smwW1cYV6KNA/TimwgLg9IIdTpl8U6gBD7ixImXtYhtmqambiuGHNhsVrx+85L87VsympTgYrfheDjgbGa9aiW4ZBwJWrB1Xd+zah273Y5pGtnvNux3rRwg3ZnCWurCsFnvnshR/dhjaiFHp/yY0ShzM6dljWxtYg4zsw9oAtt1w2azWijcmamXKsHtdozDwGrVEr7vZCFjDHXZ8vmnP1jw2xGyTJeneSKHSL2rqIqaXAWMTiijGOdp4QbYBdwJVhuC9zjrKCrNFDJttrStJcVIXRbUlXAK5ume+4eZ9UZyDkIwrDdbpnFgt9uLf7uqePXyFcdjzzjOvH71mv1+T9NYVqsWpTR3d3c0dSuruwWTHWOi73ti8KzahmHopMwuS5wTrNlDWZKzUIA/+ehzfucnP6Nd7TmeOuq6ZBwm9GuRDTvnqCpHWVkOR5mVKOXRGLSyrFvJUTwcD/g5UuxqMcOsNXVdYbWlsEJFHsceY0pyUoQs7MKiKJZQTI01BUVVC+Rl8epbW1DVDU29oSwLbOnQzghmzMvKMRvZsz/c3XH3/gNq95zSWF6//oh//B//x/nimyNffP2WxCQ9epaSOyaR+k7jiLJQN8WThj/lKE9rLVsCcfrJAfLoEclZE33k3M/MfqLQGWcN66YEJbkGIYoTNcOiFBTjWs7i+4BEMJmQIeRMVRas9htsoen9zGpVUdQlEJknsX2/fvOaX/7JL0lxRKFpm4IYxB05WVg1jrqp2O/3dN2Z58+vnuYab9+9JacsTENjQBna1mFPkio9+1mIQY++Ea1QyVMuDsG6KgnJEXMgTD1jB0MURWT0M9ZpCespJBw3Fu4777/vxSEActJtNjs2GzgeTzhXyYsx+2XoVWF0QVU0KOWFkxcmjBMQ6Gq1oXAOP45MQ09bN9i6ZfQJawq2m43Yeq3jeHjAFY4QA9fX79FasVo1oFp2uw3ffvsNV1eXwngrCoy1zH4CpXj2/BlN01DWhq6TtNf1ZsN2t1uchDP3D3coDU1boEwiRFEtrtdCLX7MOihLaRu0Vnzy8WdsNhtWqx3jOFMUJVVVs16vljI2UDcFVeXYrDdoI/kJlj1KWcpSUGbjKIaoVbNZtgXgbEXbtJImFAJVpXGu5Hw+4oPg0uq6wdmCYZgX2XFm6Caiz1hT07YrdrsLUlRLqYqEfcZAjhNET/IT8zRyd3vH3e09b55/AkqGcc451ts19v01KYwCXNFSySkjbAVLwmkoXUFVFLgnkpKg5FMWkvBjZoJYgRVhmhmHiXMv5e9AYNWKLkNZR/CROUbm4AkpLOIbublY2hNyxofIFDxFLmgrUQMqB01Z0a4qtvs9r19e8uH6mq7rWbcFP/jsNe/fv2e9rlEgN7JbCVD0+TNpsdoV0zQwzxMpywBXI98nlKHvRzGy6YyzGef0U/6FD/5pjakTtLUBbYlIfNqpm9Epk+YRP3usNtRFiXWWMabFMfvdQBH4nhwCKSXGcQDUkzmmaWoKV8ju2HtilKGOqxoUkZhlf1s1FVlBW7c4azmfjlhlWLdrgqm52G+wWqMzGKByjuwNTdsQUiDEd2irqcqGsl5JuovOrLcruv4MOXN3f83D4Y623bDdrTDG0axKSUrSmhADTdswjj27iy3T3DPPE7v9mrZtyDFCWujFyNPPGBhGT7tqhCxcVmRgGEXr3XUnpmlgtdqzv3jG/f0tWoFzNfvdM8pKwlaTLwHFer1ejCICXy2KSuTL2hFDZp4iduEKFq5g9gPOOXa7/TK1zguJWWNtQeEaQhTsttEFTbWiaVq6blxyH4AkZCdlMswJPw5LpWO5vb3lI6WlLC1rzl3P7e0d4zRRKDFKPd6ERhu0gkIbSqtwyuDQOG1wVqGM+i2LtEGrR7efJsyZ4GeGcWSYIjEpdIZ+Svio0LYkIZbfKXimKJgyltwBrRUpqgXIkZlCoEyJoq1YXWxxleaylrSk7a7l/sHjlOdq36JMZv/xC9rG8vLlq9/EmSVH3YgD1BrD/mJP15349u23aGVxhaNyz0k5kZKiH+fFoJbYbYXp8Ehz8gtPcpom1lXD88tnGOf45u1bvn7/jlVTPCHxCAGjFaWT90BZGoIPC/Tlew4VAXGlpTjzcD4yTvNT8Iiw86W819pisEQvfW/ZNBR1IYQfG8Xvr52U5sqSTMWm3VG6gqnrMVkAEbvtc4xThOh58eI1rrDMwVNUFejMdtuy3a5ABfp+QClYrSuUShSleYovu7i4QAClE85plC6wzrBafcrxdAASMY4YBXVbQVYyXbYG6xT7/UZciNZirOF0PKKsYrMVOGVRavEbaE1VNbLnzuXCmC+YhoOkLTsBjUiUWoHQZQNpYSLKJdRkpbRMlBUUhcM6caDNc1haApEYo2uGYUZbhzYV1paAoahqXFmgrFmUghM5hWVdpbBFSXfu+IN/4w/40T/072bV7mnWaz7/8Y959vwZX7+7lnI+Z0JI+JhRylBai7NWeARaY5WhtAXGRHHz5fwkGspqcQAqg9YCC/Fz4Nx7fMwUOtANnq6fl0m/IabEHIMMG7VCRYNhcSGiFuKaSHR9DFSrlo9/8CnttsY4/eS4nPojn37yiouLC7765luquuT1q59yefWclDNd3+Oyo2kb5nmi73uuLtfstxXWRBTSyoCEw9TtmqpZcXd/z/l0ktlWzqza1SJmipCEFbFvV1xdXmKLkqJQhCTqRVdUgKI79/jJY41lGKbFYRJpqoaiLL7z3vteHAI5J8apAyQauywKnFWczgfGYURrS+EkeLNQBpJCGYPVjhRgHKV/Cj5hlP1NVltRkBe5aV3XqBg53N3QtjXC5YPNtmX2I2Pf0W5bfPTsLtYYl6lqyzCKdfby2Z6hnyQNJsHYJ1zhWLUNxknmfVFaIRwZKMPSeKKwSlOXMqCqojzBxnFgu90zjWLJjWFe3G4eYy1161B6i9FuSQOyGOPQuiHPnhQcKZR4f8IY6fPFIpyX/y9IZSXf/MLJZDnMM3OKWCeZAuM4LfZVS0yB8TzStiu0Lok+UbYVxpRYU5Bixi6WXUF7RWKYCPOITgFjHefuzJe//ppx/QvOp45XLy4prOPjjz7iZz/7GX/y62/ojgPJCxPvaTfvHIV1lM5SOkPlHJV1QFwGiAgYBXhMjso5MQd5SvbDwOkc6MaZttRsN5F+nCksoLV4HVMiLf2/tAPyGskrJm1O1gqfI7YquHr5gssXW7rzgRQ8dVVxsfsRZVUxjAP399e4wvD5Z59QNQ1FVYm1egySbKQS9w8P7DbN4uqMWGvwwS9o+ETVrinqhv3FhtPxyP39PTkJy1K0C4nSSZ7Crm6oyxJXVlxcbvnBjz7n1I84W1JVLV03cHt9yzzN3N09cJpmjqcTq1X7BM39s67vxyGQEt6PdN2J9WpNXUsakPcjMYXFC51Ji4hkHmaMM1QpgdOczx1lWaC1EQOMdZDkTRPStAAmYZ4HhvGEcxHrNHMcqV2FLRKrTUm7qun6SF2v8GGmbkoSa8Zh4vJKbthpmogx0537hcU3kYnEDOeupywtKXtS9mgNVVVilWLoz+SM5AYaxzQPnM+GnBR1XQmbb73i1N9wOt8Ro6CxyrbCe8/5PFFVLQon5iBlKdya7U60+X3fEVNivV4TwhJkssBR59mL4cQsdOGc0MZSOLuQmBIpibV4nDxlWUEKhDnhNgXaFgt7YUlmnjNlVWAMArZMksYcg2c4nZinaQGYCKrMak1T1/zkJz/h9d/+u/yqv8GHhF1YCSCiH2tEzuu0EfLRIuxRC2bcaP00Xyit0H3HceR4PHI8HJkmx7kbsGqpXLQcyrBAUVm4iFpYg4/OSh4Vj8gPiSVQEjRTOKq6pC3XsHAoYkpMU+Z3fvIjjCu4vLrAFA5txT5t6rxUF4mqclRVzTyPXF7tqatSchqcbACKumVOCa0STV1Q11LVrZqWGMICxxVykc0ZnTJFJWa6V69f8827D4yT59Wrjyhtxf3dA2TFw/HIwzDy4cMHdjvhZPyP/2f/pz/z/vteHAKgaasthpEU4HToZCc7RaYxoJJDkzC6WIATiXGYOQ4dgczkZy4u9lRFyTiNdKcelTIualxTMZEZup7T4UDwE+mc5XCxin4OPH/+DB8mwqjoD4HVuiLOklWwqq9wecAP8mY+9mfp2UikOTCdE6vNCqWh8zNkxdh5+n6gdA4THdnAPMg03KkldSdphvPAarVFZS2WXWspTcu5OzFPsleuty0P5yOEJBWAn7m7fsc4TlxcXnD1YsPDw2Gh/FgJpzCOwpbM08y09JuhimzWm6VG0FhdkgtQs6XrOxSLWw9RA6ZykK2BjpTWYRMo5ZnShEfoN2bWuDGgh4nQn5iGCNWKj370Qz755CP26zUqO2Kqybbl2asrPv70Ge/frYjxjJ/TExDEFJbkFMmBqQ1FpXEm45ys8gAKZzEadJaEpMJZpmz54nbktnfMMaGcplqVFKVBG6n2HmPFSApNgaIkZzkAU1ILX9AsfoNAkcSjkZfKp90/oz+el3Wb8CPKsmW3vwSEd1CWVlqBxeVXuIJx9FT1irpqmSfFfnv5BKoty5JpGpY0qkipFVVbU1mB4EiAaIEr7BMOffYDRiHQ0Jjpu4nCaD7c31Pakh/+4IewW9PUNRcXK5KG7rMXkkwcvucxZMZYVs0OZyZSDkJ5yZqmWpHCgNEF62ZHVdV03QEfJhSG09CT4MnNVpYtcxEYZgkLSSERp5mzD5zPZ8ZxlDJMGc5DT1GUFFnR95njcUIFjx+gizPT7IUr0GiMtZzO58VKO1I5S+McU5rJPlBoA0bjbMU8ek6HgRgyq6JBpwLvJ1R2EikWJVsgJyFlWlPQdwOHw4kYM9YUhFFjVEVpC3JQEMAgII95Gjkfb/niiy/Y7ffM4VNOpzN11UiO/Ryf4KohpCdSsZ/9gtxaJuFzYPaTJBGHQFNXgABOFZpZDxSqQCdPlUFNnoBHmRltEwZFnhJ0kfBwJM8d4xw4+Uw3e1ZtTZomjC4JWaNcg6ss+4sVu/2eOEf6MEhJngXOEVUiGoVyYJ2SSiNL2yFZjUtqdfBM88TgHcfeczPAh9FQpJFNZWjaEmXESpwTkKN47VmwY1GTlBKbsdLCI0iKLHxVUogEHzBKo7PG6IJhiASvMVYSrNP5geAzRWGYxgFFhODJBG6P91hTcD6P1OWWVRsZB/E99L0YzZQyjENmGI5sVgYNhFmMWGGembzAQbURgdboZ1T2WBImGYyWIbLKUjmNXUd3PjH0HeNwIOfMetOw3wgnIzn1Xbff9+MQkIw9xzzPOOMWJHimqirZ/ZuC/cVeAkems6CltEw8bVlQtw3GWMqqILY1Tou2eoozYRFh5JyXF19RljIss9bSti3DMDL0A2phB87zjAK22y193yNWVi0EWoRXl2N+cq/NPhDnzDCMyw0nkVaCupKhGwoKUyyRVcMy6FwUePPMuTuTEYfgw8NBnGbW8fAgKCvh6kXGruPi8pKb21vmeearr74SpNgTM18m/fM8U5Y1j9mMIURurm8Qjbl5CtrwQZKM7AIxLQonGw8fUEkxMlOlEYJixuPLiFm5JezVM02DCF905Hw+8/7de96+e4sm8as//mMunr+RgaGz0hZUFbv1muFwJoyBFAVOSZIknUeM2uPXLGGxCEUppsUvrxknz/u7I9e3ExG1KA0DlSsorUblCEkxT+Hp+y9A2CWCXClIApZNSdgMRssNFVNkHAa895zOJ2Yv1OqiLBbGhH7CqmldMJ5PxCiJUX0/MI4dxsjhez4N3N3eE6OhLAUndnFxwbmblpxMwb5HL9yIEBNosQ+XVSXUYCXI/DAnAhHbFrSrLeOQOBw6Xrx4ASiOhwdSCsu6fbWg2mUIbJ6clv/m63txCEi8l0yuh6EXhLU2BL+UMCrT9yfO3Ynj8cD9/R3nocdVJa6qWG3WWCuDw0ykqCxTL9FbjyBGayXa+3Q6MU0zq9Watm1F2tkLvWcYzwvWSabxu90eAGMUGUeMnqpq0cqQVMA5B0ZIw+fzmX4YMFbShHOM9F2PtzMhCjsuuEwIonCxthARR0oLRdjQ9x0piXouLVPk0/lEVcka1BYOmyKb3ZZPP/sUHwL3h1tRMi7sdOfs8gaVQ1Qt/MAQAvd3t0sOgqw1UYqqqhfpdca6AusKfIgkLWTePoxYfcLgUKUmBI9TTnBfZHyUr88PHafTmQ8f3vP65Quc0fztf/1v8nt/7R/G1SVWK+qyYNXU1KVo3GdryUYUeh6ZK5DzUy6DJER7yko0ECprUlLCH8wSfHrzcMDHkqquaXzPqnZUVmPIkCJ939F1HfNCmX5kTOYs8mSd1SLKkaBbk4XXn5bvqT1oikLaK2sMx9NhSTUWDqEPMrS0tljox5mydBJyYlb0nWeapEKYJs/V1TNWm4Lz+YSxUunM88w8CyOgrksSoLxHISj8aZqWzAyFMY4YMw/3B66vr+m7iRcvXmGtoOvbVYtzlu12jfeyXnyUMH/X9b04BFJKdP1I01TEDH6U3AGFoixruv7MueuYJ8/xeODm5gMPxwOXz68whWO9aSnLUoAbKEprGacOV7c07Upy/haoZIyJYRjY7RxN03J9fb3AQh3OZNl7awtkzufT8nTV5PyI8RYzS1FJAEjICW2F8lJVUJaF2D1zJsx+SY6RN0UMCq0dq1WNtUuU+DwJ5tzJdL6qKzbbjQzVUkAZxRwmvJcBqbGK0/lIu26EIFwKHcfPctA4555SlqqqXoRWjrIsF1mxHKyPT8a6rinLUsAX1hJ8wDoL2aAwhJAY0kRdapqyZk6T5PRJCohwHzQ83AtU4+72ht//vb/K1J/56uuvebi95sXHb3BGs9ts2K5WVNZQW4d38oa2VcWUZqLyQvONsiWaVMYqGdxpZchZMc2RaQ4MHs5z4u440KVEs11xUTXsmoLaIX8vRvrzifO5x0dFTurpAFg2goBUd9IGyXuxbmo2283Te3OaJoGRGEVMgWEU2KxSinHqCTFSVkJpLjKE2ImDsCkXj0eW19JHqkZTlpmUDdM08nBzR+kcSzADrRXyNIuOQqqUgA8Bq2S1entzy93dkfu7E6vVhhiDaA/qAmNkiDqOw1N2xeNB8l3X9+IQyBmGcRYbsFKM0wQKqrrClY53H95DFqHIOI3EmGjbVgQ2KvNwfwcqUxUFTVWRoyErwWqnlJmmmZwzTdOw3e6YZ884TkzTzDCM7Pf7RQc/yRrMarSBrjuz2WwlnUiaSym9Y6JcVczBQ4qUdY0ylnk5dVWGyhVo1JLVV5CVllRhYygr2X4AfPj662W1qGjahs1my3othqB5nlmtW4ZhYO4n4hilRRk6Li4uSCmx2WzkKT+KZkA4DLI3ZolrFwVgKSGsSXIdH1uHsiwXBZ6VfbsPtKs1g09YXeBMhQkFxsq03wZJ/JUVq8YVcsD148jt7Q3T0PPq2RV//OuvCaPm7sN7Xrx5idWKzXrFbr2hcSVNURKKkq4bMMj3LmtDacU+G2LEe6hb4Tg+pkFNc6CfI/eniQ/3HafRM8VMUc/sryq2taUy4JBQ2HkUJHjKhpyWbMIcAYuzZskhWHInZKNLu1rx7NlztrsdVV2Qk4Bbc86LsCpxPg/LATEK1suVyyHq6UOPNQ5VCck5xhkwrDYr+v6BfDdijGYYBw7He+qyecoenOaZ/cUlSmuctVhXoJW4LU+He/rgxU9SFDx7/oyL/RX7iwumcTGKzSPr9ZqUxDiVF+rwb7dZf/r6//sQUEr9FMkWeLx+APy3gR3wXwKul4//t3LO/8c/73PJ03lC6wPD2DEMI26h1CgU0yQZgtYVNG1NVVWSiacSs5/op56+O7PZrDAqMY0J5yzTPHLuJ7quI8bIZrNhvV4TY+L29o559jKHcAVlWZGip2gqUlIL6ltuIjlFlej8YxCKTJSboKwrnCvxMZFnQYoVziHJN4ayqAlJE5JapsiSoqRU5tyd+PLLr9hfbLm6uqBpaqyzhBgkdsxIdl5ZVSitOZ1OWOfQ829mHSkK9jzGyGOc2uMcQL42t5TVQSLElpJYUnAFoe4XabaUsBatLaWpMMlS2JqqrCms6AMkEFigGlprCd6oSlJOfPGrX9HUJTnM3H14S7l6zv31O8LwY2wj5harRTdRaE3jCrpwYup63KrCOUPhNM4ur73WOFtitSVGz+Q9k0+MXvHu7sg3NydClkzCGDzrqqItFM6AToF5GiEJfz8uqcOCCpBAMqMlmEM8CUrWgW3FZrtlvdksceYa5yrWbcP9/T0pZ5ySmLhxHAHoup638f2y7swc52uuLq9o2y05S3s3Tz1tW9H3Z46ne8k4cA5XWMZ5wi3EZDFThUX9KdyEtITdhqrFz/MiO6+pyobVak1V1xyRNW/GojVYZzifOnKW2Vbb/iXgxXLOfwj8dXlhlQG+Af454D8P/A9zzv/9v+/PlRKHw4H7+xs2m5UIKhY4hrXy5Bz6gYeHA029Yre/pGmkTTDJU5el4J6jTLz9PElf5iwp2aeEoK7rnnLaH/v4x5SfnD3T1LHZbLi7O5LiiNaO0+nE6XRehpRx+eYPkDJVXaGsg3Gi64al7RCYZ4gRi9ycX379lq6fefb8is2mZZ6gXYsbr121NE1D3/dst2tijDwcbjmdTmy3W46nE2UpPe/sZag4e8/9w4MQZkG2HEVBURQ0TV7EPoK4eowfDyFQN9UyzBSIxjhO9ENPTpmyrJfDN9EPk6xoxyD9bila9IyXft17iBJvrq0FYxinmQ/X1+y2G26u32GJ+O7E4eY9w/lAYxq6h3vOh+OS3iNtRQ6RcejxaaZIJU6VYMuFIKyXFXFiGDr6eSTbgs4rvrk+cneS9inNM4WGykJhElYbYvDM47QcarIeZLmx0AarNUay0ckYtHU0qxWXL57x8tVLXFGIurMsKcqCqqlwvcPP84L6Fi+DtY7dfg8o7u/vWa1W+DmLwMqWKBTbjWKeg1Rc1gglOEWqqqKpVzzcnyjLGmvdEqNeLAPGUazDs5Tyu90OloNbsGoIkdsLSn69aYGWm5trrNuJVfx8pmkaSbD+jusfVDvwTwB/knP+9eOA6t/WpWCeZ6Z54MXLZ4Dl3J/ISdJ/jXEcjodlgAfrzY6+75+eeptmTeEM/XAm5bjw1mc5LetKBms5c39/z9u3b3n+/DmbzebJoTVNE9N0FrKQsUxj4HweqaqGaZQ/U1U14zhJZFWGjGacAome3Et46GNWYPaRGCKByNgPfPP1W27vDozjxG63pqwsL80zqsrx8UcfU5aWb99+xTzPGFssIApELNL3aKXk9Rllnff4Cu+2W+qqwi46+sf+f7PZUpYV4ziJFXU5BJpV+xQ64n0gjRMh5mVIumKab5jmwDjNNK3ocxNgnMMUC8duVszDiHU9RVHjY6AbR87njrZp2K7X3F+/py5q7g8nHt6/5eHtt1jznNPDA+fjkaHr8eNMaQt0zkTvGQ4jejKEsUKvWtxKzD/zecTqxPFwB9bw/KNP+dCduDtNjEFRaotWM+vKUtmM01loO34WSpV0cU8+BbcoTa0xWJVwxqCNo2hadpeXvProDS9evpR0aR+ockEInkRiuxeEXIwJ4xxlLRSm/f5C2itn2W63bOc17aolJyPDRl1wcbEFMoXrRJ7t5ClvjKMoJP9yGmf6vqdpWoqiWKApHj/LBkch1WVVVfR9L1sLIzZuY/XTDKHve9brDdvt9t9yKAj/4A6B/yTwv/mt3/9XlVL/GeBvAP+1Py+CDES/fXF1SUoeVzqmcZbeaL8l58w3X39DyokXL1+Ss/RHYoRxOCf9+2OGXAxRtPNh4nx7w3qdxU2mFIfDA7/+9a8ZR4mNds4xDD11XXM+n0kpcTwdqMoVt3e35HRPTpo3rz/CGklzEblnJGclh8LiSV9v1otsOaGyou8HjtOBeZxp65awUXSnM+NwZrNpaeuStKoY+x6jajarNRqYhpG2blAo+k7yCoMPUtKHRbizpBe55cmRYnp6Y9R18zQHkCj037QHwQdO57MMsIpqgWo+DhItFxeXklbkHP2poywaqqqmaCq0Etuu6uD+5pbudOLF608ZJ88wzaAUn376CZC5v70nxYxDc/3Nr/njv/v/par+KmpRFhojKjqbNRrxCUzBM3YzfhwIXU/qN8x1hSWQ/cDtzTUvPnrNq9cf8yfXPyeoAu0yKcO6Kdi1BZXNGISRIBj3hPdJBoLLfWCMBqMwKmOVBKmWTc16t+Pi6ordfsdqvaJcDtecM+M4Mw4jTdtQV83Ccaip64aqqnDO0TQNH3/8Md57Lq+e03VncQxaR9/d0zSOTMTaEgEoG4bBA56iLMUBuKRNpwVP771HK+n/y6J4Souepon7+ztSeqQLGeq6xTlH13Ws15vFN1LIOvJ85ng8fuf99w8ii7AA/uPAf3P50P8E+KcRBeY/DfwPgP/Cn/H3nsJHXr244NmzC1IK9P0JpRXzshZp2xWXl1eitnMFVb3m9uZ+cbs5zuczisw4DXLTVAXaKMqq4f3791hbst9vFpdW4Ec/+oH465c0HmO0rKFKx37/EfvdJb/85a+5u70lZ0NOmpcvX/Hhw3s22y0g09Z5cTY6pSRSTGnp/Y4nrJG4K2ccQXkudjtevfqIeR45HO5wRou01sI8TqQw0zQlVhvGOUCWfL/gI2S/mIQcbbPCGktZlVLeKfBe5hWP6cXTNC4JxHHR12f2+z0oRdfJuixnIelI7qJImZu6JbjfJCBX1lA4iSbTpSPHQBpHzscjN2/f46qS3cVLymrFerPj+csXUo11R4ieuRso7Zbj3Q1//Ad/l9cfv6S9aNmsNzx7/pJ1vebm23dSvVhLpUtJXiITfeZ46NA+s2lLVNJSOlc1/TBzf+iJ2aILTQyeolZsa4vKA/M0EiOQhW+YiU9gMgWy0WAJKc+LM9VairKkrEps4ZbNh1oO04gFzqee0+lMWVaEEJ4Gq0ophqF/irM7n88UxZp5yrJlwdDUa6xxeA9Gl/h55nQaF2oyxDRgF31M07R475ecCJlZsDzhjZHk5Jzj8pSPzH7k4UEyIJqmIadMVdY4W3A6nR7vtb+cweBvXf8R4G/mnN8DPP68/M//58D/4c/6S78dPvL7P/ssu0KGaYeTxzlNzJHD8chms2Wz2TKME0VRst5s6PuRru9JUWLMY5gxVlO4CoVis9mSNVT1gapawB3R0zQVRVFweXm5bAtq2lbimeZZk1Kg63q8j6xWa/puompWFEXB7e1bYpJvfl3XHI5HrBOia1nXT1P6eRgxKNkO5MzUj5y7CVdWhNJhdEbrLPJPYyisY54HdFORFhRU3wmAcr/dihqwLGmWwU7hCtYrSSwepuHppk2JJ6VgSonT6fj0sRDC00xBqhiZWeiFY6+0JP9Ok/T8Wht2V3ucK9FFs2ghZMAVY5S5QMikpGjWWwiZ8PoNhgRxwqqMH3ooCsYxcf3Wcn/zgd3VD9lsNnz2wx/hsuJfOf0/pboqS4IH5gnjLFZLe6OVw+gS7TKXF89wpuDbb9/z/uaOkMUdGKaetixZF5kUPdF7YlRkDCihBoOkJGkeb+5HrsgiV1Ri9skLskugaRL3js/CIkySYGS05FFkpI2s61rWrUpR183T5gCk+ri/f6Bta5wrOJ87pvG3NQENPsqW4eH8QFGUuCUzU8RpAnSZpongPfuLPatVKw9AZ6RNSXEJqPXk/Dgfaokxcj7fPN6HT7OwP+v6B3EI/Kf4rVbgMXRk+e1/Avg7/1afIMRAN5xkzddUGANXz55J+ZMyWSnmEJnmnlM3UlcNIXjapmHWmru7jlW5oi1KjLOs1nvmMHNxcYE2mZvba6qq4uWrFwzDwDB2hBCpm4p21S7kHsXNzTccHt7jXMGnn37KNEbW6y0XF5fEGDkchMq7WsvTb7Vac3F5wWotPSAZkg/kEHHaoFMmx8S5H5jnGT/PrFcV49gx+5GmrNiu1jwcZoauZ+h7EhbnKmwhNKCmaiic49mz59zd3VHYgsIUjMNAiqJySznjCvsE23ic/ltr0Fpzc3PDZruhqGqUMYspRzMvIRaME8PomafAaiE2l5sNVhlylrAUs9wwVVHw4vIZSVnpD1xFWa+om1aGZdHjNKg4E4aOpBLd8cCHt9/y8ecfsd1sUZ9f8mx3wbdffsPP/+7PBaSZBQ5TFiWlsbSmoG3WVE1J61bstxW3h3t+/eVXdP2EMi3JzzgFF21FxUwKXvp/BSFCzIq8RJRrwQgv+YgKjbQrWuvF/BSXmypLyIli2ZgIrLQwIhUPwVNUJQ8PD1IROMMw9MzzzI9+9CP2ly/pz4YYPTmLWElpOWSmaaRpGub5SPCJ4BL9OLK7aMldzzRNzMxM48Q4ThRLQvaj5mQaB1arGqUfK8DEfr9nHGdBqAdZ/WptGBZQyaNY6C9NJ7AEjvwHgf/Kb334v6eU+utIO/DFn/pvf+blg+fcSca6IJgCdf2CeZ45Hs+kCKt2Lb3ZJKrAspC1Xr30zForXFFQ1hUhZIwueP78Bcfjgb7vl4ogUVXF04T/9vaGcZQ+2lrNyxev6fuB+7ujPA13G5wraJqazz//jNvbW6ZpYrNds794SdOuqKoK4xYFXcqoAlRM5BBJw4gyju2+IvkJFrFO350Y5x7nDHG9IoeJ4+nIPI/EXLB/80JQZccz2+2W4D2ng8Ajh26iOw3kJFZmVWeGYZA9f0x0XYfWIkRarzdPA1HvvVhqU6IsqyefQYwJcqCqGlarimfPX7BqV7IKTBEfI4fzmVVdCx23qqmuntENgWkMrLBkJevIYYlvd9ZQFZZ5CvgwMZ0O3H34QPSe3W5Hudvy+vkrfvq7v88vfv4LvvjVr4SW3KwkPTkrSlfhTElVrtiuHVeXLe8+vOfu7kG4gcoS40RdFlysa2zsSYTFQaqIPhNiJmNBJZSWp7U2QvDVgFMatfw+I0/7RJbDYimhjdEYJYyGtl2hlMiuh354wr5Zu2L2E9oobm4/sFt/xDTPgGK1qpn9wFdfvUcpxYsXryiKAteUlFXF4XzL7D0XV5d0p06AsePE/d09bdMuSHWHXVucM4zTQNedGIYO6ywXlxcUhaPvZEPlXKZwivNZDibRjkhux3ddf9HcgQ64/FMf+0//2/08KcoKKATPZtMQlp1214nqSQZeK1arNVfPrjgeD+L80gqrLHUlYMfClnifiD5SNfWTBbVZ0nOPxwdevHhB2zZ89dVXfPjwjqurK+Z54nQ6sl2vePXqI07Hgb4fKFxNjPFpIGOtJkb5nBeXlzgJHRRnmA8kL5N7kxVhnBjPHSpnysKSU0BbScitqoKqtqTgUZsNhdUio51HdLFmu90JZDILM9GWhrvbO3a7HefhzOl8Yrfd4ayFJXUmhsg4joJp14Znz56xXst0uGkajBUOo7WWpmmpq4ZUSRthjGW7kfJ/tdmSY2K6eY+tK6It6PqOunQin21asAXTfGYOCYzEwocUcc7KbGW7hjlxe90zjDNKWbEvA+vNhlXzivVqy09/52d8+df/Hdzc3jLdy0GktcElReFqjClR2jKHzP39gdvbB6bRo23FOM14H6lbcXTmuUOVDokfUiQCMSliVqRFBfQba7J0APkxiXipcnLOpMVbEEIkk3BOUVSFiJqMZbVakXOS3j2MKC1eFDtrDod7/s7f+dv89MeJy6tLnNP4oBjnGW0Sxliur99RVStevXpF4SreXX/N7e0tn3/+AzJnCldQlTUpCqTliZfpRF/S92ceDvdSYSwq2O1mD2hhKE6eVbunKKQ1LopCFI/fe7KQgnkemeeJX3/5xYJHFinsRx99yjgEunOP1ha0TL5zSDRlJdbhriPGJIgoWKLIT9zfvUWpgHMF1i3ru8XJpbXsX7331HXN3e0df/Nv/Gv8/u//VVat9Pfn85nVasMvf/lLxnHgcHhgf7Fld3mx4LGU+OuVCJ5SyqiUJSdv8qQFzxW9Z/aDePajlJ2Pqx0UVGVN+Vz0+017wTSLbr4tWzncCmkBrLXkAE3ZsN1u0UYxF/LEcbZgniWTLsa0xI0JUHW324ksetEbVHWNURZtHNYUoAx1uxH0eM483N9z+uZrLl+9pL68kihwazCuYBko4KyDogJt8VNAG8PF5SVq7mEcMFHTHwMPxxMpeMZ+YOh71i8Lis0GPwdW6zWffPIZz5+/BK1FcwAUGCrlcK7EmILDwy1f3X3Fh+tbtC0Q/cCItVq4/XGmsIJHC1nckwm9fF8yKUqPb5bkIZUl2stoLa2BfsSXSf8sv+CJglxQotBPlVNZOpoGMiVdf6Lrztzf37LZbNjuNhxPD7x4eUkmkLIHEpeXe6qq4ttv35Nz4HB4ICYRnOmomecJHyN1U9PULfvtXh4sUVKVh2EgYRkneR9pLcEqXXdms97SNC3TmHi4P9I2e4qiZLvb0jQNDw8P33/ZsEJWaj7MnD6cFtljou/GBQ/unnTsd7f3zOPIN199zasXL9isBKIxzRPH7kyzXhFz5nA60Q93rFYV3gu4UynFr774AoUAQ/th4P5wz+QneVJG+NUff8HnP/gB4zDztntLVTb80R//CZeXV7Rty09+5/f4/Ce/T4yOsOCo9KPizGpUSsRZDEACv7B4PzJNnhi93Pgohm4gpbiUp5qmqRd8tqMo9CIUyaxXG3JOrJ+vefv2LUop1putbFBCYEweYwrqZo0rAttdj1aatm1pV+sn5Fhd16w2G1xVkROE2cshUNZI0kZBigNhmujPJx4OBzbPnrG2lqoopMy2lpwHwas5hykLmHqGTtqn1XrLfHpgaFp8M1NVGo2nO018/dWXvH9/w4sf/R5aW07nI+fjSQ6pzZbVekU/jnTnjgKN9ZlSGWpXcNcP/OrbazqfKNctYcpoldjWFReritpNtEVJtBbv80LREdtxTkm2A4+moSz6B200hStR1qGtXYAmkv5TFqU4TXUg5YCxhk21lpWsNrTtihgDh+M9Shnads3xdKRqaj797HP8BInENA6YRT8Qoud4krWtK0o+3HwghMRm19K0Ig0uC0keCt6z2+6Yp5nDwwNhDjw8PGB7gykUTbumXa3JOdG2DUVV0qxa+uHA3cMd2paSh7C/XB4ApRC8v+P6XhwCKWV+8YdfoJT0suNwpiprjGm4u+0EuOjFG75pdnz1xTVffPEtt+/PXD27YLdfs9o0qDny4fYdp/MBbTTrpiVOibZd01Zrgs9UruGbb7+lKDyb7UtQiZuba5p6yz/8e/8efBgoUFSrinVrGYaZH3z2Kbv9R/yVv/KP8vEnPyP5NedChj+F8rg0oZnIeiaoCdOUFEahBo+KGU9FsHuUFVS6mie0KUhpJkVxxb19P6G14eWbShRqZYVKkRxntII0zTRFgbWKOc4iE9aO2W+XoV/DeDyQVU/IiaK5wpYVZSMmobauRVMwebKXAdocZlm9lgV5GiDMqDDi6NBNQ7lak+bApqxwCXKEMSqyK9A24ownvv8FZu7Rdcs0ZFK9odpfMHQPlPbEtumIWXP99lv+4A++5Wf/zoZ2isSHEzdffs2vfv4LSm34D/z7/v38+stf86//a/8aRcys65JGG4opcThMfJlKhssdZtXS9W/ZlyM/cp4fWsWbukBFD9FQ5Cz5E0h2ITngtJbUnqxR2WIw6OyYR4MtM9rN6Dig44ROidKUlEXDEE6cxiN2grWuaIsd4ziQ7hTtaocyW5xq2OxbxqwIeRRMXtR88c1XyxO6oV6VnM8dp1OHUoaVK9hcrCUiLUwMpyM6qSUPMYHVEutmFVlncHDx4pLruzON3dFuVxSFxjpFyp4pB2oTmFTPmx8+l8/rItmseXvT052lGvyu63txCPjg8V5wYk3TsNvun6bcbdtye3vLMEzUdcPsPavNip/8zk85HO/o+o7L53vR1EcrsVLa4pzsfp21NE2DNoZp6MnZP4EaVpuWcRxkjx4z1jgaW1JVDnQk5Uy7Sny2vuSjj3/CxeUn2EJ08qWGFAMmByxRDEjGoRYKjLIGXZXoAHUJbiV3kU6RHD0pNIRpQjQsinqaMLagWTlimMnZUxSyIsspMo6yrqyMISCMf1vWTMGJI9EV7HZGKh4Qj0Up/804KwfKOBGmER8jpigoqkoqoBCIswwujdGs24bRZ1xVLwRiJyXygrZKM6R5lMzAWSo4a0pwlqosCYW0LlVdC+i0jyhtGBdf/+nhnrfffMNXX37FzYdryrrk6uoKW1hu3r/j9OEWE+V18VEgM8F70mxg1JTGsLY1m1XFbrvFGJk7xKQWLYAi+kAI8urGRRkKsIAFF04hT6YhAZdqqcaWP6a1oSxKckpc7PcoHPcPdyJAQtOsVriipd3UnLt7zsMk6cE+4ecZ1lvqqsaHCPTsdjvadi2T/JQpC4mWGzpHCOHJ9feo+fBelIJlWdK2LZtdJCkDRIpSUxSaaY50nef29oZV2wKKrhslcCSOXF+/pa4bSWD6jut7cQgoFC9evMB7z/NnL1ivxR55Wqal1ha8fLknxUyOcHV1Kbz8psCVVuAbhyPj1JOV5tnzF4vLsMD7eaHRziQydV1xcfUZ0yRBIGSRkpyPZ1b1mhg9D8cTwzhSVhVNs+bi6iVXL9+gdEFaJKRlVuQ8ouIEMZBVAqx4CbQCY1DOQMgYPEZHiDPZS2CKwkEolt5TU80e4xzWZaZTT0ozxq4wpiAF8atX1QpTt0Q02VhMVRNiWijLGm02bLdXQELl8PQmzynhhxEVZjlcF559HDJFU0ns17IG1DlTWMNud0FRN9I/WyECZzLaWlQWqzEhPTELjULoSkZjjRB8H8VKIQSCjpz7gRgz54c7bq5vuLtdPBL7Lc16jSkcz58/J5576CdImaRBOUVTOawrUNqAMdRasW4F4BnDuNibgaiBJWwk/ptBJfwpK7HAS3+jF3hEeeWUxRzV7FB5hJSJybNerZjnzN3drZi3rObU3XN9fU1RKS73l5weOi62O3a7C+Zp4nzqSN6z3q6I3uNcwWq9FkNXnCjslnGcmGcxuymlF0q0DB1lPX3EFg2rpmT2IzHM9N4zzQMf3r8np8Tz5y/YbHbUVUHfPbBaWZ4/3/Dhw3vevT995/33vTgEANpm9SR5HYZxsbnWYgAxBmsLtNPCzJ9mRj+w2W0oqxJ0ptUtw1Az+5mqrshK4cqGjDz1i0pTlOIlKIuC29trisKw2W6YphFjDLowzKPn7nDi7u6B3e6S3eXH7C5eoVxNmgXQaZ0GeogdeZ6IkxB8c1WhVxsoapQuycmSjIJ8QtETolBunU6CHDJCkdEaSm0kGCF2ZH8gzBOTCjhXk3NBxqFci6p28tRVFqzFFQGjJxlwGYFbEj1pzqTgySmQYhDjSwqYBCElIpmyrpYM+xEVPc5o+Tt+pm62KFOIC89Eye6LUZxqj9FgGlmxGdDLiu0xITmmSEwZyQFVkkGIJqEYhwFyXlSVhu1mQ2UNp8OAQhR8QUk6dTZgC8uqKQmVIy6z/E1ZsmlqUhRqsXYanYS9J7Fdwn1ISeLRtVaoxaOfUxTkmFoqByQVKMWI97JW04MgvwoDOSYeHh5EPKVLuvOZaQrcxYgrLdpm7u/u8EEUqxebC+Zx4rCsZq0tuNjv2ay3nM+dzCxiIubAOA54L8gxrS2gF+9AKSKhxavQNA1oRdcfGIYObcD7kdu7az5cv18qwQ23t2KTL4oKY2Q2ZGzgw4d333nvfS8OAdm3Fmw2G25v7+j7ge12J44sH9FKM43SB7dt4tyfmeYJly3deGa92XBxuadqGoZxYBhGYgjMPjIvhp+qXPTvOdIPHeM0cHsjq7XbuxusseSklkmyoWr3XL34hI8+/gmb3UtyNktgpAIVIJxhOpOHkTDOjFG2AkW1wlYl2dbEaNEoAah6US2qLKGYaRqZxx5FWgxASpJw5num8z3zFAjTiCs2uGJLwGCiwlCAXQPCStAWTLFMtKMnzSJDTiFAkh9KSfJSmGaOhxNzijTrlWgRzmemvqMpjUzeUySECa0FkU5eMGpaQ4pLbx1lGKrzUxKRIeFTeMogSFkAGGiDLRQqW1brtQhxcqaw4uev65LVgtd+uLvjdDxIYk7OKKMIJpJVojQak0UeXil4vlmzaWvm6YHKKTAWhQFEOWeMgEZT8Mv0Xy2R42lpCRbtgFYolXmMg5uniXme0ZNFqwjWolLgeDxQlTV9d0uIcHn5gpgS2ii8H8kxEmdPtoa6qrh/eFgOjoLd9mIxhEWqsuLu/oHz6cx6vQZk6Lff7ynLkt1u9+QDkZbFME0Ciy2bkq4/8/Bwx/5ii3WKeR7IOQixSmU+XL/l/ftr3rx5w+R7mnPNxdWemPvvvP++F4eAWCgt3gdCiEzTzOl0JifxC2QNIQjDbhg7EiKT9FEwWnPwT+EhAp7wWOcYp8DxeGKaRtqmpyzsgtaWWKZ+6DgcDlxf37C/2HP3cEThqJsdH3/2CT/9ye/x4s1naFeRU0QXCiwkP8D5gXQ64rsJ77PYX5cSWCkt+v+shVuT8pJAO6DCTMYzdUe644EwDTK/KArR0aczce7wPoFKxODQdktEkY0DU4KtQBlyCJBHuUFzIvgJPw6QAoYoz12tgMw0Dtx+eM/xdGS121E2LRkxNeU4Y6pWyuIcIcyitM8KlOTk5UWma62VTAed0DlJ9SJSSVQKaOTGYuH+pZzR1mER1aNBeATR+6WNMaiUOB+P3N/dcjoeCfOMTQntLLhENmLM0lEOxv2q4bJt0DnSDz3WWFQCkiItWQJyOqkFBiMdmoBj5LzUSkCmjwAZRSJGT4hyaBSFZC3WZUEIgeH8IGlAc+b5i9esN62YrQrL7C05XzHN9ZJpkVivWiFPT5NkU4bI2I9YW+CnmbIsWTUt2mS67kgIYQmRlQNrnucnMVKMkdPpxIYWYzJlZTBazEPPn19xPgtQ5nC4o64LmqbkdLrneJS1pbWJ9ab67vvvL/He/vu+HnkCh8OBqlrCOu/v6fuBVbsiJVitVmitxC6MX2S/4uI6njuGacQWBcZIgk7McD53PDzcMw5nyspRlZYUA+PQ09Qtzhbc3x8ga4LPHA93tKsLPvvhR/z4p3+VF68+w5br5T0V0coTfI8fzqSHe4aHB/zg0aak2taUdY12xVISi+c+R8hxIs8zcRpJU09Ms3yOsaM/HrBGodbCtSd2+GlgnBJJZUxVgB1x7QpTlEugSiIBUSV09iJyCTPz2JHChDNqcTMmSJ6hO3P94QO3N9cUZcFqs6FoWnwQzmBd1xRliQoT0U/4scOmR9ugzCzk3lLSk6Nh6bUTiRQ8wScZlCp5CgtRJ+GDtFCla3j15iPIMPU93enEPIyonDmfjvz6iy+4ublmmkaIAbfcwNkqkkqYLEYeoxXP1y21UQydQEAjggvPXvgHaE1eDD0sPb5Wi1wamVlYa55+CB/gN+KhwlrKoiAbcWA6a5iAaZ6o6zV1XdH3J1KCslxRFgWb9YqHh5l5munOHau2Zb3ePFWxc5rRaM6nDmsc26tnOGvohhOFKxb/gWaOM8ZYSZhajGOFK6ARkVpVW6pyu8w5Ivvthh//8IfCt9QaV5TcG4WfJ9abNVYrrj+85/Li8jvvv+/FITB7z+FwoG0FsJFS4ng4EkNcGIBCBbbWMgWJVipKS9vWlFVNP0+C7D6dqOuW/f6CcZy4vbllnkZ8GEnZM46R7nwixcjUzqxXO2KEomiZfaJdrfnBD3/E7/zu7/Pi9Q8wuiUltZzO8iYf+xP96UDses6njjgG2tbR2oKirFBWE3NYpMMiYldpQIWA9jNxGohhRPuZSoNyBmMUFUni1CZPfxoZ54QuHc5KUGZTlujCgo7E2BOyIpOxWRJ+wzQwzR1V5bAqQQqkONEfDxwf7pjGnu12w/bykvV2h0bjJ6Ho1k2LLSzZD4RxYOpO6MWDnpbg0LywwZNMzmSgmtJi2fXkyT8O3nnMDEQptDWY5Njutrx+/Vqeag8HTscj8zSRU+J4ODD/8k+4Py56fBDtfs74HJn9RAoThS5pS8vlqiGHmaE/oxVECRUiR0mmVoInlnmAWiCiRgjUeqFUay0EX2cN2hnSQgVKMYgCMwaGqWOMicLJatdqQ1FaTt2BaQlpub0bxTg2S0UXo7wuNzc3T0YhgegaonWCn0dxPp94eHjgeHqgqssnwrZbUGplWTEMoyQgZ0Q6bONT69J15ycY62azFbtxWTIMA2VZ8PrVK/b7SyFZn8+i2vyO63txCIDYXV++fIFzYs5Yb9YUhcRzF4WAHZyzPLu6JOaBzXZHVTfSOpwtSmXpL9cNOUfO3Yn7+3uauiLGSN+PGJs4HO5Yr9ZM88R8e49WJev1mrIq+ewnn/BXfvbXefbiE4wtSUmD0uIuQyi4OcnPXjlyUeOspl7vKdq1yIKjR+cZYoaQwEc0soO3KUKMEIJ46xXUTQ050Z8lh87PEqrpyhVlvaPZXtJcXNHuNminSHnAx0gEjDWS8BsnpvFMzl78BGEmpJF57DgcruUA2GzYXLzAVS1aO1KSexm0WGKXO3gaOsbziXLJwIsxoo1eqhuBcubHyT+Pk3e1RKU/4k4UxjmKqqZdReJU8OL1a168eM7hoef+9pbTwwPzMBC953w6cR47puCfnsYg4qvR94xTj4qBQpVsqorSCBtinnrq2jLNgQKDUTK3QQl8A6UlOTmEpzWgNUrWgDk9IbyMNWRjlrZzZp5H7GTpxiMmJnwhevyyKNFW0fUnQhCrcN8LmWkcez76+CPOpxNKweF4IMYo7EHjaBphOxhjKArH+XxiGCemaUBpSVWSSrgS5P3ChBiG4YkYrRScz0dubm44Hk9sNutlAPoIlClklVyVNM2KslhR1wZnBXX+Xdf34hBwzvHTn/54QXhlbm5K9vtXOFcwDGKr7LuedtWwv2zph3vatlogDIGmKXn18jntak1VN7Ju8RPrtmW7XfNwCPTDyKptmaeGsiqpqxW3NyfKouKTT3/Eqzev+fgHr7m8fIVxpayWQHLhlVoINSI1Lcsau7+iWe0otaMqakwh68McJnKOpBhQPmBSQpHQcaZYPO0iEpEeOvqZaZEkC0NwxcXVK5rNBapsqS+fU2y2mFUDOhFDT4iepDJEhUkjfu45HW/Zrlty6EgxEOaevjsQ44wrNPWqpmxbtKmIIOtWZTC2lBI/eVSMDOczQ39mk5J8/eS/h8zzmAOoWG4ypD3IC4Q1L3ZdYwvadk2Mltgp3rx5TVWVvD2/53w6cj4emaeR4GdiCqQgsV+i7F0CG5RhmEdiipTO0haWVVXi55HT+UjKHlNoTEg4Y0E/IrfECeicw8f0tAVQKj/pA0QZvHwcyavMS9UwTwN2tiiVqCuHtlF8/zni48zkR7yPdNcdpSuo64a1W2O0aFuGcWC1Wi0Pm3m5yS1Ky5AvLMCTqiopSkeMAWsdp9OZw0EAsykJD0Ki7OUmH8eBOUj0mkJjbcnF/oIQBHt3eDjhQ6QqG3JW9J1H68TQR+r6ex5IChCTnNZaKy4udjRNLfSZBe1sjPyAKHDGsYc8UjcNlxc7ERvFhCJyebmjaUpeXD4Tt52NzL7g+ctLmtYx+8R28wxj1+z3b/jZX/uHefH8BfXaYrQEdyqlFwr0kl2XRVfijME2Ldk2KJ8wEXRM5ODJKZOI5BzIYQQ/w1JOq5whBvzY4/sOnTykgJ9HUgxUZUVRltTtJUW1xZQ15XZPsdlBUYDVpDgT44wSpYA8yuPMNJ6YxhNu3xLmAZUi09gzjsNySArPMISEs3LzJhTGlZSPT00JP8T7WUr9ha68zBVlr27MsnXIi7DG4kyBV4Y5pqdVoLEFZVXTrtcoXXKee6xSTN2JoTsz9QNj35NClNARlYkBsBJH/lhPpJSYxgkFVEVB5Rx14fBhJCRPUomQhGsQo5LoMKUWiIiAPNUkAza9BNLID9BK5hopgk6PQaeQohdnaappVxWbTUU2AWM1wzASciQpWe/1i39hGAfW6zV939N1PVOYefnyJdvdjrffviOmhLKasihxRYH3EXxYtgNwe3tL08h6vO8HrHVM0yMWTAbN0+Tx6UxVay52F0yTZEUGn3l29ZL7BzEVla5aKp4CZ2sODwcOhyNx89333vfkEMg8PNwtp55it9twPsuTX+i8BdvtejH8nFGLOCVEDzlSVwUp9czzRDYyiGsqx7Ze0587wn7P7B3rtiZxgdIFq/aSF69WvHn9E958+mMKazFqImfR9mPyUpqmRUAi+3GNRhlLtlIeEwJ5uYFQkPMMOUCcUHGWJ2myYot+uKU/3GPw1IW89NoayroSpZ8xzNHSDSOFdTSV3PzKGaIficETc0BbeULnFEhhYu7O1JXDGUXwmRgC4zhJqo4pKMqGmDW+GzDFmmwtGYV2VsQ/OsgAcxmiFc6hVZYUn0VwpBMovSC6I6BBZYvTUu4OKRP9TIgZW5TUTUtdt2gKnBm5v/nA8e6GOIndeB7HpwNFYB7LU3l5arqkCDEwjCOFc6yKinp5cgY/Y+uSOXoSihQVac54LYKrx6+DxWUnDxGN0fppFqCVxK3JWaeegCMyD5DqabttKWtFNwt0VhdCi4o50Q09zUpi2vtuoM3iWZFPl+nHgXKq6MdevBrRkya42F+QEhwejozzRF3V4vI09okA/agSHMfxSTLvvWfyA9OcKIuKohDL/DxFxtFjdEFZZEKYmeaJqtRs1jtUNvT9xPn8vV8RWpqmRCmh2mhrBfjR1MJ7X/hwq1XDPEW6bpAUnRQZh46mXaFIlIXEkjsLMc7MkxwWF7sNd4eRmDx1XVFUK5pmy7Nnn/Py5Y8o6xVpjgiBRsl+Oway+k0gRU5edtdZoSJkG6Qfn2fUHNFKXIUxzygVUETUIgryMfJwuOfh4RZLpG4Fr52CxlbFAkJV9ONIPwqDwFaaECfy3GMKRxikBdBGvPBqKc+nacKPI1cvXqCVwRqY+4mhn9HKSclZrZlCIsyBdiHrqvxYFksmYopZHJFJuHhW3DdSMCu1RG2DRvPUtmcheBhlxfI7jwQfsY8JPNZAoSiLgtPhgeF0IKfAPI74aYIFhS4jgGX1mIWdqKOEyHbnjqqo2Kw3tIXB2IzGULQVadbi5IxLUaQCTmnZnKS0FGFCWzKLU/A3lYDs4PWSRWiNUJMfSx9rwFqF9wPn7oiNIrG2sv2lnwYu6hpbOtwyc0g5YwuHnxRfff2VQGG16D9iSlgn/zZjDcYZzueOwhU0TfN0w4N8T/XSzlhr8f43aPxzd8/Qe1btht32Eq0ttzcH6romBXkIKBxVWaPQrNdb1qcz7z98+O777+/nJlVK/S+A/xjwIef8e8vHLpDcgc8QeMg/lXO+VzId+h8B/1GgB/5zOee/+ed+fq1o2lqm3EHQzJeXe+CRntrRnTv2F3vWq4YUe4ZhIEYvWXVKJuUSihNkp82SL4elqSqGqZCQSmTg4sqSqxcvqVcbcjZPJXGKkZAC2IxywnQjpeWA0hA1cZ4YpzvG7gyjx2XZ8yssIc1kHbFL/59jpOsHzsMZ7TSbzZq6sMR5JC+GKayl63qO44BzhnZToRxMoafWK3IS6o+rhDbEMtRCKaZuJPpE3axhlqATHzJj77m4uMRojXEtOUyiTixKtCvRGVJOchPI8Yf3sjIsikKEVdEvwzUnVRGip09ZCdA1RaKXNGNblKg54OOM9xLPFWJEG0vTVJw8zNNAjhJhnnMii5yQlDNZS0S6UpCN9Mnd+UwIkf16Tds0WBUJcSSqjCodlbOEfsZPkdo4ks7MISxrTNlsaK2f/u0ZYS/kLAeDXjImH8NX5BCQV2OeR7oukc0ISta1KXrxP6REIjGHmSJ6XFkwec/Qn6RNcoJ+M85ycXVF3/UYZ6nbhmEaKVyBcZY5ePqhh5Tp+55Xr15hjOH+/h4hXFfs9ntSStzf3ZGyJuWZeZI0ZWtLSlfjp0xZ1JAFcSbMikYkyMhQsa6+Wyfw3SmFf+/1vwT+w3/qY/8N4F/KOf8Y+JeW34MwB3+8/PgvI+DRP/d6jGs2VnM8PTD7CWuFoTaOPX0vCr/D4YHDwwOHh3uhrcZIDJ7z6QAp4JwBEsfDA935SGEN0c8oEptVuwBAJenn4vKS9XpHygofMygHWZKMU4g8puHG5AlxJqWwlIswdgO377/hdP+B5EesRXb9v8WmCznjU6KfZsZ5oqgL9s8uaNYrsoGowFYlpq4YY+Q4DCRjqNY12EjIE6YAZRUhSN6AQCcVcY7kAOiC8dTLUz0vqbrZEJMmRmjqNcpUKF2icJiiEuiGMkhPsfzaWKSzCRhtKMpSpMbjSJjF9IOS6kMBOWWCj0QfZcCYJevRlSUpwzALLdqHQFUWtG1DUxVMfUeOUm1opZYQ0ix6/hRFSZniU3TWvFhqnz27WijPgcnPhBzBGJrNGm0sfvIQxQAUfBAl32OiEPKETik/MftSijK/WeaPPAaToJ5gnuPYczwdSFlmKplEPw48HB9AK66eXy0y3n5pPSQ0pGqkRbi4umR/ccFqtaJdryWlSmuGceTcd/gQyCRCDByPR87nMyCGudVqhbVWYKZWUqK22y3OFaxXGy72l1zsLtEYxnGmbdZYU2K1o3AlKYjHxs8Td3e3wh9Mf0Ercc75/66U+uxPffifBP6x5df/DPAvA//15eP/qyy1zf9bKbX7U9zBf/PnT3A6j7iioB9Ff308H7HGieJqnCmqin7oGIbI/d0dIQb2F1tyjmSVKXKkMCJQmYOXia9LJD8RvcKWFcl7snK8efMjXr78BGdKxs6TzQyFgtCTZ9HhG6yUyBlyEK03CtI8MY89eE1drES/4ERuqwgUOsiQMwb8PBHijK0cla2etPnjHCiKCls4MgrvE8En2tUKra2sQ2tLUVqm/sQce8KcyLEmeOkRy6pCJRi7Ufh+HonZ0gblGkzZoosGbSK4kqJYQBsxkuPyNMyi/X8U1MzzhNFgNUzHW4oUcfUKlZslwAVA5MA+BAqTyHjU8gR3Swsw9xOzj4SsicriyooiZbrjNTrV2HGkzgqvBJeelEIbi1ZGBpcGkk2s9jW7q4LCavxRAkoyCZLCKEPlamY30uWOoBPayoFgY3wKXSHJ10vOpKwJGXQ2RLVIonOWuULWaJTceFpeE43BqAqna9FHGQ0W1pUQgZPy4ltpRKSmcs16XaNNzbrZ0lQN4zCQY2T0ZxSIzDcpSThqV9SuJCyBJjFFSl2w3e3ouw5tNEPfLcnYEnbarnbkqDGmxE9yYysNp+M9IcycTqK6dcYx+Zn3H77BWQkz+QsdAt9xvfitG/sd8GL59Rvgq9/6c18vH/vuQyArplHx4fqOTEJbRcieh7szd7cPeB/Yuy0+BZyrwK0xLjBG0UtXpWOMM/3DJJPXLDHQ89BhciTMAR8NPtYUqy37y89pmueooClixDCippHYvSXMCV2tMFWNzsXyBll2/zmQ/IBVicvtJxSVRdtISB2JGZs9Jgrw0vuJ5APaWoq6gZgJPpBjImXJuU8hEbzHDwGXLZUqSD6hy5qyaAjTTNcdSbqgadcUNuJDIMRApUuin0hzor1YkaMmRA3aoqsVtt2RygoVA8rZBcYRfoPU8kGefCh0VqiYCNOE0ZkcR/oPX+JyFAR3mMm6FKuuUSRj8CrgTCD6M9aI2i5HT2ENU0zMAeZseRg9HjAuM5zfoXpN1XfklJi1Iy1yfkWB0Zay1JgioWrLs6sdznQc768Jflhcio65T5iocEGgI31lmMol/m02JDJaI0yHlBefvlQzSSmCEucpRqONIWqLUWZRREoWQV23bDZbGS4Gx/P9jt6cmcuBdVvx7duviFPPxcUFl/uWECJGWYyaaYorvPaooAlTAJ15++3XrDZbnC3wPrFRO5wtiClSVI7G1BirSQgCrlyi3WIMdMNZDtnSsd5e4WdFCgo/95JnkCf68R5nNOMwkqPh+sMHsvEMw4FRO5z9S5YN55yzUuq7Dct/xvXbuQNXF1t8iNwfbzFWnvAxRm5ubnh4OLHZbBnHgRCFs37/cBINdTCs1pJk3J+HBbLp2G4vUFh8CJgM3ge63lNsdvzgBz9jvb7EmhIVM9omCBO+65jP94RsKF0p7jmr0BiSySgiaepJKVA2Na7ZonIg5Q6LvIHy5AnDmbh49pW1GO2Is1QmOSbGYaIuSyEFIwi14/FEYZZE4CUGO8dEPwyMw0S1LlitW4zKzCkQgwcyyc80TbMkMidmHzBG4sWLukZbJ+tJLZPxx/xEEfdIbqI1BWZRAYq+XoJb+vOBarWlSYHsPVEpPEaAHNZgjMSbhxBkqEaWrIjF9TZ7T0hZXv8lAFVmGx5nE6MKQKKqHcTMTFx680xVGarWQR7x5wmVA+SAUm4Bqg5kpTBKCzquLFCFBLJY5zBG/As5LwpBZ2TmuKxGRfoldCHr5O+ljHg/tKZpJOOvbdbYqsBYy7rZkIO8RnVVs11vIScudnuUUlRlSXfuKeqGw0OHc5YUJ1KEqigJPnM+dbx8uaXvT5yOnbgSyVRlhVkYF94HycXM+UkvUBYFZVVSVBVKOVIMWFvSthZjEnrhHBqlqCrF0AXmyaMrz+XVBSlpEb59x/UXOQTeP5b5SqlXwOP48Rvg49/6cx8tH/t7rt/OHfjs4xf59uaWsgHvPXd3dzw8dE8QCmMMIUq+/M31LR8+3FHXjq1qaNuaafAcjmcUmtV2Q1Wu8HPk1A2EaWZKimqz56PnH/Hy5cdyKoa0aPtnwtDTH+7x0xllG0yYcWFGuWKRvkoIxDiNspJsanRhiZMk3ioCOczEYWDuexkYKkW1XqNSYhw7WenMnnkc2bYrwjwTk8SfT9NE0VpSihROCLMxZoL3GKVo6xpbVMRxJnu5KUmR5D2rVYu1hmU5JY7BolhItfKkYVH2ycz20TefpTdexDkqS6aBigUxytfl/cRSLMtMREu7JXODijAMIqpKEh1utGUez3Tn7in5SDwEkteQyITsaS9qZhUhKuqyRo+ePI8Y4zFq2ecnRYgDfh4XY5Z60iuEGBZpssKVBVXd4JzBoHAuYjRoY0nL12yMhKnmJW2AZZCMyigjDsmsWPrvmqZd0TQrmXPYErRkCUoRpRb69Za+77FW0oOqtUjMy7Ihlo66qZimAbJHq4Km3lBWFUZXaDWhlRNLeJrZbNZorRl6SRY2Wi/gm5kUI0VRUlc17WqND3Duhv8fc38Wa2ma7vlBv3f6pjXuMSIyIqfKqjqzj3vAlhhsgy9AlgAZIWRuAMMFluw7JKQWSCAsSyCmGyQuEFwgAZYlS2AhLjDiwjR22+52d5+xhqzMrMzImPa0pm98Jy6eb0eV8ck+h3P6oFpSKiN2rFg79t7rHZ7n+f9/fwyR9WrFclEyjnsWzYqcIqVbUthI09TcHl9SVTWLZsPu4S8ngejfAP7rwP94/v//5Zc+/i8ppf5V4B8H9v+gfgBIPTqMPdppFkvJZn88YapKE4LHOnkDn07tnMba0NSVjAy7I8djy2q1wehCPnbqCYcjh4cduIp/5JPf5ONPf42yWEBSci0OgTh19O0DXXuPzgGtvAh/op9VayJM92FknAYKlVFWiSkoTuTkSWki9D2+axn7lvZ4nBNtasYpEBNkYxm7VuSqZJTKdH3L6XiQN7DVFGVB4ax0n2PCaSMOQ+dIg8STh5iJkyf0PWGaZNSYAgY3C6vAKE1TNxLpnaIs/CybaI4B7SR+HcQzL5E9YvxJSs+GIWEipuhFL5BlcYYYUAYKV5J9QTSCfDdaU5cV76aJ/X6PUhpXlkxTIOSECgFbZDCwvloQbKJKGkxFOmVSrwipJ/hIe5hojxPWZPI0Se2emOXLsnkZKwvXOkvd1KQcMVmRrBUdiTZom/GTRI9lRCGplJbsAYXkVkotMluPRdWntaWpF2hrOeyPjN5T1ZIfqJVlmiJF0eBsRd97QvDUdUYrsTNvNmcsmppu6NjvQSnL5cUTmsWSlOH8rGCzPsPYgr59kLJMiQXZzCNNHgnWKVGVmnEYKcsFSjuaasE0RPpuJEWPNomqXsg0xUu/5+Lymtbf40NktVqx352+c/39WUeE/yekCXiplHoJ/A/mxf+vKaX+W8DPgf/K/PT/GzIe/BwZEf7zf9rra6N59uwpP//mJ6w3TynLknGMxPniNk0jSlse5/hPrp+y2iyoKs0wtvjgWSwgRei6gbLQTFOk60dOw8QHVy/45LPfYL15AtmiAW0SmR4/tYzDjmk8oLPCoCnmiYIyzC45CUuN2aMLQ0qeOA0olQRDPmaZeYdAPweNFK4gTp5x8hTVAnxk6gdWq6XcQEKadeFa9P5aUy8X5AzTOEkasDYi0BlGwugJs0Emh4hvW9HrWyumFOcwtsTHSB4nqXlRaG2lSa4UkVEWEBljpNQxOb33MeSsmHwg+IDSmRgnxu6E0xW5WJLnrn2KeSY6VWg9yOefJwXBB7nSzuiy4dSSFfhZ6pwdKJcoVprSlEwBbEjoEEhDT3c6EvwJayKF01glw1bBpYuGQ2lZ2CnLFMCW4rh7RHCnmKQEMm5ON1bvFzqzKMdYIw7sLO8yjCFrTVaayQeOp5bFakkMYggypqAqS1L2tKcDerVgtTpnHCQl+j7t8VOi7ycuNwWFK7HOSZDI2FMWNVXRgDKsVyVFIcAcpxPTKFkDdqYtT9M0U6nlBlU4xziNDEOPNklGjECKkWmcGKcDxiQOhyNhyoQJDseWru+YpsA4TlT1XzCVOOf8X/2OP/qn/4TnZuBf/LO87i/9JYzVuEKy3e4fHni4P3B58RTvw1zTRZSBq+srtptLYhoJMUpNXImgYvewn8dVcqVNGM4un/Ibv/1XePL8E4gGXTboGICJnERjH6YTYWqJE7iYqRZr8dUnT8qQkpfmoFOYwjCFkcl76sKglTTEHmfeYhopqKsKlTImZgqlGbqe5AOFLYghvnfQLZbSadYzCzCME3Ge1xuliT4yhBPGFihjST5Qu5I8SbS3cyV9P83kYnHB+flzmpgAhS4KFAZt+lkBKSXCLzP29ExwSoj3qagKlIb2dKRxC8pyKeNPHwhEmqpBKUvG4iMoH+YutqKqatQsTxYzz9yHUCK0aceWaBOurMhDQNlAyiMxjZyOD+TYsVnVpMnj1S8mGczTCWct2om6zoco9bwRO7HWWizQIIusKAnBz5J0wZprI6M3bbWExjxqhpVBGccUMvcPe4qqZr1aM3gxsfkgITfTGEgLJS7UAG07MQ6S8TgOgbfDa8GPb7Zst1umSaYrbS+EaVcIEakdRowS9F3X9cIw0IpxmohBbldVKbmTVVWTlaLtelIYKYslZelIKdOPwnZwzlEWBeOQ8GFisVhS18xS5F9xF2HKibc3b7l+co0rHW9u3kr9pg3KZkJKqJSoSsfHn3xIjgW3d0f82KN0KbBLP+EKS1UWPOKiVpstH334KZ/94DeoqiV+UpikyTFD9DNc02NMwNqEjhZjNM4ZtE7kaZCZs5WPZe2whaPzHcpmlE7EYWRsO0I/yNw8w6JpKIqK/tSikiL5wGG3lzFbjIT5RBV+XMIj2QfTOOIHYethrCy4mMjzZmCdoz91XFxfEcIkSTwaQhjJvcYkSNmSgGQiSUsdq4zF2Fkbn9NM/3lcWHoWQ2Wsc1hXklxBYwuU1XTdEdtsWRhDePyBZY3WVkRZs9bAaEfKCmMdRV2LXl5pVps14wzICElGapEAzoIFU1psZdBWNtvoJwqjKa0TQlKa1z4SN46WE/wxK2AaJ6Eru0LgKMbNTUExEVV1RdsldEyoOaZN4IIaZTXGivEILbkKMUOYYbWPO2TXduyPB7qu5dmzpxRFTVXWaCXSXD8JlnwaPdvNGc6UAJzavcTcWcU4ikfkcDoRY2KxWos2IAriPZPmgFyHs2buBTixQsfMYrHEp4mub1Eqo7XYibv+SFkp6kVJJjKNgTzfno0tUEnWT12571x/vxKbgHDxKxbLZg7pSHz6yfdoW09RFHTdCZMUYDAGju0JbTLLqsI6xf5w4ng8cn52SVUXxACXF2esl+d87we/zubsEq0MZdVAhBTSe82/MVAWFtUUqKLBlA3NqkFZSwgDMWWcqygLQ0pO3HsKqqbAxMgwDUxdR/bS+Q4xYYqSjKI7dVS2oEsdh8OR8/Nz2SiQhlVZVOz2O4wxVHXN4XggTxGjDDpm8RKME0obfOhQWnM8tpxvN6K7N4E4WoauJakJVyVM0YAVyW+OiWn0cjVGUpDSewSY0IJyDKRxkDBUpTG2QJsCU8qinvwos23ZWrDGzNdnN7cMFdo4XKUovceV3Wy+kpl71dRwOmGc5Xg8onIiGyPaCi0jvyYuOJ1aDnuFNY6q0OSkMLp4L+QhIUk+RSENvbmhGWMgpCQTEuRGoLIiEUX9WJb0w0B6NIUpM1ugE2BQxoiAyjjKeoFxJZMXutXD/Y7FshYNxThyOByEiuw0fS/Aj9VKEHibzYrT6cTl5SXEwO6w5/7+Hm2uKcqKGCe0kY2ubfcslzVlpfGDlDBaG4mEQ1yVj47HaeYBWFsQUoufTkwTgs+bPNZlzs7XLFcVOwt95zk7X7JYLjl1DxwOBxSZYfhLiiH7h/UIITCMPYdDJCQJEu2HgdOp5+zsgovLS1LqaRaON29foShARawr0CYjm3mm7Y5SY9ua7faCFx9+zNWTZ7iqmUnFI0Y7yDJH9jEIxkkpClfQ9QFbKUzhZmhFhy5KlK2lY58Uh9t3OKcEwtFPcnqPE4uy4HQ6ECO4qpHGlLa4ouJuf8CHyHp7RlEU7A8CUIlZXGIoTdv2TGPEKjULdzzX19e0xzeM00SzWHCYmXW+a9HWMPYtWSty8EzRkzHUtiR7T7YBn6A9HvFhwl2eEWIgxQmXpfmao2A7U5TEWuuclAVzEGaIsNE1wXvCOFCsV0xegKnEyDgFhkk882IE0tRNg9ISGV4VgsYqZ6aDtZbd/R1+mlhvHCmNrBY1H7y4ZhwCu7sddbPAad5nLeZH8IGFPHnqqiLMzc6ubUFpCuvou0EckVpIx4+hqzFFrHWgEtoarJPNLaQonf65b+LKCltWGOtIGfw8DbJOc3a+wfUWYzXWWlbLJa4QX8b9/T0hTNzdycL92c9+xrKRxKD9w55hPPHk+plsVEVNjhMeD0pMZNlrXnz4EXd3N3TdkfXmnGkaJENw6ORmUBTsdnuU7ciMHI5HjJLcyM1mibGBV6+/4vXrt5xtr9Da8vXXN/gw0iyWBJ9YLpffuf5+JTaBnLNAPnYtzknwY1GWbLYVMWXqwnJqJ/ox0vUD45BxhSYmifUexp6qLpkGz83NDU294unTD9ienVHWi9nnnslIt1WXhniamKaRRwXcNAVs0VAvGrL3jNNARs0ClAmMot3tGLqeYrsgK6m76rJkeXHF4UGyEZrljO7qR8pmiUlKTo2ixBjLODcL6wb6bqDrB8qyfE+giTlj5pHY6XSi76Weq8uSh+BnutHpfcTYEIOAUaulZAY2K5arFTFH4dq1R/rTgeXCCSs0CZlGZSQLcQZrKBwpSE1cVDUhJJS2OGtJSc36WqErWSNJyD4ErCuorZLmoDEUdUOzWpPblpRlHGeNnQk/iW6m5RjTY21FcVGz2mxZrtdCONKaPI1oImjYbhsWywUvX32LK53cvOZGsUWBVnN894AuC6qimOX/UnLlnKVJqsRQlHOmKCsKowWMqswv/tMWZYtZKh6xrsZqgXluzpZcPTnndOroh46r6494eHjgeDwSwoS1Al7ZH/a8DnfUdcPu4cCZuuSblyNlWbPZnjH5ga7r+dnU8fyD53ifGMaCxXJJs6wIcWQKE7vDjrbtubp8wursjNOh5f7+DcYG/Nhx7I+sNw3ea7746me8ffuK7dkZ/dDyzcs31LVEmG+2Z0xTz49//JPvXH+/IptAoqpLxsnLrWAYqcpI06z50Y9+zEcff8BqtZpxyx3b9TXH0463b+/ROnFqTxRFyaJZyzWrXKCVoSqbOYxD3Hxa6Vl4okhR5rH1Yk30mhwVhV1RrFaEydMPI8VihXGWGDwqKXb3D+QcMHpNCB6jxXjSd3tev3zFkD3nz5+BdQyhw5U1w/7EGCLnV5cUTcN4PJKVRlvHMHmUMiLwsZK/mKKEnpRFwel0wvuJZdOI9LRtCclTWf2e4zcoAZp+sNpye/uA0pazi0umUYCqYZLI82k4o6jNTEcSWpBSWUaWWrwHYT7NjasFgaakWaiS6PxJ4vqzzhFTwMdAXRVUVcmwfyBrTdk0VAsJick541yBVoopT0zDSN/26KwZuoGiNKisKV3FZnPGxeU1Oif293cYldE5sT5b8vGnH3HzcE8CukHKRenlWKxxZDRxDHgg1RWFcXMfIaJmf4LSCuEiznTrqkKnhLJy81GmQBmHdg4TEtaKhFkbRVUXtN2Bu3vRraQI3//B98kklsuG5fISV1hOpyOT7/n25S3aXHBz+5rDYU/MRoJuTyfKsubsbIMPnpQCwzjRtgPWKUKcmKZOyoacKMqCfhzZPTy8pwk3TcnzFx/QdZ7D4cDv/8Hfw4eOuilYb9Z0rWe323F+cSWbXVXTj56H/e4719+vxCaglOLDD19wPD1wc/OW/X5P8HB+bqjrmsNhT1YFxmaKoqQsa97dvOPdzR1lKWq1srDU1ZLt9or16pynTz+kaZaQIXgJ7ZQTL0sDSSdUXWASxFhg3ZJ6swVXEIdWBDHOoXOW9KJxJHovxg5liQjK248Tu5sHdvcHzp5fU6+2dMETjaUuNIfxAVOULDZbsrEkZdCuJCQYfaRqljjrxA4bRbATgwelJE4c6ZkcjjvGoaPvO2onzMUYE9FU7A5Hzs4uyN7z5uU3PHvyAVkb/DAK09APJN+TioIQPTYFrHGza1ba7jGmOcseyOBMScyzUSjLKU6MGCW4bu+lAWWLAlM4fAasQ1uxKmM0BlHXKdLMEOjJMeCKBcx5B217oh966sWCyyfXWKXo+o7SGqqqIOSBs6sr1tsNU/CUucT7wNDLpmJNSVk4dJ676jGhq7nzjzSUT6eTNBRn5FgIER0TdbMgawtFIRuBNhgreoDlqsIVFmsVTW1597Dj1atXrFZbrHF88/WX3N/fi9uvdrS7A5MfMFbz4oNnnJ9fEXzi7ZtbnCtE2ahlZGmMo6pqpnHi9u6O42ng2bMrQhy5f7ijbaX3cHX1lHGM7I4HUXYaQ3toMbahcCXtqccYx3pzhQ8D7ann449/AGqBD4nLqzXWWs7PL/id3/lHvnP9/UpsAimn951QGTNpiqJgGkcWiwXDeOR4HChKhVIN45BYLc4AxTgKQHOxWHJx/oQXzz/l/PwJZ9tLXFGTYyQFYbZhLBBRKqKdIkyJ9tQRRk9R1qiyIvW9aPPrRq6944RaLPD7A6UtUFoJoaeyTIcToRtwxnJ5dsnFtaDJumPPYrnG+oxyJauqxJQV3eQJgCsrhtETQmK5XOPHidOpYxgmlpdnhEnj/cQwTXjvGaaeruswRon0dhpompJh6NCFYex63nzzLeuzC3b7A3fvXrNebxm6jmk4olIk9CdcufwFLIRIjJ5HQFgKMudHKXyUn0fX9XR9QBcL0danPMuFBZElEWcanyJYg9FSk4unW6Pn7jwpMqeQUBiLm1WEU5g47B84tQeaZsn2Yiv8xixJvOfrFW/ePOBDYHO25ebuhrIq8MHT9wN931G4mkW5onIlp7GVkir9IlNAz+pGHx7dhcIa8D7hlAXtpBFqSrQR9WFVaaoClguHM4lTe6RpHE+fXZGzNCy/eflz6dhPI1134utvvmK9XrBer1jVH1JXC9arc75cfktRNMRkePLkKS9fvuKrr37MkyfXuEISlVxREMlszrZk7Rmmln7sGaeR1focZ2vGwXPz+p6bN98Qg6aoVhRFzaJes2hKqvpKSMuq4Mn1Cx52O4q5BA0+Udf1d66/X4lNYBxHfvz5TzieHtjvH/jww0+5fvaU3UNLiJ5Ipu9ODPcntosPKI1hvbpksVjRdgfquqYsKpp6y3Zzxfn2CUWxQCG3hJwjKQdU0OQcsAq5egXP6BOlW9AsRcp7f3ePKUrKWmrulDM2BGHFF6XYnic5SZTSs602sF5taRYrHrqePkSWVcUURoFtrlfYqhHxR9lQFyWnwxFb1KxXZxzTgXEYyEkSlhSZ9nRiGAb6thVFoRZ9+lSWkCN+GvHTiMNTWcvbV684O7/gfLNhaE+slgvC1JH8gDGK6AfIDdba2WMfmMYRZzTOSBxaIqO04NyCT0yDJ/qMq6x00Yki2/UenyK2sCgr8lZTVmQvzH4zC2UKY+bRnZd/v3PUZUkcPTGIZXjoTwz9ie3ZBlctmYYR5RT9NGLqkikn3j3cYQo3ZyhAWRY0dU0KHTpDYSyrZkEfR+lV+ImsZwCSgqauOfU9YRKOgZr9FNMQ0KXBFWK8ykrjioK6LkihpSgdOo/s9vcE7VksK+pqiVKGvpvYbLfsd3sWywpjwBUG6wQR9urbGzbrC5ypWC3O+Oqrb/CTYNCmITH0gWlKbC4uqZaGrjtwdrlhc3YGGk7tid3xQFaOGFv8lNC6pCyWvL69Zb1ecH15xRQG/OT57LNPMNZxe3ekKtc8uXqBjwfqeoGfeo7Hv6Bi8C/7kXPm/v6eaepm0UbmdDyRM1xdXfHzr3ccDgfGsWVVwuk4cti3aJMx1hCDxqM4Hkbub1sWdRQDjdbkGfGVcyTGCdLEkCJTP5ETVPWCwtZobdnv7tgfT6zWhil4TAatLd39AylEqqIUKkxpmeKJMA60pxPtseP88oqYNad+oFgI3dVPXsw8TYWpKpSXN3+9WRNiZOk9pqmpvKcsCvqum70KRjrgSuFjJMVEXVUMbZT8upxoT6f3yOymqjgcO25v3nF+/XRm9nmCH4hhFBNU9ECaT0dhAsToyQEKJzPxPPP5jS3ww4jSmrKyOOek0TaKki1GKQWMlSbcOAewhnHAuZKiLLCFoy4KCVWZMs7I6xTWMk3zNCNlgh9ouwM+XFA1DTFqqkVFP/UoK9TiV2/fYJWirGvag8SgV2VFmuR2EbynsAVlUcBsGMs6Sz6hkhBVa630PObZv1KPpiENyshEwAf6fqQsNSpH+r4lh56YPFMaMMngCsPQe4xVtO2Rl9/+nMPxjpvbt1gH49gxnloe7vY8NB1alXRt5t3bB/LbPZ98+ilPnz6Xen8QJqGrK7wPvHv3lpQmhrGfYaKecUwMXUBrx4urNc8/+JjSnbFcnvPRx59ineHd3WuOx4Ht+QLnFuRkubvf0w3vaI4tm82W9XbznevvV2ITKMuSy6tLprHj1B7Z7fbc3R74/me/IaqwyXN9dc39ww1KCUjhdDqyWtWCGE+a29sdRjdU1ZKqXKICJAJyHjDPXZN09aeBYRgoTYNzBXEKHLp2zpgbWaY0U3ZEl7B7eKCuKlxR4OoaKoufWm5vbjm9u6XRlsV2yzijslfbM5ntx0RVlKLFV8ImJEiJYFxJUTWA5CwWpcWae2JIaGeElluIH6CqxGU2DCMgHf6h7zg/27JoGnLbs16tePXyW9bbc8pmwdC19G0rNJ/sSNELaQeBZ8hD2AQplDIqzOIaEGFPwlmhEGENYRrxBOKcnMss2NGPISFkJu8pywLrLMZYqqoWpV0MwtBDYbVGFZZhGlFkQvR0/YlTeyBrAXPYwhJyIOSAKxwPDzsWTUVd1+x3O5wWA5BSmmEcMIcDdbWUZinMxig9p6hlhmEQEVOhmcIjwFNCXo2RXkBOMI3SbCuLzHKhORyOpNBiysjZ+ZbNdsvp1HNzc8fp2PHhRx+z2z1w8+4tw9Tx8Ucfslw33L56x3I5G9k83NzckSIsVitiBIX4D5QSwdU4jdze3/LN733JxeWasnR0fc9qsWa1ctSLhnEIfP7TL3jx5Am/8zu/S98Ffv7VS2ypcYVi8h7jOk7HAUXkYX/P6B/4/Kc/45NPv8dy+d2k0V+RTaBh7BqOx4mpLzAKqqqgfdhzf/NOhB25pq7O0A6KasTWiZwPTGmibx3Wrrm++h6XFx9R1VsxjZggoZjZo7PB5ozykLqB2B/JeU/UWkQ1w8ju5p4QAsVZQxU1qWs5TAGdMq5YYuoGVTYyWjtODIcTeVlh1xd0y5pu9NT1BUu1YeoGbHGGNxHrLBZLQmFLh46QsmZ1fo1PimBKumkiVgt6NNtmCXZPXWvubu/xMbOwFeMoYRN13XAcOrZmCVrT9h1Pnj7l62++5e3L1/zgh79Of+yJnZRAKWeMqlGpQbNAZS3GqOAJ/YG0sHJjKEQ8E1KiVY6irLHW4WMkDEeKsibHzNT24k5bL4hdj98fcFqzKgsRHoWAMwaMxlYVh/bEpBW5rpnajnEMqKqhnK/fQzdyOpwIU8I6ByFTu4bu2KOUYZoCTnv8EClsTX/qyFiUU8Q4cVQZrxLNwqGSJ8WJFBI+SmJS1ogxx9Uko/HKEExB4cQPYGwBWaOSBi/X9bSoiLZGlwUpHWnvDugxsT+0pH4iD4F+13O1/oCz80tCAqdqxqNC6ydkDK5cU9SAdpxfKU5tyxdf/QGr5ZqyrKnqkqfX55hCkcYlsX9Ke+w5vms5255RpjXrYgtkjqd7bu5vqBrLZvct5MQwvWE4ilR7c36GcQts43Glo7254dzV/N7nX5DajhcvXnzn+vuV2ATE799yf/dAYTWrRc16ueJ0OpBU4nDYceqOEiyxtiQ76+RzQKVAVS3Zrq9ZLtcCnIwRU1rRrs+wSZMNmjg76eL7TrdOif1hx/39Dh+1ZLdpiH5knAKHU0+1XFNVBQpRaqUoCUN1WeMKhVs0mLKkcZIRqJISA6I2knFfFTirGFMSr/0wMo0ToLHGMM7a8bJpMDpxOJ7mHsXw3tI6jmI9ljAUjfeJmBTaWuH7KcN2Tl5q25aUpRYeJ/EodG1HsQlgZENU2ZP8RPATOXq5JCslgM4kslZBjonL7lFl18+5emTpvOcwcwJTQCGk4+gldBWYGftxTpa2uKIQrsEcExZjJA8jx/0BP3mWKzmxjJYMAO+9jHZnK3FZlExmoh9HYtYY55hiImRJQtY5kdXjOFA4CTlEYp6k+WdL0HbOGWBmKIp11xqJH5Owm4BPnmVdoXxJGHoe7o6Mk+fq+gnnZ+AjbLYbqrrCuRo7lyOH/ZIQJrZnZ9zf33J1fcGpPXF3f8uPfvTH/PCHP2TRRG5u33HqHlhtGwpX84PPPsP7THvsubi4pFk07Pc7co5cX12yXFSUpeXdzTtIgYe7W96+fc1yucTVJe/evSVbx3g8sN8/UFaJ8+0579684x9E+/iV2ASUhmfPr1B4lk2F0Ug0V1HiSsux2zFMgzRzfKDvRrbbJcY6QogoY1hv1yw3S2xpSSS8n1A6zjprMFlBzHNu3gwnTZFu7Dkej0zTxPb8CU3TzKOyeQyWInUpja4YPWTZZMa2oyxLKu2wxmK0ZMpnFNFHjFZMc5dbZvoiXokxCmAkJ0Fnm4pxHOj7lvPzS4zOvH1zy9XFObu+p2oaEnBqW0IUeawxTkoVW0jqstKc+o7t2Rk3dw8cjkfKqmK9XrM/7emHgb4/sSWKeShDThK3lWIk+YhxIiMOs624aRaMw0AkYYwlA13XMYyjXKGVEh3CDBERWTGC8UoRRRYN/EwwftT7O2vQhUWRmfycK6A1XXsEMsvlksIZCifj3JREQ++cY6KX+b2z7I9HUlRoJ98/HxKhzBRWUVixDOcsakM9h40666XEEQ0T+jEsPQk0lRSJYcJPBm012iqs0WgqsAsgs97UnG3PsUXJqeuEBBwDWY1oK4zD58+fc3PzjpQSL1++5LPvfywKwv0OYzSH/R5nS66vrrClWOnrusJow3q14sMXH1KWIldu2wMoWK42lGWBc46hb3m4u2G3OzKNkfUHZ9T1gpfffIspa24fduxv95Tnms++/wO+/fYlV9dX37n+fiU2AWs110/OUExUznE6Hri7u+HFiw9QRrFYLsidMPpXqzW7+4MYV+A9kx2VcYWlbkrK2QGXckDn9J4YnPzE1Hf4aaIwkkq82+2IIbDdbFg0jZh2pgmtDT5GnC0oC0sYBgIaUyimsac7nXCmoKgalHVSZ+c8NyEDWsPkByEYhwIfJoa2xViLstIgs3aO/5pPqxwDrqwQTYMlSrAewzDfGlyJdRXKylhLGUs3egn+8F7CUJH6MCtwgyEGT9eeaLqWnCNaC8sAJZTZPJ/GrixlWJglQDRFcRo6JzbnYRxnDbvjMW4shMQ0TYTgKayMBDVz4u/ctMxRNoTHDActmR8YlVEkoh/R1hGmieAkVr1wBmc0OXq0UtiywFn7nrBsZ5bhEMIjKZT9fk/rMk1TsWwqSudIM5fBipMAvAc9YAqwRmNyQMWJ5BWehNaJGEvIUiaUTYHKkZzAunlTzJYpZOHSqsz+tENp8NFQNoqQBs6WF+R8ScqBuq64v3+g7zu6ruOv//W/xmYjaLGLi0sCI4fjHUrBOPWUZYV1hn440bYtzaKSJO3oORxOrJYbqmqFNnuaZs3zD16wXK+oqxXj+I5FKaEwddPgqpKLqyvWZ1uePn363evvL3Nx/1kfKScedjecuj1tzqiUGOc56RQmXFlQk3FFwdX1M8gFKY/0bcswTNT1mYy45pgoly1WS569JP9IQGf04/vEn5hF3z10HWVVYZ1jt9uhkVjqfupJObPaFox9T+gnbLWgVIb2eCDHTN1UuFKulzEnUvTiSkN+PXRHuSLH5QyMbKmqata1S8hG3w/kGZLR9/JDXyzXTLOhJyaxy66ahqpZIJFf4ov3UW5Mgjt2+ClIPLsSx93tbUeMEzF62vbA2J+whcHqAjuLflJK+MlTL/S88UhUvPfiGbTWCv03xhnh/Qucd55luKLgU+9NL1ornNHytaeAIpFjRKuMm91/2Si0pIiiMZLGNPYSMpoTVitSkOSfx5uWMRIVZp1lsWhIaSChaJqau/sDx35iDAJUXS7MLI2WVChjNSnKaBWlcM5CDKQwEWbscC40JEMKkg41tCPdeEQlw9XZE1whKU7aluyPR4rSsjvu2GyX9N2RspGg0dDXOOc4tQd++MMf8Ed//PvEGFkuJXD3o48/gqTYH/aUtQShvrt5Q1k2nJ9fiIM1B3b7Owrn5kAWw3q1xpqCaZwoy4aLS8tmvWSxXFAtGs7OLqiXC1Cavqg4Oz+j9wPrzVq0HN/x+NXYBFLk7v4d3anFjxOXFxesN2tevnzJ6CfpqgqCVJpKypGCpKq0XYs1De/evaGuNvNV9YJKFTgtPjfSjP8KkzDvYxAfeggU1mFQtIcj+9PIomlQCvqupW4WGAXt8YCyhVy/+5axPbFwDmcdYfJgBMKFMThnUFn03X7qKZwl5YD38vmnUZRyVVVROEd3OhGCRysY+x4fAs1iQXs8iFV4VpjF2RI8jR7tKsp6QUCRMihbkJTBx0nm9sFTVyWH/R5rFYumIoaRrt1T1AVOZ2xh55JI1HwpJjGrZHHaRaKEhyAde5jVf3MuXpix7I8BGSnNEUZZBEUYLeWClwWlcqQwhuQM2ovn36j8/kpODoQp058OMla1wiLwSahLWgkrUBSgktE3jIEpZApbYLRiGCKhn8COaFfitCJGSThSBnROxOCxxqBiIE4DBsAmsgGVC2FM+IG+y4zTkZAGFs2WZrWhcJb9/uF93HpVVxSFpSgL0bgcdnRdy49+9neIKXJ/f8s/+1/6L7DZbOYQ0oZ3795S1zUff/wJfd9hi5rt2Zbj6YRWGYh0/ZGcoSznuD0yxjQslmuiVyKd355htKDRj6cjU/IUhWW5WFDVlUBtQ+TQ7XGV5euXX3/n+vtTN4HvCB75nwL/eWACfgb88znn3Ywl/2Pgx/Nf/1s553/hT90EYmLyE8vlklD49yz7b755JXbawEyU6TntforGsDmriTHip4nDcc9XX31B1wVSyjTLkmqxFTpwnEhTT5p6gh/FgjmOWJ1FGlwUeO9p2x6t5Kqbk1hmnTVyTc1Qu4I09bSnI3GcsLUlToGH/R7jCqrFEqfAD73EcQX5HFZDnEainzBaWPBd2+MuL5mGgXHo3p+qMUTRFlgndB8lDjdbalmoWTH6SL1y1IsVIWb6qaUqK/wkEdYhRhkvrRYYa4lxYrmqCXkO/0gBUhBst5ZGZ9e2rH0Qnl7W+JBmX4QhhEgIEbLALKdpks8z9wNExl0xDe17pp+zFp0kSyAGL6VFThTWMCHjWqOYY93yvFFLxt/Qd1QzHsw4y5iSlE1JqFLWKLw1c0KPJSShL1unUNYxBNC9p6wjdeEIQShKMWXUvJl5MpNWuCIK7ThHklGQA9aUOKcprKEoGrQp2WwvWSwrUIp0SHRDx+XVGf14YrFczXZng/eRxWLFelPw+tUr3rx5xRdf/IzVaoH3I5vtmmEYGIZeDoHCcXd3y8X1louLC5bLFUpn3r57DRmapqYoFkxetAOH/UgMam46LiVkVEUedncYp2nqEmvgcDxQNzVTnLClIirPw+H2z78JIMEj/yvgf/9LH/s3gb+Rcw5Kqf8J8DeQzAGAn+Wc/9E/w+u+f/jgGfqRp1fPiFPg7eu3QglKmrpZoLKcWn5KfPP2FXVRUzdPgExZFVSVQ+nMfn/Py5dfcXV9zmJZoHUGLzkBaeqJs8pumkZcXc45dcLTV9qwqBvKQrTopXP0fcfYdzTLFVZnuuOeh8OJqm5QtWHsOw77HVWzpF4sIGe69iTACxKlE3ru1Hfk6HHGMA0D09BhFcQwzXmCkRhlAflxFNZHznN0uPQHlBI9QWZEKYuxmbYfOI5HjHG0XcdysSCmzOjH2eFnaYcTKRaUVSVBLDlCCqSA9EmiZxxk8ymdhGKG4LH2ETv0i4fIIBRm9r6P40RZiloz+lFwZ1rGfvqx+ThPG9QMQU3By41svooLwUkUjBnpt0CicCWucGijhKPoZyKSEvCLMXIbiBn600BROGwQLcXgI6OPWGOJSW5pIUpPQuVZRp4CVity0MSc0EYRQoVSDWVhWSxqmsWKGMXoFBkFZLuuqOpSyrfdwGq15XQ6Yo2cvqvlit/6rc+4vr7k+snFHLB7xhdffE5VX/DB82dY4xjHga5ref36NfvTPU+ePGW9Xs4/b+jajhAnlsvFHMRraI8dbTsSo+f+wbCOS4apZ5h6zuutoOsUfPXVDTmvKWrpa1gHq7O/AF7sTwoeyTn/33/pt38L+C//Gdb6dz5SysL7x/Kw26GVxY+Jzeqcqm4YfWB7fiFnhr9j2dQoJHZs0ZQoIut1zbLZ0LZHDrt74gdPoFBCqwmTnM7jSD/0lEVJ4Ur6rqesG1xRU9RBuG/aoFVm6Foe7m6pqoplsyB74e0RJggWUikLnkxdFlLTTqKmc1Z4+kbPqURhkrk5MHQt1shJE0JAk98bXyDT9x0uyYYQ4wwJVXr+vgu5JwMow+nUky3z+E1y9cqqpG2PHI9HjJbXadsTS2Mooif6kewMRDBzk6099QxtR1MuiFHSeh4NTCllwXxnGa/qWevetj1917NarbHWYWxBTP08DXFkm/CDf7+PpJTe3wqCl3SiEDwpR1RUpCjKvTybsgDKVAoT0WhI4noUGbg0ex/9/ZBFoGQzSkd89ILuMkIgHn1AOY1ByaaTE4FEqkqyE+SSxMmLvLlpahaLmqoSeAgqoPSINpb1VrQT+/2BoixFQRlgtbzkdDqSY8F2u6EsHd/77GO67kjdlGijiNGzWq05217w1Vdfc3Nzw2q9xsdhFoMNyARiRVE6Xr96RUye5WKJUopmIdSsySeO7QPaRrquJcRRbDFKpj8XZ0v2pz1DstR1hS02vPjoCd/1+IfRE/hvIpmEj49PlVJ/FzgA//2c8//rT/pLv5w7sFqVlK6i3bcM3UhTNmxW2/e18OFwS5gSm7Mzmo+WrJc1o98T40F030mxWTds11IzLRc1zmmUnt8wZIxWxPkUW202kCJFWbPebMXfPgw4V2CUZuhOHPZ7pnFgUZdoEmN7RKdEZTU6efw4kKNn0Qh1yE+jLFoNGivX3Nmqm9J8KoVInLMCyJGh74hzolHOMunQRU3KmTBN+HHAKi03hRC4f3igrGriLH89HA6cP9mI27ERkElVltJraDuMztSVo+9P2KHHzviyXJSPgUrSRY+R0/FAUy/BOIyW8Zr3UmJopeVKHRPOzZbnlMgwqwc12lhpkM6mJD0TiGKU6UNKSUw8MZBinGOxZJoiCzuDSuQsi0UHcfulGGAGo1prsUZ6BBIuyvxxub2ASJN9zvTDhNWaReUIMTHlSFmXOGPmRKJImITzoLQmkzDWUFQF9aLGzJ4IawxaZ4oSRt+jUkF3PHF7e0dTL3m42zNNkefPn2JUxXq9YZpGhqGjWZxRlgXkxGIhvg3rLMMoCdoXF+estktCmlgsFvS91P9F6aiqQhK5rSbnyOnUobWmqBRVI5mG6ES9qLCFfM/2+wFFYr1a8LC/ozudaDvFqT3w2Wff/84F/BfaBJRS/z0gAP+H+UOvgY9yzndKqb8G/J+VUr+Vc/6PQM9/OXfg448u83qxYr/bsV1tqMuGuqhJSZJ65QTSLKqa9WpFVWj6MdG2b1A5cH6+ZbNqIAXOzy65vrqgsIYcx/nNIUx54yzNYkHZNPTHE8vVhnq1wYc9qIBWWvoDp1ZO+XlSkfxEO98aVBT8dvKawmmUtaTo5dr5SAieNx2LiG+C9xDiLNyR03/oWvb39+8lwM46utORerUlTJqhayFGmmVDd+oFwHp3x7PnL8QTESLH45HLqxWkQOEcJAgps16uGIaOcRhYLrY4J9LloZNI8LxYCEdv3gS0UpyOR5rFkWqxQmk7L16JX3eF3DRSEit11w94H6ibRp6XEYqvKYghEVPAaKEED2NgnOT7E2OShixy05jfRGIMUlICxST0Xx2jZE0gZYAiY53BakNROIwdiUm8I0VZEIInhow1DgonQqgYsbahLAx56qUXoUXIZbQSd2MKKOR7UDc1q/Vqpl2PBN9RV4axH6lTjfc94zAw9J797oHkIcVM6SqMslSuYbM4Y5jumfzEz3/+Ff1w4ur6gmZRi5MvJ06nI+vNkqqqhVeR/ftma92UgDgg15vV+/5L3/e4wlCWDusKbNK0fUtV1WzPztHGEPxE156oSxlx7g5HoTntHjgc9v/wNwGl1H8DaRj+0zNhmJzzCIzzr/+OUupnwA+Bv/0Peq2yLLm+vOTV8BJnNOtlQ31xwf5wROvMclGDsZxfiFa+7/YUTnN+tiRnz/l2hcqe06Hn8uySRVNjlAhfQgyzOETSZaqqBjQhZlbrFVk7QgJjC3KKtMcjh/2e4+GIzpGpLDjtd7IZpYiPkapqMLp6f53NMaBmlLU10skW+KYR9VwITGli6EVS66eRw37H3d0NdVUzDT26htPxwPZKkoCnYaAwGqsVXdtSVzV+GslRmpZdGEXVNnT0Byfe+KQYe2k6xRBojwdSzEKZcZbJB8Z+JPo4u32lMVoWjnYYOJ2OoA2uFI5/mJ1+rijn0aAk5EzTRIoSIx9CxBQaTIEyGcxj2GcUaGeSTeIx9QctPweVHzcC9dgaFDVnzoiMR5DamSw/wyxMwTzX77IpQVJI/Hphyb2MVa1WRCOx8tZaVqsVoVeQvVB8QUbIWp5jjCDIy6pkuVqRyJzaFvKEwjCmDu4kfNT7iMbObk84225FbhwjVmuGrqNYFFRlwRdf/JS7+3cUxa9zfiFoudOplXyJQs0lIHPQTBAUmrEYq+aNQM9sDUGrLxY1TSM2dB88p7ZHa4dzlfRegmYaIjp6rBLCdIrQ1IIY+4e6CSil/nPAfxf4J3PO3S99/Aq4zzlHpdT3kGTiL/7U1wNWi4rry3PaQ4sfW0qr0AS26xqM4ez8guVqxdANHMaOmDvO1g1KZ5rKMvQncjBslgucUu+7yVpLLaiyhF5aJ6e9NhZbNfgpkNG40jEed4zjRNue2O/uqZxlWZccvNSopxBIOdOUBVpFTscdWUvWYFE1kBUqMwt/EjlmyR4Mv7D+QsJPowA1TkectVIjG83pdGDsO/TkGfsWW9eMw8Bht8OcKaw2dKeW1WYrNmBrGdsTpxlJltGMfU/pClQWfNmp7VmtFxTGoaJnHCamcRIxjk7zxljSDQPTOND1Jyqx58z5AyLXfWxU+jDNBhzJAhiniaYsRTAVEzqJ/TpOkcRMHy5KovcYN4m3P3r8TANGyYLn0eGnBK2mlBLtBTM5FxklJ52lJDAaY8EgFKBm0VB2LYMXZJqdpw/DMKA2Ehw7nnbiOCwsWs1RZUjfJCOj0Cl4ptPIqW1pKmksrlbbWanqCT5SOkNdNjhjWdQV0+CZxlbi6+JACGrOPdCkFDm1J84vzgjBM44DZVkxixkJ/Sh1DdID6vuesnIMQ0/fd/K1WEtRVNR1SVkWtN2A1o6rqw1XV0+x2rK7eyBHS2nFju1zoqk2vH37lu3HV5xt/wKKwe8IHvkbQAn8m7N67HEU+E8A/yOllPhW4V/IOd//aZ8jBI8mcrZdMhz3tKcHTod7yWc/PwNtWDQaZyNjHlnWjtFrRj9SFGIXzkHx5OqaJ1eX6CxX+EeMts4JooSDaiWhja4oQTuGoUMZkaX2KYk3YFbRKWfJKc4JSI5+EOmyVpkYxGde1QvKqnqf42e0JoZImCbGYWQcRnHgTQN+mjBzGq6fRhEJaTkLw+SJ3jN2LT5mpnGgsobDbqI9HigLJ5LTw46qket8WTjC0DEpRagamFOHY5CJg/gIujliq8BqS5g8QzeQZ+mucSLEsUZi3adxmE9tmQDIzUkWvOQUhnlaIbes2Efq1VpCTkxCm0g2UdJ+zAzw1JIiFYKnqhtyGPEpzSj0SMx5bnbORGCtZVFGCaARGCxzQ1AYBUb3on6cPQZVVdJUnmlqZ4SYhdlBeNjvub7YYo0hBjWj1cQB+cuPYRzZ7Q8oK0yCwilSNmzWl/icicHSticWdUPXdiy2C0pnCGPH8bgnliXl2Tn7/UjOiY8+/oj1ZsE09RyPUhE/PDyIH8RYjC0oywZXVCidObUiLTfhF3zJ1WqDuMoNXTfIzyIbnHOcnV1zfv6MafSURSQyop2DqSUlzbPrp+Ro2awuWTbbP/8m8B3BI//b73juvw7863/aa/5/P2IMlIVi93AihA5tHRow2nLY3xBz5ni6paobjCqY88IZ+xayRFo39RnPnl5TlcX7K7hySmS686I2xs4/eIUtJBR0nLzUzErNM3HpDZROwBWPnWg/Sh3sCokhH8eOtj3MufNSYxZlIfLP0TP0A33bM44D0Q/k5BmHgbquMVrhp2m+CMtNKKVAWRRM48DheCLnzGa5oG9PMxvAo3Km63revX3L9uJSFjwTOtfkFEkxYpQEn1or3xfvI23bC+hys4U54YgUgYSNwqOXTruZQSuejCUlKIvqfa1urSVEqdmFhx/F1JQho1HazB1+jXEFZdWgUiY7R/ATwUvEm+8LlBJ1pStBBbk9SFiJiIRCEik0VsxSSWVUzGQjqb1ZiRTZaDOLlixV4Wa/QUI7N98ooO1a0nYl74VgZriqnssAmQ5o2Y3pxxHJqJpVkcA0JYpqgdU9MfQSZosEuYxjyzS2pDQwTRNv3xzos2Oz3XB5ecHV1Rkvv/2acRxo25bdbi9BIkaayU2znhObJDEqzT2AcRQ9hrOOgPRT3rx+y3a7pVlu6IaR02lks04Er3BuwdQGgteslhesTMYta37j1y7IObO7+xWHiiil6YeRn/3sZ5Qz0rmuhCP/8HBPzBltrQSCJkPwgbpRKGVQyuJsyXazpSpLxqEnaMFgF64W0mySOlVJ7hSmKNHOiclIKaxzhHF4H6IZZhRVURbEGDBasGcZ0cT7ccCnxNC3syFpRKvVTAQ6Mg4TfhwZp5EQJikDcmSaRsqiIClN155EMzAOhDDNyUmWaRw4Hg4YrQl+oj3JDy9FAWaG4Lm5ectqs2YaB4zy0mFPCT8FtCnIOUmAZbN4r58fhxE2whKIMRCUzO+ztP/nRGBD9p7TqcVHi9GGZd0wOUuYmXzWCJxDxpZKLMlz+SCBHRqwuNJSOofVhjT3L2KcIaeuQNsR60qUdqAFMAuPbkVJUooRnK2kN5ATOmdCUKQs//aUE8YVAlpBUVb+F9OGlMjz1EJpiS9zxhDFNoHWBmUU2sz1t3lMNJZRa0ZjixLrCo6nDucVMcFysZaxIZovv/yS87M1WsnNwZiCt29fk4oFVe24vR1YLBe4ouD27p7b23eSRGysHDg+cjzKdGGxqIT/aOF0PNF3LUVRMAzCEYRMCInDsUOZWjIZkqbrBmEXaosPmZwUy/UZWQWO/Ym6qRj6nof7u+9cf78im4Dhjz9/w0Ob+fSjpxSrFcaJ+cXVkcqIJddPQRpGOoOpKVyNdQ7nVji3IsbEfn+HNY7tZgusyFQok1CFJcWJ4EdyVeHJnPoTdVOQo8Rfl84xWUvbdhhXkrSjbTvqwjGOIjcdYsvYtUxqZJgDSAdXcLY5Z2qP7O7uRXATPSlPjL5n7DsMoJIkH03jwHja4/ueqashJjHjjD0pZHRI+Ozxo1iMlVKibygd6hgZ+yNjd6AqFEMb6UfPIvOehmSToygM69WS4/EgJ1tKjENHdIayMigjHXWfAkVZSjkQPSZHxtOew1iwXa/oTwdMEJhJ7QrJO1Tg6oacPaWGFD02KbR2RFuBchirMWR8NujksRF5TszoekAFsKqA4NFqwGgRjWZFWAAAvMtJREFUEBHTbDiSsix7O8NQHhd3FgIT+X2WYFWVGJdYeYW2GoMhKEVW4kQNKhOUxWnD6DOLSth+2WayzmijhLWYNU3RgIG2n0A7grIMU4fvdhitubq65t2bI/008M23r8hKs1mueXdzoq4qji30xy9pu3dkDNuzKy6unnFoPYmG88unZFXg48D+sCNGT5wCYTRstmucMdzsdgxdix97TscdRVlS1zXb8zMmr1Gm4Gx7Tnvqebh7y9XVE6rGcXa1pu96cinuMDMNdP2RaRxYbv5yosn/oT1iglPn+e3f+Sucna1ZLWva9kiOnvOLK/zkaU8dp3Riu91QVIXEX48Tw5DwY0uOFoVl1SwxNWidxEOdFGiN0nMCD6CMpm9PTGPPpqlo24PIXGNkv9tR1TUpWtpOLKpGRSYvmC8/DgzbkWin947DHCNhHNjtj5wOB4qyEHKOhr4/iWtRGzTyOfzYk7xIiYfu9H60NvQ9pVPUZckYPH3XczqdcEUhuYuLhZhvspQjWmemkBimIBiymQyU51uHcwZrDFZrytkKnVVgnGamYEqkkDDz/N17jx9H4jTRtZ7KWU5xJFpNs1gQpxFtK5TWFM4Ro6bQcR6JSniqKzUWuYYrMm4u3QofKHxkHEds2VCETFaWNHSgvdB+ldCgBIsuDsfgg2gVtCIruRENcYAsp3eIMi0ojGO1cCxXC+73J1xTE7JiSsIayEYkxhgHylJUNV57spZSwGiZ5tRlTbmoQItxKw4R48DHkWM74EqHskJ9StpgXUXZrHBFx+HUsVxecrr/iq+/eUvdbHnYdxzbQEyGjz75PpBwRYXWMIwDOXjCOMqo02lCKKiKkuNxT9ueKEpH17d0nePqyWeElFk0DWXp2O8eqMqKcTwwecPmbMsQWu6ONyyXBQ+7t6QUZPS5Wn3n+vuV2AScNfzVv/LbXF9fUFcFfXdkvapZLi5w1kmDZLPkeGxYbTY0qzWvv33FNz//mnEYWC+WHI5HVIbiqWO1WqG1EViGGlFGkXOQUyeKmWU4HTAkDIk4Sjrw4/X78vycu9sbur6ldA4/TXgfCTnTnTp8iKAl7zCnSFUWDH3Pzbu3TJMn1DXDNFAUlqHvJAA86/n5j+IZQXudTieqekHMSpJ1cayXCyJwPJ44HVvKOqKVpigqaZwp9b5ulNeRuv8x4EMSeiUFSBuF0jJj11o60MMwvE+8fXx+VUlK0DRNM2h05HTYE02mN1BWlTyvURRVjXaWnAusiriiwboStMbMjb0YA8I0XEKcyGEix8DQHjHWUVUVOUsjUmmN1ZasFRFFehQUzX4K9X6MmAnzGFFEjY9KSin1ikJzfXHB/cP+vX15GHquzs8BxcNuz7qsiKj3gBY1Jy6puZRJCaqqpokNu8MtykSqpiBnTd9N/PzLr2maBSkk1qsNMWYeHnZcXV3hvWe9XIF7xfH4JVVZc+omPv/8Z5xfXvP06RO+/OJnGJOJfqDrOg7TJCxGCu4f9mhrWCwbYlL4AInIMPRMfk9RXYJyhDhS1+c8fXrJOE2MM5uznCynbsfxeKQfC969fYNSzKGow3euv1+JTaAoHL/7O7/FH/3x78Nmyd3dW56/eEZZG775+iucK7i+fkLVFGhbMYbA4D13ux1+GFmv1oBiGCe6bpCFY0sWuca5jLJKtOlhJMdJXIXTSF3XqCgfN7agMJr1oibHwNQPFEZEJIeTWJZTDByOLT4kUh7xc7OrdI6uPfFweytQkTSHQiaJ73JlPfvemcdRAssYhoGQMtoUkvs3TigmptIzTRPDMAjAZJDQi+3ZGdYKvy8EOVW1dsSQaU8dy9WS9MjZR6YuOQdSMqQYKIxj7AfaGEVlaMz7jSOnmcKrhCFQGM80nEjJE6xwBoZxxMbAsllgy4qUJpzNaF2gbTkvJtEC6JnA5KwlBU0ZGkKYcOUMa43ydWgjRiBNJgUZ6+WsZ1+HbFpaaYkNJ0uzkEfNAcBMHRKDNefbNeebDfthJERJW95st6zWGw53tyJaSjCGSNGUKONQ2pGUxsdM2/WE28ip2xFijy0y33z9lnESIMzQ96yWK6w2VFVDzvCw2xOmwHK5JOVMUTQ8ffqcqtngDj3923v8nG+YUuLh4YGhP3I8nSiMZbna8LDb8fbmlma5YNkuaXuPsRWH44FT280lZuL8bEF72vNwLylQ0+Q5tSeMNYyhpR0OYBJZKxaLZpaew/3dw3euv1+JTSDGwO3bb9ndvuPm7ddstktCGGhbz8Puls3mjNF3VE2DchWHuyOnvmecMVZTCBhtcCbTdi1hmiBmCtPglAa0OOdCmJFYgdoVNM7h+440iWnHaGjKip/97GekGLm4uGD38MDD3Q7vA+M4sN+fOLQD6JaxH+i7juA993d33N/eUTcN1ogKzpMY+47SFjDHVMk1WTj/0zgJ6TYmwoxFSynSdx3dOMwx545hHN8LaZyTcaYkz3oW1QZQDIPn8rIC4HQ6MQw9SjGbgR7xX3LFlumA3EiqqoKYSD7QLBoK6xi1YdkUnHYt09CCMbTHI9kr7PqS87LBlBUhWlypEQNDgSlLyBL4ZpwEfViTCDmIrBg9u//cDIKR09wYi8qJmD1CAp5DOo0h+tk/ATO1SPQfKUvHXNkCY6xszFFciddXF3TfvsEUDrTlcDjy/U8+obY/5Obl12StMK4GraUkUZaEYQqJ/anFjh0P+7f40LJeV9y8u+HYDhSuFHNY7imsIwbIIaGN5ptvX1K6grqpObWvuLx6grUlOY8smgXn5+d88803DMOAsyKEGoaeIUmg6263Y5wmqsWKUz+hdIkra6qo8dGyPdtyeX6Js4bd/sT93S2n7kTXtcQsoqqLqwtBrBmNK0sWC4m9X61W74Ns/qTHr8QmELzn519+Qekc7969BAIxjLjSslwv0U7zsy+/4Pr6Kc3qSnjyxnJ5/QSrNHXdkH3gcDyhsuJie4bWRubf+jGkUtJnc4agIotmidWw38kCVyHLbL098frlNzx/8SEpRO5uHzgeO8hwPPW03cTD7sRy4VEZ/OjpTi13N7fsHh7QWlEU9n2ybHc8sqgagYDMFKQ8a+nznLsdQiRlkbNqpZmmaU4jVnNabZxlpQFlZBGZOYuQecg4jhPOlXLt3z8wjgNVJQacGDN5jmIrC/derBSCoM6zjvT9hNaKqpJcg8opep0Y/cgwZt68eY1qBi4+/iFlvUCXFQSHrRwxJMCBdSKSmgNfReCfyPSkDKOfpItfSsdaz4tdRmOPWgHe5wIopWF+g/9CsCTqOdk4RSYs7MdIGCeO+xZnC5q6ItkCHzPvbt7x8ttX/NYPPsMpaPf3jDFQZotCY5TEzkc0/TBRoOm6jtvbb+lOFcZVXJ4/xRrDcrmkazt2u3uqqublt6+4vLxAG8OhPXE4HbEmcjz0ZDWx350YvLASP//8c0KY+PijD2Y14IFh8Nzc7WkWCyEOlYuZKGXZH3uMKbi4es56vWa3O9KfdujZT6CV4uXLr1mdbdAGjq0R5yWG+4d3pD5yf3/Ps2fPxAH6HY9fiU0gZwFDTNPA+dkFx+OenCLr7ZrNmUQ/7/dHtudX3L96xdubPdM4cnFxQQ6Rdn8gjh5i5GJzxuXlFecXF5JDaAvBccVAzpqsI1YZbAFhaAlJoUwhXXg/st/v0UrhtOXVy1e8e/OWySdQctomLLt9S1NbrHXkJOm4h92OoesYmwZnRXijNKTwGOCZ6LsWQyJM/v2JFoOc/NoWsyNNaDwpZVJMSFKQpBQPw4QtHFobtHaQ1Xvu4ORbYspUzlGWJcPUE1JAGcXgByY/Ql5QVzV920lktVLkZURZyVvsTie5YecMccJp6ZlMfuLduzesn1Si1agaVFlRZOkDqDwBBWBBxTkkZMaYzZ6KnMEHCWwxVkZ6xkrirvcTOc1KQWRer7XIrzNZCEEznkxeL8nIN4siE0QjkaZJJiwxUljNfduCceSs+NFPfozTio+eXGEU7Pf3qLlJiHagHcpWoDWHw4GURBIcQ4dWhqdXz0gZzrYbXvlX+ClQVTVVXcu/RSmGYWCz2bBdL8UvkTNNs6R72PHy5Su8H5mmAa0SZSHK1YvLK1AW7wO73Ymu9zQr0Q5IYIhmvdlwe3fAMnK+rjEp85Mf/wxbGPp+ZLnNDMOEv71jtVlSVBX9qWVdNXRDz/1uN2+2f/LjV2ITiDFBNOzuj7z4+BnXV0/IOnI47vnxjz6XmWpKjIPnyy++4eHQ8eL5c/q+59U3L2n3B5zS/OB7n/H8+XOxchaC/UI76QhjyFmLp1wVKBI+j1AscMrRngZC39H3PZvNlnEc+dnPvmC/O2Kc0Hz7KaBtwf7UczVW79OH2rana3v85Bn7AWfN+7lvYQyLpqYfRlnsj2/keUY/DSPjlKgaJdQg1fM4EJum6X0K0jRNjOMg/IpZryDec0ddLzgej+QsmQFVXRGSl0ZeXTCeBGSqzi8ktXc2DFVVhZ8mCe1ImX7sKJzDKE3wI0ZBWVjiOND1HRdFITHk2gLSUMs5EZKfQ1Ek0FQbQ06BGLMwFufGnZpFOTkjGHRjKArRduQkwFiVE0R5nTxzG1OW+TePtyfke6C1Ee2ED7NwSLNeLNgPwoi8v32g2ZxRVCU6J/7+3/89hu99wm//2g/ZnK25P+1kk8AQMcSkyBGKqmF71jAO93SnOzabS66vnvDmzVtubm55+fIlh8OJruuoqppu6FmtlixWC4qqoGt7Ntsz0TBUNbZs+PrlN1xcXAKRL7/4nKZyNM2KzeaMgKFte+7fvCMfe566hnHyLJZbDoeWn/z0K4x1/PXf+SHPnj5hmAZ8yHz51Rcklbm6/ICfffFTtM0cTy2nvmVZ11z/2q/zwQcfAPDm9bvvXH+/EptATnDz9p7Lqyf4Ud6MVVPSd4H72z2nTvzWd7d/h33bc3H9jN/8jd9gd3eP70dqW5BDZLPZUjcNoDDWgC2IyiFXVYXSpXStlUI5w+l+hyqWxNRx8pE8TuwOe55cPeWnP/mc07EVppxRZKPwIRHDhDu29INGK1Ec9p3EbfspMPSDqBYLJw7GPM+2+56YAsMwoBVEL5jvyXu0lVhy7wN1rTgejlRNQ9d1NIsF09DP7r2ePOvqq7p+3x+ATFVV3D88cHG5JaVEVVa03Ym6KUXxOE2y0BQzNizT9y3v3olyMKXE8Xicyb4WZ+R7lAuD22y4e/lWSE4xyelrFSkptNLYohIRzjy5eDQLiYLQSnNzGimKArtYkocOax2D92TynBwtI8UphveEIm0N1kmJEWOYoaWPbkPIOqOsxljH5AOT90JYSiLMX6+XDGGCERZNTblecf/wwKvXr/nt3/4N7KLi7c1OAGdKE5URXLmSxtp6cwZ5JOXE11//nHHyHI8HtIbNZsV+v5fb6/kZqERZFiyXC25e7XjYfY1xBR+8+FBky9fXPHv2DO8Hbt69RSE9p6pecr87MU2BJ8+e0bY9h2OLtQXHYw/a8MNf+02qqqYsDPd3J07tEescl5fPqBc1YYLvffprxOy5vb8FHJtVw+GwZ7lc4VzF6dR91/L71dgEpsljTMnZ5pKvvvmC+90tn37vY5p6RVUcuTx/yugDn3/xBXXT8OLpE968+pY0BZ49ucJ98IzCWDbLleDKrURtKWtRM51XLDZAiHPCTkAVK0qnwTrK1ZYcOzCWCDNU087wTE2M4EMmkvAhMU6BwmqmMTIM0yz2mRiHiaEfqapivinIzL7vW9kQciJF0eGTFCkmlMrkmHmMzp6mCVsUpPl0ezSRkDNd180TAsPFxQXWNeScqRc1RVlQNQ1106A0HNsDrihYzHHrXdtSGEsKQRqDwTPERHc8CdK7HzjudixXKxGbaJHXqtnRZmaUt0iDjfx71fy15HnyMSPHUxKUd5jmOO8QKZ0lBEn7iSmJVHgGjL13GeZZSK0eeznSL0gx8VjYSo+E+VYgUuM8W7ZjSvJ3kkiJVSENvxijwF+IvH33hvNXaz75tR9SNFu+fbcjROYysaKuFafTO1IZOb+45ObdLYfDXtiLwXNxecYnH3/E/cMdb968oSiFsxhTIKvM8+cfst8feXNzw9dff0M79Cxz5nDY0zQLPv3ke3z15RfkpLi7fUAXFWfnF+8Pk6KsaZoFD7sDu8OBs7MLLi4u+PlP/hidAuvNhn5/QJnMB8+vKUrLGEZWixU5Wy7Pn+IcTNMDX/z8K8hGRrjf8fiV2ASWyyWffvoZ6ITWTuTA2nK2EdzSD37wa7RdT/TSdb4829KeTkLtzYbCGrarFcVM+oWZMGsMypXzlU+T8yxi0Zq+3aPLJbbU+JSo1meo2FI0C6YQsUVJUdaMk/AOfQj4EMjGMIXI4dizWS2Ykse2AynBNMmG4FzPer3EGMsUZYJwOh5nqKUlh8cTTfz6OQRMeMwDgJwU3kdgvjqnjPeBqpYwj8KV5Ayr1ZqyWrLbPbBYLFgsFjjn0EZT1xWXV5d47ylcSZgiXdejUUzj+F6IA4FT27JcLMg5czqdWCwWpOxROcnGqRRlVVEUhchtlfql67pkJv7CijPbgecFG2aPREoBZzTTPM0J7/0CzDLgR7CITFZU1u+buzE9Eo1FlyGSYbEfy0akZ0l1JGfRFIQovoJ6uWaYRvkeG8WirJmGji+/+hJVOJ5/8n2urp/wcJoYpoBrtKDJiprd4Q1dG7BaUxTSdFuvzhmnieunl6w2C4yVcsVYTUiaP/7jP+Q3PvkNmmbBejVgy4qPP/se++OBv//3fp/vffYpn336Pdpjz2azpp88p37km69fcjq1bLdnPHv2jL4fybND8tSe8MFzfnmNyYmqLnn97i05J7bbI9uzDU2zYvfwwO3tPT5MZCbW28zD/QMpaVbLs+9cf9/dLfj/46OuaxaLJYfDnmfPnvJX/spf5cMXHzKNI8t6QeUKFlXNJx9+yKIsOdzecn12xvPrJzSlE/FPfyKEiaJ0xOwZp56YBWARkRoUbaSOtY4QwVU1WRuysRJgWlcszs6JaExRUS+WGGtkEcb0mK7NOE3s9kdigilEjqeOlBWg8V42gpQQFFiGaRrfh3hKglGcDfYKlTMxRKbJz7cCNXe/4zwnT+89/ORMWZacnZ1LHkJZcXl5gTGGzWbN9mzzHg6aMyyXa6bJM46B4AND13PY7+cE5ISfJkIIdKcT4zD8wjcR5Go9TJOo7ICmaXC2kJgwZebZvHqv9f8FC4BfGHTsY4hImoM9pE8hsJAZKxd/sQHkeTygtJlTgfScezDbmecJiagIE3F2fYYQRDCkDBkJGo0xgxYO4Xq1wphZhm4tq4VEyf3oR3/ET3/yU5xxbLcXNMsNi9WWarGmbFbErOknybTIecTZzPZsQVUb9rtbdrtbzi/OSDnwh3/4+6Qc+bXf/DWsk+TnmBKPROZpnPj+97/Ps6fPOT+74vrqGcHD+fkVVVVx2B9Yrzdst2eklLi6vuB73/uE8/MNOXumqeODF09JGv7gj/+Ydhg4v7wUtoVxPH3yAU25ZH934O//7d/n9Tev6fqWPK8vcSD+yY9fiZsACs6vznj38Iq7hxvqpsTHDu8H6qri3ZtvSSlzthHgZ4yey80G5xxjWcgbKCWMURgr3WUfAy5OmDinDWHFOGK0GJKMpjAF0+Sx1lDUFd2xYHNxye3wBu1KirqWhN5JFpU2mqQUUwi0/SS1ZMocj728Ca3cOLyP+ClSljBOssAXTSMnog9o48hh7oBrjfcSuhp8mCcCQg3WWr+P8ypLMVSVZcnFxQVn52cYa9lsl1SVY3u25uxsS1k6iqKQhagtOYmeoOsGTE4EL8rBnMUibYym6+SUrKqaGDzH4x5bi/MPL4IfkS4njHPwS53mDP+hW8B8qZ9bm2DKknyUzIGpHxj7nhjTHI8uTb2UHqPOkDJAScmg1BxoojRaQVLSg8hq/qxZTESkRM4a40qJIwsCZiFL07msK6zVTEPPOHTUy5qqamgnz+tX3xJ0w/Pv/xZl1dCPntWqYrWoWS0NftyBP7JeFWgFPrRcXK558/ZbvvjySz777DOaRclqu6RqSparJeEh4mNgv99juo6ykTShq6snrFYb/BQ4O7skRvjqy6/ZHQ8sFyv6ruNl1/LJJ5+yXn1AjJ7tZkUGNtslGNgdd0Q8f+Wv/i5lWfLq5Us+cM949/YdVxdXbJZbFtWCJ1dP0bqfLdENTbP4zuX3K7EJ5Bypl456UfB7f/AzUvKcn2/46MULysIw9EecK1ktVmyX1xROxmkmJzaLBcM4MI4j7UmirK6vr4QIq0AT0Vnm8MpElLb4tiMnTwSmoad0wqqLaKrlClPtcZWw/au6BjVhUsQmK2aUFPABQhTfw/HUiX9dCVdv8oFj22GdeMDdybLeCI14nPxcP0pJoJWBLKdvjKICDCGQFCIKmrvoZVlKc28WDG23WxkF+R5dCBTFlQUxJ+rlgrIsscbiqlvCsSXG+fofZ5Q5wGOnXUGYPNkV5Bg5HY4si817bULOE0rV+CBR62hFSpnHIkA6/7OcN2WhGec4k42ThHnEhB97/DR/fVk2Oh/EG6BmV+IjUOQxWeiXbxdaC84NICE8gJRmcAsSI6/0Y5q0gTkhqaxLqjkK3I89odTYpmS7XtJOitevX3EMluwacJanT8+5PK95/mzN5eWaUrWY3PHwcMup7TA20/UHmkXBqzcv2W63vHjxnKquadsTq3LDdnHG3e6Bh/2e+4cHUJrPP/+cjz76hO36jLP1Ob/1mx/w7es3lEXBxfk5P/7pj9ls1nzwwVOOxz13dzcslgtRjvoBu6q5fHoONqEtDGPHR598yNj1/N7f+w/43d/9XS7Pz/gn/5P/BLoIfPDDhq++/Jqvf/6GYYa3/kmPP7UcUEr975RS75RSf/BLH/sfKqW+VUr9vfm/f+aX/uxvKKU+V0r9WCn1n/2zbALjNPLv/Z2/xfH0QL0ouH5yzvWTc4zNWAtFYWgqiyIw9R1pmkjeU5cVdVnMoMwj+92O/e5BOvBGY8xsJCIiQLgg19KxnxNvRBGndWLyI2OMYC2mLKmXK1abNevtGYvlUjrbzkpzDAgxcWp7hinQj5Nc55PCh8g0Rdq2Y5w8IUQOhwPjOEKWLjcpv+9yP8pjH7MHpsnPC1b8BSFI80uhiQkWiyXOlZRVjXMFx8Neuv0qMYwD+8NBTtmcKcpKVGtIB/0xp2EaR9S8hMdxfN9089NEipFpHBkmT5g1+uPk50addOulDPiF+kQKA3k99b40SBLkMfQItFRm+CGIJHqaJibvJZB0XuTSf5CT/xdXA2YMmJqv/LL4Hz/no78ghZltoMwM9CxRRqS1j3yFwhmMUaicCDMifbVcUlUVX//853z++c8Yhombuwf+5r/zt/jDP/4xMSmqugIVQAU26wXBD1xenVFVBXVTcHa+5dkHz+RmOo18/fXP8X7i2bOnfPbZZ7x48YIf/vAHXF8/ZblYU7iKtu1ZLtZ8/3s/4MMXL1gsGp5/8AEvXrzgdDoyDD3Pnz9jvWpwTrHb3fLy1c/xaWB7vsSnkaKy/Pqv/4D1eom1mvZ44urykt/44Q8E7R4jm+2GzWbD5eXld66/P2/uAMD/Muf8P/vlDyilfhP454DfAj4A/h9KqR/mnL87AwkJwPgP/u7f5td+7ftsz1acn63ZrJcYBcumgZQx2hJDYr97oFUHnj1/zqIueXi0XXpxzZVlwfF0oDpUXLiNbAA5CENjvl6aGVfdjZNgplWNHwf6aSKGiLGOxWpJgcH3EkJ66jp08OgsnWvvvQR/FrUIe+a6Vmb96T0UwljLONNlK+fmhplgvOfv2i9O1F/aDNKcYMzkhT5sIkVR8OzZB5ydneGsvOGGsaeuK7Eb9x3H44miKLi7u2e1EqOQMZacFZOfsEqabHVdo5TieDzSNA3ljM9OKWGMoet76lIyFkMMOK0lQm2Gb+SZMvy+FMjp/dem57o+5cDUt5IZ4Uf8NEm/YZLegPd+nirI94EZAvLLpYEx4oSckABUNesG5PmzijAnYk4Er8lqBnFGMHNYawxyK1EKiqIQUKnR2HkDXi2XLCbLsl7z/MWH3D28ZZoCr1+/4adLww8/OeNsYbi8OGO13vD67TuefvCUq+trXFHS9QMpBYrC4sPEdrthsWhoVguKqiaQWSxXbDbnBJ9RyfDm1TsOh5YPP/qYxb7mcDjgCkeIHu9Hrp9c8+LD54ToOT8KmsxUln4cyDHTVBVNWfPu5i2LZc0PfvB9aldxttlwd3dDDJ7D4cD9/Y71ZsNm9RfYBP6k3IF/wOO/CPyrM3D0S6XU58A/Bvw7/6C/pK3m6oNLkoZPvvd9NqvVLF01vH71cz568YRhOHLY33IcHwgeVlNBeOi4effA8dAzdJHV8hybzzndRjb1Bv10SdQirTWFI6WJnA3GgNEF+12kblaMPhM82CmSfKIoayYsExY3RVQMmNORKozovgMfSOGM/lQTa41XQn+ZFAwxURqLj4bC1SzrwP3DwNhm9KLCas9isaLvB0bvCdkQsiIpjZ37G1qBMxaVYZpkbu6KiovrK4q6YHO5YYwDu9MDy0ZsqUZPvH79LYVzTEnT7u+Z2j1THymzJeBQriFHT/ADpilwpiJHjdj4NSEGlDHErNHHCeMVi7JgGCcm54UuPMM/lJGZenq8roPo1lVGW8jTCL6nMIqhG7EonLH4riWcdjD2mCRKzJQgAjkr4SnMkfEpyG3IFA4bSsYQmKaABMbIhmmNTA9iiCSViToixUGYScKZMIFKUfIQlMEnjY0GFcHmQGEz19sti7Nn+NOO/e1P+cf+2qeUruDdqyOHm8xHH15zfb1huWlYLycKo/ng4+9R1Ft+8sXXfP32NS8+fcGVMXzv2RVaFZwOI6vlFUaV3N+cmE4lOsto2IWaw5sDq23kapl5dnbBw87hUybEyHqzplSRulSsmzVtexAKdCmK0UUj5qC7uzsur65YLqQJvT5fcjfcsL7esCgNd/7E/thyvP/LySL8l5RS/zWEJPzfyTk/AM+RMJLHx8v5Y/+Rxy/nDpydNWzPzjnbnuOKmo8++h7t8cDQHXj95h2FA+9PfPvqa5HuZsvv/eHvUZULmmqNH8GYmmnM3L3bs2y2NG6NLQqiDqic8H5AKycAT1MgYRORarVmv3/geGxJ40TyEVsU+KwxaHLXo+sKV5XkqURNA0obulgyDoqgAslEYhY02RhlzjdF6VZXZUGK0LcT0WdyVO9vDfJ9kMmFuAnnJJ4819tJhEYoRbNc8PTpsxmdbtifHiirgto1TFPH/cMNu/t3bDZLnC2IvidOkf4UKNwCZwppbAZNd+rou5EwRYx2aGWFCpSl1s4oCJkwRZLJkAT3pWfX3/vjWylZjHO2AwB5Nv2mAHPoS3c6zcg3YS8SJkxKWCWfL86koPgIZlXpl8aTwgu0RYEZPT5I63FmnaKUIUVPComklZCNlYY54zBniF4yD62TkWvMkLKwCAgTNnoMNb4bOI73NBW8eL5l1ZzR3r3i3/13f8rf+vd/yiefrPlP/Se+z7Nri/cDZTHwk5/+iDEpPv3eD6m3FSMjXvVcnq/IKrNYlMTB4Kzm6mLL/n7H/f0tz59folTg9ubnXFwWnJ09pyrO6YaJh91B4tKzkdDU6Em+h2TQusCYjMmRuipQl2csmpL7sePi6opTeySowGKzpnYVl1vHcLzhtP/unsCfdxP4XwP/srwb+JeB/zkSQvJnfvxy7sBHH17k02Hge59csX944NtXb0l+IqcJYyp+//d/QggjD7s73r17wBoxxiyaJc8/+JCn1885Oz8jRwnqWG4cdZNQKhDDhMqath1YLbaMfY9daIL30jNQmbY90LYn9CjJNWWzJGWom1qwW87JhuIcxjm0jWADPsV5XOXlFIriUQ8p4WMgEihKi7aKcZrwQa72fS8KwLIsZh1CIs4z7kdBUZYVKT8kaykKR7OoOT/fMgyS2Pzpp59w9/o1MSbevn2HDwGlzOwtMPgxcDyd2K4rtJZ0X1tYQaqfTkQv7su5qY9WmjF4SBKD/pgRaZylKEphArriF0YeIyEgItqRmDFSIqUgY89h4ng40HUd0zgIsvvxJvFL2oI86wDSXI5oJZkI75uBSkmT04kPwPs4j1AlHNX7QIiRx7NOGekrKCVuPVQCM5Og536MTEiE56fnIJSu76gXNZ/88Le4vr7m+uIZ714GDqe/x5u7e949ZLQ78p/5J3+Xp1cb7nYt/9bf/LdZbbZcPdxw8WzD9nxJaFsGs2NVLTjuXtEeBgpXU9cLQurpphPFpiT4Eb878vqN59QOGFfT9iOHY8tqs+ZMb2gWMhUqXI01DucKdvs9Dy8fqJuaPAu0pMcy8Xu/93vs9jt+8P1fp9RnXF9ccLb6Pne3J74L//nn2gRyzm8ff62U+t8A/9f5t98CH/7SU1/MH/sHPpx1kB13Nztubm7oTh1GK9rTgeViw27/DW3bslxe0Z4OkqmnQKNJ0VEUNWUpwaJlZVhtDKYYGYc9x74HDHHKxLImpQk/iebcWYWfeokGs5qgNG3XwRzWYYuSqmmwBwFhYCxZG5LWKJfx2ZOjB50RabtCaSvNtRjop5F6W+FK0ZOPk8cZmcXXdU1ZFIx+QM5DiMHP9fFj2y3JqaxgGHpiDCwWC07dkbouJaTyeJTx1+QpipLNZsM4TJSViGLGcRTF3azQKyuJ0WqPrSwUrfEhShS5Vu//X7tHyEhmsVpR1Q1FWT0W8O/bghqBN5F+ye4bIuM4cDoe6Id+lvkmxmlkHEdhDc5f5XthUZIxb4qJqCSD+tFf8ZgZWRTlzDGcCD7+h/UJ8+b5qL3QzL6FWSFqjEXnPPMmH9OPHsePirJwRK+pypLrqysUipubG25u3hJSwjZL2nDi3/v7P+LJC0GGvbl/zan3tO0bjM2slzCakW3d8NWPPpd4NlPw8cef8PNvviazog8tl89LTt0b7u9v2Ww3MFV0w8Dx9oG2G1gsN4QQOBwOrFZPaRYNKUU5qIwCEvvDPbuD/PwmPzFOnruHe3LOlEUpobpWo7Ti2eUzjDl+5/r78+YOPMs5v55/+88Cj5ODfwP4Pyql/hdIY/AHwL/3p73eMEw83J1oqiNlWXM4nNjvHpimkX/kd36X7//wd/mDP/gjQijYrF6wfzhwcXnOixfPuDg/w1gh5RelRquAj3se9gHflrSDBzSb5Rn7XcIoERdJQy4w9B1+7KhKR1dIhFUEirIgKyVhpMUDyjlh69sCXEBirCZCnMT3jiZ6MChMVvST59C3nF/WFHXFfn+i6zs2q4XUzlphnRWYBxlnDcMYyRpQCaXsfO1NhBiY/CQ4rTDNZGDNH//RH/H08gkxjpRlyWJR4VzJw8NhHkOm9yfpMA4weorCkMlyAs7z/piiPNfYudWv3nfeJx9YGSukXm0IMeKUfu8UhCSEYyWOSZ0lMWjoO9q2xWpBcY29nFg+BEIUoY8scnh/FUF+HaMoPvN7q7T8mdYSh+ZcZtQT4yj8AWPMvBGIOEfnhNFgtSLN/zcKFLLJpGCIIWKdAFSM0ZjCYaolPg28/PolbWd4++od//a/9RPuHwKDKymqgm/f3fD//Jt/D20XjKcTZxfPOTy846/9o3+N4/5bxt0B70ve/fyW/XHPi49e8B/7q3+dL7/8QkaR7ZGr62sSGVs04k/wmXLyFFUNdw+cn29YrdeEFLFObl8xJl69fsVy2bBYLGkWDW03o8y1ousG3r274YMXL1guVzzs7ji/XHHq7tE2Ycr6O9ffnzd34J9SSv2j80/nK+C/DZBz/kOl1L8G/BEST/Yv/mmTAZCx2B/9wY+5vLjkw4+eMvmGYexZb7c0yy0vPvw1vvnmwN/+9/8uddFQVhdcXrygqdfEZLi93XH/cMPl5ZLlsuBud+JunxhTSUgWZwsM0HdvWS83NPWSGCOFsaLr7w6C+46JpDTGOarFgn4YMc4Kh0BrsjZgCrSL5NwRg3jaNZqUDVMEnRSFNkwp0Q4TSRvKuiYrzakbWCxqnFbz/F/qa60U2lr6fgKtyJr3cdqSISCBm8tlg3OOpm74+c+/YOg7VqsVr14f0Nqy2Z7T9SNt2+NMyThN79/kw9Azji3Ozhl/WcJR5twIUk5oLRmCEgoi8/8wTcSUmbykL8UEzEahx0Vn5gZhjpGsxOY7zSPRqioZ4ySbzDydebwBpCQtvFl3OLcZZPyYY5q9E3PS0HyTSVlCUY21hG5AKzPzBwxqphIrMk4r6sJh5yQirXnPeAx+IkxzOag1KQV0DjR1Ba5kGG75+us77m/uOR53jJPhNEbWtkbZFT/98gYf/l2eX1/z2z/8jB8++YCL86e8ffkF3//0A959e6DUS2qX8V3ix3/4ORfbp/zopz9F6xVff7GnqGo++OBjUsrc339NinD95CnWCatxuaplWhA8bSc3p6JwwoVQeWZtllIGBbkVlWXJzbsbrq+vedjf8nAwxGgZhp7Li4///JvA/y+5A/Pz/xXgX/nTXveXH1VZ8ekn32O9XLHb3XN5tUVpsM4Sc6aqV3zw4nu4v/+l5P2ZkrbzVLVnvV1RlQ1td8v+cKQol6joGYaWQ5uxxZL1as1hf8fu4UAYB7piT86wWa3ouo6H+xs26y1t3+NjBKOp6pppDgJFa0KGKWc8iqiNJPsGA1GBKcnRkqWfhs5JeIQ+McSMLipsUTFME6P3FItKVIta46zFWrHd5hxRyr0fkaV5/KURGW5VlZydbXk43nNzc8MPfvAZx+OJm3e3GFvQ1Ave7A/krGi7nmGYKMp6ViV6hnEUJ+IszkkpCRNwjmfPSZpwWX6OGDMvwCzy6JRFCv0LjeB8+sLc0JRrdkqBlCJlIQGg3Vzry8jPoY0l5UAmSOOTua8wNwPlaxdZsexRjxuA/LuU1vPN5PHfLWPVR31EnlkNhdVYo94bmmbXETFKE9clZlCLhzCSYmBztmWxqfjq61vGcWC1Knl92zL1E4MDUxTYouTbNy1Df4fKFf/cP/vP8O5di9YrQqho+1ua5Qqs4fLqknfv7vn13/wdtosDrlji+wfGfSZuNxyGG27ub+jaHmWzgGW0ZrdPNIsFDw87iqqirErOL86QbMZMWZWcXVxydfWUtu14+/aG1WrDm9dvOduc8/bdKz7/6Vecnz3l+fMPONv+xXQCf+mPuq74T/9T/wTLZcnf/rv/b0Z/4IuvfsLzDz7m+slzxmlisz3nH/vH/+O8ffUtYWypFzXnFxesN1uur1bEdEHb3qPNKM0s65jGA9aWAi9tD0xDS+csdzdvWTYLmtLxcH/L7uGW5UKMQyFnElBUJdv/D3N/FmPblqXnYd+cc7V77X7v6CNOe89tM7Mys6pYrCpVFVvLFmlIlgXZfDBMwy8C7AcDfrBg+MlPerEBPRkQYAM2YIi0LYmkDDZiUaJJFqvPyva2p42+2X2z+jmnH+aKc5NUXbLMKhp3AxfnnrhxIiJP7jnWHGP8//d7HrPpFD8KEZ5HbQWFtdQWaiRaeA5/ToDBf/vGLU2FEIpNVjNf54TCQ3i+iwVLc5JWTFVrB8cMA1ReUZYaa4zDaCPe7r4NoJR8CwDVRvPJJ5/Qbie0khYvPz8jzwtOHhxjjAOfSOlTFrlLEU4CDA2Iw9omS+/edNNM/KVwPbOUeEK5nbs2BL7/dhUolEcUtfCj2K31rHFvHutk3LZ2aC9hrWsLhHAKRu2YCPcaCCGkC3uxAOWX/bypm8PrbgMIdz+4FxPBvYxYuW5eKnzPb4aCzkOgQtXMFdyfubcey8Z3YBuHIbYpsAbqqiaIXOR3rZ2QqqtCdnf2mNzesd2uydYr6lyQURMkLZTsgFLcTgvm08/Z3zng4V6fYTvhd7/3iijOENJSU+K3fHwCkiTm6PAIWwf0WvuslxWtIGFZTFCBwqslfiiJQzfEzfMtNzdzVusN/cEQz++9NVxVdU25XjcsiTZxnLBabSmKiuPjE46Ojvni+XO++OwVg77kl37hf8jh/r+mVOI/sZeAxfyOIBzQbofkxZZBv00UewSBIi+2ZHnaSG9HoBPGOyPaSYw2Na9PzxCUDEcJWZ5SFBW7e2OsjRHSGUZubm8RQmN0SZ6tMXXJoN9jvVrgKXcgkBLZqAKjVoQUivV6jRd4KD8gLyvyytlY81pQatmo8WKscc41a3mL/drkhskiZdTuUCMptaWoDFlRvX3CB1GEl5aYbYmQ0r05cY7a+/EZuIisqq65u71jPp3xjW99xNnpGfPlguF4zGAw4ub6EmOaBKTC6efbSYc0LTBYlOc56a9Ub5+4Urn2xw8CVOCjrcFqQLvrehhF1NrSjhN29w9QfuDUgxZoEoOtrqHRN7jA1RJfKXwFWbF1sNAmwsxgfwof5n6K+6g3jG3CY7/MKNSNOcjzHPTVNopiISXS89BF1SQZi7ejhftWwynEXNSfuR8iQnN7EGAcx1hKkNKC1W4YWpScHD/A6Iq7yzWbJbRWBdsid3qFTQF+iB+EbPKav/Prv8n7j494dLiLEprDRyn9bhdVw916yuHeHqiK0aiFrXzKDGRVMhxEhLqP1zlC64qnjx+6lkRrFssFz1+8wtgS5UGtS6q6fiv/TbcZV5d3HB4e0el0MdrS7/Xx/YBO0ubk+BHXtxtur9f4XgddCb7q9bUoAgKoqoxax4x3+qzWll7/oRuU2ZIsW6FkTZrOGAwj8iynlUj8UOB5EWm2ZL1Z4wfO0CNVizDs0e0GjaouJwyDRlpaMRj0mE3nLgC0yBmNHCPOSk3civADv+Hqa+cgExY/8MjKgqKqAUGpFZUNnKLNb1HXGiF1kxTkoa1mm9WkuWHUj9DCQwsPGURsy5qsyOl3u4RRQBCVSJW7AZoxaEGjIpTOFK0NVV0zmUzJsi29bp/lfMlnX3xBr7vD4eEx0+mUm5sJSdKirlPKssb3A6I4YrFYOS09HmVVYZVz2N33/0I4CEsUO0qOqSs85QaLYRhSa0PS6dIfjR3q24JAYq1T8Ln1oH3LKNB16UhDunRMxcYpWVXVW4uRW4c6mXGta6qiaJyHzVtSuISpWpeuQAjVYNoadWYzSagaNLkbWroiLHBGI6GaHgeaAnLveHSdga5pOhqNNS69qBW36LRjev2IRzwk/1M1H75TMrtd8oMfP+dynlFoiTWCrHLJzEaGPD+dcHO75uhwn3BP09/vUqRrlPBQUczV7TWh9IhUSJ5lWOOsxJ5s45sT6jIjzTK2myX9Tpt2EuN5AiktVVUwXy1RKmK7yd7i5qyFXnfQ/B1Dvz9gtVxz+uaMvZ0dfu7nv80nH3/B5eUL1qv5V56/r0URAMPxyT47u33mC4tSBq0r6mrNejlDdhWjQZe6bLGz2yXNBFEUIIVLh213HpOlzhRkbUUYKupKMJnOMbpiMpnw5MkTB6OsHDJbSMF8saAsK+LYmXKqeuve9HVFWeXNm8W9U4RypJu8LvFVSFl71Nr1qdqA5wd0eh6L6QptLQrJJiup8TAyQMsALTy8OKHWBbqsGfkBnvKRno8KfKQ2iKYHd4ARC/fW2NpwdnrOaNynqgpubq9ZLVe8++43qWvNx598ShxFeHlFq9V22PUgYrtNqaqSMApZr3JKp7Zpemg3YHNtuSAIQrKycJAOC1YIirKi3e+QtDus1lsGgxoZONefEs2+/X5wp2usrlFKosuSzXrdYNElm8222VYojDSN0MgxFRz5uEaKxhzUrAaFchCToijBSrzEd4NC/VOz5uZAC2EQnmqWCY1Y6R5uiPs5EU6F6SLUnFOxKisoIfAqlBLEUcze7h7p9orNxh3q3fdHiMry8PiA//ev/xYvL+YopSjqEusrah0QxgmzTcn29S1rW3B9Y9kZ9XhwNGSzDYl9S42GoED5hv7QpzYzSgMaH4RhPr+j2C4o08zxCSonY7+4vOLq5o6dnWN63SFVlQOWbqfzZQsoFOdnFyRxi8lkwsPHD3j4cEQYGs4ufkyvO/zK0/f1KAICPF+SpluEVHTaHbSu8WTA1eUtQlsePuzR73l0uh4y8F3sdFYwn8/p9waMRuPG6lkQRz7rzZIoSthuVyxXa25ublENuff07IztJsOTaXNj9NDWo6pKkiT+0sGVJA2q24VYdHtdlquN60GtR1ZohKiRasvuzh4Kn+V8hhW1a0Rx+XVZZRCBmzxb5WN0hZUK2fjzhZSu96ZRvDV/KY0Ex03AheL2boKxGs+TbDYbDg+OCIKI169PAZeyNJnN2RmNEdJx/ZerVSPndRqAutZ4Ur2d8DfAY2hs0bV2B1WYxpxjDF4Q4och2zRnIJX7Xm8dD27H73byFVo7qW5Vlq4wa40uK7ZbVwSEkI0VuckU1AZj9FvvgeMEaKfme8tWcF6MIKjxPP/+uzY4Mwlo7nkC3BsQGy8CzeAQKfE831GdEW+VksbkLkuyLpvo8AKsYLPeMptO6SY9hCg4Pt4hij6i0Ibf+v1P+fTVJdrWYBWL2RLdHSNFBNrni88KZjdrjg5jqrRGVIL9cYI0KXIQ0m1Bupmxur5Gxz1u1in9TkxdeggRUeQG5Vl0DYvFhhevr8iKksPD9wijDq1Wn+PjY6aTKUVeM5+twFrWqxXBvu+KQ7PKPTgY8erla9L0q8PBvxZF4H5ffXl5Ta1LwkARh05nXWQZqmuosiW2WlMUhtqWbDc5V5dT1sucw/1jpFCMRyPyoiAvUsJAMRzvkOYpQRCRFxW+Z1ivViznK4IgxliQyr0x0iynqkqiKODmZs5kIpBiB10XbufsKY5PjonjNh9/8jnKiwhDS61TVusFJyd7mLpE2y1xyycMJZ5K3HAMiZY+WvhURlBWNbbUDl7iRyA9hPQQykNXGiNEk7brLNCh70Qyi/kSrGU46qO1RQrFfL5guVrT7fa5u7tzQaYNqkwbQ5qmBEFAlqUUZXF/elANqKOua6SnqI0mLwvXIiiFrJ12/75lcIXPGZ+EdIxC2yDE3FBPUzcyYaxuVoTgex6rxZy6GdJB4/7DHU6tteMtNusJ3fgFdKMhcHBSR+PN8owoBBc4It8WT3EPJRHSzQyaxaMrOO4WID0P6YWuXlmBEkHT0rhtQVUV5HnGYj7n7i5xw1SjyfOU+bJiZyekJuPx412CKCArVlSiJu62eXV6wWx6gZAhQdgl8U9IlyGv1huuXr3ki59MOdzt0usqfu67z6jGIaenS8pqw2C/zc1djjQRoUroJAm62jTR9oIis0gRI6WP57c4Pb1hvVpxcvKEo+NHhL5iu01ZLRZUZUWeZvR6PdqtDuvtmizNmVyvieP6K8/f16II5HnBzfUd1hpacRvfUwTKZ7acM+oNCT3J7O6Cqq4gKDG+YrNdkxeZiwOXcH5xznR2x2o5o6pyHj46wQ88VqstSbvHk6ePXWDDZsv+4SECD125vrCT9NDWcnv6hijyyfMtwiYUWUpdFpRlwWDYZzpZkrQTgiBkMStpdxL8IGSxKBiOYkytue1JlBDs7Y3odmKENFzd3bJebpGmZNcMQXrU1qXgtJXX6OdBej7aFo1X3gFFjHUqxKoyZFkGuIKU5VvM6TkH1oVrSCnZrLccHBxwdXVD4Htv9QBCuFTlWmtnTGpswdgv9/V5XhBEEe12m6IsMaZCC+3YDdaS5aUbCr59/jfrwQb5pXVNWRYOJ9b4KILACbOyLMNTihqDsdrBPKE5gLVjA2oXjuJWgc5KqK1BWDfQLKuKvCiR0nNGIOseHKLBkTlu4b168cu5gJTKJRwpD1TgOJHaoK3AF196OI11EvMsTbm6vGa8p4iiCF9KZAiZWbOtNuTlmk5H8Ys//4TesEuQRPz+Dz3+4IdnrLcleblitbqmihKisEWGx3pecHE2o9+PuLrbsrsXI72MIATRjhgMRmBqpFdT5imX51OMzpnMlmxWNUcHDyk0bNYVlxd3LFcrfvSjT/nud75NVlUMhzvk24zhcIhSHvP5nNvbO0ptydKCq7Mp77zz7leev69FEdBakxcVB3sHdDsd3rx6zW16RysO6LRb3FyfARVh5FPJilxItuvCeeuVY78b37BazinrHERNWaUsVwWbbcZgOGQwHAHgeQFFXnJ1cY2nYrabHKM9olYMuGjxMPTpdNoO0y0cYXg0HPLyxRvqCo5PTnj+5mPixGd32OHgIOLps0OEtQS+yw5wvn/J7e0NF1fXZGlFp+WzXG+JlJO3rtOcbtdQVDV5WTrACPeadtO0tAKtrYsWt4K60lxf3+L7Et8PuL6+YbFYYK0lCmNW6zVXV1ccHR660aJSFEVGUeS4K7ij7XiNv8DSKAa1k9eGQeTwZEK/XclpbdDGUYH1vST3/vre7N6rsmiu/zVKuEJltGa7StF17W4Upn67KjTNgE5rTdmEszrIihsa3k/7jbHNUFM0hcaJfMCFr9xzGrUxVLgtgbAW1QwWlXLaBCEVVkgMjjvgvpeHVAqUaWqbG1IulkuiVgDCoJSPihR3mztqCZ1hxPR6xoOjLqNxm7zO+OjZkFYMt9OK09M1dVawmM0oshZSdshtQF4lpFXFm+tr4o5k/7hPknjcLl5wvHtAIEuOdtsIs+X6ckk78UFHWFMShwMeHByT5gWt1oqDg2Nub6b89m//Lu+/+w6z6QStHWF6vV4iBLx+/YrDowM26ynGZETh1zx3wKGm3Bs/S0vmsxWmLqnynNfzG9LNnKOjMbq2UCkKIyjLEhlGhEmI73uoQFJXEaGBLF+z3S7ZpjVJu4OuDZeX1wTNE2GznXN5fc1773xEkWs22y3S8xgNB5wcHTnve5GDdbvyqirBGj766ANWyy1lLdnfn+AFljj2effdEzrdkDJL2Tvo04o6LBcrEJZWEtIb9PD8GklFVpRYpTGNoUY3/MKyqomioPEeuCHbPXm3KCrKqsaTbjW2TbcMwh6eH3B+fo7neeR5gRwozs/OATfwcrGAgvlySVXVTvyjHZDTVz5+4L09jGEQIVCkaUatmygx5YqEMS7xyfMDpybk/gnqZMNYN8HO09R9z8AnikJWi5TpdAqCLxOUmmGg/albSF3XWG0QQXMDaNKF7gEjSkqkFE2eX+HmMNJ7O+OotTMU1VK5VgrjNgPy3kzl5hBCSTwrMULhcd8OuJxKXZVYUWBlRWQVeV7QbnvErYib6QWb8o7Ia/H+o2+ijObm4oz5dMb17TkVklYgeXIycnFnkeWLz87Ic5+bm5Kz8ym6qlnnUAuDqmBebp3PxZvyRXTKoONzOI7ptAyhJ0jiiLgVc9zepTvYod8/QG6W7Owe8PTxI8oy5+L89G0gahwHRGFMp9NmvVkzHPYYjAWffv6KbreHtl/zmYCpNdlmxVm6djMjWSFCQ17XXEyuAdB3a4QU7B5G1MJdNzflnGG/i+/VCARxLLDGR9cCU1fUpSZJ2iwXKen2jH6/Q1Fsmc8mtGLFoB/jS8XN9ZzNcoUf1rRCifZ8FospgefTG+8Q+pYsnfP+ex+SpQW///0f8nO/8ISr63N6owAjS9JyS1mmtDoBStQYu6Hf6RL6HVA+i3XJejFH+AItHNdvVdaUyqOSilKot3FYZZpjaAI6PA9tBbVQVKZCaesizbRhk2asyw2RiMjKjHadkKYp3aTjDDbaUNcVWVoihDvwVV03rQZuDmGcGsEPYmoDZVaCEngYlOcGgLq2xGFM4Pl40jp3pqlAlwhj3UAw2yDqAi8Mmog0y3K9oag0vhdgbe4GdNagq5y6zqlqBzqttW3ChQTSCIdfb1DsQlokDgduhXVFs66wwrkutZWNH8FSy8q1B4rGeGVASfd7YVFS0IoCAm0xWmBN4R4+mRN9CZ1h5QZbQV0qPL8DniNOx2EbXdYuzyZUVLjAnCAc0ks6qEXBNrMc7o/54KOAb304pqoCPv/8mn/wD36PrFiw3NZMlilVrdBGUeYBufWYW0Ucenz+whKFHseH+3S7gjiJ2TvYw3RG6E1M5AkenEQYKxmOhlh8skKiZYuwPaC/t09epqymG1Lr8/Lylje3E94bjBBJ6yvP39eiCFhryTdLFusprXaIlTWr7Yajk4eodkRVK+5SQTvpUtoIYVM8pQhCjygUCFG4SruaoISkFQZIIZmXK1b1mqp0MtZ8myJVhacM7zw5xFMlcSiJg4CiyGm3FEpWWFOTbResK0MrDtjbHbBerynLOZPJDXU94cHDY5LugJ29HV68/IIwPiBoheyNh5RpSroReCoHv2b/oI3XzgmikjIvMZWFOCSXgtRYlmVFIcBTAoRPUWSNN96SxC6FuRYSIXxyI8AqVmlJrjU29JBhAEXJauOcgVpbiqxonpQ11jo1YFGlVNqRhu7VjaYBAllcKq8VTh/gCdOAUD2E8InDFr5SCGowBVYXSF0htMGWBTbfupjyBp662abkRU2728PWJfl2g68UpqowOqMqUsoyIysKitqtRsvS4CuLMAJlBMY2NmBr8aSEhsZUmoqirlxhQzXDVgPSoDwBzabF6YFcIKqwCk9ZfAnSQGELamGxtcDYEKl8lNKIeovOLVXRIU0N+Jb9vQdglsymd2ht6I9H+GGLF89PwYvY239Cv685Oz1n3BsyvfkxRVHR7Qx5dCz4xnsB3f4hN5MNL95oXp9NsUKhK0laOKn4OgvwvTZ66XOzmSPVljCO6HTXJJ1T9vfHHA89hi0HXe30uyxWCxDQSjpsiohZ6vH6zZQwGrGultQFVPKA11eWoP2vByryJ/ZqtZxZYrtN8UOPq9srkJIHDwM8FRIGLSBgZ2efOIKyKCnzjFYvoSot68WCoijYLNfUdUU7adHtdJFKuH7TuBXfcJgQxwFVtcGimczvKFKBVBF+5CNCgfElZ+dXzNcr2u0On7z4gqOjI+JOh1Jr8rpkd38P6QsePzoi6bRZLnr0ewnb7Zo0XdFvtzk63qfYbplOpuD7YEqydI3RhkGnhy+tOxzFhijykIM2Rhu8QDk2gbGYhu+flSVSSMIgJIxj8s0aYy1pWtPZH9NuJWRrQ55XhF5AXmjKckNd1y6gVEq0KSlqp1SsrEZjqYxu1mdNnHrtnpwGi5YGhRu8dbodByRp4KjmPl1ZCkcMrnUjxHErRdHIf9vtBF8JtssFSgm80Kc0pbMfawdXdWae8u36Vtgv03OttRi+RJEjJcI46Wxd66aPVxjj2inlKxQNn7ARJBljqHGkpvtWo6q1SysyNSBRDXAFaGjPAt8P3soMup0em9UGXVs+/fhT9vcOGQ33iKOIu5sLjDbs7e5ydnqGlII3L6+5urrmF37hlzElpJuUKMp4+uQB3f6QVucNKmihgogsLZhNVkwmK7TeUuSaRTrH4rYyl8KAqAmjgEEQ0o3clT+IAoQUeIFHnMTErZjeoMt8MafT7bikJAXZssN2rpnfvvzK8/e1KAIWgxWGVtJiudoQhgkWSZEZBwLJ3ZR8dzwGUTDNNyStHkmrR7otubuZEgQBWguq0rKotmRZjVWSOGlR5BVeEHPy8IiyXvP5Z6/Yph5S+AjbYtDtIT2frVjz4+efs1ltyYuc3BomtxO0kjx99IRtmRN1EshzlGfpJAFpuuDZOw8ZDHucn9Vs10ukrVBYtKmodckmnZFEHdotn7qsOTnZ4/TVK7ZVTl6sePz0iGyb8+LlC9r9IXK6osgd0rzSmtoIBxOVIVGrx2q5JVCKotIcJHtuVSdbCKUxCNKixFlyK3wcsaisCypd4UlBIAQi8LBKOiu0VBRVgdVO72+wLoHIc6Gr3U7XtQXaSXhpkD/WWHRVUWuDUB5CWYRUDTAlpJ20EFZTec7RJ42AKsPWNWXmfpWN3FgK0fgQau4zDYwxzbrvXhHcqP6EQ4rVunYafeOyKoVtAlHu/zxOwqCEamzSoiEcO12BVAKBwvNUk+34ZUakQBL4Eem24OY6pdsO8KTPi5evOD+74Vf/jV/j5MEDsrQkabd471vf5LPPPsP3JKP+MWWqiPwO5XaDEh5np68ZjsaMhhEPqyHDvT1anQ51rpnfLbm7XeD5CVUlmU5XrDcp682ass4pypI0X7AtehSrkusrFzAilUT6TYCrcgXB832M0RRFRaBit/6sqy+d2n/I62tRBPI8Iyu3dPtdFqdrHjx8RK8/wmiBDjy2qxlSSbbrFcaW5HnFzniH3d09Jnd3LJZbOokgDCKkEqTbDbPZCq+leDx4jMEQ+hGamtvJDet0TaU9xsM9TC0o6gxra2Y6Zb7d0u4kFGXG5PaGLM1orVc8EAZtNdeTO/Z2dlA2I/Jhudywu9tHVznWVlhbs1zNyTZb8q27nlsNgbR88O5TrDF0O12qfIc8b2NNjRcKWiqg3YsZD/aZrzNmL8+orSASsM1y2kkHjaTVGVBf3oKWlManlYxZr5YYG1GZAitAeJHb2QvT9Ns1ZeVaosD3CIVC+IFDrltDKL3mqSgIVeiGfhKCMKIVt2l3Onieu1FUZYHnuwyHqtaYe92+F+BZiecHhJEb1ko0Vb51wNIkdi2AhLrIyTebJrHZxcd7nkQpJwy6jynXpm5aFfFWmVm/lQy7HEcrNLW2jd+ARifgoEzSgrUSqXyk524C92nQvu9jPQewRXhvAadOsyLJ0gyjIK9L0k1FLxmRtHqcHD/i9nbCzfUt77//Ee+9+x7DwYiiSAl8ie8JHhy/RzveYXq7YrGY88F7HzKZz6irFKwgDEvaSU0cF+BBP+yy2w+J4i7tZMh6nbFNHT0qzbak+ZqizAnUPlUZcHNz40JjspSiKthmW7bbNVUJYZgABikqilQ3hfRLy/gf9vpaFAHlKcKWIk0Lh4aSPt/6xnf48Y8+RlISKI8wDlgv524g1IAl20kHJRQX55eURel4bsbieSFClBRVgbbu2qit5fzigizfOpWeJ0m6bW6vlpxfTKmQqJ2E4e4uy8WCvNa0BwP6wxFPnjwhiGPubm9J2h2kJzFZga5y+p0EXebcTiZcXV4QRxEY21iU51itSVp9VrMprSggSRyyejDoIkSPxWLBJl2xs7PDwyfHdJJdok6PEsP1zRQ/9vFCj02WNvFoEXG7S5EVhEno4trnS9apSxGurcb3FaXFhWxql8JUaWeasVJQo6hw0t5aG+IgwGJQShKEYePnL7BCEEQhXuA34A5Nmqa0o7CJCL/39gcI7aOEeosgw9MIE5LVGSIK8EzAqtw4dl7TMkggUArjeQgpUKpZjzYsAW00xjTXeqCsNGWlqY1FWyi1droDKxBKglTOdGRoskdcbiJNMpFFIjyJ3wTU1JRulWlcUXHSYqiriiwtSMsSLd1Qcnq3Yrtd0056PH0yZLlYsl5tsNaSZSk/+Yf/NUIYqjqn3FToSrKcpxgjODrcx/MltS2pbMWoH9JuWaJYY4ShncSMuhFpWhLIDcOOot+OEbLFfGFod0eMRgNq26cofVarFd1el812Q1mVvHr9krOzU3b39xmPRwShz8sXr7l4fUmeZYx3xoDl4m9P/tDz90eBivxfgL8M3Fprv9F87K8D7zWf0gcW1tpvN1TiT4DPmv/2W9ba/+BfXgYMldlisLSSiOl0ymq1ZrPeoLWmnUSEoYcVmuV6xWhnzHg0cuKUuubo6Iib6xuyLEVKSX84wAt8VvmMsqrxvZCqNlxcXVIUK6p6Q7ebUFuaLLiMSii6oosKQk7PL2i3Wox2dtiu1nh+wOXVFXc3N/yZX/01Pv/kY7LZDZ4U9Pp9ri+vmS3mZFmOEh7pNiXPSjw/Ii22VGWNMJp0tUEJSVG4Ch9FLtBDSsHxwxMmkwmIkIOHD7AeTKYrtmnJg8eP+YPv/Yh0m1FjGO/vcXd7x87uLnEnojQFq3TJznDIarVBlK7PN9ZSGu0Ohgaaw9S2itIoJ3u2AhnEGF0hlcAPInTDRyzqurl6a7TRpNsNUeSwYxLHJPSUj/FqhA5Q1jR5DwqMwfckxlMIT6IFWF2hqwJpIfR9N7REYAPfmX+wWElj9HLaBG1w8xErnJ4iL9EIKmPefo5F4HkBSNncGizaSDyaYaL0AOVMU1I4CAwOqqKtW4feQ02MdnL0VhDihS7kJssqLi5XpJsV3W7JRx9+iBIBr169oSpLdsZj6qpkb38PYwyFrrBW0W73iGOfKIwR1uIrSRzHRO0utdAEypDVGeu0II7aWF0jjGTQH6ONZbNZURcL4iCi05LM0zWVsSRdSdLR9IdddnZ3ePCox2RyQrfXZTqb4Xkevc4j9oZwe3fLYBARxxF/42//4afvXyl3wFr7P/qpIvF/AJY/9fkvrLXf/iN83S9LgNVokxK3OqxXGcvlnO9//3t40kfXJXHkIUSNH3jM6owo8olij8n0hjTNCIMQLxBkRYHQwjH4Wi1MBudnVygVoLX7S50vbjA2RxvBZLridrpgvS2pkGQ3d28HQM+ePiNdb7i5uqEdRiync44ODlAIOkmb9BZmdwuWiy3TxZy8qkjTElvnlEWNFBE7oy5ZK8PkNVVREgUxdVFzdXlDWZcEQaOKHI1p9/pM5guW6zmL7RoZ1PzZv/jLfPH5a7qdEd1elxfPX7F3uEMnafP8ech7773LOp0SxBYtMkSoyaoVdd1g07WlrG3jyzeOrGMFtQ2ocWhyJQRCRVjjpungY7QhStrYyjQBHq4332zWtHd37728Tt8hnMlJ+iECgxKu13bJQBVKWGgOv9UVReago0pIKtNAU4TE4th/96Yi07AUaiOaQFJLUdVkZY0VgqpJHbpPkBbiS8uw4wyCFQohfaQKXEK1kM3coYkva/IC79sfRyVyAzlrLL4KMNRM7u6wZounBGFkiaIOngrJi0vKKuP1m1PeffaMvCiI4xikZDQekiQRUhpaLZ+dnR2m8xvGgyH9nR4vT1+SliVWS9aLFVlQ4vsx1qR0uzW+7xG3QkbDPsJqri7OuFtpVqmh2+vy+vULojjmPf0ug8GAk5MjttstL18857PPPufZOw85Pu4zGjlF7cmD4688f3+s3AHhGo1/H/hz/78c+n/+paSglQS0kzZWK9qxReuCXjshLzRa5xhbkrR77Oz2CULBdOb6dZezZymrDG3c3nk+X7Af7KOUz2q9pcgzBIp33/uA5WrMD374u+S5ZbOu+OTjNyRJFy0k3ZbHMq/oJV32xnv8wetzVvMlwRMfXVa0/JAXn31BN0kI/Dabder6tarkbjZDKEWSjBBCsllv8GRNtikxRYWvfOpKMF8uuDi/Yfdgj8Fol8U6RfkR26xE+SHr9IpOt4sXGobjNuUna44efMDh0SF7B2MEikcPH7B7MOL4+JB//Jt/hw++9YC8mpKlGWEiqNYVaWHISmd0kjL8cgdvDVmhnWHKKMq6otACaxVJ3KasQcqQIPbA185XULtcvcOdXUxjRnL5CC4yzAANDYXAb/T4dYGuCqoiQ+uSIk9ZLRdMJ3dM76aNJ0BSlrWbLVhLbe3bpztN36+toNKWqtYUlaFobgl1Mx+4N1zVxqAapaE1rhC5VsBzMeo4BJm1Fl1pN1S8J/kIdxu4x6g7paJLgfI8pzvZbkukMIzGB9S1ZLFKiaMOrbjNb/zGP+bZO++y3rjUKXTNcOeYm+uc2WTKcPSQYBtijORg/4RNvkHYEKs1223Gzv4DkqTjYu3zilIbsiJDmxqhfKbTBZPpHZMl4LXpdXewuuD89JbryxlPnzzhvfffp91uo2hxd7Xk0bFAtgMeHu9TlAWh969PJ/ArwI219ouf+thjIcQfACvgf2et/cf/si/iBz4PH5xgTcCof4DVPtPbJaEfMBgmnJ+/wvMsiIper4U2BXd3M2qtiaLYhYqGHm0vZrVaEychta5oxW2S1pDLyztAMejt8c1vfovT01M8FbFYZixXW8pK8cGHH/HwvWNm0ymb1Yrnnz5nPV8x6g6osxJT1Nxd31IWBU8ePWKzrkgzzfXtlKKuWW3cXreXaqrCsl6XLGZXhJ5PKBXJIEGIkLwwtJI+R4ePePLsKYPRHvPlnMl0SRwn9AYd3nv/HcIgJggsUtZEISStDj/zM+9xc33HeK/L/uEIY2uSvuDJ42PaHcmrl6/pD1q8fHFGUQoCE5AVkOUuysxN+hM63QEqiED6ZGXGNiuxumQ82kGbmrjVot0NKbKMsqooF3P2Oz2qsqAs8sbi7Aw60pNIVbpeH4vyFOgKW9eNYCunyFIWizmTyYTNdguq4f836sWqrpsDz9uhoHNQflkEikpT1oaqNlTaoc7uY9IQ7sktagdwVVKhhAThWgGLwjRRbuDQb2V9n23QmKmEQal7iIttNh81nvSII7dym6/mhBd3RNHnTO5uePDghNGgx/7hCX7UYjQOmM0m3Fyfo0JFVeSo0Od2OuPlyzds0g2rVcnV7YzA79GKA67On3NytMN4Z5fbuwm3kwusyOn1euTphul8Q5YW+GGH4bCN8rt4ssVw0EJXiqvrK87e3BL7PZ6+85R+e5dvf+PnGfV22MzuiKTACxJef/GHzwPgj18E/grwn/7U76+AB9baqRDiZ4G/IYT4yFq7+uf/4E+Hj+zuJHRaXRcjZXykCGmdJO5K5nukWZ80XWOxaFtzN5lydXVNEIScnDxwQxftWICtdgvpKcqyYtg7pJOMWc5L1qstn3/2EmM0g/4e7W7Ex59+iucFtOIOO+M9OlFC76jN717+Dl9MPyOQil67TeSFHOztc3t9TbedcHdzx/XNgrwomUw3oBR+0AEUd7dr+r0e/d4us8mUbmdIttlyc7cgiNv0+jsMx3s8fPIMP4zo9UdM5gtm8yV92yeOY4JQMhx0MbXk0YMjtus56WbD/t4hus5JEkUUBbw5u6I/bBEnisfvHLLN5ihpmc2nCBEhRcJyXXNzu6TIa+IoYHd3l17fIa2tAOl7VNYQBgFWSvK8Igg03X6fhdak2xyrXTzYarmgvbOLNRqpvAZI4jYCtW4AI8agq4K6KijylCzbslwumM2mpFmKsRYvCJ0foaowzdVeN4nHUimHHdc12kClJWVtmiLghoKujaDp58VbTHtR1Sgp8ANJgMQKhUVhhcLwZe6ANs4RKRxO0hUSbZBSo6RCYBtPgjNIxa0EZMA2rfjixSnz5Zr1asl0vmJ3d8Sg3+X16RVxHHJ5ecf55Ski9Kjzkp3RmM+ev+Ds9WtGozGLRcHN7Yb5fM6jJ0+I4hGbTCBmGdvM4IcdvKDNzu4Rq+WcvNDEcZeqLhmEfVqdIZO7CUEQcnCwQ9IK0LXBU4LtesXezoijg30uzy+YbizP5+f4vs9m8986gn/8IiCE8IB/F/jZ+4818WNF8++/L4R4AbyLSyn6Z14/HT7y5GHflpkz92w2GXHsNcGMW+bzmZvmKw8vCEFYfF8Rx1ETkOlRa8P1zTVaW8bjXWpjsFKwu7vHel7gCUUcJizmK/7e3/11Hjza52D/iB/96GPG413GowNurm+x5PzMN7+FrQxFWnL08IRuu00QhpwcH1LmOcIarm9vWW1y1mmGlSFSeQSRu25ZpNM5aE3gb6kq4z7PWmbLDYNhn6TbQ0iPq+tbDIbeYIhFMJnPGY0Dbq9vubue8sG732BvPOazT74gjhK6SQtParBFYzLa8uDhA1aLBYHvMxr1CT2fPM0w1kfIhPWqQiDYrjN6na5bZ5qau+kdRZkRBj6lLuj1B6RlSpptaSctwjhC+T61yegkPW5ub9gJItd7S+fQoxHxCOmGcMI6s1WVZ1R5Spm7FOKiyNFaE8UxRVE7l6bnoYTCExLPgCkdl1E07EN3Q7CUlaAoa4qqoqwN2tL804BHcVxDF2BSY1AocLcDIZ1D82170WQbAEiBFB5Kyaa9cVwDYbRz9BnNPWzB90P8MAEVslwuKOspuq5YrF5wdXNHr9OmrHJacUSW5eR6ys7BMTeXV6y2Bek6Y+/wEfv7h2QF+H6X5fqW+aKk29vjbrphttjS63Z4+s4HGFOzWG7Is4r9vSOEsFxcnLPcTIg6EuGtiRPBcNCmlVTkeYkVS6SK8HzF1dUpWb6l1YrJphukhMPDwz/5IgD8BeBTa+35TxWGHWBmrdVCiCe43IGvlio1Lyl8pE4oCk3otdgd74E1zGYzrm9uuLq6Zm9/z4mBdM5ouEO3M2C1Xjeac4eTq2tLUWr8IOJgZ5dOu83ZiwuSVgtMgZSCskh5cHJCu52wszMG6zMcDlkul6zma4y27O8f8urFc5J2l+OTI85O39BqxYStmM3akYPy2lJbiReGICCIW2hdE/g+WZ6xmM7IthuqqKA3HNDudBESsqpEZikX11eEkc+DRw+Ik5hPP/sEX3pI4fHqxSllVvFg7zHb1ZYyzXl4+JBssyYIQ3SVMd3MMbok8Ed4qqAqSg72D8k7GcLAYr6llQxZLQuksGTbkjiK6PdDbu+WpNkMbTT7hw/odzsEkXLEGqnpDtqoIHBiGxzu/Ob2lgfP3iMInPNONsOze92xFA475tKHCqyp3Hwg8F0mQqdL1GqBDDAECFyCs01TpBcSVBVVWTUH1q39XCEwlLVxyPPaiYK0cVN9wz04yEWfuUAaJwCyDZGpNiBqi1W20Q04YpKUPlK6WDUlHWLM6ZVMQyd2DkUwCBHQ6Q0JWzPsassmdU5Gow2LVcbF5Q1g8JQijAL8xPLi9Jp0vUXbkMPdA77xwTfIsoyLi0ta7YSf+c4vsFiuWK0z1psNnXbSWKQdRerm+gYlYTQcEMchSgouZ68IWxnjwKPb9hj0FZYayNH1Fs9vM5vO+fjj32M03KfVGjOSLU5OThgO/xhkoT8sd8Ba+3/GpQ//p//cp/8q8L8XQlS4tu4/sPZfYF9qXoEf4Zkuq9WEKA7xZMjNzYTPP39OVhS4ZOyY6XSGNim+7yOFTxC0KEsDeBwdPUJbWG+29Ps7vPP0PdaTBVoX+CpkvZrTHw757nd+hk4vRAnLs3eeoLXAGIUQXVarBc9fvOTho8fMZzNmiwX9QY/FesVqu2qkx336Yczt4g5TabR0qb0tp3ukKHOqMqMoUrCand0Rj56+Q6k1SknSbIvyJIUuONo/4PHTR1zfXHF5fc63v/1t8vWCF5+fsjMYc3c1Y73ccLBzwMPjB5ydn+ErxXwy4er2yglggh7HR+/x5uUXBF7EfDsnzzI8aRh0Q+IwYDTsUhY1ZVZS1hWXVytqvcILfA6PhxweHDC9mxD7HTZLiwo0QjlmX1lV3N7doryQXq9LGAb/DBHYzQYENCEqSgp8T+IFPoYAU/vErRa9ft+tFr2IsNWjbhyB+s4NNP1GtGOKEiFMY6t2UBXXFljK+j77wDTKwSYB0TgrsC9lk5p8L2G+j3bXzhVpG7yqkCAsWgtUs1ZwNxzciqFRDmpdO+yZxcl9212svGO7TQkDt/pzfgufwHeCoyyvkEmbV6dTQt/H9zVj7XM321AUGaWGWPgcHJ1wM/0BEsHjBw8Jo4BWHLJZLVESklboBpbabRCSOML3Yb64cQ+zfE2nHXBwOOby7II4bGNMznRyyWjYQVBS6zVSWRAFy/Xtv3oR+IrcAay1f/UP+dh/xlcFnv0LXlL6KNtG2pTTV+dstylWwXS6wA8iHjx8h7qyzKZb2h2PsjTcXF/j+QEGQRDGjMY7ZHnJfJGC8EEEbDcbMFUjRfY4OtzDDyWb9YLeMKHTjmi3B2zTAs/LyPKMH//kY/7cn/+zHD96yOXFGaeXF6zzDF2XaKOJ6xbdzoD2oMcqy8mrkjTbUpmKMPTxJQy7Hdr7Y/LNhsGgQxD7KBm4+Ua1oT/s4gWKdjfh+vaS3/nd3+Ly4oyf+ZmPsEYSB13GgwOKtMYjIPJi8k1OXdQsqwU//vSHzJZzxnu77Bz9HEm8x3b9GbEvmNzOuLu+YWdnhCc1e+Mu+/tH1LXl5vqWi8sr4sjieRV+IBGqxAs0XqAZ7XSxNme1nVLrh2hryPOczWLL02fv0u12iYIQYW2zVnPkHtFQewUGJQXS9zHWp6wysC4Uo93pYIzBjxLCTFM3seSeH3L65pQyr5yrUWhAIqxCNKGhlXHDwLIZIupmRYl0SUiuINEc5vutgkVrN/mXooGNNEtFKZw/QGtLLZrQE5yS8D5s/f5GoHUN1uCHMd3eED+4BJEilI9FUVUV7d6Q0PcB69R8a0ebzrIKQYaSt7x+fcFw0CMMfWpryT/7lOcvX3C0t0fvyRPHSLQ1vrIoBcmgg65LPKGpsjWTyYSbu1uMpxn0+8ymC6S45tHJQ3zVop30KfKK9apgd+eEPC8wVpDnOa9ev6As/+QDSf9EX7rSREEPjw1ffP6K12eC0d6QIIhoJV18P0IpRZppwsgDq3j58g1CeiTtDkcnDxF4pOkGaxRGC1aLLVKA1iWBL9nb3WM06rNazyjKjKr2WC5ntJI2nU6LzSajlSS8ePWSsqpptd2VOCtLirqm02ljMdzOpmRljREhcRKz2lQusDMKkAo8TzIY9mkFPptAUdcFi9Wc3aNDlqsl55fnZPmGJ+88ppVEPH/xORdX5ySdFucXp3SDAd/48NuEXoAnQ5QvuL68Jt9m+KHH9eSa3/7N3yPpxXhBQL6VvH5xzeefvmG12wGrGI928CSUxZZBv0vS8gj8kMXsBt/XPHq0D0ENStJq++Tlmv4gwQsgjD3WqxXbdAtSUlbOnlvVNVHkWIPyLSfM/cs9DORL8nDzMSkJggDPu9e0W1oogrQiywuSuMVwtIOxgjcvX1NlRbM1aDBj2lKUFWVZOcjK/UBQGzTGZQxK1ZiZGgOQtU5p2ESdKXs/D/jSi2CFQQoPIdzKsK412BpjBB4K4QnEfSvA/b4AhqMRnW6PoiEjV1UJCKrKEPjSZS1WGgoI/YCyKthsK4rsBqsL1pstxpT0+m0QsFjMyNdrdJbx4Qfv0h906XZaGF26jMxsgzAF08mE169fMS1S9h8+YNg7Id8ors6XpMs37Ix2qaIQW3tI2yHdCNrtIZUpiFttsjTj7PzsK8/f16II1Lrm8uaCL158zsvXF+wfjcmqO3b39zg4OKI2Gj8UJF3JbHJBv2WxVUVaZfhRhPJ8hOcRRzHDrmXU6uCVNaNen+14QF4XyCBla6/QUUmdp2wL2GYpEoMyNYu7K+LWDk+PH+LXFk/6xAg2WY7JS9o7O5SmZjKdMZtfEgc7+GHIXmsXz4N+N8HUhRM3hYqqKEjaCdbCdrsgzVqstwuW6YqbxQTViQm6bRZpTtzu0mm1MDUU5Dx9/x0wlsV0wWwy583pKbu7eyTtFi9PL1llGi9RVLUiT6+Zb5dsNze83pzx7jsP2B3sMZ9NoK4pthtuL84x2nLx+jVKSj569oh3Hh+8XQfe3N4hdE25rrFlji0r0ukcUVUEgaTIa0QgMUo4JiIhWJdmbKkd5KMJ+tS1dmlKKKwXoWLl8gn9shH1KKRfg5eR9Aa02j0qFTNZlmwvr7FCUeqczMCmrkhrzaYsqHSNpQl+bQ60lNahxBoDkpGS0gU2II1G6gqpFJ6VSKMcZNg4lJumRuGUiG9dkUJSW4kwAt86D4FDlAm0qeh024zHY5bLJUWRU9capQSrzZpNtsZaDcrgK4muS8qyIFeStK5pxSG3izVGV2xrZ35SSrJcTsAa3nn/HUTgYZQE5bPdrpit17C0XF5ccH51hwx80kXKZpASR23m8w15ZZitNuSloS4rkIrLq2uOjx6wTjVxK+Dhww+xJMBv/KHn72tRBMq64Mdf/IBXr9/w5vyWVrdPIgLm8y2zxZLd/QFZuebgKGGiAzxjeP/ZU9ZlRS0UMlAIKdjZGZJ6isSz9AKB9EM63YRssWCZLyiDiEIbKiFY3q1YztdEfogylk7okwQhyXgMWcp7zx6j1zPOLwsCGyLqijLPqMua1WpNFYb0uj3ef/ddTJ0zn1zjSYijEGEdzKPdGyKEZL65Ybud0mq32Dnc5WoyZ7pJuf29PyAJQ6QX0+0O2B2OWK1mhG1FXVmul3f85LPPmE2XbAE1VSyWG3aPHuD7Pp3eHjsjw+lmwgfv73N9cU7oG0JfsDMY4CmPqqxYTmZsNinZKmM8HtKPXEuTpSuWqwViPScvwQvamFxTZa4IBFFIGAgmNqO720cr6VotGzgJsikwtkTYEluXzUrNYppkJi3Bep4DkKAQ1jjJsBYEocFrt4nHuxyohMPLBfNFQVUtqYXPVhvWWrPWFZuqKSAWrHHrRCcmEC4GruEcGeUIVUI1RaCuUJ6k0qJpCzyEFejaJUBXMnfJwZ7C9wKkHzYGpAZPjtMbKCWp0HhewO7umPPzc5bLFUoJXBS9JstWhKGHF3p4VFS6dlsSX2GMJdfGqVujCFMK8lyjpKTlhSSDEbX02JY1y2wLGLI8526dsd1sWW5LCq/FKImp84LzN6d0Bj1anRZFXZKvp9zObpEWIi/ADySrzYbLq5IsvSbwe3znu7/MV6UHfi2KQFGWnJ6fU5Q1SMX55TW7ZoeiKknLjGfVI7r9GM+L2Ds4QRlJJ2hRb7eIIGBnZ4TWJcpTBLFlsrxE2zV1oZgtlpS1Rnk+ddkw44KQeTpF14LVMmV+d8toMCJuRbx8cUavJ+kPQkbjFqgReIJtUWBWBdHaEsdQZmv6vX329oZcnp+yXC6wdc3OaIwnPJT03f7WV0RhhCcVnXabXidnm1WYuuby4pJRr083Dgn9EF95WAPz+ZKbmwl5XrJeb7m5uWWzyXjw4CHWgud5jEYjhsMB8/nc5RI+eYTQ2gV11gbP8/GUT7otCAKfpN0jyyHLa66uJmy3Eel2xWRyh1ABrVZEUYFQAuX7lGWJwbDZbgiCgDAMabXbjlHgNS4/7Xp7YZyXwAKyyQgsjXZBrliUCLDS9deOEWIQQrmf0VcMRwPe++BdJpeXfD6bUBtDXpakWUGWVVS167GtthitG+6AazekuqcHiEYyTBNvbqiFdjHoypGLHYrAWQ21dWIgbSyRdOtm2Uikgbf4M2vdCtL3HJh1PB6xu7vDdDpz60UFSilHrcaZn1TTzgjhBEpaV6TblKKqsAik8vH9EF1XtNs9ikJzenrFbB6TZY51oeuK+WLBfL7AaIPvB7hYzID1OqVC8uDRQ374kx8TBiGtuEW720UZODl6wKuX50g0p29eEfiWv/yX/uJXnr+vRRHwfd853iwcHO0zmc54c3bJ7t6YpNfle9//MY+fnuB5UG23RF7g1k1Ccnh4wNHhHs+ff871+pZOO6IwK744fYNfjalKyHRO2BUIz6PbGeJ7CbLucFXecH054ez1Ke89e8Le0YBWG4pqzun5Z6T5lDDSjHd3yHRFsgpYrG9ZrzVxz+fR4wPKckOebTk8OOT2+paiaIY70me7yVCqoDOI0UXN9HbC5OaWPM2xBnaHIzqtFrYsKdKcu/wWbTWff/aC6+s7Dg5cxFS/nzsIaOj20K1WxGDQByw/+v4PSdMNe6MRnU4XYTXb9Qbf8yjyDWVekrQUnheSFTWb9Ybl0oOLGqMrtKkZjXsYoSjqAiME3UEfnRsuz07ZlCmtXu/t90/TtNHEe18eEuHe2EboZjBXoa1oGP8S5UnAc6s5I6jrohnoC3Rd4QeK4wcHfPTN9zl9/dKFfZYV621GVUmMdZkIutZN+KibP2jHOXJtiTVgVAMyFdQalHDDQW1EE7AiXeugBOrt9N+xLSttELp2swVrqU2NrErKykPUFZ51JqBuO+Fgb4+z03OKxsBWG4sQzd+HgU26dZizn9JS1HXdFASnaVCeojY1upacn99xcXHrouUDSRj6aF2Tpimr1RohFXEUU6U5SiqQAq8WDEf7PHpccnV1SZJ0OTp8wN3NDZW2LBZLTCXRVUpdppy9ef6V5+9rUQSSJOHX/uyf5Td/83eZrzK2RYlFkJUaIzwuriYsNhs6nRbDXkToSb71+IS9/X0skK1X2Lpku1kS+JqkF7MtlqxnKfnaMN9OaA9DbGBZLSqOT94hifoIs6AVD/jlX3pMEBjiWPLg0SGfffoTynLF3t6YNM3IshUVFl2mFNs1voTjkzHWpCzmW3Z3x6zma6TwKAtDNEgYDgak2y3WanYGXW5n18yXC8ptRidOkMpjb28XhaDcbhHacvbmDe1+n1dnZ0zuZmzWBUmrw3e+c8LBwSGvX79GSsn+/i6r1ZIiTylS9/UCP2A0HDK5u8UKiZQB2lQYoZgu1oSBRqMotUDnhsV8QV0WaFNT6MCl8XYH1NawzTKMrqjqstHUQ7fbYTa5ozYxYdDFD1Rz+BUSF2qKdk/7WoPBCXWMFXjKd7t4IZBaYG3mhvueoqpLjK0JY4/3PnqX1y9fcHp+xnqbgfKhvh/2uZDU+9WkweL2CADWwUmsm+5bA0bYtwlRLpDYDQel56G8JofAvzdHWcqqAiHw/CYFqclAlEog8gDrB/hBiC4rBr0+O+MxF5eXFEWBMRbPD5ufyGJ0ThCGBEHoWiTtICV+k/ZU14ayTCnLituycng2DHGc4wf30XNO5HVPfC6LCmrNxfWMqq45PPZYrQuePH4PrUUjglJ4QczF9R2L1ZrTL86R1Pge/JN/9N985fn7WhQBIQV7xwdsy4z5eokfRbSSDp3+AINiZ/eQi6szJrMZR7/8DXpdj0KvyPKI7WqL0ZZu3KLlexR1wWQ65/JmxeZ0hSg9tDIkUY9KVCynWzqtFEnNd7/z8+yPdzjYHbPeTDByRlFU+H6Lly9Pnf8eTZ5rvDDAWsHBeJ8HRyFJf8iLl5/x6MEznjx+xD98+RskSZd+d4CSUJYFnifYGe3QH7TYbJbULUsUJRghuZ3MuD2/wmrNqNenFUasZksurie8Ob8AocizN/zMt75DkVdYA4cHR/iBwpiaV69e4inBQb+LAOKwxWAwZDab4wcxy/UG3wsZ9XcocteT5/UK4eVOeKM9sqJim5b0d3yCuIsMfIQpWE1XjFtdHj19wmKzIKs1/X6Pm+trOt1D93QTwFvmv3Q7e+lShI0VSBU44Y/AUYekdIQfIVBeTCgNfhBghCbLt2jl0ekn/NKv/CI3kymvLq/Js5xtWjkQSJPWrLXbEghpkcblEtwv+YV1Q0Nh3D7ACNvgy5tEMgRSevi+i38PbIQ1hqoqMVY3aUZOPeiUiA5sousSkecEyifbbEniiJPDI6YTJ4UOgoiwFZGXGVVV0ooT4lYLz/fZbFyP73IeGuKRtuR5ied7VJUl8GKs1biEOPHWT6GUT6/XRQpFnpdkpUFuNGVdYS6nbP7rf8Ljp08wuuLq8oKb2ymHe/ustyl7B/ucfXFBECjmszuK/F+DbPhP8lVWJRc35yy3SzrDLse9EYP+kNF4xGq94Nn77xO0AiazK+KOz3A3oNtRzGcXTC5njPt7ZJVhvcnYlgUvTl8zXWTEaZtu6NFudzjaOwIfXp6dYUrnImuFLYq8xPcDlBdS5D5aB3Tbe5zVN7x6dcM7T58Qh21WqzVWKHZ6J7R7XbZ6RbrZEPoBWZozHI4QeC7x12pqXVBXKauNxtoEX3qcHB6B5/Pq9JQqy9nMl2Ase/0hw+6Q0WDE8uIKKQK6vT5Jq8NwMOb7P/g+f+O/+C/5s3/u13j0+AHGVPR7PcbjEX6Zcnd3y6tXb4iiGM8LG9PNBmM0XRXSH/TZpjmaFSoMWazmrNKCbFsSBAmD8R6VgU8//gn9UZu446K5pQC1UKzSDGM0fuC9NQm5fte93CFzrj0hfYQvUMrFvEurUF6IELirrAbPi5G+0xoYU1GUW7QFvwUnTx7w5//Nv8Dry2v+1t/5J2y3OdJzUFkrJJWum9WfQGgDtcZIgd+YAASuQIBFGRphkOduJlZghXLZj0rhu2hl/LrC6BqpAFybgLBN4XE+AmksnnDJ05Hvc3x40PAfoLIGL/Sp6ppKGOdd8F3EHDbF90IsgrzIUcrDUx5x7GZEVVFR5SVVZRDS4gUBcRITaMda8IOEqnE9ZnmNEBJjFZPZltOLW16fXtHrd5jPJigpePjghDzd8uG77/Lu+4/JsxTlQRQFX3n+vhZFwGIpdMrxw30EAe3OkPF4D7C8OX/F3uGY8c6I8V6Hwchns72g7SuUEbSDgEQFfPLpC16fXRG0u4Rej2998D4D20NnBYv0jrp0FTpQPtO7Cb3ODrc31yRJRFnu8fyL5xijGY1G5Pk5H37483hS8OTRI0eP2b4kilqcHD8gjEIuFi/46L2PGA/HhEHEkyfvcH19x3x6x8OHh/S7IavFDXm+YTHPCIOEUX+AlYpPNimmKPCFpN1JSMKYfJMhEDx59A5+1AEr6XS6YAVxmCBFxHw6I0u3dLsJSTsm8H1aqs0yWHJ2ek5Va7r9LvPlkizN8b0QP9ywM2oRRjFhKwJP4fsRR8cPUfgEYcDewZi0XDLORxw/2qfVDqjmFWVeUFvN4yePsVikbPJ6lGqgn+6qarBOqmsdKFUaTVXmSOXheyHS89z6zFqQliBKIHAxX9o2aUdaU+YSaSVP33nC/+Df+3eZrFJ+63d+yHaTNU93l02IG8qjrUtBVtYZi0IRuGyGZoXo6oJoQlbEW8+BReAmeu4mE/g+WI0Q1tl3dflltJp2UetRYDBlhR+EoDWdVsQHH76HEZbLmxuKvKSuDL4KkdpQNRH2ZVHi+Q5OYg1o60Jxle9jDCjPJzM5QRQSxyGeLwkClxKltQWpyIqULC+II4/VtnIeB+VuVItlyjYtkNKyXi9YzNdYodluMnp+QLfbptVp0e21v/L8fU2KgGG2vOXgZIdW3KfINf1BQhRFPHvvCaOdAe1eQL8XE8drpjczJtd3PNh5xDe/8202s5zFcIs0MUYF7D94yP7JCaOoS75e8eL0M1pdn3a/wzP/CWleM59tkcIgpeHN6XNubi7BBoxHe+yMj2m3Yx48OGZ/d4+XL15wdPCYJOkgheL6ckpmKqKwy2Zd0G4rzq/OMVrz3ofPCAOLtSmdfsBh+4DlXYkpfQIvYLFaM7m+odimHB2d8OjkAbvjXTbrNboyBLHTtFelexO9fv0aEHzrW+/zK7/yb/Bbv/1PWa83HB8fUlUVs8UKowV1Ddc3t2zyjMVqhRSKvd0uaV5wN5nRHwwYjges1huCwDDq7aKkTxiFRLFPV0WMD9vEHY/nrz7j9mxKkrSo6or9gz2SpMV0tqTdOXjLq3N5gAolQTbUXyEAU2PKEqSP8v2G2GNBOOhp1AqwvqUQWwQCT7oQ9roq2ORzgmTIz//8zyFURJb+J/z+935AVd8nFEmCwHeHyRhHLNZNcKqvENYVKk+5zYRuzGQItxGojKEytlEGuhZCKeluKRhHWlKga0FVWWqtoSgIvBysIJGK0hj8KOb44ID5YsFsPme1XqOrmlarhScMVVVRliV5ViCKCovF9wNHOa7cFifPckbDIa0kwvMkSbvl2hwhqKryy5yFyq0cRQFJK6Y/aLPZrqnqgrI01LUhDH3KyuU6JO2Es6tbrrRhNBoiFTx8+MeAivz/41UUOVoUfOub3+H55294/eYVOzsjzi+uCHwIA+f42tsdYyuF7D1kls7wbRupfSgLfvab32Gx3fLF6zc8Ot6lN+pgC8Pe0ZjDJ0O0yLm8vmS0MwQRcH014+bmluPjAUni4/mam+s7FotdfuVXf4nv/cHvkRUp13fX/ODHP2BnuEsYRfzkxz9ACEF3r4U1HqtVRm2vSTqJs2sqjfAtWbom3UyYr2paYpdhd4dWnLDNMp4+fkKaZagmcTfLMtbLNVi4vLyiyJ1Kbm9vn+024+bmnDzPMUbT63Z5+s5j0u2G29srBn6EH4TErTbD3SGL9YL+YMRmkxLGLVpRm/Viw2q9YvdoRLsTcbuZsFrP6HX6LBYrRn4fa0s+e/kxo70ucRzQ7rYRQhCFivOLc1rJkLjVZdjvI4VEGEfmkUKi63vSkGiMN673Fp57ZFe6RNc1nvJQnocwEu3V+F6A0c1Tr6owZYUnDMVmjagsH7z3lP/On/8zvHp1xmR67eYNtcFvBVBbB0+1xoFPtMZ6wv0sVuA3Ia5V5cRL0ncMwtpYKq3xcSpD01iGhVIubNUKFBLP95q4+hI0FFmGFJIiz/CjECEMZZFyfLBPWZZUdcViuXKrSwTSOhBqFEdUZUXUarHZbKm1JgwVvucRBiF1XaI8QRAppDSUVe7wc2mKlIpW3EZ5As93K1Xlh2yzgna7RxB4DiXfAEjSNEUoQZqVBF4ESjBZZnR7CZd3y688f1+LIhCEAcNBm73dEZfnlwwGHbbbBYEvaEUtQh88JdFVwTDZpyMGTF59n80ClkHJ6Ztz9nZ3GOwM6PcknXZNt11gwpiqKlivlmzzBfPFhLTY8vrVJXmuee+990gSjzCCb37rGcPhhLvZa1bbI548O+D7P/geB/v7fPdPfchPfvQxKqgIIo2Qina7Tz6bE0WSo+NDWp2Ei8tTZATT5S3XFy+II8HB7hhfB7w+PeOzF8+JopDxeEiRl6xWa9I0ZdMM8dI0B2Pd/1YpKHP3+4cnD7i6uuL73/s+abbh2TvvMJ1MOT+75N1f+GVUoDh+9JDJak4+m7ItVvhewHqzYdQf4w0Uz198jhdbHj15iK0HBF5Enm65u7vhdnKK35KEgUe326GsM7yWT57mDLt9NtsNy9WCd548ctfxRrsvGjxXVTtNgBBuYiiVe8Naq5DCUtcNChw3G/NCH+m5LARdubwDaQW2rihqjaGm2qRYFfIrv/RzbNYz/tpf+8+5vLmlHXuYunJDQG1QviIMAvKyoipLvMYZWNc1UjqqcKVr5zFQksALUb6HFu49pYRESpBK3puNnUzYOOqxafJPjLBNcGqFZzyqMsegaSctTo4P2KYbTs8u2G4zqGuqqsJXnms3q9pJplsxxliiOHbEIq2pqwILqNo4/YWtkdISBArPC5p1oQuC0TVuJlK7hOFWa0g7apHlIWHoEQQBk9mEqi6pDGjpue3Pcst0nX7l+ftaFAGsZbmY88UXn2Kqig/efUaWloz3dgijkL29HZaLCTfXN/T8IYHs0u8+JFSSF68v+OzzT5lvp3xn8CFP3t+nqGe8fPkFRyc/S9Tt8+Pf/4LTi5cMBn02Z2ecn93Q7Y4Q1JyevmB3b8C77z5ld7fH3/v7L/j409/i8OSQTXHLupCouCLqaHaP2rR7xwwGY96czimKnMfvPiWIPN5cvGSbrfCiDmESYpWgqg1+1GY7Kbm6vmabbukP+nR7HQSCvf09ZtMFp1dn7O7uo4KIk90dLq6vGI1HvPvsPZbLNe2kwy/8wp/iJz/5MbUuub6+ZrFYsr+/z+fPX7F/tM9eOyErStpdl0yT5inddpe7u2t8PHqdNkWW8frFS4cvLysuzi+alZjBaoWvAmZ3M6Qn8AOfwaBP4HlcXt4QhiHdbpdmKc/9VNDYe/GPaGTE7jqr1P0cwCXf3GcJWMAq6/px64Q7Cg+FQ7pbrbG4FKhcb4iCHv/OX/qLCF3w//zP/i6n59fU1FgJYSBRgY+ULkXKlE7846PQpqKqLbXxKevCRZHVlkAEaGEwukTiICaOmipppgVIXMR544CA2nEIbBPGKtCYusDYCqUk/U7C+8+eIrB8/JNPUEoSRYFDmdUVSgqCICQIfNabDQKIopAwDKh1QVFusNZFqWldu9Yg6eEHEUbDdpu6jwcxnhcQhSF1VTKdOK5B0o4JgwFhEIGVhGHL6TaMQKmAokjJt1/zIlBVFevFCnUkefb0HeKow9XFDS++eMHhwR67wz5ojYdgvVwhqop2b8BidsfF7BbZDlnrDZPtLR8+e4/1OmeTZaTlmk1pSPodummPoi75+NOPCf2EskxRCgJPspjecn2liOKAR492CJOQTt/j0Tv7LJdLjIgYH/WZrK65vb4jTHzmyzsG4y55uaJYr7GqoCIlraDbTmj3xsxvb1kuKjbLGuF59AYD+sM+e7s71FqTphnb3IWS5kVFmLTQpuKdZ495+vQZ//Q3fpvLy2t+/ud/gYODfT799BM8z+Py8pL1esV49BRPS6ra8ONPPmW2nHL44IDeYMDt1RXKcxjvMk0p0pR1tmK5WXKwt8fTx0/otSMWyyUWQ609hIR+3Kfb7TJdLCiqguls0jjyapbLBf1uB1rOqmu1BgtKuZgzY5rhn3WKO6wDhALNE9cdNEP9dg3neQHKjzBFjdQSX4AvBcoXKC0o0iVBEPLf/Qu/wqDX4m/8l3+bz56fU1FjUeRVTVqWhJEPzUqvqlyKkRAuuLYsnZrUYsmLHNEQkZUN3c/fgEmlcDHlWIFogCNKehipsbV2eQtSNrMPQxQnjAZd+sMR2kISh4i65vd+/3uEUYzvBxRFQRTFBFGMtZYwChENwXmzWSOlcfJja6hLQ1UVzW0LN2w1omE3Bk5XURX4cYSPR124KPgql2SblLKukELRTjpU2lAUhlYcunWpMfyzPOAvX1+LIlBXNZPrGS/813zwl75N5MdMr+c8Pn7ENz58H0vN65vPWS0XvJq8YrtxeOy4FfLomw+BQyaTCy4Wt2R/sAGTc3t9Tm5zdg+fsLvbZ//oiND32a4zQr9Fvztkf3eXberz8SffJ82XPHny0F3VdcnN7R2v3rzm4vKaX/nVHRCSz148ZzVf4fkR3f4I6SvObl8zPhjRGye0x7uslhtmyzWd7i5V5rPeeNzeTrHacjjeR/keRV1R1RWn52eURc1gPHLMxNqgAsuxOuL29pLJ9Jpf+sU/Tafb49d//dcpioKT4xMuLy9YW4GQij/1p36ZTz7/Caeff0xlS/xpgOdLbu9uSFdLjnf2ib2QMi/ACHwRoBpNfOC7a60Q9ynGHmoQUmeSuNXi8uqMu9tbjvZPuLm5QdgO0bMdIgBrKesK4dFkErwNNWxuCa5wWKOd9VjIBkwKRjiEuQU8FWBlTaUzlHXjOmM0SPBDSTeKWa+2xJ7iz/3KL9JpJ/ytv/13+YMffsJqW9BJ2rQ7ivU6ddiwuiEJK4cSs6XFqzxUrfDwkGWOH3goP3aUawvWNMnHSjUhy6bZ6TeA0maZ4Hnu1qSNIfA9jg93ef/DD9nZ22OzzhgP+pwcHlCUFXd3d2RFjm8drmw2mzTbHkut3czHGIsfuJmKaWjHZVnhFIZgtPv+urZ4niKOQ/zAmdN0VdGOW3gSoiiCJjWp1UpQymOzXaO1RYkI3/NohdFXnr8/ClTkBIcbdzs7+E+stf+xEGII/HXgEfAa+PettfOGQPwfA/8WkAJ/1Vr7vX/R91DSY9zfY3d0wORqynKxYW9nj8ALaMddXr36gnSVEfo+YavE6xQUtSYZBOy9O2Cz3nKXeVRasMg1kQjZpgGT7YKdE8V8vcZULm7ruz/78/gyoshKnj9/wd7egPFozKtXXxBHbTr9HutsQ64LNlvNJtX8jb/13/DNjz4kiPq8/9EzyrxgtrwhK1MKSipCLu/u2Nnb4+L6nOU057sf/gLHxwdMrmaU9YTtes5gOGC1WbJYLeh02lR1jVCK3f19bqYLoqRFtxtyc33B1cUF40GfTjvmN/7RPyLPC5TyydsJ7VZC1SmZ3Nzx/MUrPv38OcbCwfERwjNsNkuCMKAsMqq6YNRp4wuBKAztVo8kCtksVqxXC0adLl4QgAqwwueTH72g0+3z0S8/pTfs4CnpAlVwPXHd+OtdxHmF73nOR1DfP33v7TyWuqodzFM0jYC1IF07YLTT82MkQisCFaECAXVOZWqEcJpAhaKXBGzSnCzNefLokP/+v/VvEoYR//R3foSxLlZdh5rcVhhTYmwTg95wT4qiQEhBFEUI6VKMQ+Hcq0I1DALZqB+VcnoDY5sU5Xsrsmt1kIJWEjIc9nj69CFHh7skSZt2FNNpt+j3OvyVv/I/5u/9V3+Pj3/yMdYafN8jn27xPOVs0M3tKAwjJFCW2mkSjHYZFUK6GDvPoISHr0I85eOSt0OqIqOuCogi0nTrQKnNzxeGEaWuKYvSBcDWJbauUF8dQPRHugnUwP/aWvs9IUQH+H0hxN8H/irwD6y1/5EQ4j8E/kPgfwP893BYsWfALwD/p+bXf0ERUHgy5me//XOcvrzkB7//I/7yX37E3niHKi/INhm2tkSRz8FJQvewjcZjkxtW+pag1+EX/9yvoWqP1c2csy/eEMeGceJRact6tmJ33OPl6zc8PnmCNIbr82uW8yndTsTuzh5Xl5fYOmJ6m1JaDWGE1TFl4fE7v/Oa0eCIdqtLXngIJIORpK9iTq9f8/L1x6zzlDcXr7m7WSPqNnkp6HbHDAYdyoOCz9cz8rKg1YoYDQccHR4AksVyTbffYzzeYXf/gCKb4XkGJT329g750Y9/SFkWjMdjrq9vuLm54fj4iDAMubi44PTigqrWxK22e9Komvn8DoQlCAPiMGCzXjG7W4JsuY3DKiWQgmGvT1WXbLOcpN2i1R5wWk0IvQ5CKrq9DuO9XaqsJo4j4jh2B6aJAtfGILUj/DpDjzv89wlRuq7wlOMROp+B25VbaUC4zAGMxZMeftBCWYkxtdMXBC5NuC5z6kqTxCG11igsH33wPv3BDrv7x/zm7/w+nz9/g/Jdjy2lQL+1BoPnK4zVbNONYwlgUL7ECz2UNMgm/szyZQaCkPJLRgJgpUSjMRiiOOb4+IjHT07Y2RnjKUlVZEjp0UlaVHnBw0cnfPe73wYLn33+OWEUsjMeU9YuTwAhyfKcbbp2ce+eoxLVxq37jNbUlUVrSRwpR97yQtJsjShrtK4IfEWZp0hryNPUZQ4ajReFFHXl4t2EpcxS8iJzWZb/qkXAWnuFowhjrV0LIT4BjoB/G/gzzaf9X4F/2BSBfxv4v1nHpf4tIURfCHHQfJ0/vAh4AWUFz59/wbN3ntIfJMwXU3Rl2d/dZ2/nAZO7O9J0xmJzxqTcELb6eGGfJEjwg4jRcJ/l7ZZXp5/z6Sdv3NVbSc7Orlkubhn0vsXB/jHGwPnpOQqPR4+fcns7pbrckLS6TG+2/ODHP2B8OObBs8dUpaAqJMeHQ5Rs89nnZ8ymGx4/Pma/k1DqzLn6lMezd55xO1nQii391iG2BqziYO8IXS45PY1pJTF7ewPiyEd6FYdHA+KWTxi6a2ae5sShR29/yN1kzmjY5ezsDf1hTG03fPCNJ3Q7fccZrOHg6JBAxczXO7x484L5fEnU8jG1AOMOsQXyIqfXa6ONT1WlSDR1XRDFHTbTDcvliizXHMc9Hj44IQhazO7mvHr5nCj08aXPcrWg086w1Ji6oCwrhxtHgLbOeNMQOIwxVFXtTELqXorbzAesQYhmV+AW/O4qLl1KkVYS4UeokGZSDp5vMQhaiUR6Adui5GBvwL/37/xl9vd2+Ov/r/+C09MpKEUYhmjrQkql8gjCGG0MRZ4jVem2GiLF9wNCKdB+gFUeRhtqNFbYBk7uVoVvvQoNkizptHn48ISnTx5hTU3gS4RQ1JVuhqk9ZlnBt3/mG3TbCev1guurG7AGT0rSPEd5bpKvvEZhauxbjLtEgrTN6rWmyF0RNGaLCi0qcLMOIQXbbeqArxhXDIrcCafcRYO6rt3/F2WBMX+MIvDTryaE5DvAbwN7P3Wwr3HtArgC8dMYk/PmY19ZBIz2GIwe8bs//seo7i1Cw+15zdOTX2JcdVmvVtRaEiUB6/MCWfqEoWQyuebkqMv7v/oeKk9YTqa8fHnFutKE4wEqU5y+ucMP2rRbI8bDHovZjN5gTLEtKA0EcZvrs2seHD3i4c47nL+54sP33qW32+OL588Zdnfo9x4w7B/yj/7R79BqGxZb2Mke0+2P2R/XyFDR6rS5u/iYR/sHJGGf+eSWSES09zzG4yHvvfcBsGV3NyYMCiaTTwj8kIP9LrouSSKPdL2lHWiqdEGoasryjlanojOWrLYrHn3wmKqsyDY1eSFphR1MOqXtSQJguUgptgGdeI91NSdJdqh13qTvaITZIpSPkD6lsaRljhYSL4qorWabz0HAdH5FtfFIszUPHx+hVc3pxQsQHodHxyRxxHaVMhgMCEQLrSuM57wVQigagjdSBk7mampsAwGRFvzKd4GpWe6wXoGk9CqKQKOD0HkYPHcwQwnKWnSV44mC2K9RoaAoKrwy55e+84y2+kv8zb/56/zo9Q3K90AosqxG+AoRtrBVhfQt2gjKvMSzgirMnU7Bd/24i2MFq1w6gdcIyYw11NZQSYiCgO6gx+HhPqHX0Ipp8hZ83yUDeyEHOx3m04oPnz0k/ZVf5O/93b/PcjohiBNMVWGBOA4pCqf+01WN1m6LokIfT7gEp9pUzQylxgpDWRgCG7gZjBD4SctF7wlAaDdXaJyQeZGjjSaKInzPWcD/2EVACNHG8QP/V9ba1U+nnFprrXBL4j/y66dzB3rdDrsHAz55ccVk1uX64oaWf4QfSWTgYsUm0ylmPiGO+xQUXF7MWC5ykmjFZrkhXVfM51NOTvY4ebCH7wvydUm30+OdZyccHR6z3SwAgVLKEV67CaPRAKFqDnePqdeKg+NDHj5+TH+nyzcWSz57fs5wdEC322Vn3GU6m/DRNx7R7iTUVYUfeLz/0QdMZjNOjo/RlWR2t2Q1S0nCDod7h+zs7FGVGa9e/5BaG6SuaSURURija2i3W/zsz3+byV2KsjO+/71P+Ma3vsNmk7Gzu0dabri6u2Kz3XB5PmW90OwMHyF0QT65ozMY0k4SLm7uUEFEGEUEQQhWUBQlVCX4ormKKspKk+UblNcC4WGtcwTe3N4RRRFlXbJau7+fszfnVLqgKCqmyR2r5RxFRFlq4sjH84XT8vuO8FsVlVv7SdGYZtzGwLUEtQsXEYa6KJ2N2Jf3vFJoWgfleS5DEAPaPfk8DMpzcWVk7uspKZFIvv2tb7BZbVj+V7/FZLUhK5se2GqHiZfuNiJw+/88z5FriZAeURRT+xqEdkwB6QxtUgqEsEgp8ZWHFNDpxAyGgwZ37yjFbgOinY7ZaIR0eot20iLwQr7x0UfcXN+y3GypaoP0hszWa8qyxBrjNARNAXC3AQiCgDgKGjuy+0cpxbr5c1Hkvr+QgiAMqaqKvCioqspZvj3XGmmtXcEwpole/8Nff6QiIITwmwLwf7fW/ufNh2/ur/lCiAPgHmd6AZz81B8/bj72z7x+Onfg0aN968cFj945IopDLq4v2evH5NWCVldy9HCH29kO3//Rp+ye9MirEiliDg726HQGzBcLp1+h5uhoTFnlvHr9HE/t0O11EQI22zWXF+csZnMCP2ZnZ0y/16MVh+RJi1evX1JvEparJT/8wff57p/+NuPxiD/44ad892dPuL274913nyBUxsnRIb1em+l0Sqcd43uS1WpOkadsNyW+F3ByfMjeaEwrDtmmBdb67O4e43kKzwMhaqqqpChL4sQwGnfIy5rFXcV494Ref5f56TmekCzmGXUluL2eYbSg2x4QBy1Wiw11nuPnhSMNtdu0Oj3CKCT05NuhmM5TTKAItI/WhYO3oPCDkiBUGOvEPfPlGoMrCEEQUpY1p2entNstknabwA9YzGeYShIELbI0AqvRWALT7OhV6IAhUqKkoK6ME+MISV3bpmNwmgAn5vFQkmaSLZrQUHcNN0IiPfe1hAJrKsBtFrDFW7xXFPgcHezys9/5Bp+9POWLl69JQp8gabHarGglbWQTe4YxVLp2ik0vImoVIH18YxvgiEV4DVGoMT1Z4Q5cr9tl0B808BGLBKoGTCKEoxl7nsCTina7Q0rGzs6YDz/4gIvLaz757AuUUiStFqWpETg9htWq8Qq4YuA17QI4cIu1rhglSQI4/obWmrquUcrJtcuyITs1n+v7PkkrcQGpRY62X/2M/qNsBwSOS/SJtfb/+FP/6W8B/1PgP2p+/Zs/9fH/pRDir+EGgst/0TzAfRPNfH3G4fEu22xNEPqkxZrTy88Y9Efsjvboj/ps0prW1rAtarSWjMdDut0BWZqRZylR7Ha7Ol+TxJbdwT6LWcFsdk3cgixLieOY4WCHYpszmU5YryEvV/zk449JFx57BwMm8wk//vjH1AiMrdndG3J3e8U7Tx5R6xW6qijylPliQquT8PrVC5aLGZv1AklIv99l1N3DVIbPv/iMwBuglKKV9DE2I8sr6rqgLFKCoEVtCkqTgaqYzpeMxkfkpeD16Q130wlJL+LBo0e0opjdUY/lpGQ5WyEJ8H2f2WxGaeFgf5/x3h7r7YaqyCiyFcZo4lZMt92mrmE5WZCmJVHUYrkqaCU+Uexsr54fY1AYXbPNU+I4JA4TDvcPnTVWKmaTO0xl2d05wNQF203BJt3iRSFSKAaDMZ4XIoUHeAhb4ykFzUVRNIh2bSr3JMVS1XVDH26Iv03AqBAS5QdOeFSDrh0eyFF5NFK67cSm3GCt5uHxPr1+nyjwef76DbWunOZAuBWbkzU7taMxlizL2Gy2WCGJjG2QaQaMh7QeXqAQOIS573n0uj36/T5KKScYMs4jUNfGORCF0xUIcPkTMgNrGI4GvPvsHU7PL7iZTGn3emzzjEproibmXTT5jj917t4e9PtiEEURvu8ckGmauq1HcyN3OgA3B6iqilYc00najSpRUfPHawd+GfifAD8SQny/+dj/tjn8/w8hxP8ceIMLJgX427j14HPcivB/9i/7BtqUtDqCVtzhzZuJ28Ebn4o13/vR/4ef/fafZrldELU6bLY1eW0Igog46tDt9MFq5vMprVzR6QQIm/Po4R6Pjt7lk49Pubp+SRxFDPsP6Hf7VKXl848/c2EbjfSt2+uQRAknjw/ZVgvysiDpdHnv3afEkYenDEkckaYSWxvW6zVFUWCF4frmyg3AjGEw7BEHIe12Qr7R/OjlJ4yGj9jd20cbQRDFbLeQFRVJq0UUJcwWU+bLnPkio6g181XKm4spn79wROXecMDhwUOMrlHWZza55u5qzfHBQ9J0w910hgpjBmOf+XxGXuR0uwmtqM+43yIOPTzpMZunKL+i1W7R7w8wBqJWh929PYoyd4M5U1GUGfWqxvgBrSih3erSbidYI1jMJvhSUfe7pKlEa8PNzQ2tTot+f0i6UsRxgu9HVLVL+L0PAnXUIUtZ5tS6QihBXWnKMkNXFUIKF28uGkOP8LCiaQ2Mj216Y6UgjGJMbahFRTtp0e0kTNcFTx4c0ElismzLF6/PaScdJxQSsgkydWgyi6AsKzabjZM8oxqeAAhr8ATQuPkQgigM6Pd7dLs9Zz0XNRjHOjDWcC+MdlBSgdY1Sri2qJ3EPHr4gHeePma+Wr5VEIbGoKRy4anw9oCXZUlVVW9vAW574IqEUooguI9Mz9xN7x651rQNVVVRlKXzEjQbDk/98bYD/+TtSflvv/78H/L5Fvhf/Mu+7j/zEhY/VFzfzLi9XYMVnBztELfh8vwNi+0jrKr58BvfZFtuKHRG0krY3zuk1++zXs6Zz6YsRUXwYJdeL2R3NMDzBVWdEwQeVV3SCxOGwyHptuTk5AFFkWL0ltqk7B/u4skhJ48OyfWA86tTWknMwdExxuQkLY9uO6Lb3iEKFdsyZTQckWYpd7dTR79VPv2OJi1TLvMrQq/Nzs4OrSSh3W2hJISRJQhH1HcbOr0uVW05vzhF147Vr/zQBYUWa6K4w9N3nlLVKbqGKIxIVxnTuzsuz+7otXoIZanrGi1K8jzDCBiNhhwc7PL/be/NYiRLs/u+3/fd/d64sUdGZlZulVW9L9McNmeG5GhkSiTHImGNaMCGXmwRMOAXG7Af/EBDL3q1AfvBgGHAhgXIhiS+WIJpW7IpS0MS0JAzPUtPT3VX175kZuUSGXvE3e/9/PBF5vSMps2haSqrMfUHAhkZkag+0Te+c8/yP//j2hJZZVRFShylOLGk3TFASIIgJElTOr11+hvrDEcDhAVJGiFTyNMacRxrufGkZJbPsR2bPEvJ8xrTyYDxeIRlOYyGZxiihVGvE81HlFmC5WhtA9fzodItwgsiUFFmq5Xmuldf5FpzUVOPBVLkGNJEGialFFTSQEkTDEt/qVWJa1moomJZFNTrIZ1Om+FkQVHENEOPN1+9SRTFTJcJjm3qZaarxSVCFwcwMEjTFLmM9PCQXO0vEorS1Lk06Faj57qEYYjj6LVzciVTJo0UqcrLdWYXj6IssUyTsBboMems4I03XuNsOOR4cI5A4bsuaZZpfpUQlxLtZVmuFIv0zIAmKf1oamDbNrZts1wuL9OIizTAcRyKvCBeRlqHcZVefRqeC8Zglubcu/+Y48Mxy3lCp92kv7GJEAk7+2sIO6G/2abmbyAtWCR6Xt4QBqZpUKvVaIQNsnSup8CEhcRgNp3guhb7+3vMZufUQ/8yzFrfWOd8cEISZzimx3RmgRS4gYustNxUXuTYjoGqMrZ3NjBQqConS1Ncp0aS5CwXKYOTEVme0+v1Wc5jovmM6fgp1zauc/PGqyAtwoaNaYJtpzQbfdJ0SlUJFvMFs1lCnufcfOkNJCbD8zHttS7LLGPvxnWG58c6zSkKouV8tU23IvAd2mtNvFqNwXCsWWWuzfXre+zuXCNaTDg/e8Z0NiOJMwzLx3IU48mEeRTT7Xa1Ym2ekOQxWZlSklFSUJQFjuPiWA5lrphNpnieje1IBAXjyRlRXLCxsY0hFfPJkKFlIqXeDSmkTX99E8c2KcuL++SKRcjF6nCoikrvNjSNlTpJRZEVoFIMW1K5AsuwLnfN6f0GeujHsh0sM0GVJUHg02rWODw6ZjqP6XdavPX6K3z3gw+RhiDNSqqqWHEC9H/KsiwqpUiSGGkYl6xG0/hhKF6YBpZh4boOvudhGKtWqJBIy1jNSOg7sOZLV0jAMgxKwLH1xqaqqti/vsvpYMBoOmM6HOmuTJ5rmjCs2qg6v7+4618c4Mt2X55fRlUXkYOmMa80FFZt0ixNKbPiMj34f8Nz4QTyvODB3SN8r88br73DzvY69bokTo/Z2rlGtEjIM4v+RhfHtxnPDMajCdPRjKIw8T2PnZ1dlrMxqkwpMkmeCgxL0WyFSOGzjEYItLLxyfExnu2xWCxwHEmlFJ7ngOkym0+ZLier72NFHM/xHBPXkQxOTqmKkjhKyIXBZDplNJ6jCgNLmqyvbRH4dSZnz7AMh1ajQ6+zRpQvkVaKH5i4joPtCJqtDqcnpyzmOaYRUJaS7WsvMRqfMZzc00tNpV6UsbbeYzafUmYxVIpWO8RQFvW6j+04vP76G4ymUwzLwnYd6mGNsixI04TJZMLp6SlSWggTTs+HPHigtQqb7TppHhOlU4bTAZYtKaqEghQ/8Njsb1HkFYup1k9UpRbxyNIFaZaTZJAXa+R5ysHhE4bnAxrNFnGcIU2LWs2n1WpSZKyKa4YWxFgtNBUr/UJpGHrEeKW/V5aVzmGlrrYLw9QqQsLUkmZKAiWm5WDaDsv5DMM0CDwbqpz5dEQQtlnrNGk36kyWCWWWocoSaZmrBSaag6CUDrM1q1DqQqNjaQdQFFSViRQWjmXheS6mZelBo9VGZmmarASKtFRZlmLYmqp7QaDyXYeiXqOXtrl58wYPnxxwOjgnjRM9Q7G6MV08gB+5e18IugKXsxBS6vkHz/NWcxvVj2wZUgp83yeOY2az2f8/LcK/SJimRZm5XLv+Mr/2V/8t1noht+78AdPplHbX4+nhMeenJXs7r7HMcpbJEChZLCYYQufoaZJjSAfbdrGkxWJWYroR0rBJ05hGI8S0DMbjMfPFgnkxxzQFQeAwGE5wPQfbrzGfz/G8gCDQW2Jms4m++8cJ5ycDWvU2w8GYcVSCgG57E9/VK7Ze2X8Ny3SoUotWs8/+9Veo11uwTFmm52S5dlhVpQtIs0nEfJaxsX4dx24S+F2++/638Gs2rucihhVn58es9Zrce3CH6zubhGGNqSNpND3ieEpSWfQ3N7nRvQFSENRqDEcD7t55CqthmsVySS2sU5UZ8+WUKFniOBbz5YSnhwmNVkhexNiugyEVrmnSbHVZ6/aZDGfkcY7vOlRVRKkWRNGEOM1BekxmU+7evcPg4Am99XV6cQTCwHY8omhGlkVYykVIE2lqGq4OURWGAKVMKhTGSp67UqVWKpIGQmoFo4vtwghN6xVVgVACJTRzMSv1QXJdR2sh+K6OFBB0Wg0m84iq1Jz8siioVnfNNEsxTAvLshBCkecpSWJQBPoalVVJWejowbZNbFuz+1SVc8EotmwbKYSeOagUVVlgKmClxMRqk5HrmIQ1n2sbfV595SUePTng9HyE7Tq6awGX0Ydt25d3+4tagJTyMlK4uPNfRAumaZKvNkV9sqBouJ7+t1DkxadHA8+HEzAsblx/g1duvEmz3sNx9PTUYrHk4OCQpwdH5EnAwyd3GY0H1EIDy/CYzydQSDw7IE0K6rWQjbU14mjB6ekxhYhp9zokyQLT1P8T9RjuBFEJtrc2sGxJmsb4NQ9pSCzbYnd3D8s2MS04GxxyeDggcF2EVPh+QFGMSWNNM/3iu69T5AlJEtNrr2EaNuWOge81dUtHgFIJy2jAcpFD2abX6TIZz4ijnPk0YWdrk153hzDokucZL79+Hc+rkyQJSZpSVjotWet3GZ8NGQxPEYWleW3K5e7du+zs7uAFAb21DpOJYLmYk2cJliXJ8owkTfBqLs1WjetiE9f3qFTG4ydH7IgtyiojGU/xfL0zMclj5tGcOEkBA9OQ+HUTYVicDJ6SV4ogdCmritF4jBKCosiZzqaEYR2lCmbTMefnp7TbPVwv0FPFSmmm4YrWKwwTUek7a1UqFBLDMjEtG2FaerV4WVKh/9YwJQITlSekeUlalChhIEwLvxbQ769hmi5ZIUlzWOsuOT2fMJkvdFiuFMIwsE0bVRYoJaiUgVQ6rM+yhOViTs21AC3yIajhOg6mYawyAUlZFchKYRgWEkGZF0C1aoUW6PNfUpQ5F1qovufQ7bR4+aWXuPXRXU5OB1yk6noIa1XfWRX5Lh5SyssI4aIucJEG/LBDUfwrnYIKhWGZOKuZifHoOZ4ilMLk5Zuvs97f4PHjRwQ1GI3GCGEyHs/wXJf13gZnZ8ecnBywt7vOsoiwDIM4ivHtOv21DRzLwTR9lEpJkgpllozH59i2JMsKsiTj7PSc8WiOiclbb74GIqJWC3Bdm7wsdJ5YKtrtLkLmnA2eMp2P2br2Ggsj1kKTTkA9NDAti1a9S6UysjRCrvTw67WALM+YToa4QUBRRuT5kmgxx1AFgetRFQrfDXFtKFLFdLKg17vO3t4OQuYYpqLTaxEtY5qtBusb65RlQZwmLJZzAruBF3gkBRweHWLaFjs7W+RZiuvYtJoNBucJy+VyFcJCpUo8zyYI1mi2WkxnU+RYMZ1NQBS4nl72Op1FBHaGZbrM5xnRLCH0ba7v74Gx4O7D72O6HiUVeVWytr4JyVJrEwiFH/ggYDA40avNTXMVvlogTQxp68IaimqVJggpUaWWArMcF8tyqIQJhtRTfau0QUgDQYWSluY0mDa2C2VpIClZW1/HCxpEcUlWCJZZxelwyvFgSF5Vuv9uGpiGiVlJ0jwjTRWGaWIoEyiZTDNaDQ/bCaiKnDiJNCtwlbsbhqlTlrJcpSc6FaDS25zzLNNOoCxWRVDtOGzLpFEP2dne4u233uT+w8cs4gglSqSUOI4D6JD/k7m+4zg/5E6suAAX7184DqUUtm1fjk+XVUmhdI3ADXxs1+G5HiU2TZN+r8PJyVOqqmAwiCjyhF6nz3Q+wDYltqHnojfXN3BMh5PBkM31PQzlUg+bGMJiOpkxOBuCKpGGhRdazKMJQVBnOh1hG47OoTo2VVbiOA5nZ8+o10M9K16a5AVEy4Qg9BlNpviBj+c52I5JlmcsZiP8oEXY6bCzvUMQhKTJDN9pMBoOiKIIDK2wW1aSqpKgCvI0hUpRZAVlXuE5PoFXsr/bw7JdJqMxZyfHmKbg7p2PaLc3yHOdo3a7PdJkycnpMc1mk53dPUKvRbPe5enxGbbjEAQ+nU6HNI1J0xjTlNTrIUfzqd4KXK+D1KyzRqOJ6zhkrsvNGzeZLiakWclar89ocs5oOKa23cSwJKPJGKO0sKwaeZ7jOoLuWhtpuSR5yXQ+xw/rVIbEdvSK7iCskSQJ48kYISWNegMpBLWgienoGUN919K7CTFACANh6iKhYTkI29VM+tWqcEMaK9ahzuMRJoblYjk+2DZKFSSqIBACYbiYdo4SNou4ZHe25OHBEcVijuO7ZKoizzICv0acxihRaXnxrEBgk6c5k+mYfq+FaUkW8ymL+ezSGSndC9R3+qqEQo9My5UakVCmvnOvxEsFSuf7qkIIk1oQ8M47n+POvUd845t/TKEqTNPEcRyCILhsP+e5bkEWRYHnebiue/m7Uuqy4HfRUbhwABfMwVJVCCUxLQu5cjA/8fz9aznlfwqEANtSDM4e8tbbr3H47HRF0QxYzBfkecHZ+ZC93Ze4tr7OwcNHWNKjUWtj4JLnFeeTc2bTKdPpGNMQ9HodHNeiwMEPPJyZRT2s0270WUwTKHW7ajqbsrunC2+G4VMLAhzboyoAJdjd3aWs9AGcLzNcs43t+kjTot9fI4mmZGmC4RrE0ZTjk2cso4hGu0ur08NwFGmakCU5gRsQeCGWtInLjDzN2d5eoygs8rTi8cM7jJMTikJLZQVBhzjJ8dyAvb190iim2Wrjv9LEMWpIYTGNchzHYWdnh3pYI00TBJAkMZZpYFyMztoOrh+wXMRYppasCoKQjc0NzgYnDIZnqEoQLRI67TWanSbKgPlyTq+xgecHPDl4ytZujV6/S1bC7DRGUrCYxlTRHN/3cF2LJE+xLJN8teFoeD6gLEqMdRNPSkRuYxpyNXSkWYECiRQrp2BaKGO1NRh9iAxj5QSKaiVboDUDlTS1fLjlQ5WBlFhVillIDMNd0b179Nd6JGWhRUfLiqLIqFYjy+bqDpokKZYpsEzJZDLi9Mzn2voaSlVMp9NV3i1X4bbAMCzyNKbKc0yhi4BVWWIath6HlgLTtFGqIskz0iSmEjamYbG7vcMv/9Iv8uDxQ54eHV6G87WaVgUuy/Kya3BRpLw46Eqpy8Nu2za+718Km+o1caYWLzG0QpK0dOTzaXgunECeZ8znJwS1EsNcMJsdkZwnSLkLlYMlbJbzKVVZYQobSpuX91+i1ewxnSw5OjxgMpkCFaUq8F0P2zNJ0ojlck6rGbJ/Y58sLlgulvhBSCtsMR6f4rkutVrAYjGlLAo2+5ucnk+YT+fUWyGNhku9EfLxs0M8q0lYb0Klo4L5fMpkdIIhckbnE9JkDiphPj8nLSIW8ZhCbVGWJY2gw1q3i2c7VAWoSoeRi9mEsjJxXZs79x5gBkuu7+4hpM/+9VdZLDOKEgK/zvbOPlVW4EgD2wgoMsXa2hpVVdBsNqkqPbte5BnLxfySViqEZLGMsJ2AMLxYrW3TarfxHJ/N9S18X2skokxeffl1Gms+g5M5hao4Hw7pd1sEQcBkMmaRj8grSZIq5ssB5ycTqniJbZt4rkVV5WxurFMLfJRSHB48ZT5b4NgubWFQSQtlWQjLxBACaep0RUj03gLD0gQhaSBWk4SGKRHKQEmhiT9lTlFBUYFhm1iuh6hCLWxaSIgKrTXgOFimRbfTZTifMVrMQAq9OFYobMdCmgZlqfcVmJZBsxESL+acnDyj12kQBiFJEmtVIql3EgohdTGxKCjSdDX0JFGFulRVklKstg6VGKUmDiEEruUgrYDPf/7zHBw/4//4P/8pBwcHP0L/dRwH13VXHAN9uOM4/pECoWVZeJ53mQpYqx2ShqFT1WrVzrzYFPVpeC6cgJQwmT4jyyd8fPec89Ez0rSgHjbY23uZOMo5OR6TRCl5UnGtv0fg1LGkhyTjfDBiNp8Rhh69fhu/5oBRMhyec3r6jGYjpB7WGI2GHD454603fw4pJfP5jDAMGZwPNF/brNHr9nj46Ig0S0k8xfGzEaBoNBqUmcloPGGt08SxPc6HZxiU+L7D2ckIIXI8z2Rrew0Mg8l8weD8GbYV0O9t0G2vYxsGk+EIVSlMw+Dk+AiERbuzRpEvIM8whIFh2IRhk0bd5nRwxv37jwl8F9+tMZrNCNqafBSqkka9RlWVxFlOFC04PjlmNBoznU7wAx/btqlKPTjjOVqpx3UEYdBgNl0gDYN+b5PlMsIwLDw3pN1tMJukWLbJ8HBIUPsctcDh/uOPsYKK8+kC0+nz8NFjWuEalUgYT2ZUjQDTAITCtEzSNOHxk8f0OhHtZhs/aGB4JYY0KKSuAchV5Uyxqg+YJsIw9RwBK50CqSvwF605pRQIA9OysRwbYZiYjotdKZxcwWRJUVS4rkcQajJRazJktJhiGAb1MMQSEjvPidMM0PLcYRhQDwIcS5LGS/I8xfd7pEnCbDqjqirMlVRZWaTkeUGeF9iGQJgO0jDIMj0XYBjykrFnGAau66Kkg7A8Kkx63S6/+qu/ymg6YT6fs1gsVnJkLr7vY1kWcRxfRgAXd3l9ZuRlDeAiXbAs3emAi/0KK6UnNCnr0/CcOAFBEqWYwsJ3FI2aQ9Bfp9ddY72/weB0QLvRQBYScq1Me+uD97m+/zJgYBhg2YJmJ2Rrd53jswOyJEaJkqAWkBc57337PRxTC0KkacLZ2QlxtKQWBNx/cJ/eWo/d3esoVWJbimarxeHxYwbDQ3avb7LVv87TxyccHz7jWm+PKs15+uwZu3vrOG6NIKyhqhSBg+O5mK5HOJ8zXUTE84gxA1q1ENd2GE9mTEZT0qwkLRRFGeNmC7r9OousYDSZ0Ww4zKZzpAqpMod4UZCnc5y1gFkU0+mX+HUbM28AGYVIGY6GnDw7Q6yEMoeTIWfDc7q9DXZ2tgg8H8s0yfICz/Op1XyiGBDgOjbXNjcpyi6VKhkNxlquKvBw9mpIx+bbP3ifvIp4a/8mZ9N7+DWPMDT5hXff4un9Ax4/fohpOTQaAY4fUKiCKi+ZRROCWkCURqRZgpcXqNU8gZJQoleAVUIfemFauu0mJdLUX2qFsVo5hk4hVqQY0wRztb+vwkCaDq4v8PyYJM6p1QKumxbLJOZsMuLw9IQccD2PZq1GFC0R8xm2Y9PrdXFsC0NAza8zm5SUVYphKJbLmPH5GVm0xApreodhqYeOlBKUSujURBqQp5oZWehCpjQMDFliGRUlBVkyJc0ERSXZ3Vzj13/lK2TLGQ8fPaIoK+IkRSkoKkWa5eRFheu5eK6HlII816pJUhokSYpArzZTmquEaZoURU5aaNKRZVmXWo8/Cc+FEyiKkuFRyka7h+MkOGXJeqeNH9T5+PZHnB4/pOGGeNgU8wXDySGnJ4eEocX65jadNR8vFZh+SW4sKM0lUTZmd+cmVb7GZDQkTpdsbW6x0VtDFQbnJ2fYpsA2od2soSo9f55Vc0xzSafhc/hwwcn9E7Yb11EYeGUDqxyynJ6y5m8xzxXHT4+oyMgqCLwaVZYRzQrsXFHz+pyPn7GYnGEXCaOBTVYoTk4nmGYdywoJOzWieIpwJbGa0NvcIkoXxOWS2WzJ/Fxw8PgZnbU6i8UZEy9lY3+PYXGICgRZ1EKpKa5TEk9GZGaGLH06/XVymXF0dIoTdAjr27RbLlUZgwTXs1EqodGwaXc7nJyc4LiCutfk8OiQ8ckIzw0J6wE3X3mD23fucfvolGvba6SyS72TkKZzru/5FPkxZyfHpHFBq+3T6m6wzBfkyZxOK6SxHoBdMI0nnJwdI5WJ024hXV3RF5lWKaqEAW5Na3yjkDJASU8XAis9jSiUrsJDhWXpQ1ZUBYiKQhkIQ+LXPHobBtF8SRjUWUOAhKOzU+4+eES2jKgKQa3eQFFg2zW2Nrs4tqDIEz2yrLRoRykcbKeiMCuy5Yx8OUMGHoAeRbZ9iqyiAAppo4TAdJfMJgvyDHyvhuc5qKpElRFJNGc0nDAazjDNgHZ/g9e3e2z9e/8Og/GEj27f5ff/+R9yMhgiLBffr2O5Fa7v41sWRZaB0jUXPciUkecZcZzieb4e1670zsay0MQsQ14sV/nJeC6cQFVW7O7ssd5ZJ88PkNYGVAZJlHNwcMhyMeLazQ0CUaesSpI0Ya3fI6j5ZHlOLahRb9WZLkdMxhPa7SbC0KGxLWygYvvaNRbzGQ9m91lrb7KMIna3+zSbDvPlgFLBaDQhzxKm0ylJtMBzXHa395iMtLhop93l8OgJvu/y+uuv4tc8Hp484u7dj7FdSbtZxygVNb9Gs9lmkRckSYpje5p8hMB2bEzToNGoI4RHt7/Gk4M5B08Pkaak2eowO5zS6bb53ve+Q7f2Nrdvf0R4XNDo2RiuT63Z5N79e2wWHsdPn9BsCtbWAtqdNmQF0VTRaTfY2Gpz86UIoZoI5eiJNQWGKajXa8RxhELpTUN5RpQmNE2T6XRKvIxwHV9TkK9vc3I+QBiS9Y0NHMfj9PSMdsul2Qo5OHhCnheMJ3Nefu1lrm1tce/BDzBskzRP6XQ7TM7nHJ+eEC9KZGES+i4SkyqPkVLnuIbtYRs20XKJUQk827tk9EkEK/UMtHyRFuaUUmJiUFYKaZiYq/TC93wsaUKlcByP3d0d3nrrLZ4cHfPhnXuAYDIZo8qc/lqX63u7LBcjFouSdrvJfLGk0agT1HyUquivddne2cR1ba0zsJoYVLat9QUMoWcdSj3e7NdCFjO9ezDLCqoyI88T4iQmihacnw+IoxPG8wX7L7/KjZs32K5gsUwRoFu7jpasdxwXISRJkupBJbFaWpprYtFisVjVB3QRMcsykixBmQrfCyiLEst6zp2AlIKbL7/EztYN4lmN0dzh2WBKWUiajR5rvYA4TkiSEXmeUSiohQ1qYQPT8pnOI8ajCaPpENsTZLlLkibEWYrKFc1mHVUWHDw9oN/dZjQacfzshMC3mC9KojghCOscPzvjSRoReJIP3r/N9uYua2t94mVJUKvT6TXZ3d1ld3cbQamXZ5QZ9XpIqRKEUDiurtYapoEsKzqdLo3NTcwyR4lS75a3BFm+xDAFg8ExqioZjkas9et6MaWQ9Hpd/q9H32AWetiOgeOD61k8evSQyWyJaXl88P7HDI4VYVjw1pv7BFtN1tc3qO12ePjgLgeP7tGoN+n3r2GKGrWazWIRo1RJHEe6ILtIAEjSFEOaTKdzDGnx0ssvo5RgbeMaZ6cnmKbk9Tde4Rd+4edxHcEPfvBNXM8jL2PqYcibv/ZzHB6d0Gy1cRybTqdLRcTZ2RF7O7s0r/do1HpkUcV0PuPg8JC97Q1MqUd6lVJYSHIikiLGrYPd6CKU3iy80uBmVelCKU38MQyBNCxUrnRrWOrtSLZtI5RiMpogsxLb9vjc229zPBhyeHKmNzqVCWGg9f981yNaCDzXpd/v47qzlTyZi22b7O5usbe7jetaCFmtmHk6ArEdC8PQY+x6VNnEC13iqMJxPdI0pkJQKEWptLMyTJOiylkuI8ajEUEtpNbQRKIvfemLnI+nDKdzlLBQWYHp2IiypCpyLYqyIhBdsAtBa0fEcaznC4RaqQppNuF8Pv/U8/dcOAHXc7l9+yM+/v5dOm3FPHnGIlGUQmDYku5Gn4MHD9nr7/L48RMG0yFKuNQWMbXQwzBsjg6POTh6hONLvECiZMlmp49n2biWQVIW9Htdtq9dQwoXz/Gp1T2Onj0gzxer1k2bg9MnvLS/w1tvvkO0iDl4esTO9j6WbTEcji4VW46On3J6dsRwdMbOjW2CepNoNmMyGRMvE+RognR9rXlXCJazhDRfUMmSMKzRW1snywUffnSPRrPB5uYGYUMX7YQwuf3RbVzXwnUlouUjzQlpHq00Ek/4zd/6S3zj20NaL3WYzZ+SxBknJ2eEjonXbzI4P+Po8AC5LXCdIfWalqNOU4llBRRFznKZ0Wq1KMqSRqOFYVrcunUL07Z56803+O733ieKFjx8qNWMoVrtBKz43Dufw5A53//etygTA8OEv/SVX+L4ZMB4NsZ2HM6H51i2Q1XpzTkoST2sUyYlURyRFzm1uk9VZaRpRpzEpMuURQZtw6V9wSdYpbMCdNJbVXpQZ8WoQ2gCD5VadZAAFPEyYjqdUVbg+CGbO9f5+Z9/l1sf3+G973yHwLeo+Q4nxyeEgUWz2WQ0PGU8GlEp6HW0+KvMFb5nE8czjCnUVEPrYhYlCnk5fAQS07CpciiLijQvkJaNKAsMUWJUNpblEDZabCiLsFEgTRvTtJjN5mSFwjJN3nnnHc7HM/7ln3yLrFQIw2SxXFIPfMKgjpSalZgkCWVZ4vu6QxCtBEcNw8BxbQzToMhyTCGZR8mnnr/nwgnkec7jg6d4qkZVWmRlwpvv/CLfv3VIXmYso5TReIRvnJJXJa4fUlSCo2enCDmm3e7SaXV5/Pghw7Mh3bWQs7MxZix449UbzMdjXMdl69omDx7cZ2P9Os1mh9H4nMdPj/ACSVYZ2IaPY4c4dkCrEZAnp6yv17m+d4OTszNG8wGng2NG4zO6bpu8KghDjyhZ4NVMTk6PmQxGCGXg1RrgeCyzgnKhIC/YuNZi41qbNI9YRGOqyiKOp0hT0V3r0Om0GY1j1ta7/Mtv/DMc12I6H/Lo4RO++ptf5L3vfoNXXv0S3e4e6/1dWvUNLLNJPYQiHyOUII1SDp4+xTINGo0aYd2n3a6zt3udmie4e+d7RHFCvdEgSROy84zB+YhaLcRxHYq8Yv/6DsvlgulszHg2YbmY8dqbb5LlGePxgKOjR2xdWyNKYurNFuOzObP5mMVixmgyJIpjlMyxLYf9/R0mowmT8ZTjwymO4XNzd59Ws6s1DJSDYzskaUqcJMS5oEDrFVIopCtR8kKyXNcDyjLT5BxDt+mqFYNOKEWWpAjToCrz1eBMTqUEURRhmAY3b+zzy7/8ZZ48fcrZ6RGSCqoU21J84d23MeUajx8/Ik5TamGDJMsJbZdkMmZwfszG5hZb27sEQYhleWBaCHSofUFtLjOYTmckWablxYWhmZK2gyNKLeTiNgijAoXEcl2E1GPNSV5Sr9f54he/SJIVfPeDW4wnc0zLYrlcUBUZtVqwGjPmso2oBUYUYRhgmiaWbRGnMVmqW4Y1P/jU8/dcOIGyKqmFAQ2zQ57NyAuDRriOZMj33v8Btz4c4JkKMp8bL71OWuU8eXSEZXiUZcTR0Rmua9Fudag3Ntm81ub+g49ZnA/4zjcH/NKXf4lOt814PKPf6+L7HmG9zny5oL9+jf5mC5AcP8l5/bXPUWZzvvvdD9jd2qXb7vMnf/JNFtGCV157CcMqsWzB6OCUKE9orXdwQ1+LmgQezWCX6XiOND2Ozofcf3rIq3ufoyxgMdcTXYt4xCKaUSmTRsMnK0tu377N/s2bHB6M6HQ1XfXatU3u3R6xvdfny1/5Eg+efoQQ8NqrbzEeTqh5bUwzoLVVJ0vO8cwaNi0m50tUVXFj/zr7N2/QbGzjuxaL+YDz4Sknp2e8+ebbdHtdDo+OtW5+WOf05FS31IIap2cntFpNnp1o4tbGepc4i/kH//Dv47om/X6T8/NzOr0+k/MlSbLkm9/6BkpY7L/0Eq12iDRKhsNTvvHH38ISLq1wjWQxJE9yVJUh6SJUjm1ClhcUZYVhuHi1JmFYX6n0SM0fqC72FGhJblWVl3r7oFWITNOilDlqJbiRZxmO7VAhqITFdDKl1u7yV37lVzg9O+V/+71/DCq/5N/fvv0xtcCBFVXX932iZYTMC8bzqRaBcSxqoY8QCt9XlJmJYTl4nrWaQ1AgDWbzBa7XQAmJYVqoosCxfGzXxJI2QmaUKtNphGmS5gWGKWgGIR3bZef6TfZvvkznf/8n/Is/+CMWywjTMrFtkzAMLxmGSZJcjhyD5kXo2QWBJQ0qIcnSlLDW+NTz91w4Adu2aHc6tN0Nau46jx9/zO/+g3+E5a+TJhVlGrOsIgbHC3obO1SGwfl4wt52F9ep8Ud/+IfE0ZLPvf0K77z1OqZVUvNshkcHbKx12d/f4/bHd0hziOIxTw8HeN4hpVJkZcrtu/fxnBq98GUCv0FU5Wysb9HpdFkuYh7cf4QwBOubPSazMf31NqXKefbsKZNkwrXrW6RZAqXi6PAp7VaPbrdDa2OD/vYOTtXFlVrPbjR9SEFCvowJak1uvPQyy7jg1ke3UYbJcqaoqlQTlzbXuXv7mLDR4c6DD/nCl97l/ASeHZ1QiZx3f/6XOXp2ijQmlJnFgwcPMas6oaejCiXnnJ+fMJvkUNlYRkq306DVbtJfX6PeaHN0fApKMp9p6fFWs8VsOqXVdRAGpHmMYTncv3+Ho+ND3n3353Aci2fPjrj98S0MQ+IZPp0gZzQ6p93bYLFYMJmOSLIF49EZICmKiryoGE+mJFGEIMcQBYu5hWkIfN+nEiaW5+C5Hp5fQ9jOZf6vywGrhaGqolIlotL9d0PqcXBWKj+L2Zz5dIqQEt8NUEKSVYInT5+wbdr0N67x1a9+lWeHT7n94fephzWm4ynPDh/z0s09ajWfslJMJhOmsyXtmkfNFjTqPaSsOD97RhxFeH6dJK+ohW22dms4lkuaZiRZzmwRsd7XXSU9LaWdQVmWKCGRhonj6nqGEJoqbbkelu0yi2JmiwVra2v89m//Nr/xG7/JnXv3+frX/wW3P/qQKIoIw5Bms8lsNrtMAS7oxEmS4Nj2qisgsaTx5xca/YtGnusuwNP5kLff2Off/q1/l+98/yH/5Pf/GCFMpLSZL87Z6PTob1wjqxSvv27TrHfJk4JWs0OVl2yub6FK+MFHt6hUwrVWSM3z+OC732MynxInFW7QIV5m3Lt3gO3a9LfWKKsKy/V5773v8dGHt/jaX/81siTm++/fYntrly984QuUqiRKZ4zHYxotn+VyhqIkiubcufsxzWaT6WgMOTSbLfI8Z5kkzGYzQqvJG2+9xv2H75FmOeubbT68fYvJdEZ/fQfPD7l+cx/XrfHg41tsrr/CbF4ym03JihRpwXvvfZO/+ut/BUqLOM4wTIVj1PC9BXE8oR422FgHWQRQmngeLOIp9+7dwTI6tBprBG7FbDYhrLe4f/8eYaPN9s4Os9mSj29/jOM4tJpNTfE1DWQBrVaTg6NDkizlF7/0CxRVQavTQt6q6PTafP0Pvs7GVpvRaECSxfT7fRqtNoXKaTR3ePLYJs9KRoM5+/s3qXvnDE6OkYahF6cuKyxL0Gg0Nee/NHHCRHcBkVx2t8WP1AVXwzN6QAbDgiLXQjB5znQ6YT6aUK+FWoHH9ZjGGc8Oj/DDJn6tzs61Lf7yV77C0dNHHB48pVl36ffW8Fb8fNO0saXF/v7LiDwmW4zp97tAyeHTx0jDJmx0iNOK3vo2/Wt7Wu0oy7XzmM5wXJc4yhDodegUijTNkKIEtVI6LirmiwWGaePXm/j1Jo4fYtkzJvOIWr3B22+/xTvvvMOv/Bt/mVu3fsDBwQEfffQRJycnlzMEFynBRb0g8H3KIkciaNQbmo79KRCfFDe8KgghBsASOL9qW/4c6PLZth8++5/hs24//MV+hl2lVO/HX3wunACAEOLbSql3r9qO/6/4rNsPn/3P8Fm3H67mM3x6ovACL/ACPxN44QRe4AV+xvE8OYH//qoN+HPis24/fPY/w2fdfriCz/Dc1ARe4AVe4GrwPEUCL/ACL3AFuHInIIT4N4UQd4QQ94UQv3PV9vy0EEI8FkL8QAjxvhDi26vX2kKIfyaEuLf62bpqOz8JIcTfFUKcCSFufeK1n2iz0PhvVtflAyHE56/O8ktbf5L9f0cIcbS6Du8LIX7jE+/95yv77wghvno1Vv8QQohtIcTXhRAfCSE+FEL8J6vXr/YaXCw2uIoHYAAPgH3ABr4PvH6VNv0ZbH8MdH/stf8S+J3V898B/ourtvPH7PsK8Hng1p9mM3qf5D9Fc3S+BHzzObX/7wD/2U/429dX3ycHuL76nhlXbP8G8PnV8xC4u7LzSq/BVUcCXwDuK6UeKqUy4HeBr12xTX8efA34e6vnfw/4G1dnyr8KpdQfAaMfe/nTbP4a8D8pjT8BmqsV9FeGT7H/0/A14HeVUqlS6hF6Qe4X/sKM+ymglDpWSn139XwO3AauccXX4KqdwDXg4BO/H65e+yxAAb8vhPiOEOI/XL3WVz9cw34C9K/GtD8TPs3mz9K1+Y9X4fLf/UQK9lzbL4TYA34O+CZXfA2u2gl8lvFlpdTngb8G/EdCiK988k2l47nPVOvls2gz8N8BN4B3gGPgv7pSa34KCCFqwP8C/KdKqdkn37uKa3DVTuAI2P7E71ur1557KKWOVj/PgH+MDjVPL8K11c+zq7Pwp8an2fyZuDZKqVOlVKmUqoD/gR+G/M+l/UIIC+0A/r5S6h+tXr7Sa3DVTuA94CUhxHUhhA38TeD3rtimPxVCiEAIEV48B34duIW2/W+t/uxvAf/r1Vj4Z8Kn2fx7wL+/qlB/CZh+ImR9bvBjOfJvoa8DaPv/phDCEUJcB14CvvWv275PQui1S/8jcFsp9V9/4q2rvQZXWS39RAX0Lrp6+7ev2p6f0uZ9dOX5+8CHF3YDHeCfA/eA/xtoX7WtP2b3P0SHzDk6v/wPPs1mdEX6v11dlx8A7z6n9v/PK/s+WB2ajU/8/d9e2X8H+GvPgf1fRof6HwDvrx6/cdXX4AVj8AVe4GccV50OvMALvMAV44UTeIEX+BnHCyfwAi/wM44XTuAFXuBnHC+cwAu8wM84XjiBF3iBn3G8cAIv8AI/43jhBF7gBX7G8f8Ag1cGsYI8cBsAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.imshow(input_batch[0]/255)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The image above is a Golden Retriever, class 207 in ImageNet. So we look for class 207 in the top 5 predictions to verify our model works as intended:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Class | Probability (out of 1)\n" + ] + }, + { + "data": { + "text/plain": [ + "[(160, 0.32290387),\n", + " (169, 0.266499),\n", + " (212, 0.16812354),\n", + " (170, 0.07066823),\n", + " (207, 0.03341851)]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "predictions = model.predict(input_batch) # warm up\n", + "indices = (-predictions[0]).argsort()[:5]\n", + "print(\"Class | Probability (out of 1)\")\n", + "list(zip(indices, predictions[0][indices]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Labels 150 to 275 or so are dogs in ImageNet, so look for those as other common predictions in addition to our correct 207 class." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "__Baseline Timing:__\n", + "\n", + "Once we have warmed up our non-optimized model, we can get a rough timing estimate of our model using %%timeit, which runs the cell several times and reports timing information.\n", + "\n", + "Lets take a look at how long our model takes to run at baseline before doing any TensorRT optimization:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 85 + }, + "id": "eMu3dZlM96bh", + "outputId": "537a88e2-ad7d-413a-f815-abd91f010e21" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "46.8 ms ± 514 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit\n", + "\n", + "result = model.predict_on_batch(input_batch) # Check default performance" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Okay - now that we have a baseline model, lets convert it to the format TensorRT understands best: ONNX. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "__Convert Keras model to ONNX intermediate model and save:__" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The ONNX format is a framework-agnostic way of describing and saving the structure and state of deep learning models. We can convert Tensorflow 2 Keras models to ONNX using the tf2onnx tool provided by the ONNX project. (You can find the ONNX project here: https://onnx.ai or on GitHub here: https://github.com/onnx/onnx)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "id": "aG3tXUEx8quf" + }, + "outputs": [], + "source": [ + "import onnx" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Converting a model with default parameters to an ONNX model is fairly straightforward:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 68 + }, + "id": "QxLAvWp68quk", + "outputId": "d750962a-d098-4a63-c195-c3442211cdc1" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Assets written to: my_model/assets\n", + "2021-06-09 19:48:30.462380: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n", + "/usr/lib/python3.8/runpy.py:127: RuntimeWarning: 'tf2onnx.convert' found in sys.modules after import of package 'tf2onnx', but prior to execution of 'tf2onnx.convert'; this may result in unpredictable behaviour\n", + " warn(RuntimeWarning(msg))\n", + "2021-06-09 19:48:31.938818: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set\n", + "2021-06-09 19:48:31.939684: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcuda.so.1\n", + "2021-06-09 19:48:32.010614: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1747] Found device 0 with properties: \n", + "pciBusID: 0000:07:00.0 name: Tesla V100-DGXS-16GB computeCapability: 7.0\n", + "coreClock: 1.53GHz coreCount: 80 deviceMemorySize: 15.78GiB deviceMemoryBandwidth: 836.37GiB/s\n", + "2021-06-09 19:48:32.011850: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1747] Found device 1 with properties: \n", + "pciBusID: 0000:08:00.0 name: Tesla V100-DGXS-16GB computeCapability: 7.0\n", + "coreClock: 1.53GHz coreCount: 80 deviceMemorySize: 15.78GiB deviceMemoryBandwidth: 836.37GiB/s\n", + "2021-06-09 19:48:32.013128: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1747] Found device 2 with properties: \n", + "pciBusID: 0000:0e:00.0 name: Tesla V100-DGXS-16GB computeCapability: 7.0\n", + "coreClock: 1.53GHz coreCount: 80 deviceMemorySize: 15.78GiB deviceMemoryBandwidth: 836.37GiB/s\n", + "2021-06-09 19:48:32.014344: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1747] Found device 3 with properties: \n", + "pciBusID: 0000:0f:00.0 name: Tesla V100-DGXS-16GB computeCapability: 7.0\n", + "coreClock: 1.53GHz coreCount: 80 deviceMemorySize: 15.78GiB deviceMemoryBandwidth: 836.37GiB/s\n", + "2021-06-09 19:48:32.014373: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n", + "2021-06-09 19:48:32.019097: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11\n", + "2021-06-09 19:48:32.019146: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11\n", + "2021-06-09 19:48:32.020281: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10\n", + "2021-06-09 19:48:32.020567: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10\n", + "2021-06-09 19:48:32.021254: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.11\n", + "2021-06-09 19:48:32.022280: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11\n", + "2021-06-09 19:48:32.022445: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8\n", + "2021-06-09 19:48:32.030879: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1889] Adding visible gpu devices: 0, 1, 2, 3\n", + "2021-06-09 19:48:32.032680: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set\n", + "2021-06-09 19:48:33.010741: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1747] Found device 0 with properties: \n", + "pciBusID: 0000:07:00.0 name: Tesla V100-DGXS-16GB computeCapability: 7.0\n", + "coreClock: 1.53GHz coreCount: 80 deviceMemorySize: 15.78GiB deviceMemoryBandwidth: 836.37GiB/s\n", + "2021-06-09 19:48:33.011970: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1747] Found device 1 with properties: \n", + "pciBusID: 0000:08:00.0 name: Tesla V100-DGXS-16GB computeCapability: 7.0\n", + "coreClock: 1.53GHz coreCount: 80 deviceMemorySize: 15.78GiB deviceMemoryBandwidth: 836.37GiB/s\n", + "2021-06-09 19:48:33.013195: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1747] Found device 2 with properties: \n", + "pciBusID: 0000:0e:00.0 name: Tesla V100-DGXS-16GB computeCapability: 7.0\n", + "coreClock: 1.53GHz coreCount: 80 deviceMemorySize: 15.78GiB deviceMemoryBandwidth: 836.37GiB/s\n", + "2021-06-09 19:48:33.014389: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1747] Found device 3 with properties: \n", + "pciBusID: 0000:0f:00.0 name: Tesla V100-DGXS-16GB computeCapability: 7.0\n", + "coreClock: 1.53GHz coreCount: 80 deviceMemorySize: 15.78GiB deviceMemoryBandwidth: 836.37GiB/s\n", + "2021-06-09 19:48:33.014428: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n", + "2021-06-09 19:48:33.014458: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11\n", + "2021-06-09 19:48:33.014478: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11\n", + "2021-06-09 19:48:33.014497: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10\n", + "2021-06-09 19:48:33.014516: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10\n", + "2021-06-09 19:48:33.014534: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.11\n", + "2021-06-09 19:48:33.014552: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11\n", + "2021-06-09 19:48:33.014571: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8\n", + "2021-06-09 19:48:33.022970: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1889] Adding visible gpu devices: 0, 1, 2, 3\n", + "2021-06-09 19:48:33.023016: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n", + "2021-06-09 19:48:35.609734: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1287] Device interconnect StreamExecutor with strength 1 edge matrix:\n", + "2021-06-09 19:48:35.609783: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1293] 0 1 2 3 \n", + "2021-06-09 19:48:35.609797: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1306] 0: N Y Y Y \n", + "2021-06-09 19:48:35.609806: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1306] 1: Y N Y Y \n", + "2021-06-09 19:48:35.609816: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1306] 2: Y Y N Y \n", + "2021-06-09 19:48:35.609825: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1306] 3: Y Y Y N \n", + "2021-06-09 19:48:35.619000: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 203 MB memory) -> physical GPU (device: 0, name: Tesla V100-DGXS-16GB, pci bus id: 0000:07:00.0, compute capability: 7.0)\n", + "2021-06-09 19:48:35.620513: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:1 with 14206 MB memory) -> physical GPU (device: 1, name: Tesla V100-DGXS-16GB, pci bus id: 0000:08:00.0, compute capability: 7.0)\n", + "2021-06-09 19:48:35.621962: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:2 with 14206 MB memory) -> physical GPU (device: 2, name: Tesla V100-DGXS-16GB, pci bus id: 0000:0e:00.0, compute capability: 7.0)\n", + "2021-06-09 19:48:35.623398: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:3 with 14206 MB memory) -> physical GPU (device: 3, name: Tesla V100-DGXS-16GB, pci bus id: 0000:0f:00.0, compute capability: 7.0)\n", + "2021-06-09 19:48:35,625 - WARNING - '--tag' not specified for saved_model. Using --tag serve\n", + "2021-06-09 19:48:43,221 - INFO - Signatures found in model: [serving_default].\n", + "2021-06-09 19:48:43,221 - WARNING - '--signature_def' not specified, using first signature: serving_default\n", + "2021-06-09 19:48:43,222 - INFO - Output names: ['predictions']\n", + "2021-06-09 19:48:43.250962: I tensorflow/core/grappler/devices.cc:69] Number of eligible GPUs (core count >= 8, compute capability >= 0.0): 4\n", + "2021-06-09 19:48:43.251124: I tensorflow/core/grappler/clusters/single_machine.cc:356] Starting new session\n", + "2021-06-09 19:48:43.251388: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set\n", + "2021-06-09 19:48:43.252059: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1747] Found device 0 with properties: \n", + "pciBusID: 0000:07:00.0 name: Tesla V100-DGXS-16GB computeCapability: 7.0\n", + "coreClock: 1.53GHz coreCount: 80 deviceMemorySize: 15.78GiB deviceMemoryBandwidth: 836.37GiB/s\n", + "2021-06-09 19:48:43.253259: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1747] Found device 1 with properties: \n", + "pciBusID: 0000:08:00.0 name: Tesla V100-DGXS-16GB computeCapability: 7.0\n", + "coreClock: 1.53GHz coreCount: 80 deviceMemorySize: 15.78GiB deviceMemoryBandwidth: 836.37GiB/s\n", + "2021-06-09 19:48:43.254444: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1747] Found device 2 with properties: \n", + "pciBusID: 0000:0e:00.0 name: Tesla V100-DGXS-16GB computeCapability: 7.0\n", + "coreClock: 1.53GHz coreCount: 80 deviceMemorySize: 15.78GiB deviceMemoryBandwidth: 836.37GiB/s\n", + "2021-06-09 19:48:43.255627: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1747] Found device 3 with properties: \n", + "pciBusID: 0000:0f:00.0 name: Tesla V100-DGXS-16GB computeCapability: 7.0\n", + "coreClock: 1.53GHz coreCount: 80 deviceMemorySize: 15.78GiB deviceMemoryBandwidth: 836.37GiB/s\n", + "2021-06-09 19:48:43.255663: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n", + "2021-06-09 19:48:43.255693: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11\n", + "2021-06-09 19:48:43.255712: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11\n", + "2021-06-09 19:48:43.255730: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10\n", + "2021-06-09 19:48:43.255748: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10\n", + "2021-06-09 19:48:43.255765: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.11\n", + "2021-06-09 19:48:43.255783: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11\n", + "2021-06-09 19:48:43.255801: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8\n", + "2021-06-09 19:48:43.264001: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1889] Adding visible gpu devices: 0, 1, 2, 3\n", + "2021-06-09 19:48:43.264071: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1287] Device interconnect StreamExecutor with strength 1 edge matrix:\n", + "2021-06-09 19:48:43.264086: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1293] 0 1 2 3 \n", + "2021-06-09 19:48:43.264097: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1306] 0: N Y Y Y \n", + "2021-06-09 19:48:43.264106: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1306] 1: Y N Y Y \n", + "2021-06-09 19:48:43.264116: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1306] 2: Y Y N Y \n", + "2021-06-09 19:48:43.264125: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1306] 3: Y Y Y N \n", + "2021-06-09 19:48:43.269085: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 203 MB memory) -> physical GPU (device: 0, name: Tesla V100-DGXS-16GB, pci bus id: 0000:07:00.0, compute capability: 7.0)\n", + "2021-06-09 19:48:43.270297: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:1 with 14206 MB memory) -> physical GPU (device: 1, name: Tesla V100-DGXS-16GB, pci bus id: 0000:08:00.0, compute capability: 7.0)\n", + "2021-06-09 19:48:43.271732: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:2 with 14206 MB memory) -> physical GPU (device: 2, name: Tesla V100-DGXS-16GB, pci bus id: 0000:0e:00.0, compute capability: 7.0)\n", + "2021-06-09 19:48:43.273448: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:3 with 14206 MB memory) -> physical GPU (device: 3, name: Tesla V100-DGXS-16GB, pci bus id: 0000:0f:00.0, compute capability: 7.0)\n", + "2021-06-09 19:48:43.293134: I tensorflow/core/platform/profile_utils/cpu_utils.cc:112] CPU Frequency: 2198860000 Hz\n", + "2021-06-09 19:48:43.355209: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:954] Optimization results for grappler item: graph_to_optimize\n", + " function_optimizer: Graph size after: 1253 nodes (930), 1908 edges (1585), time = 33.193ms.\n", + " function_optimizer: function_optimizer did nothing. time = 0.577ms.\n", + "\n", + "2021-06-09 19:48:46.008484: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set\n", + "2021-06-09 19:48:46.031017: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1747] Found device 0 with properties: \n", + "pciBusID: 0000:07:00.0 name: Tesla V100-DGXS-16GB computeCapability: 7.0\n", + "coreClock: 1.53GHz coreCount: 80 deviceMemorySize: 15.78GiB deviceMemoryBandwidth: 836.37GiB/s\n", + "2021-06-09 19:48:46.033674: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1747] Found device 1 with properties: \n", + "pciBusID: 0000:08:00.0 name: Tesla V100-DGXS-16GB computeCapability: 7.0\n", + "coreClock: 1.53GHz coreCount: 80 deviceMemorySize: 15.78GiB deviceMemoryBandwidth: 836.37GiB/s\n", + "2021-06-09 19:48:46.035311: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1747] Found device 2 with properties: \n", + "pciBusID: 0000:0e:00.0 name: Tesla V100-DGXS-16GB computeCapability: 7.0\n", + "coreClock: 1.53GHz coreCount: 80 deviceMemorySize: 15.78GiB deviceMemoryBandwidth: 836.37GiB/s\n", + "2021-06-09 19:48:46.036940: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1747] Found device 3 with properties: \n", + "pciBusID: 0000:0f:00.0 name: Tesla V100-DGXS-16GB computeCapability: 7.0\n", + "coreClock: 1.53GHz coreCount: 80 deviceMemorySize: 15.78GiB deviceMemoryBandwidth: 836.37GiB/s\n", + "2021-06-09 19:48:46.036986: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n", + "2021-06-09 19:48:46.037035: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11\n", + "2021-06-09 19:48:46.037062: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11\n", + "2021-06-09 19:48:46.037086: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10\n", + "2021-06-09 19:48:46.037110: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10\n", + "2021-06-09 19:48:46.037133: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.11\n", + "2021-06-09 19:48:46.037157: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11\n", + "2021-06-09 19:48:46.037181: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8\n", + "2021-06-09 19:48:46.046998: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1889] Adding visible gpu devices: 0, 1, 2, 3\n", + "2021-06-09 19:48:46.047077: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1287] Device interconnect StreamExecutor with strength 1 edge matrix:\n", + "2021-06-09 19:48:46.047095: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1293] 0 1 2 3 \n", + "2021-06-09 19:48:46.047108: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1306] 0: N Y Y Y \n", + "2021-06-09 19:48:46.047120: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1306] 1: Y N Y Y \n", + "2021-06-09 19:48:46.047131: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1306] 2: Y Y N Y \n", + "2021-06-09 19:48:46.047142: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1306] 3: Y Y Y N \n", + "2021-06-09 19:48:46.052418: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 203 MB memory) -> physical GPU (device: 0, name: Tesla V100-DGXS-16GB, pci bus id: 0000:07:00.0, compute capability: 7.0)\n", + "2021-06-09 19:48:46.053664: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:1 with 14206 MB memory) -> physical GPU (device: 1, name: Tesla V100-DGXS-16GB, pci bus id: 0000:08:00.0, compute capability: 7.0)\n", + "2021-06-09 19:48:46.054881: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:2 with 14206 MB memory) -> physical GPU (device: 2, name: Tesla V100-DGXS-16GB, pci bus id: 0000:0e:00.0, compute capability: 7.0)\n", + "2021-06-09 19:48:46.056098: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:3 with 14206 MB memory) -> physical GPU (device: 3, name: Tesla V100-DGXS-16GB, pci bus id: 0000:0f:00.0, compute capability: 7.0)\n", + "WARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/tf2onnx/tf_loader.py:603: extract_sub_graph (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Use `tf.compat.v1.graph_util.extract_sub_graph`\n", + "2021-06-09 19:48:46,541 - WARNING - From /usr/local/lib/python3.8/dist-packages/tf2onnx/tf_loader.py:603: extract_sub_graph (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Use `tf.compat.v1.graph_util.extract_sub_graph`\n", + "2021-06-09 19:48:46.600644: I tensorflow/core/grappler/devices.cc:69] Number of eligible GPUs (core count >= 8, compute capability >= 0.0): 4\n", + "2021-06-09 19:48:46.600797: I tensorflow/core/grappler/clusters/single_machine.cc:356] Starting new session\n", + "2021-06-09 19:48:46.601148: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set\n", + "2021-06-09 19:48:46.602435: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1747] Found device 0 with properties: \n", + "pciBusID: 0000:07:00.0 name: Tesla V100-DGXS-16GB computeCapability: 7.0\n", + "coreClock: 1.53GHz coreCount: 80 deviceMemorySize: 15.78GiB deviceMemoryBandwidth: 836.37GiB/s\n", + "2021-06-09 19:48:46.604322: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1747] Found device 1 with properties: \n", + "pciBusID: 0000:08:00.0 name: Tesla V100-DGXS-16GB computeCapability: 7.0\n", + "coreClock: 1.53GHz coreCount: 80 deviceMemorySize: 15.78GiB deviceMemoryBandwidth: 836.37GiB/s\n", + "2021-06-09 19:48:46.606193: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1747] Found device 2 with properties: \n", + "pciBusID: 0000:0e:00.0 name: Tesla V100-DGXS-16GB computeCapability: 7.0\n", + "coreClock: 1.53GHz coreCount: 80 deviceMemorySize: 15.78GiB deviceMemoryBandwidth: 836.37GiB/s\n", + "2021-06-09 19:48:46.608049: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1747] Found device 3 with properties: \n", + "pciBusID: 0000:0f:00.0 name: Tesla V100-DGXS-16GB computeCapability: 7.0\n", + "coreClock: 1.53GHz coreCount: 80 deviceMemorySize: 15.78GiB deviceMemoryBandwidth: 836.37GiB/s\n", + "2021-06-09 19:48:46.608091: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n", + "2021-06-09 19:48:46.608129: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11\n", + "2021-06-09 19:48:46.608153: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11\n", + "2021-06-09 19:48:46.608176: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10\n", + "2021-06-09 19:48:46.608198: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10\n", + "2021-06-09 19:48:46.608220: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.11\n", + "2021-06-09 19:48:46.608242: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11\n", + "2021-06-09 19:48:46.608265: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8\n", + "2021-06-09 19:48:46.625482: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1889] Adding visible gpu devices: 0, 1, 2, 3\n", + "2021-06-09 19:48:46.625560: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1287] Device interconnect StreamExecutor with strength 1 edge matrix:\n", + "2021-06-09 19:48:46.625578: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1293] 0 1 2 3 \n", + "2021-06-09 19:48:46.625590: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1306] 0: N Y Y Y \n", + "2021-06-09 19:48:46.625601: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1306] 1: Y N Y Y \n", + "2021-06-09 19:48:46.625612: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1306] 2: Y Y N Y \n", + "2021-06-09 19:48:46.625623: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1306] 3: Y Y Y N \n", + "2021-06-09 19:48:46.634557: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 203 MB memory) -> physical GPU (device: 0, name: Tesla V100-DGXS-16GB, pci bus id: 0000:07:00.0, compute capability: 7.0)\n", + "2021-06-09 19:48:46.636578: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:1 with 14206 MB memory) -> physical GPU (device: 1, name: Tesla V100-DGXS-16GB, pci bus id: 0000:08:00.0, compute capability: 7.0)\n", + "2021-06-09 19:48:46.638422: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:2 with 14206 MB memory) -> physical GPU (device: 2, name: Tesla V100-DGXS-16GB, pci bus id: 0000:0e:00.0, compute capability: 7.0)\n", + "2021-06-09 19:48:46.640290: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:3 with 14206 MB memory) -> physical GPU (device: 3, name: Tesla V100-DGXS-16GB, pci bus id: 0000:0f:00.0, compute capability: 7.0)\n", + "2021-06-09 19:48:47.379855: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:954] Optimization results for grappler item: graph_to_optimize\n", + " constant_folding: Graph size after: 560 nodes (-640), 1215 edges (-640), time = 399.986ms.\n", + " function_optimizer: function_optimizer did nothing. time = 1.17ms.\n", + " constant_folding: Graph size after: 560 nodes (0), 1215 edges (0), time = 101.728ms.\n", + " function_optimizer: function_optimizer did nothing. time = 1.017ms.\n", + "\n", + "2021-06-09 19:48:47,938 - INFO - Using tensorflow=2.4.0, onnx=1.9.0, tf2onnx=1.8.5/50049d\n", + "2021-06-09 19:48:47,939 - INFO - Using opset \n", + "2021-06-09 19:48:52,720 - INFO - Computed 0 values for constant folding\n", + "2021-06-09 19:49:05,218 - INFO - Optimizing ONNX model\n", + "2021-06-09 19:49:06,920 - INFO - After optimization: Add -1 (18->17), BatchNormalization -53 (53->0), Const -162 (270->108), GlobalAveragePool +1 (0->1), Identity -57 (57->0), ReduceMean -1 (1->0), Squeeze +1 (0->1), Transpose -213 (214->1)\n", + "2021-06-09 19:49:07,076 - INFO - \n", + "2021-06-09 19:49:07,076 - INFO - Successfully converted TensorFlow model my_model to ONNX\n", + "2021-06-09 19:49:07,076 - INFO - Model inputs: ['input_1:0']\n", + "2021-06-09 19:49:07,076 - INFO - Model outputs: ['predictions']\n", + "2021-06-09 19:49:07,076 - INFO - ONNX model is saved at temp.onnx\n" + ] + } + ], + "source": [ + "model.save('my_model')\n", + "!python -m tf2onnx.convert --saved-model my_model --output temp.onnx\n", + "onnx_model = onnx.load_model('temp.onnx')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That said, we do need to make one change for our model to work with TensorRT. Keras by default uses a dynamic input shape in its networks - where it can handle arbitrary batch sizes at every update. While TensorRT can do this, it requires extra configuration. \n", + "\n", + "Instead, we will just set the input size to be fixed to our batch size. This will work with TensorRT out of the box!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "__Configure ONNX File Batch Size:__" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "__Note:__ We need to do two things to set our batch size with ONNX. The first is to modify our ONNX file to change its default batch size to our target batch size. The second is setting our converter to use the __explicit batch__ mode, which will use this default batch size as our final batch size." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "inputs = onnx_model.graph.input\n", + "for input in inputs:\n", + " dim1 = input.type.tensor_type.shape.dim[0]\n", + " dim1.dim_value = BATCH_SIZE" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "__Save Model:__" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "id": "jFT6-13f8qup" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Done saving!\n" + ] + } + ], + "source": [ + "model_name = \"resnet50_onnx_model.onnx\"\n", + "onnx.save_model(onnx_model, model_name)\n", + "print(\"Done saving!\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once we get our model into ONNX format, we can convert it efficiently using TensorRT. For this, TensorRT needs exclusive access to your GPU. If you so much as import Tensorflow, it will generally consume all of your GPU memory. To get around this, before moving on go ahead and shut down this notebook and restart it. (You can do this in the menu: Kernel -> Restart Kernel)\n", + "\n", + "Make sure not to import Tensorflow at any point after restarting the runtime! \n", + "\n", + "(The following cell is a quick shortcut to make your notebook restart:)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "uZUnHVHE8quu" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Restarting kernel in three seconds...\n" + ] + } + ], + "source": [ + "import os, time\n", + "print(\"Restarting kernel in three seconds...\")\n", + "time.sleep(3)\n", + "print(\"Restarting kernel now\")\n", + "os._exit(0) # Shut down all kernels so TRT doesn't fight with Tensorflow for GPU memory - TF monopolizes all GPU memory by default" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. What batch size(s) am I running inference at?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have actually already set our inference batch size - see the note above in section 1!\n", + "\n", + "We are going to set our target batch size to a fixed size of 32." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "BATCH_SIZE = 32" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We need to do two things to set our batch size to a fixed batch size with ONNX: \n", + "\n", + "1. Modify our ONNX file to change its default batch size to our target batch size, which we did above.\n", + "2. Use the trtexec --explicitBatch flag, which we also did above." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. What precision am I running inference at?\n", + "\n", + "Now, we have a converted TensorRT engine. Great! That means we are ready to load it into the native Python TensorRT runtime. This runtime strikes a balance between the ease of use of the high level Python runtimes and the low level C++ runtimes.\n", + "\n", + "First, as before, lets create a dummy batch. Importantly, by default TensorRT will use the input precision you give it as the default precision for the rest of the network. \n", + "\n", + "Remember that lower precisions than FP32 tend to run faster. There are two common reduced precision modes - FP16 and INT8. Graphics cards that are designed to do inference well often have an affinity for one of these two types. This guide was developed on an NVIDIA V100, which favors FP16, so we will use that here by default. INT8 is a more complicated process that requires a calibration step." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "USE_FP16 = True\n", + "\n", + "target_dtype = np.float16 if USE_FP16 else np.float32" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We generate a batch of repeating Golden Retriever images, as before. Make sure that for TensorRT the image is resized to the size your model expects. Tensorflow and TensorRT have different behavior for handling 'oversized' images - so this is a safe way of ensuring consistent results across the two." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from skimage import io\n", + "from skimage.transform import resize\n", + "from matplotlib import pyplot as plt\n", + "\n", + "url='https://images.dog.ceo/breeds/retriever-golden/n02099601_3004.jpg'\n", + "img = resize(io.imread(url), (224, 224))\n", + "input_batch = 255*np.array(np.repeat(np.expand_dims(np.array(img, dtype=np.float32), axis=0), BATCH_SIZE, axis=0), dtype=np.float32)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Only we must now cast the input batch to the proper FP32/FP16 precision:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "input_batch = input_batch.astype(target_dtype)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. What TensorRT path am I using to convert my model?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "TensorRT is able to take ONNX models and convert them entirely into a single, efficient TensorRT engine. Restart your Jupyter kernel, and then start here!\n", + "\n", + "We can use trtexec, a command line tool for working with TensorRT, in order to convert an ONNX model to an engine file.\n", + "\n", + "To convert the model we saved in the previous steps, we need to point to the ONNX file, give trtexec a name to save the engine as, and last specify that we want to use a fixed batch size instead of a dynamic one.\n", + "\n", + "__Remember to shut down all Jupyter notebooks and restart your Jupyter kernel after \"1. What format should I save my model in?\" - otherwise this cell will crash as TensorRT competes with Tensorflow for GPU memory:__" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "id": "h60Gmotx8quz", + "outputId": "065384aa-c848-4194-c72c-cad0d80449ca" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "&&&& RUNNING TensorRT.trtexec # trtexec --onnx=resnet50_onnx_model.onnx --saveEngine=resnet_engine.trt --explicitBatch --inputIOFormats=fp16:chw --outputIOFormats=fp16:chw --fp16\n", + "[06/09/2021-19:49:25] [I] === Model Options ===\n", + "[06/09/2021-19:49:25] [I] Format: ONNX\n", + "[06/09/2021-19:49:25] [I] Model: resnet50_onnx_model.onnx\n", + "[06/09/2021-19:49:25] [I] Output:\n", + "[06/09/2021-19:49:25] [I] === Build Options ===\n", + "[06/09/2021-19:49:25] [I] Max batch: explicit\n", + "[06/09/2021-19:49:25] [I] Workspace: 16 MiB\n", + "[06/09/2021-19:49:25] [I] minTiming: 1\n", + "[06/09/2021-19:49:25] [I] avgTiming: 8\n", + "[06/09/2021-19:49:25] [I] Precision: FP32+FP16\n", + "[06/09/2021-19:49:25] [I] Calibration: \n", + "[06/09/2021-19:49:25] [I] Refit: Disabled\n", + "[06/09/2021-19:49:25] [I] Safe mode: Disabled\n", + "[06/09/2021-19:49:25] [I] Save engine: resnet_engine.trt\n", + "[06/09/2021-19:49:25] [I] Load engine: \n", + "[06/09/2021-19:49:25] [I] Builder Cache: Enabled\n", + "[06/09/2021-19:49:25] [I] NVTX verbosity: 0\n", + "[06/09/2021-19:49:25] [I] Tactic sources: Using default tactic sources\n", + "[06/09/2021-19:49:25] [I] Input(s): fp16:chw\n", + "[06/09/2021-19:49:25] [I] Output(s): fp16:chw\n", + "[06/09/2021-19:49:25] [I] Input build shapes: model\n", + "[06/09/2021-19:49:25] [I] Input calibration shapes: model\n", + "[06/09/2021-19:49:25] [I] === System Options ===\n", + "[06/09/2021-19:49:25] [I] Device: 0\n", + "[06/09/2021-19:49:25] [I] DLACore: \n", + "[06/09/2021-19:49:25] [I] Plugins:\n", + "[06/09/2021-19:49:25] [I] === Inference Options ===\n", + "[06/09/2021-19:49:25] [I] Batch: Explicit\n", + "[06/09/2021-19:49:25] [I] Input inference shapes: model\n", + "[06/09/2021-19:49:25] [I] Iterations: 10\n", + "[06/09/2021-19:49:25] [I] Duration: 3s (+ 200ms warm up)\n", + "[06/09/2021-19:49:25] [I] Sleep time: 0ms\n", + "[06/09/2021-19:49:25] [I] Streams: 1\n", + "[06/09/2021-19:49:25] [I] ExposeDMA: Disabled\n", + "[06/09/2021-19:49:25] [I] Data transfers: Enabled\n", + "[06/09/2021-19:49:25] [I] Spin-wait: Disabled\n", + "[06/09/2021-19:49:25] [I] Multithreading: Disabled\n", + "[06/09/2021-19:49:25] [I] CUDA Graph: Disabled\n", + "[06/09/2021-19:49:25] [I] Separate profiling: Disabled\n", + "[06/09/2021-19:49:25] [I] Skip inference: Disabled\n", + "[06/09/2021-19:49:25] [I] Inputs:\n", + "[06/09/2021-19:49:25] [I] === Reporting Options ===\n", + "[06/09/2021-19:49:25] [I] Verbose: Disabled\n", + "[06/09/2021-19:49:25] [I] Averages: 10 inferences\n", + "[06/09/2021-19:49:25] [I] Percentile: 99\n", + "[06/09/2021-19:49:25] [I] Dump refittable layers:Disabled\n", + "[06/09/2021-19:49:25] [I] Dump output: Disabled\n", + "[06/09/2021-19:49:25] [I] Profile: Disabled\n", + "[06/09/2021-19:49:25] [I] Export timing to JSON file: \n", + "[06/09/2021-19:49:25] [I] Export output to JSON file: \n", + "[06/09/2021-19:49:25] [I] Export profile to JSON file: \n", + "[06/09/2021-19:49:25] [I] \n", + "[06/09/2021-19:49:25] [I] === Device Information ===\n", + "[06/09/2021-19:49:25] [I] Selected Device: Tesla V100-DGXS-16GB\n", + "[06/09/2021-19:49:25] [I] Compute Capability: 7.0\n", + "[06/09/2021-19:49:25] [I] SMs: 80\n", + "[06/09/2021-19:49:25] [I] Compute Clock Rate: 1.53 GHz\n", + "[06/09/2021-19:49:25] [I] Device Global Memory: 16155 MiB\n", + "[06/09/2021-19:49:25] [I] Shared Memory per SM: 96 KiB\n", + "[06/09/2021-19:49:25] [I] Memory Bus Width: 4096 bits (ECC enabled)\n", + "[06/09/2021-19:49:25] [I] Memory Clock Rate: 0.877 GHz\n", + "[06/09/2021-19:49:25] [I] \n", + "[06/09/2021-19:49:42] [I] [TRT] ----------------------------------------------------------------\n", + "[06/09/2021-19:49:42] [I] [TRT] Input filename: resnet50_onnx_model.onnx\n", + "[06/09/2021-19:49:42] [I] [TRT] ONNX IR version: 0.0.4\n", + "[06/09/2021-19:49:42] [I] [TRT] Opset version: 9\n", + "[06/09/2021-19:49:42] [I] [TRT] Producer name: tf2onnx\n", + "[06/09/2021-19:49:42] [I] [TRT] Producer version: 1.8.5\n", + "[06/09/2021-19:49:42] [I] [TRT] Domain: \n", + "[06/09/2021-19:49:42] [I] [TRT] Model version: 0\n", + "[06/09/2021-19:49:42] [I] [TRT] Doc string: \n", + "[06/09/2021-19:49:42] [I] [TRT] ----------------------------------------------------------------\n", + "[06/09/2021-19:49:48] [I] [TRT] Some tactics do not have sufficient workspace memory to run. Increasing workspace size may increase performance, please check verbose output.\n", + "[06/09/2021-19:51:05] [I] [TRT] Detected 1 inputs and 1 output network tensors.\n", + "[06/09/2021-19:51:06] [I] Engine built in 100.683 sec.\n", + "[06/09/2021-19:51:06] [I] Starting inference\n", + "[06/09/2021-19:51:09] [I] Warmup completed 0 queries over 200 ms\n", + "[06/09/2021-19:51:09] [I] Timing trace has 0 queries over 2.99006 s\n", + "[06/09/2021-19:51:09] [I] Trace averages of 10 runs:\n", + "[06/09/2021-19:51:09] [I] Average on 10 runs - GPU latency: 5.48546 ms - Host latency: 6.30948 ms (end to end 10.0032 ms, enqueue 0.539108 ms)\n", + "[06/09/2021-19:51:09] [I] Average on 10 runs - GPU latency: 5.48946 ms - Host latency: 6.31468 ms (end to end 10.9038 ms, enqueue 0.516052 ms)\n", + "[06/09/2021-19:51:09] [I] Average on 10 runs - GPU latency: 5.48004 ms - Host latency: 6.3107 ms (end to end 10.8822 ms, enqueue 0.513507 ms)\n", + "[06/09/2021-19:51:09] [I] Average on 10 runs - GPU latency: 5.49315 ms - Host latency: 6.34006 ms (end to end 10.4643 ms, enqueue 0.512753 ms)\n", + "[06/09/2021-19:51:09] [I] Average on 10 runs - GPU latency: 5.52059 ms - Host latency: 6.36953 ms (end to end 10.2954 ms, enqueue 0.498505 ms)\n", + "[06/09/2021-19:51:09] [I] Average on 10 runs - GPU latency: 5.50788 ms - Host latency: 6.3551 ms (end to end 9.11696 ms, enqueue 0.518701 ms)\n", + "[06/09/2021-19:51:09] [I] Average on 10 runs - GPU latency: 5.49774 ms - Host latency: 6.3454 ms (end to end 10.9278 ms, enqueue 0.495056 ms)\n", + "[06/09/2021-19:51:09] [I] Average on 10 runs - GPU latency: 5.50585 ms - Host latency: 6.35638 ms (end to end 10.9322 ms, enqueue 0.505725 ms)\n", + "[06/09/2021-19:51:09] [I] Average on 10 runs - GPU latency: 5.50247 ms - Host latency: 6.35249 ms (end to end 10.5564 ms, enqueue 0.513574 ms)\n", + "[06/09/2021-19:51:09] [I] Average on 10 runs - GPU latency: 5.51249 ms - Host latency: 6.36059 ms (end to end 9.63242 ms, enqueue 0.498096 ms)\n", + "[06/09/2021-19:51:09] [I] Average on 10 runs - GPU latency: 5.4911 ms - Host latency: 6.33875 ms (end to end 8.90275 ms, enqueue 0.474237 ms)\n", + "[06/09/2021-19:51:09] [I] Average on 10 runs - GPU latency: 5.50072 ms - Host latency: 6.34651 ms (end to end 10.4826 ms, enqueue 0.498499 ms)\n", + "[06/09/2021-19:51:09] [I] Average on 10 runs - GPU latency: 5.49602 ms - Host latency: 6.34083 ms (end to end 10.92 ms, enqueue 0.486401 ms)\n", + "[06/09/2021-19:51:09] [I] Average on 10 runs - GPU latency: 5.49089 ms - Host latency: 6.3358 ms (end to end 10.8925 ms, enqueue 0.490247 ms)\n", + "[06/09/2021-19:51:09] [I] Average on 10 runs - GPU latency: 5.48907 ms - Host latency: 6.33452 ms (end to end 10.1912 ms, enqueue 0.482959 ms)\n", + "[06/09/2021-19:51:09] [I] Average on 10 runs - GPU latency: 5.47534 ms - Host latency: 6.31992 ms (end to end 8.9359 ms, enqueue 0.484119 ms)\n", + "[06/09/2021-19:51:09] [I] Average on 10 runs - GPU latency: 5.47952 ms - Host latency: 6.32281 ms (end to end 10.4421 ms, enqueue 0.481885 ms)\n", + "[06/09/2021-19:51:09] [I] Average on 10 runs - GPU latency: 5.48701 ms - Host latency: 6.33408 ms (end to end 10.9013 ms, enqueue 0.491455 ms)\n", + "[06/09/2021-19:51:09] [I] Average on 10 runs - GPU latency: 5.48179 ms - Host latency: 6.33092 ms (end to end 10.885 ms, enqueue 0.505078 ms)\n", + "[06/09/2021-19:51:09] [I] Average on 10 runs - GPU latency: 5.48776 ms - Host latency: 6.33756 ms (end to end 10.3106 ms, enqueue 0.494629 ms)\n", + "[06/09/2021-19:51:09] [I] Average on 10 runs - GPU latency: 5.47145 ms - Host latency: 6.31754 ms (end to end 9.37426 ms, enqueue 0.481995 ms)\n", + "[06/09/2021-19:51:09] [I] Average on 10 runs - GPU latency: 5.48057 ms - Host latency: 6.32472 ms (end to end 9.55609 ms, enqueue 0.480151 ms)\n", + "[06/09/2021-19:51:09] [I] Average on 10 runs - GPU latency: 5.48557 ms - Host latency: 6.33252 ms (end to end 10.4543 ms, enqueue 0.486841 ms)\n", + "[06/09/2021-19:51:09] [I] Average on 10 runs - GPU latency: 5.50972 ms - Host latency: 6.35627 ms (end to end 10.9478 ms, enqueue 0.488062 ms)\n", + "[06/09/2021-19:51:09] [I] Average on 10 runs - GPU latency: 5.50054 ms - Host latency: 6.34517 ms (end to end 10.0418 ms, enqueue 0.483325 ms)\n", + "[06/09/2021-19:51:09] [I] Average on 10 runs - GPU latency: 5.48201 ms - Host latency: 6.32832 ms (end to end 9.67512 ms, enqueue 0.481812 ms)\n", + "[06/09/2021-19:51:09] [I] Average on 10 runs - GPU latency: 5.48279 ms - Host latency: 6.32742 ms (end to end 9.18972 ms, enqueue 0.484082 ms)\n", + "[06/09/2021-19:51:09] [I] Average on 10 runs - GPU latency: 5.47712 ms - Host latency: 6.32109 ms (end to end 10.879 ms, enqueue 0.482202 ms)\n", + "[06/09/2021-19:51:09] [I] Average on 10 runs - GPU latency: 5.47788 ms - Host latency: 6.32166 ms (end to end 10.8823 ms, enqueue 0.481006 ms)\n", + "[06/09/2021-19:51:09] [I] Average on 10 runs - GPU latency: 5.48203 ms - Host latency: 6.32615 ms (end to end 10.6967 ms, enqueue 0.481055 ms)\n", + "[06/09/2021-19:51:09] [I] Average on 10 runs - GPU latency: 5.46802 ms - Host latency: 6.31384 ms (end to end 9.47229 ms, enqueue 0.477344 ms)\n", + "[06/09/2021-19:51:09] [I] Average on 10 runs - GPU latency: 5.4967 ms - Host latency: 6.3428 ms (end to end 8.9686 ms, enqueue 0.48147 ms)\n", + "[06/09/2021-19:51:09] [I] Average on 10 runs - GPU latency: 5.49275 ms - Host latency: 6.33767 ms (end to end 9.57681 ms, enqueue 0.481714 ms)\n", + "[06/09/2021-19:51:09] [I] Average on 10 runs - GPU latency: 5.52278 ms - Host latency: 6.37007 ms (end to end 10.9759 ms, enqueue 0.493896 ms)\n", + "[06/09/2021-19:51:09] [I] Average on 10 runs - GPU latency: 5.49238 ms - Host latency: 6.34084 ms (end to end 10.7861 ms, enqueue 0.49917 ms)\n", + "[06/09/2021-19:51:09] [I] Average on 10 runs - GPU latency: 5.48333 ms - Host latency: 6.33235 ms (end to end 10.4963 ms, enqueue 0.500806 ms)\n", + "[06/09/2021-19:51:09] [I] Host Latency\n", + "[06/09/2021-19:51:09] [I] min: 6.28442 ms (end to end 6.327 ms)\n", + "[06/09/2021-19:51:09] [I] max: 6.66431 ms (end to end 11.2405 ms)\n", + "[06/09/2021-19:51:09] [I] mean: 6.33588 ms (end to end 10.2251 ms)\n", + "[06/09/2021-19:51:09] [I] median: 6.33411 ms (end to end 10.8945 ms)\n", + "[06/09/2021-19:51:09] [I] percentile: 6.38745 ms at 99% (end to end 11.0925 ms at 99%)\n", + "[06/09/2021-19:51:09] [I] throughput: 0 qps\n", + "[06/09/2021-19:51:09] [I] walltime: 2.99006 s\n", + "[06/09/2021-19:51:09] [I] Enqueue Time\n", + "[06/09/2021-19:51:09] [I] min: 0.413086 ms\n", + "[06/09/2021-19:51:09] [I] max: 0.796997 ms\n", + "[06/09/2021-19:51:09] [I] median: 0.486877 ms\n", + "[06/09/2021-19:51:09] [I] GPU Compute\n", + "[06/09/2021-19:51:09] [I] min: 5.4425 ms\n", + "[06/09/2021-19:51:09] [I] max: 5.82251 ms\n", + "[06/09/2021-19:51:09] [I] mean: 5.49097 ms\n", + "[06/09/2021-19:51:09] [I] median: 5.48969 ms\n", + "[06/09/2021-19:51:09] [I] percentile: 5.53986 ms at 99%\n", + "[06/09/2021-19:51:09] [I] total compute time: 2.00421 s\n", + "&&&& PASSED TensorRT.trtexec # trtexec --onnx=resnet50_onnx_model.onnx --saveEngine=resnet_engine.trt --explicitBatch --inputIOFormats=fp16:chw --outputIOFormats=fp16:chw --fp16\n" + ] + } + ], + "source": [ + "# May need to shut down all kernels and restart before this - otherwise you might get cuDNN initialization errors:\n", + "if USE_FP16:\n", + " !trtexec --onnx=resnet50_onnx_model.onnx --saveEngine=resnet_engine.trt --explicitBatch --inputIOFormats=fp16:chw --outputIOFormats=fp16:chw --fp16\n", + "else:\n", + " !trtexec --onnx=resnet50_onnx_model.onnx --saveEngine=resnet_engine.trt --explicitBatch" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "-\n", + "\n", + "__The trtexec Logs:__\n", + "\n", + "Above, trtexec does a lot of things! Some important things to note:\n", + "\n", + "__First__, _\"PASSED\"_ is what you want to see in the last line of the log above. We can see our conversion was successful!\n", + "\n", + "__Second__, can see the resnet_engine.trt engine file has indeed been successfully created: " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total 508284\n", + "drwxrwxr-x 8 1000 1000 4096 Jun 9 19:49 .\n", + "drwxrwxr-x 5 1000 1000 4096 Apr 5 23:28 ..\n", + "drwxr-xr-x 2 root root 4096 Apr 6 01:13 .ipynb_checkpoints\n", + "-rw-rw-r-- 1 1000 1000 34748 Jun 9 19:46 '0. Running This Guide.ipynb'\n", + "-rw-rw-r-- 1 1000 1000 502649 Apr 5 23:28 '1. Introduction.ipynb'\n", + "-rw-rw-r-- 1 1000 1000 23645 Apr 5 23:28 '2. Using the Tensorflow TensorRT Integration.ipynb'\n", + "-rw-rw-r-- 1 1000 1000 210995 Jun 9 19:49 '3. Using Tensorflow 2 through ONNX.ipynb'\n", + "-rw-rw-r-- 1 1000 1000 334050 Jun 9 19:17 '4. Using PyTorch through ONNX.ipynb'\n", + "-rw-rw-r-- 1 1000 1000 7052 Apr 5 23:28 '5. Understanding TensorRT Runtimes.ipynb'\n", + "drwxrwxr-x 2 1000 1000 4096 Apr 5 23:28 'Additional Examples'\n", + "drwxr-xr-x 2 root root 4096 Apr 5 23:28 Getting_Started\n", + "drwxr-xr-x 2 root root 4096 Apr 6 01:09 __pycache__\n", + "-rw-rw-r-- 1 1000 1000 4085 Apr 5 23:28 helper.py\n", + "drwxrwxr-x 2 1000 1000 4096 Apr 5 23:28 images\n", + "drwxr-xr-x 4 root root 4096 Jun 9 19:48 my_model\n", + "-rw-rw-r-- 1 1000 1000 3228 Apr 5 23:28 onnx_helper.py\n", + "-rw-r--r-- 1 root root 102169836 Jun 9 19:49 resnet50_onnx_model.onnx\n", + "-rw-r--r-- 1 root root 102470353 Apr 6 04:18 resnet50_pytorch.onnx\n", + "-rw-r--r-- 1 root root 51398352 Jun 9 19:51 resnet_engine.trt\n", + "-rw-r--r-- 1 root root 161081907 Apr 6 17:38 resnet_engine_pytorch.trt\n", + "-rw-r--r-- 1 root root 102169844 Jun 9 19:49 temp.onnx\n" + ] + } + ], + "source": [ + "!ls -la" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "__Third__, you can see timing details above using trtexec - these are in the ideal case with no overhead. Depending on how you run your model, a considerable amount of overhead can be added to this. We can do timing in our Python runtime below - but keep in mind performing C++ inference would likely be faster." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. What TensorRT runtime am I targeting?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We want to run our TensorRT inference in Python - so the TensorRT Python API is a great way of testing our model out in Jupyter, and is still quite performant.\n", + "\n", + "To use it, we need to do a few steps:\n", + "\n", + "__Load our engine into a tensorrt.Runtime:__" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "dX2jFwrA8qu6" + }, + "outputs": [], + "source": [ + "import tensorrt as trt\n", + "import pycuda.driver as cuda\n", + "import pycuda.autoinit\n", + "\n", + "f = open(\"resnet_engine.trt\", \"rb\")\n", + "runtime = trt.Runtime(trt.Logger(trt.Logger.WARNING)) \n", + "\n", + "engine = runtime.deserialize_cuda_engine(f.read())\n", + "context = engine.create_execution_context()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note: if this cell is having issues, restarting all Jupyter kernels and rerunning only the batch size and precision cells above before trying again often helps" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "__Allocate input and output memory, give TRT pointers (bindings) to it:__\n", + "\n", + "d_input and d_output refer to the memory regions on our 'device' (aka GPU) - as opposed to memory on our normal RAM, where Python holds its variables (such as 'output' below)." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "id": "q3UJcdWy8qu8" + }, + "outputs": [], + "source": [ + "output = np.empty([BATCH_SIZE, 1000], dtype = target_dtype) # Need to set output dtype to FP16 to enable FP16\n", + "\n", + "# Allocate device memory\n", + "d_input = cuda.mem_alloc(1 * input_batch.nbytes)\n", + "d_output = cuda.mem_alloc(1 * output.nbytes)\n", + "\n", + "bindings = [int(d_input), int(d_output)]\n", + "\n", + "stream = cuda.Stream()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "__Set up prediction function:__\n", + "\n", + "This involves a copy from CPU RAM to GPU VRAM, executing the model, then copying the results back from GPU VRAM to CPU RAM:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "id": "6R-F8JtV8qu-" + }, + "outputs": [], + "source": [ + "def predict(batch): # result gets copied into output\n", + " # Transfer input data to device\n", + " cuda.memcpy_htod_async(d_input, batch, stream)\n", + " # Execute model\n", + " context.execute_async_v2(bindings, stream.handle, None)\n", + " # Transfer predictions back\n", + " cuda.memcpy_dtoh_async(output, d_output, stream)\n", + " # Syncronize threads\n", + " stream.synchronize()\n", + " \n", + " return output" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is all we need to run predictions using our TensorRT engine in a Python runtime!" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "id": "AdKZzW7O8qvB" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warming up...\n", + "Done warming up!\n" + ] + } + ], + "source": [ + "print(\"Warming up...\")\n", + "\n", + "trt_predictions = predict(input_batch).astype(np.float32)\n", + "\n", + "print(\"Done warming up!\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Class | Probability (out of 1)\n" + ] + }, + { + "data": { + "text/plain": [ + "[(160, 0.3112793),\n", + " (169, 0.27026367),\n", + " (212, 0.17321777),\n", + " (170, 0.07165527),\n", + " (207, 0.033843994)]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "indices = (-trt_predictions[0]).argsort()[:5]\n", + "print(\"Class | Probability (out of 1)\")\n", + "list(zip(indices, trt_predictions[0][indices]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that we have recovered our same predictions as before!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Performance Comparison:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Last, we can see how quickly we can feed a singular batch to TensorRT, which we can compare to our original Tensorflow experiment from earlier." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We use the %%timeit Jupyter magic again. Note that %%timeit is fairly rough, and for any actual benchmarking better controlled testing is required - preferably outside of Jupyter." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "id": "XAtWnCK38qvD" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6.41 ms ± 846 ns per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" + ] + } + ], + "source": [ + "%%timeit\n", + "\n", + "_ = predict(input_batch) # Check TRT performance" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Next Steps:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Profiling

\n", + "\n", + "This is a great next step for further optimizing and debugging models you are working on productionizing\n", + "\n", + "You can find it here: https://docs.nvidia.com/deeplearning/tensorrt/best-practices/index.html\n", + "\n", + "

TRT Dev Docs

\n", + "\n", + "Main documentation page for the ONNX, layer builder, C++, and legacy APIs\n", + "\n", + "You can find it here: https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html\n", + "\n", + "

TRT OSS GitHub

\n", + "\n", + "Contains OSS TRT components, sample applications, and plugin examples\n", + "\n", + "You can find it here: https://github.com/NVIDIA/TensorRT\n", + "\n", + "\n", + "#### TRT Supported Layers:\n", + "\n", + "https://github.com/NVIDIA/TensorRT/tree/main/samples/opensource/samplePlugin\n", + "\n", + "#### TRT ONNX Plugin Example:\n", + "\n", + "https://docs.nvidia.com/deeplearning/tensorrt/support-matrix/index.html#layers-precision-matrix\n" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "name": "ONNXExample.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/Notebook Tutorials/4. Using PyTorch through ONNX.ipynb b/examples/Notebook Tutorials/4. Using PyTorch through ONNX.ipynb new file mode 100644 index 0000000..b90f9d4 --- /dev/null +++ b/examples/Notebook Tutorials/4. Using PyTorch through ONNX.ipynb @@ -0,0 +1,992 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Using PyTorch with TensorRT through ONNX:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "TensorRT is a great way to take a trained PyTorch model and optimize it to run more efficiently during inference on an NVIDIA GPU.\n", + "\n", + "One approach to convert a PyTorch model to TensorRT is to export a PyTorch model to ONNX (an open format exchange for deep learning models) and then convert into a TensorRT engine. Essentially, we will follow this path to convert and deploy our model:\n", + "\n", + "![PyTorch+ONNX](./images/pytorch_onnx.png)\n", + "\n", + "Both TensorFlow and PyTorch models can be exported to ONNX, as well as many other frameworks. This allows models created using either framework to flow into common downstream pipelines.\n", + "\n", + "To get started, let's take a well-known computer vision model and follow five key steps to deploy it to the TensorRT Python runtime:\n", + "\n", + "1. __What format should I save my model in?__\n", + "2. __What batch size(s) am I running inference at?__\n", + "3. __What precision am I running inference at?__\n", + "4. __What TensorRT path am I using to convert my model?__\n", + "5. __What runtime am I targeting?__" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. What format should I save my model in?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are going to use ResNet50, a widely used CNN architecture first described in this paper.\n", + "\n", + "Let's start by loading dependencies and downloading the model. We will also move our Resnet model onto the GPU and set it to evaluation mode." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Downloading: \"https://download.pytorch.org/models/resnet50-19c8e357.pth\" to /root/.cache/torch/hub/checkpoints/resnet50-19c8e357.pth\n" + ] + } + ], + "source": [ + "import torchvision.models as models\n", + "import torch\n", + "import torch.onnx\n", + "\n", + "# load the pretrained model\n", + "resnet50 = models.resnet50(pretrained=True, progress=False).eval()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When saving a model to ONNX, PyTorch requires a test batch in proper shape and format. We pick a batch size:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "BATCH_SIZE=32\n", + "\n", + "dummy_input=torch.randn(BATCH_SIZE, 3, 224, 224)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we will export the model using the dummy input batch:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# export the model to ONNX\n", + "torch.onnx.export(resnet50, dummy_input, \"resnet50_pytorch.onnx\", verbose=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that we are picking a BATCH_SIZE of 32 in this example." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Now Test with a Real Image:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's try a real image batch! For this example, we will simply repeat one open-source dog image from http://www.dog.ceo:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(32, 224, 224, 3)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from skimage import io\n", + "from skimage.transform import resize\n", + "from matplotlib import pyplot as plt\n", + "import numpy as np\n", + "\n", + "url='https://images.dog.ceo/breeds/retriever-golden/n02099601_3004.jpg'\n", + "img = resize(io.imread(url), (224, 224))\n", + "img = np.expand_dims(np.array(img, dtype=np.float32), axis=0) # Expand image to have a batch dimension\n", + "input_batch = np.array(np.repeat(img, BATCH_SIZE, axis=0), dtype=np.float32) # Repeat across the batch dimension\n", + "\n", + "input_batch.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.imshow(input_batch[0].astype(np.float32))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "resnet50_gpu = models.resnet50(pretrained=True, progress=False).to(\"cuda\").eval()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We need to move our batch onto GPU and properly format it to shape [32, 3, 224, 224]. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([32, 3, 224, 224])" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "input_batch_chw = torch.from_numpy(input_batch).transpose(1,3).transpose(2,3)\n", + "input_batch_gpu = input_batch_chw.to(\"cuda\")\n", + "\n", + "input_batch_gpu.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can run a prediction on a batch using .forward():" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(32, 1000)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "with torch.no_grad():\n", + " predictions = np.array(resnet50_gpu(input_batch_gpu).cpu())\n", + "\n", + "predictions.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Verify Baseline Model Performance/Accuracy:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For a baseline, lets time our prediction in FP32:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "31.5 ms ± 72.1 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" + ] + } + ], + "source": [ + "%%timeit\n", + "\n", + "with torch.no_grad():\n", + " preds = np.array(resnet50_gpu(input_batch_gpu).cpu())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also time FP16 precision performance:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(32, 1000)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "resnet50_gpu_half = resnet50_gpu.half()\n", + "input_half = input_batch_gpu.half()\n", + "\n", + "with torch.no_grad():\n", + " preds = np.array(resnet50_gpu_half(input_half).cpu()) # Warm Up\n", + " \n", + "preds.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "19.4 ms ± 5.42 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" + ] + } + ], + "source": [ + "%%timeit\n", + "\n", + "with torch.no_grad():\n", + " preds = np.array(resnet50_gpu_half(input_half).cpu())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's also make sure our results are accurate. We will look at the top 5 accuracy on a single image prediction. The image we are using is of a Golden Retriever, which is class 207 in the ImageNet dataset our model was trained on." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Class | Likelihood\n" + ] + }, + { + "data": { + "text/plain": [ + "[(207, 13.121688),\n", + " (208, 9.614037),\n", + " (257, 9.361297),\n", + " (205, 8.777787),\n", + " (160, 8.557351)]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "indices = (-predictions[0]).argsort()[:5]\n", + "print(\"Class | Likelihood\")\n", + "list(zip(indices, predictions[0][indices]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have a model exported to ONNX and a baseline to compare against! Let's now take our ONNX model and convert it to a TensorRT inference engine." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let's restart our Jupyter Kernel so PyTorch doesn't collide with TensorRT: " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "os._exit(0) # Shut down all kernels so TRT doesn't fight with PyTorch for GPU memory" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. What batch size(s) am I running inference at?\n", + "\n", + "We are going to run with a fixed batch size of 32 for this example. Note that above we set BATCH_SIZE to 32 when saving our model to ONNX. We need to create another dummy batch of the same size (this time it will need to be in our target precision) to test out our engine.\n", + "\n", + "First, as before, we will set our BATCH_SIZE to 32. Note that our trtexec command above includes the '--explicitBatch' flag to signal to TensorRT that we will be using a fixed batch size at runtime." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "BATCH_SIZE = 32" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Importantly, by default TensorRT will use the input precision you give the runtime as the default precision for the rest of the network. So before we create our new dummy batch, we also need to choose a precision as in the next section:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. What precision am I running inference at?\n", + "\n", + "Remember that lower precisions than FP32 tend to run faster. There are two common reduced precision modes - FP16 and INT8. Graphics cards that are designed to do inference well often have an affinity for one of these two types. This guide was developed on an NVIDIA V100, which favors FP16, so we will use that here by default. INT8 is a more complicated process that requires a calibration step.\n", + "\n", + "__NOTE__: Make sure you use the same precision (USE_FP16) here you saved your model in above!" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "USE_FP16 = True\n", + "target_dtype = np.float16 if USE_FP16 else np.float32" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " To create a test batch, we will once again repeat one open-source dog image from http://www.dog.ceo:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(32, 224, 224, 3)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from skimage import io\n", + "from skimage.transform import resize\n", + "from matplotlib import pyplot as plt\n", + "import numpy as np\n", + "\n", + "url='https://images.dog.ceo/breeds/retriever-golden/n02099601_3004.jpg'\n", + "img = resize(io.imread(url), (224, 224))\n", + "input_batch = np.array(np.repeat(np.expand_dims(np.array(img, dtype=np.float32), axis=0), BATCH_SIZE, axis=0), dtype=np.float32)\n", + "\n", + "input_batch.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.imshow(input_batch[0].astype(np.float32))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Preprocess Images:\n", + "\n", + "PyTorch has a normalization that it applies by default in all of its pretrained vision models - we can preprocess our images to match this normalization by the following, making sure our final result is in FP16 precision:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "from torchvision.transforms import Normalize\n", + "\n", + "def preprocess_image(img):\n", + " norm = Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n", + " result = norm(torch.from_numpy(img).transpose(0,2).transpose(1,2))\n", + " return np.array(result, dtype=np.float16)\n", + "\n", + "preprocessed_images = np.array([preprocess_image(image) for image in input_batch])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. What TensorRT path am I using to convert my model?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can use trtexec, a command line tool for working with TensorRT, in order to convert an ONNX model originally from PyTorch to an engine file.\n", + "\n", + "Let's make sure we have TensorRT installed (this comes with trtexec):" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "import tensorrt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To convert the model we saved in the previous step, we need to point to the ONNX file, give trtexec a name to save the engine as, and last specify that we want to use a fixed batch size instead of a dynamic one." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "&&&& RUNNING TensorRT.trtexec # trtexec --onnx=resnet50_pytorch.onnx --saveEngine=resnet_engine_pytorch.trt --explicitBatch --inputIOFormats=fp16:chw --outputIOFormats=fp16:chw --fp16\n", + "[06/09/2021-20:23:03] [I] === Model Options ===\n", + "[06/09/2021-20:23:03] [I] Format: ONNX\n", + "[06/09/2021-20:23:03] [I] Model: resnet50_pytorch.onnx\n", + "[06/09/2021-20:23:03] [I] Output:\n", + "[06/09/2021-20:23:03] [I] === Build Options ===\n", + "[06/09/2021-20:23:03] [I] Max batch: explicit\n", + "[06/09/2021-20:23:03] [I] Workspace: 16 MiB\n", + "[06/09/2021-20:23:03] [I] minTiming: 1\n", + "[06/09/2021-20:23:03] [I] avgTiming: 8\n", + "[06/09/2021-20:23:03] [I] Precision: FP32+FP16\n", + "[06/09/2021-20:23:03] [I] Calibration: \n", + "[06/09/2021-20:23:03] [I] Refit: Disabled\n", + "[06/09/2021-20:23:03] [I] Safe mode: Disabled\n", + "[06/09/2021-20:23:03] [I] Save engine: resnet_engine_pytorch.trt\n", + "[06/09/2021-20:23:03] [I] Load engine: \n", + "[06/09/2021-20:23:03] [I] Builder Cache: Enabled\n", + "[06/09/2021-20:23:03] [I] NVTX verbosity: 0\n", + "[06/09/2021-20:23:03] [I] Tactic sources: Using default tactic sources\n", + "[06/09/2021-20:23:03] [I] Input(s): fp16:chw\n", + "[06/09/2021-20:23:03] [I] Output(s): fp16:chw\n", + "[06/09/2021-20:23:03] [I] Input build shapes: model\n", + "[06/09/2021-20:23:03] [I] Input calibration shapes: model\n", + "[06/09/2021-20:23:03] [I] === System Options ===\n", + "[06/09/2021-20:23:03] [I] Device: 0\n", + "[06/09/2021-20:23:03] [I] DLACore: \n", + "[06/09/2021-20:23:03] [I] Plugins:\n", + "[06/09/2021-20:23:03] [I] === Inference Options ===\n", + "[06/09/2021-20:23:03] [I] Batch: Explicit\n", + "[06/09/2021-20:23:03] [I] Input inference shapes: model\n", + "[06/09/2021-20:23:03] [I] Iterations: 10\n", + "[06/09/2021-20:23:03] [I] Duration: 3s (+ 200ms warm up)\n", + "[06/09/2021-20:23:03] [I] Sleep time: 0ms\n", + "[06/09/2021-20:23:03] [I] Streams: 1\n", + "[06/09/2021-20:23:03] [I] ExposeDMA: Disabled\n", + "[06/09/2021-20:23:03] [I] Data transfers: Enabled\n", + "[06/09/2021-20:23:03] [I] Spin-wait: Disabled\n", + "[06/09/2021-20:23:03] [I] Multithreading: Disabled\n", + "[06/09/2021-20:23:03] [I] CUDA Graph: Disabled\n", + "[06/09/2021-20:23:03] [I] Separate profiling: Disabled\n", + "[06/09/2021-20:23:03] [I] Skip inference: Disabled\n", + "[06/09/2021-20:23:03] [I] Inputs:\n", + "[06/09/2021-20:23:03] [I] === Reporting Options ===\n", + "[06/09/2021-20:23:03] [I] Verbose: Disabled\n", + "[06/09/2021-20:23:03] [I] Averages: 10 inferences\n", + "[06/09/2021-20:23:03] [I] Percentile: 99\n", + "[06/09/2021-20:23:03] [I] Dump refittable layers:Disabled\n", + "[06/09/2021-20:23:03] [I] Dump output: Disabled\n", + "[06/09/2021-20:23:03] [I] Profile: Disabled\n", + "[06/09/2021-20:23:03] [I] Export timing to JSON file: \n", + "[06/09/2021-20:23:03] [I] Export output to JSON file: \n", + "[06/09/2021-20:23:03] [I] Export profile to JSON file: \n", + "[06/09/2021-20:23:03] [I] \n", + "[06/09/2021-20:23:04] [I] === Device Information ===\n", + "[06/09/2021-20:23:04] [I] Selected Device: Tesla V100-DGXS-16GB\n", + "[06/09/2021-20:23:04] [I] Compute Capability: 7.0\n", + "[06/09/2021-20:23:04] [I] SMs: 80\n", + "[06/09/2021-20:23:04] [I] Compute Clock Rate: 1.53 GHz\n", + "[06/09/2021-20:23:04] [I] Device Global Memory: 16155 MiB\n", + "[06/09/2021-20:23:04] [I] Shared Memory per SM: 96 KiB\n", + "[06/09/2021-20:23:04] [I] Memory Bus Width: 4096 bits (ECC enabled)\n", + "[06/09/2021-20:23:04] [I] Memory Clock Rate: 0.877 GHz\n", + "[06/09/2021-20:23:04] [I] \n", + "[06/09/2021-20:23:20] [I] [TRT] ----------------------------------------------------------------\n", + "[06/09/2021-20:23:20] [I] [TRT] Input filename: resnet50_pytorch.onnx\n", + "[06/09/2021-20:23:20] [I] [TRT] ONNX IR version: 0.0.6\n", + "[06/09/2021-20:23:20] [I] [TRT] Opset version: 9\n", + "[06/09/2021-20:23:20] [I] [TRT] Producer name: pytorch\n", + "[06/09/2021-20:23:20] [I] [TRT] Producer version: 1.9\n", + "[06/09/2021-20:23:20] [I] [TRT] Domain: \n", + "[06/09/2021-20:23:20] [I] [TRT] Model version: 0\n", + "[06/09/2021-20:23:20] [I] [TRT] Doc string: \n", + "[06/09/2021-20:23:20] [I] [TRT] ----------------------------------------------------------------\n", + "[06/09/2021-20:23:24] [I] [TRT] Some tactics do not have sufficient workspace memory to run. Increasing workspace size may increase performance, please check verbose output.\n", + "[06/09/2021-20:24:49] [I] [TRT] Detected 1 inputs and 1 output network tensors.\n", + "[06/09/2021-20:24:49] [I] Engine built in 105.672 sec.\n", + "[06/09/2021-20:24:50] [I] Starting inference\n", + "[06/09/2021-20:24:53] [I] Warmup completed 0 queries over 200 ms\n", + "[06/09/2021-20:24:53] [I] Timing trace has 0 queries over 2.9909 s\n", + "[06/09/2021-20:24:53] [I] Trace averages of 10 runs:\n", + "[06/09/2021-20:24:53] [I] Average on 10 runs - GPU latency: 5.35326 ms - Host latency: 6.18286 ms (end to end 10.1932 ms, enqueue 0.460231 ms)\n", + "[06/09/2021-20:24:53] [I] Average on 10 runs - GPU latency: 5.35654 ms - Host latency: 6.19131 ms (end to end 10.2018 ms, enqueue 0.473865 ms)\n", + "[06/09/2021-20:24:53] [I] Average on 10 runs - GPU latency: 5.38982 ms - Host latency: 6.22551 ms (end to end 10.2071 ms, enqueue 0.460098 ms)\n", + "[06/09/2021-20:24:53] [I] Average on 10 runs - GPU latency: 5.3761 ms - Host latency: 6.24244 ms (end to end 10.2638 ms, enqueue 0.456512 ms)\n", + "[06/09/2021-20:24:53] [I] Average on 10 runs - GPU latency: 5.36218 ms - Host latency: 6.22775 ms (end to end 9.37773 ms, enqueue 0.441846 ms)\n", + "[06/09/2021-20:24:53] [I] Average on 10 runs - GPU latency: 5.35991 ms - Host latency: 6.22073 ms (end to end 9.77996 ms, enqueue 0.443829 ms)\n", + "[06/09/2021-20:24:53] [I] Average on 10 runs - GPU latency: 5.38082 ms - Host latency: 6.25148 ms (end to end 10.0299 ms, enqueue 0.44693 ms)\n", + "[06/09/2021-20:24:53] [I] Average on 10 runs - GPU latency: 5.39341 ms - Host latency: 6.26748 ms (end to end 10.0738 ms, enqueue 0.456384 ms)\n", + "[06/09/2021-20:24:53] [I] Average on 10 runs - GPU latency: 5.38766 ms - Host latency: 6.26089 ms (end to end 10.2009 ms, enqueue 0.461377 ms)\n", + "[06/09/2021-20:24:53] [I] Average on 10 runs - GPU latency: 5.37385 ms - Host latency: 6.24359 ms (end to end 9.65547 ms, enqueue 0.442078 ms)\n", + "[06/09/2021-20:24:53] [I] Average on 10 runs - GPU latency: 5.35819 ms - Host latency: 6.21615 ms (end to end 8.21369 ms, enqueue 0.436646 ms)\n", + "[06/09/2021-20:24:53] [I] Average on 10 runs - GPU latency: 5.34844 ms - Host latency: 6.20999 ms (end to end 9.77367 ms, enqueue 0.433765 ms)\n", + "[06/09/2021-20:24:53] [I] Average on 10 runs - GPU latency: 5.35132 ms - Host latency: 6.21758 ms (end to end 10.6213 ms, enqueue 0.435864 ms)\n", + "[06/09/2021-20:24:53] [I] Average on 10 runs - GPU latency: 5.36421 ms - Host latency: 6.23065 ms (end to end 10.5457 ms, enqueue 0.436438 ms)\n", + "[06/09/2021-20:24:53] [I] Average on 10 runs - GPU latency: 5.39054 ms - Host latency: 6.25834 ms (end to end 10.4534 ms, enqueue 0.444727 ms)\n", + "[06/09/2021-20:24:53] [I] Average on 10 runs - GPU latency: 5.36874 ms - Host latency: 6.23105 ms (end to end 8.89895 ms, enqueue 0.443665 ms)\n", + "[06/09/2021-20:24:53] [I] Average on 10 runs - GPU latency: 5.35729 ms - Host latency: 6.21859 ms (end to end 8.51741 ms, enqueue 0.437866 ms)\n", + "[06/09/2021-20:24:53] [I] Average on 10 runs - GPU latency: 5.33851 ms - Host latency: 6.19753 ms (end to end 9.1334 ms, enqueue 0.438574 ms)\n", + "[06/09/2021-20:24:53] [I] Average on 10 runs - GPU latency: 5.34199 ms - Host latency: 6.21041 ms (end to end 10.6064 ms, enqueue 0.44613 ms)\n", + "[06/09/2021-20:24:53] [I] Average on 10 runs - GPU latency: 5.33002 ms - Host latency: 6.20233 ms (end to end 10.5858 ms, enqueue 0.458911 ms)\n", + "[06/09/2021-20:24:53] [I] Average on 10 runs - GPU latency: 5.38256 ms - Host latency: 6.25411 ms (end to end 9.77722 ms, enqueue 0.460205 ms)\n", + "[06/09/2021-20:24:53] [I] Average on 10 runs - GPU latency: 5.3837 ms - Host latency: 6.2543 ms (end to end 9.4882 ms, enqueue 0.448364 ms)\n", + "[06/09/2021-20:24:53] [I] Average on 10 runs - GPU latency: 5.35146 ms - Host latency: 6.20986 ms (end to end 8.36691 ms, enqueue 0.434412 ms)\n", + "[06/09/2021-20:24:53] [I] Average on 10 runs - GPU latency: 5.34351 ms - Host latency: 6.20732 ms (end to end 10.1922 ms, enqueue 0.439209 ms)\n", + "[06/09/2021-20:24:53] [I] Average on 10 runs - GPU latency: 5.3502 ms - Host latency: 6.21951 ms (end to end 10.6236 ms, enqueue 0.451489 ms)\n", + "[06/09/2021-20:24:53] [I] Average on 10 runs - GPU latency: 5.34368 ms - Host latency: 6.21904 ms (end to end 10.4949 ms, enqueue 0.462231 ms)\n", + "[06/09/2021-20:24:53] [I] Average on 10 runs - GPU latency: 5.33777 ms - Host latency: 6.21189 ms (end to end 9.99021 ms, enqueue 0.455859 ms)\n", + "[06/09/2021-20:24:53] [I] Average on 10 runs - GPU latency: 5.33193 ms - Host latency: 6.19707 ms (end to end 9.02058 ms, enqueue 0.445972 ms)\n", + "[06/09/2021-20:24:53] [I] Average on 10 runs - GPU latency: 5.33115 ms - Host latency: 6.19114 ms (end to end 9.11257 ms, enqueue 0.433862 ms)\n", + "[06/09/2021-20:24:53] [I] Average on 10 runs - GPU latency: 5.34673 ms - Host latency: 6.21465 ms (end to end 10.6074 ms, enqueue 0.442139 ms)\n", + "[06/09/2021-20:24:53] [I] Average on 10 runs - GPU latency: 5.38572 ms - Host latency: 6.25532 ms (end to end 10.3253 ms, enqueue 0.446631 ms)\n", + "[06/09/2021-20:24:53] [I] Average on 10 runs - GPU latency: 5.36335 ms - Host latency: 6.23845 ms (end to end 10.6406 ms, enqueue 0.45625 ms)\n", + "[06/09/2021-20:24:53] [I] Average on 10 runs - GPU latency: 5.36877 ms - Host latency: 6.24153 ms (end to end 10.2023 ms, enqueue 0.449341 ms)\n", + "[06/09/2021-20:24:53] [I] Average on 10 runs - GPU latency: 5.36023 ms - Host latency: 6.21748 ms (end to end 8.45557 ms, enqueue 0.436719 ms)\n", + "[06/09/2021-20:24:53] [I] Average on 10 runs - GPU latency: 5.34392 ms - Host latency: 6.20728 ms (end to end 10.1899 ms, enqueue 0.438428 ms)\n", + "[06/09/2021-20:24:53] [I] Average on 10 runs - GPU latency: 5.34636 ms - Host latency: 6.21821 ms (end to end 10.6184 ms, enqueue 0.447217 ms)\n", + "[06/09/2021-20:24:53] [I] Average on 10 runs - GPU latency: 5.33555 ms - Host latency: 6.20952 ms (end to end 10.5899 ms, enqueue 0.459546 ms)\n", + "[06/09/2021-20:24:53] [I] Host Latency\n", + "[06/09/2021-20:24:53] [I] min: 6.16092 ms (end to end 6.17383 ms)\n", + "[06/09/2021-20:24:53] [I] max: 6.2887 ms (end to end 10.8184 ms)\n", + "[06/09/2021-20:24:53] [I] mean: 6.22352 ms (end to end 9.90214 ms)\n", + "[06/09/2021-20:24:53] [I] median: 6.22021 ms (end to end 10.6108 ms)\n", + "[06/09/2021-20:24:53] [I] percentile: 6.28583 ms at 99% (end to end 10.7902 ms at 99%)\n", + "[06/09/2021-20:24:53] [I] throughput: 0 qps\n", + "[06/09/2021-20:24:53] [I] walltime: 2.9909 s\n", + "[06/09/2021-20:24:53] [I] Enqueue Time\n", + "[06/09/2021-20:24:53] [I] min: 0.424072 ms\n", + "[06/09/2021-20:24:53] [I] max: 0.49585 ms\n", + "[06/09/2021-20:24:53] [I] median: 0.445618 ms\n", + "[06/09/2021-20:24:53] [I] GPU Compute\n", + "[06/09/2021-20:24:53] [I] min: 5.30127 ms\n", + "[06/09/2021-20:24:53] [I] max: 5.42108 ms\n", + "[06/09/2021-20:24:53] [I] mean: 5.35895 ms\n", + "[06/09/2021-20:24:53] [I] median: 5.35571 ms\n", + "[06/09/2021-20:24:53] [I] percentile: 5.41693 ms at 99%\n", + "[06/09/2021-20:24:53] [I] total compute time: 2.00961 s\n", + "&&&& PASSED TensorRT.trtexec # trtexec --onnx=resnet50_pytorch.onnx --saveEngine=resnet_engine_pytorch.trt --explicitBatch --inputIOFormats=fp16:chw --outputIOFormats=fp16:chw --fp16\n" + ] + } + ], + "source": [ + "# step out of Python for a moment to convert the ONNX model to a TRT engine using trtexec\n", + "if USE_FP16:\n", + " !trtexec --onnx=resnet50_pytorch.onnx --saveEngine=resnet_engine_pytorch.trt --explicitBatch --inputIOFormats=fp16:chw --outputIOFormats=fp16:chw --fp16\n", + "else:\n", + " !trtexec --onnx=resnet50_pytorch.onnx --saveEngine=resnet_engine_pytorch.trt --explicitBatch" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This will save our model as 'resnet_engine.trt'." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. What TensorRT runtime am I targeting?\n", + "\n", + "Now, we have a converted our model to a TensorRT engine. Great! That means we are ready to load it into the native Python TensorRT runtime. This runtime strikes a balance between the ease of use of the high level Python APIs used in frameworks and the fast, low level C++ runtimes available in TensorRT." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 15.9 s, sys: 556 ms, total: 16.5 s\n", + "Wall time: 19.3 s\n" + ] + } + ], + "source": [ + "%%time\n", + "\n", + "import tensorrt as trt\n", + "import pycuda.driver as cuda\n", + "import pycuda.autoinit\n", + "\n", + "f = open(\"resnet_engine_pytorch.trt\", \"rb\")\n", + "runtime = trt.Runtime(trt.Logger(trt.Logger.WARNING)) \n", + "\n", + "engine = runtime.deserialize_cuda_engine(f.read())\n", + "context = engine.create_execution_context()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now allocate input and output memory, give TRT pointers (bindings) to it:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "# need to set input and output precisions to FP16 to fully enable it\n", + "output = np.empty([BATCH_SIZE, 1000], dtype = target_dtype) \n", + "\n", + "# allocate device memory\n", + "d_input = cuda.mem_alloc(1 * input_batch.nbytes)\n", + "d_output = cuda.mem_alloc(1 * output.nbytes)\n", + "\n", + "bindings = [int(d_input), int(d_output)]\n", + "\n", + "stream = cuda.Stream()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, set up the prediction function.\n", + "\n", + "This involves a copy from CPU RAM to GPU VRAM, executing the model, then copying the results back from GPU VRAM to CPU RAM:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "def predict(batch): # result gets copied into output\n", + " # transfer input data to device\n", + " cuda.memcpy_htod_async(d_input, batch, stream)\n", + " # execute model\n", + " context.execute_async_v2(bindings, stream.handle, None)\n", + " # transfer predictions back\n", + " cuda.memcpy_dtoh_async(output, d_output, stream)\n", + " # syncronize threads\n", + " stream.synchronize()\n", + " \n", + " return output" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's time the function!" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warming up...\n", + "Done warming up!\n" + ] + } + ], + "source": [ + "print(\"Warming up...\")\n", + "\n", + "pred = predict(preprocessed_images)\n", + "\n", + "print(\"Done warming up!\")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6.28 ms ± 1.07 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" + ] + } + ], + "source": [ + "%%timeit\n", + "\n", + "pred = predict(preprocessed_images)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally we should verify our TensorRT output is still accurate." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Class | Probability (out of 1)\n" + ] + }, + { + "data": { + "text/plain": [ + "[(207, 12.44), (208, 7.508), (220, 7.492), (160, 7.426), (226, 7.383)]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "indices = (-pred[0]).argsort()[:5]\n", + "print(\"Class | Probability (out of 1)\")\n", + "list(zip(indices, pred[0][indices]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Look for ImageNet indices 150-275 above, where 207 is the ground truth correct class (Golden Retriever). Compare with the results of the original unoptimized model in the first section!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Next Steps:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Profiling

\n", + "\n", + "This is a great next step for further optimizing and debugging models you are working on productionizing\n", + "\n", + "You can find it here: https://docs.nvidia.com/deeplearning/tensorrt/best-practices/index.html\n", + "\n", + "

TRT Dev Docs

\n", + "\n", + "Main documentation page for the ONNX, layer builder, C++, and legacy APIs\n", + "\n", + "You can find it here: https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html\n", + "\n", + "

TRT OSS GitHub

\n", + "\n", + "Contains OSS TRT components, sample applications, and plugin examples\n", + "\n", + "You can find it here: https://github.com/NVIDIA/TensorRT\n", + "\n", + "\n", + "#### TRT Supported Layers:\n", + "\n", + "https://github.com/NVIDIA/TensorRT/tree/main/samples/opensource/samplePlugin\n", + "\n", + "#### TRT ONNX Plugin Example:\n", + "\n", + "https://docs.nvidia.com/deeplearning/tensorrt/support-matrix/index.html#layers-precision-matrix" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/Notebook Tutorials/5. Understanding TensorRT Runtimes.ipynb b/examples/Notebook Tutorials/5. Understanding TensorRT Runtimes.ipynb new file mode 100644 index 0000000..05e8b3d --- /dev/null +++ b/examples/Notebook Tutorials/5. Understanding TensorRT Runtimes.ipynb @@ -0,0 +1,107 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Runtimes: What are my options? How do I choose?\n", + "\n", + "Remember that TensorRT consists of two main components - __1. A series of parsers and integrations__ to convert your model to an optimized engine and __2. An series of TensorRT runtime APIs__ with several associated tools for deployment.\n", + "\n", + "In this notebook, we will focus on the latter - various runtime options for TensorRT engines.\n", + "\n", + "The runtimes have different use cases for running TRT engines. \n", + "\n", + "### Considerations when picking a runtime:\n", + "\n", + "Generally speaking, there are a few major considerations when picking a runtime:\n", + "- __Framework__ - Some options, like TF-TRT, are only relevant to Tensorflow\n", + "- __Time-to-solution__ - TF-TRT is much more likely to work 'out-of-the-box' if a quick solution is required and ONNX fails\n", + "- __Serving needs__ - TF-TRT can use TF Serving to serve models over HTTP as a simple solution. For other frameworks (or for more advanced features) TRITON is framework agnostic, allows for concurrent model execution or multiple copies within a GPU to reduce latency, and can accept engines created through both the ONNX and TF-TRT paths\n", + "- __Performance__ - Different TensorRT runtimes offer varying levels of performance. For example, TF-TRT is generally going to be slower than using ONNX or the C++ API directly.\n", + "\n", + "### Python API:\n", + "\n", + "__Use this when:__\n", + "- You can accept some performance overhead, and\n", + "- You are most familiar with Python, or\n", + "- You are performing initial debugging and testing with TRT\n", + "\n", + "__More info:__\n", + "\n", + " \n", + "The [TensorRT Python API](https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html#perform_inference_python) gives you fine-grained control over the execution of your engine using a Python interface. It makes memory allocation, kernel execution, and copies to and from the GPU explicit - which can make integration into high performance applications easier. It is also great for testing models in a Python environment - such as in a Jupyter notebook.\n", + " \n", + "The [ONNX notebook for Tensorflow](./3.%20Using%20Tensorflow%202%20through%20ONNX.ipynb) and [for PyTorch](./4.%20Using%20PyTorch%20through%20ONNX.ipynb) are good examples of using TensorRT to get great performance while staying in Python\n", + "\n", + "### C++ API: \n", + "\n", + "__Use this when:__\n", + "- You want the least amount of overhead possible to maximize the performance of your models and achieve better latency\n", + "- You are not using TF-TRT (though TF-TRT graph conversions that only generate a single engine can still be exported to C++)\n", + "- You are most familiar with C++\n", + "- You want to optimize your inference pipeline as much as possible\n", + "\n", + "__More info:__\n", + "\n", + "The [TensorRT C++ API](https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html#perform_inference_c) gives you fine-grained control over the execution of your engine using a C++ interface. It makes memory allocation, kernel execution, and copies to and from the GPU explicit - which can make integration into high performance C++ applications easier. The C++ API is generally the most performant option for running TensorRT engines, with the least overhead.\n", + "\n", + "[This NVIDIA Developer blog](https://developer.nvidia.com/blog/speed-up-inference-tensorrt/) is a good example of taking an ONNX model and running it with dynamic batch size support using the C++ API.\n", + "\n", + "\n", + "### Tensorflow/TF-TRT Runtime: (Tensorflow Only) \n", + " \n", + "__Use this when:__\n", + " \n", + "- You are using TF-TRT, and\n", + "- Your model converts to more than one TensorRT engine\n", + "\n", + "__More info:__\n", + "\n", + "\n", + "TF-TRT is the standard runtime used with models that were converted in TF-TRT. It works by taking groups of nodes at once in the Tensorflow graph, and replacing them with a singular optimized engine that calls the TensorRT Python API behind the scenes. This optimized engine is in the form of a Tensorflow operation - which means that your graph is still in Tensorflow and will essentially function like any other Tensorflow model. For example, it can be a useful exercise to take a look at your model in Tensorboard to validate which nodes TensorRT was able to optimize.\n", + "\n", + "If your graph entirely converts to a single TF-TRT engine, it can be more efficient to export the engine node and run it using one of the other APIs. You can find instructions to do this in the [TF-TRT documentation](https://docs.nvidia.com/deeplearning/frameworks/tf-trt-user-guide/index.html#tensorrt-plan).\n", + "\n", + "As an example, the TF-TRT notebooks included with this guide use the TF-TRT runtime.\n", + "\n", + "### TRITON Inference Server\n", + "\n", + "__Use this when:__\n", + "- You want to serve your models over HTTP or gRPC\n", + "- You want to load balance across multiple models or copies of models across GPUs to minimze latency and make better use of the GPU\n", + "- You want to have multiple models running efficiently on a single GPU at the same time\n", + "- You want to serve a variety of models converted using a variety of converters and frameworks (including TF-TRT and ONNX) through a uniform interface\n", + "- You need serving support but are using PyTorch, another framework, or the ONNX path in general\n", + "\n", + "__More info:__\n", + "\n", + "\n", + "TRITON is an open source inference serving software that lets teams deploy trained AI models from any framework (TensorFlow, TensorRT, PyTorch, ONNX Runtime, or a custom framework), from local storage or Google Cloud Platform or AWS S3 on any GPU- or CPU-based infrastructure (cloud, data center, or edge). It is a flexible project with several unique features - such as concurrent model execution of both heterogeneous models and multiple copies of the same model (multiple model copies can reduce latency further) as well as load balancing and model analysis. It is a good option if you need to serve your models over HTTP - such as in a cloud inferencing solution.\n", + " \n", + "You can find the TRITON home page [here](https://developer.nvidia.com/nvidia-triton-inference-server), and the documentation [here](https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/)." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/Notebook Tutorials/EfficientDet-TensorRT8.ipynb b/examples/Notebook Tutorials/EfficientDet-TensorRT8.ipynb new file mode 100644 index 0000000..a38ef2c --- /dev/null +++ b/examples/Notebook Tutorials/EfficientDet-TensorRT8.ipynb @@ -0,0 +1,665 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "877efa3c", + "metadata": {}, + "outputs": [], + "source": [ + "# Copyright 2021 NVIDIA Corporation. All Rights Reserved.\n", + "#\n", + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# http://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License.\n", + "# ==============================================================================" + ] + }, + { + "cell_type": "markdown", + "id": "44b821d9", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "id": "dc433756", + "metadata": {}, + "source": [ + "# Optimize Object Detection with EfficientDet and TensorRT 8" + ] + }, + { + "cell_type": "markdown", + "id": "9b0f88f5", + "metadata": {}, + "source": [ + "## Overview\n", + "\n", + "This notebook will show how to optimize a pre-trained TensorFlow EfficientDet model checkpoint using TensorRT 8.0.1. NVIDIA TensorRT is a platform for high-performance deep learning inference. It includes a deep learning inference optimizer and runtime that delivers low latency and high-throughput for deep learning inference applications. After optimizing the pre-trained TF EfficientDet-D0 model for object detection with NVIDIA TensorRT, inference throughput increased by up to 2x to 3x over native Tensorflow depending on the batch size and precision used for TensorRT conversion.\n", + "\n", + "One of the most important problems in computer vision is object detection, where objects of interest like cars, people, obstacles, etc., need to be detected. Since the inception of deep learning algorithms, there has been a lot of research in developing model architectures that can help detect and classify such objects in photos or videos. [EfficientDet](https://arxiv.org/abs/1911.09070) is state of the art object detector which is efficient and accurate while requiring less computational resources. Such a network with accuracy, low compute, and memory requirement is perfect in robotics and driverless car systems. " + ] + }, + { + "attachments": { + "efficientdet.png": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAACXIAAAOECAYAAAAGs1thAAAABHNCSVQICAgIfAhkiAAAABl0RVh0U29mdHdhcmUAZ25vbWUtc2NyZWVuc2hvdO8Dvz4AACAASURBVHic7N13eFRl3sbxezKppAAhBELvQUAREFRABQUEBREbYkPBFcWyKHZd0cWCoK4FOyuvsthWEVRApCgIAoJICzWE3hMIkELazPsHmyEnmZ6ZnAS+n+viSk79PTPJPJzJc89zLHa73S4AAAAAAAAAAAAAAAAAgGlCzG4AAAAAAAAAAAAAAAAAAJztCHIBAAAAAAAAAAAAAAAAgMkIcgEAAAAAAAAAAAAAAACAyQhyAQAAAAAAAAAAAAAAAIDJCHIBAAAAAAAAAAAAAAAAgMkIcgEAAAAAAAAAAAAAAACAyQhyAQAAAAAAAAAAAAAAAIDJCHIBAAAAAAAAAAAAAAAAgMkIcgEAAAAAAAAAAAAAAACAyQhyAQAAAAAAAAAAAAAAAIDJCHIBAAAAAAAAAAAAAAAAgMkIcgEAAAAAAAAAAAAAAACAyQhyAQAAAAAAAAAAAAAAAIDJCHIBAAAAAAAAAAAAAAAAgMkIcgEAAAAAAAAAAAAAAACAyQhyAQAAAAAAAAAAAAAAAIDJCHIBAAAAAAAAAAAAAAAAgMkIcgEAAAAAAAAAAAAAAACAyQhyAQAAAAAAAAAAAAAAAIDJCHIBAAAAAAAAAAAAAAAAgMkIcgEAAAAAAAAAAAAAAACAyQhyAQAAAAAAAAAAAAAAAIDJCHIBAAAAAAAAAAAAAAAAgMkIcgEAAAAAAAAAAAAAAACAyQhyAQAAAAAAAAAAAAAAAIDJCHIBAAAAAAAAAAAAAAAAgMkIcgEAAAAAAAAAAAAAAACAyQhyAQAAAAAAAAAAAAAAAIDJCHIBAAAAAAAAAAAAAAAAgMkIcgEAAAAAAAAAAAAAAACAyQhyAQAAAAAAAAAAAAAAAIDJCHIBAAAAAAAAAAAAAAAAgMkIcgEAAAAAAAAAAAAAAACAyQhyAQAAAAAAAAAAAAAAAIDJCHIBAAAAAAAAAAAAAAAAgMkIcgEAAAAAAAAAAAAAAACAyQhyAQAAAAAAAAAAAAAAAIDJCHIBAAAAAAAAAAAAAAAAgMkIcgEAAAAAAAAAAAAAAACAyQhyAQAAAAAAAAAAAAAAAIDJCHIBAAAAAAAAAAAAAAAAgMkIcgEAAAAAAAAAAAAAAACAyQhyAQAAAAAAAAAAAAAAAIDJCHIBAAAAAAAAAAAAAAAAgMkIcgEAAAAAAAAAAAAAAACAyQhyAQAAAAAAAAAAAAAAAIDJCHIBAAAAAAAAAAAAAAAAgMkIcgEAAAAAAAAAAAAAAACAyQhyAQAAAAAAAAAAAAAAAIDJCHIBAAAAAAAAAAAAAAAAgMkIcgEAAAAAAAAAAAAAAACAyQhyAQAAAAAAAAAAAAAAAIDJCHIBAAAAAAAAAAAAAAAAgMkIcgEAAAAAAAAAAAAAAACAyQhyAQAAAAAAAAAAAAAAAIDJCHIBAAAAAAAAAAAAAAAAgMkIcgEAAAAAAAAAAAAAAACAyQhyAQAAAAAAAAAAAAAAAIDJCHIBAAAAAAAAAAAAAAAAgMkIcgEAAAAAAAAAAAAAAACAyQhyAQAAAAAAAAAAAAAAAIDJCHIBAAAAAAAAAAAAAAAAgMkIcgEAAAAAAAAAAAAAAACAyQhyAQAAAAAAAAAAAAAAAIDJCHIBAAAAAAAAAAAAAAAAgMkIcgEAAAAAAAAAAAAAAACAyQhyAQAAAAAAAAAAAAAAAIDJCHIBAAAAAAAAAAAAAAAAgMkIcgEAAAAAAAAAAAAAAACAyQhyAQAAAAAAAAAAAAAAAIDJCHIBAAAAAAAAAAAAAAAAgMkIcgEAAAAAAAAAAAAAAACAyQhyAQAAAAAAAAAAAAAAAIDJCHIBAAAAAAAAAAAAAAAAgMkIcgEAAAAAAAAAAAAAAACAyQhyAQAAAAAAAAAAAAAAAIDJCHIBAAAAAAAAAAAAAAAAgMkIcgEAAAAAAAAAAAAAAACAyQhyAQAAAAAAAAAAAAAAAIDJCHIBAAAAAAAAAAAAAAAAgMkIcgEAAAAAAAAAAAAAAACAyQhyAQAAAAAAAAAAAAAAAIDJCHIBAAAAAAAAAAAAAAAAgMkIcgEAAAAAAAAAAAAAAACAyQhyAQAAAAAAAAAAAAAAAIDJCHIBAAAAAAAAAAAAAAAAgMkIcgEAAAAAAAAAAAAAAACAyQhyAQAAAAAAAAAAAAAAAIDJCHIBAAAAAAAAAAAAAAAAgMkIcgEAAAAAAAAAAAAAAACAyQhyAQAAAAAAAAAAAAAAAIDJCHIBAAAAAAAAAAAAAAAAgMkIcgEAAAAAAAAAAAAAAACAyQhyAQAAAAAAAAAAAAAAAIDJCHIBAAAAAAAAAAAAAAAAgMlCzW4AAAAAAACVid1ud7nNYrFQuwLrBrMdvtQ1s3awnvuz8XcNAAAAAAAAqOwIcgEAAAAAKq0//vhD27Ztc7tPaGiobrzxRr9rLFq0SHv27PHr2CZNmqhr165+1/7888/9PrZ79+5q1KiRX8fu3btXCxcu9Lv24MGDZbVa/Tr2jz/+UGpqql/H1q1bVz179pTkX+Bn2rRpOnnypF+1O3XqpFatWvlVOyMjQ3PmzPGrriRdd911ioiI8Kv2kiVLtGvXLp+Da5KUkJCg3r17+x2u+vrrr1VUVORX7fbt26tNmzaSfH/MBw4c0C+//OKxbseOHZWcnOxXDQAAAAAAACAYLHZ//poGAAAAAEAFGDZsmCZPnux2n4iICOXm5kryLoxR+m3wwIED9cMPP/jVvsGDB+uLL77wunbJ+na73e8wlCRNnTpVQ4YM8bp2ycc9Y8YMDRo0yO/aWVlZqlatmk/hl+L6w4cP9/gzdeXyyy/XvHnzyqx3146SjzsxMVHp6el+1X7zzTf10EMP+VV76dKl6tatm191pVPBpMTERJ9qF9e/6aab9M033/hVt0uXLlq2bJlPNUvWjoyMVH5+vl+1X3rpJT311FNOtzlrQ8mf888//6y+fft6rDFhwgSNHj3a47kBAAAAAACAisKMXAAAAACASsffzxyZ+Vmls7F26bolQzAV2aaz8bk3q3Zl+DxgMNtQfG4CXQAAAAAAADADQS4AAAAAQKVRGUIi8B8/P5wp7HY7YS4AAAAAAABUOIJcAAAAAIBKwZsQ0A033KBOnToZ1oWFhZWr7tChQ9W1a1ev2lA62NGmTRu/61osFr3yyiuGde7ql659/vnn+127bdu25aodHh7ud+0bb7xRrVq18qpuaU2bNvW7riQ999xzys7O9rmupHLdGrFx48aG59uX2haLRTExMX7XvvXWW8u8Zjy1ofjnXb9+fb/rSqduj1hYWOhT7eL6l112md91W7VqVeb3W5KmTJmiDRs2eH0ewlwAAAAAAACoaBY7H5cFAAAAAJjM1VvT4cOHa/LkyY7lFStWOA2lBKu+O4EKeJyNtcv7p4jytKGq1jbrea/Kv2ula06ZMkV33nmnY3n8+PEaPXq0x1qEuQAAAAAAAFBRmJELAAAAAGAqXwIedru9zP5mBWuKj/W3flWubWaQqjxtqMq1y/Mzr8q/a77W9reeu1rMzAUAAAAAAICKQpALAAAAAFApeRvIKLmft2GLQE5O7U/II1D1fa0dyLqS7+GeQD/v3rYh0JORV5Xagaxv9u+55P4xm/k4AQAAAAAAgEAhyAUAAAAAMI2r8EV5Z9WRXM+sEwzehmuCUb8iQy7OzhvoQJGvwTxPMyl5qzyBQLNqV/TP3Mzf82Ce11md0o+RgBcAAAAAAAAqAkEuAAAAAEClEuhZfCqSu7BHsNvjqrYZz4O/bSi9X0XMfOXsOH/DZIGsXZ4Qm1m/axVR21+VtV0AAAAAAABASRY7f8kCAAAAAJjAm9m40tLSdOjQIcfyueeeq+joaFPa5stsPOUJ1gSytq9v+Uvv723dQMwM5W6/YM6E5Kl9ZtWuiNmfqvLvuS+Ka6Wnpys1NdWxvnHjxkpKSnJZ19l6ZuUCAAAAAABAMBHkAgAAAACYwtNMSGaqyJDJmVC7PEEub3/mwXgOKnvtYP/cq+LvWiDqOONtaIsgFwAAAAAAAIIpxOwGAAAAAAAAAGaqLCFSAAAAAAAAnN1CzW4AAAAAAADestvtVWJGnPLcci6QtctbtyKe6+IaZt9msLLV5ve8/MrbnqrS3wAAAAAAAODMwa0VAQAAAACmqMy3ViwPV8GPinhszmoHu643QRdv2mBWkMtT28yq7UtdMwJVZv6ee8vXtnhzK0WCXQAAAAAAAAgmglwAAAAAAFOciUEuTyGPYD4+d7XNqutPG0ruV54wky81y1PXzNqV9WdemV7HvrSFIBcAAAAAAADMRpALAAAAAGCKigpyVcRsRb6GOwLZBl9qB/qxm/m4fWlDsP70UdlrB7p+Zfh5+4ogFwAAAAAAAKoSglwAAAAAAFMEO8hVUbMG+RvsCER9s2qXJ8xi1vN+pgSa/Hnuq/Lvmrs2+DPLmy/n9ybYBQAAAAAAAAQSQS4AAAAAgClcvR01I2TkT81ABTrOxtpVOUhmVm2znnczf9e8re/p3N7U9ja0RZALAAAAAAAAwUSQCwAAAAAqKX/DB1VFMIJc5X0+PNUO9vPtrv6ZWNvXn3Ug21FValfkc1+8zlXNigp1BSuM6WofXx5vVe5zgarC7P+LAVR9Z/r7KAAAAJzZCHIBAAAAQCUQ6LdmVWVgIpBhrqrymOGas597RfxcfQ34BLu2mY/ZV5XtdedLoMtT2wlyAcF3pvZFACqO2bewBgAAAAKNIBcAAAAAmKSi3o5V5gEJd88Bn6Svekr+PPhzQ+UT7J9JZXk9BuJxEuICguds6YsABE9FXGfSlwAAAMAsBLkAAAAAoIKU59Zf3nI34FBZByP8CXNVxsfibZuC8Tbcm9pm1Q1mfU/tMHOQrzIMMNrtdo/t8PcWk5XxNVhaoG/TWhUeM1CZBbo/kvybWQ9A1VXeD4G4Ql8CAACAyoQgFwAAAAAEmb8DDgUFBUrbsUtbUtO0bccu5eTkKr+gQNaQEIWHhyupTqKSWzZTcstmqlG9epnjq0qoqyqHbcr7PAZzwKmy1jbzNlpnU+3S9Uouu2pLfn6+Nm9NU8qmLdq9d59O5uXLbrMpIiJC8TWq65zkFmrTupVqxdf0uk0Wi8XU2dkCGQypTP0mUFX4eg1kt9u1e+8+pWzcos2pacrOzlFBQaHCwkJVLSpKLZo1UdvWLdWkcUOFhISUOb6qXPsA8I0/t2PPys7Wps2pStm0RQcOpSsvL1+SFBERrsSEWmqT3FJtWrdUbGxMmWO5FgAAAICZCHIBAAAAQBD5Muhgt9uVlZ2jOfN+1dxfFys1bYcKCwu9qlM7oZa6dDpfg/pfqZbNmxq2VfZBTTNmanLHXXuC/XydbbX9nQ3qbKodqN9hZ8Euu92ugoJCzf3lN/04Z762pKapqKjI4/kTayeo+8WdddO1Vyupbh2P7S29LlC/a8Ga5ZDBW6D8fA1wpWzcov9On6mVf63Viawsj+ePiorUeW3P0fUD+unCzh3KbOd1DJwZvLmmKbkuOydHP86ep5/mL9KOXbu9ug5oWL+errisqwYN6FvmwzHM0AkAAAAzEOQCAAAAgCDwddBh2/ad+vb72Zq/cLHj0+L+Oie5hQZe1Ue9e16i0NBQw7bKOhjh6q3pnDlztG7dOsfy7bffrjp16jjdt6TK8JiAiuZtgEs6NdA548ef9c33s3TkaKZf9UJCQtT94i665YZrdE5yS8O2iuprfAlzbdiwQbNmzXKs79Gjhy644ALDvoQ/gPLzNsRut9v1+/KV+vyb77V+wya/6zVp3FCDB/VXn8sv9eq6x916AJWHu9lFSy9nHDmqr6fP1A+z5ionN9evemFhYbryist083UD1LBBPcO2yvoeCgAAAGcmglwAAAAAEGCeBh1KrsvLy9fHn36ub7+fHfCZqZo3bawnRt2nVi2alRloqGyDEa4e+/DhwzV58mTH8ooVK9SpUye352JQBWcjXwY716Vs1NgJb+vQ4YyA1b/pugG6Z+gQhYWFGdZ70/eUl7dhrilTpujOO+90rBs/frxGjx5t2I/QB+A/X8KkJ05k6dW3PtDipX8ErH7rVi303ON/V72kOl5d5/C6Biofb4Kgpff59belmvDOh8rOzglIG8JCQzVi2G26YeBVHvsN+hEAAAAEQ6jnXQAAAAAA3vA0gFl6++q1KRr/9gfat/+g0/PViq+hc5JbKrlFcyW3bKaEWvEKDw9TUZFN2Tk5Stu+S5u2btOW1G1KTdspm81mOH7b9p269+GndcsNA3XHLTcoPCzMMdhQ3JaSgw92u73KD0ZU9fYDvvB1sLOoqEhTv/5O//f5f2WzlT22ZlyE2reurXNb1NY5zWopNjpMISEhyssvVNqeTKVszVDKtnRt2Vl2Bq+vp/2g1WtT9I/HH1LD+vUMfU3pfkYK7GvVYrEE9RatADzzJcS+Zt0GvfjaOzqcXjZMGhYaonYta6ld8wS1bVlbSQnVFBZmVWGhTYeO5Chla7rWb0vX+q0Zys0z3n5605ZUDX/wMT0y8m71vvxSSe6vc4LRHwHwny/vpex2u/Ly8vXOR5M1c84Cp+erVzta5ybX1rktEtSqcU1FR50Km+ecLNTWXUe1PjVD67ce1q79JwzHFRQWauJH/6cVq9boqUfuV43qcW6va+hDAAAAEGjMyAUAAAAAAeBuANPZtmk/zNY7H/5fmfOEhITo4i4dNah/X3XpdL7XAwMHD6frh1lz9cNP85zeJq11qxZ6bewziomJlmQctKwMnywP1IxcDKTgbOHrYGdRkU3Pj/uX09lvWjWuoVuuPkc9uzRWiDXEY+1dezP15exN+mnJDhUUGgOkkZERev3Ff6jtOa0kue9rXK3zh6c/bzEjFxA8vlwDzft1sV5+/Z0yYdLY6HBd36ulbujTStXjojzWzM3N1/e/pOq/czbr4JGyt1G7+YZrdO9dtxnWVbZrHwCneXtdU/IW0Y88PVZbUtPKHNepbaJuvbqNOp9br8w2Z1K2HNLUWRu1eNVelW5GQq14TZzwT9Wtkyip4q5rAAAAcHYjyAUAAAAA5eTNAGbJdV99+70+mDy1zHm6XXSBRt033DFQ4I/CwkL9MHue3v9kinJzTxq2tWzeVK+9+Kyqx8U61rkajKjoQYhABLkYOMHZwtfBTpvNpvFvfaA58xcajomJDtOTwy7UZV0a+dWOI5k5GjdpuZau2W88b0y03nxljFo0ayKpcoS5yhvk8rQNOFv5cg20bMUqPTN2QpkZRK/v1VL3DT5fEZHGW7N6w1Zk09c/bdKH/12rwiLjee++42bdetMgSadfv4S5gMrH3XWNsz4lLy9Pj495RWvXbzQck5RQTc/d11XtWvn3XmrX3kz984Ol2rzjqGF9vbp19M6Ef6pWfE1JhLkAAAAQfJ4/ZgkAAAAAcMnVAKbdbi8zAGG32/Wfr6aVCXHFxcZozBOjNO75p8oV4pKk0NBQDRrQV5998C917tjesG3rtu16+KkXdDTzmNvBEWePC0Dl4KzPcRecsNvtem/SZ2VCXO2TE/TZy1f5HeKSpPga1TR+dA/9/baOCgs9/SemrKxsPfaPl7R7z74ybfQUQjNbZWoLUNl5ugYq+W/Nug0a88obhhBXXEy4xj18qUYN7exXiEuSQqwhuvnqNvro+d5qWDfGsG3SZ19qxsyfy7TNU/sBVBx31zXO+pTCwkI9P+5fZUJcV1zUSP/3Uj+/Q1yS1Kh+DX04po+G9GttWL/vwEGNfvZFHTt+okyb6TcAAAAQDAS5AAAAAMBP7gYwi7+W/LdoyXL9e8pXhmMa1EvS5PfeUK+elwS0bXXrJOqNl5/TkBuvNaxP27FLL7z6pstgBQMTQOXlaeYbZ/8WLFyib7+fbTiuW4d6evupXqodH13+RlksuuHK1nr90R6GMNfRzGN67uXXVVBQ4LRv9PTYAFRu3lwDFX89fiJLY155Q/n5BY79q8dG6IPneqtbxwYBaU/LJrX08QtXqkWjGob1b33wiTZuTi1z3eOqP6IvAiqOs+uY0ttKf53y5TQtW/GX4TyDr0zW8/d3V7VqEeVukzXUqpG3dNQTwzob1u/YuVvj33qfUCgAAAAqBEEuAAAAAPCDLwOYkpRx5KjeeHeS4ZjGDetr4mtjlVi7VtDaOXL47brzlhsM61avTdE3M2YR5gKqME+z/0nS8RMnNPHjTw3Hnd+6tsY+cIlCrIH9k1CHtnX1zwe6yRpy+nZC23fu1pfffu+0baUHbEtuA1C5+XINZLfb9d6/P3PMZCNJ1aJC9a/He6hhUvWAtiu6WoTefKKnGtY5PTOX3W7Xa+98qKKiIkPI1Vl7AVQcV687dzNy7dy9R59/M92w/9WXNtUDt3YMePv692yp+28+37BuybKVWrRkWZm+pHS7S7YdAAAA8AdBLgAAAAAoJ08DmMWDiMdPnB7ErJNYW+9MGKta8TWD3r7hdwzRLTcMNKz7+NMvtGPXnkoR5mKgA/DM3SwVpdcV//tw8ufKPHbcsU+9xGiNf+QyhYVbg9LG7p0a6u+3GQdTP/tymvbs3e+x3ZVJZWwTUFl5ugZavTZFP89fZDjmxQe7q2WT4ITYq8dF6c0nL1e1qFDHurQdu/T1tB89trcYfQAQXJ7CoMXfl/xns9n0+sSPVVhY5Ninwzm19cTwCyWLRcFw89VtdH2vloZ1b3/4f8rKzja00dljKf09AAAA4AuCXAAAAADOWEeOHNHIkSO1YMECFRUVeT7AS97c9qPkH/Z/+W2plq9c7TjGYrHo2cceVM0agZ2Jwp2/3XWrkls2dywXFBTojYkfl2mru0/HA/DOsGHDdN1112nlypUBOZ+n2/c463e2pm7X7Lm/GPZ9/K4uiooKD0ibXBnUq5XOa5XgWC4oKNC7kz5z20cGe9AzUOekH0RVMnfuXHXq1EmTJk2SzWYL2Hl9uQay2+16+8PJhuP7XdJUnc+tF7D2OJOYEKP7bmpvWPfpl9/oyNFMt9c8BDCAslavXq2aNWvq3nvvVX5+fsDP767/KLn8629LtX7DZsdx4WEhemL4hbKEBHeI697B7ZVYq5pjOePIUU356juvZxilLwEAAIA/CHIBAAAAOGPl5+fr/fff1xVXXKGkpCSNGDFCc+fOVWFhod/n9HUA026365vpMw3nGHzdAJ1/blu/2+CPUKtV/3j8IYWHnw5xrN+4WZu2pBraXPx9ya8AfLNmzRp999136ty5s+Li4jRo0CCtWLEioDXc3dLHbrdrxuy5hv37dG2sTu2SAtoGpywWPTH8QoWWuHXj8pV/6eChdLd9S2Ub9KwMbQDK49ChQ1q1apX+9re/KTw8XJ06ddJHH31UrlCXr9dAa9dv1M7dex3HVI8J14O3dPC7vi+uvaKVzm15etav/PwCzZm/0NDGYrzeAdcyMzOVmZmpDz/8UBEREapVq5YefPBBv0Nd3vQjJbcX/yt9XTP0mraqXyfOrzb4IjIyXI8N7WxY99PcX5WfX+DyOgwAAAAoL4JcAAAAAM4Khw8f1kcffaQ+ffqoTp06Gj58uH766ScVFBQE5PyuPkW+YfNWbdq6zbFfzRrVdffQIQGp6avGDRtoyPXXGNZ98/1stzPkVJWZcgJ9LiBQTpw4oenTp6tLly6KjY3VNddco6VLl3p9vLsBz+LvS792c3Jy9ctvvzv2sVike0vNThNMjepV19WXNjW0cdbPCzzOWlEevP4B14qKirRq1SqNGDFCYWFh6tChg95///2AzdTl6hpo5s8LDPvdevU5io2JDEhNjywW3XvT+YZVM39eYHjMZlz3AFXdkSNHNHHiREVERCghIUGjRo3ya+ZjV9cDpT8QY7fbtXP3XsNsXNVjwnXL1W3K90B8cFGH+mqffHq20eMnTmjx0j8M7S1GXwIAAIBAIMgFAAAA4Kxz5MgRffLJJ+rXr58SExN155136scff/T4yXJvAxUll7/9frbhHNdefaUiwoN7azN3ru3fV1ar1bG8cPEypWcc8frT8QxEAP7LysrSDz/8oK5duyomJkb9+/fXkiVLXO7v6XXpauaKBYt+18mTeY71F52XpNq1YgL5UDy6pmcLw/Lseb+qqMhWZnC2uN0lv5b+HkDg2Gw2rV69WiNHjlRYWJjat2+viRMnepyt1NdQ6fETWVr0+x+O7VarRVdd2iyQD8Wj81rXUeOkWMfyvv0H9dfaFKe3bSv+vuTX0t8DMMrIyNBbb72l0NBQ1a5dW48++qjbUJerGTndhbjsdrtmlbpVdL/uTRUaZi1zrmAaWOq65oc5893OjFryKwAAAOArglwAAAAAzmqZmZn69NNPNWDAANWuXVu33Xabpk+frpMnTxr28yVQUbxcUFCg35f/6VgfGhqqa/tfGcyH41FCrZq6/NKujuWioiItXrrCZbACQHBkZ2dr5syZ6t69u2JiYnTVVVdp0aJFXh3rKjxR/G/J8pWG/UuHqipCq6a11KpxDcdyesYRbdqaWma/YPc15Tm/qwFn4Exhs9m0du1aPfjgg4qIiNC5556rt956y22oy9tQ6Yo/VxtmPb2kU31Vj4sKwqNwb0CP5oblxcvKXvNIvLaB8kpPT9frr7+u0NBQJSYm6oknnjCEunz9QExJv5e+rrm84q9renRurJjoMMfymnUbdOJEltd9CX0MAAAAfEGQCwAAAAD+5/jx45o6daoGDRqkhIQEDRkyRN9++61yc3NdHuMuUJG2Y5dhELNzx/aKr1nD2WkqVL/ePQ3LG/9360d3A5sMdgLBk52drdmzZ+uyyy5TdHS0+vbtq/nz5zu2e5r9pvS/1LQdjv3Cw0LUtX39CnssJfXs0siwnLpth9MZuSTz+hp3A8fA2cRms2n9+vUaNWqUIiIi1K5dO73xxhvKz8/3KphQ+rW9pUQ/JEk9OzeSGS6/sFQ/lFa2H6oMfRFwJjl8+LDGjx+v0NBQ1alTR0899ZTLj+iDrgAAIABJREFUfV3NLlr8LysrWwcOHnZsb1o/Tg2Tqget7a6EhVvVrYPxespZf1KMD8cAAACgPAhyAQAAAIAT2dnZ+vLLL3XDDTcoISFBN954o7766itlZWVJ8i5QsXGLcfaZtq1bVuhjcKV1K+On2DdvSa20M88kJCSoUaNGjn8RERFmNwkIqpycHM2ZM0e9evVSdHS0+vTpo7lz5zq2uwoeFa8/cjRTRzOPOdY3b1hDIaHm/PknuUm8YXlr2naX/aUU/D4nJibG0J9Ur356ILgy9HdAZWGz2ZSSkqLRo0crMjJSbdu21YQJE5SXd+qWrd5cA20rFeRKbmrsDypK7Voxios5fUvrtB27ZLPZnO5LPwAE3qFDhzRu3DiFhIQoKSlJzz77rCQfwunbdxjO18qkvkSSkhvXNCxvLdXPFWNWLgAAAJQXQS4AAAAA8CAnJ0fffvuthgwZosTERF133XX64osvdPz4cbeBik3/m+mq2DnJlSPIFRsTrQb1kxzLu/fuV1Z2doV/otyb840bN07bt2/X9u3btWPHDrVr167c5wSqitzcXM2bN099+/Z1hLp++uknSWUHPYvXpZYaVGxVatCxIrVqYqydmrbT5b6uZr8pz2u69LGDBg3S9u3blZaWprS0NA0fPtzvcwNnC7vdro0bN+qJJ55QtWrV1KZNG7366qsuZys9Hb44/XqPigxV/cTYimpyGSX7opMn87R3/wGfZ+UCUH4HDx7Uyy+/LKvVqvr16+uFF17wOCPXtlLXDsmNzLuuMQbU7drqxwx/AAAAgDcIcgEAAACAD06ePKkZM2bo9ttvV926dTVo0CBNmTJFmZmZZQIV+/YfMhzbqkUzM5rsVHLL5oblffsPuhxIcaaibnlW0TWByurkyZNasGCBBgwYoNjYWPXp00czZ84sM3C4/4Cx32ne0LzbuVaPi1KNuNOz6O0/eMjtjFxS8F/n7s5fWWcmBCoLu92uzZs365lnnlFcXJzatGmjV155Rbm5uYbXclZWtrKysh3HNW9QXbJYzGq2WjQw9oPF1zyV7boHOJscPHhQY8eOVXh4uBo3bqyXX37Z6Sxd+w4ar2tamBjkat7odF8SqkLt3XfA5z6DvgQAAADeCDW7AQAA4Owxa9YspaSkmN0MAGeRwsLCoJ4/Pz9fM2fO1MyZMyVJcXFxGjdunPr376+aNWs6bkFULDYmOqjt8UVMtLEt7c9tq48+nqTDhw7K8r/B1tJfi7la7ytPAxnOtntTs7ztgmebN2/WkSNHyvyO45SMjIygnj8vL08LFy7UwoULJUnnnnuunn76afXv37/MzyQ6KiyobfEkNipMmcdPtSk/P9/xui75Os3OztZ7H/1bVruxz/alrwlmCMtVHwhzHT16VJs2baIfcsHVjFmBYrfbtXXrVo0ZM0ZjxoxRtWrV9Mgjj+ihhx5SXn6+YV+z+6HS9Xte2k233HabMg4fluS+r+H1fnZIT0/XsWPH+HmXEhYWprfffjvodfbv368XXnhBL7zwgmJiYvToo49q9OjRstvtOlmqj68WZd6QVnTk6du0hllsSt+3XZL79yx2u53fKwAAAPiMIBcAAKgwn3/+uaZOnWp2MwCcRRISEiq03vHjxzVy5Eg98sgjevrpp1VQUGDYbrVaK7Q97oSFlX07+NS4fyljG4FboLyqV69eofXWrVunIUOGqG7durr3/r8btlmt5g4eWkNPTwZfVGRzDGiWHPRclbZfz70yQUWZB81oInBGCgur2PBUTk6OXnzxRY0bN06PPvqYYZvZ/VBoaNmbUnzx5VeyFxY42RuAmbKysvT888/rpZde0n/+8x/ZimyG7VariTeZCbHIYpHsdskiu/Jzs7Vw4ULVqVNHbdq0KbN7yRAXgS4AAAD4giAXAACocH379lWvXr3MbgaAs0BhYaGefPLJCquXkJCgF198UX379lXNmjU16qkXDNsLCgoqfGDVlbw842wZvT78VfN+mi1L6+66p11dNa0ZJcn5LBWuvveVN7PllNzH21oMkgQfM3K5t379eh07dqzC6rVv315PPfWU+vXrp+k/zpEWr3Bsyy+wuTky+PILihzfFwdISw9m7imwKqTHYBVl5+qihnEa2CpR1hCLT31NsGbkYoaeyiszM1MbNmxQfqnZn3BKTk6OFixYUGH1YmJi9PDDD+uBBx5QXn6+Ft99OlSaX2hyP5RfZFjufOejWrFmnawnj6lz3VjFl5ixi9f82al4Ri4YmfG+pXr16nrkkUf08MMPy2azactHnxq2FxQUuTgy+GyFNhVfWhTYrYqrUV0TJ06U1WrV66+/rgYNGhj2d9V3EOoCAACAJwS5AABAhRs9ejRBLgAV4sCBA0ENckVHR6tv374aOHCg+vbtq+joaNlsp2acsdlsqhYVZWzPocNqWL9e0Nrji4OHDhuWx3ero5fjbtI3Gw/rQ4s0vmtz/f3CRrJYLI5/UtlgV7Bvr1i8DyEuVCWdOnXSjh07gnb+6OhodevWTaNGjdKll14qu93u6HeioiIN+2ZkBvf2am7Z7MrIPOlYrBYVVWZGLovFops7NFGNl57UwBkbtOxkoY7ViNOPN5ynJjWjvO5rXPUlvgS8vA1w0M+gKpg6dWpQg1whISFq06aN7rjjDt1zzz0KCwtzXANZLMYZc9KPmtgPSUov1Q+OG36DJu27RV+kHNKyEIs+7NdSQ9vXM1zzlOx7AhVgB6qiX3/9Najnt1gsatiwoR544AHdf//9stlOBT+Lv0aXuq45fPSkzglqi1w7nJnj+L5AYYpPrKc9x4+ooKBAY8aM0auvvqratWuXOY7gFgAAAHxl4jy0AAAAAFD11KxZU7fffru+/fZb7du3T1OnTtWNN96o6OhoRzig+Guzxg0Nx27cnFrh7XWlZFsiIsLVtH6SJvdvpX/1bi5ZLHp83jYN/jZFJ/IKJQVmZht/MfABSHFxcbr22mu1aNEipaena/r06brsssvK9DuNG9Y3HLd5x5EKb2uxXfuPKa/ETDhNGp2aqaI4eFby+74tain17i5qUy9OG/cdV/OPlmnmlsNOz1tezoIZ9DOAZ1arVR07dtR7772n7OxsrVy5Ug8++KDCw8MNr+nw8DAl1U10HLdr3wnl/+96wgyl+8GWTRrow34t9HKPJpLdrhEzt+jRuVtVZDt9rVPyusfMayDgTGSxWNSkSRO9/fbbys3N1ebNm3X//feXuaax2+1qVOq6ZsuOjApvb7HN248alls0b6oePXpIko4ePaqxY8cqKyvL5fH0JQAAAPAWQS4AAAAA8KBOnToaMWKEZs+erf379+uTTz7RgAEDFBl56hPizgb77Ha7WjZvajjPhs1bK67RbuzZt18nSgwyNG/aWCEhp0IMf+uQpIW3tpdiwzV90yGd88kKpRxyPSABIHji4+M1ZMgQ/fXXX0pPT9dXX32lLl26OLY7G/Bs3LCBQkJO/7lny07joGNF2rzTGJ5o1qSR23BEoxqRWnFnJ93XuYF0slDXfLFaT8zdoiKbPSiDnwS4AM/Cw8N18cUX67PPPlNeXp6WL1+uu+++29HPlA5mFn9t1qSR4xw2u13bdpvTF9kKbUrbc9yxnBBfU3GxMbLb7Xqwc31Nv6GNFGHVxBV7ddVXa3U0t4CwBRAEFotFLVq00KRJk5Sfn68tW7ZoxIgRkly/lyrdl0jmXtdsLXVd07xJIw0cOFAWi0VWq1W7d+/Wiy++qJMnTzraT38CAAAAfxDkAgAAAAAnGjVqpFGjRmnRokXas2eP3nvvPfXu3VtWq7XMAEOx0utLB7n+WptScQ/Ajb/WGNuR/L92Frf/gnqx2j3sAl3StKYOZeTq/E9W6KuUA07PVd7BiUCGKAhk4EyQmJioYcOGacuWLTp8+LCmTJmidu3aldnPWXBCksLCQtWgXl3HfvsOZevosZwyx1eElK3GWTOaNz01GOsuzBVuDdE7/Vrry+vbSeFWTViyQ90/Xan9J/Lc1nL1+i9Pv8BtFXG2ioyMVM+ePTVjxgzl5uZq8eLFuuWWW8q8Xl1dA0lS88bG8MW6relBbrVzm3dkqLDI5lhu1tQYKO3VNF6rbz9f8fFR+jXtiM6ZvFJb0s3pM4EzTUhIiFq3bq3PP/9chYWF2rRpk4YOHerY7um9lCTVT6qjsLBQxz4btmXIVuI1XZHWpRpnCm3RrLGSkpLUuXNnFRUVKT4+Xps3b9Zrr72moqIiF2cBAAAAPCPIBQAAAAD/07p1az399NNauXKldu7cqTfeeEPdu3c3zG5TzNVgZsn1tRPiVSu+pmM5bftOrV2/MUit9960H2Ybltu0blnm8dSKCtWcm8/TU90aSwU23TYtRQ/O3qT8QgYlgECrX7++HnjgAe3Zs0cHDhzQpEmT1Lx5c5dBSU/9T+kQ6ezftgeh1e4VFhTp56U7DeuSWzRz2lc6e5w3tK2rTX+7SI0TY7R851HV/2CpFmw373ZKwJkuOjpa/fr107x585Sbm6v58+drwIABZfZzNsuMs2Bpq5bNDMfN+i0tiK137ceFxrrJLU63y9Fn1qqmlKEd1aNZvI4cyVW7ySs1Z1u6YZ/SxwBwLiQkRG3bttW0adNUWFioDRs2aPDgwYZ9nM1U5er6wGq1qnmTxo51x7Ly9fvqfUF8BM4dTM/Sqg2HHMtRUZFqUC9JknTttddKOhX2rlOnjlauXKnPPvvM7fnoSwAAAOAOQS4AAAAAZ7UOHTpo7NixSklJ0caNG/XSSy+pU6dOPv1x3dXMOJLUu+clhn2//u7HALTaf6vXpig1bYdjOb5mDV3Q4TzHcsm2WyQ936OZZtx8nhQZqvdX7NHFn/6pPcdOVmCLgTNTkyZN9PjjjysjI0N79uzRO++8o3r16vl0Dmf9lN1uV89LLjKs++HXbVIFDxj+smKnTmTnO5bbndNKtRNqOdpY8qur71smVFPK3V10e/t6Una+rvh0pcYu3Obz4Kc/s2gx8xbOBnFxcRo0aJCWLVumrKwszZo1S1dccUW5zln8+myT3MLxmpekbbuPadO2ip2V6+TJfM0tESgNsVh0WfeLDO0s/lo9wqqZN52rh7o0kPIK1f+LNXqjVBgVgHNWq1XnnXeeZs6cqaKiIq1fv16DBg3y+nh376WcX9dU/O3qf/gl1XApdVm3ixwzNbdo0UJt27ZVRkaG+vXrp7Zt26p///6G4wluAQAAwBcEuQAAAACcdbp27arXXntNaWlpWrVqlZ599lm1adPG7TG+/vG9eP++V1xquB3Ib0v/0M7de3xvdIBM+WqaYfnqPj1ltVoluQ6F9GuRoNS/dVGrurFaveeYGn+0TPPSAjcYW97AhMViIXSBSs9isahly5Z6/vnndeLECW3fvl2vvvqq4uPjy+zrrr/xZpauNsktVS+pjmN5z8EsrVi3vxyt9920ucZB1iuvuNTjMc5m6IgKs+rTQe006Zo2kjVEzy1IVe//rFJ6Tn6Z4931A770EcG4TSNQWcTHx2vIkCFat26djh07pmnTpunCCy/0+3zOghfSqVl5rrism2Hdf+du8buOP2b9tl25eYWO5Q7t26l2rXiX/WiIRZrQu6UmDWgthVj0+NytumtGivIKzbmNG1CZhYaGqmPHjpo/f74KCwu1Zs0aXXXVVY7t5Q0ulTy+20UXKDIiwrG8bM0B7Tt4vFzn90V+XqF+XGSc3a9vqeuagQMHSpJ++eUXvfjii6pdu3aFtQ8AAABnHoJcAAAAAM4Kl19+ud59913t27dPS5Ys0ejRo9W0aVPPB3rg7pY7drtdcbEx6n5RZ8c6m82msePfVmFRxd+i8Mef5umPP1c7lsPDwsrMGCY5H3hpXCNKf97VSXeeX0/KKVCfKav00qI0Pl0OeNC2bVuNHz9eOTk52rJli8aMGaOYmBivj/c0a5Wr7b17GAMU//pspQryK6bfmbUwVetTT98GMSYmWhd37limjb4Y1rGBVt99karHR2l+arpqv/+7lu3ODEh7gTNdYmKihg8frtTUVGVkZOjzzz9Xu3btglav+HXe67KuCikRgPx5yQ6t3XQwaHVLOnosRx9/s9awrk/P7h5v6ypJd7Svp99u7yhFh2nKmv3q+ulK7T/ObKRAaGiounTposWLF6ugoEB//vmnLr/88oDWcPYajYqMVLeLLnCst9ntev3TFQGt686/p61VRubpPqB508Zq1qSRoa0dOnRQjx49dNNNN8lmI/wJAACA8iHIBQAAAOCMFR0drU8++UTp6emaP3++Ro4cqaSkpKDUchdOGNS/j8JCT8/KtXnrNn069eugtMOVffsP6O0PJhvW9e3dQ3GxpwIlnmYAstvtigy1atI1bRwz4/xjQar6f7laR3MLyt0+f2e6YYYcVEaDBw/Wu+++q4KCAq1fv16PPfaYIiMjK6R28Wv58ku6Kjb2dGBs98EsfTpjXdDrHzueq4lfrDas63/lFQoLC3PaTl+CXe2TYrVzRFdde04d6XieLv7kD721dIfXx3vTX9Cn4EyRnJysBx54QLt379bBgwc1adIkNW/e3Ktj/Q1clg5gxNesoUu6djHsM/6TP1RYEPxQ6ZtT/lRWzunrk4b1k9SpxK2kS3IWHLmoYQ2lDe+i5KRTs5HWn7Rcf+6ruBmAgMqiXr166tq1q5YvX66CggItX75c3bp183xgKd7MKOpO/ysvl9V6ejjrj3UHNXdJmpsjAiN15xF9OXuzYd11A6502m888MADuuiii7iWAAAAQLkR5AIAAABwxoqNjdVdd92lWrVqmdqOenXr6JYbrzGs++zLafp18dIKqX/s+Ak9M3aCck+e/iR5w/pJuuX6a9wc5dpdHerrz2FdFFUzSrM3H1b9D5dq9f4TgWouUOU9/vjjGjlypEJLBDgrWnR0Nd1583WGdf+ZuVFrNgZvNhxboU1jP1yqE9mnb3tYv15dXTfgyoDVqB4Zqu9uPl9v9k2WJI36abOu+2q1jp08dfu0YA2eMiiLquaCCy7QO++8owYNGpjajqFDrldMdLRjeef+E3pn6qqg1py9aJsWLN9tWHffsNtkDfHtT+ENq0dqxdALdH2bU+HRzp/8oa/WV+xtagGztWrVSkuWLFGXLl087xxEDerV1cB+vQ3r3vzPKu09ELyAZXZOnv75/u+ylQhtdWzfTt0uvMDNUQAAAED5EeQCAAAAgApwVe+eandOK8eyzWbT8y+/oXm//BbUukczj+mhx59TatoOxzqrNUR/v/cuhYeHuT7Qg/OTYrXnnovUt1Vt5R7NVYdJyzT5rz3laquzoMTYsWN1wQUXOP5t2rTJ7f4ATrukaxe1a5PsWC4qsuuxNxZq644MN0f5yWbXPz/8XcvXHjCsvu+uWw0zEgbK3y9uoqXDukhxEfpuw0E1/Wip1ngIlM6aNUudO3d2/Js6daphO30KEHhxsTG64+ZBhnXT5m/VJ9+uCUq931bu1rhJfxjW9el5iZJbNvPrfNXCrfr6xvP0Qs/mUqFNQ75Zp2fmbeXW0oAJrrumr+om1nYsH8/K10PjFij9SHbAa+WdLNCjE37V9r2ng2IR4eEacectAa8FAAAAlEaQCwAAAAACwFMAwGKx6L7htyk25vSsFEU2m/45/i19+/2soLRp1+69evCxfyhtxy7D+luuv0ZNGzc0tNmfAEPNqDDNHHK+XryihVRk07DpKRo2fb1OBvCWSTt27NCqVasc/7KzAz9QA5xJSr+u/3bHYEVFnb6tY+7JQj386i8BDXMVFRbp1U+Wa/4yY1/Tr1cPnZPcwm07yxOeuqhhDR2+r6uuaJGgoxk5On/SMv17letAaUZGhqE/OXDggMt9gbNZIG53XPL7Ht0vUvt25xj2nTw9Rf/5fr1/DXRhyao9GvPuEsPsObUT4nXrTQPdHueq3SW/f/bSZvru5vZSuFUv/5ama75cray8wgC2HjizBSIsHR4WpnvuvFkhJWbXO5SRo1HjFuhwRla5z18sJydPT761SOtTjddKt988SLUT4iW57iu8kZmZqQkTJuj4cW7XCgAAAOcIcgEAAABAOZT+w727wcCE+Jp67vGHFBcX61hvt9v15nv/1t+ffF77DgTmlmdFRUX6/OvvdNfI0dq5e69h240D++maq3q7ONI5i8XicoDCYrHomUuba94dnaRqYZr8116d/+8/tP1ort/tB3CaNwEDd+vqJtbWE38fobCw0zPwHcvK1z3Pz9V/f9pY7vbtPXBcI174WT8uTDOsv7DT+brz1hvc9pGBkFAtXHNv66h/Xn4qUHr3jBQN/W69cvIDFygFUD4Wi0Wj779bzZo0Mqz/8L9r9eQbv+pE1kkXR3qnsKBIb09ZqSf/tUgFhTbH+upxsXrusQcVXa2aoS3OvvfEYrFoYOs6WjP8QllrROrHTYfUdtIyrneAcvLlvZQktUluqfvuMs6KtXP/Cd3xzGwtWmG8pao/UrYc0h3PzNbK9cb3Zdde1Vv9evXw6hye+pbJkyfr119/1ahRo7Rr1y63+wIAAODsRJALAAAAALzga/jA1SBEowb1NObxB1Wjepxh/1Wr12noiIf1yZSvlJ5x1K82FhUVaeHiZRox6im9/8l/lF9QYNg+5LoBumlQf48DJL4ER4pd0SxBu+/tqg4Na2jz/uNq9sHvmrXlkF+PAzjbuXuteRuMKvl6bt2yuR4ZOUxW6+k/AxUW2fT21L/06IQFfs3OlZubry9+TNGdz87W5h3GPuu8tq016r67ZP3fjBne9p/uQqOejvvHZc01f+gFUnS4pqzZpzaTlmlzOjP4AcHm7bVDZGSEnn5kpOon1TEcv+Svfbrj6dn6eXGabCVCWF6x27Vs9V7dPeYn/ffnLYZN0dHV9I/HHlTdOolu2+fssbhzbp0Y7b/nYl3cJF67DmWr2UdLtTAYt6sFqrjyBrfd9SndL+6sO4dcb9g/K7tAz7z9m17+aKn2H3J/q2VnjmTm6P0vVmnki/N1MD3HsK13z+669aZrPbbLWyNGjFDnzp114MABPfLII/rzzz99bi8AAADObNbnn3/+ebMbAQAAzg7Tpk3TunXrdMcdd6hZs2ZmNwcA3PJ18MH+v9v4lPzq7HtJio2JUaf27ZSyeauOnzh9G5DCoiKtXpeib6b/qNS0nYqMjFBsbIwiIyNc1i0sLNTWbTv0409zNXb82/pxznxlHDGGKkJDrRo65HoN6NfLcSuSkJAQR2ii5Pcl/zlbX/zcOBu4iIsM1bD29ZSRW6gVO4/q83UHVGSzq0eTeL8Hc77//nutXr3asXzPPfeoXr16hrrA2aZkf+Lp++KvdRIT1KBekv5al6KiotOzVe09mKUZv2zTms0HFVstTLVqRCo8LNRpXVuRTdt2H9UXMzfohQ+W6vfV+1VYZDfs0+G8Nnr0gb8pPDzcZT/jrG8pvU4qGxDx9JpvWrOaRpxXT4v3HtOGfcf17up9al0zSu0ST82CuHbtWk2fPt2xf+/evdW1a1fHsi8BOgCneXMNZLFYFB4Wpgs6nKv1G7fo2PHTQYuck4Va9Ocezfxtm2S3Kz4uQrExrq99Dmdk6ZflOzX2g9/135+36OjxPMP2mjWq68lR96pp44aO6x5X/VDpr95c90SHh+qO85J0MCtff+7K1P+t3a860WHqXL96AJ5N4OxUss+wWCwu30sVf23WpJGiIiO0ftNWw/VP6q5MfTN3i9J2H1XNmAjFx0UoNNTqtGZBfpE2bkvXpG/X6pWP/9CaLYdlL7VPn8sv0d23D/bqmkaSy76kZH8SFhamSy+9VHl5eVq7dq0WLlyomJgYJScnB+z5BAAAQNVmsZe8ygUAAAii2267TVOnTtXcuXPVq1cvs5sDAB65GpgsXrbZbIZ1xcs2m83wfcl1JY8rKCjQ9Jk/a8aseSqyuZ6FIrF2gpJbNlNCrXiFh4WrsKhQOTm5StuxS2k7dqqgoNDlsS2aNdF9w25Vg3p1ywwshISEuBzI9GVA01nA4Yt1+3TLjBSpwKbLmtfSf68/T7Wjw716vksaPny4Jk+e7FhesWKFOnXq5LY2UFW5CmI5+1eyb5Hksf8p/rr/wCG98/Fn2rFrj8t2NE6KVevmtRRXLVzWEIvy8ouUtjdTm7YfVZ6LWxaGhlp1643X6qrePcr0Jc76F1fhCl/7GmfPYZHNrqfnb9WEJTskSSO7NNQbfZL11RdTNXToUMe+48eP1+jRow3Hl2emHuBM4k04q+R1kKvrHmf7FRQUaOo33+uneQtd1q8eE662LWspqVaMwsOsKigo0qHMHKWkZigj0/WtGDud30733nWr4mJjPAa3vAm1ewqUvvvHLj0we5Nks+veCxrqnataK9TKTTAAyXM/4uq9lDfvq4r325K6XRMnfeZyRuMQi0UtGlVXqybxio4MkyxS7slCbdl5RFt3ZaqoyPnwWHR0NY24c4gu7HS+V9c0zvoOm82mefPmqWfPnoqKipJUti+ZM2eOJk6cqKKiIl111VW67777ZLU6D54BAADg7OH8I5YAAAAAAKeK/+he/Gnx/2fvruObuN84gH+SNHV3g1LaUqwGbaG4y4DBgMGGjAmyDaa/jTGDMvjBBvyYMEEnMBjubqUFWihVWqxG3d01+f3BEpL0ok2byvN+vfLK3eXue09C83C5e+77FUwLXhMsF50WXSbahpaWFma9OBkDvT2w5+8jSElNZ9xnfkEh8gsKlYpTT08Xs6ZNwuRxI8HhcGReiFSm1xtFixle9bCHp40xxhyKRnByEay3h+LuXG/4O5oq9T4I6UoE+UKZdaXlGmmv2dlaY+2qD3Do5HlcuhYi1juXQFpOBdJyFB+WyKmbA959cz6cujsKYxM8y8svrVE4xWGz8N34XhjW3RTTT8Tj1/AM3Mwqx+stuDAqmvMJ6UpE85JkjmJ6Tdoy0W24XC5em/sS+vd2w56/j6CktKzZfssq6xHFxFJEAAAgAElEQVQanaNwnPp6unhl5lSMHz1cakGosnlIEcv9u6OflQFGH4rF9ogM3C+qwqk5XrDUl128TkhXJZkjAOabOeQd1wi2c3PpgQ1ff4I/DhxDWHhUs3Z4fD4S0kqRkFaqcIwefd3x9hvzYGlhLjeXyMofu3fvxvnz51FcXIwFCxYwrjNx4kTY29tj/fr1OH/+PLKzs/HFF1/A0NBQ4XgJIYQQQkjnQz1yEUIIIaTNUI9chJCORrJ3HFk95Ch6B7m0bfh8PpJSUnElOBThETFoaJTey5YsTt0cMH7UUAzxHwg9PV3GnigAKN0Tl6I9U0iqqGvEGycf4NjDXIDDws+TemO5f3eZn7UoeT1yKRIDIR2JKr3gAJDZC5e0Xi+KS0px+fpNXAsJQ02t9B5upOnftxemjB8Nb4++YjlCXn5RJM8om2skPz+B1JIaTD0Si4fZ5QCbB+xfAyTeA8DcI5esfVGuIV2Juo+BADCu19DQiNDwSJy7HITM7Fyl47S0MMPksSMxenhAi497JIdIUyYXpRRXY+Q/UcjMr4KemR7uvToA/WyoEIN0barmEUWOZ5h+W2Xn5OHclRu4fTcCjY3MPYhKw2az4T/QC1MnjIaLs5PU/CCrdy5BO4JtcnJysGLFCrDZbGzfvh3W1tZSc0lOTg4CAwORkZEBBwcHBAYGwsHBoSUfPyGEEEII6cA4gYGBgZoOghBCCCFdw/HjxxEXF4fXXnsNPXv21HQ4hBCiNEUu4ksWYchbT5S5mSl8vftjzIghsLa0gLGRIfh8Piorq6S2Z2pijD7uLhjiNwBzX5qKl6dPhrNTN3C5WoxDKSpSbCGt0EL0M1C0uEJHi405/W1hoqOFS8lFOJ9QgITiakx2tQRXgaGHTp8+jZiYGOH80qVLYW9vL7YOFVeQzkiyBxsmknmBqQhM1np6urro16cXxo0aCisLCxgY6KOhoQGVVdWM+zM00Ecfd1cMH+yLBXNmYOrEMbC1sZKbX6TlGsF7a0nxhDymelws9rJHUU0DIrIqgP6jAS1tIPU+xo8fhyFDhjTbhgq5CGmupcdAotuLvs5ms+HUzQHjRg6FW08nGBsbgcUCKioqhQVgojgcDpydusFvgCemTRqL11+dhV6uztDW5so85pGWj2QVlwriViQfm+lxscTLHjH5lXiUWYZfY7MxwNoA7pZUzEWIgLw8ItlTl7KMDA0wwLMfRg0dBAszU+jp6aK2rg41NczF6mYmxujftxdGDh2ExQvnYMzwAJibmTLmBlWK042NjVFdXY0HDx6gpKQEw4YNk/pZGBkZYcyYMUhOTkZCQgKCgoLQq1cv2NraqvRZEEIIIYSQjo165CKEEEJIm6EeuQghHZEiveNI641CXm84THejS7YPAPX1DcjJy0dtbR0aGhvAZnOgzeXCytIcJsZGjBccZRVWSLsgIe9ihGj7AspcaLmVVozhh2OBynp0tzbAlbk+6GVpILPoTZEeuVSJhZD2SrL3CsGztIeg0EHZ3riYcplguqqqGrkFhaivr0dVeiZMe/aAkZEhbK2tGHONMoWiyvT6p2yukZVL+Hw+lv92GNszdQFtPSAtDl/34SDw0w8Y16diLkJadgwk+Qw07zlQ1jFQQ2MjsnPyUFtbh/LUNBg5dYeeni7sbW3A5WrJzSPKFrDLy0cC8nIAj8fHl9cT8e3NpwCA9WNd8eUIFzX+qxDSsSiSR6TlBFnHM8r8liotK0dhUQmqikvANTSAtrY2LMzNYG5mopYcIlmgLvqoqanB0qVLUVZWhs2bN6Nv377C9Zg0NTVh586dOHPmDDgcDt59911Mnjy59f6BCCGEEEJIuyT/9mdCCCFEAWcuXMWqwI1YFbgRVVLu4CeEEEI6MnkX8aQVICha9CDrNW1tLpy6OcDdrSf693FHX3dXuLn0gKmJcbMiLNGHMhcnmYomJN9nSwsYhjmZI/edIQjoYY70/Cq47wzD8YfKD6NESGcm63sm+f0VXSa6rSLFDbLyhKGhAVydneBibwu9o6fh5uIMOxtrhQonVCnSUuYzUIXgQu5gwzpg1/tAQRrg5IF1NW64kVokcxtCiGrHQLK++4oUTmhznx/76J48D0cjAzh1c2jW81ZL8pBk/OrITWw2CxvH9cKB2R6AFhtfXUvCnCOxqGlQbpg3QjobWXlElVyhSB4RPJuZmsDNpQfYfx9Cvz694ObSAxbmpi3eD9P7knxdX18fCxcuBADs2LEDfD5fZi7hcDh45513sHz5cvD5fGzbtg07duxAUxPlEEIIIYSQrkRL0wEQQggRd/HqDeQXFIpdOBCeHMCzZ44WB90c7ODcozvsbKzB4XA0Equok+cuISEpBd0c7WFgoN/i9mpr63DmwhXU1tUBAOa8NBU6OjpKtVFXV4d7UfcRHhWD3Lx8lFdUgsvlwqtfHwzy84FH394KtdPQ0ICo2HjciYhGQlIK2Gw2TIyM4NTNAb4+nujXxx3a2lyl3yMhhJCOicViMf4/zTQUCNMJflFsNrvZsEGi7QumFd2HIhc0FL3AKtq+tPhVKbawMdTBzUW+WHU1AVtup2LWoVh8HOCEb8e5QUuBoRYJ6aqk5R4AYnlCmRzE1L7oftJOn4NNVgHqy8qga2YmXE80LyhSNCqZYyRjU0fxhMKKMp4Vc019D/Ach7F7o/DNKBd8MdxZvfshpBNS9BiIKSexWCzhMQ/T8Q9T+wDQUFMLq/RcZJw6h37vLmnWpjLHPaLbSE5LxtFSr3rYw83cAH7/RONIfC4eFNfg8qvecDDWbXHbhHRkTN9zydcB6XlEQJk8AgDp127AMTkL5ekZMO3h1KxddTykvZcJEybg/PnzSEpKwtWrVzFhwgS5n9OUKVNgb2+PDRs24NSpU8jKysKqVaugr9/yc66EEEIIIaT9o0IuQghpZ06eu4QHjxIUXl+by4WbqzNmTp2EMSOHQkur7VN7aVk5EpJSAAB+A7xa3F5CUgrWfvsD0jOzhMtefGGCwoVc9fUN+O33fTh74aqwEExUzP0H+Oufo5gxdSJWLFkks920jEwEbvweSSmpjK/vPXgMujo6ePvNBZj54mS6+EMIIZ2QtIsBsoqrBMsVKaJgs9mMQ4EwFVVItiOrIEIdFx9k7aslOGwWNk9wx5Bupph5Ig5bw9JwO7scJ2Z7wtZIucJtUfLucCeko2HKA9LWkzUt7WKn5H4kt2u6eANaYCH5nyPov3yZwrlG0R4BFX0fipD2+TAub6wDTm7BLD93HGtywuqgZIRkluHAjP6w0OeKbSsrtxPS2al6DCQvJwHMxz9Mx0Cpx0/Bgg9wg8LAWr5U+LqsfKRMgalke5KxtuT77utgguxlAZh8MBqxmWVw3BGGsFd8MLibqcptEtLRyMsjTCRvZJH2fVQ0jwBA6ZHTMASQtvcgzNasEq4jLZeoOtw8Uy5hsVhYunQpVq1ahb/++gvDhg1TqCDLx8cH33//PQIDAxEREYGPP/4YgYGBsLW1lbstIYQQQgjp2Oh2Z0II6eDqGxrw4FEC1m3+CfMWv4/snLYfnigy5r5w2r8FhVw8Hg8HDp/A0g9WiRVxKaOwqBjv/udLHD15TljEZW1lCf+B3vD18YSxkaFw3ZNnL+GnHX9IbSslNR1vrfhUWMTVv4873pg/Bx+++xaWL3kNQwf7gsvVQm1dHX74bQ8+X/stDcFCCCGdnLwT9PIuLCp6cVFyHaZtJIcMkTZUmrxhFmXFKqvgQh1e6mODJ0sD4GRtiLtpJbDfEYZbacWttj9COgpZhU7yvqvK5h7J4VkF0421dbAtqQIA8MOimuUcaW2pmm/kvffWMIhbgog3/GBiroeriYWw3hmG8MyyNts/IR2JIsdATMuUfUjmmppbdwEAFlV1qC8rkzmktKJDS8vKP7LyryrsjHRw9w1/zPe0ByrrEfBHOPbGqHa+g5COTloeYVpP1d9RTHmE19gEx4x8AIBJZHyzXKHOPCIavygPDw8MGTIEJSUlOHTokMKfmaOjI77//nt4eXkhPT0dH374IR49eqTw9oQQQgghpGOiHrkIIaQd+/zj5Rg1PABaHC1oaXHA4/NRWlqGgsJiFBYVoaCwGHciohEWHgkAyMnNw3sr1+DXrf+FjZVlm8V5L+pZIReHzYaPZ3+V2sgrKMT6zT8h5v4DAICdrQ0qK6tQUVmpVDvbdvyJJ4nJAABbGyus/uxD9O/jLjyB0tjUhBshodiw9Wc0NDTi9PkrmDJxLPq6u4m1U1/fgLXf/YC6unpw2Gx88cl7mDBmhNg6r8yajieJyfjPV+tRVlaO23ciEBYehSGDBqr0GRBCCOl4BP+/SBbySp64l+wNR9rd46IPFuv53evSeoCRvBii6IUNyXWZtpW1H6b3qCo3C308WDwIS848xD9xORjxVyQ2jXXDJ0N7qKV9Qjo6QS4QzQmC5aKY8gRTLlK054rUsxcg6DPGorgcDdXV0DE0FK6r6kPW+1Rmubp42xnh6eJBeP30Q5x+nI+AP+7hhwm98N6g7q26X0I6OqZjIFnHKsDzHCRrODTRfMdrbIJ5doFwnZQTZ9DvrUUyj1/kHfdI24YpZnXlHx0tNv6e5QEvG0OsvJqARSfiEZtXic3je4HNph7+SNelyHdM3u8qpvVF8wiLxULyuYsw/vd1owYeCuIfwtqjn9K5RJHfT0y/lwTPixcvxr1793DixAlMmjQJdnZ2Cn1ORkZGWL9+PX755RfcvHkTRkZGCm1HCCGEEEI6Lk5gYGCgpoMghBDy3NlL11BQWAQAmDB6BHq59gSHwxGeNNDX14OVpTmcujmij7sbxo8eDq/+fRF0MxRNTU2oqq4Gm82G/0DvNomXz+fjh192o6q6Bh79emPGlIlKt1FWXoEFS95HemY2AGDqpLHYuOYzXA4KQXl5BQBg3sszoCtnaMWHTxKx7d8etnR1dPDHr1vh7NSt2Ynjns5OsDA3w+07EQAAU2Mj+Pp4irUVFh6FoyfPAQBGDB2EJYvmMe7T0sIcujo6uHMvCgCQm5ePqZPGKvsRdBnHjx9HXFwcXnvtNfTs2VPT4RBCiMJknZCXnFamLXnrSF4kEEwz9aCjyN3jqt5d3lqFXIKLv1wOG7P62sDaQBvnkwtxJakIMXmVmOJmCTafh549e2LYsGEYNmwYxo4dK/fiRWsXfhCiSa2Rb5hyTeovu2Bc/OxYnAMWcvS0YO3loXQeUXSYRXkXQVUhWWTL4/FgZmaGoUOHYujQoRg1ahS6d+8OHS025vazgYmOFi6nFONiYiHiCqow2dUCOlocxs+MkK5CXcdAkttIK6QS3UfK+YswCIsRvlZSWgKHmdMUPvZpb8c9Q7ubYZCDCfY/yUdYaglCs8vwYi8r6DLkGUI6E3l5RNo6stpT5juc+sN2GJdWCLfPqiiFw9iRYrlCHblENDamOAwNDVFfX4+4uDgUFhZixAjxG0ZlYbPZGDRoEEaNGqVwARghhBBCCOm4qEcuQgjpBAZ6e+DNBXPx2559AIDL10Ow7M0F0OK0/snAjMxs5P9beKbqsIq1dXWorKqGiYkxVn34LoYF+KnUzvXgUOH01MnjYGlhJnXdsaOG4Yff9qCurh5xj540e/1ORLRweoi/7B62Ro8Ygu9/3Q0ASE5NUzbsLmX//v0AAE9PTzlrEkJIx8FiscR6tJFG0bvHJXujkNY+0wVR0WlFLzLIWkd0P+q+mMnkHb9u8LUzxqAjsTj1KA/O+eW4MWcCZs6c2er7JqS9kfzuy8oHsqjSAyCvsRHmqdngAxDspfrGbbBfmyfctyL5RN4wi6LvVdpnoE4+Pj7w9pZ+w8sHg7tjsKMphh6NxYmHebieW44bL3vBy85Y6jaEdGWiuUMwL0paj6Ky2hLNSaVXbsBQ0BYAq+wCYe+AHfW4Z7KbFR4tCcCQf6JwNbEQfXbdQcirA+BmaaC2fRDSkQi+X5LF15KvK9qWZB5pqKmBZZr4MY1+zEOx3voEzy19MMUtmUvmzJmDy5cvIzQ0FLGxsfDyUu5cqrW1tVLrE0IIIYSQjokKuQghpJMYN2q4sJCruKQU2dm56N7NodX3ey8qVjjtp2IhFwAMHeyLzz58F2amJiq3MXbkUPTo7oDiklIM8h0gc109XV1YmpsjKyeX8WSR6JCO2lyuzLYMDfSF07W1dUqdrFbW1q1b8d1337VK221BcMLJw8NDw5G0TGRkJBwdHTUdBiGkjckqqmC6AMH0f4HggoG0YgrRtpnaZ4pJ8lmRixGyXpcVf1vwczRB/rLBmHssDkHJRfDcfRf7pvXFPA+685wQAVUKuuS1JfqcdOEy9Buf5zM+AIv0XDTV14Orq6vyRU/J/cp61pTB3UyQt3QwXj0Rj+vJRfDZE47dU3rjTR869iNdl6KFpfKGmpbWruRxj+BhmpQuLL5gAdDms5B84gz6L5ov3L4jHvf0tjJA0pLBmHkkFsHJRei16w4uz/HCeBfLNouBkLamaB5pSdtM7ScfOQETiXtpzGoakHk7DN2HDxWu35KCLslYZNHT08Mbb7yBrVu3YseOHdi2bRs4bXAjLiGEEEII6ViokIsQQjoJC3NTsfma2ro22W/4v4Vchgb6cHdzUakNS3MzbFyzqsUnTvu4u6KPu6vC69c31AMA9PX0mr1mYvx8yKbM7FyZ7RQVlwinrSwtWvUEcGVlJfLz81utfaKYpqYmTYdACNEQZS5AKFvYy1TAJWhH3naSz/IuRjAtF21PVmFFW1zotNTXxpX5A7D2RjLWhaRg4fF43E4vxfcT3aHDpQsdpOtQRwGpvLaZ2iy5HAR90XUB6PKA5NPn0PeVl4XbK3PRU3I9yVgk49dkQZelgTYuzfPB+pspWBucgsWnHuJmWil+ndIHepSDCBESzUOyvrOi6zEVsQvWEc1HqVeuw7CheU+mlUG3wXp9QYc/7jHX4+LagoH48OJj/Hw3HRP2ReL7ie74MKBHq+yPkPZM8jhEkXVl3RADPD8+qrp2E0y3jOacOAunEcOUyiXy8otojKLPktNjx47FmTNnkJiYiEuXLuGFF15Q9KMihBBCCCFdBFvTARBCCFGP6ppasXltbuvX6jY0NCA6Nh4AMNDHU+U7yDgcTptfqCkrr0BxSRkAwLNf72avjx0xVDh9OShE5lBYwbfuCKeHDvZVY5SEEEI6ElUvArDZbLHhxwTTos9Mw5OJLpe2rmBe1vBmylyAaEtsNgtrx7ji3HxvQE8L2yMyMfivCGSU1crfmJBORJELg+rMOXw+Hyb/9oIjqexqiNi6snKUvCIupoKutqBIsQnwLAetHumCywsHAAZc/BWTjf677yKxqLotwiSk3ZFV3CQtJynykHY8k3/+crMYnvUOmIOm+nq1HfdooohLgMNmYdsLfbB7ej+AzcJHF5/gjZPxqG+Ufv6BkI5M1jENU15hyhfK5JHq4hJYZxUwHtMYxicK96NKLpGMU9r7kvbeli1bBgDYu3cvqqqqlP0oCSGEEEJIJ0c9chFCSCcRFh4pnNbT1YWDfesPP/TgcQJqap9dTPVvwbCKmnDhynVhr0rDA/ybve7l0RcB/gMRFh6JjMxsbNz6Cz778B1oaYn/1xn34BH2/H0IwLNeyea8NLX1g5dgY2PT5vvsavLy8jQdAiGkHREUOohOMz0LSLu7XLCuIkMqympDclpWQYe89SSnZe1PHUQ/S2kmu1kjZelgTD1yH7GZZXDaEYbLsz0wrifz8EOaKjwjRBMk8xHQ8pyTcvkaYy84fAAmKRng83hga2nJLdBSpGBCdLo1iicUyTHyjOtpicxlQzDj6H1EpJfAfUcYjszoj9n9bNUSIyEdibxjIEC5HklltWfwKKX5+njWO2DSmfPoN3e21GMbZY57RNfRlLcGOMLdQh/DD8Xgz+gsxBdV4dxcH1gbams0LkJagyJ5BFA8l8hqL+HAIZiDuQ3j+iY8vXgFblMnK5xL1JlH+vbti5EjRyI4OBgHDhzAkiVLlNpemoKCAly+fBnz5s3TeG4jhBBCCCGqo0IuQgjpBMrKK7D3n6PCef+B3tDW5rb6fiOi7gunfTtQIVdtbR1OnL0EABg9Ygh6OHVjXG/dV59g84/bcelaMC5evYGw8EgMC/CHlYU5eDweYuIfIu7BY/D5fNjZWOOLT1bAsQ0K6EQdO3YMM2fObNN9dkXTp0/H6dOnNR0GIaQdUbaYqyXtCZbJ2lZ0WtaFCMnl0raR1ram9DDTR+Sb/lh+/hF+j8rChH1R+GaUC74c0ZMuUJAuQdniCUWGOWNqDwDyzl6CPdM2AAwa+Ug+fwm9Z0wTbiuviJTpNdFnWdOtRfRzUmSf9sY6CF00EF9cS8KW0FS8fDgWy/1L8P0kd3A51Nk96bpaUswlK6+l37wNk7pGqduWXAoC+9U5wm0lnzvicc8wJ3OkLQ3A2H+iEZFeCpsdoYieNxDedkZtGgchba0lxVzyjo/qb4TJ3HfeqfNwf3GKcHvJZ3nHNqLPknExTUt66623cOfOHZw+fRqTJ0+Go6OjzHgVsXHjRjx+/Bj5+fn44IMPwGbTcQohhBBCSEdER3GEENKB1dTW4nrwbbz78ZdIy8gCAHC5Wlg0b3ab7D88MgYA4GBnC3vbjtErVH19A7745jtk5+TB0EAfH7z9ptR1dbS18eUn7+GT95ZBR0cbZeUVOHfpGv48cAR7Dx7D/fhH4PP5mDppHPbt+hHeHv3a8J0QQghpTxS9WCh4MA1rJjotOVSiIkMrympH3kPWe5Cc1hQdLTZ2v9gPv8/oB3DYWB2UjBf+iUFxTYOmQyOkTTB9J6UtY8oX8nKOYNrgUbLMOAouXFU4v4jGIy9uTRCNTR4tDhubJvTCyVe9AV0t/BKeDr/fw5FeWtPKURLSvkj7zsjKQYrkJNHnjBPnpO6fD8AkOR28pibGbTvycU93Uz3EvDUIL/W1Acrr4LPnDo4+yNVILIS0Jnl5RDCt7LGN6HNZRiasC8oYh1UEnuUS04RUNNU3KJVLROOUlz/k5RJLS0ssWLAAr732mtp623/vvfdgamqKK1euYNOmTcLRCAghhBBCSMdCPXIRQkg79vfhE7gWchva2trQ0eaCxWKjqLgEefkFyC8oQkVlpdj6XK4WVn20HG4uzmLLN/24HXn5BXL3x+GwsembLxWKrbyiAo8Tn13k8RvYMXrjamxsxOoNW3AvKhaGBvr433+/hoW5mdT1S8vKseuvAzh78Rp4vGfDyzg62MHIwAB19fVITc8Ej8fD2YtX8TQtHcuXLIJH395t9XYIIYRoGIslPmSXYJ7pWZn2JLeT12OMrOIORQoo5BWHtDbJz1Ge170d4GNrjOGHYnApoQD2O8IQOscbA+yN20XBGSFthSlXKJN/JNcFgNSQWzJ7wQEAw8cpwt4dZF3QlHexkym2lnyHpeUSZXOMvHim97ZBytvGmHI45tlwr9tDcW6mB17oZa3SPgjpiES/V0zTih4DSTt20o17LH0bPOsdMOn8JfQR6R1Q8llWgYUi+UhTDLQ5ODbHC2tvJGPtjWS8fDgWq0dWInC0S7uKk5CWkpVHAObeuJiWSftN9mTvP7CQtX8A+o18PD52Ap4LXhXbt7zjGHX+fpo1a5ZK20nj7OyMTZs24fPPP0dISAjq6+vx+eefg8tt/ZEbCCGEEEKI+lAhFyGEtGMJSSlISEqRux6Hw4HfAC+sWLoITt2ad8Md9/AxUtMyFGpHUVEx8cITJf4dYFjFsvIKbPjfzwi9GwFrSwtsWf8VnHt0l7p+VVU1PlwViOSnaQCA6VMmYt7L08V6HquorELI7Tv44dc9ePAoAe99uhob13yGAP+Brf5+CCGEtA+KFHMxUXboRXkxSE4rU8glbZm0+fbAy9YI6csCsOBEHM4/KYDv7+HY/UJvvDWQebhkQjoLaTlHdJrpO6tozsk8fhby+oMwqm9C8uVrcJ04TuFirdYu4mprzmZ6iH5rED648Bg7IjIwZX80vhjeE9+McQWH3XHeByEtoUwxl6LDmAJAZkQkzKrr5a6Xd/YS+r70oli7yhZyMcXUHnIRi8VC4GhXeNgYYvbxOHwTnIzY/Ersn+kBA23Fz9sQ0t5JyyOi88reHCPQdDtcofWKz14Ba+E84T4Fz8ocy7S34xpHR0ds2bIFn3/+Oe7cuYPAwECsXr0aOjo6GouJEEIIIYQohwq5CCGkHevu6ABdXR3U1tahqakJenq60NXVhb6eLnR1dWBmagL/Ad7w9fGEvr6e1HZ2fL8RPD5P6uuhdyOxbtOPGDF0kMKxhUfFAgDYbDZ8vPor/qY04G5ENDZu/RlFxaXo6eyE/63/CpYW5jK3+e33fcIirjkzp2HFkkXNTsIYGRpgysSxcO7RHcs+WIWmpiZ8tX4LTu7fBSMjw1Z7P4QQQtoXWRcdgOa95Cjbpmg7oq8zbaPIs7T1Za3THpnqauHMK9747lYqvrieiMWnH+JWRhl+m9IHuly6yEk6L6acA0ClvCN5kVT7/iOFYsg8dQ5uk8ardJFTExc7JT8zRbeRRUeLje3T+mKEkynmn36ADTdTEJRRihMve8LGkC6Ukq5BVi86qhatPz18Eor0b2f4OEXhgnVFXmea17RZfW0RY2YA73+icOpRHjz3VOP6qz5wMpV+/oeQjkZdxVyi2+Y9eAir0mq5++YDsHiahbrKKugZGwnbEX1mWtaei7gEbGxshD1zRUdH4+uvv0ZgYCD09fU1HRohhBBCCFEAFXIRQkg7tvi1VzB6xJAWtyOryKuisgq/7t4LA319fPD2mwq1x+fzcS8qBgDQ190NhgYGLY6xNdTV1eHnXX/h5NlLAIAB3h7Y8PVKGBjIPmlRX9+Aa8G3ATwrVJs3e7rMkzB93d3g6+OJiOj7qK+vR9DNULz4wgT1vRFCCCHtnqq95EiuL+9ChrRtmeYVKaBoLxczVSmyEGy3argzBv8ybUAAACAASURBVHUzwdgjsfgzOguhuRW4MMcLPc3pIgXpvJi+M8rkHabtM+8p1gsOAOjGPREOryhoT/RZ3jLJWNRFVi5RJs8omm8BYJ6nPXxsjTHhcAzCUoth+1sobrzsiZE9ZA3oREjnoWhBu+S0tHY4kXEK7deovglJl6+h16Txwu1F25JcJm0dafPthZedEfKWBWDqwRjcSy9Bjx1hCJnrjeE9ZN+cRkhHwlQUCig+vKJkOwl7D8JKkf0C0OYDD/7+B37LlzXbv6IFopIxtBeWlpbYvHkzvvjiC8THx+PLL7/EN998AyMjI02HRgghhBBC5GDLX4UQQkhntm3HHygqLsG7ixfCwtxMoW2ycnKRm1cAAPAf2D6HVSwoLMKKT78WFnGNHzMCW9Z9KbeICwCepqWjsrIKANCzR3eFPpcBns97JYuJV6wnA0IIIZ2LvIuFTPNMDzabLTYtOi9rXaZ5yYfkvpliY3ov7d0YZwtkvj0EA7uZIiGnHC47wnD2Sb6mwyKkVcm7eCjtey+5TJA3Ug4fV3jfprWNeHozVGa7TPFIxtrRco00fawN8XhpAF71tAMq6zHqzwhsvJmiUoEqIR2RogVTsvISi8VC3qPHsKioUXi/mSfPdonjHmsDbdx+3RdvDnAEqhsw4q8I7I7M0HRYhKiVIsc1gmdZeYTFYoFzN1qpfZdfvK5wLpGcFo2vPeYSU1NTfPfdd3Bzc8OTJ0+watUqlJWVaTosQgghhBAiB/XIRQghXdiDRwm4cCUILs5OmDppnMLb3YuMFU77DfBujdBaJD0jC++tXI3iklIAwPyXZ2DZmwsUPqFSX/+8JwIul6vQNmzO89poHk/6MJbqdvHiReoWvQ1cvHhR0yEQQjoI0R4nZM0LlinSQ4W8dpjWY5pX5I5xTV58EL0TX9ntAMDBWBdhb/jh40tP8PPddEw7EI0vhvfEujGuYLPb30UVQtRBMqeILmPq3U/Wd5ytYC84AqlHT8Jl5DDGdttrbxWK5BlVYzPQ5uDALE+McjLDsvOP8MXVRASnl2L/Sx6w0FfsNwUhHZnk90vaMY6sY57EA0egTD9TOvefyCwaY4pR1nx7xuWwsWd6P3jbGuL9i0+w5PRDROdW4sdJ7tDi0L3apHNg+n+a6ThG1u+op7fDYF5Vp/A++QAsswpQmV8AIxtrsf1ITisy3x4ZGRlh48aNWLNmDR48eICVK1diw4YNsLCg3kMJIYQQQtorKuQihJAubM++gwCAtxbOFRsaRZ7wf4dVNNDXR29311aJTVUVlVX4LHCjsIjrrYVz8fr8OUq1ITpUZH5BIXg8ntzPJze/QDhtbGSo1P5aYteuXdi1a1eb7Y8QQohiZF3MZFqm6AVORS4UqHqxoSNchJCHy2Fj2wt9MKy7KV459QAbbqbgVlYZjsz2hLWBtqbDI6RVqVpEwefzkffwESzKFe8FBwC0ouOVutApbZm6qVoUqki7iljq2w1+9iYYejgGlxIKYLk9FOFzvODnaKr2mAhpb+QVYQjmAebi9MawCKX2Z1rbgKe3QtFz+NBm7Uubl7e8vXtvkBP6Whlg3KFY/BqejrjCKpx42YsKRkmnIe93k+S85Hf56d+HYK3M/gBogYX7v+/FsC8+bbZPafPSlrVX+vr6WLduHdatW4fo6Gh8+umn2LhxI2xsbDQdGiGEEEIIYUC36xBCSBcV9/Ax7kXFwrVnDwwL8Fd4u8bGRkTFxgMABnj3hxaH01ohquS7739FZlYOAGDqpLFYNO9lpdtwsLeDqYkxAKCouAT3H8geKpHP5+P2nXvCea9+fZTeJyGEkM5H2sl+yWE3ZA0BxLQ+03byhhESXSatnfaAKY78/HwkJiYKH3V1dTLXB4C5/e3wcMlg2FrqIySlCDbbQ3Eno7TV4iakvVDkO86UL578fVjpfZlX1iEzKlqpXKNpFRUVSEpKEuaT0lLxvKCuGH3sjZH9dgBe7G0NlNXC//dw/Hw3TS1tE9LeyRpyTFYuKnqaCquSKihbhply6HiHPe5R1dielkhcGgB7KwPcTCmCy64wPMqv1HRYhKiVrFwiOS+6TCfmoUr7q7l+S+Fc0tp5JDY2FnFxyvWUqghdXV0EBgbC398fubm5+PTTT5GVlaX2/RBCCCGEkJajQi5CCOmi9uw7BAB4c8FcpU4+PHqSiOrqZ3fr+w/wapXYVJWTm4/g23cAAD2dnfCfFUtVOrGirc3FjCkThPM/7/wL5RUVUtf/++BxFBQWAwCsLM0xcthgpfdJCCGkc5J1kl/ehU51vCarEEzwenv3+eefw93dXfiIj49XaLs+1oZIXBqAmf1sgfI6BPwejm1USEG6AGWKFwTLGu9EqrSvxANHVdqvppw8eRLu7u7o3bs3evfujT179rTavkx1uTj1qg+2TOgF8IH3zj/GrMOxqKhrbLV9EtKeKFKILrrs0b5/wMKz3nGUoRX9QKH9Cl7rLFwt9PFw8WBM7GWFsuIa9N19B+cS8jUdFiFqp0weeXLuIkxqlf9/lg/AqqAMhckpzfahSDzq9ODBA3z++ef46aef0Nio/mMGLpeLr7/+GsOHD0dhYSFWrlyJtDT6jUQIIYQQ0t7Q0IqEENIF3Y9/hMjo+//2xuWn1Lb3ou4Lp/0GeKslnv/9vBMlpWXNlhf+WxwFABv+9zO4XPH/tmytrbFi6SLh/MWrQcLppqYmBH77vcIxDB3ki8njRwvnp0+ZiKOnzqOyqhpPEpPxzkdfYvb0FzBksC8szEzR0NiIR08ScfFqMC5ceb7f+XNegpZW6/33OmzYMHz22Wet1n5baGxsbNXPqC2YmJhoOgRCSAfDNESI5GsCTMOIqLIvVV/vTAx1tHBsjhd+CDPBR5cT8P75x7idUYY9L/aDgXb76lWUEHWTlnckc0BBUjKsSirBh/IFFLzw6C5RMNES/xnqjMGOphh2JBbHH+QiJLccQXN90N+m7YZjJ0STRHOBrHxUc/MOjFVo37yyFllRMXAc6CN3/52Nia4Wzr/qg1VXE7D5diqmHojGt2Pd8NnwnpoOjRC1UjSPpB86DlUGChQUkcbv3ovR366VG0Nr6tevH/r374/4+HicPXsWM2bMUPs+OBwOVq5cCR0dHVy9ehUrV67Ef//7X7i6uqp9X4QQQgghRDUd+yoqIYQQlRw5dQ4A8NLUiUqfiAiPigEA2NlYw8HeVi3x3L0XjZw82XeOht6NaLasp7OT2HzYvWjhdFp6JtLSMxWOwdrKEpNF5i0tzLHzx++wcs0GZGblID0zC1t/2YWtv+xi3J7D4eA/K5Zi2uRxCu9TFWPGjMGYMWNadR+tqaGhAevXr8fatcwnxgghpLOTVbQlbR1p6yrzf3hnvoipiA8DesDP3gTDjsTiUFwObueV4+ocH7hbGWg6NEJanaxCUgB4uO8fqFqibllWjbxHT2DTx73Z/shzQ53MkPfOEMw5dh/ByUXw2BWGP6f2xSJvB02HRkibkpaPynNyYZlfCj5YSheUAsCT/YfFCrm6Uh5is1nYNMEdnjZGWHj6AVZdTURMXiX+mN4PulwqWiedj7Q80lhfD8OHiSoVpgvbuHWXcV9tbdmyZXj//fexf/9+jB49ulVuJuRwOPjoo4+go6ODoKAgqceJhBBCCCFEM6iQixBC2hlzM1PYWFsBAHR0dNTePo/HQ1RMHADAx7O/UttWVFbh0ZMkAIDfQPUNq2hpaQ6eCicMLM3NxOa1tbnCz05ZxobN74jv5miPnT98i2OnL+BORBQePEpoto6tjRUG+Q7A1Ilj0LsX3bkmz/Xr17Fnzx4EBgZ2qZPrhBAijay7y2Wtq2zb5N9CireH4KWjsQh9WozeO8NwZEZ/zO6nnsJ0Qto7afmm9tZdlQu5WAAe/X0QthsCWxRbV2BtoI3rCwbim+BkrA1Oxusn4hGcWoJfp/ShYgvS5Ujmo7i9+2GgcumF7N4Bu4oFXvboZaGPQQdjcDAuBw+Kq3HxFW/YG+tqOjRCWkWzPHL4GAwaWlaMZFVWjeyY+3DwUd85T1W4uLhgwoQJuHTpEvbt24cVK1a0yn5YLBaWL1+OWbNmwdaWfhMRQtpGdU0NyssrxJaZmBhDT/f5MQuPx0N+QaHYOro6OjA1bR+jZJSVV6CmpkZsmaWlBbQ49LuOEKI+VMhFCCHtzIbVrTtsXkpqOsorKmFmagJHBzultmWzWPh+42oAgKO9vdpi+vV//1VLOz9vXqeWdkQZGRni9fkv4/X5L6OsvALZOXmoq6sDl8uFqYkx7O1suvwJY2UcO3YMWVlZCAsLw5AhQzQdDiGEtCuK9Nal6LYdlbJ3gvP5fKXeu7WhNkJe88UX1xKx6dZTvHw4Fh8GlGLz+F7Q4rCVDZeQDkvwvSnLym5RLzgAUH87XH2BdXJsNguBo10xtLspJhy9jz+isxCcU45Lc7zhaqGv6fAI0QgWi4WKoNtoSR+ZTL0DdkX+jqbIWjoYEw/GIC6rDA477yD8FW/4OZpqOjRCWhWLxULeiXNQRynSw9/3wWGbZgu5AGDRokUICQnBxYsXMWXKFDg7O7favqiIiwBAXV0d0jOzwWaxoKOrAz1dXZibmSr8e3v4pFnNlt28eEzdYZJO4FZoONZt/kls2ZrPPsS40cOF83X19Xh50Tti64waHoB1X37SJjHKs+OP/Thz4YrYsmP7dsLaykJDERFCOiMq5CKEkC5G0BuXl0dfpS/6GhjoY4CXR2uE1SGYGBvBxNhI02F0WDweD7t2PRuacseOHVTIRQghcnSW4qz2hsNm4bvxvTCkmwlmHI/HD2FpuJ1VjlNzvGBnpP7eUAlpz+L2HmhRLzh8AJZFFShOS4e5U3f1BdYCLRkaSNniUFWNd7FE5jtDMONILCLSS+G2IxRHZ/THrL50IZV0PdUlJbDILmhRQSkLz4aJtaHeAWFvrIt7b/jhjVMPcDAuB/5/3MPf0/thvqf6bsYjpL2pq6qCaVJ6i4ZVFODcjVZHSC1mamqKV199Fb///jt27tyJjRs3ajok0gmlZWTi6KnziHvwGE/TMsDj8cRet7ayxCBfHwwa6I3B/gOgo62toUhJZ8JiN8/UfNDwroQQIoluOSaEkC7mYcKzoRFdnZ00HAnpam7duiWcDgoK0mAkhBBCCDC9tw0SlwWgp60R7qWXwP63UASnFmk6LELaVMWN2y3anoVnJ5bi9+5XSzztWUsKxJg4GOsi7HU//GdID6CuCbMPxeL9C4/R0MSTuy0hncn9ff+Ay1e9iEuAegd8TpfLwT+zPfHtODegiYcFx+Kw8vIT8Hh0kZR0TrF/7Ycur+VFXABgVl2P5KBgNbTUctOnT4ednR1iY2MRGhqq6XBIJ5KRlY01G7diwZIPcPLsJSQ/TWtWxAUA+QWFOHPhCr5avxmvvrkcZy9eVfsxMel62OzmpQnt5c+Kz+dj/eaf8NW6zfhq3WZUVFRqOiSioPvxj4T/bvsPn9B0OISoBfXIRQghXYxg/HEDfRq6g7StY8eed6edkZGB8PBw+Pv7azAiQggh7YmqJ4Rb0oOOq4U+4hcPwtIzD/F3bDZG/RWJTWPd8Omw1hu6hJD2orqkBLZZheppKygU+FotTbUqRfJMW/XKBQBaHDa2THTHsO6meOlEPLbdScPNzDKcnuOFbia6bRIDIZpWcjlILcOhWbez3gHbg8+G90Q/a0NMO3ofm2+nIq6wCodnecJIhy4JkM6l6MwlteQRgaS/D8Fl9Eg1tqgaLpeLxYsXY926ddi9ezf8/PzA5XI1HRbp4GLjHmJV4EZUVlUrtV1BYTG+++E33Ay7hw2rV4LD4bRShKSzY7MY+piR+J3G5XLx1SfviS2zsbFqzbAAAA8fJ+LStefFvB8tX8y43pQJY+DVr7fYMiOjlgwUTlrqzMWrCL59BwDQxGvScDSEqAf1yEUIIV2MV/8+mDx+NFxdemg6FNKF8Pl8HD9+XGzZ0aNHNRQNIYQQ8pwel4N9Mz3w27S+AAtYeSUB0/+JRllto6ZDI6RV3d97QG1tWeaXoKKgQG3ttYb23HvAjD42SH47AO52xojJLEX330JxMal9f56EqENdVRXM0nLV1l5X6B1QWVPdrRG/ZDAMzfRw8UkB+u66g+Ri5S7eE9KeVRQUwiIzT61t6sY8VGt7LREQEABvb2/k5ubi5MmTGomhtrYW9+7d08i+iXrdCruHj75Yy1jEZWFuilHDAzBl4lhMGjcKPXswF0aH3o3A4RNnWztU0okx98gl/ltNi8PBxHGjxB7eHv1aPbbrNxXr/bBfn17N4tPTpRtxNKWhoQG3wqh3XtL50O03hBDSxSya97KmQyBd0L1795CZmSm27MiRI9i0aZOGIiKEENKetLTAQrB9S3rRedu3G3ztjOF3KAanH+fDeWcYgud4w8PWqEWxEdJeVWXmoNTKROY67IZGsJua0MTVAl/OXfd6t8LQ76UX1Rmi0tRVrCWtV67W7K2rp7k+YhcPwvvnH2FnZCYm74vCVyN6Yu1oV7DZbdNDGCFtLTM8AqWWsvMQi8cDp74BPDYbPG3ZPdFw8qgAkkk/a0OkLg3AjMMxuPW0GK47w3BtrjfGOFtoOjRCWiz9ViiKLU2f9eYi4/9oVkMjOE1NaNLigK8l47LYv+1kRcfCwcerFSJW3tKlS7FixQocPHgQ48aNg5mZWZvuf8uWLQgNDcX8+fMxf/78Nt03UZ/iklKs2/wjGhrEb1jq2aM75s95CaOHBzTr8S03Lx8/bv+jWYHEzj8PIMBvAHo4dWv1uEnn015/2/D5fNwIoWFsO6KI6PtK9zJISEdAhVyEEEIIaXWSvXEBQGpqKiIjIzFw4EANREQIIYQ05+tggsK3h2Dusfu4llQIz913sG9aPyzwstd0aISo3djN6+Wuc2H2Qlg9TkXDikXwf5t5WImOQJUCr7YcYlFAR4uNHS/2w4geZlhw+gHWh6TgRkYpjs72hI2hTpvGQkhbcBk9Uu7wZfcPH0PTNz8gt7sNJp+nXp1VZaHPRdDCgfjg4hP8Gp6OsXsj8dMkd7w3yEnToRHSIv1eelGhQvLLS1bAIiwWFXNfwIivP2+DyNSnR48emDx5Ms6dO4c///wTH330UZvuf/To0YiMjMT+/fuRkZGBjz/+GNra2m0aA2m57b//jerqGrFldrY2+HnLehgZMg8JZ2tjjQ2rV+KXXXtx6Php4fLGxkYcOHoKX/xnhcrxVFfXIDLmPqLuP8DjJ0morasDV0sLpqYmGDTQG8MC/GBjrdhQetk5ubgbEYOImPvIzM4Fn8cDAHR3dIBX/z7wG+ClcNFZUkoqwiOiERkbj8KiYvD5fLDZbLj27AGv/n3gP9Bb4bikeZqajnvR98WWTR43CkZGhqiuqcH5S9cRE/8QObn5APhwsLeDn48XJo0bKXV41evBt1FYXCKcd7S3w5BBA8Hn83Hy7CWER8WioLAIk8aOxOwZU5ptz+PxEP/wCa6F3MbT1AxU19SAz+fB2soKfd1dMWHsSNhYWSr0/h4nJCEoJBQPHieiqroaxsZG8PPxxLTJ42FibMTcI5fEfGNjI46fuSi2TPCeZKmurkH0/XgkpaQiMSUVLBYLDrY2cLC3w8hhg2Bs1PxGvWs3bqGopBQlZWXILywSe+3MhSvQ19cHAPTv0wt9e/cCAERG30dyarrYulMnjoW+vp7U2OrrG3AvKgbBt+8iJy8f1dU14HA40NPTRS8XZ4waFoA+7q6Mn49AWHgkMrJyhPOW5mYYM3Los/deU4MbN8Nw+24E8vILwWaz0d3RHjOnTUIfd7cW/ba9cTNM7LNxsLPB0MF+AICKikpcDwlFaHgkCouKoaXFgYtzD8yaNgkuPXso1H5xSSlu3AzDnYgoVFRUoa6+Hnp6unDt2QODBvogwH9As/hLS8twOegmACAk9K7Ya1nZuWI9B05/YTx0dOj3NOl4qJCLEEIIIa3u2LFjjMuPHj1KhVyEENLFqXO4M3UUXljoc3F5/gB8E5yMtcHJWHg8DrfSS/HT5N7Q1pJ+Qo0QQtRlvqc9vG2NMeHf3nNsfwtF8MteGNHDXNOhEUI6MC0OG9+Pd4GLbgP+cy4O7+9NR0ikLb6d3B/GhgbQ09ODvr6+zAuIhBDNWLhwIYKDg3H16lVMmzYNrq6ubbbvoUOHwtbWFoGBgQgJCUFubi5Wr14Nc3M6LukoUtMycOFKkNgyLS0trPvyE6lFXAIsFgtvvzkfEdGxSH6aBgN9ffh49YNnv94qxxMZE4cNW7Y1K5oRCAuPxA+/7cH0KRPw4buLoSWlZ+CGhgbs/eco9h48Dt6/xVuinqZlIPj2HQDA+NHDseyN+VKLsCoqq7Btxx/NPieB5KdpuHQtGFpaWpj14gtYNG+23M9OmkcJSdi24w+xZd4efZGRnYMvv9mEwqJisdcSkp4iKCQU+w+fwOb1X6KbQ/MbzY6ePo+4B4+F88MC/BHgPwBrv/sB127cEi63sjRvVsgVG/cQ337/KzKzcyApIekpboWFY8++Q3j15elY8tqrUo8TKioqsW3nn4yfYVRMHE6cvYR1X30CFkv+0IoNjY3NPqNRwwOkFnI1NDTg9IUr+OPvwygrr2Bc59fdf+G1V2dj5ouToSNSjHr45Fk8fJzIuM2efYeE04tfe0VYyHUtJBRnLlwRj29YAGMhV1NTE46dvoDf9x1CVTVzr1FRMXE4eOw07Gxt8J8VSzDI14dxvQtXbyBIpNewPr1cMWbkUCSlpOLr9Vua/Rs+epKIS9eCMWHsSHz1yXsqny87duYCYu4/EM4H+A/E0MF+iHvwCKs3bG32N/vwcSLOXLiCuTNfxIqli6S2W11dg593/YWzF68ynhu8H/8Ix09fgGvPHvjmy/+I/e0XFpc0+xsReJqWIfbahDEjqJCLdEhUyEUIIYSQVhUXF4ekpCTG144ePYqNGze2cUSEEEK6gqqqKhQWFqKwsBBFRUXCZ8np0tJSNDY2gsfjoampSezZrrYBeRU12MHj4XcWYKbLAQv8ZutJTmtra0NfXx/6+vowMDAQTkvOKzLN9Jqurq6mP15C2h1pRaEtKRZlKg5VtmC0pKRELO9Iy0UVFRXNcopBUxOMymtRUVuPkf/lw0SHAwOtZzHIykGCaQ6HI8wbgjwi+qzKMtFpLVlDUxFCWk1VVRWys7ORnZ2NnJwc4XNRUREqKytRWVmJqqqqZs9VVVVoaGgQa+vovw9R2trawqIuPT094UPWvKrr0jENIYoxNjbGvHnzsHPnTmzfvh1btmxp0/27uLjgxx9/xNq1a5GQkIAPP/wQa9asgYuLS5vGQVQTIdH7EwAM9vOBu1tPhbbX0tLC5x8vB4/Hh5urs9TCKkVcD76NNRu3KrTuqXOXkZtXgE3ffNGseIjP5+OzNRtxLypWobauBN1EWHgk9vy8GfZ2tmKv1dXVYfF7nyI7J09uO42NjTh0/DRCwyOw+6dNMntgUkZCUgp2/LEfpWXlUtfJysnFB5+twd87f2q2X8nfJ3V1dQgKCRUr4mJyNyIan379X7m/mZqamvD3weMoK6vAyg/eZlxny887cT34ttQ28gsKseKTr/D2GwuavdaS32xFxSV4f+UapGdmyVyvsqoav+7ei9t3I/Djt4HgtODvWFE8Hg/ffPcjrodI/1xE5eTm4ZOv1uO9ZW9gzktTm70u+e9cXVuL0rJyfPLVehSJ9Mgm6fK1YLj06I55L89Q7g38iy2x39raOuTk5uPTrzdILU4DgEPHT8PFuTsmjx/d7LW6+np8+PlaPHrCXEQnKiklFW9/+Dl2bdsEe1sb5d8AIR0UnXEhhBBCSKtiGlZRICkpCbGxsfDy8mrDiAghhHQWdXV1SExMREJCAhISEvDkyRPhdFER8929qmoAkF/2bJrFYoHD4YDNZgufuVyucLqurg7FxcUoLCxUawwCbDYbenp6Mou/dHV1weFwoKWl1ebPLW2jLU6oEiKgjp78ysrKxPKPYDoxMRFVVVVqihQo+/chIJqLBA8tLS1oa2uDzWajqakJFRUVKCmRflK/JbhcrsziLz09PY3mkpbukxBNy8/PR3R0NKKiohAdHY34+HhkZmaiooK5pwlZtLW1YWhoCENDQxgYGMDQ0BAcbV3cz6tETXU1uLwG2Ojw0dRQh5qaGtTU1KCsrEx+wy3EZrOhq6srtwBMR0dHeIzQHvMFHdeQtjBt2jScP38eDx8+REhICEaMGNGm+zc3N8emTZuwdetW3Lx5E5988gk+/fRTDBkypE3jIMq7//Bxs2W+3p5KteHu1vKivYrKKvzw255my+fNno6AQQNRXV2DPfsOIiHpqfC1uxHRCI+MwWC/AWLbhN6NaFbENXSwL16cPAH6erqoqKzE/QePcez0eTQ0NAJ4Vsjz1fot2P79RmhrPx+i8PCJs2JFXCwWC1MmjsH40cPB5wMVlZW4FXYPl64FC9fJyMzGdz/+hrWff6z058D02+PX3ftQUVkJG2srzJw2CTo62oiIvo9bYffE1isoLMb+wyew5PV54m1CvM38gkL8eeCIzDjSM7KwesP/xIqo9PX1sHDuTHj07Y2S0jKcvnBF7HM+c+EKRg8PgN8A8XP5N0PvMhZxDRnkC/+BXmhq4uFKUAgeJyRj996DMuNSRk1tLVau3tCsiCvAfyAG+XqjuroGV2/cQorIMIixcQ/x14GjeHPhXADAAG8PWFlaICU1HRmZ2c3aEfytOHV3VDq+X3bvbVbExWazMWHMCPTp5Qoej4fbdyOaFVtu2/EHujvaN/u7l/zbyc3Lx087/kBRcQkszE3h7uaK2tpaRMXGN4tl36HjmPPSVNV+5zDsd/NP21FVXQ1bGyu4ODuhvKJSrFc4gT8PHMGkcaOaxf7dD781K+Ia7DcAL02dBH09XUTFxuOfo6dQW1cHACivqMT/tu3ElvVfgcViwUBfHyOHDopdMgAAIABJREFUDQYA3Aq7h6amJmE7Fuam6N/3ea+BXPptRzoo+sslhBBCSKuSVcgFPOuViwq5CCGka5J116W01/bu3YvvvvsOUVFRePr0KeN6WlpacHZ2hpWVFSwsLGBpaQkLC4tm0xYWFjAzMxNeaBMUQkhON/CAjy89wZ6YHIDNxppRLlg90kVu8UdtbS2qq6tRVVWF6urqZtMtfa2goECtQ1O2J7IuiGpra8PR0RG9evVq9qDu8okqBN8jRQq6cnJyEBERgXv37uHevXuIiYlBXh7z3ft6enpwd3cX5h1pucjS0hJGRkZS8xCbzcb9vCqMORGPutJasMz0Ef7KAPg6mCj0/urr68XyiKBnHsllsl6Ttay0tFTxD7sDERTGSctH5ubmcHNzE8tBvXv3hoWFhaZDJx1QU1MToqKicOPGDdy6dQuRkZHIymres4Suri6cnZ1hZ2cHe3t7sWcrKyuxQi3RZy6Xy7BXoKq+CfOO3cfpx/nI5LJxbKYHZvZ91lMJj8cTFnXV1NSgurqacbql84I8ou4i/PZE1nENl8uFo6OjWD5xd3dHr169qMcyIobD4WDp0qVYvXo1fv/9dwwePBjaIsODtQUdHR2sWrUK3bp1w4EDB7B+/Xq8/vrrmDNnTpvGQZTz4OGTZssGePVv8zhOnLmAklLxIuGFc2di6RvzhfO9XF0wa+FSsaESj5+52Kyg5fDJc2LzQwb5YuOaVWLH88OHDMKEMSOw5P3PhEUeiclP8TghCZ79+wjXOyLR1oI5L4nFBDwbNm/oIF+s3vA/4bLrwbfxyYqlMDIyVOj9CzD95qiorITfAC+s/+pTYW9bs158AReuBGHD/34WW/fQiTN4Y8EcsYIcFlu8zcysHDTxeNDX18MHb7+JPr1cYWZmiuKS58fth0+eQ3V1jdh2G9d8hgFeHsL5kcMG44ff9uD46QvCZVt/2YUDu7eJvY9f9+xr9p7eW/o6Xn5pqnC92dNfwK69/+Dvg82vFah6XuPb739FQlKK2LLX583GmwtfEe73lVkv4uv/bsHtOxHCdf48cARDB/vC3c0Fy15/9m+948/9zWL77MN3YGFuplJsmdk5OHz8jNgyA319bP9hI3qIFIXNnjEFl68FY93mn8TW/WnHH/Ab4CVWEC5ZsFdXV48r10Mw88XJeOfNhdDVfXY+5OGTRKz8+r9iw0xWVlYhKjYe/gO9lX4vkn+zOXn5yMnLx5sL5mDB3JnC48zImDh8tmYD6urqhetm5+QhKSUVbi7OIstyceV6iFibQwf7YsPqz4S973l79sPo4QFY+sEqYTFXeGQMQu9GYOhgP9jZWmP9V58CAF6YvQgVlZXCtvq4uwlfI6Qjo0IuQgghhLSapKQk3L/fvPtuUUePHsW6devaKCJCCCEdRWFhIc6cOYOSkhI4OTkhLS0NALBt2zbhOtbW1mIXuwTPrq6uUi9YqkIPwO6Z3hjR0wqLzjxEYFAybmeV45+XPGChL30/urq60NXVhbm5udpikVRTU8NY5FVfX4/GxkY0NTV1+OeGhgbU1tYKh8AEgPT0dISGhop9FlpaWnBzc4Onpyc8PDzg6emJgQMHwt7evtU+f9K+SV4QmDJlCsLDw4XLu3XrJreNhIQEnDp1Crdu3UJERARycnLEXmez2XB2dmbMRd26dWtxb18CQwwNkbPcHK+djMfZx/nw23MXP0/qjeX+3eVuq62tDW1tbZiamqolFkmNjY2MRV41NTUqf/c1nXeYnkXzKgBkZWUhLi6u2edhZ2cnloe8vLzg4eGhtr8F0nk8fvwYZ86cERZvlZc/H06JxWLBzc0NPj4+GDBgAHx8fODt7Q1ra2u1xmCgzcHJV7yxJigJ64JTMOtQLAJHV2L1SBew2WxhD3ttoaGhQWrRV2c/rsnIyEBYWJjY58HhcODm5ibMJR4eHvD19YWDg0Ob/HuQ9snX1xe+vr6IiIjA8ePH8corr7R5DCwWCwsWLEC3bt2wdetW/Pnnn8jIyMD777+v1t9gRH0qGYY+s7e3ZVizdZmZmmLqpHGoq6tDXX09amvrMGGMeM9ylhZm8B/ojTv3ooTLomKbH29VVFSKzfP5vGbrAICbizO+/vR9aGtrw9rKEjZWFjAxMRZbp1yiLdEiMlGjhgfgk/eWwdrKAtZWlrC2soShgb70NywF0xEhl6uFT95f1mzIxEnjRuH0+SuIf/S8GK+urh5P0zLECmMkC3yaeDzo6uhg639Xo1+fXsLlpv++dz6fj9t3xHv78vHsL1bEBTz7vi9fvAhXg24KP6fMrBzk5OULh7grKS1DZpb4byQ7WxvMnP6C2PEvm83GG/Pm4NqN28jJFb8RRpVCrtKycgSFiJ8T4HK1xIrHni3j4v1lb4gVcvH5fNy4fUctPc1JExQc2mzZsjfmixVxCYwfMwKXrocgPDJGuCwjMxtPEpPRt/fzfz/Jgj0A6OHUDe8tfV2ssK+vuxtee2UWtu38U2zduAePVSvkYljm5dEXr8//P3v3HRXF+TVw/EsvKnZBUcSGBQsoiogodsEaezf2ghqNiRqjP43RGEvsBaLGEitWjLHFEguiKEWKKIiCIM2G0vv7h6/EdZeyuLCAz+cczoFnZ565uwzDzuyde4dIvNatzJoxuF8v9juflNrux/vrp5XmAMaNHCrVQrWOsREjh/Rn159Hssdu3/PCum1ruZ+DIJREIpFLEARBEIRCk1c1Lnh/8drf3x9TU9MiiEgQBEEozp4+fcrJkyc5deoUt27dkriAamBgQOvWrWnVqhWtW7emdevWVKlSReY8hfVh+RgzQ8wM9OhwxIt/Al+g73SLO0PMaJXPqjiF4UProS9JSkoKISEh2W3sPnz5+voSEBBAQEAAR478d6GvWbNm2NnZYW9vj7W1tWiZ9gWrVKkSFSvmfEf1hw8Q3N3dcXFx4dSpUzx6JFnBwMTEhDZt2mBhYUHr1q0xMzNDV1f+D3AKoqKOBn8NN2et61O+vxTEjL8DuB4ay66+TSirpbz9Wl1dHT09PfT09PJeuJTIyMggOjpa6jgUEBBAcHAwkZGRXLhwIXv5qlWr0qNHD+zt7enevbuo2vUFi4yM5NChQxw4cABPz/8+pNbU1MTGxoZOnTpha2uLhYUF5cqVK5KYVFRUWNa5Ac31yzH4pC9LrwbjG53Avq+aoqtZdG0BNTQ00NDQ+KKOJfC+aqKs9zV+fn48fPiQhw8fcvTof+2xTE1NsbOzw87ODhsbG5E48wWaPHky5cuXp1u3bkqNo2PHjhgYGLBs2TIuX75MZGQkixcvpnx55Z0bCbKpf9LiVVVVFU0lHDv62HWlj13XPJcz/P8EoQ9SUlJJS0uTON7p6Un+j3Rz9+SX37Ywa+p4ypWVTEDuYts+1+2V1ysnUanqwNFTaGtrM3xQX4mKzyoqKvTr1T3P+PMi63qFrU277MSoT5e1bd9WIpEL4GHgY8lELhmXQDp1aCeRxPWxR0HBvHz1WmKsTSvZHTM0NTVo3rQJN93cs8d8/QKy4/WTUfGts42V1H73Ya6O1pYcPn5aYrwgBbnc3D2kEsA6WLdFT8b7pxrVDVj8/SzU1NTQ1tZCW0uLKlUK74Y7gEvXbkqN9ejaUeayKioqdLVtL5HIBe+fo0Qil4xftH23TjKvcXTuaC2VyPWmgG2zP02wAujTo4vMeDp3tJZK5Pq0Et/1j/YleP83aFK/DrK0MmsmkcjlI6N9oyCUVuLqpSAIgiAIheb48eP5Wu7YsWMikUsQBOEL9ezZM/bt24ezszN+fn7Z41WqVKFPnz706tULS0tLqapKyqps0tygHCFTrBh98n1VHItdd3Cyb8xki7wr+wiKoaWlRcOGDWnYsKHUYxEREfj6+uLj44OPjw83btzA19cXX19fVq9ejZ6eHl27dsXOzo4+ffqgry99sVz4Mrm5uXHw4EFcXFyIiIjIHjcxMaF///706NGDVq1aoaenp/TKSt9Z16FtzfLYHPXB2S+S61FvuTTEHFN9+dq6CAWnpqZGjRo1qFGjBra2thKPJSQk4Ofnl30cunfvHu7u7uzfv5/9+/ejqqpKmzZtsLOzo3fv3rRs2VL2RoRSIzU1FWdnZ/bu3cvVq1ezq7rVrVuXYcOG0aVLF6ysrJSemD3I1ADPirq0POTJ8QdR+L5J5NJwc2qVF+39CpOmpmZ2S8VPRUVFZR9LfHx8uHnzJv7+/vj7+7N27VrKlStHly5dsLOzo1+/fuJ9zReiZs2azJ07V9lhANCwYUM2btzI0qVLefDgAd988w1Lly7F2NhY2aEJHylfXk+i6lROFaeKUlZWFqFhz4mNfUtcfDxx8QkkJSejrqbO02dhUsunpadLJHJ1bGeJh5dkF4jzl/7lnyvXad60CdaWrWjS2IT6dY3RyaNNrU07S1z+viAxtuvPw+x3PkHrli1oa9GSRib1qWtcSzHJszLOJVqYNpax4Ht1jKUr8AYGP5WcUkaijY1VzlWLfPwCpMbuevnw6o3slukhoZK/k/v+D+nR1RZ4n1T2qdoyqk59UKe2rGsn8mdyud65JzWW2+vYvYvsJKrC8OLlK56EPJMYq13LEN1c3uvVq1NbaszL54HEz7LOQk0byU7Wq1ypImXL6BKf8F9Fvk8Tqj5HkxySBI1qGaKioiKRZPdxAllWVha+nyRjZWRksMlpt8z5Xn+yTz55GkpcfIJUwqYglEYikUsQBEEQhEIRHh6Ou7t73gsCR48eZcmSJYUckSAIglBcJCYmcvz4cXbv3s3Vq1ezx+vUqUO/fv3o378/1tbWMu/6Kw4qaGtwepgZq28+ZcHlIKb89QDXsFgcezdBR6PoKlcI0j4kVvTo0SN77MGDB5w7d45z585x48YNTpw4wYkTJ1BTU8PW1pbhw4czYMCAXCs1CaXTkydP2L9/PwcOHCA4ODh7vE2bNtnHokaNGik9cUuW9rUrET2tHYOP+XD9ySua7nBjb+8mjDETLbeUrUyZMlhaWmJpaZk99urVKy5evMi5c+e4cOECt2/f5vbt2yxZsgQTExOGDx/OiBEjZCZyCCVXdHQ027dvx9HRkejo9y2EqlSpwtChQxk5ciRWVlZKjlCaeQ09oqe0w/6wFx5hsRg53eLmUHOsa4v/kcpgYGCAgYEB3bv/VwHm0aNH2e9rrl27xqlTpzh16hQzZsxgwIABTJ8+nQ4dOuQyqyAoVpUqVVi7di1r1qzBzc2NuXPnsmDBAlq3Fq2viovKlSoSFh4hMRYfn0C5ckV/E8Cr1284fPw0V2+4ER3zIt/rfZp8Zt+9E5evu3LfVzLRJSMzEy8fP7x83t8kpqKiQh1jI7rZtseuWycqV5L+f/b1iMHc9fQmIlKy3V9KSio33e5mt4FTV1enkUl9enTuQPfOHaTaIOaXrFOLunVybpderYp0JdeExCTJOWWsZ9pY+sanDxKTkqXGPL198fSWbmMpy5OQ0Ozv4xISpB6vrp9zK+iqMp5PQVorJsl4DrJ+v8rwabtOgBrVc0+2lvWaxX/y2sq6RlaxguwqiCoqKujqKiaRS9b5cE7b1dLURENdndS0NJnbTU1Nlfp7jk9I5Nipv/MdT8izMJo1aZTv5QWhpCqeV8UFQRAEQSjxTp48mfdC/8/f35+HD0VZXEEQhNLOzc2NCRMmYGBgwJgxY7h69SrVqlXj22+/xcvLi+DgYNatW4eNjU2xTeL6QEVFhfk2dfn3awsoo8k+7wia7rxD8OvEvFcWilSTJk2YO3culy5d4tWrV7i4uDB58mQqVqzI5cuXmThxIgYGBvTp04eDBw+SIONCtFB6JCQksGPHDjp06ICJiQnLli0jODgYMzMz1q5dS1hYGG5ubixYsKDYJnF9UK2MJldHt+J/HetBeiZjT/oxwcWf5LQMZYcmfKJy5coMHz6cffv2ERUVxd27d1m2bBnNmzcnMDCQn376iYYNG2JhYcG6deskqsIJJY+HhwdjxozByMiIn376iZiYGOzt7fnrr7+IjIxky5YtxTKJ64NqZTW59XVrxpoZQkIa7ffe5Q/PcGWHJfy/hg0bMnv2bC5cuMDr1685c+YMU6ZMQUdHhyNHjtCxY0eaNm3K1q1biYuLU3a4whdCW1ubRYsWMXjwYJKSkli6dKlc1wWFwiUrQcT3QdFfh30Y+Jix077l8PHTciVxgXQil5aWFqt/WkgX2/ZoamrmuF5WVhZPnobitPsAY6fOwdvXX2qZKpUrsmHlUizMm+d6HSI9PR2/Bw/5bcvvTJo1Xyo5Lr9UZKRdqeWyXVkxfajumdsy2tpaUmMfpH2UZFMQHydRpaWlSz2uqZXz70RWRbiCtFaUlUhXoXzxaM+ckpIiNaamlnttHTV16ZsCUz/z95Samirx86cJgPmlqpr/c+KsrCypuD/ebqqM/UVespL4BKE0EhW5BEEQBEEoFPltq/jBsWPHWLRoUSFFIwiCIChLRkYGx44dY/369dy5cwd430Jm4MCBjB07lp49e6KuLnlq+mkZ9k8Vp8SKjsaVeT7Vin5H73PvWSz1ndxw+aopfRuJ1jbFUdmyZenbty99+/Zl69atXLp0icOHD3Py5EnOnDnDmTNn0NXVpXfv3gwbNgx7e3u0tHK+AC4Ubx8fS8LCwti8eTM7d+7k7f+3djA0NGTEiBGMGjUKU1PTYnVsyS9VVRV+6lwf69oV6HHMhz88w7ke8ZZzQ8yoX1lX2eEJMqioqGBhYYGFhQWLFy/G39+fgwcPcujQITw8PPDw8OD777+nY8eOjBgxgkGDBlGhQgVlhy3kw507d1i4cCFXrlwB3v/PmTJlCjNnzqRBgwZKjk4+muqq7PmqKWYGZZlz4RETXPzxjo5nfY+GqMnxYZ5QuHR1denVqxe9evVi7dq1HDhwgO3bt3P//n1mzJjBggULGDVqFNOmTaN58+bKDlco5VRUVBg3bhy1atVi06ZN7Nixg7CwMKZPny51vicULavWLTn3z1WJsXtePrSztMj3HHFx8QQ+foJ5i6YFuukqLS2Nn37dwNu37yTGW7ZoSuuWLWhsUh8D/WpkZmay88/DXLnmKrFcZoZ08o+urg5LF8whKTmZO3e9uHLjFrfu3CMlJVVqWYC37+KYt/gXThz4nbJlJNuyVTeoxvqVS3j7Lo4bt+5w9YYbHl4+ZOTQhvJZ+HN+WLaKP502yH0OIWv59PScb8RISpZOCtLIx99UbslhmprSLSL72HXDrFmTPOcFJFoEaslI2vo00exjspJwClKRq1rVKlJjsmJRBi0ZyYXp6bknMMl6/NN5ZP7t5bL7fZpQpalR0GNx/vdxmYl9H21X1mtTpXIlpk0Yne9tGBvJas8pCKWPePckCIIgCILCvXjxghs3bsi1jkjkEgRBKF3evn3Lzp072bx5M6Gh78vum5iYMGPGDEaOHEmlSpXyTNaS9XhxTLSooaeN29et+e5iIBtvh9LvkDfz29dhRZcG4sPOYkxdXZ2ePXvSs2dPnJycOHv2LIcOHeLMmTM4Ozvj7OyMnp4eAwYMYNq0abRp00bZIQsFcPfuXdavX8+xY8fIyMhATU2NoUOHMnHiRDp27IiqqmqxPK7Iq3u9KoRNbUf/o/fxCIulgdMtjvdvyoAmBsoOTciDqakpK1asYMWKFbi5uXHw4EGcnZ25evUqV69excHBAXt7eyZPnkzPnj1Lxf5a2vj7+/Pjjz/i4uICvG8VPWvWLMaPH4+eXvGoDFFQs62MaVKtLD2c77P5dih+L+I5PrgFFXWkPwAWlOtD4uCUKVO4desW27Zt49ixYzg6OuLo6Ii1tTXTp09n0KBBuVavEYTP1bVrV6pXr87PP//MpUuX6NWrF/Xq1VN2WF80SwtzNDTUJRIsbt3xYOr40TITemQ5cuIv9h46hn61qth1tcWumy01quf/fabvg4eER0RKjLVpZcbqZQtRU5OsRCTrvU5OCVUAOtra2NpYYWtjRUpKCv4PA/H2eYCXrz++fgES6yYlJ3Pm/CWGDewnc67yeuXo3bMrvXt2JS4+AV//ALx8/PHy8edRULDEsqHPwnH38MbSwjzP5y/5BKWHgp+G0rxpY5mLR0ZFS43pfdIWU973h2XLlpEaq1fHiO6d5W/NW0GvnNRYdPSLHFvfPY+MkjEqfyJXrRrVpeeOiKJhA+Ufb8rKaFsq+3l/9HiE9O+5TBnJG3PkPQ1ITf0kkauA//9l7V+yKssBpKZJJ1Jqavy3XQ0NdTQ1NSWqhamqqhZo3xOE0q5496oQBEEQBKFEcnFxkVkmOTf379/n8ePHhRSRIAiCUFSePXvG7NmzqVWrFt999x2hoaF06tSJ06dP8/DhQ2bOnEmlSpWAvC82fvp4fi5OKusDbnU1VTbYNeLokBagqcaqm0+x3XePmHjZdwMLxYuWlhZfffUVzs7OxMTEcODAAXr37k1SUhJ79uzB0tISS0tL9u/fL9WeQCieTp06hY2NDW3btuXIkSOUKVOGuXPnEhwczMGDB+ncuTNqamqlKimmZnltbo9rzRyr2pCSwcAj95lz/iFpMioYCMWTlZUVmzdvJiIigvPnzzNmzBi0tLQ4deoU9vb2mJiYsGHDhuyqcoJyPX36lDFjxtC8eXNcXFyoXr0627dv59GjR8yePbvEJ3F90L1eFQIntaVaFV2uBr+i3o7bPHwh2hAXZ+3atWP//v2EhYWxcuVKjI2NcXV1ZeTIkRgZGbFo0SLCw0W7TKHwmJqasnHjRhYsWCCSuIoBXV0dLMwlq/I9j4xik9PufK0f/DSU/c7vW2VGx7xgz8GjjJk6h4SExHzH8OBhkNRYr+6dpZK4APwDHkmN5fc6s5aWFi1bNGP86KFsXr2MP3/fSKWKkpVNHzzK3/XncmXL0M7SAodJY9m5eTWbVy+Tijcgn3N9TFYCjLffgxyXD3kmfbxu0lCy0qc8re8AWplJV2l8Ghom1xwfmNSX/ht/GPQkx+WfyNhOQVorWrVpKTX2abLdxxavWMuCpStZvmYT67ftxPnkGfk3mk/VqlSmdi1DibGw8AiSknNuCRgULP2atWze9JMRGQlVOZzPZmZmSlX50tAoWCK+PKfMnyaPfbpdFRUVWplJPq+YFy/lOp4IwpdCJHIJgiAIgqBw8rZV/ODYsWMKjkQQBEEoKi9fvmT27Nk0aNCAjRs3kpyczOjRo/Hy8uLKlSv06dOnQAkTKioq2V8lwSBTAwImW1GzahluPn2NvuMt3J7FFni++JTcy+8Lile2bFlGjBjBX3/9RXR0NBs2bKBBgwa4u7szevRojIyMWLx4MREREcoOVfjIh2OEq6srbdu2ZcCAAbi6ulKnTh3WrVtHWFgYq1evplat/LdhkHnncTE/FqmrqbKuZyOOD20BWupscAvFavddwt/m/KGBUPyoqanRo0cP9u7dS3R0NH/++SeWlpY8fvyYOXPmYGhoyNSpU/Hz81N2qF+kFy9eMHPmTBo1asSff/5J+fLlWbVqFcHBwUydOrXAH5IVZw2qlOHRpLZ0qV+FN68SabzjNueCXsg9z8vEVDa6hSg8PkG2qlWrsmDBAoKDgzlz5gx2dnbExMSwYsUKjI2NGTBgAJcvX1Z2mEIppa+vT7t27ZQdhvD/hg3sKzXm8vcFjp36m/Rc2uAFPwlh3uIVUq3yevXoIlUtKDey2sbJSs667/uAqGjp/y8fL5uRkcHziChcb9/loPNJ/jp3Kcft1qpZQ6qFZNpH7ebS0tIICQ3j3xtu7DlwlFt3PHKcy6y5KQ0b1M1xrs9x7eZtqYpl8P65Xrx6XWq8SWOTT0bkO0epXcuQGtX1Jcbuet7P8fn8c/U6l6/dxPO+LyGhYRJJN40b1pda/pb7PZm/87j4BFzd7kqNF6S1on61qjRt3FBi7K/zl4mLi5da9lHQE/694Ybr7XtcuHyNE6fP4eWT9/vogv5+VVRU6NLRWmr87MUrMpfPzMzkzHnp/8c27SSrgsuTsCcrds0CJ3LlP51E1o1vn1b+s7ZsLbWMu4e3zPmehDzj7MUr3LrjwcPAx0THvMhzf5GVTCYIJZFI5BIEQRAEQaHevn1b4AuBIpFLEASh5ImPj2fZsmXUrVuXjRs3oqamxrx58wgJCWHfvn2YmZkpO8Qi16hqGR5OtmJwUwOIS6Hdbnc23Q6Ve57AlwlU236L6PiUQohSyI+KFSvyzTff8OjRI86ePYu9vT0xMTEsX76c2rVrM3ToUG7evKnsMAUgODiYQYMGYWNjg7u7O6amphw7doygoCC++eYbypaVbm9Rmg1oYsDjqVbUNyiHR1gstRxvceGx/EkX6RmZ2B30JOKdSARTFh0dHUaNGsXt27e5e/cuY8aMIT09HScnJ5o1a4atrS3Hjh2T+WGZoFhZWVns2LGDhg0bsmXLFjQ0NFi4cCFPnz5l3rx56OjoKDvEQlVBW4OLI1vybTtjSEnH/oAna12f5nv9jMwsBhz1YfaFR3g8F1XlipKqqiq9evXi7NmzBAUFMXfuXPT09Dh58iRdu3alcePGbN68mXfv3ik7VKGAYmJiOH36tLLDEIqxli2a0bF9W6nxjY5/MHy8A84nzxAeEUlcfAIJCYk8eBTEJqfdTJv7IzEvX0msY1y7FtPGj5Zr+2XLSLfyu3z9lkRCRvSLl6xYu1nm+qkfJaVcuHyNYeMdWLD0V7b/sZ+Njrt4EvJM5npx8QncdHOXGNOvVjX7e8fdBxg9ZTaLV6xl15+H+W3L78S+lX0sfBryjEeBkhWfDPSrylw2NzJbR2ZksGaTk0SCVFZWFqfOXCAsXPIGoqpVKlFdv5rEmLwVuVRUVLD+JMEtIjKa/UdOSCXJuHt4s2zVRpauXM8385cyespsvHz8sx+vWKE81Q0kk8LCwiM4fFzymJSekcHvew6QkChdeSmrAK0VAbratpf4OS4+nl/WbSE5+b9rKImJSWzYtlOXMcBfAAAgAElEQVRq3f69ekj8rK4qnTJx6d+Cn+t36iCdyLVr3xGehT2XGMvMzOTwMRcCAiWru9WrU5v6dY0lxuS5qShTRrJTflupfkqee5nys12rNq2kltm2ax+xsZLvD+Pi4lm3ZQcr121l/pJfmDRrPstWb5R6HVTVJH933j7+vHz1Jv9BC0Ixpa7sAARBEARBKF3OnDlT4LtVPDw8ePr0KXXq1FFwVIIgCIKipaam4uTkxPLly4mJiUFdXZ0pU6bwv//9jxo1auR7HhUVlQLdfZnTXMVFGU01nAe3YFOtCnxz4RHfnHvIzbBYdvdrShlN6fYRn0pMzcDqgAdJb5IY6+LP+ZHSbQOEoqOiooKdnR12dnY8fvyYrVu3snv3bpydnXF2dsbMzIyZM2cyYsQItLW1lR3uF+XNmzf8/PPPbN26ldTUVPT19fn5558ZN26cROsTeY8zxel4UlD1KuniO9GSmWcfstMznJ77PVncoS5Lbevn+8Oe+ZeCOP/oBSPS/bg6ulWpeF1KMgsLC/bu3ctvv/3Gjh07cHR05Nq1a1y7do2aNWsydepUJk+eTNWq8n+oKOTO39+fKVOm4OrqioqKChMnTmT58uXo6+vnvXIpoqqqwm89GtJcvyxfn/bn+4uB3I+OZ2dfU7TUc79n/LuLj7jx5H0ywIjT/vhPbou6mrjPvKjVq1ePtWvX8vPPP3Po0CG2bt2Kp6cns2bNYuHChYwaNQoHBweaNv20nZNQXGVkZDBv3jxiYmKoX78+TZo0UXZIQjE1f/Z0wp9HEvxU8iajqOgXbHbazeZ8tFrU1tLi5x+/Q1tbS65tt23Tko2Of0iM3XRzZ/6SX2jS0ITI6BguXrlOeno6TRs3xO+T9orbdu5jxKB+VK1SGeu2rdGvVpXomPc3KaSkpDJhxvd062RDWwtzdHV1qFypIg8eBnHs9FmJxCxNTU2GD+qX/XPv7p05/fdFklPeJ/7EvHjJsHEO9LHrimkjE3R1dahYoTy373py4q/zZHxUGay6fjW6d+ko1+sAss8zdLS18fT2Zey0bxnUzw4tTS3ueHjhevue1LIzJn0tNUdB3qOPGNyfK9ddefX6vwrif+x3xt3Th842VmhoqOPj/5B/rt6QWK+9VRus20omgX09YhAr122VGHPafQAvH39srNqQnp7OuUv/Evj4CWXLliE+/pMWzQW8JGTXvRMnz5wn9KPkqJtudxnnMJfONu3IJIsr11yJiIyWWK9NKzMsLcwlxmrXqik1v9PuAzx6/ITaNQ3Rr1aFPnbd8h2bsVFNevfsypnz/1WMi4uPZ8zUOfS3704dYyNSUlO5eOW6zJaQ3zpMkvF7zf/vWVVGYpq6esHSQuTZv9Tysd1qVSszdEBfjpz4L9kvKvoFQ8c7MKB3T6obVOPtuziOnvqbNx8ld2lqajJ3xmSp+Y1r1eT+2//ak6ampTFu+rd069wBbU1NunWyoY6xUb6fgyAUF+JMSRAEQRAEhSpoW8UPRFUuQRCE4i0rK4sDBw7QqFEjZs2axYsXLxg8eDD+/v44OjrKlcT1JZjVtja3xrWBcloc9Yui0e9uPHyRkOd6k/96wOvXSQBcCHzBbq/wwg5VyKf69euzfv16nj9/zrZt22jSpAne3t5MmDCBmjVrMn/+fEJD5a/AJsgnLS2NDRs2ZP8+1NXVWbx4MUFBQUycOFEiiQvku/ic07IlMYlJW0ONHf1M2TegKair8vO1J3T604OYeOmWF586/TCadbdCALgW/IrNd2RXOhCKXpUqVfjhhx948uQJJ06coFOnToSHh7No0SJq1arF6NGjcXd3z3siIU9JSUn88MMPmJub4+rqStOmTblx4wY7duz44pK4PjbWzBC3cW2grCb770fQdvddouJyriD65/3nbHD7739jYFQc69zE/0pl0tHRYfz48Xh4eHD79m1Gjx5NWloajo6ONGvWjI4dO+Ls7KywtmFC4VFTU2Po0KEAODk5KewmGaH0KVe2DOt++R8tmhYs2a92LUO2rF2OsZF0wkteataojnlz6QRRN3dPdv15mLMXr5Cenk5Nw+r8smQ+NQ2rSyx30+0u0+cu4sLla5TXK8eqpQvQ1vovmSw9PZ1z/1xlycp1fL94BeMdvmPtZidCQsMk5pkweij6Vatk/1zH2Ij/zf9GYpmExEQOHz/N4hVrmfvjz4x3+I7f9xzk5avX2cuoqqryzbTxaGlqyv1ayMrFmT5xDOX1yhEd84KtO/axbusOmUlc1m0t6NRBVstS+c9TqlSuxIrF89HQkEyy8XvwkE1Ou/ltyw6pJC7D6gYs/NZB6ryoZ1dbmjRqILUNdw9vftvyOxsd/yDw8RPKlS3LnGkTpJYr6HFLV0eHX/43Hx0dyZupwp9Hsu/wcfYfPiGVxGVU05C5MyZJzdW2TUvKl9eTGv/3hht7Dx3jzj3Zrf9yM3fmZNq2lrwpLyMjg+N/nWPtZic2O+2WmcS1cO4MmjdtLDUuT+W1T8+HQXaL0/yQK5Ern9udOmEUbVpJVvBPTExiv/NJ1mxy4vc9ByWSuADmzphEXRkJWXbdbKXGYt++4+jJM/x55ASv38RKPS4IJYFI5BIEQRAEQWESExM5f/78Z80hErkEQRCKLw8PDywsLBg1ahRPnz6lS5cuuLu74+zsjImJSYHn/dzkCBUVlWKdYGFlVIHoae1oX6cS4S8SaPy7G8f8o3Jc/vd7YRzwkWyfMP7sQ0Jjkwo7VEEOZcqUYdq0afj7+3P58mX69+9PbGwsq1evpl69enz11VdcuXJF2WGWSufOnaNJkybMmTOHN2/eMHbsWAIDA1m2bNkX10Ixv0a3MMRvkhUGVXS5/uQV+o63uBn6OsflQ94k0e+En8TYNxcf4R8TX9ihCnJQU1PLPtb4+/szbdo0NDQ02L9/P5aWlrRp04Z9+/aRkiJa9BbE+fPnadq0Kb/++ivq6uqsXLkST09PrK2lW+V8idrWqkDYZCsa19DDOzyW6k5uMlsmejx/yxgXf6nx+VcfE/xaur2SUPQsLS3Zt28fYWFhrFy5ktq1a3P9+nWGDh1K7dq1WbJkCREREXlPJChNjx49qFu3LkFBQVy6dCnvFZQsJSWFBw8e5L2goHCVKlZg0+qf+NZhklQ7vJxoaWkyuH8vdm1ZQ8MGdQu87eWLv8Okfs7rt2llxu8bV1GxQnnmfzNNZkLIB/XqGrPXcR3dOnfI17arVa3C2uWLGDG4v9RjNu0s+X3jKlqZN8/XXCb167Bz82qs27bO1/KfUpGRdFWhgh6/b1ol1UrvY9062bBgjnQSFcjfWvED08Ym7Ny8hsYNpZOwpLbfuQM7t6ymXDnp8y1VVVUWfT+Lxib1c1y/cqWKrPppAU0aS187+pwEVKNahuzYtAqT+nl3+OjYvi2/b/yVGtUNpB4rW6YMyxZ+SzkFnk+qq6nxy//mMWH0MDTzkfRX19iIbb8tx65bJ5mPq8qRsCerMlZRJHLJqgQma7vqamr8unQBo4YNQEsr99emSuVKbPh1KfbdO8t83L57Z/r37iHzMUEoyVSyRHq+IAiCIAgKcvz4cQYNGvTZ84SGhmJkJMrdCoIgFBdJSUksWbKEdevWkZGRgbm5OatWraJbt/yXlc/L55yaFuckro9lZGax6EoQv954CsDMtrX5rbsJGh+1FfKMeEernbchQ/r1aFenEjfHWpSY5/slCg0NZdu2bezatYtXr963j2rSpAkzZsxgzJgxlClTRuHbfPDgQaG10Dk3aDQGD0NImzGWNlMnFso25BEVFcWsWbM4evQoAJ06deK3337D3Py/thh5HUvyejy3v6/S8LcXn5LOhNMPcPaLBFUVVndpwHfWxhLPLTU9k1Z/uOMnIyHDxKAcfpPbShy3hOLl3bt37Nmzh61btxIYGAhA1apVmThxItOmTaNWrVoK32ZAQACNG0tXDVAEH+fjZCzbQJSRPnZni+amn8jISGbPno2zszMAPXv2ZNu2bdSpk/cHhF+ipLQMxp7y46hfFKircqh/U4Y1e19JJSY+FX2nW/BOdjJhh7qVuTbWQuZjgvJkZmZy5swZtm7dyj///ENWVhbq6ur0798fBwcHbG1tC23bhXU8uThpBlXd7hM31J4Oi39Q+PzFga+vL/Pnz6dixYrs3LkTHR0dZYeUo5UrV+Lq6sr06dOxt7dXdjhfrKysLHz8AvD29Sfk2XOeR0aRkpKCuroa2traNKxfF6s2rWjetHGeladseg6UGrtxXrprQ0pqKnfuenH5mivhEZFkZGRQs0Z1Ora3pHMHa4nkrdjYt9y8fZegJyFoa2pStmwZOlhbSrXAexb2nLte9/EPCCToSQgZGRmoqqigo6NNsyaNaNmiGa3Mm6Gjrf1pOFIePArC28cfv4BHhDx7XxVbVUUFPb1ymDczpWWLprRobop6Lklmebnueocff14tMfbDtw7Yd+9MSkoKZy5cxvX2PV69foOujg6NG9ank40VzUxzPjbedLtL+CcJt4P69ZKrlV7w01AuXLlO8JMQYt++IyMjA71y5Wht3hzL1ua5JuF9kJGRwflL/+J215Pw55GoqKhgWN0A8+am2HXvhK6ODolJSZw+e1FiPfPmTWnYoF72z+np6Rxz+VtimVqGNXJNnsvKyiLgURBn/7lKSGg4CYmJqKqqUqG8HlatW9KurQU18pG8+Cb2LVev3+L2PS8AyujqUrVqZbrZtqdBvffvBe963if4aYjEen3tuqOrm/NxNzEpCTd3D667uhPz8hVJSUloaKijq6tL8yYN6WDdlvp1jXM95/Tw9iUo+InEWK/uXWQm1wEcPu4i8XN1fX06tm+b4/w5uXXnHs/Cn0uMDehjj6amhtSyKSkpnDwjeaN/ndpGUq0sP5aYmMQ119u43rnH6zexJCQkoq6ujkn9urS1MKd1yxa5vrYf+D14xL83b/P4aQi6OtqU0dWlScMGdOtsQ9lCuBYjCIVNJHIJgiAIgqAwI0eO5ODBg589z7p165gzZ44CIhIEQRA+19WrV5k8eTKPHz+mTJkyLF++nFmzZsm8y+5zFfT0tKQlV5x+GE2/k36QnI6FUQVcBreghp42b5LSMHC8RWpsco7rbujZkG+sjIsuWKFAkpKSOHToEJs3b8bb+30LhvLlyzNu3DgcHByoXz/nO5XlcePGDXr27Mnx48fp2bOnQub8WHFJ5MrKysLJyYkFCxbw9u1bqlevzoYNGxgyZEiOy+c1nyylPYnrY1vdnzHj/EPIyMK+UTX2929KRZ33F+Id/g5gm3vObRTnt6/Dr90KXoVRKBpZWVlcvHiRLVu2cPbsWTIzM1FTU6Nv377MnDmTTp1k3+kvr4yMDBo0aECPHj3YunWrwt8fFGUiV2ZmJo6OjixcuDBfxxpB0vJrwSy+8hiAhTZ1WWJblw77PLgT+ibX9fZ81ZSxZoZFEaJQAIGBgWzfvp09e/YQG/u+NZGpqSnTp09n9OjRlCtXTmHbunfvHjY2Nhw+fJh+/fopbF74MhK5AFasWIGrqytDhgzh66+/VnY4Ofr777/Zvn07mZmZ9O3bl0mTJuVafUkQSpMbt+6wcJlkItfs6RMY2FckNQqCIAj/EbfPCYIgCIKgEKmpqZw5c0Yhc4n2ioIgCMoXGxvLpEmT6NKlC48fP6Zbt274+fkxe/bsQkniysnZs2dZuXJl9ldUlGRLwpKYXNG3kT6Pp1hR16Ac957FYujoxtUnLxl50jfXJC6A2ZeCePgioYgiFQpKR0eH8ePH4+XlxY0bNxgyZAgJCQls2LABExMTevXqxfnz5z+rEh28r2aQmJiInZ0dhw8fVlD0xYufnx/W1tZMmzaNuLg4pk2bRkBAQIESK/z9/SWOJ+7u7oUQccng0MaIuxMsUaugzdmHMRg43sIz4h3H/KNyTeICWHXzKTdCcm7LKBQPKioq9OjRg7/++ovHjx/z3XffUb58eU6ePEnnzp1p2rQp27dvJyHh8/6nHD16lKdPn+Lo6MiIESNIS0tT0DMoWhEREXTu3BkHBwfi4uKYPn16gY81X6pFHevhMtwMNNX45cYT+hz2zjOJC+Drcw+JiU8tggiFgjAxMWH9+vU8f/6c33//nRYtWuDv74+DgwOGhobMmDFDYS3yVqxYQXJyMv3792fPnj0KmfNLM2HCBNTV1Tlx4oTUeVNx0qtXL5YvX07ZsmU5ffo0S5cuJTFRtFoVvhTS1zCSk0UbbEEQBEGSSOQSBEEQBEEhLl26xLt37xQy161bt4j4pBy0IAiCUHROnjxJkyZN2LlzJxUrVmTPnj1cvHgRY2PjIo/l+PHj/Pjjj9lfz58/z3ulEqBeJV38JloyxqwGJKTyzaXHnHv0Iu8V0zMZfNKX9IzMwg9SUIj27dtz5MgRQkNDWbx4MVWrVuXs2bPY2dnRsGFDNm7cWKD3UH5+fpw7dy775xEjRuDo6KjI0JUqKSmJH374gZYtW+Lm5kazZs1wdXVl27ZtlC9fvkBzenp68uOPP7Jo0SIWLVrEjRs3sh8riUmhn8vCsDwxU9th17AqqbHJdD7qzeBTfvlat9NJX94mpxdyhIKi1KlThzVr1hAeHs6OHTswMzPD39+f6dOnY2hoyOzZswkKCirQ3KtWrcr+/siRI/Tt27fEfRh/6dIlzM3NuXbtGqampty6dYutW7cW+FjzJevbSB/fiW2xa1iVi0Ev87dScjozzz0s3MCEz6arq8ukSZPw9vbm5s2bDB8+nJSUFLZu3YqpqSmdO3fm2LFjpKcX7H9DQEAAp06dyv553LhxrF+/XlHhfzEMDAwYMGAA6enp7Nq1S9nh5MrMzIx169ZRo0YNPDw8mDNnDpGRkcoOSxAKnazzji/xXEQQBEHInUjkEgRBEARBIVxcXLJPOlVUVAr89WH9kydPKvPpCIIgfJGioqIYOHAgAwYMIDIykiFDhhAQEMDYsWOVHVqppKOhxt6vmrHEth6+EW/zvZ7f87esuPGkECMTCkONGjVYtmwZYWFh/Pnnn7Rp04agoCBmz56NoaEhDg4OBAQE5Hu+1asl23FkZWUxbdo0VqxYoejQi9zFixdp2rQpv/76K+rq6qxcuRIPDw/atm2r7NBKnUo6Gvw93JzlneujoaICqRn5Wi8jNpmZZ/O/vwrFg46ODhMnTsyuFjh06FASExPZuHEjDRs2pGfPnpw5c4bMzPwlC1+8eDG7fewH58+fp1u3btkt2IqzzMxMlixZQo8ePYiJiWHcuHHcvXsXS0tLZYdWosWlpHMuv0lc/8/ZL5JzQflIaBeKBWtraw4ePMizZ89Yvnw5NWvW5OrVqwwePBhjY2OWLVsmdzWolStXSo19++23LFq0SFFhfzGGDh1KxYoVcXV1xdfXV9nh5KpmzZqsX7+eFi1aEBYWxpw5c/Dzy19SuSCUVLJytlRVRSKXIAiCIEkkcgmCIAiCoBBOTk5kZmaSlZVFZmamzK8RI0aQlZXFxYsXc1zmw/oODg7KfkqCIAhflF27dtG4cWNOnDiBoaEhLi4uHDlyhGrVqik7tFItMi6Fn+6GgZwd9pZee4JnhGIqYQpFS1NTk1GjRnHnzh3u3LnD6NGjSU1NZdu2bTRp0oSuXbvi4uKSayLFs2fPOHjwoMzHFi1axNy5cz+7baMyxMTEMHLkSHr06MGTJ0/o3r07fn5+LFiwAA0NDWWHV2qpqKgQ+jaZl6/kq6L05/0Ijj8ovm2bhNy1b9+ew4cPExoaypIlS9DX1+fChQv06dOHBg0asHbtWt68yb0t3sfVuD5269YtOnToUKzbekVHR9O9e3eWLVuGtrY2e/bs4Y8//kBHR0fZoZVokXEptDviBZny/w+y/+sB8Smi0l9Joq+vz48//khISAgnTpygS5cuPH/+nCVLlmBkZMSwYcMkql/mJCQkJMf3NStWrGD69On5TjAV3iftfv3118B/1+qKs3LlyrF8+XLs7Ox49+4dCxcu5OLFi8oOSxAKjazqW6qqakqIRBAEQSjORCKXIAiCIAiCIAjCFyw8PJyuXbsyceJE3r59y5QpU3jw4AF9+/ZVdmilXkZmFgOO3oeEVPlXzsyizwkfktPyVz1HKJ7atGnDvn37CAsL4+eff8bQ0JDLly/Tv39/6tWrx5o1a3j9+rXUeuvWrSMjI+ff/bp165gwYUKuyxQ3+/fvp1GjRhw8eBB9fX0OHDjAhQsXqFu3rrJDK/UO+ESwwyO8QOsOOu1PxLtkBUckFKXq1auzdOnS7ATRdu3a8eTJE77//nsMDQ2ZNGkS9+/fl1rv3r17XLlyJcd5fX19sba25unTp4UZfoH8+++/mJubc/nyZRo3boy7u7uoPqoAKemZ2B/2hvgCvK8BeJvMwiuPFRuUUCTU1NT46quvuHTpEgEBAcycORMdHR2OHDlChw4daNGiBY6OjiQkJMhcf/Xq1bm+Z9m+fTsjR44kLS2tsJ5CqdO1a1caNGjAkydPuHDhgrLDyZOamhozZ85kypQpZGZmsmHDBnbu3Fnsk9AEoWBkJHKJ1oqCIAjCJ0QilyAIgiAIgiAIwhfq7NmzmJmZcfnyZUxMTPj3339xdHRET09P2aF9EX64FMjt0NyrneQm4kUCP4oPPEuFatWqsWjRIkJCQjhy5Ajt27cnJCSEefPmYWRkxPz583n58n2bqtevX7Njx44859y9ezeDBg0iJSWlsMP/LImJiYwbN47Ro0cTGxvLpEmTCAgIYMSIEUUaR0msYKYIATHxjDrtX/AJktIZdcrvi339ShMNDQ2GDx+Oq6srnp6ejB8/nqysLHbu3ImZmRl2dnbcuXMne/mcqnF97MmTJ7Rr167YtMnKyspixYoVdO3alcjISEaNGsXdu3cxNTVVdmilwrQzD/AO/7yWmptvh+L+mXMIytWoUSM2bdrE8+fP2b59O02bNsXHx4dp06ZhZGTEihUriIuLy14+OjqaP/74I895Dx8+TL9+/UhKSirM8EsNFRUVpkyZAsDevXtzTKIrbvr168fSpUvR0dHhxIkTLFu2TPzOhVKnlmF1xg4fJPHVuGEDZYclCIIgFDMikUsQBEEQBEEQBOELk56ezvfff0/v3r159eoVkydPxtvbmw4dOigtpoImAZTU5AGXh9GscQ357HnW3Qrheoh0xSahZFJXV2fIkCHcuHEDLy8vxo8fT1paGqtXr8bY2Jh58+axdu1aEhPz1wLv1KlT2NvbS3xgWpw8ePCA1q1bs2fPHqpVq8b58+f5/fffqVixolLiKanHk4JKTM2gu7M3pH1etYurwa/Y4v5MQVEJxYG5uTm7du0iPDycVatWUbNmTc6fP0/btm3p2bMnJ06c4Pjx4/maKyoqChsbG9zc3Ao56ty9fPkSOzs7Fi1ahIaGBjt27ODPP/+kTJkySo2rtNjq/ozdXs8VMtew0/6kZ4gqPCVd2bJlmTp1Kr6+vly7do1BgwYRGxvLokWLMDY2Zvny5SQmJrJu3bp8J52fO3eObt26ERsrkv3yo0mTJnTs2JF3795x6NAhZYeTbxYWFqxfvx4DAwPc3d2ZO3cuMTExyg5LEBSmVs0aTBw7XOLLtLGJssMSBEEQihmRyCUIgiAIgiAIgvAFCQ0NxcbGhrVr11K2bFkOHTqEk5MTOjo6yg7ti/HkdSL9TyiuOkm3k77Ep6QrbD6heDAzM2PXrl0EBQUxZcoU0tLS2LRpE05OTnLNc+XKFTp16pRd0au42L17N61bt+bBgwfY2tri7e1N9+7dP3vewkrGKo1JXlPPPCD8hWIqdMy6GEhATLxC5hKKj8qVKzNv3jweP37Mli1bqFmzJhcuXOC3336T628iNjaWrl27cvHixUKMNmeurq6Ym5tz4cIFGjRowO3bt5k4caJSYimNroe8ZsbfAQqb72l0PKtci19LTqHgOnTowNGjR/Hz82PYsGHExsayadMm3rx5w7Zt2+Say9XVlY4dOxIdHV1I0ZYu48ePR0tLCxcXF54/V0yyZVEwMjJiw4YNNG3alJCQEGbPnk1AgOKOM4IgCIIgCMWdSOQSBEEQBEEQBEH4Qri4uGBubs7t27cxNzfH09OTYcOGKTusz06QyMrKKjFJFslpGdgd8QYFJl6lxibzzflHCptPKF6MjIxwdHQkKCiIkSNH8vq1/BXYPDw8sLGxITw8vBAilE9CQgJjx45l/PjxJCcns3jxYi5dukT16tULfdv5OU6UlGPJ5/rDM5w/70cobsL0TPof9yFNVNEplbS0tHBwcODx48esWbOGe/fuyT1HYmIivXv35ujRo4UQoWxZWVmsXr0aW1tbwsPDGTJkCB4eHrRo0aLIYijtnr9LpuMRb4XPu+jfYIJeloxWcEL+NW7cmEOHDuHn58cff/zBH3/8QXy8/EnAPj4+WFtbExISovggS5mqVasycOBAMjIy8tWauzjR09NjxYoV2VXY5s+fz5UrV5QdliAIgiAIQpEQiVyCIAiCIAiCIAilXGpqKrNnz6Z///68efMGBwcH3NzcqF+/vrJD++L85hZCYJTi29z94RnO2UDRcqQ0MzQ0/KwPrx4+fEi7du0IDAxUYFTy8fPzo3Xr1uzbtw99fX0uXrzIsmXLUFNTK/Rty5OgVdqTuXyj4pigwOo5HwRGxfG/q48VPq9QfGhpafHq1StSU1MLtH5aWhpDhw4tkmSC169f07dvX+bPn4+amhpbt27lyJEjlCtXrtC3/SWpUU6LfwY356sm+qCqoriJM7IY+9eDUn88/lI1btyYTp06sWHDhgLPERwcTLt27fD391dgZKXToEGDqFy5Mu7u7nh6eio7HLloaGgwZ84cJkyYQEZGBmvXrmXv3r3i2CAIgiAIQqknErkEQRAEQRAEQRBKsadPn2Jtbc3GjRspX748x44dY8uWLWhpaSk7NIUrCRf0f+xQD88pVsy2qg1lNRU6d3E9EnYAACAASURBVK9T/rxMLNiH60Lx5+zs/NmVJ8LCwmjfvj1eXl6KCUoOu3btok2bNgQEBNC5c2e8vb3p0qWLQrdR2MeAknCMyQ9dTTV+bGcMeor/P/DrjafcDJW/apxQMsTFxbF9+/bPmiMrK4vJkyfz66+/KigqaXfu3KFly5acOXOGunXr4urqyvTp0wtte18yFRUVutatwomhZkR/Z8ua7iZUqayrkLndQl6zy1P5lSSFwvH7778XqMroxyIjI7GxseHOnTsKiqp00tbWZvz48cD7170kvp8ZOHAg//vf/9DR0eHYsWOiGpsgCIIgCKWeSOQSBEEQBEEQBEEopY4fP465uTn37t2jdevWeHl5MXDgQGWHJaEkfpDwucxr6LG+ZyPS59pyfnRLhjevDhoKOD1PSGXKGcVX2RGKh9WrVytknhcvXtCxY0euX7+ukPnyEh8fz6hRo5g4cSIpKSksXbqUf/75BwMDgyLZPhTsOFOaj031KumyvEsDsuba4j7JkjlWtRWa1GV7wpc4BbaPFYoPJycn3r59q5C5fvjhB+bNm6eQuT62YcMGbGxsCA0N5auvvsLT05NWrVopfDuCtGplNPnOug4vZtnw7zgLhjWrDmqf9/5m0oVAouJSFBShUFykpqaydu1ahcz15s0bunTpwqVLlxQyX2nVqVMnevbsycyZM1FRUWD1vCJkaWnJ2rVrmTt3LnXq1FF2OIIgCIIgCIVKJHIJgiAIgiAIgiCUMikpKTg4ODBo0CDevn3L7NmzuXnzprjgXcyoqarQo35VDg5sTvy8zuwb0JRO9SrDZ3y2csI/ikO+EYoLUigWLly4gLe3t8Lmi4uLo0ePHpw5c0Zhc8ri4+ODhYUFBw4cwMDAgEuXLrFkyRJUVcXlqOKidc0KrOvZiMxvO3JtXGumWtQCXY3PmjMjNpkZZ0VSaWmTmprKunXrFDrnmjVrmDhxIhkZGZ8919u3bxk4cCBz5swBYP369Zw4cYLy5ct/9tyC/DoaV+bQoOa8/M6WDT0bUrNqmYJNlJLOjLMPFRucoHR79+4lIkJx71cTEhKwt7fn+PHjCpuzNJo1axampqbKDuOz1KlTB1tbW2WHIQiCIAiCUOjElTNBEARBEARBEIRS5PHjx1hZWbFt2zYqVqzIqVOnWL9+PZqaim3jpwh5Vbz5kqrolNFUY3QLQ66MsSBiri2/9WhIfYNyBZprxJkAIt4lKzhCQZkUVY3rY8nJyfTv358DBw4ofG5437rH0tKSR48e0bVrV7y9venUqVOhbEv4fCoqKnQwrsT2Pk1I++59tcAxZjVAS61A8+3zjuDEgygFRyko0/79+4mMjFT4vLt27WLo0KGkpha8NbCHhwctW7bkxIkT1K5dmxs3bjB79mwFRikUVGVdDb6xMiZsRntuTbB8f1xRl+8jieMPojj9MLqQIhSKWkZGBqtWrVL4vGlpaQwZMoSdO3cqfG5BEARBEARBKGoikUsQBEEQhCI3c+ZMQkJClB2GIAhCqXP48GFatmyJl5cXVlZWeHt7069fP2WHVSA5JWSV1EQteVQvp8W37YwJmtYOv+nWzGtfR762Z8npjHHxL7wAhSJ17949rly5UihzZ2RkMGrUKDZv3qywOePi4hgxYgRTpkwhLS2Nn3/+mQsXLqCvr6+wbchSGMcMWet+CccgdTVVetSvyt6vmpE8rzMnh5kxyNRA7uSLgaf9iRQt0UqFrKysQkko/eD48eP06tWL+Ph4udfdunUr1tbWPHnyhD59+uDl5YWlpWUhRCl8LiujCuz9qhlvvrdla+/GmFTXy/e6/f4OEC1bSwlnZ2eCg4MLZe7MzEwmTZpUqMcrQRAEQRAEQSgKIpFLEARBEIQi9/DhQ6KixB36giAIucnKysr3V1JSEpMnT2b48OHEx8fz/fffc/36dYyMjJT9ND6bgYEB9evXz/7S1tb+IhIpPjDVL8uqbiZkzOnI1a8tGGtmCJp5V8i5/PglTnef5Wv/EYq3wqha8alZs2axZMmSHPeNLPK3v3h7e9OqVSsOHTpE9erVuXz5MosWLSo2rRT19PQkjieVKlUSfwd50FJXpX9jfY4OaUH8/M4cHNQM+0bVQDUfPWCT0hl9yk8ch0oBFxcXHj16VKjbuHTpEl27duXVq1eS+8bHx6KPxuPi4hg2bBgzZszIrvDj4uJCxYoVCzVO4fNV0NZgemsjHk214u7ktkxqVTPv9zbvUpj/T6A4lpRwWVlZ/PLLL4W+nfnz5zNv3jyZ+8f7w4rYZwRBEARBEITiTSVLvFsVBEEQBKGIuLu7Z98d7ebmRtu2bZUckSAIQvHwOadljx49YvDgwfj5+VG5cmX27t2Lvb09Kir5+JBdyT6ngk5uz68kPPfPkZSWwelHMey+H8mFoBeQ08uloUrQtHbUq6Qr1/yl/fUrSUJCQqhfvz4ZGRlFsr0ZM2awcePG7H3g/OAxGDwMIXXGGNpMmZjjeioqKmzfvp1vv/2W5ORkunfvzv79+6latWqRxA3yH08+Hc9pv5c1Lv5G4E1SGicDotnnF8W1J69yPg4Bm+0b4dBG/sRi8ToXHx07duT69etFsq3GjRtz8eJFDA0NAfB1Pk7GzxuJMtKn599HAfDx8WHw4MEEBQVRo0YNjhw5grW1tdhnSrD4lHQO+0WyyeM5vs/f5ric6/g2WBlVkGtusV8UHxcuXKBnz55Ftr2JEyfi6OiIqqoqFyfNoNptH94NsafD4gW5rif2mZItKyuLlJQUtLW1lR2KIAiCIAhCgakrOwBBEARBEL4cbdq0oV+/fri4uCg7FEEQBKWTJ3krp2X379/PtGnTSExMxNramsOHD2d/8PnxOuLDiNJFR0ONIaYGDDE14EVCKof9ItnpEyX9wWdaJlYHPIlysEY1P9Vz/p/Yd4oPY2Nj4uPjefbsGaGhodlfH/8cFhamsESvLVu28ObNG3bv3o26ev4uGb17945JkyZx9OhRVFVVWbFiBT/88IPYd0q5ijoajG9Zk3HmhsTEp3L0QRR7/aK59+yN1LIzzz6ka53KNKxaRq5tiGNR8XH16lUiIyNlHoNCQ0MJCQkhISFBIdsKCAigffv2/PPPP9SvX1/q8R07djBr1ixSUlKyk0arVKkCiH2mJCujqcaEljWZ0LImPlFxOHmEs90nEj5pp2h9wIPk7zuhKUerV7FfFB89evQgLi5O5nHkw8/Pnz9XWKWsnTt3Ehsby/79++VaT+wzJZujoyOBgYEsW7aMcuXKKTscwsPDuXXrVq7LaGtr07dv3wLNHxQUhJeXV/bPuf39fLo/d+7cOft/qDySk5M5ffp0ntuTtV0TExPMzMzk3ibAlStXePnypdzb1dHRoU+fPgXapjyv76fb7dKlC5UrV5Z7m0lJSfz1119yrwfQqFEjmjdvXqB1P3595aGrq0vv3r0LtE1fX18CAgIKtK69vT1ly5aVe713795x/vz5Am3T1NQUU1PTAq37zz//8OaN9LlSXsqWLYu9vX2BthkYGIi3t3eB1h04cCBqanlXg/9UVFRUgW8AsbCwoG7dugVa18XFhZSUFLnXq1q1Kp06dSrQNr29vQkMDMx1mcqVK9OlS5cCzS8IoiKXIAiCIAhFqn///ri4uIiKXIIgfJHyOv3K7+lZYmIiM2bMYO/evQD88MMP/PTTT6ipqeX5YUNx+jBCVOSSX26vTeCLBPb7RrDaJ5LU2OTs8V+71ud76zp5zl2S9h3hPxkZGUREREh8GPrpV1JSklxzVqlShdDQUK6NmfK+IpfDGFpPmSC1nJeXF0OHDiU4OJgaNWpw6NAhbGxsgKLfX0RFrqKT23Eo7G0yR/wi2e0XTUDku/8eKK9F8sz2aKjlnXzxpR7fS7rXr1/neAwKCQnh1atXcs/p6emJ6qPHZC7fREiNyhyvVpYDBw6gqqrK0qVLWbhwISoqKmKfKcFyO54kpqZz7EE0Wzyfc+9ZbPb4Etu6/K9jvTznFvtFyZSenk54eLjMJK+QkBDCwsJITk7Oe6KP6OjocHDwKGp7BPBuiD02i+bLXE7sMyVfcnIy8+fPJygoiLp167JixQrKly+v1JiOHTvG4MGDc12mWrVqREdHyzXvh+Pnhg0b+PbbbwsU27Vr1+jQoYPc24yIiKBmzZoF2uasWbPYuHGj3OtlZWVhZWXFnTt35F5XX1+fyMjIAv0dr1+/vsCvr6urK1ZWVnJvNywsDCMj+avZAnz33XesWbOmQOtaWVlx+/ZtuderWbMmYWFhBdrmggULWLVqVYHWffToESYmJnKv5+fnR7NmzQq0zcWLF7Ns2bICrWtubl6gpCpjY2OePn1aoG3+9ttvfPfddwVaNyEhAV1d+aq7A5w/fx47O7sCbdPJyYnJ/8feecc1db1//JPBChtB9hZBhojKVhT3Qq1ate62amtbW63VamuH1tZqh53f2qodWn+1WrXWrXUPKo6CAooIKAgiqOwNye8PmktuSEISMhjP+/XilXtO7j3nSe7DuSf3+dznzJ+v1rG2trZq/d7o37+/2sKzRYsWtTiehYeHq/V/RRAAZeQiCIIgCIIgCILQOoqCVKq+l5ycjDlz5iAxMRF2dnb45ZdfMHz4cLnHSN80pCfM2yfKiFF8bAVYFdsN7w/0xsWcYiw9mYFLOcVYfioDI7xtEWjf/GlVSR9Q1nfIb9oWPB4Prq6ucHV1Rb9+/Zh6yfNZWFjICoJKZ8GQfjL40aNHGDFiBF61dUYtRACHA5FIxDr34qw4tbW1GD58OLZt28Z6op/Gmo6HMuOQi4URlkR5YEmUB+48rsSv1x/gg7OZQEkNVp3OwAeDmmdYUuU6ReNQ28XGxgY2NjYICQlh6iTPZWVlZbOxR/IvLy8PQqGQ1ebAgQPxxaIlMKwux8p/buBuWQkcHBywfft2DBw4UGY/NO9pHygznpgY8DAz2Akzg51wq7ACb5xIx+H0R1h1LguTe9g3y/JHY0nHgM/nw8PDAx4eHkydtL8UFBTIFHqJX6XnNVVVVVhxZD++tnEHr6EetVVVMDA27vQ+U1NTgz///BMjR46EhYWFvs3RCMbGxli7di3effddpKamYtmyZfjoo4/UyorUGiR9SZmHlEQiUbO5dkvtagJxv7pGX/lF2ku/lH+FIAiCAEjIRRAEQRAEQRAEoTVUFWkp2r+urg7fffcdPv74Y3h7eyMmJgbbt2+Ho6OjQgGOoqCDMjeL9QHnP9GIovc7C6pkGZKsi3K1woXZfVBbL8SfqXnYkZSDN8KdYWxkBGNjI7ntKOM7bdVviEZk+YadnR1sbW3Ru3dvmfuVl5czwdDk5GQUFRUhKysLP1+9ijdNbMGtq2Ptf/LkSXz55Zfgcrn46KOPsGzZMtb/bXvwmbZmT1tH2WuWZJ23jQneG+iF9wZ64er9IvyRko/knEJ4dTGDsbERuFyu3HbE50eRT8mqJ9oO0udVIBDAz88Pvr6+MveVzsKTkpKCR48e4cMfN+PRgwdwsbDEwIEDsX37dtjb28sVXLQ075H3HqEb1BlLAMDXVoD9U4JRUy/Ezhv38fPVe1ge5QojQ0NmXkNjScdE3rzGzs4Offv2lbmf5LLUycnJKC4uRlZWFj6/loR3dh1Ggqkpope8pvY8WFZ9e2Tbtm3Ys2cPHj16hJdfflnf5mgMgUCANWvWYPXq1UhMTMTSpUuxdu1a2Nvba73v1gpwSMBDEARBEIQYEnIRBEEQBEEQBEFoAXUDVeKnYm/dvoObt+8g7U4mbqdn4l7OfTTUN6B7SD8IGxpQz+FgwZJ30L2bF3y7eaF7N08EB/nDwtxcZnCzvQUj5Im52pqd2kRZf5Gkrq4OScmpSLmZjpRbt5Galo7y8goAwPn/9rG0MEcPXx8E+PnA3687ggN7MMtyKus7bdVvOjvq+IxIJIKpqSm4fEOU1whR0cDHnbxiFJYJwfMJwFsAeAeOweDEOfj6eCPA1wf+fj4YNmwYnn76aWapbEmxlj58RtGYoW5QjJZVVN+nsu7l4HryTaTcuo2UW7eR96BxuaD4//Yx4PPRzdsT/n4+CPD1QUhwIKwsLeT6UHsSCXZ21J3/8Hg8CEzNUdPAwaOyWjyuFCHzYRkcfXvByb8PRA0NqOPx8eaq9fD/7xoWHOgPF2dHleY94vfIb3SPMmOHNPX19UhKvonUW7eRfPM2bqalo7SsHAAw7uvGfSwszNGje7dGv+jR6BcGBgasNmksaX+oe/0BAGNjE9TUCfG4pApPKoVIzcxHSWk9uF49sAIATpyDICERPXx94O/bDf5+3RHSMxAGBk3hss7gMxMnTsThw4dx+PBhjB49mpUFrb1jbGyMVatW4cMPP0RCQgIj5nJ2dtZan8rON42NjbFy5UpWnampqZy9WyY6Ohpr1qxR61hPT0+1jrOwsFC7z7CwMLWOA4AFCxYgLi5O5ePMzJpnplaW1ny/6i6PaGVlpXKf4vFI/NtMHdT9fluT0W/EiBFqHy+ZDVoVHBwc8OGHH6p1bP/+/dU6DgAWLlyI/Px8lY+zsrJSu8/+/fur/VkNDQ3VOq579+5q99ma8WHlypWorKxU+Th1/08BYMyYMejatSurrqqqSu0xgyCk4YhI4k0QBEEQhA4ZP3489u3bh/j4+Fb9uCQIgmjLKBOYkrXcQmlZOY4cP4W/Dv+N3Aeq3+AxMDBAbEwkxo8ajh6+3VjBBWUECfoIRrT0k1TVJZHae0BFjCIfkvVeRWUl9h/+G7v/OoRHj9nLybREV7sumBg3EmNGDIFAYAIACn2nLfgN0YQyWdvk+VNDQwNOnb2IHXv2IyPrnkr9CkxMMHrEYEwaOxJd7WzbhM+oksFOGTq7kEsV0YVYhHzpyr/4bfdfuJ58U6W+DPh8DIntjykTxsDd1QWAYr+Rdx460/lpi6g7/0nPyMKO3X/h9Pl/mi2v2BJhfXphyoQ49A4OVHnsIX/RDcr6gWS5qqoaB46ewB/7DqKg8LFK/dl2scaEuJGIGzkUZqYClccS8gv9oqqAS3K7qroah4+dwq59B5H/sFClfm2srTBhzAjEjRoKSwtz1nuqzHHaG7t27cJPP/2E4OBgrF27Vt/maJz6+np88sknOHfuHKytrfHRRx/B3d1d4/0ommv+8ccfmDx5MlNevXp1MyGXrmzRtL+qMsfWZN+drd+23CdBEGyqqqogEAiYcnh4OP755x89WkS0Z0jIRRAEQRCETiEhF0EQHRlVMw2Iy8Ulpdj0y284fuoc6urqNGKLj7cn5s+Zhr4hPVUOPujyBqCmf5K255uXqgSuxK/19fXYtmMP/th3CJVVVa3qX2BigknjRmHm1Ang8xszEsjzHX37DdGIOj4j3j5+8iy2/Pq7ygFyabhcLoYM7IeX5s6CpYW50j6jDX8hIZfmUDZoLt6+fC0J3276Bfdyclvdd0Rob7z6whw4OjQtgST+3tvaNYxoQlUBMgBk38/Dl99twbWk5Fb3383LA6/Mn43gQH8A8n1GVh35jHZQRcDVJDAW4v927sXvfx5ARYXqWRUkMTY2woQxIzBn+mQm0xKNJW0bdXwGaBSm79x7AL/9sQ9l/2WiVRdjIyOMHTUUz8+cAiOjpuXIO6owtK6uDi+88ALy8/PxzjvvIDIyUt8maZyGhgZ88cUXOHHiBMzNzfHhhx+iW7duGmu/pXmmtJBr1apVWLlypcZ8RpPzXG33Sf1qn/Y6FhFER4GEXIQmISEXQRAEQRA6hYRcBEF0VNQJYIpEIpy9cAlfbvwRRcUlCtt3crSHbRcbGBoYoKFBiIrKStzNvo/a2lqFx40cGouX5s5SOSOBrm4AkpCrEXUCV7kP8rFm/Ve4lZ4ht10DQ0N0cXCGobExRCKgproKjx/cR0N9vdxjfH28sPKNV+Hs5CAz4NkW/IZQz2dEIhEqK6vw+bebcPLsRbltc7hc2Do4w0hgCi6Xg7raWjx5+AA1VfKD6l1srPHWklfQOziwsY0WfEdRnbooGk9UHWs6a8YnRX4ly6fq6uqwaesO7Np7QH6jHA66dHWAiZkFuDwe6utqUVxYgMryUrmHmApMsPiluRg0IFrlcUheHaE9lBFaSJZFIhEOHTuJb374BdU1NXLbtbDuAnPrLuDx+WhoqEdFcRGKH8vPssPhcDBt8ng8O+1pZslg6fdlbcsqE61D2WuUZF3+w0Ks+fQrpNy8LbddvoEhujg6w9DIGABQW12Fx/l5qK+TPx/29nTHO0tfhbtb82x/NJa0HVQVEItfCx8/wUeffo0kBZkg+QYG/82HGzPP1tZU40l+LuoU/I7ycHPBu8teg6eHW4f3mYsXL2LNmjVwdHTExo0bmaVJOxIikQjffvstDh06BIFAgNWrV8Pf318j7bb03h9//IEpU6Yw9WIhlyTq+I2mfkcr27c+f7drsu/20K+i/ltqpz2OQQTRkSAhF6FJSMhFEARBEIROISEXQRAdEWUCD9J15RWV+OSr73Hu4iWZbbq6OGHkkIHw9+uO7t28YG5m2myfhoYG3MvJRVp6Bi78cwXn4xPQIGNJItsuNli+aAH6hPRk6pQRWejiJiAJuVQPdopEIpw5/w8++ep7mVm4bB1d0DOqP5y8fGDn6AIul8t6v6GhHoW5OcjLuoOk+HN4kp/XrA1jYyMseXkeBg/sB6C5v+jbbzo76vgM0Lh82fsfb8CD/IJmx5taWCE4Ogau3fxg7+oBA0PDZu0/KchH3r0M3LpyCdm3mwdMORwOnpk4FnNnPwMOh6NzASAJuVqHqtey/IcFeG/tBty+k9msLSNjEwRFxcDd1x+O7l4w+i94LknJk0fIu5uB9MSrSL/xLyDjHA2N7Y8lr8yDoaGhSuOQvDpC8ygrthBvV1fX4JOvNuLUufhmbXG5PPj1CYV3UG84uXvBzNKq2T6V5WV4cC8Td2/eQErCRZlCDH8/H7z35iJ0tbNl6uj6pTtUFYSKRCJc+OcyPv7iO5lZuGwcnBAcFQNnTx/YObmAy+Ox3hc2NKAw7z7y7t7B9fhzeJR3v1kbRoaGeO3F5zBi6EAaS9ogygpApd+/dOVfrP38W5SWlTdr09rOAcHRMXD28oGdsyt4PD7rfaFQiMK8HDy4m4Hr/5xH4f3sZm0YGBjglXmzMXbUUAAd22dWrFiBpKQkPPfcc5g0aZK+zdEamzdvxp49e2BkZIT33nsPvXr1UrstZTPBKiPkkqQl/6GQbsdA1nlW9twqM8a0x3GIIDoCJOQiNAkJuQiCIAiC0Ckk5CIIoqOh7DJmkq+lZWVY9u5amcHvmOhwTIgbiT69glS25dHjJ9h/+Dh27z+CkhJ2phM+n4d3ly1C/6gwpk5WEKI9i7na481KdYKdp85exJpPv252rJOXDyKGjYanX6BKNmTevIGE44eRm9k8A8byxS9h2KAYAIrFXNLl9ngu2guqiG0kX+/ey8GiFauaLTlkZdsVYcNGwb93BHh8dpBTEQW5Obhy6ghuXmkuRh0/ejheffFZvQgAlQ2qtURHCcwqi6p+9fhJEV5d9h4ePGSLAgVmFggbOhJBEf2ZjDnKUPSoANdOH8P1i+cgFDaw3osI7Y3Vb73ebMlXEuboH2VFXE0Z3Orx1up1uJp4g9UOj89HSMwg9I4ZCnMra6X7r66sQNLFM7hy8iiqK9ljm4uTA75ctwo21k1isLYy7+nIKDOvkX49H38Z73+8AUKphxEc3b0RMXw0PHsEqXRe7qalIuHvQ8hJv9XsvSWvzMPo4YMByB9LOtv4r2/UEXGJRCJcvpaEtz/4BA0N7GuGvasHIoaPhndAL5XOW/adW0g4fhj30lKavbfwhTmYEDcSgHLjiLy6tkxWVhYWLlwIIyMjbN68GdbWyo/F7Y1t27bht99+g4GBAVauXInQ0FC12mnpwQoxqgq5CEIZKDMXQbRNSMhFaBISchEEQRAEoVNIyEUQREdClWCD+LWktBRvrPwQmXfZT3072NvhzdcWoG/v4FbbVVJahi+/24Ljp86x6rlcLla+sRAD+0cCUD6Ape2bgJ1VyKWOICfhaiJWrvkE9fVNQSsul4d+Y55C39jhan8HIpEI184cx9n9uyFskGybi/eXL0Z0RF+Wf5CYSz+oK+LKy3+IRcvfx+MnxazjA8KjMWjCNBgaGaltU+bNGzi6/UdUlpex6qc/PR7Pz5qqcwGgJrJydbZsXKoG0UtKy7B4xSrczWZnvfHoEYSR05+DwMxcbVseZGfh0C8/NFs+b1BMFFa8/jJ4/2XiITGX/lFGxCW5LRQK8cEnX+HsBbb406arI0bPmY+uTq5q21JeUoRDv/6InHR2pkBvT3ds+OhdmP2X1bStzHs6Kupco/5NSsby9z9GncSSzxwuF9EjxyJs8ChwpLKKqsK/507izL6drOWkORwOVr6xELExUUxZ1qv0tqwy0XqUvf5I+01yahqWvfsRaiQy8nE4HEQMH4OIYXHNstGqQlL8GZze83uz5TrFDzd0ZAHgN998g0OHDmHYsGFYtGiRvs3RKrt27cJPP/0EPp+PZcuWoV+/fiodr8qDAxUVFcjPz2fKXbp0gZVV84yTHQHJz69L/+9s/SrTZ3sbfwiioyASiZCZ2fTQrrGxMZydnfVoEdGe4b3//vvv69sIgiAIgiA6Dzt27EBaWhrmzp0LFxcXfZtDEAShNqqIuMTbFRWVWLRiFbLu5bCOHTNiCNa+vxzubpoZF42NjDCgXwR8u3kh4WoiE+QQiUQ4F58ADzdXpi9lghHavgnY2vall3BrjygT7ExLz8Dy9z5GXV1TQNLUwgpPv/Q6fHuFtuo74HA4cPLwhldgMLJSb6C2uorp+3z8ZQQH9kBXO1uVs5m09/PSlmkpc5t4Wyy6KSh8zOzH5fEwcuZcRA4bo1IWLllY29nDPzQSefeyUFb0hKm/kXoLZmYC9PD1Yer07TOtXYakI/qzqkH0YS9B+gAAIABJREFUuro6LH33Q9zJvMvaN2bsJAyeNL1VokAAMLe0RmB4NIofFeBx/gOmPuteDsorKhEmIXZWdL2icUi7qCriEolE+PqHn3Hs5FlWO359w/HUvIWwsLJplT2Gxibw7xsBLo+LnPQ0pr6ouATXU29hWGx/RtjRFuY9HRFVRVwikQiZd7OxVEqMIzAzx8QFi9GjT0Srz4Ojuye8g3rh7q1k1FQ1Ldl44dIVBPp1h4N9VwDK+wL5hWZpaRwRb0v7Tc79PCx5Zw2qqquZ/YwFppjw4msIDItu9XlycPWAT3Bv3EtLZWX6u5hwBb4+3nB2cuiwPuPr64sjR44gLS0NERERsLFp3djclgkICICFhQUSEhJw4cIFODg4wNPTs1VtyhN3GRoawsbGhvkzNlY+Y2lrEYlEes+wrc3+O1u/qvTV3sYfgugocDgc1phvYWGhb5OIdoz6jyYQBEEQBEEQBEEQAJQTcYlEInyz6ZdmGUxmTZ2INxctgMDERON2RUeE4qt1q2Bp2XTjQCgU4dOvf0BB4SOZT7grEocQmkOZwLdkXV1dHdZ/uZEV7DQWmGLSS6/Dwa11QQdJ7J3d8PTLS1gZderq67H+y+9RU1OjMEiv6LMRrUdRUFy6TnL7+5+240F+0/J3HA4Ho2bNQ4/e4RqzzdTcEhNfeK2ZL/7w82/Iyc2TaZc8Wusz2gpadMRgiDLnQfq87dj9F26m3WHtO2D80wgdNEJj35GhsQlGz3oB3oG9WPV79x9B4vUUmXbJslu6jtAM6oi4riXewF+HjrPa6d6rD0ZNex4Ghq0T/4nhcLmIGBaHfqPHs+pTbt7Gzj37GVto3qN9lJnX1Nc34JOvNqKqqkmMY2hsggkLFsPZo5vGbLFzdMHTL78BM8um7DcNDQ1Y/9X3qKquVmijZJ30NtE61BVxCYVCfPr1D6ioaBLmGRgaYcKLi+Dq7asx+7rYO+Lpl5fAwroLUyf+DVVWXqH0+NHefMbS0hLTp0+HSCTCxo0b9W2O1omLi8OiRYsgEonw2Wef4fbt5svMaxLJ65AufEOZeTdBEARBEG0XEnIRBEEQBEEQBEGoSEtZBsSvkn/n4y83y0Tx/MwpmDdnmlZt9fbywDeffAAb66YAVnlFBdZ9ubFNBTU7okhCHuoIcnbuPcDK5CYOWtk6OGncPms7e0xc8DqMjJvEhXn5D7F1xx6lBVyEZmlpzJE37iReT8HRE2dYbQ2ZPBO+wX01bqOhkTEmvrgIto5NywbU1dXh8283tygA1KXv0JPrslHmHOXk5uHXnX+yjgsbOgp9Bw7XuD1cLhdj5rwAt+49WPWf/28zIyptyZ9ofNIdivynpqYGX3z3I2t/d98AjJo5r1XL5skjfOgYhA5m++TW3/cgNy9fpo2yXqW3CcWoOq8RiUT48+BR3L6TxezDNzDAU/Nfhb2zm8bts7SxxaSXXoeJqRlTV1D4CD/+ulPuXLilz0JoHll+Il0+ePQEUm41iW14PD7GzX0Fjhp8qEGMuZUNJr30OgRmTQ/EPH5ShE2//NahBYBjxoyBs7MzUlJScO7cOX2bo3WGDRuGZcuWYeLEiejevbvW+pHlB+3NNwiCIAiC0C0k5CIIgiAIgiAIosNSX1+PioqKlndUAVVFXADwpKgYG/63mdXOsMEDMGf6ZI3aJg8PNxd8+M4yZlkhAPg3KRl79x9h2Sveloc+bja///776NmzJ/N38+ZN1vvaFl5o2n8UIU+UkJuXj6079rD2jRn/tFaCVmK6OrsidhJbZLhz7wFkZN2TG1wTlyVfpbc7C/fu3dNYW4q+S+n3JM9FTW0tNny3hbW/f2gEekbGaMw2aYwFphgz50VweTymLulGKg4fO9WiAFAXPnPgwAH07NkTwcHBCA4OxtatW5n32rKISxvjkDLnQPqcffG/Lairq2P2cfLyQb9RT2ncNjF8vgFGz5rPEl/k5uXj19/3suxS5X+kM1FcXIzS0lKNtqmOqHTb73uRl/+QOc7E1AyjZ80Dj9e6ZV0V0X/MJDh5NmV1qq2tw+f/ky8q7Uzoel4syyceFjzCT9t3strpN2YCXLx8oC262Dth8OQZrLo/DxxBWnqmQmGomM48llRXVyM/P19j7SnjM7LKj58UYfPW31ltRY6Mg7uU4FeTWNvZY/i0Oay6A0f+RnJqWocdR/h8PubPnw8A2LJlC2olsgF3VAYMGIDnnnuuVW20td/QgPwlzQmCIAiCaB+QkIsgCIIgCIIgiA7Lo0ePYGdnh/Hjx2Pr1q0oLi7WWl+KAhC7/jyIktIyZt+udrZY/NLzWrNFFoH+vpgxmR1w3/r7HlRX1zB2thTY1wc5OTlITk5m/iorK1s+SIPMmjULb731Fg4ePIiioqJWt6fMdyztPzv27GeLJzy7IThyQKttaYmAvpFw9w1gykKhEP+360+FQTZxWfJVerszMGHCBBgYGCAsLAxbtmyBUCjUaPuKBFziulNnLzIZaIBG8cSAcVM0aocsutg7InzoKFbdtp17IBQK5QqFJOtkbWuKoqIi1nhSWFgIoO0HuDZt2oT58+fjl19+QXp6eqvbU1Z4IVm+nnwTiTdSmXouj4dhU2Zp/bsTmJljwHi26Hn3/iMolxCjKCPo6owcPHgQlpaWcHd3x+uvv46CgoKWD1KAOoKd8vIKRjQuZuBTU1jiPG3A4XAwdMoslqg08XoKbqTcahOiUn3y/PPPY8WKFdi/fz8eP36stX7kXadEIhF27TvIzD8BoKurB0L6D9aaLWJ8g/vCOzCYKQuFIvy6U74wVFzf2blz5w4cHR3h4OCABQsW4P79+y0fJAd1rj/iv937j6BC4neAraMz+sZqPiOkNF7+PdE9hJ3JdOuO3TJtFtdJvkpvtwdCQ0PRt29fFBQUYPfu3fo2p83Rns6nrua4HA6nWV+y6qhfgiAIglAeEnIRBEEQBEEQBNGhqaqqwr59+zB79mzY2dlh+PDh+P777/Hw4cOWD5ZCmYCfdDCzqroah4+fYrXz9huvwMzUVJ2P0yqenTEZPt5NWZzKysrx9+lzcsUVHSEQ0VqqqqqQmJiIb7/9FjNmzMAbb7yBvXv3atx/5AkRKiurcOrsxaZG/gtO6+om8ZDJM1hZUy5cuoLiklIKeCpBfX09Ll++jLlz58LAwAC9evXC119/jfr6eqXbUCXgKVknEolw8Bh73Ok/diIEZubqfhyVCB8yGtZ29kz5YcEjXLl2Xa6fk9+0zP379/H777/jtddew5w5c/DDDz8gJSVFoyJBRQH0A8dOsvYNHTQcXewdNda3IgJCo+Dq48eUa2pqcOL0BYX/A+JtyVfp7c5CdnY2NmzYAHt7ezg5OWHBggXIzs7WWPuK/ObEmQuokcjm4urTA/59IzXWtyJsHZzQdxBb5HHg6AmFY1Bn8Jna2lokJSXhu+++w8yZM/H6669j9+7damVcUmVeLK6vra3FidPnWe0MmzyTlTVWmwyeNAN8A0OmfOnKv3j0+IlCmzuDXyjDw4cPsXHjRri6usLe3h5z585FVlaWRtpW5DcikQh1dXU4LrVE/ZDJM7Wa2U+SQROegaGRMVO+mngDD/KbxLEd0WfmzZsHLpeLnTt3alX0SXQsxGImXQuaOlu/4r4JgiCIjg0JuQiCIAiCIAiC6DTU19fj2LFjePHFF+Ho6IiYmBh88cUXSgU01RVU/H3qPMormp4eDw7yR+/gIE18HJXh8/mY9cxEVt2eA0fliisIsAKLIpEIqamp2LRpE5599lksXLgQO3bsaFVmAnG70mXx36lzF1FVXc285+EXCFsHp1b1pwpWXezQLTiEKdfXN+D4ybMyg2zKBHQ7K0KhEElJSXj11VdhaGgIf39/fPzxx6iWOLeq0FLAM+teDm6mNWVuEpiZ60w8AQA8Ph+9+sey6iSFQOQzraOgoAB//vknli5dihkzZuCbb77BtWvX0NDQ0OKx6ggvysorcO5iArMPl8tDSMwQTX0cpegzcCirfPDYSaXGIILNgwcPsHHjRri7u8POzg5z5sxBWlpai8ep6jfiv0NSQva+sezzqG16xwwGl9uUlevsxQSUlpV3ap+RDvzeunULW7ZswXPPPYdXXnkF//d//6fy8sDKzIvF9WcvJqCsvCmjnks3P9i7uqv6MdTG3Moavr2bsisJhUIcOX5aoc1EcwoKCrBlyxZ4eXnB1tYWs2fPbjFjpLrjiEgkQnzCNRSXNC0V6+DuBWeJ5VO1jam5JXr0DWfVHZK4DknSUXzG1dUVcXFxqKmpwU8//aRvczokJMTp3EiKwFQVg5HvEARBdA5IyEUQBEEQBEEQRKdEJBLh3LlzWLx4Mdzd3REaGoq1a9fi9u3bKrcjuS3999fh46z9pzw1RiP2q0v/yDDYd7Vjyvey7yPxRgoJcuTAk1iWSZqMjAxs3boV8+fPxwsvvICtW7ciIyND5r7qBq+O/H2G1U7PqP6t+ThqERQRwyof/vuMXB+QFwglmhCJRLh58yZWrFgBgUCAbt264Z133kFpaWmz/aS3lQ14HjnB9hv/sCidZa1g+uwbCR6/qc/4hKsoKi5pk4FyffffGoqLi3Ho0CGsXLkSU6dOxWeffYZ//vkHtRJZkJRB0f/0qbMXWMu7egcGw9TcolV2q4pXjyCYW1kz5cy72bh9J4tEpa3g0aNH+OWXX+Dn5wcbGxtMnToViYmJKrWhaMxPz8hC5t0moby5lTU8/QJbZbOqmJpbwiugJ1Ouq6vDyTMXOrXPKJrXZGZm4tdff8WCBQswb948/Pzzz3LnxYrGDMntlq5PepnXRErPa07TWNIKHj9+jK1bt6J79+6wsbHBtGnTkJKSovTxisaRNuU3UQNY5SMnzjDLRndUn5k2bRrMzc1x8uRJ3LlzR9/m6BWhUMjMhWSJaBQJa1TdX9a+2sz6JEtQ1BqRkab71XTfbaXf1uyjTD8EQRBE+4eEXARBEARBEARBEACuXLmCt956C76+vggMDMS7777LBDTVFVQUFZfgbnZTtiYHeztER4Tq6BPJhsfjYULcCFZd4vUUuZ+HUI6cnBzs2LEDCxcuxLPPPovNmzcjNTW1xe9QUQaImpoapN3JZOoEZhbw9g/WuO0t4ebjB6suTeK/nNw8lJaVKQx4Ah0jgKVtRCIRMjIysGbNGlhaWsLd3R1LlixBQUGBwmNkBQsl/5JT2Rl2giJ0H/A0FpjCp1cfpiwUCnEzLV3lQLmu6Ai+WVFRgRMnTmD16tWYOnUqPvroI5w5cwZVVVXN9lXlWpaUcot1bGCk7v2Jw+UiIDyaVZeceqvF/wdCOYqKivD7778jJCQEFhYWGDt2LC5cuACg5TmQuNzSOBQQHg2OjpbPkyQoii3auXEzTe51l3ymidzcXOzcuROLFi3CnDlz8P333yM5ObnZcq6qjCX19fVIvdWUtcnIRACfnr1184EkcPboBhuJ7KYPCx+hoPBxs7FDVplQTFFREX777TcEBgbC2toakydPRmJiotrjCNA4f0i51SQoNDA0gl9ImA4+DRt7Zzd0dWnKHvekqBi5D5ovSdqRxhJzc3PMmzcPr7zyCry8vPRtjl7ZsmULli9fjuLiYrWOV0cgJGtfTQqN1MkCpY9+1T2mPfcrfaw+l3AkCIIg9AcJuQiCIAiCIAiCIKRISUnBBx98gJCQEHh7e2Pp0qWIj4+XG3CQRlyfls7OztQ3JJi1VJ++CO8bwirfSm8UCyn6PJKv0tuaQN322mKg5OHDh9izZw/eeOMNZtmzxMRENDQ0KB28AoCsezmsgKmzVzdwFWTS0BYcDgfO3bqz6u5k3JUbqO1IASxdk52djc8//xz29vZwdnbGSy+9xCz9qozwQBwolxSQmlpYwqarg/aNl4Grty+rfCfzrkz/UOQzuvSljuS31dXVOH/+PNatW4cpU6bg/fffx7Fjx1BWVtZsX0XnRCQSISNTYpk1DgcuXt2hD1y7sf0pPeuuTPGINCQqVY2ysjLs378f/fr1g6mpKYYPH46jR48226+la0B61l3W/tLnT1e4eHUHJAKfGZl3Aci+7ooh4Q6bgoIC7Nu3D8uWLcOMGTPw9ddf49q1a6ivr2ftp+j7EolEyL6fx8ru5+jhDT7fQGt2K8JNel7zn1+IaemzKLNfZ6e4uBi7du1CSEgILC0tMWHCBFy+fJm1T0vjiEgkwoP8AlRVNS1Fbe/mCQNDI11/HACAq0/L82FJOsJYMmTIEIwaNapN/IbVFzU1NUhKSsLNmzexaNEi3L17V6v9qSP2UlboQ2Ks9tWvqpDQiyAIomPReWdfBEEQBEEQBEEQSpCZmYnPPvsM0dHRcHV1xcKFC3HixAk0NDQw+8i6gd8o5MpkteXv202ntsvDw80FxkZNAZC0O5kkyNESRUVFOHToEN5++21Mnz4dGzZsQEJCAurq6loMXGVk3WO11dXVTU+fArB3Yfd9J+ueUv7REQJY+uLBgwfYuHEjPDw84ODggOeeew63b99u0W+kA+V2zvrzm65SfpMuESjXVtBTW77Wnn24vr4eCQkJ+OKLLzBt2jS89dZbOHjwIJ48ecLaT9Y5qaysYmUcsbGzh6GRfgLokplQACAj816L/w/t+by1BSorK3H8+HGMHDkSJiYmiI2Nxe7duxUeI1MAiObnT1cYGhnBxs6eKec+eIjKyiqVfYZ8qZHi4mIcPnwY77zzDqZNm4bPP/8c8fHxzHKuir5TabGUvYurrs1nsJMeT7JIoK5NysrK8OeffyI8PByWlpYYN24c4uPj5e4v+b3fkRKF6tNvujo3n9fI8hEaSzoWRkZG+PTTTxEZGYmCggIsWbIECQkJ+jaLIAiCIIgODgm5CIIgCIIgCIIglCQvLw//+9//MGzYMDg5OWHevHk4dOgQamtrZQZ7bmdmsY7v4eujD7ObwePx0N2naXmM8vIK5D14qPAYbQpyWttmewmwlZWVMcueTZs2DevXr8eFCxdQXV3N2o8Jgt/NZtVLB490ibQYSCzkAhRnU5BFezhXbY3CwkJs3boV/v7+6Nq1K6ZPn46kpCTmfcnvO1PKb+z1KAC0dXQGl9uURS4jK1uhf2jbZ5Rpp6P7p1AoRFJSEr777jvMnj0bS5cuxd69e/HwYdM1QPIc3c3OYR2vT0GpsYkAlja2TLlRtFhPolIdUVNTg7Nnz2Ly5MkwMTFBv379sHXrVpnjfl1dHbLv5zHHWtrYwthEoC/TYScl/Mi8m00+owEqKipw8uRJfPjhh5g2bRrWrVuHs2fPspZzZQnUpec1rvoR9wGNy+RJckfi+qQI8ovWU15ejgMHDqB///6wtLREXFwcTp8+3WwcEW9nSfuNiz4F6tICwHssX1AkUCfaN8bGxli5ciUmT56MqqoqrF69Gnv27NF4P5rIqCQrS5e2skMp6kubfXe2fpW1jSAIguhY8PVtAEEQBEEQnQtxhoji4mI8evRIz9YQBNHRKSoq0lrbjx8/xs8//4yff/4Z5ubmGDlyJMaOHYuhQ4dCIBBAJBKhtLScdYyrs6PW7FEVNxcnXE++yZRLy8thWV4OoVAIDocDLpfL3AyU9yq9rS6KAhxCoRAGBk3L7lRUVKCkpETmvu3p5qV42bPz58/DwMAAvXr1QkREBEJDQxn/KSllL4Fm2cVOT9YCVrZdWeXCggKkp6cDkL+kh7xtcbkjI5mxT9MUFxfjjz/+wB9//AEzMzMMGDAAixcvRnh4+H/jjrTf2MppSfvw+QYws7RCadFjAEBZWTlEIhFz/sX/+0KhEBUVFRoZaxSJwWpqaljjSW1trUrjib78VnKJVU0iEolw69Yt3Lp1Cz/99BM8PT0RERGByMhIODs7yx6HbPTnTwBg0cUOJU8af0PU19fj+o0bMDczZZZ8Eo81kmXJesk66e2ORmFhodbarqurw6VLl3Dp0iW88MILCAwMxJw5c/Dss8+Cx+OhorKK5bcWehyHgObXsNKyMlRVVaG+vp7lM/KuVbJ8h2BTU1ODCxcu4MKFC+Dz+QgODmbmNaamprKvT3ocT6xs2XOqwsJC3L59W+bcRVFZTEf1DW0vp1dZWYmjR4/i6NGjMDExQWRkJF599VUMGTKEEUWVSP2e0u98uKlvHupRVFwMoGnuIe0H0nOejuonnQUOh4M5c+bAzc0NX3zxBbZs2YKcnBy8/PLL4PP54HA4rRLukX8Q6kB+QxAE0XEhIRdBEARBEDrl2rVrAICRI0fq2RKCIDoDtra6CRCVlZVh586d2LlzJ4yNjTF48GCMGzeuWaYlIz0tRyULQ0NDVnnlijfh7e2NjIwMPVkknyFDhjDb69at02nfkqIPbVFXV4fLly/j8uXL4PF4CAgIQHh4OGpq2P7D14Et8pD+HhIunsO2zd+ysm4QTVhaWuqkn/Lychw8eBAHDx6EiYkJoqKi0G/AYNY+PL7+/AYA+AZNY01dfaOgXzrgWVxcjDfffFOrwhMxkuOJWHTQ1vH399dJP1lZWcjKysJvv/0GJycnhIeHw9TCmrWPPsehxv7ZtzIHDojRkyVtH11cv4BG4WpSUhIWL16MJUuWoHv37nhm+gzWPvofh9j9f/7Jx+jduzfz27QzYWpqqvU+6uvrcfXqVVy9ehVcLhf+/v4ICwtDdTV7zqDP8YQn1ff1awmIitrYbO5OADdu3NBJP1VVVTh58iROnjwJIyMjhIWF4d1330WtxHLRQKNIXF9I9m3EqUdh9m1s2LABDQ0N8PX1ha+vL7p168b8zpInsCBRV/tm0KBBsLe3x5o1a3Ds2DHk5eXh7bffhoWFhdptkj8QqkI+QxAE0fEhIRdBEARBEHojICBA3yYQBNHBsbCw0Hn2v759+yImJgZRUVE4dyWF9Z5QKNT6k+3KIutpYQcHB9TW1jar1+dNwry8PFZmNS8vL5iYmOis/wcPHuisLwDw8PBAQEAA/P39cfFaKus9kVB/S7NIZwQyMDSCubm5wsxTnfnmMp+v+9stPXr0wPjx42FsZg3gClOv7yV9RCJJ3+GwRFzibS6XCwcHB63/bxcXFyM3N5cp29vb60zw2xp0JciRJDAwEAEBASgqrWTV69+fJPoXCmFkZAQej4f6+nqZ+3fmcUggEGg1M6ksHB0dMXjwYIwYPhzHz0uKpPTsNzKy2tna2sLFxaVNZd/TBY8fP9Zpf+7u7sx48m9qJus9fc5rpH2Cb2AAMzMzuRkQO7JPKEIf8xkA8PHxwdixYxESEoILV66z3mPPK3QLaxlFcACIcOPGDZSWluKff/4B0PideXh4wM/PD76+vvDz84ODg0On9aGOSkBAADZs2IBVq1YhOTkZixcvxnvvvQc3N7dmc6WzZ89i1apVTPnZZ5/FjBlNgmd9+oaqfWtqHqjOZ9ZE3+2pX4Ig2ie1tbUYMWIEU/b398c333yjR4uI9gwJuQiCIAiC0AubNm3C3Llz9W0GQRAdnPz8fDg6anc5Qz6fj9jYWIwdOxajR4+GnZ0dhEIhRCIRDA3ZwfeqqmqYmgq0ao+yVFWxMw6s+WgdunfzZC0tJGuJRU0vM9TSjdHnn38e+/fvZ8obNmxAnz595O6v6Zuk48aN02h70nA4HPj5+SEiIgJhYWGwtbWFSCSCUCiEoZR4o662Rqu2KKJOSuAXGTMQxw//BS6Xy1rCTNp/Ouvyin369NF6sNzQ0BB9+vTB/PnzMWnSJGbc+evQcdZ+9TLEmbpE0m8NDQ2YLBSS2SgsLS3xwQcfyPUbyVdFKFpWEQC2bduGOXPmMPXr16/HkiVL5LbXVgQeX3zxhdb7sLOzQ0REBMLDw+Hr6wugUcCZcDWRtZ/0WKBr6mqa+ufy+biTkQELc3NmHJIcg6SXytPGNawts337dlagWFt4enri6aefxquvvgpLS0sIhUKUlJay9pE8b/pA2m9fX7oc4X1DmvlJZ/CZyZMna70PX19fZl7TtWtXZl5jIJVRr65Of34h7RO9w6Jw/NBfcucx2p4Pt2WSk5O13gePx0NQUBCee+45zJo1C1wul5nXSIuZ9es3TXOaKpEhvN3dUFqQDQAICwtDbW0tbt++jTt37uDOnTs4cOAAAMDKygqbN2+GsbFxh/aVzoa9vT0+/fRTrF+/HpcvX8aSJUuwfPly9O7dm7VfQUEBTp8+zZRjY2NV7qu1S4yr2o4qxysjdNKE38taurQj90sQRPukoaEBp06dYsqVlZUK9iYIxZCQiyAIgiAIgiAIQkWMjY0xfPhwjB8/HqNGjYK5uTlEIhETqAIab/DZ2tggDU3ZBzKy7qFnYA99mc3iTuZdVrmLjRWA5kt9SC+BpklaugmqztOv7WGpEnGwSiyasLS0bOY7ANClC3tJs8cPH8DOyUXn9jb2nccq29o02ib2e/F33tL33x7OT1vG2NgYkZGReOWVVzBy5EgmyCn2GZFIhC420n6TJ6spnVBTVYnykmKmLOk30mIuaWSNReQ7msXFxQWRkZGIiIiAl5cXaxwSv3axtmId8zhff/4EAE8k/NnIyBCC/7K4yfIpyWsI+Y7m4HA46N69O6ZNm4aFCxfC2NiYNQcSiUQwFQhgZGSImv8EXE/0OA4Bzf3W1saafEaDcLlcBAYGMvMaa2trxheAprmCnfS8Jj8Pjm6e+jBZ4bxG1vVJm/Phzgqfz0dISAjmz5+P6dOns8YQSd+xlZrXPMnPg6u3rz5MlhpLOLC374o5U8fjk08+wbVr17B69Wr4+voiNzcX6enpSEtLQ1paGurq6mBsbKwXmzVNdXU1du3aBYFAgIkTJ+rbHL0jEAjw7rvv4scff8TevXvx/vvvY968eYiLi1Pq+JbGFFXHHH2MUfoaFztbvwRBEETng4RcBEEQBEEQBEEQSmBubo5Ro0ZhwoQJGDFiBExMTGQGGyRfu3fzxIVLTUuc3UxLbxNCrpqaGmTdzWbKdrZdYG1lqZS4Qhqn448TAAAgAElEQVRtiStayqzT3jAwMEDv3r0RFRWF0NBQmJqaMr4j6UNiRCIRvD3cWHWFuffgFxKqS7MZCu5ns8reHm5yBVzSwU4S4LQOU1NTxMTE4PXXX0dMTIxM0ajkq6eHK+v4gpwc3Ros2Xcuu28vD/dm44ym/EMbY4Ys29q7P3t7eyMyMhJRUVFwcXGROQ5Jvrq6OIHP46H+v2VUC+7rz5/Kip+gqqKcKXu6uYLL5bbKp9r7+dQVXC4XAQEBmD17NhYsWAADAwO5cyCgcfz3dHPFrfQMAEBVRTnKip/A3MpGL/ZL+i2fx4OLMztbK/mB6ohFOJGRkQgLC2M91MBafk5i29OdPa8pyM0GEK0rk1kUKpjX0FiiPQwMDNCnTx+8/PLLmDJlSrNxRBJmXuPOntc8lDp3uuRhbnO/6dOnD2bMmIFt27bh448/xieffAJXV1e4ublhyJAh4HA4CpcilyQ7OxsPHz5EQEAABIK2kcVZmsLCQvz+++8wNDREbGwsbGz0M663JbhcLubOnQtXV1d8++23+P7775GTk4MXXngBPB5P3+YRBEEQBNFOISEXQRAEQRAEQRCEHLp06YKxY8diwoQJGDJkCAwMDBQKb6Rffbw8WPukpt3Rid0tcTsjCw3/iUCARsGZssErXQeq4uLi4OzszJSdnJzaTbDM2NgYYWFhiIqKQt++fWFkZAQAcgNW4vfEeEkHrvQpyLl/j1WWDKpR9grNY2lpiUGDBmHZsmUIDQ2VK7QRI1lvb2cLU4EAFf+l8C/Mzdbb/0xBs4Cnq8z9VMnM1Rp69uyJlStXMu1GRER0Cj/t0aMHoqOjERkZCXt7e5l+JG9M4vF4cHN1RuZ/4t/ykiJUlpdBYGaufcOlkA7ee8oQXohpK9ew9gyfz0evXr0wb948PPfcc8z3rMwcCGgUlYqFXEDj+dOHkKuyvAzlJUVM2c3VGXx+4y1xZfyBsgM2YWxsjL59+zLzGhOJjHiyxhPpVy9PKSGXHuc10oIcz//E8zSWaB5DQ0OEhYVh4cKFmDhxokrjiEgkgpfUgw36FBQ3EwB6ugNo/L2Sk5OD06dPY+3atVi7di3z/wGAGXMkkeVDx44dw549e8DlcuHh4YGgoCAEBAQgKCgIlpaWWvhEquPq6orRo0dj//79+OWXX7B48WJ9m9RmGD58OJycnPDhhx/i0KFDyMvLw/Lly+Xur+lsXARBEARBdCxIyEUQBEEQBEEQBCGBk5MTnnrqKUyYMAExMTHg8XhyxRNi5AWsvD3dwOVwIPyvfOnKv6isqmKWgtIXJ89eZJW7e8te1kZfQSrJ73ncuHEYN26czm1QF3Nzc0RERCAqKgohISEwMDAAoJx4S/rVydGetTRVbmY6aqqrYGSsW/+pr69D9u1bTJnL4cDDzYWxlQLcmqFLly4YMWIEli9fjoCAAIU+I0ZeBgtPdxck37wNAKitqUZuZjpcvLtrxW5FZKZcZ5XFQXxZ4ht525okODgYwcHBTB8dFS6Xi549ezLiLWvrpiXDxLS0zRKVergxQi4AyEy9jsAw3WfRyUpl+5O3e3NhII1BrcPQ0BChoaF4+eWXMXXqVACyfUISeWIMb6nsS1mp19EtsJemTW6RTCm/8fKQPQ6R78jG1NSUmdf07t0bhoaGCn1CkZ/Y29nC1FSAiopGofGDexmoqiiHiamZ9j6ADIQNDbh3M4VV59VCplHyDdUwMjJCVFQUFi1ahLi4OLV/SwGApYU5rCwtUFxSCqDx4YLykmKYWVo1b0iLiIRCZKUms+q8PZsyjc6fPx/5+fm4desWNmzYgBUrVqg8R+7evTuioqKQnJyMzMxMZGZmYt++fQAAZ2dnRtgVGBgIe3t7zX9IJZk+fTpOnTqFv//+G2PHjoW3t7febGlrBAUF4fPPP8eqVauQmJiIJUuWIDw8XOV2aMwhCIIgCIKEXARBEARBEARBdHo8PT0xceJETJw4EeHh4UxArzWIRCIYGRkh0N8X11MaRTAVlZU4fPwUJo4dpQmz1aKysgqHjp1k1fUNCQLQcoCBglqysba2RlRUFKKjoxEUFMQsoaGMDykKaHG5XAQH9kDC1SQAQH1dLVKv/oOQ6FjNGa8EtxOvoLqygin38POBkZER+YMGsLe3R1xcHN566y14enq2GOhU9J5kfUhQACPkAoDr8Wd0LuQqflSI7Ns3mbKpwAS+Pt4tZt8CKHilDnw+H71790Z0dDQiIiJgbt6UMUuRzyjja32CA/D36fNM/fWLZ3Uu5KqtqcbNqwlMmcPhIDioB2OnqhlzaMxqwtjYGFFRUXjttdcwduxYpl7VeZC0ILBXT3/WfOrm1QQMGDcFhv9lp9QV1y+cZZX7BAdQlqUWsLKyQmRkJKKjo9GzZ09WBjNpVBGFikQihAQF4Pw/lwE0CqpSLl9A34HDtfI55HEnJREVZaVM2cfbE6aCJpE8zYfVw8TEBNHR0Xj99dcxcuRIAK1f3lhMryB/nD7/D1OfnHAeEUPHtM5gFcm6lYyy4idM2cPNBVaWFkzZwMAAS5cuxfLly5GQkIDt27dj5syZKvURExODmJgYAEBOTg6Sk5OZv9zcXOTm5uLIkSMAADs7O0bUFRgYCDc3N0VNaxQLCwtMmzYNP/zwA77//nusX79eZ323FRT5tqOjIz777DOsW7cO165dw4EDB2BgYIC6urpmbdD4QRAEQRCEPEjIRRAEQRAEQRBEpyQgIAATJkzAhAkT0KtXyxkiVM1EId4eM3wQI+QCgD/2HcKEuJF6u2l76NhJVFZWMeVeQf5wdXZi7UMBqiakb7iLsbe3Z8RbPXr00Np3NCy2PyPkAoAbF8/pXMh1Pf5cM5s6u1+0BmdnZ0yYMAHLly+Hk5NTi/urGiQHgNiYSPzf7n1oaGhcQvV24lUMmjANxgJTjXwGZbjxD1s8ERMdASNDQ5313xkQL3MWHR2N0NBQCAQCrfQT1qcXLC3MUVJaBgB4cDcDj/LzYOvQsv9qilvXElBXU82UewX5w862i8zrFWXVaRlTU1PExMTgjTfewKBBg7TSRxcba/QK8se/1xszH9XVVOPWvwnoGdFfK/3J4tGDXDy417S8o6WFOcL66D4rWFuipqZGZr2dnR0zr/H39weXy1W6TWUySIoZGtuPEXIBjfMaXQu5bkjNa4bHyvZJGj9aRiAQICYmBkuXLm02lqgr4pI1rxka248RcgFAcvx5hA8ZrdNzcv0ie14zfFBMs30sLS3x1ltvYf369QgLC2tVf66urnB1dWVEcQUFBUhJScGNGzeQkpLCLOV4+vRpAI3iKklhl5eXF/NwiTYYM2YMDh48iOTkZFy4cAHR0brP1NkWEfusqakp3nvvPWzatAk5OTlMZjWCIAiCIAhlISEXQRAEQRAEQRCdhr59+zLiLV9fX5302adXEBzs7ZD/sBAAcD/3Af48cBRPxY3QSf+SFBeXYOuO3ay6uBGDWzyuMwevJJ+ednV1RVRUFPr166fSEiKqBLKaZa4IDkQXG2s8flIEACjMzUZGShK8A4KVbrM15NxJQ25GU2YnMzNTRIaGNLNZHf/oTH7l4eGByZMn480334SNjY3W+7OytEDfkJ64dCURANBQX4/LJ4+g/5iJWu8bACrKSpF04QyrbsTgmBZFN0TLiJc5i46OZpY50xTyxIF8Ph8D+0di38FjTH380b8QN/tFjfWtiPq6Olw+cYRVN0yO8IKQj6WlJQYPHoxly5a1uMyTJgQYADAsth8j5AKAy38fhn/fCPD5Bmq1ryrxR/9ilQf1j2QJG1QZfzrKWGVkZMTMa5ycnNCvXz9ERUWhe3ftZW2U9Isgf1/WvPhJQT7SEi/Dt1eo1vqXJO9uBu7eavJJE2NjREf01UnfHQUzMzMMHDgQy5cv14h4R5nxxs/HG67OjsjJfQAAKHnyCClX4hEYGtXq/pXh4f17yExperDC0NAQA/o1jaOS44Obmxu++uorZnl1TdG1a1d07doVsbGND3SUlJQgJSWFydiVmZmJ+Ph4xMfHAwBiY2OxdOlSjdogCZ/Px/PPP4/Vq1djy5YtCAsL0/hnbu/weDy8+OKL+OOPP/RtCkEQBEEQ7RASchEEQRAEQRAE0WHhcrno378/I97S5ZITYjgcDkYPjcWWX3cydd9u/gV9Q3rC1UV32UwAYN2XG1FUXMKUnRzt0Ts4sMXjdB24bGlpS13a4+Pjw2S8cXV1VauN1izVyeNyMXhANHbuPcDUndi1Ha7d/LS+PFV9fR2O/76VVRcbHd4sSNMRAtuapkuXLvDx8cH06dOxZMkSmJmZ6dyGYbH9GSEXAFw5eQx+fcJh5+ii9b5P7f0NNVWVTLl7N094uLm0KV9pS7a0hLW1NUaOHIno6GgEBwdrNcOGNOLxa1hsP/x16Dgzlt3+9woyw27Aq0eQ1m2IP7YfxY8KmLK1lSWzJDAhHyMjI9jY2GDEiBFYsWIFAgNbvt63FunrXd+QnrC2smTmHsWPCnDp+AFEj3xK67ZkpCThduJVlm1DWyEAbE9jhiI8PT0RHByM6OhouLu767x/DoeDoQP7Ydvve5m6U3t2wMM3AEYm2skqKKahob5xXiPhozHRYTA21u1yn+0NHo8Hc3NzDB48GMuXL29RCKothg7shx+372LKZ//cCa8eQRCYmSs4qvUIhUIc27GVNbZFR/SBmWlTllPp8UEX12lLS0tERUUhKqpRzFZZWYmbN28iOTkZKSkpCArS/nUyIiICwcHBSEpKwr59+zBp0iSt99mWac1SogRBEARBENIonyOZIAiCaHfk5T/E8AkzMHzCDGyTyr7RGoRCIerr6zXWHkEQBEFoi65du+Ls2bNYtGiR1kRc0jfuJcvi7aGx/eHu6szU19TUYvX6L+Qu26cN9h/+G+fjE1h1L8x+ps0GJuXZpWt7P/30U0ydOlVtEZeqyPKfcSOHwMbaiqkvK36C84f2aN2Wf47uR1HhQ6ZsbmaKCWNHNrNTXdqq72mCY8eO4fbt23jvvfc0KuJS5TsLDuyB4MAeTFkobMCxHVshFAo1Zo8sMlOvI+3aZVbdnGnNA3viz6IpP9DGmCHrWH347ezZs7Fw4UL07t1bq8FhRefEycEewwaxRTAndv6KWonlDrVBYd59XDlxlFU3c8pTSn8PHXmcaYlJkybh8ePH2L59u8oiLk2N8TweDzMmj2fVJ/x9BI8e5La6fUXUVlfhxK7trLrhg2Lg6NBV5nW2M7Fu3TpMmzZNoyIuDoej8LuUfm/U0FjY2TZlp6woLcGZv3ZJH6ZxLp9k+55AYILJ43W7PF97pEePHigtLcXevXu1IuJS5rcUAAwZGA1Hh65MuaqiHKf3/a5xe6S5duY4Cu7fY8rGRkaYPmmc1vtVFYFAgD59+mD27NlYv349RoxQLvvzX3/9hXPnzuHx48dq9Tt//nxwuVz89ttvKC4uVquN9oYmBVsk/iIIgiAIQh6UkYsgCEIOhY8eY+3n30IkEkH6J5X4NgKHw4GpqQBe7q7w9HCDp7srXJwcdfqEtCIuX01CZWUVACA40L9VbZWUluHX3/cg4VoS7uc+QEN9PVxdndHN0wNDBkYjMqwPuFz19MENDQ1Y98V3zJJBo4bFYvCAfq2ylyAIgiDaEnw+D6++MAdvvr+OEUPfup2BFavW4cN3l8FIg0tjyeLEmfP47OvvWXWjhsaip4TIQx8ok3lL8n1lAm0dMRhnYmKMebOmYN2XTefw3zMnYOvgjJ6RMVrpM/VKPC4dP8Sqm/3MRFiY6z6zFKE+82dPxeK316C2tlE0mn8vE8d+/wUjnnlWK/09zM3Gwa2bWHXDBvVHj+7dtNIfoVtmTH4Kl64korikFABQWvQYf/34HZ6atxA8vuZvMZYWPcHeH76CUNjA1AUH9kBMVJjG+yK0x4DocJw+/w9upKYBAIQNDfhz09eY+tqbMLO01nh/9fV1+HPLtygrfsLU2VhbNROUEfrDyMgQ82c/gw8/+5apuxF/DraOzugdM0QrfaYlXsHFQ/tYdTMnPwVrK0ut9EdoHgMDA8yfPRWr1n3F1N28/A9sHZwRNnikVvq8k5yIs/vZD8ZOe3osbLtof5lsXdDQ0ICffvoJNTU1ABofgvL392f+PDw8WrzX6+npiWHDhuHIkSPYtm0bFi5cqAvT2xwkyCIIgiAIQtOQkIsgCEIO1TU1uHwtSal9T5+LZ7a72tliylNjMGbkEAhMTLRlnlKI7TcxMYa/n4/a7Rw/dRaffv0DIwoTc/deDu7ey8Hfp8/Bx9sTa1a+ASdHB5Xb37H7Lxw+foop9wpqneiMIAiCILRBa5bHAwA3Fyc8MzGOtZTMpSv/4s331uLj95ZrbVmXo3+fxkeff8vKwuPs6ICZUxqXNZL11Lu8J+H1IZLqiMIsSeT5lXR9WJ9eCO3dE5evXWfqju/cBiMTE/j2CtWoTXeS/8WR//uJVRcU4IeB/SKa2UhoB8nz35KPyNuXw+Ggq50tnh47Etv/+Is5LuXSBRgLBBg4bopGbX5S8AB7vtuA2uqm3wzWVpaYOWWC3scRQj2k/clUYILnZ0zGZ99uZva5l5aCg9t+QNzsF8FR88EeWVSUleKP/33OEuMYGjYG8Vu6RimTnY38ULtIj1vz5zyD19/+kMlEWvLkEXZ9twFTFy6DianmBMLChgYc+HkjctJvsernzZoCExNjln3y7CbUR5m5snifkJ4BiArvg4uXmpa/PLVnB4xMBAgIjdKoXVm3knFo2yaWbX7dvTFkYDTLLunttjQfbo+09reTrPYC/LpjQL9wnDl/iak/t383jAWmGn+4ITv9Jg78vBEiid9Q3p7uGD1sEMsmeba2BxoaGvDiiy8iNTUVqampyM3NRUFBAU6fPg0AMDExgZ+fHwICAuDv7w9fX1+YyLjXPWvWLJw5cwZHjx5FXFwcPDw8dPtBOiDiOb48srOz4erq2uZ87eHDhzh//jzu3LmDyspKVFdXaz0bcHuDx+PBxMQEZmZmCAgIQFRUFCwsLPRtFtEKRCIRUlJSEB8fj8LCQlRWVqK2tpYEnlIYGhpCIBDAxsYG4eHhCA4ObjMJMQiirUJCLoIgCCXx8nQHn8eDgQEffB4fNbU1ePS4CE+Kilk/SAoKH+HrH37GweOn8OXH78PKUj8T8YaGBlxNbAz09Q4OBF/NJ6RPnDmPD9Z/xUw8/bp7IyQ4CPV1dSgrr8DxU+fQ0NCA9IwsrPn0a3zzyQcqZebKyLqHzVt3qGUbQRAEQWgTdYIPigQVIpEIY4YPQsbdbFbQ6uq/1/Hi4hV4+42F8PH21Jj9NTU12PTLDuzcu5/1OSzMzfDGK3NhZNSUBUydIERbu2ncEZDlc+LlikQiEV58djqy7+fhYcGjxjdFIhzauhkVJcXoPWCoRmxIPH8Sp/fuZAWtuthY4+XnZ7DskWW75CuhPZQR/0lujxkxGClpd5B4I5XZ9+qp46itrkbsU8/AQAMZAe/dvonDv25GZXkZU2dgwMeiBc/BVGDC2CTLZumyNnxI3fG8oyP+jNKZD2WNQ+K6qPA+uJ56C8dPnWfeT0+6hj+3fIsR057ViCinIDcHB37+HkWF+az6F2Y/g652tizbpbel7Sa0jyxRqeR7HA4HDl3tMH/2VHy7eRvz3pP8POz4ej3i5rwIWwenVttRUVaKI//3I+7eTGbVjxgyAKG9g1u8fhHKoymx8dyZk3H33n3k5Tct43z0t59RUVqC0EEjNHJursefxak9v0HY0JTZz9rKEgvnzQKXy231WEL+oxmU8Rfx+3OmTkTm3Rzk3M9jjj++cxsqy0sRPniURkTFyZcv4uSuX9HwXyZloPE31KIXn5XrN+3RFwwNDTF8+HAMHz4cAFBSUsKIulJTU5Geno5///0X//77LwCAy+XCw8ODEXb5+/vDzs4OVlZWmDp1Kn766Sf88MMP+Oijj/T5sbSKrPFOXbHGyZMnkZubixkzZqh0L/3hw4dYvnw55s6di0GDBrV8gBYpKyvD7t27cfbsWZw/fx7p6el6tac9wuPx0LNnT/Tr1w+xsbGIi4tTO45D6I60tDTs27cP586dw4ULF1BUVKRvk9od5ubmiIyMRP/+/TFmzBj06tVL3yYRRJuDIyJJKEEQhExycvMw7fnGdNCGBgY4sV+22KihoQFFxaV4kJ+Pg8dO4dCxk8wPuG5eHtj89Xq9KMtTb93GC4tWAAAWvfQ8Jo4dpXIbOffzMHP+a2gQCmEqEGD120uYG6Bisu5mY9l7HyH/YSEA4M1FCzBmhHKp8Ovq6jDv1TeRkXUPBgZ81NU13iCZP2caZk6dqLK9RPvA0dER+fn52LRpE+bOnatvcwiCIBQivqZLvsr7EwqFcl+lt/+35VecvZjA6ovH5WL65Kfw/+ydeXxM1/vHPzPZ9wUhEbIIQRKxxB571a6o1hq0lH5VUVRRpXxr+bVVS6tVilpDbSXfEntEEmoXCUlENlsIsm+Tmczvj5jrzp1779zZMpM479drXnc795xn7n3mOWfu89znTBr3ASwsLHSS+/adu1i99lc8evJUab+zkyO+XTALnh7uEIvFlCOCuU5fMj+AarCFro4Lff0tNUUHilAdUugImz7R17Of5+CbFWuQm1eg1I5PyyD0H/sxbO0dtJKztLgIkXu3Iy1ROSOto4M9/rtoDjzc6/Pqi+LDpTdM/aEvCW+g/xYUukBfZ9MXxUslbLZGsV5eXo7vftyI5NQ0pfZcG3hg8MSpqOfuqZW8MpkUsceP4Oq5UwBNdrFYjC8/n4oO7YIF6Q3bPkA7W8NlT/QRyFVTdVaIXjFtEJ8dkkqlWLdpOy5duaHUjr2TCwaGTUEjP3+t5bwRfQYXjx2CTCZVOvbxuA/Q/50enPqkcIDS9YhNzwD992G1FX2PgeRyOf4XeRY79h1WasfcwgI9ho9C6y49tZY1PSkBkbu3oaRIuW/s1rk9Zk6bJGi8w9d/AaTvArTro/hsiVwuR86Ll1i84ie8fKXsAPXyD8CA8R/DzkG7qQ/LSktwev8OpNy6rrTf3s4WyxZ+Aa9GDTltibr+idgS4WhrRwDw/pfKzcvHklVr8TznpVJ7nn7NMShsstbTtpaXleLMgV1Iuq78P83WxhrLFn6BJj5evLYEAK8tuX//Pn755RcsXrwY9evX10rG6qSiogIpKSm4e/cu7t27h7t376KgQNnO1q1bl8rW9ffffyMnJwdLly5Fx44djSS1YdEmkOvAgQMYPXo0tb1s2TLMmTMHkydPRmFhIUJDQzF37lxYMl6uYLMpxcXFmDFjBp49ewZbW1uEh4fr/OxAG/Ly8rBhwwasX78er169Un8CQTA+Pj5YuHAhJk2aZJR7S+AnPj4e3333HQ4dOkSyzemZAQMG4JtvvkHnzp2NLYpOlJaWwtbWltru2LEjLl++bESJCDUZs2+//fZbYwtBIBAIpkhBYSEOHTsBoOrNiIljR7KWE4vFsLW1QX23egjt3B6NG3kgKqaqY36Vm4dWgS3QUIvpBnXln5PncDO+6k3UmdM+hpOj5k691Ws3IvPhYwDAd4vnoVP7tip/Il2cnVCvjivOX7wEaysruLnVRceQNoLq37ZrP6JiLsHMzAzjPxxOZQlo1zoIwYFkesXaypo1a1BUVIShQ4eibdu2xhaHQCAQtILeHzKdWswlc59IJEJImyC8zM1DRtYjpXNvJ9xDROQZFBeXoGFDd9jbvfnzrw6pVIqoi5ewZuMWbN/9FwoKi5SOu7o4Y9mC2Wjo0YDTcakuCIfNoclcNyamIoe2MDMRsOmPna0NWge2ROy/1yF5PUUVAOTlPMeduGiUl5WgTn0PWFpbqzbAQlF+Li6f/h8id2/Di6ePlI7Z2thgyZefo5GnB6++CAmgAFSd4DX9fhkC5jXhu0ZMpxFTV+jrYrEYHdoG41bCPeQXvMmaVVpUiDuXLiLvxTM413WDnYOwbMKS8jLcjj2P4zv/QPrdOyoyf/7JBHTp2E6Q3lSXk1yTc7nK1nadZcuCwtmPtQ7Cg4wsZD/Poc6XlJch8Uocnj3MhIOLKxxd6ghqVyaVIvFaHCL3bMPdq5cglys7RUYNH4Qh/fsIDgokQTmGg3m9NBkDiUQiNG3iDZlMhnspD6jzKisrkZ4YjweJ8bCysYFrfXfB9yU9KQFnD+zB5ZMRqJCUKx1r36YVZv/nY+rFOi47JGT8Q//uRGfeoE0fRV+Xy+WwtbFBm1YtEXflBsolEqpM/sscxMdFo6y0CHXqu8PKWnU6NzaKCvLx75njiNy9Fc8eZiods7a2wuJ5M+Dr3VjwGFioLVF3PQjKqPsvRd9mG99YW1uhXXAALl29ibLyN7/9glcvEB8XjZKiAtRp4AErG2H/pYoLC3D13Amc2L0V2ZnpSscsLS3w9dwZaObnyxvEJeQ/1L59+3DlyhXcuXMHvXv3NvmsO2ZmZnBzc0NAQAB69OiBkSNHokePHmjSpAkcHR1RXl6O7OxsZGZm4saNGxg7dixu3LiB1NRUDBw4UKMsU7UNur7a2NggICAAgwcPxuDBg9GrVy80atQIbdu2xZUrV5CcnIxbt26hY8eOsLbmngZYJpNh2bJlePCgqg+tqKiAo6MjmjdvXj1fCkBubi5WrlyJsWPHIjIyEqWlpepPImhEXl4eIiIisGPHDlhZWZHp50yEmzdvYvr06fjiiy+QmJiot5cRCW9ITU3F1q1bERMTAx8fH3h5eRlbJK0Qi8Xw9PTEkCFDMGTIEAwYMAD+/tq96EQgkIxcBAKBwIHQjFxszF64DNdvVk1r2Ld3dyyZP8sgMvLx2bzFiE+4B7d6dXFw5yaNHyilpKZh8owvAQBejT2x6/d1nHWUlZXjec4LeDZ0F/wn/W5SCj79YhHkcjkmjf0AjRt5YPn/rQdAMnLVdhR6VFpaqvSAgkAgEEwRNqekYsmXHcDD0N8AACAASURBVIcv+wDwJnPOqfMXsffgMZSXS1TaFovFaBsciMAWzdDMrwn8mzZB3TouVF9bUlqKtPQsJKc+QFLKA1y5fguvcvNYv0fb4EBMnTgadeu4sjqnmBkIjOHQ1OWvqSk7ztQ5ttmy4KjLXlFZWYkn2c+w7rftSsGACsRmZmgSEIyGvn5o4O0LN4/G1PR5kvJyPH+UiaeZaXiSnoq0xDuorJSp1NHY0wOzpk2ixndc+iI0IxdAMlcIRZvsFULsj1wuR1FRMTbv2Id/r99ibdvTrzkaN20Gd+8maNDYB9avHaAymRQvsp8gO+MBnmamI/XOLZSXlqic7+hgj8+mhKFtcKAgvQH4M1cAmjvJ1dkSdcf52qipOssVQMGlU4rj6uyQRCLBrr/+RuSZC6zt1m/kDS//FnD3bgJ3L1/Y2jtAJBJVZVPJycaTjDRkZ6bhQUI8igtU+y8rK0tMGvM+enfvomRj1DnQSVCOfhCafYnL9rDZIMW+0+djsGPfYVTQApIVOLnWhW9gKzTw8oWHdxM4udSBSCyusmH5uXiSkYanmQ+Qee8uXmQ/ZpV9aP8+GP3+UFhaWqjohlD9AUB0hgVN+ygAvHqhWGY/y8H63//Eg/RMlTbFYjP4tAyCZ5OmaODli/qejWFhaQUAqJCU49mjLGRnpeFJ2gOkJcarZPQDgIYeDTBr2iR4N/ZU2zcJyaxE9EIYQu0IV6ZRvj5ILq/K6Pbzlp1ISU1XaVskFsOnRRA8m/jB3dsPbg0bw9JKoTcSPH+SheyMNDzJeIAHCbeVplFU4N7ADbM//UgpE5c6vWHaFcU+kUiEiooKLFq0CPfu3UPXrl2xaNGiGq87BQUFuHfvHpKTkxEWFoavvvoKCQkJmDp1KoYNG2Zs8fQO2zhS6D4F9Hv+6tUrLF++HKmpqXBzc8O3335LBS8wdWP9+vU4efKk0j4HBwds375dKfuLobh8+TI++OADPHqk+v8TqJI3ICAAoaGh6NChA1xcXGBra1vjdVzfyGQylJSU4NmzZ4iLi0NMTAwyMjI4y7dv3x4HDx5E48aNq09IAoVcLsfq1avxzTffQCZTfW4CALa2tujYsSNCQ0MREBAAOzs7WL3ubwhvkEgkKC4uRkpKCmJiYhAXF4fCwkLO8vPmzcPq1atJICPhrYYEchEIBAIHugRyHfz7H6zftA0A0MzPF1t/+cEgMnJRXFyCQR9MhKyyEoP798FXs6drXMcfO8KxI/wgAGBy2ChMGveh3uQrKyvHR5/NxaPHT+Hp4Y4/N/2E6NjLJhfIFRgYSL3lRCDUFGJjY0mmMwJBj2ji/FbnyGRzTMjlcjzLeYEtO/Yh4V6KIJnMzMw4HyAxsbezxaQx76N7145KDkoAGjvDAcM6NN+mQC7Fkk13AHaHJ5tDvKKiArsPHMWJ01Fq5RCJRJBXNay2bP93emDcyKGwtLRk1Q99ODyZ64Q3qAv+E+Lw5FvK5XKcvRCLnfuPQCJRDaJQ4vW9kwuYNiI4sAWmTx4PF2cnQUFcdIenvp3kfPZE20Cumq6vmgRfAMICLxTL67fu4Ldte1BYVKxWDrHYjDV4lImPVyN8PnUCPBrU19oOVWcfVhvR9xiIWS7r0ROs37Qdj55kq5VFqN44OznisylhCA5swaoHfOMevsyAbMu3FU37KE3GyFKpFH8d+QdHT5xRK4cm45p3enbFhFHDYWVlJciWMPWA2BLt0daOaKI3MpkMh45F4u/jp9X/nxCJIIKw/x29unXGR+NGwsbaWrDeAOzj4aqmq9bz8vIwa9YsvHjxAuPGjcO4ceO0u7gmSmpqKmbNmgU7Ozts3boVDg7KM0QkJydj48aNCAwMRGBgIAICAuDkpN0UqsaAqTuaBnEpoNuMsrIyrFmzBpcuXYKNjQ0WLlyIdu3aKZU5cOAAtm/fzlrXqFGjMHHiRKFfQSs2btyIOXPmQCJRfQGtS5cumDt3Lnr16gUXF+2mNX3befToEU6cOIEffvgB9+/fVzlet25d7N27F3379jWCdG8v+fn5mDhxIo4ePapyzNLSEpMmTcLHH3+Mtm3bkmkwtUAmkyE+Ph67d+/Gpk2bUFKi+qJYz549sW/fvhoxHTGBYAhMO3crgUAg1FBsbd+kfJewvGFqaG7GJ0L22tnSvk2wVnVcvHyVWm/h31Qvcin4/c89ePT4KQBg3sxpsHqdIcLUKCsrQ1lZmbHFIBA0olKAo5VAIBgOkahqOir6krlfsa3ArW4dLJozHbH/XseJMxeQlpHF24aQIC47W1v0CO2Iof37wMXZiWpTqGOSXo6+j21dXyiuUW1F3fdTXFOm/jCX9HLm5uaYOHoE2rUKwKGIk7iXkspZv5Br28LfDyMGvYvAlv6sTkshS+b3Ufd9CdzQdYapP0x9oe8Tco/69OiK5s2a4FDESfx77Rb3+IHmoOfCw70+hvbvg56hnbSazo6rrKHg+y2+bXrJ9n2ZOsdmf5jLdq2D8P2yBTgccRJRsf+yZllSoC4Yx9XZCQP69kT/Pj1gYWHOqU98emOMPqw2IqTfYtoldX0Y3WY1auiOFd/Mwz8nzyHybLTKlNB01OmNtZUVenXvjOGD3oWTo4NGAVl8docE6QiDSxfo60KW5ubmGDNyKFoHtcTBiEgk8rzkIGRc49/UF8MHvYvWQS3V6gTffuZ3ZVsnsKPp+F5TOyISiWBmZoYPhg1EcGALHIqIxJ27ydwNyOVQJ01TX28MH9wPIW2CdBoLM7+XAmdnZyxZsgRffvkl9uzZA29vb3Tt2lWNVDUHPz8/9OnTB2fOnMH+/fsxZcoUpeN3795FamoqUlNT8ffffwMAGjVqRAV2BQYGol69esYQ3WhYW1tj0aJF+PPPP3Ho0CF8++23mDZtGoYMGQIAiImJ4QziAoAjR45gyJAhcHV11btspaWlmDZtGnbt2qVyrEePHliyZAl69+6t93bfNjw9PfHJJ5/g448/xr59+7BixQrcu3ePOv7ixQv0798fy5cvrxWZ/GoCCQkJGDFihEpgnbW1NaZMmYL58+ejUaNGRpKudmBmZoY2bdqgTZs2WLBgAdasWYONGzeiqOjNf4KoqCi0bdsWBw4cQJcuXYwoLYFgHEhGLgKBQOBAl4xcO8MPYsuOcABAQItm2LR2lUFk5GLdxj9wKOIEAOB/f/0JJ0cHNWcoI5FUoM/Q0dT2Pwf+hKODA6RSKVJS05B8Pw2VlZVwsLdDyxbN4OnhLrjuG7fvYNZX3wIA+vXpgcVfzgQAnD4fbXIZufz8/EhGLkKN4+rVqwgJCTG2GARCrYIt84BiyfcWOdeb4wBYyyk+6ZkPcToqFpeu3mR945WPJj6N0bdnKDp3aAur19mUFB8hWUvUZVTiCsLQF2x/T588eYLc3Nw337FJE6WpeU39IaammQgAdv1Ql+FELpcjNS0T/zt5DldvxgsO7DUzM0OHdsEY1LcnfL0bc+qBthkItHF2ve1oqjNCMldw6YxcXjUt0fEzF3Ah5l+UlZcLlrNFsyYY9G5vtGsdqHLvhWS94cvIRd+vQBOd4XrUlZ+fT00HI5fL0aBBA9SpU0dQ/TVdZ5l6xadbgGpGLqF2KD+/ACfPXcTpqBhBGboUeDVuiEF9e6Jz+7YwN1cN4BKaVcmYfVhtRJ9jIAAqOqPYLpdIEHPpKv45HYWn2c8Fy+fq4oz+vbuhT4+usLOz5dQXALz2iFmW6Aw3huijuPqn9MyHiDh5Dleu34JMJnRcI0ZI61YY3K8X/Hy9We2AJtm4iC3RHW3sCKA+0yiX7mQ9eoL/nTyHy9duCc5iLBaL0S44EIP79YZ/U19O+6BOb4A3Uysq1rnGwzExMVi5ciWsrKywZs0a+Pr66umKG59Xr17h4MGDGDNmjEpGLrlcjocPH+LOnTtITEzEnTt38PLlS6Uybm5uCAgIoAK7TClYgjnGZBtzco1D6bDZDJFIhJMnT2Ljxo2QyWSYM2cOPD098dVXX/EGyQPAwIEDMWPGDLXtaoJUKsWgQYNw6tQppf1OTk7YsWMH3nvvPb22R3hDZWUlfvrpJyxYsEDFjs2fPx//93//ZyTJ3g6SkpLQuXNn5OUpT/3eqVMn7N+/n0xzaUBevHiB8ePHq0wja21tjfPnz6NTp05GkoxAMA4kkItAIBA40DaQSy6X46Ppc/EgPRMAMP7D4Zj28XiDycnGmMkz8OjxU/g3bYI/fv5e4/MfP8nG6I8/AwA0qF8PB3ZswoWYy1i/aStyXrxSKd+mVSA+HD4YoZ3b89ZbXFyCif+Zg2fPc+Bgb4+9f2yA8+ssITUhkMvDq4kRpSEQ2HmSqRxsSAK5CAT9o4nzW4gzE1B1ZLJ9pFIpnmQ/Q3rGQ6RlPcLDR09RWl6GigopzMRiWFpaoG4dV/h6NYKPVyP4NPaEvb0dr8OK6VjQdGohQwfjsP09nTx5stIbyFevXkW7du2o7ZrgONPEgSVUj/gcWOXl5UjLfIj7DzJwPy0Tr3LzkFtpBpnYDHVEFXCr44KmPl5o4usFH69GVNAfAE4dUBeYoziXODz1A5eu0Nf57IomgVz045kPHyM1PRP3H2Tg6bMcSCQSyCsrYWllCUcHB/j5NIafrzf8fL1gb8dubzQJugG49UZxTIEmOsP1qGvXrl1KU898//33mDt3rqC6a7rOahJ8QT+mTXCg4tjjp89w/0E67qdl4vGTbORWAGUQwQkyONtbo4lXI/j5esHP15s1i5JQneLq26q7D6uNaKI36vougH38Q7djcrkcL17lVunNgwxkZD1GaVkZpBIJzCwsYGNjjcaeHmj22g7Vd6sneNzDNdYhQciawacT9HXmfRUSHMr1KZdIkJ7xEPfTMnA/LQMvX+UhVyaGzMwcrqiAWx1n+Pl4wc/XC75ejWBlZaWTLVHcZ2JL9IM2/6U0GQuz6ZtcLodEUoH0rId4kJaJlLQMvHiZi4qKCsjlclhaWqKOqzOa+lT1Qb7ejWFtbcWpK0L+N3GNhxX72Ja7du1CeHg43NzcsH79+ho1xaA+efbsGRXYlZCQgMePHysdd3JyUgrs8vX1pa53dcM2xhS6TwGXzVDsv337Ng4dOoSpU6di/vz5yM/PVyuXWCzG77//joYNG6otK5SpU6diy5YtSvtatWqFQ4cOwc/PT2/tELiJiorC6NGj8ezZM6X9mzZtwrRp04wkVe3m+fPn6NSpE9LT05X2T58+HWvXroWlic4sU5uorKzE0qVLsWLFCiVbWq9ePVy+fLlWBT4TCOogUysSCASCntm2az8VxAUAoZ07VGv72c+eU9MWtm+n3bSKz3NeUOuODvb4X+QZ/N+63wAA9vZ2qOPijNKycqrczfgE3IxPwKK5MzCgby/Oejf8vh3PnucAAOZ+/gkVxFUTsLKxxcbIq+oLEgjVzMbFM3Hm0G5ji0EgvFWIRMrTxTCRy99M90Ff1zQgwcLCAo09G6JRQw9079qRUxbFkisoQl1gFlsdXO0IlZ3AjkJ36DrEVgYQpkds20DV24otmvmhedM3QeijT2bhbqEU5wc1Rh0bc6Vz+YInhOgQXRZmGYL+YNMbrt+mpnZHcc98XgeG9u0ZSh0rPXgMNiOHqtSr7Yd+PltdXPIRdIPL7vBdWzbbI1SnxGIxPD0awNOjAXqGVr05vSDmKc5nl2JLaH10bGCnVI8+dUuT70jQHuZ1ZY6N2PRHLBarZIwUi8VKwR11XV1Q19UFnULaUGXKDv8PVu8NhMhMOVCCvq6uz2Irq84usn3Ptx0+W0LfT79u6sbDbHpBx8rSEs2bNYF/0zeOu0lnHuJGXgVO9G+EhvYWSvXpo1/i+u4E3aHrijo7om5so4BpRwDAysoS/n6+8PfzxcDX+yTRcbDs3kWlPua6JtnZdLEl48ePR2ZmJuLi4vDdd99h1apVMDd/+1x29evXR/369fHOO+8AAPLy8pCQkEB90tPTERcXh7i4OACAjY0NWrZsibZt22L48OHGFF1v0PUjODgYfn5+mDt3rqAgLqAq8OHPP//E119/rRd5Vq1apRLENXr0aGzduhW2trZ6aYOgnp49e+LGjRsYMWIE/v33X2r/Z599hsaNG2PAgAFGlK72UVpaiiFDhigFcVlaWuKPP/5AWFiYESV7uxCLxfjvf/+Ljh07YuzYsSgsLAQA5OTkYODAgYiLizPIVLIEginy9o0KCQQCQY9UVlbiVW4enue8wKMn2Th6/BTiE97MXz50YF8EtGjGW0dZWTmeZj9DnToucHTQbApENq7eiKfWO7ZtrVUd5bRpnF6+ysOPG35H0yY++OyTiQgOaglzMzMAQFpGFnb/dQSnz0UDAFav/RX16rgipK1qAFns5as4fuocAGBA317o0yNUpQyBQCAQCKaIUIcVfb+6dXUOKzYHB1cAhzZOK7byXHWwyU/QH2zXle7wZJZhlqc7rpgZdeh69KBYivz8cs6gB12CJzR1eBJdEo7iHqpzkNP3cTnM6bpCtz9Me0NvR3r/AcqOnoTNyKFKmSYUS+aHz/FJP0fdd2bKTtAvXHrFVg5Qr1Nc/RmznTxJZZUdArcNoq+rc6Qzz6NvM/WH6JV2aDIG4rrmfOt8bdLbKDtxHuZ+PrAIDtTLuIfvfIJwNLUlQteFtPOwRMY5rmG7x5pOm8isiykn0RXhaGtHuMbD6v5H0eumt1H5KhdFv+2Aa5eOEFtqFvynLnOfpmMdupzz5s3DnDlzkJiYiI0bN2LWrFmCzq3NODs7IzQ0FKGhVc+OS0pKkJiYSGXsSklJwfXr1yGVSk02kIupy2z7uZDJZFi5ciUePnyoUZuxsbFITk6Gv7+/Rucx2b9/v0pA2NChQ7Fnzx6jZUJ7m/Hw8EBkZCQ6d+6MpKQkAFU68uGHHyImJgbBwdq9yE9QRi6XY9y4cbhy5YrS/m3btmHcuHFGkurtZvDgwThw4AAGDx4MqVQKAEhOTsawYcNw5swZkh2N8FZAArkIBAJBAJKKCvQfEQZLSwtYWlrC0tICFRUVyHnxSmWecgV9e3XDzE8/5qzz8ZNs/LZ1Fy7EXqb2uTeoj+GD+2HkewNhYWGhlaxXb94GAFhbWakNIuPC0vJN2y9f5cLXxwsbvl8Gezs7pXK+3o3xzZczUVlZibNRMaisrMTBY8dVArnyCwqpjF6eDd0xe/pkreQiEAgEAsHYsDkFuB7GMp0PTKcDVwAOVyAOWztcjnB1TgiuslzfWZP9BFWY95EraIZenmtbiPOKXjfl9KKdzxWQQ18X4vDkOpfN6Ub0RTPU6QafQ0iIvjDtD71NehsVcddQ+TIPlSmpMGvhz3mvufRFm0AcY8C0128jfN9fnU7RA7oUeqT4KOmVoh4RlByBQvswdTrFJjfpw/SHLmMgpg6xBQDSdYfeljTzIWSPs1ERdxVWbVop1afLuIfve6pbJ7Cj7hoJ0QuuD7OPos4TcY9rdPkwvxexJfpBqB1hu9dCxjdcdgQAyi9ehjy/CNKb8bDqFEKV59MboZmNub6runWgKpPu0qVLMWvWLJw8eRI+Pj4YOnQoZ71vI7a2tmjfvj3at28PAJBIJEhOThZ8fk5ODhwcHGBtbW0oEXkRYifouvrLL7/g1q1bWrW1fft2rF69Wqtzgapr9emnnyr91wgJCUF4eDgJ4jIizs7OOH78ODp16oTnz58DAIqKivDRRx/h2rVr5N7ogZ07d+LIkSNK+5YvX06CuIxMv3798Ouvv2Lq1KnUvosXL2Lt2rX46quvjCgZgVA9kEAuAoFAEEhxSQmKS/jL2NraoH3bYAzp/w460qYBYJL18DE+m7cYefkF8PFqhBb+TVFcUoKLcVfw6x878SznBWb/R/NgJ5lMhmuvM3K1CQ7QOhiMGbA1avhglX0KRCIRPpsyAWejYgAAV67fQnFxCezs3qRZXvPLZuTm5cPMzAzfLpgDWxsbreQiEAgEAsFY8AVVqJsiT90+vvaYQRVsdbEFQ6hzQrCV4XNiMdsi6I4QRzi9LB2+YC42BxYAyEVvjjP1gLlfW0cnn8zq9hO4Eer0BNgdn0IzALK1UbzvKACg9NhJWAW0UGpbF31RZx/VlTUEb0MwF/P+8gWV8ukb13VSBGIw66CWtHKa9GFCHerMc5ny1vb7ayj0MQZiu/ZsgTv0ehXL8lPnAQAlx07DYcYUwTpD1xeuMprYKMIb2OwHUx+Y11Cb/onZHr0NMXVMVRcUS76PurEx23cgtkR7NLUj6sYJgLCstPT6CzftAgCU7DoAmy4dlOpl3nttplbksk1CqF+/PhYvXoyFCxdi8+bNaNSoEdq04X6u/LZjaWmJoKAgweU3btyIa9euwc/PD4GBgQgMDERAQAActJgZQ92YSRtEIhG2bt0KS0tLmJub49SpU1rXFR8fj2vXriEkJESr8xctWoS8vDxq28vLCxEREWQ6RRPAx8cHERER6NmzJ0pLSwEAN2/exJYtWzBt2jQjS1ezKSwsxIIFC5T2ffTRR/jmm2+MJBGBzieffIK0tDSlINXvvvsOEyZMgLu7uxElIxAMDwnkIhAIBAGIxWKM/XAYysrKUVZWBjMzM9hYW8PWxhrW1tawsbZG40YeaBXQAubm/KZVJpNhyco1yMsvwJzPPsF7g96l3pq4czcJXyxchkNHj+P9IQPQyNNDIznvP0hHYVERAKB9G+3T6jrY2yttd+7Qjrd8vbp14OfrjdS0DFRUSBGfeI8652xUDM5HxwEAPv14HPyb+motF4FAIBAIpgCbY0DoQ3pFn69xEA5HgIGmTiuhDk1m/Vzt6hNtH37XlOALIQ4stu/B9f34MlfQ61VxQgrMyCXE6cnnuGLWT9AffM4irmutSQZARR0VGVnU+RX3UnmdlNrqC1tdxqSm2BN9wmUr+K6FNsGBItGbjFwK/aCXVyy1dZxzyUHQL9qOgZhBfnx1M/VGcjOBKiNLug+Llv6C+y1THvfUVhTXiusFBCaaBqdT9YveBNzwZeRibmuS1Y9+LkF/6GJHhE6pqJLZ7/FTqkxF1mPIy8phZmuj1LbQPodvmkWmLMzvxvc9AwMDMX36dPz8889YtWoV1q1bBw8PzZ4LE9hxcHCAra0tUlJSkJKSgsOHDwOoClIKCAhAUFAQAgICULduXaPJGB8fj9TUVL3UtX37drRr105j23X9+nVs27ZNad+ePXvQoEEDvchF0J0OHTpg9erVSlOwLl68GB9++CFcXFyMKFnNZvny5cjOzqa2mzVrhk2bNhlRIgKTlStX4sKFC7h06RKAqox08+fPx65du4wsGYFgWEggF4FAIAjA3MwM0ybpJ43q2QuxeJCeieCglhg+pL/SsaCWzTF/1n9gb2cHVxdnjeu+ev02td6+XWutZXSrVwfWVlYoKy8HAFgKyOzl2dAdqWkZAIDi16nLXrx8hR9/2VwlT9tgfDh8iNYyEQgEAoFgbLiCJzR1QgCaBeGoCyzQJLBCaMAEV7AFQT9w3V+hGQmE1M1Vr74DKNicV8QRrh+YtkXbAFKh7TB1RhJ7hSoje/ocsrQMWPj56k1nuPRGqMOTC10yIqirtzbosL70SpvgQEXNbDqgSR+mrm9j0yFd9eptRx9jILqtEao30ifZqHjwJqi0PPYKrAJbaDz2UScfGfdojj77KE2D06trXENsiX7Rhx1R1/dw6Ux5VOybSiRSVFy+CvM+PTS2JZqOYzRlwIAByMjIQEREBJYtW4a1a9eSTEh6YO7cuZDL5cjIyEBiYiISEhKQkJCAzMxMZGZm4vjx4wCABg0aKAV2NWzYUHAbXPot5Lzy8nK9BXEBQHp6Os6fP4/evXtrdN7MmTOVgiXHjRuHrl276k0ugn6YPn06Nm/ejMTERADAixcvsHTpUmzYsMHIktVMUlJSsH79eqV9a9euhaWlpZEkIrAhEomwYcMGdOjQgbK1e/bswfTp09G5c2cjS0cgGA4SyEUgEAjVzIXYywCAd3qw/xF6t3d3reu+crMqkKtuHVd4NRL+Z5OJmZkZ/Js1we07dwEARSXKUyWyIRa9eXBW+Xowde1mPIqKigEAV2/cRo+BHwhqf/Ofe7H5z70AgMAW/vht7UqNvwOBQCAQCIaE6bCiO5Z0eXhPd3LyOa0UZenr6pwPQssZw2mla+CFvgNbDIU+nZ5s9TJ1B3ijk+LXIRRikZj1Pmvr+GTKQRzh1YO+9YZNZ8qiLymVL4+9AqtmftS5ujjLmXJUF0JsTW0J2NIGphNSX7oFvL72ojd2iC2DjmKpL3tEMAx8YyDFcV3qpddZFnNZqUzpxctw/s9HSu3wjXdMddxT2zG0Laka17wuI9bvuIbYkupBnR1RlNG2XmadpWcvKpUrOxcLu769lNrRVDeY5zD3M7+D0O8zdepUZGVl4fbt2/j+++/x7bffCvvyJk50dDTOnj2LJUuWwMzMrNrbF4lE8PHxgY+PDwYPHgwAePr0KRXUlZCQgKdPnyI7Oxtnz54FALi4uCAgIICajtHb25vKyKYuKFETbty4oduXY2Hnzp3o1q0bLAS8nA0Af//9N+Li4qhte3t7fP/993qXi6A75ubmWL9+Pd555x1q36+//oovvvgCPj4+RpSsZrJ48WJUVFRQ2wMHDsTAgQONKBGBi5CQEHz00UdU5kC5XI758+fj4sWLas4kEGouJJCLQCAQqhGpTIYbt+4AAFo2bwYAKCwsQm5ePiwsLOBWr47Wf2ZLSktxJzEJQFX2K10fNrVs3owK5EpKTkX9evzppV++yqXWbW1sdGrbFCkvLUHM8cPGFoNAUCHmxBFji0AgvFUwgyf04cikOxzo9QkJENPWAU4/n8vZSaheNHF6MnWGeR5rNrfXC7FYpJcACnp7+nReEZTRt81h6g5XvdIn2ZDQsuAAQMmFODhPHq/UlqbOcroc1R08oYljrbYHcwnRK0C3/ozehmKbnpGLK4OOYsnWL2mqW8QW6Rc+vVHs21XUrgAAIABJREFUA/SrN2XRyoFc0ifPIX2QDsumTahz1ekIvR0y7tEvxrIlAG1qRQGBoZr2U+qWmnwXgjJC7Qig/fiGqTPSB+moyHqiVL7kyk24FhbBzMmROl+InnAdFyqnEMzMzLBo0SIsXLgQAwYM0EudpsDff/+NpKQkREZGYtCgQcYWBwDg7u4Od3d39O3bFwDw6tUrpYxdGRkZiImJQUxMDADAzs4O06dPR69evfTSvkJnHj9+rJf66Dx//hz//PMPhg0bJqj81q1blbYXLVpEpvY0Yfr06YMRI0ZQ04TKZDLs3LkTS5cuNbJkNYuXL1/i6NGj1LalpSXWrVtnRIkI6li1ahUOHTqE/Px8AEBMTAzu37+Ppk2bGlkyAsEwkEAuAoFAqEayHj5GUXEJxGIxPNzrY8ufe7H34FFIpVIAQL26rhg6oC/GjxoBc3PNTPTtO3chk8kAAB3aBussa4D/m8HPtVvx6BHaibNsaVkZEu4lA6hKcx7YoipIrVVAc3wzfxbneXTiE5Nw9J+TAIBe3bsgtFN7AICTo4NW8huCNXOnGFsEAoFAIJgoXI5Mvgf6dIcDcx+X04qrHuZSiLOBrSxXfcxjpkpNCL7QxOnJ9l3o57PtF5LdQGgABXObaz+zLoJ+0YejnM/esNVXevGySh0VGY8hffgYll6NlNoR6iinH2PKxiW3puia3U9I/bVRz9n6HsV+QPP+jFNPBdgPPl3i2sc8n23JXCfoF036MeY5XHpT+eIlyu/eVzmv7OJlWDXzE6QzXGOe2jLuMQX4+ih6GYBfJ+j6IDSAWVFGH4Gh9PP59IO5TtAfbHYEEK43zDoUy5KzLJk6ZJUoi46Dw3sDqfMUS13HwPrQGQcHB/z888+1StemTp2KOXPmYNeuXejZsyfs7OyMLZIKrq6u6NatG7p16wYAKCoqwt27d6nArvv378PFxYW3Dqb94yunICkpSTfBOQgPD8e7776rdnrOnJwcREZGUtv29vaYOXOmQWQi6I+vv/6aCuQCgF27dpFALg3Zt28fJBIJtT1y5EgSEGTiuLm5YcqUKVizZg21b+fOnfjvf/9rRKkIBMNBArkIBAKhGlEEbFlbW2HBt6vx8mUuxn0wDC7OTniQkYXT56Kxddd+PHySja/nzlB6GKWOKzduU+shbVrpLGvHkDao4+qMl6/ycOL0eYwaMQSeHu6sZU+cOk8FkbVuFQBnZycAgId7A3i4NxDUnkgEKpCrqa+3TlNMEggEAoFgSNgcVsxj6hxWXOcxHQ98TitmfYqlUGe4uvLMdgzlSDB04IUpos7pKTR4Ql199DqoqRVfTwdCb4fP6cnc5tMXtrqY6wT9oY2jnO1cNp0pvRDLel7Zxcuw8m6sV53hkk+faGNnamvQlgJ1jkZd+zNW20Yrw8ygQ1/nc5jz6RWzHoL+4eu/uIIw1NXDVVfhxUus5xZfiIPLJxOo84ToDds+Y4173hbo10+dLWGzR7y2RGlcw59Ji9gS00OIHVEcE1oPX13FZ6JZzy8+Ew3HYYME6w3fcaa8+rAltU3/mjdvjp49eyIqKgrh4eGYMsX0X5a1t7dHhw4d0KFDBwBAeXk59eI13zhKLpfj0qVL8PPzg5ubG5KTk3HkyJts+t27d0fXrl2p7Xv37hlE/sLCQhw8eBATJkzgLRceHk75LABgxIgRJhloR1Cmbdu2CAgIQGJiIgDgwYMHiI2NVdItU6agoACOjo5GlWHnzp1K2+p+KwTTICwsTCmQa/fu3Vi+fLnJ9JtSqRQ//vgjtd2wYUOEhYUZUSLhlJSUwNLSUuMkIwTDQe4EgUAgVCOKwURJSSkgB7b/ugbW1lbU8WGD3sX0uV/j1NkLGNK/D1oHBQiu++rrQK5mfj5UIJUuWFtbYXLYaHy/fhPKyyVYtnotli+aB/cGbirt/rz5T2r7nR6hOrdNIBAIBIKpoy9HppC6APVTK9LXhTga1JVj1k2oHtjut9CMJvR1VifY64VYLFabuUKx1NZpRRzh+oftPivWFega+KfYJ3vxEmWJqllwAKA4KgYuE0crta2tzujb4UnQHC5dACCoLxLqRGfec6Ydoh8T2pfx2S5mncx1gm5oMgYSErTDVUfpBfZALkn6I5XsgGTcY1y4+ijmcSFjG0VZtWNrDcY12tgSpkxs6wTtUWdHAP7xsFA7Uh6fAOnzl6wylN66i8qXr2Ber67GesO2TpeFwM5HH32EuLg4HDt2DIMGDYK7O/sLw6aKlZWV+kIAsrOzsXLlSgCAo6Mj7O3tcfHiReTn5yM/Px+LFi2igm2ePXuGvLw8g8l85MgRDB48GK6urpxldu3apbRdUwIOCFX3asGCBdT2rl27akQgV2VlJcaOHYvg4GCEhoaiS5cucHLS3Z+lCSkpKbhy5Qq17e7ujnfeeadaZSBoR3BwMFq1aoX4+HgAQEZGBi5evIju3U0jMURFRQUWLlxIbXfs2LHG2NUbN25gw4YN6NixI0JDQ9G2bVtYWFgYW6y3GhLIRSAQCNWIrY0NtR42eoRSEBcANPPzRa9uXRB5JgpR0ZcEB3I9z3mJzKxHAID2bXSfVlHBgHd7I/LsBcQn3ENSygOMmTwD/fr0gKdHA8hkMly6egN3k944efr27o5B/XrrrX1j8+WXX+LVq1fGFqNWYgpv3dRWPD09jS0CgfDWINSRKcRZxVeH4hifHMx1LscUn0OTrx1DOSTUZcmpzVl0+JyeQh2eQp1gzHPYdIFrKcTpTZxX1QOXztCvuTq7I0RnSqLjOGUoS0qD7FkOLNzrK7WtL50h+lP98OmVJsHJ6vo/po3j0wWhfRlX/8ZVL8EwaDr2EXJuZX4BSm4lcp5XGh0HqwmjBdsgtmP0JZtcBM3gsh+A+r6Jrz514xpU47iG6IXh4Pr/o2v/U3z6AveJcjmKz1yAy7gPlOrSh97Q6yO8oV69enj//fcRHh6OP/74A998842xRTIIIpEIffr0QWpqKh4+fIiCggI0adKEOn716lUsXrwYfn5+StO6GYLy8nLs3bsXM2bMYD2enp6Oa9euUduenp7o3bv6nu1XVFRoHSRQXFyMvLw8lJaWwsrKCg0bNtRodhM2pFIpsrOzUVZWBnt7ezRoIGymEWMxfvx4LFq0CJWVlQCAAwcO4LfffqsRtqeyshI3b97EzZs38csvvyAoKIgK6uILPNQXf/31l9L22LFjYWZmZvB2gaqxjFQq1Ur35XI5cnNzUVhYCIlEgjp16ujlehUVFeH58+eQy+WoX78+7O3tda7TkEyYMAHz5s2jtv/66y+TCeSq6RQVFeHs2bM4e/YsbG1t0aFDB4SGhiIkJASWlpbGFu+tgwRyEQgEQjVSx9WFeqDQyNODtUxQS39EnolC1uMnguu9dvPNtIrt27XWWU4F5mZm+GH511j83Q+4euM2ZDIZjp86x1q2e9eOWDTnM53/MJkS06ZNM7YItZLnz59j8eLFWLVqlbFFIRAIBL2iqYOK61wuZxdbQA7bthDnFV85vvXqhG+qitoCVwAFfZt5/fmym3A5wQBA9Dp1hYgjc4VSWQ2cVXy6UhMeINc01OmMpvWw6UxxFHcgFwAUR8fCZfT7Sm1qozP61g9D2Ayhv8HaBFfghS6BGIp1+r0XkpGLa6lOl0yhD6vt8PVZuuiMoq7Ci5eASu7fbuH5WLhOGkuV51rWpHFPbYMrIIdPL5h6xVUX8GZcIxYJG9doqifMdYL+UTf21bX/qayQovBcDG+5wtNRcB3/IXUO11KbsQzRH3Y++OADnDx5EpcuXUJ8fDxatWplbJH0jru7O+bMmVOVFa68HFu3bsUPP/wAJycnODo6wtHRkQpgqQ4iIyMxfPhwNGzYUOXY9evXlbZHjhxp0Gf7iYmJ2LJlC44cOYKcnByUlpbC398fffr0wbRp09Tqg0Qiwe7duxEeHo5z585RAUwAYGNjgw4dOmDKlCkYM2YMZ1DO5MmTkZKSAgA4ceIE7O3tce3aNaxcuRLnzp1Dfn4+VbZnz56YNWsWhg0bplTHJ598gqSkJADAzJkz8cEHH0Ad586dw9KlSwEAdevWxeHDh3W2Ew0bNkTXrl1x8eJFAMCrV6+QkZEBHx8fneqtbuRyOeLj4xEfH4/ffvsNLVu2RGhoKLp27Yq6desapE2m7o8aNcog7QBVAYLHjx/H5s2b8e+//yI3NxdisRidO3fGu+++i5kzZ8LBwYG3jqysLGzduhXh4eG4f185e7abmxsGDhyIzz//HG3btmU9PzMzE+PHjwcAdO7cGd9//z0qKyvxxx9/YNu2bbh69Sr1e7Kzs8OYMWMwf/58NG3alKrj4cOHGDt2LLV99OhRQUFkixYtonR09OjR+Oyzz9Seo45Ro0YpBXIx7ydBP5SUlCAqKgpRUVGwtramgrrat28vOEslQTdIIBeBQCBwYGtjg359egCA3uYEtra2go93Y6SlZ6KoqJi1jKItTRwOV15Pq2hpaYmgls11F5SGra0N1qz4BjGXruLU+WjEJ9zDq9w8WFiYw8nRET1CO2FIvz5o4uutUzvu9etT19vXu7EeJCeYKkeOHMHhw4fx22+/VdubLgQCgWAouBwQ9HVtnJhcdXOV41sX4rgU4gw3JWpTIAWbc1IfOsSsi75fE13QRH/Y2icYFiHBFJrUJcvLR8nNBN5yRedi4DpmpMY6o845biydEZL5sLbDFjyhiQ5xBWnw1a2u39FGr9i2CYZDV/vDpzfFUbG855bdS4X02XNYur/JlKFtv0UCd/QH19hVm/5IrS0RvSmrj3GNqfZRtR2h/6XUnc+mM6VXb0CWV8jbftm9B6h4+AhWXm+eQ+raD3Ht0yc1+b+QtbU1Jk6ciLVr12Lz5s3YsGFDrXopmImVlRXc3NyQlZVF7Vu6dClGjRqF1NRU7N69G4WF/HqqK5WVldixYwcWLVqkcuzOnTtK21xBILoilUoxd+5cbNiwQeVYcnIykpOTsWXLFqxYsQJz5sxhfV6cnp6OESNG4NatW6xtlJaW4sKFC7hw4QK2b9+OAwcOsAaZ3L59mwr6kEqlWL9+PebMmaMUFKZAEcDw999/47333qP2+/j44I8//gAA2NraCgrk2r59O2JiqoJLZ86cqbffcNu2bakgGaDqnta0QC46crkciYmJSExMxO+//47mzZujW7duCA0NRb169fTWDl33zc3NDRZUmpmZiWHDhqnorUwmQ3R0NKKjo7Ft2zbs2rULXbp0Ya1j//79mDJlCoqKiliPP3/+HH/++Sd27tyJFStWKE23qaCsrIzSPwcHB5SWlmLQoEE4f/68Stni4mL88ccfOHXqFK5fv04F03l6eiI3NxeJiVVZcyMiIjBx4kTe719SUoINGzaguLjKF/rjjz/ylheKp6cn6tWrh5ycHABAQkJCje4bawJlZWWUzlpZWSEkJAShoaHo2LEjrK2tjS1erYUEchEIBAIHdVxdsPjLmXqvt6V/U6SlZyLhbjKa+fmqHE/LeAgAcKsn/G2DpV/NxtKvZgMwzMMCkUiEbl06oFuXDgAM88AgsKU/Alv667VOgmmybt06vHz5EqdPn0b//v2NLQ6BQCDojCGDuYS2z7cu1FFlSs5w+vVcuXIlvvrqK2q7cePaF/CtqxNLWCOKhWYOT6HHmN+HYDj4HOX049roTnF0HG8WHAAouX0Psle5sKhbR6n9mqAzw4YNo96el8vlenUG1HT4grno6zr1azR90DaQS90+troIhoMvmAuASmCFEL2pLCpG0b/qs5QURcWgzljlKdH4lsxyfOsE7akWW6JUt2b9ELElpoeQAEBtdKbwlKpzmo2iU1GwnvrGEa2t7jCPGYqnT59ixYoV+PTTTxEYGGjQtgzFO++8g4iICKSmpuLMmTN49913jS1StWJmZobmzZvDx8cHmzdvrpY2Y2JikJKSgmbNmintj4+PV9oOCgrSe9tlZWUYMmQIzpw5AwBo1KgRFi1ahCZNmqCiogKnTp3Cli1bUFJSgvnz5yMtLQ2//fabUh0lJSUYNmwYJa+trS2++OILvPfee7C3t0dBQQH27duHjRs3oqKiAufOncOkSZNw9OhR3nHAzp07MXv2bDg5OWHmzJno3LkzbGxscOvWLWzcuBGpqakAqqZxS09PpwLDxo0bh6+//hoAcPbsWbx8+RJ16tThvQZHjx6ltidNmqTl1VSFec/i4+MxdOhQvdVvbJKSkpCUlIQtW7agWbNmCA0NRWhoqE7TXhYXFyMtLY3abtq0qUGyC12/fh0DBgyggo369euHqVOnwtnZGU+fPsXu3bsRGRmJtLQ0dO/eHadPn0avXr2U6oiNjcX48eMhlUoBAP7+/li8eDGCgoJgZmaGR48eYfXq1bhw4QIqKyuxcOFCeHt7Y/To0Ur10PX+xYsXmDZtGs6fP49WrVph5syZ8Pb2hlQqRUREBLZt24bS0lJkZWVhxowZ2LdvH1VHWFgYFSh28OBBtYFcx48fp4K4mjdvjg4dOuhwRZUJCgrCuXNVswcVFRUhPT0dvr6q/laC/ikvL0dsbCxiY2NhaWmJdu3aUUFdtra2xhavVlF7Q90JBALBROnXp2qu5kPHTqC8vFzpWFFxMc5frJrKpEfXjoLrVDwIr66HS+QhFkFbXr58STnPjhw5YmRpCAQCQX+ocxIyP1z7RaKq6abo6/RtvrJC9teEIC4mbm5uaNq0KfWprem72e4BU1fo5YTqhJgxlaJYrKpXbPrC1gZTTuLsNB7qrj2X7qjTGXVTEAEA5HIUR8fVSJ1xcHBQsifOzs7V1nZNQFO94rv3rH2Zoh4WOySk7zJVvXrbUTcGUiyF6k1x7GXIpTK17RadixHUj9FlYdMltu9A0A1D2xKxWEw5Nej7iC2puehbZ+TlEhSqyeynIP/keZ11h3nMkNy4cQNpaWn47rvv8Pz5c4O3ZwhEIhGmTp0KANixYwdKS0uNLJFxuH//PmQy9f2dvti2bZvKPmZWopYtW+q93VWrVlFBXO+99x7u3buHTz/9FH379sXAgQOxbt06nDlzBpaWlgCATZs2qWQJmj9/PhXE5eXlhQcPHuC7775D+/bt0aJFC3Ts2BFr167F3bt3YW9vD6AqW9DWrVtV5KH/Tr/88ksEBQUhIyMDy5cvx4ABA9CzZ0/Mnj0b0dHRVHBWQUEBTp06RZ3n5eWFHj2qZhaRyWQ4duwY7zU4ceIElXmtVatWaN26tfALqAZmIBczy1ptIiUlBdu2bcPHH3+Mzz//HPv378eTJ080ricxMVEpgNgQ2bgkEgnCwsKoIK7t27fjxIkTGDFiBHr37o1x48bh+PHj1PSAMpkMEyZMQEVFBVVHWVkZRo4cSQVxffLJJ0hISMD48eMRHByMwMBA9O/fH+fPn8eaNWuo86ZNm4anT58qyUN/JnTr1i3s2rULCxYswM2bNzF58mT06dMH/fr1wy+//IJNmzZRZY8dO4aysjJqe9y4cdRv6NSpUygoKOC9Dvv376fWJ02apNd+8m3SfVNGIpHg0qVL+OGHHzB69Gh8++23OHPmDGcGOYJmkEAuAoFAqGZaBwWgd4+uyHr0GPOXrMSdu0l48TIX12/G48vFK/A85wV8vRsjpE2wsUUlEPQOPXgrIiJCoylECQQCwdRhcwpyOYf4nA9sHyXHFYvDSp3jga0tNrnYtgnVB5sDiM+JRV9XpwvMNjT5cLXJJj+h+uDSF3X3jEtn5MUlKPr3hqC2C89e1FlnqsvhSdAMbfRKsC3R0G4J1WV18hMMjxCdEao3BecFBJQCKL51F7JXuRrbHzLuqR64xgn6sCUikQhyqA/80sWWcH0HguHQp86UxFxCZUm5Sn1slGc8QnnSfcFjGjZZq1NXBg0ahH79+qGgoADLli1TcrDXJAIDA9G1a1fk5ubir7/+MrY4ekfI807Fi67VRXx8PK5evUptM7MSNWvWjAqm0hfJyclYvXo1AMDCwgLr1q2DnZ2dSrnOnTvjo48+orZ37txJrVdWVioFg/z000+c2Zj8/PyUsnmHh4erlKH/XmUyGfbs2cP6coe7u7tStqHIyEil42FhYdT6oUOHWOVRYMhgloCAAKUgnbclmOXBgwfYsWMHpkyZgunTp2Pv3r14+PChoHOZ18gQmejWrFmDe/fuAQB69OiBiRMnsvYdy5cvh5ubGwDg0aNHiIqKoo5fuHAB2dnZAKpecPzhhx9gbq460ZpIJMKsWbMQEBAAoCrw8Pjx4yplFFRUVCAkJAT//e9/Wae2DQsLo35jpaWliI6Opo55enpSWcMkEgn+97//cV6DoqIi/PPPPwCqAsnGjx/PWVYbSCCX6SGVSnHlyhX89NNPGDt2LJYsWYJTp04ZfArh2gwJ5CIQCAQjMOvTjxHauQNu3E7A9DlfY/i4KZi9cBkS7iXDq1FDrFmxBJaWFsYWk0DQOwcPHqTWnz59isuXLxtRGgKBQDAMfA/2hToz1QVlqfvQ62auM2Xhk51gHIQ4xOnrQj5igQ5Pvvb4ZCO6YzzU/ZaF6kxRzCVBWXAAoPDqLVQWFumkMwTTRhu9oq+z2RfFQ0gRdOvTmLJwyUuoXoSMK9TpjbxcgsKYK8IalMtRGBXDqnts8pBxT/XDNUbQ1ZaIRCJQU0brOCYm4xrTQl86kx95TqN28yPPCtYXU7Aj06dPR8uWLZGeno4ff/yxxr4kOXnyZJibm+Pw4cN49uyZscXRG+ruh+K4IsijOlmzZg3V/rNnz5Rkbdq0qd7b27dvHyQSCYCqKc69vb05y/7nP//B0qVL8euvv2LChAnU/mvXruHFixcAAF9fXwwfPpy3zRkzZlDrcXFxKjOi0H+zXbp04Q3iCQkJodYzMjKUjo0cORLW1tYAqjIT5efns9ZRXFyMiIgIAFVZz8aNG8crv6bY2dnB3d2d2lYE/rxNZGRkYPfu3Zg2bRo+/fRT7N69W+V+0WFmqzKE7v/555/U+uzZszn7ChsbG3z//fdYtWoVtm/fDj8/P+rYiRMnqPVPPvkETk5OnO2ZmZlh+vTp1DYzqx2zfYX9ZUMkEvHqPj2Ike7rYRIREUFlXHz33XfRsGFDzrLawLxvb6PumzJSqRTXrl3DunXrMGbMGHz99dc4ceIEp60ksMP+KyUQCASCQXF1ccaqpV/hxu07iLl0FUXFJbC1tUG3Tu3RJjiQNRKeQKjp5Obm4uzZs0r7Dh8+jM6dOxtJIgKBQDAcIpFI6aGo4qEJ3z76gxW5XK6VU0CIA1XIeQTjw9Qh5j5t9UeoA0qbMgTjos7uqNMZoVlwAACyShRFx8F5SH+V9ri2ufYRTBtN9Ip5fzltkUi4fhC9qnlw6QygXm+KL12BvEwiuK3CsxdR54NhrG1puo9gWAxiS6pOJOOaWoq2OiMrLETBxX81aisv8hwafPEfiF4/jzV1fbGwsMDixYsxa9YsxMXFYc+ePXrPdFIdNGjQAMOGDcPBgwexfft2LFiwwNgiCUYfwXPVnZELeDNFYL9+/VQCnGxtbfXeHv0FXnVZj4KDgxEcrDpDCT0TVuvWrdX+9pydneHh4YEnT56grKwMly9fpqZBBJSnmOvbty9vXa6urtQ6cwo5JycnDB06FH/99RcqKioQERHB+js8fvw4SkpKAAADBgygsi/pExsbGwBVtkEmkyE3N1fvbegTvoAkXcnKysLevXuxd+9eeHp6omvXrggNDUWTJk2oMobW/ZcvXyIlJYXaVqf79MxvdOi636ZNG7XtKjJyAVWBXPT+kelv1EX333//fUyfPh2lpaU4ceIEioqKqClN6dCzLXJ9R11Q6L1IJIKFhQWKi4tNQvfLysqUshuKRCKTkEsdFhaGSyxSWVmJmzdv4ubNm/jll18QFBSE0NBQdOnSRUnXCKqQQC4CgUAwIm2Dg9A2WP+pWwkEU+TYsWPUnO4KDh06hB9++MFIEhEIBIJh4Qveou9Xt0/bdtXtE3LMWLAFMTGPvw1oGgCo2M8VOKEor0lAn6k5rgj8aBM0CkCzLDivyT9zAS5DB3DKoG6fsTFFmUwVbfVKxT5xZDjhak+bfQTTgE1n2PYz9Sb/3EWN2im4chOVhYUwc3RkbYdPNkL1oy9bolQnyLimNqONzhScjRacYVRBxfOXKL11B3btWrO2zyWXMXF2dsaSJUswb9487N27F97e3ggNDTW2WBozevRonD59GtHR0Rg6dChatmxpbJF0QmiAV3Z2NvLy8gwsDTu///477OzsVIJXrKys9N7WtWvXqHV/f3+t6khMTKTWAwMDBZ3j7++PJ0+eAKiabo0eyEX//fJlCAOqshwpYAazAFWZiRTBKgcPHmQN5KJPq2iIYBYAVGYwCwsL+Pj46D3rl77hy+KkTx49eoT9+/dj//79cHd3R9euXTFgwACVQC596/7169epdUtLS7V6xoZUKkVycjK1LUT36b+xJ0+eIDc3lwqSYfZbXl5evHXx6b6DgwOGDRuG8PBwlJWV4fjx4/jwww+VyhQUFFAZxRwdHfHee++plV9TFHovl8vh5OSE3Nxck9H9Pn36KG2bilx8tGnTBgMGqD7f0TdyuRzx8fGIj4/Hr7/+isDAQISGhqJfv34G6YdqOiSQi0AgEAgEQrVw4MABlX3p6em4desWWrduzXIGgUAg1A7UOTPpx9icAroENNVkRyZXMJepy20IhAYFMvdz1VWb9YZQhaaBpMVx/6KyVPmBtjoK4q5CVloKMxsbk9AZPpuhbdYE4vBXhu1aahJ0QU2tKMAO8dXzNt+DmoaQvpwKKJXKUBAVq1kDskoUXIiFy5ABKvXytUkwLrraEuZ5ZFxT+9FEZ/KOn9GqjbwTZ6hArprS/zRp0gRz587FypUrsWbNGnh4eMDX19fYYmmEra0tJkyYgJ9//hmbN2/G2rVrTe46C0WT8aYhsnHZ2dnBzs4ONjY2sLKygrm5OZWlU9lHAAAgAElEQVSFRyKR4PHjx7C1tUVpaSnS09NVMuhUVlbqXSZ6nc7OzlrVochmBQAuLi6CzqG3VVZWpnSMrl+aZIYqLCxU2devXz/Uq1cPOTk5iIyMRGFhIRwcHJTO+eeffwBUZTgaPHiw4PY0QXGdLSwsYG5urlXgUHVijNloQkJC0KFDBzRo0EClfX3rPr0+BwcHpaAooSimJFQgRPeZ+kzXfbre29nZcU6ryAab7oeFhSE8PBxA1Yv6zECuo0ePUgFzo0ePprJn6RPFdRaJRLC0tIRIJDIJ3ZfL5bhz5w61bWtrqzRlpqlCn6K1uvD29kZISAjat29Pgrg4IIFcBAKBQCAQDI4idTcbhw8fJoFcBALhrYAr8EaTY5q0oc3x6kZI5i1NgpSElqmpCAkKZDuuSd3aHieYJkIDSfPORWtct1wiRXHMZTi925u3bULtgkun6Me4jmtSv6bHCKaLOr1QHC+5egOywhKV4+ooOHsRrkMHqm2bYFroakvUuYDJuKb2IURnpC9eovDKLa3qzz0VBY8FX0BsoeoyM2V9CQ0NxZgxYxAeHo5ly5Zh/fr1WgfMGIt+/fohIiICKSkpOH/+PHr3Zh9bmgr6mFbx3r17vMfNzc3h4OAAW1tbWFtbw8LCAmZmZhCJRKisrIRUKoVEIkFpaSmKi4uVPnwUFxcjNDQU+/fvx7Jly5SOMQOe9IGrqytevXoFQPvfkUz2JsOe0AAgermKigrOY46MjJ58MGeZAKoCp0aPHo2ff/4Z5eXlOH78OEaNGkUdj4iIoK7r2LFjDRaooGgjPz8fSUlJSpnQTBFDBA0ycXZ2RteuXdG9e3cEBAQo3XfmfdC37tepU4da14feA8J0n1mGrvva6j3Arvt9+/ZF/fr18ezZM/zzzz8oKSlRyvJHz0Q3adIkjdoTiuK+yeVyPH78GAMHDsSvv/5qkLY0obS0VOladOzY0STkEkJMTIzB2/D19UVoaCi6d+8ODw8Pg7dX0yGBXAQCgUAgEAzOsWPHVP44Kzh06BCWL19ezRIRCASCcRHqvNJnWzWVmi6/IeDTH/pxrmNCrym59rUHPp2pyoJzSat6885cUArkIjrz9iA0OIf1XJ6p0NS1RajZ8OlN/tkLWtWZH/MvZCWlMLN986Y/0Zmag9a2RCRsqlZBdRFqFHw6k3vyLKBlkI0srxBFl67AsXsXlXZMnfHjxyMzMxNxcXH47rvvsGrVKlhYWBhbLMGIxWJMnToVixYtwo4dO9CjRw+tMtgYE02Du/Ly8hAYGAi5XA6ZTIaKigqUl5dTwVgSiQS5ubnIzc3Vq5xisRgxMTFwc3NDvXr1lI6xTR2oK15eXkhNTQUAlenshGJnZ0etcz1bZkIvZ2lpqXSMmZlIGznohIWF4eeffwZQNWUgPZCrOqZVBJTvnSEyH9UUnJyc0LVrV3Tr1g1BQUGcwU/Ma6Rv3W/cuDG1rg+9B4TpPjPgiq772uo9V3lzc3OMGTMG69atQ3FxMU6ePInhw4cDAHJzc6kX+ps2bYpOnTpp1J5QmPftbdZ9U8fb2xvdu3dHt27d0LBhQ2OLU6MggVwEAoFAIBAMzsGDBzmP3b17F8nJyUrzuBMIBMLbgj6yl6irl1B7MYT+EN2p3bDpTPG/1yAr5H97n4v86EuQV1RAzHCQmBraTK9IfgvCIbaIoA30e1wpkyHv7EWt6pFLpCiO5c4OSKg5EFtC0BSVDKNaTquoIO/EaTj16KqrWNWOSCTCvHnzMHfuXNy9excbN27E7NmzjS2WRrRu3Rrjxo1D586da1wQlzaIRCIkJCRUe7v0TEgeHh6wsrKiAk3u3r2r9/ZatGiBs2fPAgAePHigVR0NGjSg1jMyMgSdk5WVRa0zp5ujB/dokomJORWlgpCQEPj7+yM5ORnHjx9HcXEx7OzskJeXh8jISABAQEAA2rVrJ7gtTcjJycGLFy+obR8fH4O0Y6o4OjoqBW8JsR/Ma6Rv3Xdzc6Oy0RUWFiInJ0clcFIdFhYWShntMjIy1E59R9d7kUikpLPa6j3ArfthYWFYt24dgCrfjyKQ68iRI1Tg2aRJkww2FmPet7dN900dLy8vdOvWDd27d4enp6exxamxVP9EtAQCgUAgEN4qCgsLqT+uXBw+fLiapCEQCATTRZFRgPnR5ryahD7lrWnfXZ9oqj/a6huh9qC45/laTKuooLK4DIVxV/QolW4YWofJb0Q9xBYRtKHkZjykr/K1Pj/3dJT+hCGYBJraBWJLCJLHT1Acn6RTHXlnYyAr1f/0ctWBtbU1lixZAkdHR5w6dQpHjx41tkgaM27cOPj6+hpbDL3TuHFjjBkzhvoEBQXhgw8+MKpMgwYNgrm5OVq0aEHty8rKQn6+9n0xG3379qXW1U0nKZFIcPHiRdy5cwcPHz6k9vfp04dav3Pnjto2ZTIZkpLe2IKePXsqHaf3DeqmoqTDlcVIJBIhLCwMAFBSUkI9Az927BgkEgkAwwazMK9Jq1atDNKOKeHg4ID+/ftjxYoV2LNnDz7//HO0bt1acBBoUFCQ0nZ8fLxe5ROJRHjnnXeobXW6n5WVhcuXLyMpKen/2bvvsKbu74Hj77CHLEVA6sAt7llxoNaK2jrr1jrr1rpHW3EhVm1rravuuq11UPfEqnVRd9W6xT0QFdkzJL8//JEvyIaEAJ7X8+RJ7s29n3uA+DHJPfecJFX4MvvaT5zYVLt2baysrJLElCAzr3tI/bVfo0YNKlasCCRtI7p161bNMRP+behCbn3tGxkZ0aNHD82tZcuW+g4pxxQrVowvv/ySZcuWsXTpUnr06CFJXNkkiVxCCCGE0Km9e/emW0bYx8cnh6IRQoi8J7UTU3KCSmREsteJvG5EImqVKstVcBJktR1aTpNWfvqlUChQJ/xeFZlrhybyv5C/sp5QCu+qA6r+/2SpyL9Sev+rUMicLd7JbjUuAFVUDGHHs/e+SJ8cHR2ZMmUKhoaGrFy5kkuXLuk7pHwnxTbl6VQQ/Pjjj9m4caPm1q5dO0qVKkXNmjV1FWa6ypYtCyRPaMlIskhmfPLJJxgZvWsMdfjw4TTbw125coVGjRpRtWrVJMlXn3zyiSZB59KlS+m2m7x8+bImoaRUqVKUKVMmyfNZTWhJrSoRvEtCTJDQlSKhraKBgUGS57Xt/SSk9/+m+UWBAgVo3rw5M2fO5Pfff2fkyJHUqFEjSxX8ypUrh6mpqWZZ2697IEnyzr59+9Lc9pdffqFevXq4uroyb948zfrmzZtrHidUtkvL6dOnUzw+ZC+RK7XXfuJErbCwMHx9fXn9+jVHjrz7//jTTz+lWLFimTpWZuTW176xsTGbNm3S3KZPn67vkHSqaNGidO/enaVLl7J8+XK+/PLLJO1FRfZIIpcQQgghdCqttooJLl68mKT8rxBCiA9Ldk/ASUKAEFkTcelKtqrgAAQfPYVaqdRSRLqV0SqHQoicFeybvYRQVUQ0YX65pzqgECLnBe07rJVx3mohIUyfKlWqxPDhw1GpVMyZM4dnz57pO6QPllqtTnZLTJ9VuVJL5NJ2ZSIrKysaN24MwIMHD1izZk2q2y5YsEDzuG3btprHNjY21K9fH3iXLDJjxoxUx1Cr1UycOFGz3KJFi2TbJG4xp61ELhcXFxo1agS8u6D5+fPnHD78bk5q2bJlui3xsuP9JKTcksyiDZaWlnh4eDBjxgx+//13Ro8eTc2aNbPdfvX9anRPnjwhODg4u+Em0aJFC81rbdGiRQQEBKS4XUhICGvXrtUsJ37tJ07k2rp1K2fPnk31eA8ePGDJkiVJjp9Y4td9bGwsykx8fk8viTHh8/P27dv5888/iY+PB95VotMVlUrF9evXNcuOjo6Zbl8pss7Z2Zlu3brx66+/smLFCnr16kWJEiX0HVa+JIlcQgghhNCZiIgI9u/fn6Ftpb2iEEIIIUTOCjmS/Wpa8SHhhF+4rIVotENXiViS4CWEbkT+d4PYgFfZHkcb85kQIm+KvnOPaH/tXBwYcvIs8aGhWhlLX1q2bEmbNm0IDw/Hy8srzSpIQn+qVKlChQoVcvy4zs7OmlZp77ciO3PmjNaPN2fOHE0SyYQJE1i+fDkqlUrzfHR0NN999x2bNm0CwMLCgm+//TbJGHPnztVU9lq8eDFz5swhKioqyTZv375l4sSJHDt2DIBChQolGweyXpkocQWnlCRUJgoPD2fIkCGaRBldJrMA+Pn5JVnOLe3lssrS0pJPP/0ULy8vNm/ezJgxY6hdu7bm768t7ye8afu17+zszLhx4wCIioqiVatWyaokPnr0CA8PD00SWdu2balTp47m+eLFizN+/HjN8pdffqlJEEzs33//5csvv9R0RPniiy80yY8J3v8sq63XfrFixTQV9Hbt2sXGjRuBd0mcX3zxRYaPkVlXr14lMjJSs5zXX/d5gbOzM127dmXx4sWsWrWK3r17U7JkSX2Hle9pd+YTQgghhEhk//79mnLW6fHx8WH06NE6jkgIIURupVAo0m1LIYTQHrVaTfCR7LUzSxBy5G+s3Oqkv2EukNZcI8laQuS8EC3NQ8FHT1FsmhKFlk/0CSFyv7f7fbU2lloZT/DhYxTq1E5rY+rDoEGDCAgIoH79+hgbG+s7HJGKTp06MXPmzBw9ZkI1LoA6depgbGysSfbbuXMnERERmkQvbahduzaenp54e3sTGhrKkCFDWLBgAeXKlSMuLo4TJ04QHh4OvKsatGDBAhwdHZOM8fHHHzN79mwmTJiAUqnku+++Y8mSJTRp0gQbGxvCwsLYuXMnISHvKg0bGhry+++/p9jeK3FlosSP0/N+4tj7OnXqxNdff01MTAx79uwBwM7OjjZt2mT4GJl18eJFbt68qVkuW7Ys9vb2OjuerlhYWODm5oa7uzs1a9bMkTmrfv36bNiwQbO8ceNGPv/8c60ew8vLiwMHDvDff/9x6dIl6tSpg7u7O4UKFeL58+f8888/mm2LFSvG3Llzk43x/fffc/LkSc6ePYu/vz8tWrSgQYMGVKxYESMjIx4+fMiBAwc025cvX541a9Yk+1z7/mtdm6/9Xr16cezYMUJCQjh58l174q5du2JhYZHhY2RW4r8dkCxxTWiHk5MT7u7uNGrUiNKlS+s7nA+SfLIVQgghhM5kpK1igtOnT/Py5ctkH9aFEEJ82NatW8epU6c0y1OmTEnyhawkXgiRNVH/3dRKFRyAt4eP89GksSgy8YWwPvzzzz/89ttvmuXOnTvj4eEByFwihL689T2ulXESqgPmlaRSIYR2qNVqgvZqL5EL3rVXzOuJXIaGhnh5eek7DJGOunXrUrx4cR4/1k5FuYwoV66c5nHBggVp2bKlJvEoIiICHx8fevfurdVjenl5YW1tzbRp04iMjOTmzZtJEpAAbG1t2bBhA61bt05xjPHjx+Po6MjYsWN5/fo1T548SZbMAe8qLa1cuZK6deumOE7i9/xmZmYZ/hnSq2Bka2tL27Zt2bZtm2Zdt27dMnWMzFq/fn2S5Z49e+rsWNpmbm5O3bp1adSoEbVq1crxhNMuXbowatQoYmNjgXdJjGFhYVhZWWntGObm5pw4cYI+ffqwZ88eVCoVf/+dvIKsm5sbPj4+ODs7J3vOxMSEv/76i9GjR7NmzRri4+M5ffo0p0+fTrKdoaEh3bt3Z9GiRdjY2CQb5/3Putp87Xfs2JFhw4YluZi/T58+GR4/s+Lj4/n999+TrMtLr/3cztHREXd3d9zd3ZMk/gr9kEQuIYQQQuhEVFQUe/fuzfD2arWanTt3MnjwYB1GJYQQIq85ceIEa9as0SwPGTIkxStrhRCZE/KX9tqQKYNCiLzyH5Y1cndLg7t37yZJ5CpfvrwmkUsIkfOi7/oT8+iZ1sbLS9UBhRDaEfnvNa0lpicIO3+FuMBXGDsU1uq4QrxPoVDQqVMn5s2bl2PHfP/EfO/evTWJXPCu0o22E7kUCgXjx4+nb9++bNu2jfPnz/PixQuMjIxwcnKiXbt2eHh4ZKh9YefOndm5cyfnzp3jzp07xMfHY2VlhYuLC927d6d69eppXqDxzTffaH6+9Kr4VK5cWZMwUqhQoXR/zpkzZyZpJ9egQYN098kqpVLJ5s2bNcsKhULT3jG3MzAwYPPmzZiYmOgthoIFC/L555+zc+dO4N15jO3bt9OvXz+tHsfOzo7du3dz6dIlDhw4wOXLlwkPD8fCwoJKlSrRuXNnqlSpkuZr1tLSkpUrVzJlyhR27NjB5cuXefnyJcbGxlhZWVGvXj26dOmCg4NDmmMkTn5KL3Fu8ODBms/JtWrVSnNba2trdu/ezevXr4F3f19dvvYPHz5MQECAZtnNzY0yZcro7Hgfkrp169KwYUN9hyESkUQuIYQQQujEgQMHkvQqzwgfHx9J5BJCCCGEyAGF+/bAvlvHNLcJWLWe4CN/U3TiCArUrJ7mtgbW2rt6WQjxYTApVpTKR3zS3Cbq5m0eef2Idb06OI8akua2ChNpHybEh8asfNl05xGA11t28HrHXooM7oPNJ43S3d7Qxlob4YlsePDgAStXrqRv375JqkjlFelVe719+zb79u2jb9++ODg4EBgYmCMxvZ/w0KZNG2xtbQkODgbg6NGjPH78WCcXT9nb2zN06FCGDh2a5THMzMzo1q0b3bp1y9L+TZo0yfC2Tk5OdO/ePcPblytXLsdeq/v37+fVq/8lsTZo0ICSJUvmyLG1QZ9JXAl69+6tSeQCWLt2rdYTuRLUrFmTmjVrZmuM4sWLM2rUqCzta2JikqnXct26dVOtapeSnLw4at26dUmWtZ14+iGTVsy5jyRyCSGEEEInMtNWMcGxY8d4+/YtdnZ2OohICCGEEEIkMLJN3nIhmfh4lK+DMTQ3x9gp9St8hRAiKwzMTDFIZ26JefoM5etg1HFxMg8JIZIxtDDH0MI8/Q0VoHwdjIGpqcwlecS1a9f4999/WbFiBXPnztV3OMC7RCi1Wp3uutTExcVx8uRJ9uzZw927dwEoVqwYHTp0YNmyZVqP931FixZN1lLN1NSULl26sGLFCgBUKhWTJ09O1rZP5B7x8fFMnjw5yTpJZsm8Vq1aUahQId68eQO8qwZ/8OBBWrZsqefIRGouXbqUpH2piYlJlpM6hcgLJJFLCCGEEFoXExOTpCx3RimVSvbs2SMfPoUQQuhMdHQ0T5484cWLFwQEBBAaGopCocDIyAhDQ8Mkt8TrEh6ndJ/WYxMTE6ytrTEwMND3jy6ESINarU63coK2BQYG4u/vz8OHD4mIiNDMNwYGBlqZixLGSVi2sLDAwsIiR39GIUTOUCqVREZGEhMTQ3R0NDExMUluqa1TqVSYmppibm6Oubk5ZmZmKd4nfpzTc6UQ4sPUqlUr9u3bx40bNzhx4gSNGqVfSS23ev36NQcOHODgwYOEhIQA4OzsTKtWrfDw8MDIyIjNmzdrntOV1KpFDRkyhJUrV2oS0jZu3MjQoUOpV6+eTuMRWbN06VKuXbumWS5UqBBdu3bVY0R5k4mJCf379+fHH3/UrBs9ejTXrl2TykS51IgRI1CpVJrlbt26SUEAka9JIpcQQgghtO7QoUOEh4dnaV8fHx9J5BJCCJEpSqWSEydOcPLkSS5fvsydO3cIDg7WnLSMi4sjPj4+yRc++qBQKFAoFEmSNIyMjDA2NtbcTE1NNbeEE6fm5uZYWlpiaWmJhYUFBQoUwMrKCisrK6ytrbGxscHGxgZbW1usrKwwNjbGxMQEExMTzMzMkl11LYTQHX9/f/755x+uXLnC3bt3efnyJW/fviUsLIyIiAiioqKIi4vLFfNRanORiYmJZh5KnMSRMAdZWlpSoEABrK2tNXNRwhyUcEsYI2Eeyg3tU4TIq6Kjo7l9+zb379/nxYsXPH/+nOfPn2sev3jxgtevX2e4Ikx2mZiYZCrxKyPPpba9qalpikmrQojsiY+Px9DQUN9hpMnQ0JABAwYwbdo0Vq9ejZubW558P7Fz505Wr16tee9Xu3Zt2rRpQ82aNTWfD+Fda7z9+/frNJayZcumuL5GjRr069eP1atXA+8ucBg5ciTnzp2T5N1c5s2bN0ydOjXJOm9vb6ytpR1sVnh6erJu3TpevnwJvGt5unDhQsaNG6fnyMT7Nm3axJkzZzTLBQoUYPbs2XqMSAjdk089QgghhNC6rLRVTJCQBFagQAEtRiSEECK3y+jJx6tXr/LHH3/g5+fHw4cPefPmDTExMRk+joGBgaZSVkKSgUqlQq1WZ+mWEPv7y4nvVSpVkmOoVCqUSmVmfj1a4zjjf48TvpRPOIGQ0rKBgUGy+/cfJ1QQen85cXWhhOpAaVUWMjY21jxnbGysSSwxNDTExMREs1y4cGHq169P3bp15WSuSCY7iQxpVeVK/FxkZCTbt2/nwIED3Lhxg5cvXxIcHJypuQjAyMhIk7RpbGys87koYf5J2CY+Pp74+Pgs/a6yo0kq81DC/fu3hDknpbkopVviOSi9uej9+8RzUcI8lPiWsM7S0pIaNWrQuHFjChYsmOO/Q5F/Xb9+nXPnznHz5k1u3LjBzZs3efjwYZrJnwYGBjg6OmJlZZUkCTNxcnhq6wwNDYmOjiY6OpqoqCiioqI0j1O7T3is68ox6VIYUO4H4zQrFWakmmF62+h6bBsbG0qVKkWJEiVyfWKNyB+Cg4OZOXMmn376KZ999pm+w0lTnTp1qFWrFhcvXmTHjh25tupQWu0VS5cujZmZGR4eHrRq1QpnZ+ck+719+5bVq1dz9OhRnceZWkUugNmzZ+Pj46OZ2y9cuMCKFSsYPHiwzuMSGfftt9/y9u1bzXK1atXkb5QN1tbWzJ49m6+++kqzbsaMGXTt2pWiRYvqMTKRWHBwMN98802SdZMmTUoynwqRH8k3nkIIIYTQqtjYWHbt2pXl/WNiYti/fz9dunTRYlRCCCHyIn9/fy5fvpxkXf/+/VPc1sLCAkdHR0qVKkXVqlUpX748Tk5OODk5UaRIEZydnXNF0o9SqSQoKIi3b98SGhpKcHAwISEhhISEEBYWRmhoKOHh4YSHhxMREUFkZCSRkZFJTpwmVBqLjY0lLi6OuLg4lEolSqWS+Pj4FBM7VGo1vHdy4f0kj7zIxMQEOzs7ihUrRp06dWjdujUtW7aUVpYiRRl5raeUzHXixAlNAqm/v3+alWeNjY2xtramcOHCFC9eHGdnZ4oUKULRokUpXrw4JUuW1JzQ07fg4GDevn2ruQUHBxMaGkpISEiSuSil+Shxm7bY2FhiY2NRKpWaCogpJZvl13nI0NAQKysrnJycqFSpEh4eHnTs2BF7e3t9hybygPv373P06FH++usvjh07pqkIkZiTkxOurq6UKVMGZ2dnzbyScO/o6KiXBCC1Wp1qkldGEsEy8lxMTIwm6TThfY5SqeRVeDTKOCUmJgrUKhWxsbFERUWhVCrz7HxibGxMiRIlKF26NGXKlKFatWrUqVOHypUr54r3sCL/ePToEbdu3eL27dsUK1aMypUr6zukNA0cOJDLly+zdetWPDw89J5AnVbSVkqqVKnC+vXrU33vZ25uTlhYmLbCS5WBgQGlSpVK9XkHBwemTp2apBLRyJEjqVixIu7u7jqPT6RvyZIlrFq1Ksm6hQsXymffbOrbty9Lly7l/PnzAISGhtKmTRtOnjwpF5rnAnFxcXTo0IFnz55p1pUuXZqxY8fqMSohcoZ8AhBCCCGEVvn6+hIaGpqtMf78809J5BJCiA9I4spVf/zxB6tXr+bcuXMpJksYGxtTvHhxatasSd26dWnSpAk1a9YEyBNtH4yMjHBwcMDBwSFHj1vi11M8Dozg5cRPcLBM2pJEpVIRHR1NbGys5j4uLo7o6Gji4uI07SkTJ4+9f5/wWKlUpphgljjJI2FdSrfEJ2rff5xwCwkJ4fXr10RFRREbG8vLly95+fIlFy5cYOnSpQDY2NhQunRpGjVqxLBhw1JtIyI+HJk54RYWFsavv/7Kli1buHHjBnFxccm2sbW1pXz58tSpU4cqVarw8ccfU7Vq1Tx1IiWhDWLJkiVz5Hgdt17hz+sBHO9Xm8YuhZI9n3gOSph/Eu5jY2OTzEUJ9wnzSsLjxHNTWvNPwuOExLOE+5TmopTmoaioKF69ekVYWBjx8fEEBwcTHBzMrVu38PHxYciQIZiamlK0aFFq1KhBr169aNu2bY78nkXud+XKFbZu3cr27du5c+eOZr2BgQG1atWiUaNGVKpUCVdXV1xdXbGzs9NjtKlTKBSatog5rcqKf/jvWQj/jWlEcdukx1er1Sm+l0jrfUZGntPmWImfCwoK4t69e/j7+3Pv3j3u3bvHoUOHND+Pubk51atXp06dOri5udGsWTMKFy6c079ykY9Uq1aNQYMGsWzZMmbOnMmCBQtwdHTUd1ipKl68OJ999hn79u1j/fr1jB49Wt8hJREbG4uJiUmaCV4pJXElfHY1MzNjypQpLFq0CF9fX53F6eLikm5ryhEjRvDbb79x48YN4N3P1r59e/z8/NKs5iV0b9++fYwcOTLJum7dutGoUSM9RZR/KBQKFi1aRL169TT/hv/991+6dOnCnj17pFqmng0YMIBjx44lWffLL79gamqqp4iEyDmSyCWEEEIIrcpOW8UE+/btIyYmRt6QCyHEByA2NpZZs2bx+++/4+/vn6R1kEKhwMLCgoiICM26M2fOUKtWLX2Emm8ZGBhgYWGBhYWFvkPJtOvXr3PixAnOnj3LhQsXePjwIREREYSEhHDp0iUuXbrE/PnzNW3QevToQb9+/XJFNSShfdmpwvL06VOmTp3KoUOHCAgISPKcsbExZcqUwc3NjXbt2tGqVSupjqIDJiYm6Z5gzI1ev37N8ePHOXv2LH5+fty+fVvT9tff3x9/f3+2b9+OoaEhpUuXplWrVowYMSLHEgXKVgoAACAASURBVOhE7nD9+nV+//13tm3bxt27dzXrK1SoQLNmzWjatClNmjTJtUlbeYlCocDY2BhjY2N9h5JpwcHB3Lt3j7t373Lx4kXOnz/PxYsX8fPzw8/Pj4ULF6JQKKhZsyYtWrSgRYsW1KtXL0/+rEK/2rZty8OHDzl48CAzZszg559/ztXvj3v16sXx48fx9fWlTZs2lC5dWq/xxMfHc+HCBQ4fPsytW7dYu3YtxsbGGa7W9f4FSIaGhowePZqCBQuyZcsWncSckQtbjI2N2b59O/Xr1yc4OBiAoKAgPvvsM/755x9JItWTy5cv07Vr1yQt0StWrKi5iElkX926dfnxxx+ZMGGCZt2BAwcYPnw4y5Yt02NkHzYvLy/Wr1+fZN24ceNo06aNniISImfJt05CCCGE0Jq4uDh27tyZ7XHCw8M5fPiwvCkXQoh87OjRo0yZMoV//vknSfKWhYUFtWvXpl+/fvTq1YuBAweyZs2aDI2ZUks0kb9VqlSJSpUqMXToUM26yMhI9uzZw4EDBzhx4gSPHj0iIiKCU6dOcerUKYYNG4azszPNmzdn6tSpkkzxAUjthJpSqeSHH35g+fLlPHr0KMlz9vb2NGnShGHDhtGkSRPNepljxPvs7e3p1KkTnTp1SrLez8+PnTt3cuzYMa5du0Z0dDR37tzhzp07/PLLLxQoUIDatWszevRo2rVrp6foha4dO3aMH3/8kYMHD2rWValShc6dO9O5c2cqVKigx+hEbmNra0vt2rWpXbs23bt3B95VTr158ybnz5/n+PHjHD58mIsXL3Lx4kVmzZqFlZUVTZs2pVWrVnTs2FHvbedE3jFs2DCePHnC9evXmTt3Lp6enrn2fY61tTXdu3dn1apVrFixgh9++EEvcbx48YLDhw/j6+tLUFAQAJaWljx69IgyZcqkuE9QUFCS6ovFihWjaNGiKW7bp08fjIyM2LRpk9Zjz2iFYldXV/78809atGihqUp7//596tevz59//kmVKlW0HptI3d69e+nVq1eSi9scHR3Zv38/tra2eows/xk/fjwPHjxgyZIlmnXLly8nPDycFStW5MkL3/KquLg4xo0bx6JFi5Ks79SpEz/99JOeosoYtVrN2bNnNctWVlZUqlRJjxGJvCzv1HsXQgghRK539OhRzRVb2fXnn39qZRwhhBC5R3BwMEOHDsXOzo5PP/2UM2fOoFKpsLGxoV+/fly7do3w8HCOHz9Onz598lSLMpF7WFhY0LVrV9auXcv9+/eJi4vjjz/+oGXLlpovu58/f87atWspVaoUDg4ODBo0iKdPn+o5cpHTPD09mTRpkiaJy8XFhSlTpvDq1StevnzJ1q1bkyRxCZEZ9erV44cffuDcuXNERUVx5coVBg4cSIkSJVAoFJr/79q3b4+ZmRmffvppknZqIu9SqVT4+PhQt25dmjZtysGDB3F2dsbLy4ubN29y9epVpkyZIklcIkMMDAyoVKkSffv2Ze3atTx//pwrV67w448/0qxZM2JjY9m1axeDBg3CycmJVq1asWHDBsLCwvQdusjljIyMmDx5Mg4ODpw5c4aNGzfqO6Q0tW3bFmdnZ65du8aZM2dy7LixsbEcO3aMb775hv79+7NlyxaCgoKoUqUK48ePZ+PGjUmSuN5Phjt69CgNGjTQ3NauXZvm8b788ktcXV21/lk4M60RP/nkE1atWpVk3b1793Bzc9NJkplITqVSMXnyZNq2bZvku3YLCwv27t1LiRIl9Bhd/rVw4UJatWqVZN2mTZtwc3Pj3r17eorqw/LixQs++eSTZElc9erVY8OGDbk24ThBdHQ09erV09z69++v75BEHiYVuYQQQgihNdpoq5hg165dKJVKaVsjhBD5gJ+fH2PGjOHcuXOayjgGBga4ubkxY8YMmjZtqucI867ouHjM5x6HaOW7FQZpfKlloMBx7vGUn1P9f8UiYwOCxjXBzjz/tOgxMDCga9eudO3aFYCAgAB+/fVXNm/ejL+/P69evWLlypWsXLkSR0dHOnbsyJQpU3ByctJz5CIzUqu6lXj98ePHkz1nZWVF+/btmTlzZrLqCClV+ZPKfyn7aud/rLny/N1cks481GTdxdSf///9l7dyZVDtYtoPVI+qVq3KihUrgHcn537//XdWrlyJn58fMTExHD16lKNHj2JmZkajRo347rvvJJEwj1GpVGzatImZM2dqqq9UqFCBCRMm0LNnzzzZOjSnKeNVGP98HCLjQE2680mJBSdTfk6tfre/kQEBYxrhWMBUB9HqT9WqValatSoTJkwgMjKS48ePs337dnbs2MH+/fvZv38/5ubmfP7553Tv3p1WrVrl6rZ5Qn9sbGyYOnUq48aNY/Pmzbi4uODu7q7vsFJkZGRE//798fb2ZtWqVdSpU0enbUX9/f05dOgQx48fJzw8HAA7Ozs8PDxo0aJFmp8VMtpiMTUzZ87E19dXay3djIyMcHFxydQ+vXv35vHjx0yZMkWzLjIykp49e3L69Gm8vb0pVKiQVuITSfn7+zN8+PBkCf7GxsZs3ryZ2rVr6ymy/M/Q0JAtW7bwySefcP78ec36a9euUbt2bRYvXkyPHj3kokMd2b17N4MHDyYgICDJ+nLlyrFr1y55LyM+ODLTCCGEEEJrwsLCaNq0aZq3jz/+mOrVq9OoUaM0t6tRo0aS0uNCCCHyngULFvDRRx9Rv359zp49i1qtxsnJiSlTphAVFcWpU6fSTOLKypff2fnCPC8yMzakYyn7/61QqbN2+3+NitnlqySulDg5OeHt7c29e/cICQlh0qRJmiuaX758yZIlSyhSpAhFixZlzJgxvH79Ws8Ri+yaNWsWBQsWTFYBYeDAgbx9+5Y1a9ak2uJGZEy7CoX/N5dkdR5KtH+7Cg76+2FygIGBAT179uTvv/8mNjaWLVu2UK9ePYyMjIiOjubw4cN88sknWFpa0rp1a/z8/PQdskiDWq1m69atVK5cmd69e3Pnzh3c3NzYsWMHN27c4KuvvpIkrgwyMjSgZ5nC75KwIOtzyf/vX9vZOt8lcb3PwsKCzz//nNWrV/Py5Ut27txJt27dUCgU+Pj40KlTJxwcHOjduzf79+9HqVTqO2SRy5QqVYrx48cDMG/ePPz9/fUcUerq1atHtWrVCAgIYPfu3VofPyIign379jFixAhGjBjB3r17iYyMxM3NjWnTprF+/Xr69u1LkSJF0h1LoVBkOfnf3NycYsWK4eCgnfdDJUuWzNKFspMnT2bDhg3JWsotXboUFxcXJk6cSGBgoFZiFHDr1i169epF+fLlkyVxffTRR/z999+0bdtWT9F9OCwtLfn777/p1atXkvUhISH06tWLihUrsn79evn/VEvUajV//vknNWvWpF27dsmSuFq3bs3Zs2cpXLiwniIUQn+kxIUQQgghtOaPP/5Id5v27duza9cu/Pz8cHNzy4GohBBC5KTw8HBGjBjBH3/8QXR0NPDuS+zatWszb948GjZsqNk2raSrjFTXEe90reSAz42A9DfMgG4V83fyxPusra35/vvv+f777wkODmbWrFn88ccfPHnyhGfPnjF//nzmz59P2bJl8fb21lT1ErmfUqlkwoQJrFixgqioKOBd8oxKpdJsU7ZsWc1jqbSVPS1K24OxIcTFZ3sstxJ2+T7x4n1dunShS5cuAGzcuJGFCxdy6dIlIiMj2bdvH/v27cPa2pq+ffvyww8/yNXoucju3buZOnUqV65cAcDNzQ1vb2+aNWum58jyri6VHNl45blWxurh6qiVcfIKExMT2rVrR7t27YiIiGDPnj1s3ryZgwcPsmHDBjZs2IC9vT39+vXj66+/pnjx4voOWeQSDRo0oGfPnmzcuBEvLy8WLlyoaUme2wwcOJARI0awefNmmjVrho2NTbbHvHbtGocOHeLUqVPExsYC4OzsTPPmzWnWrBkFCxbM9jEy482bN8ybN483b97QqlUrfH19NXFlReL3vJnVs2dPqlWrRocOHZK0lgsPD+enn35i8eLF9OzZk08//RR3d3ecnZ2zfKwP0e3btzl16hQHDhxgx44dST6rJGjSpAlbtmzRWmKfSJ+5uTnr16/Hzc2NMWPGJPn3d/v2bfr06YOXlxe9e/fG3d0dNze3ZAmPInUxMTGcP3+ekydPsnnzZq5du5ZsGwMDA7y8vPD09JTP6eKDJYlcQgghhBBCCCG0YuLEicyfP5+4uDgATE1N6dSpE/Pnz8fe3j6dvf8ncbKWs7MzFSpU0Cybm5tLwsV7Pi/nAEYGoEz+pW9mfVHhwzrhmZitrS0//vgjP/74I4GBgXz//fds27aNFy9ecPfuXbp168bAgQPp16+fJFLkYiqViokTJ7J48WLNF+6mpqb069ePpk2bMnXqVM2277eDkVaKWWdmbEjHsoW1klTao9KHOw/BuxOmPXv2RKVSsXr1apYsWcKVK1cIDQ1l4cKFLF68mMaNG7Nw4UIqV66s73A/WEeOHGHSpEmatjs1a9ZkxowZtGrVSs+R5X3NS9uDiSHEZj8xtEPFD3c+sbS0pFu3bnTr1o3g4GB27NjB5s2bOXr0KD/99BPz5s2jffv2jBw5kkaNGuk7XJELdO/enYcPH3Lq1Cm8vb2ZM2eOTlsXZlWpUqVo3rw5hw4dYsOGDXz99ddZHuvWrVv8/PPPPHv2DHiXDNm0aVNatGhBlSpVtBVypkRHRzN9+nTevHmDu7s7w4YNo1evXowZM4YXL15kacxy5cplK6YqVapw4cIF+vbty86dO5M8FxUVpWlTD++qf9WtWxcbGxssLCykBd174uPjiYiI4NWrV5w5cybNimYGBgaMGzeO2bNnY2homINRigTDhg2jVq1adO3alUePHiV57v79+0yfPh141/ayRo0aVKpUCQsLC/muIAUxMTFERUVx+/Ztzp8/T0xMTKrbFi5cmA0bNtCiRYscjFCI3EcSuYQQQgghhBBCZMv69esZMWIEoaGhANjZ2TF58mTGjh2b7bFnzJjBjBkzsj1OfmZpYkjbMvbsvpW9tha1i9viZPVhVcFJjYODAwsWLGDBggX4+/szcuRIDh8+TFhYmCaRokmTJixYsEASKXKBhOTPmTNnMmfOHCIjIwEoUKAAo0ePZtq0aZqTSB06dNDK8SS5K7lOFbVTHbDjB1ZBJzUGBgYMGDCAAQMGoFQqmTZtGsuWLSMoKIhjx45RpUoVXFxcmDx5Mv3799d3uB+MgIAARo0axdatWwGoXLkyXl5efPHFFzIvaImpkQFdyzuw5VrWEhYSlC9iTQlbcy1FlbfZ2trSr18/+vXrx+PHj/n1119ZuXIlPj4++Pj4UKNGDUaOHEn37t0xNZX3gh8qhULB2LFjef78OTdv3mTx4sWMGTNG32GlqHfv3pw4cYKDBw/SunVrXFxcsjSOg4MDL168oHTp0rRs2ZImTZpgaWmplRizUklapVLxww8/4O/vT4UKFRg3bhwKhQJra2t+/fVXvv/+ey5evJjpcbNTkSuBjY0NO3bs4OTJk3h7e+Pr65vidg8ePODBgwfZPt6HzMjIiB49euDp6ZntJDyRfXXr1uX27dusXr2aOXPm8Pjx42TbxMXFce7cOc6dO6eHCPMPR0dHxo0bx7Bhw7Q2FwuRl0kqtBBCCCGEEEKILDl79iylSpWiT58+hIaGYmxszOTJkwkKCtJKEpfIuK5aqGDzobUfyqjSpUuzb98+oqKi+O677yhYsCAqlYqjR49SpUoVSpYsyW+//abvMD9oS5cuxdbWlqlTpxIZGYmZmRmenp6EhITg5eWlSeLKyAk1ad+ada3KFQbD7CWy1Cpmi7O1XMH+PiMjI77//nvevHnDnj17NAmkDx8+ZMCAARQoUIBBgwZpEqqF9qnVapYtW4arqytbt26lSJEibNiwgStXrtChQwdJ4tIybbyv6fWBtYvOqOLFi/PDDz/w9OlTli1bRsWKFbl8+TL9+vWjePHiTJ06NctVf0TeZ2ZmxtSpU7GxscHX1zdZ9aXcws7Ojq5du6JSqTSVoLKiYMGCrFq1ikWLFtGqVSu9Jw6sXLmSs2fP4uTkxLRp0zAxMdE8Z2ZmxvTp02nYsGGmxjQxMdFqG1V3d3cOHz7MP//8I1UotczY2JgBAwZw+/Zt1q1bJ0lcuYipqSlDhw7l3r17rFq1itKlS+s7pHzF2dmZ+fPn8+DBAyZMmKD3uViI3EISuYQQQgghhBBCZEpAQAANGzbEzc2NBw8eoFAo6NSpE6GhoXh7e+s7vA9Sm/IO2U6g+JDbD2WEkZERs2bNkkSKXGTLli04ODgwfPhwQkNDMTIyYvjw4YSHh8tcpAdWpka0LJPxNrop+VLmoXS1bt2aa9eu8eTJEzp06ICxsTERERGsXLkSW1tbGjZsyIULF/QdZr5y/fp1GjZsyNChQwkJCWHo0KHcvHmTnj17SssoHfmsjD0YZ6+NVCdXJy1Fkz9ZWFgwePBgrl+/jq+vL61bt+bVq1d4e3tTokQJevbsqWkdKj4sDg4OTJ48GSMjI1atWsWlS5f0HVKKvvjiCxwdHbl8+TLXrl3L8jhOTtqfK7JyYcDevXvZtWsXlpaWeHl5YWNjk2wbQ0PDZGMbGaXdeKl06dI6actXt25d9u7dy7Nnz9iyZQtff/011apVk/8XM8HU1JQGDRrw7bffsnfvXgIDA1m5ciWlSpXSd2giFcbGxvTv3587d+7w77//smjRIrp27Yqzs7O+Q8tTChcuzBdffMHPP//MuXPnePToEaNGjcLcXCqpCpGYtFYUQgghhBBCCJFho0aNYvHixahUKgBq1aqFj48PJUqU0HNkHzYrUyM+L1uY/Vlsr+jqLO2HMqN169a0bt2ap0+fMmrUKPbs2aNJpFi1ahX169dn/vz51K5dW9+h5kt3797ls88+w9/fH3jXgq579+6sXLkSM7PUqzkpFIp0T6xJVZ3s6eLqwMHbr7K8f6dKkniRUUWLFsXHxweVSsXs2bNZuHAhgYGBnD59mjp16vDRRx/xzTffMGLECH2HmmdFRUXh7e3N3LlziYuLo3LlyqxYsYJ69erpO7R8z8zYkE7lCrP9etbatRYtbEn5wlLNIaOaNWtGs2bNuHfvHosWLWLNmjVs2rSJTZs2Ua9ePUaNGkXHjh3TTRgR+UelSpX4+uuvmT9/PrNnz2bp0qXY22cvWVvbjI2NGTZsGJGRkVSpUiXJc9HR0Rw5coTdu3fj7e2No2POJYqn914zpeevXbvG8uXLMTQ0ZPLkyRQrVizFfZ8+fcqZM2ewtramW7durFy5kiFDhmBoaMjatWsJCQlJto+uqzo5OzvTpUsXunTpAkBISAgPHz4kMjKSqKgozfcG4h1DQ0MsLCywtLSkbNmy0s42jzIwMKBatWpUq1aNr7/+GoDHjx/z+vVrIiMjiYmJkUrPiSgUCkxMTLCwsMDOzk6SFYXIIHnnLYQQQgghhBAiXWfPnqVt27YEBr5LFProo4/YuHEjTZo00W9gQqNbRYcsJ3J96Srth7IicSLFrFmzWLRoUZJEiqJFizJx4kRJpNCi4cOHs3TpUtRqNQqFgtatW7N+/foUqxboiiR7pa5teUdQXIcsnLeoVtSGYjbSVjGzDAwM8PT0xNPTk6NHjzJhwgQuX77Ms2fPGDlyJBMnTqRLly788ssvFCxYUN/h5hm+vr4MHToUf39/zM3NmTFjBuPGjcPY2FjfoX0wulZyzHIiV19pF50lZcqUYcGCBXh7e7NmzRoWLVqEn58ffn5+FC1alGHDhjFo0CAKFSqk71BFDmjevDkPHz6kQIECuS6JK0GdOnWSLAcFBbFnzx72799PWFgYAGfOnOGLL77Q6nG1naBRvnx5GjRoQK1atahWrVqq2/n4+KBWq2nTpg3t27enSJEiVK9eHVNTU9zd3dm8eTM7d+4kPj5es0/ZsmW1Gmt6bGxs0vwZhMivihcvrtU2pkIIITUuhRBCCCGEEEKkSqlU0qlTJ9zc3AgMDMTQ0BBvb2+ePn2qsySu9JIkJIkiZW3KO4BB1n430n4oewwMDJg8eTIvX77kr7/+ombNmsC7q+ZHjhyJubk5ffr0ISgoSKvHDQwMpFatWsTGxmp13Nzo9OnTFC5cmCVLlqBWqylVqhS3b99m9+7d2NraprhPSnNFWvNHZrcXyRWyMMa9ZNZO8PeUtorZ1rRpUy5evEhAQAA9evTA1NSU6Oho1q9fj729PXXq1OHEiRNaP66HhwenTp3S+rj68OrVK3r27Enz5s3x9/enefPm/Pfff3z77beSxJXDPi9bGIyydvqigyRyZYu1tTWjRo3izp077N69m2bNmvH06VMmTZpEsWLFGDBgQLZa2aUmJiaGcePGoVQqtT62yJpBgwbRo0cPfYeRrkePHvHLL7/Qt29ftmzZQnh4OA0aNODnn3/WehJXWjKa4PX+diYmJnz77bd4eHikuk9QUBB//fUXpqamtGnTBnjX3jChopOFhQX9+/dn+fLluLm5afbL6UQuIYQQQmiHJHIJIYQQQgghhEjRjh07sLOzw8fHB4CaNWvy/PlzJk+erPNjp5Y8IUkVqbM1M8ajTOavlpf2Q9qVkEjx8uVLunfvnmIihbYSHgYPHsylS5dwcXEhNDRUK2PmNkqlkvbt29OwYUNev36NkZERs2fPxt/fP8snpiRhS7e6ZrHCX6eKklCqLQ4ODmzatIno6Gh+/vlnnJ2dUavVXLhwgcaNG+Pk5MScOXO0cqwLFy5w5MgRGjduzI4dO7Qypr4cPXqUKlWqsGnTJs3v8NChQ9L+RU8sTAxpX7ZwpvczsTWjhrO1DiL68BgYGNCmTRt8fX3577//GDRoEAC//fYbVatWpWnTpuzatUtrrdPWrFnDvHnzaN++PVFRUVoZU+Rvly9fZsqUKQwdOhRfX18MDQ1p06YNq1atwtPTE1dX1xyLJbNVujK7/c6dO1EqlbRs2RJr69TnOGdnZ6ZOncr333+Pq6srRYsWzdRxhBBCCJE7SCKXEEIIIYQQQogkQkNDcXd3p0OHDoSHh2NmZsbatWu5ePEiDg7aa8GXkcpbCdskfpydMfO77hUz//eR9kO64eDgwO+//65JpChSpIgmkcLd3R0nJyd+/PHHLI8fGRnJ7t27AXjx4gUlSpTg6dOn2go/V9i+fTu2trbs2rULgFq1avHixQu+/fbbbI+dMKdkdG4RGZeVSjiuzta42JnrIBoxduxYnj17xpkzZ3Bzc0OhUPDy5Uu+++47TE1N6dKlCwEBWWtfBzBkyBAAVCoVHTt2ZPny5doKPceoVCqmT5+Oh4cHL1++5Msvv+TWrVt5ogpNfte1Uubnk68lKVQnKlWqxPLly3n69Clz5syhWLFiHDt2jPbt21O2bFl++eUXQkJCsjx+fHy8JsF03759eHh48PbtW22FL/IRpVLJkSNHGD58OJ6enly8eBE7Ozv69OnD+vXrGTp0KEWKFNHZ8bXdVjEj40dERLBv3z4MDAwyXGGsRo0a/Pzzz/I+VwghhMijJJFLCCGEEEIIIYTGokWLsLe311QMat68OW/fvqVPnz56i0m+fM64dhUcM91eUdoP6d7YsWN5/vx5skSKb775JsuJFKNHj05SASM4OJhy5cpx/fp1bYef44KDg6lXrx6dO3cmIiICc3NzNmzYwIULF7C3T7nqnC6q+EnlrqwpYmVKrWIpt7tMTS9pq6hz9erVw8/Pj6CgIPr374+5uTmxsbFs27aNIkWKUK1aNQ4dOpSpMR89esTFixc1y2q1miFDhuDl5aXt8HUmICAADw8PvLy8MDU15bfffmPjxo3Y2dnpOzQBtC5XGAwzdwqjo7yv0amCBQvyzTffcP/+fbZu3UqDBg24f/8+Y8eOpWjRoowYMYI7d+5ketzNmzfz6NEjzfLp06dp1KgRz58/12b4Ig8LDw9n27Zt9OvXj3nz5vHgwQNcXFwYO3Ysa9eupWvXrlhZWekltqwmd2V0v/379xMVFUWTJk20emGVEEIIIXIvSeQSQgghhBBCCMHjx49xdXVl5MiRxMXFYWtry+HDhzl06BBmZmY6O642kyIkwQIKmhvzScmCGd5e2g/lrMSJFP369UuWSFG9enV8fX3THUelUrFu3bpk66OioqhRo4bWWjfqw7x583BwcOCff/4B4PPPPycoKIiePXvqOTKRGd0z2V6xs1TQyTG2trasWrWKyMhIli1bRvHixQG4evUqLVu2xN7enunTp6NUKtMda+DAgSmunz59OsOGDdNq3Lpw9OhRqlevztGjR3F1deXcuXN89dVX+g5LJFLA1IjWZTPRNtrKhHrFbHQXkNAwMjKic+fOnDp1igsXLtC7d29iY2NZvHgxFSpU4PPPP+fQoUMZSlRRq9XMnj072fr//vuP+vXrc+/ePV38CCKPePv2LcuWLaN3796sWbOGN2/eULNmTWbOnMmSJUto1qwZxsbG+g4zVaamphQqVEhzs7TMfEv7v/76C4BOnTppOzwhhBBapFAosLe319zk4hCRHZLIJYQQQgghhBAfuDlz5lCyZElu3bqFQqGgb9++vH37Fg8PD32HJrKgeybaEEn7If2wtbVl9erVyRIprly5QvPmzbG3t2fGjBlJKm4lNn36dGJjY1N8Li4ujsaNG7Njxw6dxa8LwcHBVK5cmXHjxhEXF4ednR1//fUX+/bty3YyaVaSPCUxNHs6ZmJuKeVkRZlCFjqMRqRm8ODBPHr0iMuXL9O4cWMMDAx48+YNXl5emJub07ZtWx4/fpzivsHBwRw5ciTVsZcuXUqHDh10FXq2vN9KsVevXpw/f57KlSvrOzSRgq6ZqNg3tIKjzN96UKtWLdatW8fjx4+ZPn06Dg4OHDhwgJYtW2paMkZHR6e6/65du7hx40aKzz169Ij69etz+fJlXYUvsigqTpTRXwAAIABJREFUKoqIiAidHyc+Pp59+/ahVCpp1qwZv/76KzNnzqRmzZo6P7Y2tGnThsDAQAIDA7l69SojR47M9Bjz58/H09MTFxcX7QcohBBCa8zMzHj16pXmduDAAX2HJPIwSeQSQgghhBBCiA9UdHQ09evX57vvvkOlUlG0aFGuXr3KmjVrcjSO7J5wUygUctIukfYVHCGDv45O0n5I7xInUjRq1EiTSDFt2jTMzMzo0qULgYGBSfZZsGBBmmOqVCo6duzI8uXLdRm61hw6dIgiRYpw/fp1FAoFAwYMICgoiKZNm2ZqHF3PAzLPZJyLnTnlimSs2l8/aauod9WrV+f48eOEhYUxbNgwChQogFKpZM+ePZQoUYKqVatqqoEkGD58eLqVdnbs2EHDhg11GXqmpdRKcf369VmqkCJyRpvyDmCYsfm3UyaS2YX2OTo6Mm3aNB4/fsz69eupVasWN2/eZMiQIbi4uDBr1ixCQkKS7ZdSNa7EXr16RePGjTl+/LiOIheZFRgYyNixY5k9e3aqFx5oi729PRMnTmTt2rWMHTuWkiVLprpteHg4S5cuZdWqVTqJJaX/9zLaHvHJkyeMGzeOWbNmJal6mZExzczMaNCgQSajFUIIIUReJolcQgghhBBCCPEB8vPzw8HBAT8/PwAGDRrEkydPpBpFPlDY0gT3koXS39DKBDdpP5RrVK9enb///puQkBCGDRuGpaUlcXFxbNu2DScnJ+rUqYOfnx8rVqwgNDQ03fHUajVDhgzBy8srB6LPupEjR9KyZUuio6OxsrLi7NmzrFy5UuvHyUwSliRsaUfPDLZX7FJJKgPmFhYWFvz666+EhYWxYcMGzcnya9eu0axZMxwdHfnpp59QKpVs3bo1Q2OePn2aihUrZqhVo65JK8W8ycbMiJZlMtBe0dyIxiUy3l5a6I6JiQm9evXiwoULnDp1irZt2xIYGIinpyfFihVjwoQJPH/+HIAjR45w7ty5dMcMCwujRYsW7Ny5U9fhiwywsLAgLi6OS5cu5chFQO7u7hQsmP6/75iYGA4fPszOnTt58uSJzuNKi1qt1tyePXuGp6cnISEhWFtbY2hoqNfYhBBCCJH7SSKXEEIIIYQQQnxgJk2aRIMGDQgLC8PMzIyDBw/qvXJPVhMnJOEiZd0rpp9AIe2HcqcCBQrw66+/Eh4ezrJlyyhatChqtZoLFy5Qv359vv3220yNN336dIYNG6ajaLMuODgYV1dXFi1aBMDHH39MYGAgderU0dkxM/J6l38T2pORin/FHSwpZy+VkHKjnj17cv/+fa5cuYK7uzsKhYLAwEAmTpxIqVKlMpWYdfPmTVxcXAgPD9dhxKmTVop5X7cMVO7rU94RQwOZw3ObBg0asGvXLv777z/69OlDdHQ0c+fOpUyZMgQFBTFr1qwMjxUbG0vHjh1ZvXq1DiMWGVGgQAGmTp2Kubk5Pj4+HDt2LFP737p1i1mzZnHp0iWtxlWoUCG6du2KSqVixYoVWh07qwICAvD09CQoKIhPPvmEESNGyPtNIYQQQqRLErmEEEIIIYQQ4gMRGhpKtWrVmD17Nmq1msqVK/Py5UtatGih79CElnV0Tb/CjbQfyv0GDx7MkydPOHPmDHXq1MHa2pq3b99mepylS5fSoUMHHUSYNQmtFG/duoVCoWD69OmcPXsWMzOzbI+tqxNjcsIt81wdCuBkb5HmNv0qSjWu3K5q1aqcOHGC4OBg+vbti7m5ebJ2rxnx7NkzihcvTkBAgA6iTJ20Uswf2pZ3gHSStDpXylgVQKEfFStWZO3atfj7+zN69Gi6devGnTt3Mp0ApFKp6N+/Pz/++KOOIhUZVbx4cSZOnAi8a/t97969NLdXqVScOXOG8ePHM3bsWE6dOsX+/fu1HleHDh1wcHDg4sWLnD9/XuvjZ0RCa8RXr17h6enJ69evadiwIaNGjcLAQE7LCiGEECJ98o5BCCGEEEIIIT4Avr6+ODk5cfXqVRQKBRMmTODatWtYW1vrO7Q0jRkzBgcHB83t6tWr+g4pT3AoYEI9lzTaj0j7oTylXr16nDt3DgeHrJ+k3rFjBw0bNtRiVFmTUivFadOm5cixt23bhqOjo+a2dOnSJM9Lspb2fZVOUmlnSeTKM6ytrVmzZg1eXl7ExMRkaYy3b99SunRpbt++reXoUiatFPMPO3NjPi2VRttoE0OalcpA+0Whd8WKFeOXX35h9erVmarG9b5vvvmGCRMmaBJmhH7UrVuXXr16ERsby4wZMwgODk62TXR0NHv37mXgwIHMnDmTGzdu4OTkxJAhQxg/frzWYzIxMaF///4ArFixQm+tfYOCgvD09CQwMBA3NzfGjx8vLRWFEEIIkWGSyCWEEEIIIYQQ+dzw4cNp3rw5UVFRFChQgBMnTuTKq9hTSqIIDQ3l9evXmltcXFya24v/6ZFGe0VpP5T3XLhwId1KB+k5ffo0FStW1MsJLX20UnxfdHR0kvkkMjIyx479oeqYRjs058KWVHIskIPRCG2YM2dOtvaPjIykSpUq+Pn5aSmi5KSVYv7UPY1Kop3LFcbUSE515CXXrl1jz5492Rpj7ty5fPXVV3pL1BHvdO/enQYNGvD69Wu+//57zd8jKCiIdevW0adPH5YsWcKLFy+oUKECkyZNYtWqVbRt21Yr1VhT4u7uTuXKlXn27Bl79+7VyTHSEhISgqenJy9evKB27dpMnDgRQ0ND+fwqhBBCiAyTTzdCCCGEEEIIkU+9fv2acuXKsWTJEuBd4sSrV69yRVUeoXsd06h0I+2H8p4hQ4ZoZZybN29SsmRJwsPDtTJeRuiylaLI3Wo6W2Nom/Lf+StXae+a12zatImgoKBsjxMXF0fDhg3ZvXu3FqJKSlop5l/tKjhAKkkQXdJIGhW50+zZs7Uyztq1a+nQoQPR0dFaGU9kzbhx43BxceH69ev89NNP/PLLL/Tt25ctW7YQHh5OgwYN+Pnnn5k3bx4NGzbMkfaCgwcPRqFQsGnTJkJCQnR+vAShoaF4enry9OlTatSowaRJkzA2Ns6x4wshhBAif5BELiGEEEIIIYTIh3bs2MFHH33E3bt3USgUeHt7S+LEB6aIlSl1itslf0LaD+U5jx494uLFi1ob7+nTpxQvXpyAgACtjZmaESNG6K2VosgdRqSSsNW5krRVzGu+++47rY2lUqlo3749v/32m9bGlFaK+Zu9hQmNS6XQFtrIgM/KFs75gESW+fv7s2XLFq2Nt2fPHpo3b56jyToiKTMzMzp16oShoSEnT57E19cXQ0ND2rRpw6pVq/D09MTV1TVHYypdujQeHh5ERESwYcOGHDlmeHg4kydP5tGjR1SpUoXJkydrkrjSq8YVHh7Od999x5UrV3IiVCGEEELkcpLIJYQQQgghhBD5TM+ePenQoQOxsbHY2tpy6dIlJk+erO+whB58mUJ7xS7lHKT9UB4zcOBArY/59u1bSpcuze3bt7U+NvyvleLixYsB/bRSFLlDpxQSuewLWVDVyUoP0YisOn78OE+ePNHqmGq1mgEDBmS7Mo+0Uvxw9Eih8labMvZYmhjqIRqRVT/88AMqlUqrY548eZJGjRrx8uVLrY4r0hYfH8+RI0cYPnw4c+fOJT4+HniXtDRp0iSGDh1KkSJF9BZfnz59MDc35+DBgzx48CDb46WVjBUZGcmUKVO4f/8+FStWZNq0aZiamqJQKDLUUnHz5s1cvXqVw4cPZztOIYQQQuR98s2tEEIIIYQQQuQTCVV2Nm3aBEDTpk159eoV1atX13NkGaNWq3W6/YcopfaKnVNI7hK5V3BwMEeOHNHJ2JGRkVSpUgU/Pz+tjqvPVorZmRdkTtGN+sVtoYBJknUDpA1anjNixAidjT1p0iRGjRqVpX0Tt1I0MzNj9erV0koxH2vv6gjv5UPI+5q85fnz56xdu1YnY1+9epX69etz//59nYwvklOr1axbt44HDx7g4uLCmDFjGDBgAGq1mnnz5vHq1Su9xmdnZ0f37t1RqVSsWLFCJ8dQKBRERUUxdepU7t69S7ly5Zg+fXqm3vc+ffqUPXv2YGpqSr9+/XQSpxBCCCHyFknkEkIIIYQQQoh8YP369ZQsWZInT55gYGDAwoUL+euvvzAyMtJ3aEKPitqYUb2o7f9WSPuhPGf48OE6TTCKi4ujYcOG7N27Vyvj5ddWipLklXUKhYLBFZImbnVOIclU5F43b97kv//+0+kxFi5cSLdu3TK1T0qtFOUEeP7mYGlCfZdE7RUNFLQuJ4lcecncuXOJi4vT2fj379+nfv36XL16VWfHEP9jZGTE4MGDmTlzJkuWLMHDw4MOHTrg4eFBSEgI3t7exMTE6DXGdu3aUaRIEa5cuaL1ixcAoqOj8fLy4tatW5QuXZoZM2ZgYWGR4f0VCgUrV64kPj6eTp06YW9vn6EKXkIIIYTI3ySRSwghhBBCCCFyMbVaneZNpVLRtm1b+vTpg1KpxMHBgRs3bui0cobIO9RqdZL2iq3L2GNhLF8F5BVKpZJt27bp/DgJ88hvv/323hzz/8+TfhKTtFIUaelY8X8JpOZ2ZtQoYiXJcXnIoEGDcuQ4W7ZsoUmTJprlhLkIQK363+sltVaKlSpVypE4hX4kvB56uP7vfU3TUgWx/T/2zjs8qmpr4+9Meu+VSYeQhN4TEkIndEIIVQVFrlhQUbiAV7AgXAWvfiA2QBQEAQ0gSEd6LyG0AOkkhCSk9zqZme+POJM502cyJWX9nidP5uyzz957zlmzzp6z3lnbnH600FYoLi7G5s2bdd5Pfn4+IiMjcfny5WY/8o8LoXuP9omIiEDfvn0ZZYsWLULXrl2RlpaGjRs3GmhkTZiYmGDBggUAgK1bt2pVSNjQ0IDPPvsMiYmJ8PX1xWeffQZra2u12oiPj0d8fDxcXFwwbdo0rY2NIAiCIIi2DT29JQiCIAiCIAiCaAXIE2opIjU1FR4eHjh8+DAAYNKkScjNzUXXrl31MWStomlQhYIxTSiyn1ixJcym/xP81MTeCP1z+/ZtBAQEwMnJCWZmZjrtSyAQYMGCBfj888/l7pdnM4ZcSlEdVLFx+hy0DHl2MsynOYPObD8npfWJ1oWpqSk6deoES0tLsNm6fZx84cIF9OjRQ2agXSAQIC8vD6NGjcKnn34KMzMzbNu2jZZSbIco8gsxwc3zmhlBNK9pS6Snp2PmzJkYMWIEunbtCgsLC531VV5ejtGjR8vMOEr2oho5OTn46aefcOnSJbWPNTExwapVq+Do6Ijz589j//79Ohih6oSFhaFXr154/vw5Dh48qJU2uVwu1qxZg7t378LLywtr166Fra2tWm3weDz89NNPAIBXXnlF5/N9giAIgiDaDvRzFYIgCIIgCIIgCAPRkoDBt99+i8WLF4PP58PY2Bhbt27FvHnzpNqlZRnaL6raj4+9BUI8bfHoeSUmBipeVpFsp3UxaNAgPH78mFH27Nkz3Lp1Cw8ePEBKSgqysrKQl5eH4uJiVFdXtzjLwIcffoinT5/ihx9+UKn+okWL8N133wEAbGxscObMmVaZhYsCtLpDlXNrbMTGS708sfNeLl7ry1GrTfJFhufMmTOM7aqqKiQkJODevXt49OgRMjIykJOTg8LCQlRUVKC+vr5Fn7nExER06tQJT548YZSfPXsWc+bMQUFBAYKCghAXF4du3bpJ9UU20zZR1WbcbcwwwMcBt7JKES2xbKuiNskuDM/AgQMxcOBARllJSQmys7MV/mk6t6mrq8PkyZOxZcsWTFShPtlLkzjp6tWrOH78uGh5yuDgYAwZMkTtthwdHbFy5UosW7YMv/zyC4KDgxESEqLtIavMwoULsWjRIuzduxejRo2Cg4ODRu2wWCwIBAI8ePAACQkJ8PT0xH//+1/Y2dmp3dbRo0eRnZ2NkJAQDB06VKPxEARBEATRPiEhF0EQBEEQBEEQhJ5RFKhSFsRqbGzE2LFjce7cOQAAh8PBlStXwOHIDoy3hYBESwUWwuNb6/vTNsrOl6z9LwW74m8LE9ibG8vcL+vctQXb6YhwOBxwOBxMnTpVbp3Hjx8jISEBiYmJSE1NxdOnT5Gfn4/S0lLU1NSAx+Mp7GPz5s0oKSnB/0L+WSZHIG1X5eXlCAsLQ3JyMoCm4Oz58+dhbm4OgUBgEJvRllhL3vgN9b5aI5r4odgQF+zMLMaATrZyj1fki+jctx6sra0RGRmJyMhIuXUKCgoQHx+P+/fvIzk5GU+ePEFubi6Ki4tRVVWFhoYGhX0UFhbCx8cHN3ftAY/Px4ZrF7Bpw38hEAjw4osv4vvvv5e7fBXZTNtBE18CAC8Eu4INwMXKhOY1bRxHR0c4OjqiV69eMvcLBAIUFBSIRF1Pnz6VEnrl5uaCz+fL7eO1117DR1NiEA1AAOnsW2QvTdm3jh8/jtOnT6OiogIA4OTkhDFjxiAqKkrjdoOCgvD2228jOTkZgYGB2hquRvj6+mLs2LE4duwYduzYgcWLF7eovb59+2LFihUICgqCo6OjXH916NAhzJ07V7S9cuVK/Pvf/0ZFRQV2794NQHoJY5pzEgRBtE3q6urg6tq8DPiAAQOkfhRDEKpCQi6CIAiCIAiCIAg9oKl4S3xfdnY2Bg4ciJqaGgDAnDlzsHPnTrntSD787WgBifaGPDtRRbwSG+IORwsTldomIUXbJzg4GMHBwXJto76+Hvfu3cPdu3fx8OFDpKenIycnBwUFBcjLywOLxUJcXByeBdzF9oFDAWMjxvFxcXF4++23UVhYCBaLhY8++ggff/wxo05rshlNBF4UQJNNS4TIo/2d8VHvTiq3L+8eRtelbeDq6orx48dj3Lhxcuukp6fj9u3bItFpVlYWnj9/juzsbPB4PBQXF6PH1MnoZmsPbqUJzM3NsWnTJrzyyisAaN7TlmmJLwGAqUGuAFRrg+Y1bRsWiwU3Nze4urqiX79+Mus0NjYiLy+PIe569uwZsrOz8eeff8LW1harDx1A0ZBheP/vizDz9YFVn56i41X1Je3NXmRl32KxWOjfvz/GjRuHgQMHwsjISEkryhk9ejRGjx7d4na0wUsvvYQLFy7g77//xqRJkxAQENCi9iIiIpTW4XK5qKqqEm3X19cDAH777TdUVVVh1KhR6NKlS4vGQRAEQbQOBAIBKisrRdvV1dUGHA3R1iEhF0EQBEEQBEEQhA5RR3wjWVZdU4PU9EykpGUgOS0DB+P2gOMbgEZuA7oGdUPE8FE4d/EqAjv7w9OjaWkZ8QCDoqBDew1ItEdUsRXJ8sKiYjx8nILEpBQUFhWjvr4BPLDx8SkTuLu6IKRrF3QLDoSTY/OSIkJbUCakIJtp/SizGVNTUwwYMAD9+/dn1KmsrMKj5FQ8SkpFSnoGKu7exUEWH7yjp2B67yGCu3ZGSFAXsNlsFBUVwdnZGUeOHMGAAQPk+hSymfaDqr5IWFZbV4ek5DQ8TErBk6xnqKuvQz1PgFWXjWFjY42gLgEICeoCPx8vsNls0fHKfBHdv9oOyu5V/v7+8Pf3x/Tp00VljY2NSH+SJbqHXTp2BDY1tbCys0V4bG9Y2jnjwcMkBHb2g6mpKc172iDq+hIAKCouwcOkFDx8nIL8wqLmec1ZE7i6OKNb1y4ICQqEi7Oj6Bia17QflNmHkZEROBwOOnXqhNDQUFF5cUkp3l68FA+TU5GbmwdeejqOVZSj9o8/YX7rDkK6dkZIUCDcXJ1V8iXtxY8oy74lnkmkvWFnZ4cXXngBW7ZswdmzZ1ss5NKUrKwsHD9+HBYWFpg3b55BxkAQBEEQROuGhFwEQRAEQRAEQRA6QJMgFQCUlJbh6Mkz+PvcZTx9lsPYZ+PiJXr9vLQau/cdEm1bW1thQN9emDJ+DHr3CAEgHcBqjUuFaWspNGFbbT2wIo4q9iK+XVxSiv2HjuHUuUsoKi5RqQ9XFyeMGR6JmEnj4OhoLypXZDvtJYjVHlHXZoAmsc2xk2dx+MRpZD59JnX8w+IyoLgMuJeIg0dPAgBsrK3x+qL38P7it+Hn48X47BnKZlqSsU5RmxT0V8+uGhsbcfr8Zfx55CRS0zPA58s//0dPNi0xYWFhjv59emHG1AnoERIkU3RBvqhtoernUXz79p37+OPgUdy5n4iGBm5zJSsHlFg5oARA9uM03HucBgAwNjZGSFAXxEwciyGDB8HIiE3CnVaOuveoktIy/Hn4BE6evYCCwmKV+nB2csSoYRGYNnm8QlEX+ZK2gSbzmvLyChw4fAInzlxAfkGh7IZLSoF7iaJNJ0cHjIgMx/To8XB1cVb6Haot+hF9Zd9qC0ycOBGenp4YOHCgwcawdetW8Pl8zJgxA46OjjLrtEU7IwiCIAhCe5CQiyAIgiAIgiAIQssoCzDIKkt8lIx9fx3Dpas3wePx1O6zqqoa5y5exbmLV+HrzcGUCWMwYcwIRrYKym7SdlBmM+Kvnz7Lxe64gzh9/hIaG9WznYLCYuz640/8fuAwRg6LwOxpk+HjzZGyCXmBT7KZ1oMyvyO5XVZegX0Hj+LQsVOorFIv3X9lVRUepFThlTeXoH/fXnghdgr69OoOoO3ZTGscU2tCVbuqq6vHwaMnse/QMZWFpEJqa+tw6eoNXLp6A8Fdu2DWtEkYGh7KaL+t2VVHRp05kEAgwJkLV7B3/19Iy8hUq5/GxkbcT3yM+4mP4e7mgulTJmDSuFEwNTUFQMKd1oY696hnuXnYs+8QTp25CG5jo1r9FBWXYO/+vxB38ChGRA7G7Ngp8Pf1Jl/SBlHHZgQCAZ7nF2DP/r9w4vR5phhUBYpLShF38Aj2/3UMw4aE4YXp0Qjw82kXfkRW9i1HR0dERUW1++xb8jA2NjaoiKukpAQpKSlwd3dHdHS0wcZBEARBEETrhiXQ5s+fCYIgCIIglBAdHY1Dhw7h2rVrjJT3BEEQ7QVlQQbJfdU1Nfj+p504duqs1sfC8fTA8vfeQI+QIACyAw2SZfoORij7ShofH4/09HTRuEaPHg0HBwe59Vt7MEUVVLUhgUCAA4eP48dtu9QOdMrDxNgYC195AdOmjGecS3mvxWkP574too7oT/j69t37WPu/b1FaVq61cUwcOwpvv/YyzMyYIgrJ14rKWoIqGYCysrJw48YN0XavXr0QGBiocEz6GHtrRB27Sk1/gtXrNiA7J09r/Q8e1B/L3n0ddrY2avmijnBtWiuaZJH8/OvvEH/nvtbG4OfjhVXL3oW/r7eoTNk8h2xGd6jilyVfHzlxGt9u3YH6+gatjMHIyAgLXpqJmdMmy1zCVfK1OGQb+kcTmwGAE6fPY+MPP6Ouvl4r42Cz2XhlznS8MHMq2Gx2m/YjH374Ie7cuQMWi4V+/fp1uOxbukLV7NsAsG/fPsycORNAk62MHj0aRkZGGDdunGhpR3NzcwwfPlxuf4psLCsrC48fP1Zn+CIGDRqk8Lu1POrr63Hu3DmN+vT19UVQUJBGx964cQOlpaVqH2dubo5hw4Zp1GdLzm9oaCjs7e2VV5Sgrq4O58+f16hPPz8/dO3aVaNjNT2/FhYWGDp0qEZ9pqSkICMjQ6NjIyMjYWlpqfZxVVVVuHz5skZ9du7cGZ07d9bo2KtXr4oEtepgaWmJyMhIjfrMzMxEUlKSRseOGTOGMXdSlaKiIsTHx2vUZ/fu3cHhcDQ69uzZs2hoUH/+6OjoqLG4NykpCZmZmYyy2tpaxMTEiLYHDRqE69eva9Q+QZCQiyAIgiAIvUJCLoIg2ivq/mocAK7fSsBX326Vm72kk4c7QoIDEdQlAF07+8PZyREmJibg8Xioqa1FRmYWklIzkJySjodJKWiUIeZhsViYOiEK/3plDizMzRnlsl7L2tYVqnwdVbQ8kjxaczBFEaoKJwQCAcorKrF+4w+4euO2zLacOGZw97OAm68FnDqZw8SMDQiAhno+ip7VoSCzFs+f1KAkV/aDroH9emPFe2/Cwd6ORBStFE0EXI2Njfjp1734/cBhmW3aOBrDI8ASbn6WcPE2h5mFEVhsoJErQOnzehRk1iI/qwb5GXWQ9fH15nTCR8vfRWd/X1GZvmxGnYCavHIScqlvV/sOHsWW7btlikktbIzg2bnJnlx9zGFubQw2G+BxBSgvakBBZi0KntYiL60WvEbpfp0cHfCfJYvQVyLbm6LXisoI3aGuiP3W7bv4/P++lykmNTZlwbOLFVx9zOHmZwEbexOwjVng84DqCm6TH3pSi7z0GtTX8KWONzU1wZuvzsWUCWNUthmyF+2jri+pqqrGl99sxsWrN6SOAwBHT1O4+1s2zWs45jA1bQosNtTzUZxTh/zMWuRn1qL4mWwhT9/ePfCf99+Ck6MD+ZJWiibzmqrqavzf99tw9sIVmW06eJjCw98Srr4WcOaYw9TsH7tp4KNEzG6KsmXbTc/uwVj173fg4uwEoG36kfj4eDx+/LjVZt9qbGxEWVkZnJ2dDT0UuezevRuRkZEMcYGmQi4/Pz8EBQWhuLgYN2/eFNVxcXFBfn6+0rHIsrMNGzbgvffeU3qsLC5cuKCRQCQ3NxedOnXSqM93330XGzZs0OjYsLAwjQQR7u7uyMvT7AcHLTm/V69eRVhYmNrHZWdnw9vbW3lFGSxduhRffvmlRsdqen45HA6ys7M16nPFihVYt26dRscmJyczfpSjKomJiejRo4dGfa5atQqrV6/W6Ng+ffrg7t27ah/n6+uLJ0+eaNTnV199haVLl2p0bHV1tUZCuRMnTmDcuHEa9bl582a89tprGh3r7OyM4mLVluIWZ8iQIbh48aJGfS5evBgbN25UWIeEXERLoKUVCYIgCIIgCIIgWogqAUzJOr/98Sd++nWvVFsmJsYYFhGGqROj0KNbsMJ+A/x8MHp404PXsrJyHD11FoeOnkJefgGj/wNHTuBu4iN8tXYVHOztROXG82OTAAAgAElEQVTiy4W0xqVlZJ3X1jAuXaBOFq7iklK8/e+PGNcZAFgswK+XDXqNdIKLl4Xcvhw9zBE4oOmXwQVPa3H/TDGe3K9kCHNu3r6L19/7ABu/+BTubi7/tM9SaDfyygjto47oT0hDAxcr13yJWwn3pI519zdHrxFO8O5mI/f62buawa+nLQCgsrgBDy6UIOl6GRobxJf5zMEb73+IdZ+uQJ+e3VulzSgSkLaG8RkSde3qy29+xLFT0tkYHD1M0XOEEzr3tQXbSPavuO1czeAdYgMAqK3k4uHlUjy6XIq66mZhTnFJKZauXIMVi9/AmJFDGctYtTa76sioOwc6cuIMvvp2i1Q7lrZG6B7pgODBDjCzlP3I2tbZFB7+VgCARi4PKbcq8OBcMcoLm5dRa2jgYsMP2/DkaTYWv/GqzKXRZC2zR/aiPdT1JSWlZXh3xSd4JpnVjwX49bBBr5GOcPWRH0h09DBHl/5N85qiZ7W4d6YYGfcqIRDT+SXcfYCFiz/AN+s/hae7W1Pz5EtaDerajEAgQEVlFRav+ASZT59JHevTwxq9RjjC3c9Kbp+O7ubo3K/Jbopz63D/bDHSEyrAF7Ob+4mP8a93lmPjuk/h49VJ7n1IfGytzV769++P/v37G3oYMqmursYnn3yCsrIybNiwAVZW8q+Xofj555+xb98+XLhwAT/88ING2WmEmJqaIiAgAAKBQOMMT+oIyDRtT5W6+upT8riWHqvJ57Ol75XuIwRBEISmaD7rIAiCIAiCIAiCINQKYAof5P2y6w+ZIq7Rw4dg/84t+Gj5YqUiLkns7e3wwoyp2PvLd1i59G3YWFsz9mdkPsXiFZ+guKRUNA7xh6HKMooRukNVGxJm4lqyco2UiMupkyliV/hj1MschSIuSVy9LTDqFQ6mf+APZ44ZY19BYTH+vWotSkrLZI5F1tjllRHaQ5a9KPM7PB4Pa7/aJCXisrAxwriFHEx+xw8+3W1VDjLYOJlicIw75nzcGf69bBj7uFwu/rN6PR4np8m0E7KZ1om6QfTvt+2UEnEZm7IwbI4HYpcHIHCAvVwRlyQWNiboP84Vcz7ujO5DmMvPCAQCrNv4Ay5dvSnTtsXr0X1M/8g75/LuE+cvXcPX322VaqfXSEfM/qgzeo9ykSviksTYxAghgx0w4z8BiJjuBiMTpv86dPQUftqxR+F8R1tBaaIZdX1JZVU1ln/8uZSIy8HDFLHL/DB6PkehiEsSZ44FRs7jYMYH/nD1Yc5riktKseTDz1BYVCxzLKqMn9A+iuY1sj67AoEAtbV1WPHJF1IiLntXE8Qs9UXUq14KRVySOHmaY/iLnTDjwwC4+Zkz9pWVV2DpyjV4nl+o8D4k7/3okszMTCQkJOitP21jbm4OMzMz5OTkYN26deDzpbMsGpKdO3di3759sLCwwJIlS1ok4gKAwMBAmJiYIDs7G5WVlVL7DeVrJJ8NyPszRJ/a7r+j9UsQBEG0bSgjF0EQBEEQBEEQhIZoIuLa9ute7N53iHGck6MD/v3OQoSHDmjxmNhsNqJGDcOAfr3x1bdbcPFK8xI1Wdk5eHf5J/jqv6vg6uwEgUDA+EW5cKyUoUJ/KAoeStpQTW0tVnzyBbIkglbdIx0waJILjEyMNB6HvasZpiz2xa0jBbh/vlRU/iw3D/9etRYbPv8Y1tbNATFFdiMcM9mO7lFmM0K/8/V3PzF8AQBwgiwxbI4nLG1NNO7f3MoYo17hIOl6Ka4eyBdl56qrq8fyj/+LDV98An9f5rIgsrJYCMdKNmMYFAWiZf3fHXcQcX8eYdR39jLDyJc6wc6VKZxQB2NTIwye5gFOsDXO785DXRUPAMDnC/DZ+o3478fL0a93Dyn/Q77HMKjif8T/xyfcw9qvNjGOs7AxwvCXPMEJZIrP1YHFYiEk3BEeAZY482sOY8ng3+IOwtrKEjOnTZZrN/JeE+qj7ry4vr4B/1m9DmkZmYzjggfbIyzaFcamms9r7FzMMPkdX8QfL8TdMyXAP0N5nl+IpavWYuMXn8DOtkmILDmXoXmN/lDXZgCgvqEBK9d8iaSUNMaxXQfZYXCMG0zMNLcbWydTTHrbF3dOFSLhZDGEwyksKsaSDz/DN+s/haODvUHnwTU1Nbhw4QJOnjyJlJQUuLm54eeff26T9mlkZIQVK1bg3XffRXx8PLZv34758+cbelgAgL1792LPnj0wNzfHZ599xli+TV0BX1BQEN5//30kJycDAKKiojBhwgRGHet/foSlie0MGDAAy5YtU+sYIZou32djY6NxnxERERodBwBz587F0KFD1T7O1tZW4z5bcn41XX7S1tZW5T4l7UWT8yNE0/Nrb2+vvJIchg0bpvGxjo6OGh3n4uKC5cuXa3TskCFDNDoOAObPn4+cnBy1j9P0fQLAwIEDNX6vJiaaPSfw9/fXuM/evXtrdBzQtGxrdXW12sf5+flp3OeoUaNgbm6usI6mfpYgAIAlIGkvQRAEQRB6JDo6GocOHcK1a9cQGhpq6OEQBEG0CFWCDeL/T5+7hM//73tGG/6+3tjwxSeiJQ+1zY7dcVLZv0KCumDjF5/C2Lgp0CF8+Cf+EFDea22iytdR8TqqjqOtBDJUFXEJX/9v0xYcO3WWUS90igt6DnfW6rgeXi7BlX35jLIRkYOx8t/vAJC2F0W20lauRVtBmc3I8jsnTp/Hl99sZtT1722DkXM7gcXW3vV5/qQax77PRiO3eVycTh74adN6mJmaAtCtr1EnqCavXFb/qpa1ZeTZlSx7uvfgEd77z2rG8e7+5hj/uneLRBeSlBfU469NWait5InKbGyssf37r0T3S3F7UuR72tv1ag2oK+IqKSnFy28uRZVYcMXS1giT3/WFrZOp1sbFrefh6PdZKMiqZ5Rv+OJj9OwWrPL9i2xGM9TxJQDw7ebtOHDkBKON/uOd0XeMi1bHlXyjFBf2PGeUhYcOwGcfLgGg+ryG7EK7KPMjssoEAgG2bN+N3w8cZhzbZ7QjBkxw0+r40m6X4eyuPJEIEAAG9O2F9av/A0Cx3QjRps08evQIJ0+exMWLF1Ff3+Tj3N3dMWbMGEybNk3jgHtr4OnTp3jvvfdQW1uL5cuXt0iIog3279+Pbdu2wczMDKtXr0aPHj0Y+9UVcgn3FRcXIzMzE/369VNqG+RvCHUhmyEIgmh/GH3yySefGHoQBEEQBEF0HPbu3Yvk5GQsWLAAHA7H0MMhCILQGHVFXAWFRfhwzf/A5XJFx3UJ8MPGdZ/C3k7zX4gqo3ePbjA3M0P8nfuissKiEpiaGKNHtyBRmbJghC4eDKrSpqLgiKZttgbUFXHdf/gYmzZvZ9TrM9oRfce4an1srt4WYBsBuak1orInWdkICgxAJw93AIqDnRT01A3qBsgFAgFKy8qxau1XaGhozlDDCbbE6Fc4Ki97pyrWDqZw8TZD+p0KUQaLisoqsAD07tnNIIFyddvqiEIudeyqvqEBH6xeh4rKKlE9p06mmPCGN0zMtZv039zKGJwgK6QnVIDX2DSGhoYGFJWUYkjYQJXuVeSL9IMiHyQSIn+7BSlpGaJjzCzZmLjIB/YtyOAmCyNjNvx62SD7URVqq5pFgI+SUjF+zAgYifk9Q8x72jOK/IdkmUAgQFJqutQymz2HOWhdjAM0Lbdoas7Gs6RmIWH2s1z4+3rBx6vpmQTNawyLKjYDAOlPsrB+44+MeiER9giLdtf6mBw9zWFhbYSnj5rtJjcvHxwPd/j9k21U1z9qKC8vx9GjR7Fx40bs27cPGRkZYLPZiIyMxGuvvYbXXnsNPXr0gJGR9oTUhsDOzg4+Pj64cOEC4uPjMWDAgBZloNGE48eP45tvvkFtbS22bdsGExMTfPzxx+jVq5fGbUp+x7O0tISnpyejTJF9aGo7ikRl2vZh6uQJ0WbfHa3f1twnQRAEoTu0+9SOIAiCIAiCIAiiA6CqiEL4x+fzsW7jj6iubhbGcDw9sHHdp6JlXXTJnOnRmP/iDEbZ9t37kJr+hDFWWe+jNdBRHkhKXgfh64YGLr7+lhns9O9to5Ngp5A+o13QdRAzS9yGH7ahtq5Opr2Iv1YkUiM0Q5HYRpbdCP9+2LYTlWKiG3tXE4zRgYhLCCfIBhHTmcHUPfv/QmZWtkKRh75tRjKDk6yMTh0BZQFzybLdfxzEs5w80TFmlmyMW+gNUwvtiriEOHmaY8x85pI0Zy9cwa2EewptX9H7I1qOOnMgALh5+y7OX7rGaGPMqxw4uitehkRTzCyNMf51b5hZNvu5p89ysTvuoFI7JzRD2WdN8rzzeDx8tWkLo65PD2uE6kCMI6THMCd0i2Au/fTN5u2oqq6Wez+SHL+8/YT6KPMjsl7zeDx89e0W8Pl80bGcIEuET9Od3YREOKLncAdG2Xc//YqKyiqdzWP4fD5u3bqFtWvX4sUXX8S2bduQnZ0NPz8/vP7669i1axeWLVuG3r17t6u5S2hoKF588UXU19dj9erVKC8v12v/N27cQHp6OrZt2wZjY2OsXLkSffr00esYCIIgCIIgxCEhF0EQBEEQBEEQ7Zbi4mLMmzcPhw4dQl1dnVbaVDeTkkAgwOnzl3H3/kNRPTabjVXL3oGNtZVWxqQKc2fHontIcwYuHo+Hr7/7SeWgZkcNXK1fvx7nz59HTU2N8soqoor4Sbz80LFTePosV7TfzJKN8Gm6E3EJCYt2haVt8y/88wuKEPfnEbkCCkXvp6OxefNmrFmzBo8fP9ZZH7L8j/j5f5ychjMXrjDqDJnpodXl72QRFOoAz0AL0XZjIw8//LxLaaBcHH3ZTFsRcB08eBBxcXHIzc1VXllFVL0GwutWVFyCPfsPMeqERbvB0la3Szl5drFGUChTVLppy3ZRIF9SvCUsI4Dk5GQsXLgQp06dYmQDbQmaCNm/3bqD0UZwmB08AnQ7/7G0M8GgycyMlbv3HURRcUmHv3d9/fXXOHv2LKrFlrnUBYrmCsdOnUVG5lNRXRNzNiJidSfGETJwkiusHZqFp8Ulpdgdd0hqfELaqw2oS0lJCV566SUcOnRItKRfS1DFjwhfi/+dPn8ZyanNmf2MTVkYMt1D5/fx/uNcYOvcfK8rK6/Ar3v3M8Yo67X4+1CF/Px87Ny5Ey+//DI+/vhjXLlyBWZmZhg/fjw2btyI7777DpMnT4aNje5/BGQoZs+ejfDwcBQWFmLt2rVobGzUW98hISGi12+++SYGDBjQovZUve6K6snyS/LqyBK0K6vfEjRpg/rVPXTfIgiCaF+QkIsgCIIgCIIgiHYLl8vFr7/+iujoaDg7O2PGjBnYu3cvKisrtdqPIhGOQCDA/r+OM+rPnRWDkKBArY5BGUZGRlj177dhYdGcASM5NR0PHiWpFVDRNoZcRk0Vrly5gvXr12PWrFn46KOPcOrUKVRUVGjcnioZCCS3j5w4zWgjdIorLGx0K54AAFMLYwyOYQrGjp46Bx6PLzMIQA+Om0lJScGqVasQEhKCkJAQrFq1Cnfv3tWoLXVsRrzsr+N/M9oJCtW9eEJI5HQPGJk0fx5vJdxD3vMChYHyltqPvM9/S/xCa1lWMTMzE7/88gsWLFiAN998E7t27cKTJ0+01r68ayB+vU78fR6Njc1L1XkGWiBwIDO7ja4YNNkVFjbNAsSc3Oe4c/+h3IC/cFv8v+TrjkB1dTW2bNmCqKgouLq64uWXX8bhw4e1IsSQRJ5PunMvETm5z0X1LG2NpARWuiIo1AGeXZii0hOnz8u9d3UUm7l+/Tr+97//YdasWVi5ciWOHz/eosw3is6leLn4vsMnzjD2D5zoAis73c9rTMyMpLJGnjh9niF0JF8iDZfLxa5du0Tfp+bMmYMDBw6gtrZWq/3I8iPiZX8dZ86H+49zho2TqVbHIAtjUyNEzGDazakzF1FX1+xLlX0O5NkMl8vFhQsX8J///AevvPIK9uzZg6KiInTv3h3vv/8+du3ahUWLFqFLly7afEutFhaLhSVLlsDX1xeJiYn48ccf9dLvuXPnsGNHs+hY3Tm7rn2CLLGWNsVJqrSji351fUxb7lfyWG2de4IgCKJtQUIugiAIgiAIgiA6BNXV1YiLi8Ps2bPh7OyMSZMm4ZdffkFJSYnKbagiqBDfLxAIcP/hY6RlZIrKnRwdMG/O9Ba+G83w9HDHnNhoRtmBv07IDVoREAX3GhsbER8fjw0bNmD27NlYsWIFDh8+jOLiYq30Iy/oeS/xEbLFljKzdTHRm3gCAPx728GZYybaLiouwc3bd6TGqWkAqyPw+PFjrFmzBn369EFAQACWLVuGGzduqBw0kXytzOcIBAJUVVXj4pUbon0sNtBvnIu23pJSbF3MECSxNOeRk82BewqUa05mZiZ2796Nt956CwsWLMDPP/+MpKQktc6XuuJAHo+HY6fPMdoYMF5/9mRmaYxeI50YZUdPnlEoZBS+Fv8v+bojUVZWhh07dmDy5MlwdnbGrFmzEBcXp1ZWJlX8vOT2kVNnGW30HOGks6U4ZTFgAtNOj506Bz5fWozckexCKOTj8XhISEjApk2bMGfOHCxbtgyHDh1CUVGRym2p60sEAgGSUtKQ/iRLVGZlb4yQwcyl63SJd4gN3Pyaf9RQVl6BK9fjpeyBfIlsqqqqsGfPHkybNk30I5k//vhDZV+iqh+RLHuS+RRJKWmiMgsbI3SPdGzp21EZTqA1I9toVXU1Lly5LtNu1GH37t1Yt24d7t69C3t7e8TGxmLLli1Yv349Ro0aBXNz3SxB25oxNzfHRx99BBsbGxw7dgzHjx9XflALuHz5Mr766isAwDvvvANbW1tcvHgRDx8+VHKk9mgN/kSRWExX41PWp676by39tqSOKv0QBEEQbR8SchEEQRAEQRAE0eFoaGjAkSNHMH/+fLi6umLUqFH4/vvvkZeXp/xgKBZUSD58O/DXCcaxMZPGwthYf0FMSaInjIGJSXP/l6/fRH5BIQly5GBiIp0hQiAQ4P79+/jhhx/w0ksv4b333sP+/fuV2o86wSvh39FTTPFEUKid3jMCBYUxhWNHTp6V+YBYWdCTADIyMvDll18iNDQUXl5eeOedd3DhwgXRMnGqosxuzly4gvqGBlEd727Wesl2Ik6QRGD+5JkLaGxslGk3ukaTz0xbWHIxNzcX+/btw/vvv4+5c+fihx9+wP3798Hj8ZQfLAN5n+nbdx4gv6BZ3OHoYQo3X/0tDQwAgQPswDZuviZXbsSjrLxCbsCLfI98qqqq8Pvvv2PGjBlwdnbG1KlTsWvXLpWzMimaA4lvl5VX4OqNeFE525iFwAFMgaeucfO1gqNHc8ae/MIixN+5r9Ru2rMtmZmZSZUJBAIkJiZi8+bNmDt3Lt59990WLecqz5c0zWuY4r6gUDuw2Pr1t8FhzPvTETnCUKD9XX9tUlNTg7i4OMycORPOzs6IiYnB7t27Vcp8rMyPiJcJBNKi0MCBdmAb6Te0JWk3R8Xmw/LsRpkvGTFiBAYMGICVK1di586dmD9/Pjgcjg5G37Zwd3fHBx98ADabjR9++EFnoqrr169j3bp14PP5ePfddzF27FjMmzcPALBlyxb6/BM6pSXCMbJNgiCIjgEJuQiCIAiCIAiC6NDweDycOXMGb731Fjw9PREeHo6vvvoKmZmZjHqq/CJS8kFcQwMXN8SyF5mamGDy+DE6eR+qYm9vh1HDhoi2+XwBrt64zaijb0FOSwUTLBbLoKKL5ORkbNu2Da+++ireeust7N69G1lZWXLrqxK8EpZfv5Ug2mazga56zMYlpHM/OxibNp/f+Dv30dDAVVtAQQ+cmeTk5GDTpk0YNmwY3N3dsXDhQpw6dQqNjY0ANBP+Cf+uitkNAASH6d9unDzN4eLdLBgoLSvH4+Q0nQooFPkBdXyELpZp1DXFxcU4fPgwVqxYgRdeeAEbN27ErVu3GEuGAZrZ1ZWbzHtEUJj+sucIMbcyhl8Pa9F2YyMPtxLuyf0MCOkIwpyWUFdXh4MHD+Kll16Ci4sLxo8fj23btkllZdJkDnQr4R5jOU6/HtYwt9K/kF3SXq/eTJCqQ0JkJqmpqaLlXN944w2Zy7lqeo+6Jnb+WSyg6yD935/8e9vA1KI5LHL3wSPU1taSL2kBdXV1+PPPP/HCCy/A2dkZkydPxs6dOxkCUU38iPDvusS8JihU/3bj28MG5lbNdpP4OBkVlVWibU3EGF5eXvj0008xePBgGBkZKT+gA9G7d28sWLAAjY2NWLt2rVrZAlUhPj4e//3vf8Hj8bBo0SKMGdP0HT0qKgr+/v5ITU3F6dOnlbRCSKLJ54D61bxvgiAIon1DQi6CIAiCIAiCIAgxrl69iqVLl8LPzw99+/bFmjVr8PjxY9F+dYJV6U8yGUHMgf17w97OVn9vRg5RI4cyth+nppMgR0s8efIEu3btwhtvvIF//etf2L59O1JSUjQKXuU9L0BNTa1ov5u/BSxs9JtVCQBMzY3QqWtz9h0ej4fMp9kaCSgI2RQWFmLLli2IioqCi4sLXn75ZRw+fFi0BJY4is6nyPeILedqbMICJ8ha7jG6xK+nDWM7LSNTSsAoS0BBtIyKigqcPHkSH3/8MWbPno3169fjypUrqKurk6qr7HpI2hMgfV31hV8vZr+pYvYkC7In9eByuTh+/DgWLFgANzc3jBw5Et999x0j26Q6c6BUSbvpZSC7kbDX9H/shuY9qpGVlSVazvXVV19VupyrvIC2QCBAUXEpysorRGUu3mawdjCVqqtrjE2N4BXUPK9pmrPLFuAbKkDflmloaMDhw4cxd+5cuLi4YMKECfjll19QWloKQH1xekVFJQoKm5cyd/Q0hZ2LdGY5XWNkzIZ3CHM+lZb+RK6NcLlcqc8K2ZJ6REdHY9SoUSgrK8Pq1avRIJZttiXcvXsXn332GRobG7Fw4UKMHz9etI/NZmPhwoUAgO3bt6O2tlZeMwq5dOkS1q1bh4KCAq2MWVP0ZXOyPgf68J8drV+CIAiiY0FCLoIgCIIgCIIgCDncuXMHq1atQkhICIKDg/Hhhx/i9u3mzCTKAshJqemMspCuXXQ2VnUI6hLAyCyTnJIuM3AiRB8iC00z3bTmDDk5OTn4448/sHjxYrz88svYsmULEhMTwefzVQpepWUws184dzLX91sQ65sZMEtN10xAQQ+3lVNWVoYdO3ZgypQpcHFxwezZs7Fv3z5UVcnO+iD5mS0qLkF5RfOyRo6dzMDW87JVQpw5TJsVCjvkBfmVvW4pqviL1uxTNKGmpgbnz5/H2rVrMWvWLKxduxbnzp1DdXW1qI6i68Hn85GR+VRUbm5tBCt7/QtKAWkfmCZmT4a+h7U3+Hw+zp49i0WLFomylX799deMbJPK7gFpEkIuZ46FroarECt7E5hbN2e6ych8Ch6Pp5LNEEzy8vJEy7nOmzcPP/74I+7duyc6n+LIOrdS8xoD2QQAOHGY85o0GQI/We9J/D+hHC6Xi2PHjmH+/Plwc3NDVFQUtm7dKsqupEwQKBAIkCYhsjPofNiLabOpMuymsrIScXFxePXVV/HBBx+grKzMQKNtH7z99tvo2rUr0tLSsHHjxha39/DhQ3z66afgcrmYP38+pkyZIlWnR48eCA8PR2lpKX7//XelbUrOHevr6/HLL7/g8uXLePbsWYvHrCmSwnyCIAiCINoW+s9nTRAEQRAEQRAE0QZJTk7G559/js8//xw+Pj6Ijo5GdHQ0wsLCwGKxZAZ+klMzGG20FiGXlZUlvDmeyMrOAQDk5D1HZVUVbG2ks2UIBAKZwgZ55domIyMD+fn5ou2ePXvCyspKwRGtk8LCQhw6dAiHDh2CnZ0dwsLCEBYWhh49ekgtpdKczYQZuHLiGDBwJdF32pNMAPIDcELbENqJvuylvVFVVYXff/8dv//+O8zNzREVFYWpU6diwoQJsLVtyu4nGaSREk8YMuApEaRPU5BBSVv2IbQ3WRQVFSElJUW07evrCw8PD5Xbbes0NDTg6tWruHr1KoyMjNC7d2+EhYUhNDQUNjY2jPuX8HV2Ti7qxTJgOHP0nwVFiI2zKUzN2Wio4wMAMp48lbrW4tdJuM+Q97D2wrVr13Dt2jUsXboUffv2RUxMDKZOnYouXbrIFb9kPGkWAJqas2HjZBgBINBkt8+SagAA9Q0NyM7Jha+3l8y64rYh7zXRvJzr4cOHYWtri9DQUNG8xtjYWKY/SW9N8xopQU6WXH9CvkQ7NDY24u+//8bff/+NN998E0OHDkVMTAyio6Ph6uoq5UOa7SaT0Y7ktdMnkvfA1PRmcWJ+fj7++usvnDlzRpQBs0+fPqiuroa9vT35Eg0xMTHBypUr8c477+DcuXPo2bMnoqKiNG7P0dERdnZ2iIqKQmxsrNx6r776Km7cuIEDBw5g7NixcHd3V7mPAwcOoLCwEP3790ffvn2V1id7IAiCIAhCFiTkIgiCIAiCIAiCUJOsrCxs3LgRGzduhJubGyZPnowpU6Zg6NChYLPZosBD7nPmUgpdOvsZaMTSBHb2Fwm5ACDveSFsrKWXX9Nn0EGWAGPt2rX45ZdfRNu3bt1Cv379GGNra5SXl+PEiRM4ceIErKysMGDAAISGhqJ3794wNW1aYkggEOB5QSHjOEcPwwkoHD2Ywdb8giKFAgrA8CLA9kZdXZ1IDGhiYoLhw4eLRF3Ozs4iv1NQWMQ4zsGAdmNubQwLGyPUVjYtMZtfUKj0+utS/Hf8+HHMmzdPtL1+/XosWbJEtN2R7JLH4+H27du4ffs2vvvuO3Tr1g2hoaEIDQ2Fk5MTgH/8UL6EH3I3nD2xWCw4eJgi/0lTgLy6pgaVVVWwsbaWElwI6wshUan2SEhIQEJCAlauXIlu3bph6tSpiI6ORkhISHNGmqoqVNfUiI5x8DA16Hl39GgWcgFN9zAfL2zktjoAACAASURBVI7MumQf6lNRUYFTp07h1KlTsLS0RP/+/REaGoo+ffrAzMxMZBd5UvMa/S+rKOpbwpcJ70+AYgEX+RLtwOfzce7cOZw7dw7vvPMOIiIiMHXqVEyZMoUhmBEIBHguNa8xoN14SNpNEZKTk/HXX3/hxo0b4PP5MDY2xogRIxAdHQ0/Pz+yEy3g5OSEVatW4eDBg4iMjGxRWx4eHvj2229hLeN7rzju7u6IiYnBH3/8gW3btuHDDz8U7VP2o4F9+/bByMgI//rXvxTWFbalK8hXEQRBEETbhoRcBEEQBEHoleTkZADA0qVL4eDgYODREATR3jE31/0v/fPz87F161Zs3boVAPDCCy9g0qRJGDFiBBrEspgAgHUryiRlZWXJ2P5g+VJMnDgReXl5jIe9ih786uKhsOSD7tLSUpFwCwB27dqFw4cP66x/fVNdXY3z58/j/PnzAICwsDAMGjQIffv2RUM9035MzdgGGGETJubMvjPSU7Fo0SI8f/5c6jqoYj/t4dopgs3W7bXicrmioDkAdO7cGW+88QYmTZqEegm7MTGg3QBNtiMUctU3cAFIf84rKyvx008/oUZM+CGJOjYjL2CWk5PD8Cf379/Hp59+qlKbhrRZHo+n0/YFAgESExORmJiIn376CSYmJpg5cyZCQ0Ol7cncwPYkZs8CPh8vvfgSrK2tUF9fz6inyC8pKmtP2MjIsqltHj58iIcPH2LNmjUAgCVLlmDSpEnw9fNn1DO4H5Lof/3nazBz5kykpaUBUO2+pWxfW0HXy2vV1NTg4sWLuHjxIgBg0KBBonlNfYPk/clIVhN6QdImnmY9wZtvvonCQqbYrKP7EnNzc3z77bc67UMgEODSpUu4dOkS3n//fTg7O2Pp0qWIjo6Gh4eHjHmNIe2muW9zNCA34xH+7//+DwUFBbCyssLYsWMxYcIEODk5UXZaLRMUFIQVK1ZopS1lIi4hM2fOxN9//40rV67g/v376Nmzp9y6wmu8fft21NfXY8qUKejUqZNoHyDtf/VhD/qyOWWCeuqXIAiCINSHhFwEQRAEQeiVsrIyAMCVK1cMPBKCIDoCzs7Oeu/zt99+w4MHD5CVlQUul8vYp2txhzoYG0t/HXz8+DHS09MNMBrFuLq6il6LL4umD0xM9LsU1LVr15CVlYW8vDw0NjLth2VkuIfDkqZbV1eLuynpuHv3rmEG1MppabYAdUlLS8MXX3yBpKQkBHXvw9jHNqDdSPbP5/NlZjjhcrl49OiRVABdF4j7k+LiYhQXF+u8z5YSEhKi1/64XC4OHDiAp0+fwss3gLGPxTa0PYk5IxYL169dg5GREcrLyw03qFZK165d9d7nN998g8TERLw0dx6jnG1wu5HuPzU1FQkJCQYYjWHR9/LUN27cQFZWFnJzc6XnxYac10j0XV/fgISk+0hMTDTQiFonlpaWyitpmaKiIqxfvx5JSUlYtmwZ+Hw+Y78hv06x2CyABUAAsFl88PkCmJqaonPnzliyZAnc3NxUWpKTaBtYWFjglVdewddff43Nmzdj06ZNCr/PJycn4/z587C1tcXs2bOl9ncEWzDUe+xo/Rq6b4IgCEI/kJCLIAiCIAiD8N5772HWrFmGHgZBEO2ciooKjB49Wi999evXD1FRUZg1axZ8fX3B5/Ox+IPVjDoNDVyYmupXGCQPyV+3f7b2c1hZmKG+vl70gJrFYjH+hKiauUITJH+pvGbNGhw5ckS0vX37dgQFBemkb1ksW7ZM530AAIfDQd++fTFs2DB4eXmBz+cj6evvGHV4jbrNoqGIRi6zbw7HByveWwQADPsQvhYPckjuF//fXvn5559F2Uh0iaurKyZOnCha2pXFYuHPIycZdQxpNwDAa2gOwJqYGDMyUwBNtmBra4vly5eLtsXRxGbkZZw5fvw4IwPX22+/jTlz5kj1JQtD2uyhQ4fw6NEjnfcjXOq1f//+6NevH4yMjHDtJlPowje0PXGZ2cl2794NGxsbKR8kee+S3C+kPfuirKwszJgxQ+f9mJqaYtSoUYiKisK0adNga2uL0rIy/PjrPlEdXiNfQQu6hydxD1u8dDn8vDwRGxsLNpst136EtCebWblypV768fT0FM1rvL29IRAI8OWmLYw6hrw/8bhMm3R398R/liyCQCBQOA/uaL7EyEh/2a8cHBwwYcIETJw4EVFRUaLl6iV/fGJIf8Jr5AP/mG29wBjOttaora3Fs2fPsGTJEsyePRvjxo2Tsg9JKDtX22HkyJE4fPgwUlNTcfLkSYwbNw6A9PKKAoEAmzdvBgC8+OKLKmf9UoS8JeqJ9kFLri/5D4IgiI4BCbkIgiAIgjAIISEhGDhwoKGHQRBEO+f58+c6bX/QoEGIiYnBlClT4O3tLco2I/zluLXE8oV5+fnw8eLodEyqkvs8n7FtZWkJL44nI2AlDG7KClrpSpAj+fDS1NSUkWmFw+GIsoy09QeYPj4+GDx4MMLCwtCpUyfRexfaj5VEFoSqMi7sXc30Pk4AqC5vZGw7uzijb9++DNGWuL0oE1NIvm5v/P777zpr28PDA9HR0Zg6dSrCwsJE5UL/Y2VpwahfXdog2YTe4PP4qKloFt4IxyYewBQIBDAyMkLnzp2VBs1VRV4Q5ObNmwx/Ymtry8ha1FqFXKampjpr28bGBqGhoQgLC0P37t1FQXt59lRVypXVjN6oKmv2RWw2G6GhYTAzM5XphwxxD2tNyMq8qS1MTU0RFRWFqVOnYsKECbCxsWHMgSzMLRhBbvHrZggk7dbK0gLu7u5y7UWRrbR1m9Hl+L28vDB48GCEhoaKxFvif9aS85rSBjh56n4ZdFlUScxrnJyd0KdPH5l20NF9SX5+vvJKGmJvb4/JkycjJiYGw4cPh5GRkQy7kZjXGNCfVIv5Eh6M4e7lj5VL38KePXtw+vRpbNu2DWfOnMHChQsZ2TRJuNV2YbFYWLhwIZYuXYodO3YgMjJSZmbDs2fPIiUlBT4+Phg7dmyL+lNlv7YFXcr61YWATBtz+/bWr2QdWf2raiMEQRBE24eEXARBEARBEARBECrCYrEwePBgxMTEYOrUqSLxjfhyYeL/A3y9cfdBcxaVpOS0ViHkEggESE5JE21bmJvDw91VagkQ8QeH+nggqO6D0rYYFAkICMDgwYMRHh4ODw8Pke3Ish8/b6atFD+rAyew5b/u1oSiZ3WMbT9vDiOzkvh/WbTFa9Wa8PT0RExMDGJiYkTiLXHfI243Pl6dGMcWPavX+3iFlBU0MDKu+Hp7yczIpQqq2lBLAi4dxU7t7OwQFhaG8PBwdOvWTZT1RFyIDDSdD18JP1SUUyfZnN7g1vNQXtgcRPd0d4WJSdOjTVl+SN/3sPaOmZkZxo4di2nTpmHChAmwtraWshvhOTcxMYanuyty8poEIOWFXHDreTAx0192H3Ek7db3n7mYLHsR3ya7UY6vry/Cw8MxePBg0bxY/A9oPre+3sz7U/GzOvh0s9X7mIV9iyP0deRLdI+dnR0mT56M2NhYjBw5EsbGxoz5jKTdSM9r6uDf207v4wZk+BIfDqytrfHaa69h1KhR2Lp1K9LS0vDBBx9g+PDheOWVV2Bvb09+RY9wuVyYmGg3A3ZISAiGDh2KCxcuYM+ePViwYAGA5qxcdXV12L59OwBg4cKFUpnsVJmbqmsX4vU1nfuq06cqAiNd9Ct5TEfpVxvHEgRBEG0fEnIRBEEQBEEQBEEogM1mY8iQIZg2bRqmTp0Kd3d3AJAKNgjLxP93CfBjtPU4OQ1Ro4bpZ+AKeJaTh6rqGtF2gL+P1MNgbYsrOjJdunRBeHg4IiIi4ObmJtN2AGn78fP1YuwvyjacgEKybz8fbwDSAU9l4hyyF9XhcDiYNm0apk2bhtDQUABMvyPPfrw5nmCz2SJhhSGFN5ICQH9fpt1IvhantdlKaxuPujg4OIhEpJLiLUnfI/7aysoSLs6OKCwqAQCUPW8Ar5EPI2O2dCc6pji3TrSkFQD4ybAnWduE5pibm2PcuHGIjY2VEm/Js5/me5i3SMgFQdP1c/eTzmKia3hcHsqeN2cmdHF2hLW1lVwRsroi045IQEAAwsPDER4eDk9PT5XmxAKBQHQPEGJIobHkvCbA11stgbok5HcUY2trKxJvjR49GqampnIFxJL//Xwk5sOtaF7Txc9HdO0DAgLwxRdf4MyZM9i1axfOnTuHmzdv4sMPP0SPHj0MNOKOgUAgQE5ODpydnbF8+XL0798fL774olY/k/Pnz8f169dx9epVzJs3TyQWY7FYiIuLQ0lJCcLCwtCrVy+pY3XtG9QRWWlrLJoInLTRd0frV13oPkQQBNG+ICEXQRAEQRAEQRCEBEZGRhg+fLhIvOXi4gJAWkShLGDVJcCX0W78vQf6eQNKuH2XOY6u/wjOVAlC6SpQpemvXFtr4CwoKAgREREYPHiwSLwFyH+f8gJX4uKo/Mxag73f/Cc1jO0AP8UBT8n32RqvUWvEy8sLsbGxmD59OmMJann+RtZrExMTeHXyQFZ2DgCgqqQR1WVcWNlrNzuBKuRnMO2ms1jAUxUxl7ZRxc+0Vp+iCU5OToiIiEB4eDiCg4MZ4i1JFN3PAvx8REIuPh8oeFoLD3/9C3Lyn9QytjuLCS8AzQSC7el6awsLCwtMmDBBJN6y/Gc5PHXnQJ19vXH52i1RnecZNQYRchVm10FMJ4IAGX6I7EA5gYGBIlG6u7u7XF8CSPtakdDYqxOMjIzA4zUtuZufWQs+XwA22wDzmkzm/UlSZCakrYiNWyPW1taYPHkyZsyYgTFjxsDMzExtPwIAnp7uMDUxQQO3KSNjYVYd+Dw+2Eb6FxTnZzLvQ7IExaNHj0ZYWBh+++033Lx5E35+flLtENqDz+fj888/R0JCAoKDg5Gamgo2m41Zs2ZpNTOXi4sLVq9ejcDAQEa7+fn52L9/P4yNjfHqq69qrb+WoE/fZCg/2NH6VYXWPDaCIAhCM0jIRRAEQRAEQRAEAcDY2BgjR45EbGwsoqOj4ezsrFR8I0RewMrJ0QGuLk4oKCwGAGQ9fYbbdx+gX2/D/ip7/+HjjO1uwYEU1GwhLBYLwcHBiIiIQEREBMN+hKgb8DQ3M4M3x7NZkFPaiNy0anTqot/lFQuza1GSx8xm4mBvJxor2UnL8PHxwfTp0zF9+nQMGDBAVK4oSC5eR/y/8HXXLv4iuwGA5Jtl6DvGRcsjV0xjAw/pdyoZZYGd5YtG1RXkqIumYtG2hqurq0hsERQUxBCDqiIGlCwTCAQI6hKA67fuiPYlXy83iJAr+XoZYzuwi7/oNd27WoalpSUmTpyI6dOnY/z48bC0tFR5DiSJsL749QGAlBvl6D1Sv34IAJKuMe0mqHPTuFQV9XVk8V9QUBCGDBmC8PBwuLq6qmQLCoXGxsbw9/VGavoTAEBtJQ/PkqrgHWKj/cEroOR5HQqymrOB2dnawM3VWTRO8iWaY2VlJRJvjR07FmZmZgA09yMCgQBsFgudA3zxKCkVAFBfw0fmg0q9L69YXliPvLRmIZeVlSU4nTyk6gkEAtjY2OCNN97Ayy+/DCsrK1F5R/UluoTNZsPNzQ21tbVISEiAk5MTPvroI60vrwhAZma1bdu2gcvlIjY2VpShkCD0CfkRgiCI9gsJuQiCIAiCIAiC6LCYmJhgzJgxiI2NxZQpU+Dg4CDap8lDWFnBq6gRkdj5+5+i8riDRwwq5IpPuIfMrGzRtouzI/r06i6zrqIAgzaDDy194C08Xt8PMVksFrp37y7KeOPo6Nii9mSdh5GRg/Hzb3Gi7cdXyvQu5Hp8pZSxPXzIYKk6FPhUD19fX8yYMQPTp09H//79ReXqBsnllY8YEoZTZy+JtpOul6HPKGew9Jj1JP1uJRrqmtPg9OoeDGcn5mdEVlAT0PyzrK3gWVvLwOLu7i4SkQYGBqp8nCyxoKxzODR8EH7de0C0/FXGnQoMnuoKUwv9PVbMS69GWQFXtO3h5orgwM4A2t71ai1YWVlh0qRJiI2Nxfjx42FhYaFRO/IEX8GBneHu5oLn+YUAgLICLvIyqvUqAmyobUTG3WZBKZvNxtCIUEYdspNmWCwWQkJCRPMaZ2dnmfWUCUHFyyXrjowcLBJyAcDjq2V6F3IlSc5rIgcrnMPoaz7cVhH6khkzZmDcuHEwNzcX7VP1vqxMcDxiSJhIyAUAj6+V6V3IJSkKHT4kDEb/ZLqUZz/CjIaEbuncubPotbOzM2xtbfXS74MHD3D58mU4ODhg1qxZADRbgk8e6iyZSHQ8Ovq9hyAIoiNAQi6CIAiCIAiCIDoUZmZmiIqKwvTp0zFp0iTY2akWBND0weno4RH4/c8jaGhoCkBfvXEbaRmZ6Ozvq1F7LUEgEODXvQcYZRNGD4cRmy3ar2jJs44YrOJyuYxtNpuNnj17ipZNtLe3b1H7ygKgw4aE4dff/0RjYyMAIPNBJapKG2DtYNqiflWltpKLtIQK0TaLxcLoYeGiMXY0e2gJ/v7+mDFjBmJjY9GvXz+FdVsSJAeAwM7+8OZ44umzXABNyytmPayEbw/9BLYEAgEeXiphlI0dGSnap2+70VSY25rt29PTE0OGDEFERAQCAgJ00ofwvDk62KNf7x64lXAPANDIFSDpehl6Dpct8tAFiReYwotRw8Ll3q9UEWO09uurK6ytrTFp0iRMnz4dY8eO1Vi8pQzh+R09NAI7/2gWsydeKNWrkCvpehkauc2f//59esLRwb5Di//q6uoY2ywWCz169BDNa1oiSlfF1w4JG4Bfdsehvr4p0+fTh1WoKGqArbN+5jX1NY1Iia9glEUNH6LUl5D/YCLM4jdjxgy5QlBtik4GD+yHn3f9gZraJvvNSa5BaX49HNzMtNaHIhrqeEi+Wc4oixoR2aF9SWshJSUFGzZsANC0pHRycjK2bNmCN954Q6f98vl8bN68GQAwb948KdGerB8qyENVW9GVsEvV/rX5mdbk86ELcVxr7pcgCIIgSMhFEARBEARBEESHICYmBrGxsZg4cSJsbLT3y39F2UwEAgGsrawwNDwUf5+7JCr7bP1GbP1mPUxNtb/kgyIOHD6OO/cTRdtmZqYYPXyIXsfQ1jAxMQGXy0Xfvn0RERGBsLAwlcV/miBpP7Y21hjUvzeuXI8HAPB5wJUD+Yh61UtnYxDn2sECNDY0j6l3zxA4OzlqJUDVEYJcHA4Hc+fORWxsLPr06dPi9tQJJIwaFo6fdzVnc7v6Zz44Xa1gbGrU4nEo49GVUhRlM5etGtivN6MOZXFTHzc3N4wYMQIRERHw8/PTevuKltIbM3yISMgFALdPFiOgrx2s7HR/H8t+XIkn95uzKhkZsTFiSJhKx5J9Nd3HhBkAo6KiGNly5KGNoKVAIMDwyDDs3n8IPF5TNrcn9yrxLKkSnCDdZ2CqKm3A7RNFjLKoEbIFOx0Jc3NzVFVVoXfv3loVpSuyGfF9lpYWGDyoH85dvPbPPuDyvjyMf92nRWNQlRt/FaChtjlbZPeQrnB30/+Sn22V2NhYzJgxAxMmTNBatill36WApu8sEWEDGNlGL/2Ri0mLfPXyGb51pAB1VTzRdtfO/vDx6tTidgsKCrBjxw68+OKL8PCQXqaRUExRURFWr16NhoYGzJ07F5GRkVi8eDEOHz4MPz8/jB07Vmd9//3338jIyEBAQABGjRqlsK6ubFRRpkBdjEWT/rTRr7w22nu/BEEQBEFCLoIgCC3wPL8Af/x5BAAwNDwUvXqEGHhEBEEQBEEATZkn/vjjD4wfPx5WVvrLACFJ9ITRuHjlBuobmrIPZGQ+xdYdu/HWv+bpbQxZ2c/w/U87GWWTx46ElZXyIIwuA53a/GWvLsa5ePFi9O/fv0Xiv5a+x6kTxuDajdvg/9NO1oMqPLlfAb+eus2u9CypEmm3mVkrYiZESdXriIFwZUyZMgVz585Fr169DDaGYRGhOHD4JMrKm65hVUkj4o8VIjTaXaf9VpdzcfNIIaNsyvjRMDY2NogvacnnT9Z4DWHvw4YNQ0xMDLy9vfXaL9B8/vr26gZfbw4ynz4DAHDr+Liy7znG6FhU2tjAw6W454yyEZGDYWenn+xybRkPDw/89ddfGDNmDMzM9JO1RvLzZm9ni+FDBuP0+cuisktxzzF9uaXORaVX9ueDW988Hj8fL/RuwbOa9nKve/PNN9G3b1+9LT0mi+jxTfNiocDvWVINUuPL0KV/ywRlyshLr0bSdWZWpWkTpec1BBMrKyvs3bsXEydONOj3qUlRI3Du4jVw/8lS+zy9DsnXyxAU5qDTfguyavDwCnNZxdgp47QiCo2Li8O5c+dw8eJFREVFYc6cOS1eqr2jUFdXh9WrV6OkpATDhw8XLW34wQcfYNWqVfj+++/h5eWFbt26ab3vmpoa7NixAwDw+uuvg/1PduvWgr7vVYa6N3a0fgmCIIiOBwm5CIIwOCWlZbhzL7HpF3xofsjGQtOkWDg5trO1ga/P/7N33uFRlNsf/+6mbXpCegIJEEhQem8iIEgHKSJNiiAoVkT0XjuIXq+Na7k/FURUrBdEBKS3AKGEQAiEltAJhPSeLdnN7u+PsMvuZPrupp7P8+wzM++85czM2fO+u+fM+0YjqFlAvRswHz52Auv/2goAGGrHrBYmkwlXrt3AseQU5OTlQ6erhJubK1rHRKNHt06Ibh4l6dqLi0tw5PhJXM+8jcLCIhiqqtC2dUt07dQe98W3rXf3kSAIgiAcjY+PDyZPnlzXYiAsJBizpk7Et2t/t6T9788tiG0VgxFDBzm9/YLCIrzx7keovBtIBlQ7NB+bMFZU+aY8Zhg8eLDddZhnHZKa17zfumU0Rg4bjK0791nyJa7PRlCEB/xCnOOcLy+qxIHfawZP3N+uLavMcmjMevXggw/WtQjw8vTEvMcn49P/+86SlnagCJHx3oi+zzmz4VTpq7D/59vQa+/NdhLTIgrjRlbPVGD9zM37da0HDSE4o0uXLsKZHID1DGlMO6RUKrFw7gz8c9lHlnPX08px/kgR7u/nHCe6yWTCoXXZKC80WNIC/P3w+GMTnNJeYyMiIgJjx4rr55lI6beEys18bDySU06jpLR6VrWyAgMS12dj4PRIp333zh0qxI2z5ZZjpUKBhXNnQKlUstohMdR3OyGWQYMG1XqbTL1oERWJcaMexsYtOy1pR/7MQUi0JwJCnTOuUZfokfBLlk3agL490bnj/Y3m2ToLHx8fTJkyxWn1i7U34WGhmDB2hOVFWgA4tikXoS090SxCeKZBOWjK9Nj/cxas/q5G7x5d0LNbZ9m2xJonnngCfn5+2LhxI7Zt24a9e/di/PjxePTRR+s0aK6+YzKZsGLFCly+fBnx8fFYtGiR5VzXrl3x5JNPYtWqVVi+fDm++OILhIaGOrT9pKQkFBcXY8CAAU4JFCMIgiAIggAokIsgiHrA1Ws3sPTf/xGd39fHB61bRqNf7+4YN+ph+NSDH7ZJJ1MBAD4+3ohv01pWHQmHjuLLld8jN7+AM0+L5pFY9tpitI3lX0bj7Pl0rPzhF5xOO1/jz5C9d9+E7de7B15b/CwC6E1mgiAIgqgVHh78AJJPnUFq2nkA1X9A/+vT/0KvN2DsSP7lGOwhL78AL/zjHdy6fceS5ubmiheemgNXV+cvsUY4hmmTxiLpRCryCwoBAJqyKmz9+ibGvdjS4Uubacr02Pr1TVQU3wue8PfzxawpFDzR0OjTsxu6dU5CyunqJVVNJmD3mtsYvbAFwls79neUscqIPT9mIeuSxpKmUCjwzLzH4eJSv22NddASwU2b1i0xYuhAbN+dYElLXJ8ND08lYrs6fsnZIxuycemE7ayA8x6fDG8vT4e3RTgPb28vzJk+CZ9/84MlLSO5FB7eLujrhBkCL50oxuENOTZpo4cNRmyr2lm6jxDH5HGjcCTpJHJyq5e/1KmN2PrVTTzyQgx8mrk7tC1thQFbv7mJMqugUB9vb8yZ/qhD2yGcz/hRD+NI0gncyqp+2aBSe09vHP1yQ6XGgG0rM1GSp7ekeXmqsGD2NIe14eXlhZkzZ2Ls2LH47bffsG3bNvzvf//D1q1bMWXKFIwdOxbu7o79PjQGfvnlFyQmJiI4OBhvv/023NxsfwuNHz8e169fx65du7Bs2TJ8+umnopYVFsvgwYMRGhqKkJCQBvFCAEEQBEEQDZP6NecnQRCECMrKy3H67Hl8/d1PmPj4AmzdubdO5dHr9Th11zHSvUtHyU4Kk8mEL1f9gLfe/8QmiCs8LAQtmkfC0+qHZuatLDy96DUcSDzGWd8ff23Fc0veROqZcxaHhKUuz3t1HUk6gRdefRt6vZ6rKoIgCIIgrOD6g5aZzvV2tuLubBCBAfeCqE0mEz76/Gv89PsGGI1GOJpLV67h2SVv2QRxAcCsKRMR3TySs5wj3jAXg5i377/55huo1Wqo1WpoNBp069bN7jobIp4qFZ5+YrrN8ygrNGDb1zdRVljJU1Ia5UWV2PbNTZTk2o4R58+eCh+fe4E/5LCoPfhsCt95M/NmPgZfq2dXpTdhx6pbyL5a4TAZDZVV2PdTls0MOAAwbtTDgi+hOBOmPZgxYwbUajUqKipQUVGBF198sY4ka5goFApMf3QcwsNC7iWagP0/ZeFqagl3QYkYjSYc+ysb5xJtl7Lq06Mr+vS07QOkfCfIbtUu1vf7gT490bOb7VKzaQlFOP53DkxGx/Xbl08WI+FX2zFPZHgopkySNzMZIQ6FQiH6+2XO5+7uhmfmPg4Xl3vuiYpiA7Z+fROl+Y4b16hL9di+8iaK7tjW+eTMx+Dvxz87JdkP+3DEjK3MfVdXFzw1Z5rNyyjmlxuKc3XyhWWgKdNj+6pMFNyyrfOJGZMRGCAcuCz12gMCArBw4UJ8++23GDRoEMrLy/Hdd9/hjtNlqwAAIABJREFUySefxK5du1BVVSWpvsbMgQMH8Ouvv8LDwwPvvPMOAgPZZwV99tlncf/99+PatWv45JNPHP4bsX379igqKsKiRYuQnJzs0LoJgiAIgiAAwGXp0qVL61oIgiCaNll3crBz7wEAQFhoCL74cBkmjRuFyY+MxpSJYzBm+BD079MTXTt1QHzb1ghq1gyZt7NgNBphMBiQeCwZIUFBiG8rbyYse0k7fxFbtu8BAEyZMAbxbWMllT+SdAKff73Gcjx10ji8889FmPv4FEwaNwpTJo5Du7atkXL6LLRaHaqMRpw+ewGTxo2sETSWcjoNS//9H8uP0ykTx2GpVV1TJ43DffFtkHL6LDRaLYpLSuHh4Y7OHe638y4QhHg+/fRTlJeXY9y4cYLOeIIgiIaCue9l21rve3i4o0eXTjiechoardZS/mRqGo6fTEXH+9s5ZLZMvV6P739eh/c/+RKlZWU252ZOmYAxI4ZYnG7mZYaY+1wfoKbDzlnOLfNyXq6urnB1dYWLi4uothqas02s/oSFBCOoWQBOnEqzlNWWVyHjeAn8QtwQGG7fm+bXzpRix6pMmxkrAGDujMkY2L+3LH3h0h/rLVEToXvD1BXrdKbeeHl64v74tjiSdBKGu47AKoMJGcdLoFCYEN7ay65nUZClxbavb+LOFY1N+oP9emH+rClQKpWcemO+VuY563Tr+2GvzigUCri4uFhsilJZ8/1Gtjb4AnYbMubrsNYjITvk6uKC7p074mhyCrRa3d104GpqGTRlBkTGeUHpIv+90bLCSuz8NhNXU22DAu9v1xYvP/ckXF1catgeLlvElm6+bgrQcBxM/eHSnR5dOuB8+mXkFxZZ8mdf1SD7agWi4r3hrpI/c59eV4VD67JxYnu+zRJoQc0CsOyfi+Dv5yuq/yKdkQ7zXjD1wbxsHpteBAc1Q1hIMI6nnLGU0amNSD9eAt9AVzSLtG9cc/N8GbZ9k4nSPNvg9JlTJuDhwQ9w6oS5b6gv4+GmgNC4xno/qFkgmkeE4diJVEu+So0R6UnF8PJ3RXCUfXpz62K13hTn2OrN1Lv/UcsdCwPCOuPj44P+/fujb9++yM3NxeXLl3Hs2DEkJiYiKCgILVq0sOvaGjoZGRlYvnw5qqqq8Nprr6Fz586ceV1cXNCrVy8cPHgQ6enpAIBOnTo5VJ4LFy5g06ZNuHz5MkaNGsU6riQIgiAIgpALBXIRBFHnWAdyBQUGYsGc6QgM8Ie/vx98fX3QLDAAzaMiEN+2Nbp26oBBD/TBuFHDcPrseeTlVy8tcyL1DB59ZHSNqZRrgy3b9+D02QsAgBcXzrN5212IqqoqvPHexygpqV4u4rn5s/HE41NsZjtwcXFBTIvmeKBvT2zathtGoxEajRbRzSMR27qlJZ/RaMRb73+CgsLqt5YXzJ6O+XOm1agrunkU+vfugb+27oTJZEJBUTEmjh1hzy0gCElQIBdBEA0Fqc4YIee3ed/H2ws9u3ZC8qk0qDX3Ah/y8gvx9449KCtXIyoiHH6+PpJl1lVWYs/+Q3jvky9xIPFYDWfIE9MftSzj2FAdmo0xkAsQ57gCgJbRzeGp8sDpcxcteasMJlxNLUNpfiUCwz2g8naV1HZRjg7H/spB8tZ8VOlt5ZgyYTTGjuB2WokNqKBALsfCFnjDtW8ymdAs0B9tW8fg6PEUm9n/si5rkHWpHL5BbvCVuJSVpkyP1D35SPgtG5oy25kienbthEULn7AEXwrpDVsaUFNXakNnmkogF1/whXUamx3y8vJElw734XDSSVRazfCcl6nF9bQyePu7wj/EXdJ9qtRW4dyhQuz7KatG0EVsq2i8sfhZqDw8LHrCDBCUaoMo+MKxiBkDubq6ole3Tjh99gKKS+4tmVlWaMCl5GK4eijRLNxdUiCgscqIK6ml2Lv2NrIuqW3O+fn6YOk/X0REWCgAcNoh5pZ0Rhih6xcboG4ymRDdPBJ+Pt44dea8pbzRYMK1M+UoytYhINwdnj7SxjUluTokbc5F0uY8GCptbduE0cMwadxI1ufekMbDjRFr3eALADSZTIiKCEdQYABOpp61lDdWATfSylGYpUNAmDu8fKXpTWl+JY5vycHRv2rqzZgRQzD90Ud4xzTmIB6+fggQN9NbYGAgBg8ejM6dO+PWrVu4cuUKDh48iBMnTiAyMhJhYWGSrq0xUFBQgNdeew3l5eWYNWsWRo4cKVhGpVKhc+fO2Lt3L1JTUxETE4Po6GiHyRQTE4PTp0/j8uXL8PX1Rbt27RxWN0EQBEEQBAVyEQRR51gHcvn7+WHSOOEfYp4qFQY90BcbNm2DoaoKVVVVaBEVUSfLdqxc8zPyCwrRPDICc2ZMllQ2/dJV/Py/PwEAIcHN8PY/FnEuzejv54tr1zNx7UYmAMDb2wsD+vWynL989TrW/LyuOq+/H959fTFcXdn/tPD398O5Cxm4nZWN4pJSTH5kNDw8pDluCEIur7zyCgDg3XffRXh4eB1LQxAEIQ/zn+5swTfMdLaZCby8PNGrWydcvHQVRcX3lqMyGo04dyEdf2zahrPn0+GiVELlqYKvD3dQl1qtwbkLGdiweTve+/gL7ElItHGQAoCHhzsWzJ6KYQ89WMMxxeXIdIQTwhk0pEAuKcEfZj0RclwBQNvYllB5eODshQwb/SrM0uHcoSLk39LCXaWEytsFrm7sDnFthQG3M8px7K8cHN2Yi8Is26VjFAoFpkwYjQljhrMGTrDNXMEV1GWujwK5pCNGZ6wdnuZjNud5SHAQWsc0R0raeej192ZdKy8yION4KW6cK4OLmwIeXkq4e7L/JjFUViHnuganduXjwG/ZyLqsgYmxKmzfnt3wwlOz4ebmxqs39dVJ3pgDucQGXzCP2fo0X18fdLw/HqfSzkOjuTfDpLa8CldOleFKSgmUSsDdU8kZXFplMCI/U4O0AwXY//Md3DhXgSqDrQzt4mLxjxeegre3F2/gBXMGnfrahzUGxIyBuGbXcXNzQ89uHZF++ZrlJTQAMOhNyDxfgQtHiqCvNFbrjY8r67MxmUwoytHhUnIx9v2UhfSkEujUtoYoNDgI/3xpIaKbR/HaG7Gz6VhfN+mLOMQGqJtMJsS2ioGPlxfSLqTblCvKrsT5xGLk3tTAzUNRPa5x5x7XZF2uwLHNuTj8Z06NJfEUCgUmjR2BKRPHsI55ufopsiXOQciOWO9zjWtaRjdHoL8fzpy/CKPVEq3FOZW4cLgYOdfVcPNQ8uqNTl2tN8e35CHxj2zkZ9ZcnvGRkUMxa+pEwbEwc+ziCFsSGhqK4cOHIzY2FteuXcPVq1exZ88eXLhwATExMWjWrJlgHY0BnU6HN954A7dv38agQYPw1FNPiS4bGBiI5s2b49ChQzh+/Dh69uzp0PvWunVr7NixA+np6Rg+fDg8PDwcVjdBEARBEE0bhYntlTuCIIha5ETKabz0+rsAgOjmUfhl9Reiy7774WfYvf8QAGDooAF455+LnCIjF6VlZRg9eQ4AYPyY4Xj5uQWSymfeysLeA4dRUFSE5hHhmDJpHG/+3zdswv99uxYAMPCBPnjvzVcs525l3cHehEQUFBUjLCQYMx6bwFvXyjU/4+d1GwEAG39ZjeCgQEmyyyUpKQk//vhjrbRFEET9pX379nj22WfrWgyCEERoRgHzx2g01tg3Go02++YtAMuxwWDAtl37sX7TdugNBnYhAPj6+CCubWsENwuEu7s7DAYD1BoNrl6/icxbWbzX0OG+ODw1ZxrCQkNYg274grmc7dAU83PUOo/YtuqTI01Id5h6BIBVb9jS0i9fxZer1iK/oJCzff9QNwRFquCuUsJkAvRaI/Jva1Gar+csE9QsAM/Nn4X74tqwBksI6QtfUA7blrBFqs6wHXPpjMlkQn5BIf777U9Iv3yVUwYvfxeERnvC3dMFSmV1kEVRthaFWZXg+tp6uLvjiRmPYvCAvqKc5EIzVwDSArm4ZpQSm5erfrFp9RmmY5xPt6TaofKKCqz84TccP3mas32VtxIhMZ7w9HaB0lWBKr0JJXk65N+uhNHA/iyUSiUeHTcCj4x6GC5WyynaG3hBdkg8csdAXLoD3NOrqqoqrP9rG/7atpvz++jqrkBItAo+AW5wcVPAaDChokSP3Js66LVG1jIA0L93d8yb+Rh8vL1r2Bo5weukM/fgC7Lh0gsAvH2S9bkr127gi5U/Iicvn1MGvxA3BEdZjWt0d8c1edzjmsAAfzz75Ex0uC+OVwekzKxEeiEOqXZEyrjGvH/95i18+e1a3MnO5ZTDL7hab9xUSigUQKXWiIIsLUpyufXG388Xzz45E1063i84Bq6NoFCj0Yh9+/bhp59+Ql5eHgDgwQcfxOzZsxERESGprobG77//jrVr1yI+Ph4fffSRrBU5fv75Z/z6668ICQnB559/joCAAIfJ99lnn2HXrl0YPXo0/c9FEARBEITDkDa/LEEQRD0jKuLeVNIVajVPTueQYjWFd69uXSSXb9E8UtIsXvrKew5eL09Pm3PNIyMwe7qEugz3lj/x8lSJLmcvFy9exNdff11r7REEUT8ZPnw4/cFFNCgUCgWno5F53rzP/MPenGbeAtXLHo8ZMQTdunTA97/8gXMXL7HWX1ZejpOnzkiS2c/XB1MmjMaQgf05gyPEOBWYgRSOROi+WudpiEFcfFjLyaY7zC3bc4pv0xofvvMqvv9tAw4fO8F6L0ty9bxOKqZM/Xp1w5zpk+Dj7W1JE9IXsYE2Ys4T3DhCZwAgOKgZ3nrlOfy1dRc279iHysrKGm2pS6pwPa1ctGxxbVph4RMzEBkRJsnO8OkDmy2Sihgb0xRg3ge++yJWp8xpPt7eWPzMPOxJSMTvf/6N8oqav8u1FUZknq8QLW9URBgWzJ6GuDatOPVIjE4xyxHykDoGEmOPlEolHpswGh3ui8Oqtb8jJ7dm4I6h0oQ7lzUANDXOseHn64OZj43HgH69JNkf0hnxSLGpYvSCqR+xrWLw73dexQ+/bcDBI8dZ2yrN0/MGbTHp06Mr5j4+GX6+PpxjmLoeDzcFxNoRKeMac3rL6OZ4743F+GX9JiQkJtksIW2mNF/P+xIDk57dOmH+rCkI8PevN7ZEqVRi6NChGDhwILZu3Yrff/8dBw8exNGjR7F27Vr4+/s7rK36xuTJk6HT6TBu3DhZQVwAMGPGDNy4cQOHDx/GunXrsGCBtJex+Zg9ezYOHTqE7du3Y/To0WjZsqXD6iYIgiAIoulCgVwEQTRodFZLgri6si//4UySTqYCqP4x3bVze6e2ZTQacSQ5xXLcr3d3u+rLys4GADSPioBnLQZyEQRBEERDRsj5DUDQ+cBWJjI8DK8vfgaZt+9gz4HDSDx2AlptzaU9xNCubSweHtQfPbt1hpubK6vzW4oDgilrXdAYHGdMhyYzqALg1h2uLVC93Paz8x7HxNHDsHV3Ag4eSbJZNk8M7m5ueLB/L4waOgjhYSE1HJtiHJ/W57mun5APXyCOVJ0Bqn+/TBgzHEMH9ceehMPYtT8RpWXiA7fM7Xbv3AFjhj+E+LatJc/EVpdOcuvvHJssTQnmvTCniXWom8sOGdgf/Xv3QELiMWzbnYA8npkCubgvvg3GDBuMzh3uq6FPZrmk9GFs10nIR2gMxAzasU5n2iHz9v52bfHp8tdxIjUNf+/ch8tXb0iWKzI8FKMeHoQBfXvBw8Ndkv1h6hbzmtj2CXaY98j6eQsFGFuXVak88NScaZgwehi27UlAQmISa8AxH25urnigT0+MHjYIkeFhopdHrO/j4caAGDvC9jz47AgAeKpUmPf4Yxg3cii27zmAhMRj0Omk6Y2rqwv69+6BscMfQvOoCNbnzjUWrk1b4ubmhvHjx2PYsGHYsGEDKioqGnUQF1D98tPs2bPtqkOhUODll19GbGwsHn30UQdJVk1gYCCmTp2K77//HitXrsQHH3zg0PoJgiAIgmiaUCAXQRANmhMp95ZviIluXqttm0wmJN9t//52cZZZC5zV1v/+3IKz5y8CAEKDg9CvVw/Z9RUWFeNI0kkAwIB+vemPKIIgCILgQMjZYA1bUIV1Prb+lpnWIioCc6ZNwpTxo3HqzDlcuZ6J6zdv4UbmbWh1NQO7FAoFwkOD0TKmBVrHtECn++Mtjgc5H3OdQmMDGjvIR64Di21rTXhYCObOeBSPjhuOE6fSkHHlOi5fvY47OXms7USGhyK2dQziWrVEj24d4evjI0tXmPKw5eO6VoIfpuOS7TwAWTpjPvb18cH40cMwathgnExNQ/rla7h89QYyb2WhimVGi6BmgWjTKgZtW8egW+cONoF/5nrl2Ju6cpI3dT201i3reyFVp8z7KpUHhg95EEMH9Udq2nlczLiCS1dv4NqNTNblg/39fNG6ZTTato5B5w73IaZFlOz+S0wfxmdDiZpIHQMxbZKYQHag2kHfq1tn9OzaCZeuXEPa+XRcunoDl6/dgFpdczYulcoDrWOq9ea++DY2y+XJ1Rm+e0Cww7QfTF1hO8+3tS4HAKEhQZg9dSImjhlWPa65eh2Xr9xAVnYOqzwR4aF3+6eW6NmtI/x8fe3SB7IljkGsHWH2RWJn42ISEtQMMx8bjwmjh+HEqTO4dPU6Ll+9gazsXFY5IsJCEds6GnGtW6JH104I8PeTtUwic5/vWh2Fl5cXZs6c6fB6GzMqlQpTp04Vnf+rr76Cu7s7pk+fDi8vL96848ePx/bt23H69GkcPXoUffv2tVdcgiAIgiCaOBTIRRBEg+XwsWRcunLNcjygT89abf9W1h3k5FY7xnp16+SwenU6He5k50JXqUd+QQFu38nBlh17cP1GJgAgqFkAPvtwKdzd5U0lDQCbt+1GVVUVPDzcMXHMcEeJLouew9ph+JxedSoDQRDOpzivHP998c+6FoMg7EKMwwrgd2ay1cmGl5cn+vXujr69ugEAqqqqkF9QBK1OB71eD6XSBR7ubghqFgiVyoPTkS3kiLCWgS2YgimfM5wQfA4eOXXVNxzlwLI+x3adSqUSJpMJ/n5+eOjBfhg8oNp5UFZegeLiEryVWoGrOhO+7u6DVqGB8PRU1Xj2zgieEOvgIsThKJ1RKpWWpYcUCgU83N3Rt2c39O3ZDSaTCZWVeuTlF6BSr4fRaIK7uxv8fH0Q4O/HqTdc9oZNDj4HOZ/cQveGK+BNro2RYrcbCswACq48APcsgdZ5mPtAdWBOt84d0K1zB5hMJugNBuTlFeCrC+U4WFiFt+7zQLcIPzQLDGDVCTH9l5COM/MR9iFnDMT3PeZ6Lm1jW6FN65YAqmclzy8ohFqjhcFggKurK1QqD4QGBzkkcItr3CPGNhH3kGJLrNP4vrvW+Pr4YPCAvhj0QB8AQHmFGkVFxVh+pgIXNSZ80dUbbcMD4X03yEKKLWHKwaUD9NwdA58dYY5vhMYz1uMYNny8vTDogT4Y2L83AECt0aCwqASVej1gMsHd3R3NAvzh7e3l0DEwnx4x7wVRv7ly5Qq2bt0KLy8vTJ48WTC/m5sbnnzySbz33ntYvXo1evToIXsZSIIgCIIgCIACuQiCaIBotFps2bYb33z/syWtS6f2aBfXplblOJFyxrLfs1sXh9V79XomFrz4jxrpEeFheGTUwxg/eji8vfnfAuLj0JHj+P6XdQCA+bOmITwsVHZdjqBN1+aI69qiTmUgCML5FOeW1bUIBOEwuBwPbLOaCAUjsDkh2Jwcrq6uCAsNrlEvsw1HOSLYHJtc94KQhxgHFjNdSJ/Y8PP1ga+PN+6k3sBNfSWCQ0Pg5eUm6MCU47ziu1ZCPFxOcak6w6VHZszBf2wBGR4e7mgeFQGjRgv1iq/h88ZLohyWUnSHrTxRNzDvPbM/47NHgLBD3d3NDZERYSi6YMBNgxoeAUEIauZTo265/RVfH8Z2fULpBDdc9sn6PCBsf4QCHEwmE1xcXBAaUj32qfjkv/CaPwvKAH+bfM4Y9wjJ19Th0wG2+8b2goN1XmYZc99kNBotbVm35+PtBR9vL+SeuYmbeh0Cg0Lg4+1hI4Mj9MK6Lua+mOsmuBFrR4TsiRg7Yt56e3nBy9MT6s9Wwvulp3nHIlLtB3Of7VqE5CXqHytXroTJZML06dNFL13Zr18/dO7cGadPn8amTZscvoQjQRAEQRBNCwrkIgiiXlFYVIQvV/0Ad3c3eLi5w93dDRqNFjl5+cjJy0dubj5y8vKg199bliEsNARL//kSlEqlTV0JiUdx4tQZZhM2REWEY9qjj8iS9XhKKgDA28sL7eKdH0Tm6+MFg8EAtUYjO5Ar6cQpvP2vT2E0GjFmxBBMnjDGwVISBEEQROODy9lg7VywPjYj5MQ0Yx1MYe2sYrbL5xwQ4wgXmi1HyLkg5loIfphOJb58fPtCQRPWbZn3i/RGQF0FN6XSZtzM5dAWM4OFGP0Tuj6CH6k6w2V35OiMyWSC8eRpVB4/BYVGC8XdWSvM5+11fAo5PMWki0Xo/jmjzfoGX1/GNbsSIN6BztaXMWdVMSoUgLoKKteadsi8pT6sfiE0BmKDK3DHGi6bxDb2Mep0qDyWAvfO7eExfIhN+3J0RqiPYl4j6Qw3bP0GWx6+fS5dMNsUZlvmcuVVJkBdBXc3F1HjESn9k9A1M6+F4MdeO8KlQ1LsiMlkQtW5i9DtPQzv2VOhCG5mU589doQpH/O66lpXDhw4gJMnT+Lxxx9HaGjdvszbUEhMTMTZs2cRFRWFsWPHSiq7YMECPP/88/jtt98wdOhQBAQEOElKgiAIgiAaOxTIRRBEvaK8Qo11f24RldfNzRWjhg3BwrmPswY2JaecxuZtu3nr6Ni+naxALoPBgJOpaQCA7l07wtXFRXIdXIQEB+HFp+dCqVSiqLgYeQVFSE45jYzL15Bx+RrW/LwOi5+dj0dGD5NU76Ejx7H0gxUwGAyYOWUi5s+ZXud/JhAEQRBEQ8PaicQXZMV0XDL3pQZUmOtk5mFuuZwRcpcgEuPQcgRcDh4p5eszchxYAL8eWc9YwdYWU0+VVuXkODyFArv4rl3MtRLiEHu/gZoOTiGHJ1NnFAoFNH9uBQxGVB44DM+7S7Lb4yhn02cuh6ccneGzJVLsDFfbjUWPxfRlfLNMMvfltGNPHyYmqKs2+7CmAtfzZMvHtc+0SWzBf9ZtAUBV0kmYNJVQr/8bqhFD7R73sOUXozOkS9ww+w42hMbGYupn0z0FuIOxpI5nhPSEsB9H2BFrxNoRhUIB7V87AJMJum174DV7iqDOiF2inmuMU19syYYNG3D58mUkJCRg7NixmDJlCvz8/GpVBiGSk5PRoUMHeHp61rUoqKysxOrVqwFUB2W5ukpzobZq1QojRozAtm3b8OOPP+LFF190hpgEQRAEQTQBKJCLIIh6hYtSCX9/P2g0Wmh1Ori5ucFT5QFPTxU8VSqoVCq0ad0S/Xp1R/euHeGpUnHWVV6uBgDMnz0NfXp2Y83DV56P8xcvQaPRAgB6du0sqw4ugoMC8ej40TZphqoqHEw8hg8//xpqtQaffLnybiDbQ4L1qdUafLnqe/y9Yy8UCgVefm4+xo8Z4VCZCYIgCKKpYv4jnm82E4A9oILN6SDkgOBqX6xjUshhyeV0YLbDJQ8hDJcDi3k/2WYxYbvnQjO6sdUhZkYuOTpEDk/HwfzuczqwGTZISGfEODzNZYw6HfTpVwEAlYnH4TV2hN1OTy79qE8Oz8YMl+OcrS/j6wsA4Vne2Gyctc5InZGL+rD6h5h7KKYfY563DiwFAN2xkwCAqts5UKg1UPh4O0RnhGyS2GtsivDZErHLLjLtifm5m+2KtS4wZ/cDrAPU79kTqXrB7LP45OU6RzpiH1LtiJQZRm3GNHo9dEdOAAA0uxLg88Q0m7odZUu4ZK9LPXn//fexfv16bN68GRs3bsSOHTvw6KOPYsKECVDJ/F/ckaSkpGDZsmWIjY3FihUr4OLAl6Wlcu3aNSQlJSE3Nxfdu3dHz549ZdUzc+ZMHDhwALt27cKYMWMQGxvrYEkJgiAIgmgKUCAXQRD1iqjICPyy+guH1FVeUQEAaNO6JeLatHZInWaSU05b9nt1d2wgFxuuLi54aGB/qDUafPjZ1wCAX9f/hZEPD+b9MyDt3AUs/+gL3MnJhbu7O5b+8yUM6NfL6fISBEEQRGPD2jFl7UgSmn2AWYec9qwdYnwzpPA5J5lOK6481nVyOSScBZ/zT6hcQ8Ysv1AwICDPeWXRI8W9OvieN1OHxM58wyYzBU84B+t7KGSDxNomNp2pSrm3TL3uXDqgq4TCU2W305NPb5zt8BRjZ5qKjkrty9gCQ4WCkrn6MqWCe2ZA6305+sQsT9iP0BjInM6ET2es+zJrG8TURaNeD11yqiVvZdIJeD482JLHnnEP23WSzkjH0eNia7vC14aJZyziiH6Kq25CHs62I9b1MvXQZDLBcCrNks9YUIyqzNtwi2lhySfWlvD1PdZy1Cdd8fX1xdy5czFu3Dj8/PPP2LNnD3766Sf8/fffmDp1KkaOHCl51ilHcfPmTfzrX/+C0WjEyJEj6zSIa/Xq1di4cSNcXFygVCqxYMEC2XX5+/tjxowZWLVqFb755ht8/PHHDpSUIAiCIIimglI4C0EQRMPEHMjl6+Pt8LqPp1T/kRgRHobIiHCH18/F6OFDEBYaAgC4kXkb129ksuYzmUz4c/N2PPfK27iTkws/Xx988eEyCuIiCIIgCCfA5TwW++e/9Xnzxzq/dTnr80J5uD5yHOJs10lIQ8w9FfNhe55sz5d5ztKOQDm+dCHHlfV1EPbjCJ3he45COqM9nHRPmEoD9MkpNfLY4xDnulaibhD6Tkv9cPVlAKBQKmrYMDk6S31Y3SOmPxDqy/jSDafPwVSusdSpPXScU2eE0khnHAvfc7feF7IRYvswrnEN2ZKGjyPD9SlCAAAgAElEQVTsCN/3X6lUQrs/0abeyoTDvLakMf6GCg4OxqJFi/D111+jX79+KCoqwtdff40FCxZg//79sl6osYeSkhIsXboUarUaEydOxIgRdbt6RKtWraqD/gwGDBo0CC1atLCrvjFjxiAqKgrnzp3DoUOHHCQlQRAEQRBNCZqRiyCIRktFRfXSij7ejg3kKiuvwIX0ywDsn41Lo9WisLAYhUVFaB4VicAAf978CoUCgQF+yMnNAwCUq9U18phMJnz632+xaetOAEBEWCg+ff8ttGgeaZesBEEQBNHUUSi43yQ3/ykv9Fa52fEkZjlFtvq55DJvmft8Dgaxzoe6ZsOGDUhOTrYcP//884iKiqpDiRyPtW6Zj8VinpWArS7rrRL3njvfkmbW+1I+TLnrm/OqscG0R0KIWVIRsNIZgwGau0sQmdEmJsFr8ABWPWEe89kW67zMNOb1yNEZ5vfJmlOnTuF///uf5XjUqFEYMGCAqPYai/7y9WXmNGukzrDDtxSadT62PsceG8SlT4RjkKo3cutn6o0uMckmnzY5FdDpoPTysmm3MY17GiJixsXmfNaIWWqcqRs12rYK7GL2I/baETaZCfnUlR0xarTQHk62yavelwi/uTMcojMNrf9p0aIF3nzzTaSnp+P777/HmTNn8PHHH2PDhg2YPXu27OUEpaDX67F8+XJkZ2ejd+/emDt3rtPbFML69+W5c+dQVlYGX19f2fW5urpiwYIFeOedd/Ddd9+hd+/ecHd3d4SoBEEQBEE0ESiQiyCIRkv53UAuD5UHEo8m41x6BjQaLXy8vDD4wX6IbRUjq96U1DTLnww9u9oXyPXuh58j8ehxAMD82dMwa9qjvPlNJhNKSsstx14qVY08v67/yxLE1TwyAl9+/C6Cg5rZJacz+e3DPdjweUJdi0EQhJOp1BrqWgSCcAhSHBBCAVhcdbPVb10nsx0uB4T1vhiHpnV9bHUy23MGbAEY27Ztw/fff285njx5suWP9vruKGHCpz9Cz1ls3cw6LXXfrUrJ47yW4vhuqM6rhoYYnZEbBMinM7pTZ2xmwQEAzdGTgMEAhbu7aB3hO28tR23pzdmzZ/HRRx9ZjoODg20CuZo6Uhzq1vojFJAM2PaJCigEAy+s96XaoLrqw5oqcgMxmDpknaZQKGCqqoKaEcgFvQH64ylwZQkqtd5vKOOehg5fH2VOs0aqnrDVyaxDAfagcrljGT59IL1wHs6yIwCgPXIcJm2lTTnD7RwY0i/D/b44h/Q/bMfMa6lvOhMfH49///vfSElJwffff48rV67gnXfeQYcOHTB37ly0a9fOaW1//vnnOH/+PFq3bo1//OMfNi+Z1AUmkwkrV64EAERHR+PmzZv44IMPsHz5cruWe+zZsye6d++OM2fOID09HR07dnSUyARBEARBNAEokIsgiEaLeWnF55a8ibz8QptzP/72B7p26oC3Xn0BIcFBkupNPnUaQPUP8G5d7PsBdn98G0sg1x+btuGxCWOhUnlw5j9/8RLuZOcAAPx8fRAVGWFz/kTKaXyz5mcAgL+fLz59/816HcRlhgI8CIIgiMYA02kAiAvmsnZAWB+by1tvuZwBXM4rLocE3zFXnYRjEHJ6mtMB+/SHTR+Be0sQWbdj3udzXgml822Z+4T9cDk8xThBxeiM9tCxGuVMGh30J1Lh+UAf0U5PtnRrOdhk4zvvTJqSjvLZIWv7Yz7PV57rHLMvUygUwN26aiz5ytOHMY/FONC55CbsQ6zeiLU/zH6PGfynSU2DsbisRh3qg0fhPWSgpZx5K2R35Ix7SI/kw9VPAdL0RM64Ro4tYbMtbLIT9mFv/2NdTowdMZlMUO89yFqPeu9BqNq3s2lLrM5w2RFmXcy0+ki3bt3QtWtXHDx4EGvXrsXZs2exePFi9OnTB3PmzEF0dLRD2/v999+xb98+BAYGYunSpVCxvKRc2yQkJCA9PR0xMTH46KOP8PLLLyM1NRWrVq3CwoUL7ar72WefhUKhQFhYmIOkJQiCIAiiqVC3oe4EQRBOwmAwQKerftsqLCQEry1+Fr9+9yVWff4hFs6biWaBATh15ixeem0ZdDqdpLqPn6wO5Lovvg18fexbtnHooAGWP5yKikvwwYr/Q2WlnjVvYVEx/vPVasvxpHGjagR9/bz+L8v+8jeWIDIi3C75CIIgCIKoCZ/TT6oTQKlU2uybj9m2zA9fGet6me0IOSGEHGuE8+B7Jmy6I0Z/2AIm2HSIT3+kOD4bmvOqIcB3L5nnxOiNoM0xmVBxkDELzl3UB4/WKCNkY4ScnqQzdYOYZyDGkS2lD1NY1c1me7hsm1j7w7wGrusk5MOnN1J1Rqg/YgsoBQD10ZMwVep5dcZR4x5CGEfqBNf3n29co1SKGw9LsSVMu8J1nYQ8xOiMPf2P9THKyqE+nsoqR8XeQ4DRKMuWWMvGvKaGiEKhwMCBA7Fq1So8++yzCAwMxLFjx/DMM89gxYoVyM3NdUg7hw4dwtq1a+Hu7o533nkHwcHBDqnXHrRaLdasWQMAWLBgAXx9fbF06VL4+Phgy5Yt2LFjh131h4eH13oQF99ytbXdHrVLEARBEPKhGbkIgmiUKBQK/Pfj5QCAdvFt4GG1Bv198W3Qr1d3LFz8Om5k3sa2XfsxYewIUfXezsq2zIjVq1sXu+WMCA/F449NwNrfNwAA9h08jOuZtzBq6CB07ng/PNzdUVJahqSUVOzYnYD8guqZxcLDQjBx3EiburJzcnHy1BkAgI+3F5JOnELSiVOi5PD29sLMqZPsvh4x+Pn5oU2bNrXSFkEQ9RfzsmgE0VBRKPjfJpcyk5KY+sTIw9zyOUT48rJtmfuEffA9b2YaIH7mCq6yQPWSigDuObYgHIjItc/l6CQdcR5ibIQcPWHWBwCaU2dgLKk5Cw4AVBxKQkhVFZRubpay5q0YPeFyepLu1D18+iTXBtWYcVCkHsixQXyBAcx9wvFItUfWZZjlzWkVB46yljNpdKg8kQKvAf1E64x5ny/NesvcJ/iROq4BauqIja1gqYdrXMO0C+Y063Nc+2RL6hdi9ISrDLO8eVuekAhUGVnLVhWWoPJUGjx7dXOIznDpSUPSGRcXF4wePRpDhgzBpk2b8Mcff2DPnj1IT0+3LD0ol/T0dHz66acAgCVLliAuLs4RItvN+vXrUVBQgN69e6Nr164AgMjISLz22mt466238H//939o3rw5OnToILuNW7du4ciRI5ZjNr1WqVQYN26cpHrNun/p0iWcOiXOB8BkyJAhkgLqzG1qtVps3rxZVptxcXGWey0Fk8mEffv2IT8/X3JZT09PjB07Vtb3MSMjw677GxQUJLldjUaDLVu2yGqzXbt26NSpk6yycu+vl5cXxowZI6vNtLQ0XLhwQVbZUaNGwcfHR3K50tJS2UGa7du3R/v27WWV3b17N4qKiiSX8/HxwahRo2S1mZGRgdRU9oBmISZNmiRredns7GwcPMg+G6YQPXr0QOvWrWWV3bRpk+RJOwAgJCQEgwcPltVmamoqMjIyePMEBQVhyJAhsuonCArkIgiiUeLi4oLOHe/nPN8ypgUGP9gfW7bvRsqZs6IDuZJTTlv2e3brbLecAPDk7Glwd3fD6rW/AwCuXruB/377I2f++Lax+Ojd1xHg72eTfvjYCct+eYUav1jNziVESHCzWgvkmjBhAiZMmFArbRH1l82bN0v+g4IgCKI+w+W45nJoigmoYHNaMdtkO5biyBSzZWvLWUh9e1VsoFt9hM/BKRTIJ1Z/zGnWcC1pZt6XokdsusJWJ+EYpDrKpQRSWNejPnCEM7+xXA1dyhl49ekhy+Zw6QxTJqJ2YAZOsJ0T0iehPsy6nLk00w6Zy3JtpdggCrxwPmL6IEB8H83UQ4VCAU3aeRjyCjnLlCccgc/AByz5zduGNu5prEgd1/CVFxrXKEQs1WreStUVvn3CPoTsiHkfkB4Yan1cvvsAb7nyPQfg3aeHTXtydKWx6YxKpcKUKVMwatQorFu3zu6gq7y8PLz77ruorKzErFmz8MADDzhIUvvIzc3Fhg0b4Orqivnz59uc69q1K+bPn4+VK1fivffew+effy5pZi1rfTx69CimTJnCmz8kJAQ5OTmCOsM2btu6dSsWL14sWjZrEhIS8OCDD0ouV1hYiKlTp8pq84UXXkCXLvJekH/jjTeQlMQ+czAfYWFhuHPnjqzZsuy5v4cPH0bfvn0lt5uXlyeoM1wsWbIEH3/8sayyb7zxBo4dY58RlY/mzZvLDuT65Zdf8OGHH8oqm56eLss+3bx5U/b9feutt/Duu+/KKvvqq6/KCqpq2bIlrl27JqvNLVu2YMmSJbLKVlRUwMvLS3K51NRU2fd35cqVWLBggayy8+bNQ0FBgeRyAwYMkB3I9cMPP+Dzzz/nzdO7d28K5CJkQ0srEgTRZAkK9AcAzqUM2Ug+VR3I5empwv3t2jpEDoVCgdnTJ2PV5x9i2JCBnFHucW1a45UXnsJ/P16OZoEBNc7ny4jmJ4i6IDk5GStWrKhrMQiCIOyGK3iFy3Ek9OFbEoZraUW2fNZbvo+QzETtwheIIEaXhJYUsqqc9TybzrClWcvBlJvrOgjnIORYlKIr1tvyBO5ALgCoSDgsavlWNjmsZWReB9u52qSpLoPCd/+FnqNYPbPMBMhoS6gPE+rL2GRkuw7C8XDpDdfzENO/WKfxBZQCQEViElBVReOeeoQY225vH2XznK3qdrQt4bomwrHw2RExtkTIjlTl5kOTep5XhvL9iTWWarXHjjQ2nfH19cW8efMwYMAA2XVoNBq88847KCoqwpAhQ2QH/ziDNWvWoLKyEo888ggiIyNrnH/kkUcwfPhwlJaW4t1334VWqxWs096l9WjJPoIgCIIgzNCMXARB1Dndu3ZCwtZ1Dq9Xr9dDq6uEr4836/nrmbcl1zl04APo16s7AgP84erqWBN6X3wbvPXKC1jy/AJk3cnB7axsVFZWIjQkGGGhwQgLDeEtP3/WNDw5s/78GCYILtavX4/ExETk5eUhJIRfrwmCIOo7CoW4ZWCYSF0mxFyGmYftWIxDla0OLmdmY3NI1CfY9AcQnrlCjv6YXZ5SZ8IRs2XWw3aOcAxcNsd6X6wdYqtXm3Yehnz+F0TKDhxB2D9fhMLFxS6dqY+2piHP8mcPfH2OWH1i5mOr11ySzw5Z7zcWvWqs8D1re2deKtuXyFvOWKaG9mQqvPv2stTDtaVxT+0hdlwjt4+yrhN3t0oF/0yj9myZMhCOh8uOWB8L3Xsu3SrfnSDYvrFCC+3R4/B56EFB3RH6rcUmV1PHaDTiww8/xPXr19G+fXu88MILdS2ShXPnzuHgwYPw9/fHtGnTOPM988wzyMzMxPnz5/HJJ5/gjTfe4P2NTxAEQRAE4SgokIsgiDpHoVDIWmuZjw2btuHzb9bgvvg2+OY/H9T4gVVWVo6jx08CAPr16i663sEP9nOonGx4qlSIbRWD2FYxksox/wgniPqKeXrnX375BYsWLapjaQiCIOxHyOkg1VklpV22YzEOLKE0rmNnIveP74YeeMHUH+s0h+qQ2e+JmrMFSAmOqG960xQRcpTbQ8V+/uAJAKgqKoX29Fl4de8iO8DGmTrjLCdaQ7c1QnD1Zdb79tsh7llL7Am+YO7zpRGORUwwl9TlFnXpl6DPyhFsu3x/Inz69ba0x7cVSuM6JqQjNK5x/HhYuK8hW1K/ERvMJVWHynbuE9V+6c598B0yUJbuMPf50hozarWadfmt1atX4/jx4wgPD8dbb70FNze3OpCOnX37qvVj9uzZvEuHubm54a233sILL7yAI0eO4Oeff8bMmTNr5JMy/lSpVHjzzTdt0ry92V9EF0P//v3x3nvviZKFqZutWrWS1aafnx/ee+89SddtbrtXr16y2gSAhQsXYuzYsZLb9fHxkd2m9f2V2m50dLSsNgMCAmyeqZQ2+/TpI6tN4N79lYqfn5/sNkeMGCG7fHBwsKxy4eHheP/992WVtWeGwueffx7Z2dmSywUE1FydRywDBgyQfa3u7u6yysXFxclu0x778Oabb0KtVksuJ/d7CgBjxoxBaGioTZpGo5H1/SUINhQmChMnCKIRkptXgCeeWYzSsnI8MnoYFi2cZ5lBq7CoGEs/+A9OnTmLoGYBWLvyM/j5+taxxATRNDhx4gR69uwJABg+fDh27NhRxxIRBEE4DuZPK+tjrnNsefjKcSHWUSnGcc517Gy4rnXevHn4/vvvLcfJycno3t02EL8xOEvYrp9LF8Tqj/Vx29XJuJWnxs0X+iLEu/oPOTnBgHz7bMeE87BHZ7jyXRo7HfqsXMG2g6Y+gvBX782qIDdogivNHvjs5tq1azFnzhzL8UcffYSXX35ZtExNQb/59MpeOzRt43n8dTEPu2Z0woDoQMt5qUEYfPt8aYTzELI9zGM+vcn7ag3yvvtVsE2XQD/E7VwP5d3/ehrauKexI7aPkjoWNh93+yEFF+6UIeOZ3mjhr5IVHMq3z5dGOAexv6XE6E7llWu4/Nh8Ue0q3FwQv2cDXO7+N2zPOJgrrbHz9ttvo6KiAvPmzcP9998PANi+fTu+/PJLeHt7Y8WKFWjRokUdS1mTpKQk9OzZU9TL0VevXsXLL78MnU6H119/HQ888IDlnNBv9j/++AOPPfaY5XjZsmV4/fXXHfZiu1wXrz26ao9bmdp1Lk3RBhFEfUKj0dgECPfu3RvHjh2rQ4mIhgxN30IQRKMkNCQI777+Mjw9Vdi0dRfGTp2Ll15bhueWvIWJjy/AqTNnERochC8/Xk5BXARRi/zxxx+W/b1796K4uLgOpSEIgnAsQg4ktmPrdPO+Uqm02bc+Zn6Y5/nyMtthHvNdi7Ox9/0ik8nU4JeyEHIEMZ8ZWxqbDjCXMOPSKym6w9znuwbCedijM2zPvnoWHOEgLgAoubvsmRg94ZKXTYeciRQb0dDtiT3wfbe5+hIunRJjh4RsD5/ds05nk5eoPfhsD9/4h01vSvceFNWmeXZAIfvD1CWmvGzHhP2I6aPMWzF2hWlPzBZFypiGq10x8hLOh2+cILX/KRE5GxcAmPRVKN9/iLcNpgxC8jYlysvLkZOTgwsXLmDJkiVYvnw5bt26hejoaAQGBuL111+vl0FcQLVzXewKF61bt8aSJUsAAJ9++imuXLkCgH/MaP6Nysxz+PBhfPrpp5znxWLvb2Cp5e2VV267css05HaZZR117wmCIIiGBQVyEQTRaOnetRPW/fA1Zkwej5gWUUi/dBU5eXno1P4+vPriQvz63ZdoERVZ12ISRJNi3bp1ln2DwYDNmzfXoTQEQRCOh8thyTyW4rDickg5I/CmqTog6gtczmY+x6doHbq7tqKUoAlyXNV/uBzPcnSmbN8h0e0acgugO3vBLp1xBs52bjQl5wnfM5PjXFcoFFDezadUsNshPvvEJwOf3ETtIUZnhPRGd+0GdNdviW6zbN8h0TrDbJftmHAsXGNPOfaD+THJLGcuI1ZGonYRsuti9adku/hALgAo2b6XU1/4ZGA7bmr4+Pjgq6++wvPPP4/AwEAcPXoUTz/9NPbv348VK1aga9eudS2iw+jfvz9mzJgBnU6HH3/8kXNcKBRwk5WVhYSEBFy4cKFGGTFjTUcH9LAFC9VGAJHYdh3ddn1p1548YtohCIIgGj6udS0AQRCEMwnw98PT82quW08QRO2TkpKCa9eu2aRt2LABs2bNqiOJCIIgnIdCobD588z85z5XmvWf/8xjZn4xbUs5llq/o3H0n9CNxZHCp0N8+iJ0D/icTXJ0p7Hc74YM8/kz08XqTMkecbPgmCnZexBenTtwysJ2zJXmbOTYmcZkT+Qi1JdJ7cfuVSS+L6qvOkWwY+/4p3yfNDtUvOcgIv7xImfABduxUDrheLj0gM2WWB+LrFyUXSBb0nAQshvMY6Yd0Zw5i8qsHEltlh0/jaqCQrgGB9Vok+uYK60p4uLigpEjR2Lw4MHYuHEj/vjjD2zbtg379u3DxIkTMWnSJHh6eta1mA5h+vTp8PT0xMiRI1nPixlzdunSBfn5+Vi5ciX+85//1NAjCsJpGtg7mxrZH4IgiMYPzchFEARBEEStsH79+hppO3fuRHl5eR1IQxAE4Xy4/uzneotbaEYCvrr46mDm56qHqH+IeV6i9UdRM789ukN6Uz+RojPWx7qr1yXNggPAEvhFOtO4EfMsxdohMTPoCLXBJh9Rv+DTGSG9KdlzQFJbhrxCaM6ckzTuMZ8jah+ptoSZxnoeZEsaK3L7n5Lte6U3ZjKheOdeUTpknU7YolKpMG3aNKxZswZjx46FXq/Hr7/+innz5uHvv/9GVVVVXYtoNwqFAhMnToRKpZJdR5s2bdCqVStcunQJe/fK0Nd6TF0tAdjU2jW3TRAEQTRuKJCLIAiCIIhagS2QS6fTYevWrXUgDUEQRO0g5ETkc3Q6+hyXDI2NxvaHJp/zSIoeCNUvRXcao940JsTqjPVzL9srbRYcAKi8nQ1d+iXW+tjaJho2cu2QmGcvJQhDKJ2oP0gd/1TeyoIm/RprGT5KGfZL6tiLqF3kBPqRLWnaSOl/UFWF4p37ZbVTvHW3YBt86cQ9/P39sXDhQqxcuRL9+/dHcXExvvrqKzz99NNITEysa/GcBtfv0JEjRyIjI8Pyef7557FgwQIAwA8//ACNRlPrMjmjHWZbtRHg1NTaJQii/qNSqXD58mXLZ8OGDXUtEtGAoaUVCYIgCIJwOqmpqbhy5QrruT///BNTpkypZYkIgiBqF/Of/Wx/7Fk7Apjn5TgJhMrUJ8cD3x+d5nMdOnTAsGHDLOl+fn5Ol6s+wqVDUvXHnsC++qQ7hDBSdKZ4d4KsNkr2HITnffG87dcWXPbEOj0qKsrGnrRq1UqwTuZ1NOWlTMToFN95pdUx3z2Ue46of4gd/wBAyZ4EWW0U7z6AyCXPCcpA1C/sHdeIsSdkSxoPYvufsmPJMBSVympDfe4SKm/egkdMC14ZCPFERkbijTfewMWLF7FmzRqcPXsW//rXv9CuXTvMmzcP7du3r2sRawVvb2/Exsba6G/Hjh3Rt29fHD16FOvWrcPs2bMd3q65vaY8diUIgqhtFAoFYmNj61oMopFAgVwEQRAEQTgdttm4zGzduhVardauackJgiAaCnzOKeZ5a7gCFKTMutRQWbRoERYtWlTXYtQbpDjFucpL0YeGrDtENUI6o7t5C5oM6bPgANUBYOHPz2dtrz7y0EMP4aGHHgJg69wC6rfc9Q25fZnlPKTPZkLPp2EjFOwHAKUSl1U0U5mVA82F9BpBpaQzDQPZ4xqF7VKtUtsjGiZC/U+xnGUVrctv242whXM52yTuodfr4ebmJipvu3bt8NFHHyEpKQlr1qzBxYsX8corr6B379544oknEB0d7WRp6x42fZ03bx6Sk5OxceNGjBgxAmFhYXUgGUEQBEEQ9RVaWpEgCIIgCKezbt06znMVFRXYuXNnLUpDEARRP7B3qRgxsyc11KU/hJYkoCULnPt8G7LuENxwPddSmbPgAID2Wia0V66RzjRhyBYRcmA+28rsHFSkpcuur2T3AdZ6iYYD2RJCKsznatTqUCxjqWhrCrftYa2bsKWwsBALFizA1q1bJZXr3bs3vvrqK7z44osICgpCUlISnnnmGXzxxRcoLCx0uJx6vR4vvfQSNm/eXCtL7Uk9Fx4ejvHjx0Ov12P16tUOl8msv6THBEEQBNEwoUAugiAIgiCcypkzZ3D58mXePLRWOEEQTR2pQVpi6iCaDvbqD+lO08P6eRfvljcLjpnSvfaVJxoP9tgiR/SDRMNEoVDYbUeKdx8gnWlEkC0hpKJQKFB2IBFGtc6uenTXb0F7QX5QaVOgsrISy5cvR05ODk6fPi25vIuLC4YPH47Vq1dj1qxZ8PDwwI4dO/Djjz86XNbNmzcjPT0dR44ccagt4AvaysjIkFTX1KlTERgYiCNHjiAtLc1e0WpQWzaQzd7Whg1uau0SBEEQTQsK5CIIgiAIwqnwLatoZvPmzdDr9bUgDUEQRMOBaxYuObNzEU0PTv0g3SGsqLyTjYqz9jksi3YlOEYYolFibWtMZnujoABSwhZ7A0q11zOhuXzVQdIQ9REum6FQ0OxJRDVFW3fXq3oaK5999hnS09PRpk0bvPzyy7Lr8fDwwNSpU7FmzRqMHz8eM2bMcKCUQHFxMX777TcolUo89dRTDq2biy1btuCll17iXZWAiaenJ2bPng0AWLVqFYxGo7PEqxXqyh43tXbNbRMEQRCNGwrkIgiCIAjCqYj5A6OkpAR79+6tBWkIgiAIgiAIMyV77J9NS5N+FbrMWw6QhiCIpoihoBDlKWftrqfUAfaMcBwGgwFarRZlZWUoLCxETk4Obt26hevXryMzMxO5ubkoLS2FTmffDEoEAQBVpaUoOZTkkLoKt++BqYEH0ziL33//HQkJCWjWrBnefvtteHh42F2nv78/FixYgNDQUAdIeI+1a9dCrVZj+PDhaNWqlUPr5iI+Ph5ubm5Yu3YtkpKE9dEciDN06FC0bdsWV69exc6dO50tJlFPsHf2SYIgCKLx41rXAhAEQRAE0XhJS0sTPa34hg0bMGLECCdLRBAEQTQEFAoF75IVdfHHpcFgQHFxseVTVFRkc8yXptFoLH/OKpVKKJVKyz7XVu45Zh5XV1eoVCoUZlUARlcsyVyPIH9feHp6wsvLq8bH29vbsrXeN58nGhfFuxMcUk/p7gSEzH3cIXXVFg3VAaLT6VhtjZiPVqt1mu0ROufq6orzxQZAa8K/zzXHurBAVrvDlWZtk1xcXOr6MRAOpGTfQYCnzxdL0a79CHv6CQdI1PQoKCjAnTt3WD95eXnQ6XTQ6/U2H4PBwHlsMBh4x3FMFAoFPDw84IndUeQAACAASURBVOHhAZVKBZVKZdlnS1OpVMi6VgxUKvBe/laEBPjWOK9SqQTHNp6eng22LyBsKd61HyZDlUPq0ucWoiI5BT69ezikvsbCkSNHsHbtWri7u+Ptt99GcHBwXYvEyZUrV7Bz5054eXlh1qxZDq+f7beqQqFAXFwcXnzxRXzyySf4+OOPsWLFCkRHR3Pmt96fP38+Xn31Vfz0008YOHCgze8u67xSbKuU6xGDo9uWYn8d2XZ9bpeZh619oXqoXyMIgmg8UCAXQRAEQRBO448//hCdd+PGjfjmm2/IMUMQBEEA4A7mctYfkyUlJbh+/Tpu3Lhh2Vrv5+fny6rX7Jw0mUwwGo2WbV0tm/FTmvyyCoUCnp6erEFefAFgYvN7eXlBqaSJw2sLfX6BQ2bBAYDCnfvqPJCLz2bIdcKw2RtnO0eys7Nr2B9rm1ReXi6rXqYtsrZHtc2O8/aV9/DwkGVjxJ5zc3NzzIUSoijetd8h9WgyrkF38xY8ops7pL7Ggslkwq1bt5Ceno709HRkZGQgMzPTEqiVnZ1t96xYrq6ucHNzswSPu7m5WY7N+9bHVVVV0Gq10Gq10Ol0NtuSkhJJbX+bLF9u87iGyz6I3fKdo/83aoeiv3c5tL7CbbspkMuKK1eu4OOPPwYAvPTSS4iLi6t1GfLz80UHj61cuRImkwnTp0+Hv7+/kyWzZfDgwbh27Ro2bNiAZcuW4bPPPoOvr6/g+LF9+/YYMGAADh06hF9//RVPPvmkqHGoPYFGUse0jgookzOWNpdpKu06oixBEATR8KFALoIgCIIgnMb69etF5y0oKMDBgwcxePBgJ0pEEARB1CfEzLxlfV7OW6zWGI1GZGRkIDU1Fampqbhw4YIlQILPceji4oLIyEgEBAQgMDAQAQEBNh++NH9/f14nnnUwBVuAhb3nqqqqoNFoMOqnY8jJL8EPY9rC3aiHWq2GWq2GRqOx7FdUVHBurfcLCgqQl5cn+CzkoFKpeJ2jERERaNu2Ldq2bYu4uDi0atWKnKQyKdnruGXI1OcuoTI7B+7hYQ6rszGj1Wpx9uxZpKam4vTp08jIyMD169dx8+ZNaLVaznIqlQrR0dE17I2Yj7+/P2+gpJAtsdcu6fV6PL85FfvT7+DfA6PRxs/FYlesP0L2x7wtKytDUVGRMx4PXF1deYMzAgMDERsbi7i4OMvH29vbKbI0dqpKSlF6PNVh9ZXsSUBoA5sd0JFcvHgRKSkplqCt9PR0XLp0CRUVFZxl/P390apVK0RERCAiIgKRkZGW/YiICISFhUGlUnEGZbm6ujrU0WwymVBZWVkj0Iu5P/+v07iRV4zPhsbCx8VY47xWqxUc25i3hYWFsgP2hXB3d+cN9AoKCkJcXBzi4+PRrl07xMbGwtWVXDZS0OfkoeykHW8qsFC8+wCav7kESgrsRWFhIZYtWwadTodp06Zh4MCBtS6DWq3Gc889h5iYGDzxxBNo164dZ97ExEScPXsWUVFRGDduXC1KeY85c+YgMzMTx48fxwcffIDly5eL+r0yb948HD9+HFu2bMGoUaMQFRUlWIbN/tbGzNZyApwc0XZTa1cqFPhFEATRuKBfBQRBEARBOIVz587hwoULksps2LCBArkIgiAIG+T+GalWq3HmzBlL0FZqairS0tKgVqtr5HV3d0dsbCxatmyJmJgYxMTEWPZbtmyJqKgopznVzIEVzg5G8jiqBvwqMHLMYIR6u9tdnzkAjCvIgu+cUP7i4mIUFBSIksPd3R1xcXHo1q0bunfvjh49eqBbt25QqVR2X2Njxy04CJHPz+XNo7l6A4bcPKji28ItkH9GA2NZOUCBXDXIz8+3sUOpqalIT0+HwWCokdfHxwft27e3sT/W27Aw593f2rBFgecMgDYYfQb3wMCWQXbXp9frZdkYseekzAzUvHlzdOnSBd27d0f37t3Rq1cvpz6vxoKhpBSRz8zhz1NaDs35i3AJagavtq1587pHNK17fuXKFezfvx/79u1DQkIC7ty5UyOPp6cnOnfujPj4eMTHxyMuLg4tW7a0BGrVt2WTrZdZ5JtJx/eaL3C7BBMeexDRAZ52t2sd+CUm+EtKAHx5ebnowFNXV1fLuKZHjx4Wm+Lpaf81NlaqSkoR+ZzwsqrazNvQ374Dj9at4B4q3AdVFRRBGR7qCBEbLHq9HsuXL0d+fj4eeOABPP543QTK5uTkwMvLC2lpaVi8eDH69u2L2bNnIzo6uoa83333HQDgySefrLOgSKVSiVdeeQWLFy/G6dOnsXLlSjzzzDOC5UJCQjBp0iT8+uuv+Pbbb7F06VJZ7ddmME9dBQ41tXbFUJ9lIwiCIOShMDljUWWCIAiCIJo8S5cuxbJlyySViYiIwO3bt+nHJ0EQRBPCUT9Jy8vLcfDgQezbtw979+5FWlpajSXDPD090bFjR3Tp0gVdunRBx44dLbNQNPYl/WL+LxE3cyuQ86pjArmcjU6nY3WK3rp1CxkZGZbPpUuXUFxcbFPW29sbQ4cOxdixYzF69GiEh4fX0VU0fDKXfYj89X8j9st/wW/wgLoWRxA+eyLV1nCNR4XGqRUVFdi7dy+2b9+Onf/P3n3HU/m/fwB/HRyrodJEVmWWElqIhpYoSWnvLa1PS7SkvUubipakIm2V0lBoEUVINJQ0zYPz+6Nf59vJOjjHbVzPx8Pj8zn3eN9X3N7uc9/Xua6rV5GYmFhoG3l5ed481L59e+jo6EBVVRXy8hVPbqrKbE8/w9kXHxE8QTiJXKKWn59fZILGly9fEBcXxzcXpaSkFKog2bFjR1hZWcHKygodO3Zk8F9Svf0Kf4K48Y5o2M8cqptdmQ6HUZ8/f8aVK1dw8+ZN3Lp1C0lJSbx1YmJiaN++PYyNjXlJW5qammjZsmWNfH/d7kAoot59R9I84SRyiRqHwyky6Ss1NRUvX77kq6KWnp7Ot6+MjAx69eoFKysrDBw4EAoKCgz9K6q3Dzv34+OBY1BZvQiNhlgxHU61sHHjRgQHB6NNmzbYuHEjpKSkGIslLy8PV69exYkTJ/D161eIiYmhV69eGD16NJo0aQIA8PHxwdGjR9GxY0esWbNGpPEUdV3577IPHz5g7ty5+PXrFxwcHNC/f/9Sx83JycGUKVPw5csXrFmzhq4fiMBq4t96Qgip7agiFyGEEEJE4syZM2Xe58OHD3jw4AG6desmgogIIYRURaW1VywOh8NBaGgobty4gRs3buDhw4d8FW6aNm3KS5T486WhoUFt+KqJP9U4GjZsWOq2ycnJiIiIQEREBMLCwnDnzh34+/vD398fLBYLZmZmmDhxIoYOHUoVLWq48s4nZRm/KDExMbh06RIuX76MkJAQ5Obm8ta1atWq0FykpKQkshiJ8IiLi6N+/fqoX79+qdv+/PkTT5484c1FwcHBvP9fuXIlVFRUMH78eEyYMAEqKiqVED2pKZ49e4bAwEAEBgbi0aNHfEnqurq66NGjB3r27Alzc3OB/mYSZrDZbMjJyZVYZeyPd+/eITw8HBEREQgPD8ft27d55wCLxYKJiQkmTpwIOzs7autKRObUqVMIDg5Go0aNsHz5ckaTuIDf1eosLS3Rq1cvnD9/HmfOnMH169cRHByMgQMHok+fPvDx8YGYmBimTp3KSIz/Xoe2aNECS5cuhYuLC/bu3QslJSW0a9euxP2lpaUxceJEbNq0CY8fP6ZELlIqSuAihJCaixK5CCGEECJ0MTExePHiRbn29fPzo0QuQgghRcrIyEBAQAB8fHwQFBTE1yZRTk4OPXv2RK9evdCrVy9oaWkxGCmpTC1btkTLli0xePBgAL/bPt64cQMXLlxAQEAAgoODERwcDAcHB9jb22PSpEno1KkTw1GTylaWJK/SHohwOBwEBwfj/PnzuHjxIl9VnPr168PKygr9+/dHv379oKioWKG4SfVQr149dO/eHd27d+cte/LkCS5cuIALFy4gPDwcq1atwurVq9GzZ09MmjQJNjY21AaWFJKXl4fr168jICAAFy9eRHJyMm9d8+bNMWDAAFhYWKBHjx7UvrOGUlRUhKKiIgYNGgTgd9vHmzdvIjAwEAEBAQgJCUFISAgcHR0xbNgwTJw4ke6hEKG6f/8+vLy8ICkpieXLl1epiqHS0tKwt7eHpaUlfHx8cOHCBZw7dw4BAQHIz89H//79C7VcZFKHDh0wZcoU7N+/H2vXrsX27duLnLv/vvbs0aMHWrRoQe9nSYkogYsQQmo+aq1ICCGEEKFbvXo1VqxYUa59VVRU8ObNG+EGRAghpMor7q1pdnY2Ll68CB8fH1y8eBFZWVkAft/ENzExQa9evdC7d2907NixxrdHrIjq1lpRWPLz83HlyhV4enriwoUL4HA4AIBOnTph8eLFsLGxoZvgJahurRWB0lsolra+uPMhMzMTV69exblz5xAYGMjX0lNPTw/9+/dH//79YWxsDAkJ+txkUapba0VhevnyJTw9PeHl5YXU1FQAQJMmTeDo6IhZs2ZRJaUS1JbWirGxsfDw8MDRo0d55wgA6OvrY+DAgRg4cCCMjIzob9b/q26tFYWloKAA165dg6enJ/z9/XkVII2MjLBixQpYWloyHGHVRa0VBRMfH4///vsPOTk5WLJkCV+CclWUlpYGd3d3PHz4EMDvD/eMHDkS/fv3F/n1WHHXlEUt37lzJ65evQpVVVVs3ryZr0owk/N6WY8trMfJ5fk3C+PY1em4hBBCCN1ZIoQQQojQlaet4h9JSUmIiIiAgYGBECMihBBSnXA4HFy5cgU+Pj4ICAjAr1+/AAAyMjIYOnQo7O3tMWDAAF4lE7oxSoojLi4OS0tLWFpa4vPnz/D29saBAwfw6NEj2NraQktLC4sWLcLo0aPBZrOZDpcIgTDbK379+hUBAQE4d+4crl+/zkskZbPZ6NOnD2xsbGBlZUVVt0iptLS0sHHjRqxduxaXLl3CoUOHEBgYCBcXF2zcuBFTp07F/PnzoaCgwHSopBJlZmbC19cXHh4eCAkJAfB7Duvduzfs7OxgaWlJ8wvhIyYmhn79+qFfv3748uULjh8/jn379iEsLIyX7EcJXaS80tPTsWrVKuTk5GDkyJFVPokLABo3bowfP34AANTU1JCYmIi9e/fi3LlzGDNmDMzNzRl/r3jnzh2cO3cOiYmJePToEVJTU+Hr61umuATdtrwfWKhIHIJcdwvjZ/DvGDX9uISQ6ik3Nxd9+/blvdbR0YG7uzuDEZHqjD6uTAghhBChevXqFSIjIys0hp+fn5CiIYQQUl2wWCykpKTAxcUFysrKGDRoEE6cOIHc3FwMHDgQR44cQXJyMnx8fDBkyBBqR0XKrEmTJpg/fz5iYmJw9uxZdOrUCS9fvsTEiROhrq6Obdu2ISMjg+kwiQgVFBQgNzcXOTk5yMnJQX5+Pt/6Pw9d7t+/j7Fjx0JBQQETJkxAQEAAWCwWhgwZAm9vb3z69AlXr17F9OnTKcmClImEhASsra0REBCA6OhoTJgwAdnZ2diyZQvU1NQwZcoUxMXFMR0mEbHY2FjMnDkTLVq0wPjx4xESEgIlJSW4uLggPj4e169fx9SpU2l+ISWSl5eHo6MjoqKicOrUKejq6vISujp16oSLFy8yHSKpZry9vZGWlgYTExOMGjWK6XAEEhwcjJiYGLRs2RI7d+7Etm3boKenh48fP2LTpk2YPXs2wsLCGI3x06dPuHPnDpKTk5Geno7IyEh4e3sDKD3hh8VilTnhq6QvUSjtmHRcQkhtkp+fj+DgYN5XREQE0yGRaowSuQghhBAiVL6+vhUeoyIVvQghhFQvXC4X169fh42NDVRVVeHm5obU1FT07t0bhw8fhp2dHQIDAzF+/Hg0btwYjx8/5tufbpSSsmKxWLCxscHDhw9x48YNWFhYICUlBfPnz4eysjJWrlyJL1++MB0mEYHjx49DRkYGsrKykJWVxfbt23nrfv78CXd3d+jp6cHExATHjh2DmJgYRo8ejXPnziEtLQ1+fn4YPXo0GjRowOC/gtQUWlpa8PT0REJCAubOnQs2m41Dhw5BS0sLdnZ2dNO/BgoNDYWNjQ20tbWxd+9eZGVlYciQIbh06RKSkpKwevVqqKmpMR0mqWbExMQwfPhwPH/+nBK6SIVMmzYNw4YNw4IFC6rFe6zs7Gx4enoC+B27uLg4NDU1sX79eri6ukJdXR0JCQlYsWIFFi1ahPj4eIYj/o3NZsPHxwd37txhOhRCCCGEVGGUyEUIIYQQoRJGIldcXByioqKEEA0hhJCq6tu3b9i+fTu0tLTQp08fnD9/HvXr18e8efMQGxuLa9euYdy4cZCUlGQ6VFKD9ezZE9euXUN4eDiGDh2Kb9++YdWqVVBRUcHcuXORnJzMdIhExCIiIjB16lQoKChg9uzZiIqKgq6uLnbu3In379/Dy8sLgwYNgoyMDNOhkhpKSUkJ27ZtQ1JSElasWIGGDRvizJkzMDQ0hIWFBW7cuMF0iKQCuFwuAgMD0b17d3Tt2hXnz59Ho0aNsHLlSqSkpMDPzw/9+/eHmBjdpicVQwldpKKkpaUxfvx4SElJMR2KQPz8/JCWloZOnTqhY8eOfOsMDAywa9cuLF68GC1atEBUVBSvBSPTjI2NAQDHjh0rVB32b9UhmY4QQgghokPvEAkhhBAiNHFxcXj+/LlQxqL2ioQQUjO9efMGDg4OUFRU5CVtGRgYwNPTE+/evcOWLVvQunVrpsMktYyBgQF8fX3x8uVLTJ48GRwOBzt27ECrVq0wfvx4xMTEMB0iEYFdu3ahU6dO8PDwQH5+PkaNGoWQkBBERkbCwcEBcnJyTIdIahF5eXmsXLkSSUlJ2LZtG1q2bImgoCD07t0bnTt3xtmzZ8HlcpkOkwiIw+Hg6NGj0NPTg5WVFUJCQqCmpobdu3fj7du3WLFiBZo2bcp0mKQGooQuUhsUFBTg3r17EBcXx5QpU4rchsViwczMDPv374ezszP09fUrOcqitWzZEvPmzcOmTZsgLi5e5DaUxEUIIYQQCaYDIIQQQkjNIYxqXH/4+flhxYoVQhuPEEIIs54/f44NGzbg9OnTyMvLg7S0NMaNG4eZM2eiU6dOAEAPqMshm5OPw0/flbqdTkNZtJBm48Tz95CSKPkzXSPatUADabawQqxW2rRpg4MHD2LVqlXYunUr9u/fj6NHj8Lb2xtDhgyBi4sL9PT0mA6TlFNeXh7f6+TkZLRp0wbTpk3DuHHjIC8vz1Bk1d+l2E9I+p5d4jZSYix0Vm6A4MSviP6cUeK2RgpyMFSsnYl0derUwdy5czFr1iwcO3YMGzduxKNHj2BrawsdHR0sW7YMw4cPL/bhL2EWl8vFiRMnsHz5ciQkJAAAOnbsiIULF8LOzo5+bqXg5BfA48m7Uq8J1epJoY5yA/hGf4Qsu+Tv6TDdFpCXrX3XNX8Suuzs7ODr6wtXV1deQpeRkRFWrFgBS0tLpsMkpFzExMSwY8cOREdHQ1FRscRtJSQk0K1bN6Eev6LvW3v37l3mfXJzc6laNSGEEFKLUCIXIYQQQoRGmIlckZGRiIuLQ5s2bYQ2JiGEkMp3+/ZtbNiwAZcvXwYAyMnJYebMmZgzZw6aNWvGcHTVnzRbHMseJOHrl0yBtn/49lvJG9SRxDSDlkKIrHpTUFDA5s2bsWzZMri7u2P79u04c+YM/Pz8YG1tDRcXFxgYGDAdJhHQz58/sW/fPqxbt45v+dixY+Hp6QkWi0WVDyoo7P0PrLwVL9C2pc5DAK6Npd8vNpuNCRMmYNy4cTh//jzWrVuH8PBwjBo1CitXroSTkxNGjx4NCQm6vVtVXLp0CU5OTnj27BkAoEePHnBycirXA/vaii0uho2P3iIx9ZdA25c6n8hIYEpHJSFEVn1RQhepqdhsNtq3b890GHxE9cGkixcv4vjx49i6dSuaN28ukmMQQgghpGqh1oqEEEIIEZqAgAAkJSWV+GVvb48WLVrg4sWLpW6rpFS7b7gSQkh1xeVycf78eXTt2hXm5ua4fPkyFBQUsHHjRiQnJ2Pt2rWUxCVE03SE972cpt0MYmKU0PJHw4YN4ezsjKSkJGzcuBFNmzaFv78/DA0NMWDAAISGhjIdIilBamoqnJycoKysjMWLF+PbN/4H/m3btuUlcFFFwIoZqi3Eh4riLPRQbSS88ao5MTExDBkyBGFhYbh8+TK6deuGuLg4TJgwARoaGjh48CA4HA7TYdZq9+/fh5mZGSwtLfHs2TMYGBjg6tWruHnzJiVxlcN4HeHNJ2M1m0JCnB6BANRykZDS/KmiWBV9+PAB3759w9atW+malRBCCKkl6F0MIYQQQoSmZcuWUFZWLvErKysLHz58QKNGjUrdVkZGhul/EiGEkDK6cuUK9PT0YGNjg9DQUGhqauLQoUNITEzEwoULUa9evSL3K+8NabqRDQwT4gPPobpNhTZWTVKnTh0sXLgQiYmJ2L59OxQUFHD58mV07doVFhYWuHPnDtMhkr/8+PEDixYtgqqqKtavX4+fP39ixIgRcHV1LXE/mk/KT7dZXTSVlxXKWBP0FCjxohj9+vXDvXv3cOPGDZiZmSExMRFTp05Fq1at4O7ujpycHKZDrFWioqJgbW0NY2Nj3LlzBxoaGvDx8UFYWBj69OnDdHjV1jBd4V3X2Akx2b2moISu2uvatWuU+FuMFy9ewMHBAc7OzoiPF6zC6L9EeR05btw4KCsrIyoqCufPnxfZcQghhBBSddBdEUIIIYQQQgghFRYTE4P+/fujf//+iIqKgqGhIc6cOYPo6GhMmjQJkpKSTIdYY+kr1EfdhkJIfpaRgLkKVcEpiYyMDObMmYOEhAS4u7tDWVkZQUFBMDMzg5mZGYKCgpgOsVbLz8/Hvn370KZNG2zevBkAMHPmTLx+/RrHjx+HsrJyucemJK/STRZSUunwtpR4UZqePXsiODgYd+7cgYWFBZKTk+Hg4AB1dXVs374dWVlZTIdYo6WkpGDcuHFo3749Lly4AEVFRezfvx8vXrzAsGHDqFVrBWk1qQPlpnUqPpCkOCxaNa74ODUUJXTVLh4eHti+fTu2bdvGdChVUlZWFpo0aYLHjx9j9uzZWL9+PT58+FDktsK8JhR0LDabjf/++w9iYmI4cuQIkpOTBdovLy+vIuERQgghhEGUyEUIIYQQQgghpNy+fPkCBwcH6Onp4cqVK1BRUcGpU6cQFhYGW1tbiImV/razojfDuVxurU+ymCWE6hXjNJtRFRwBSUlJ8RKEDh48CHV1dV5CRbdu3XD58mWmQ6x1goKCoK+vjxkzZuDz588YPnw4Xr16hd27d0NVVRWAYHNNbZ9LKsJWGJVvZCTQS02+4uPUEqamprh27RoePHgAS0tLvH//HvPmzYOqqio2btyIX79+MR1ijVJQUIBdu3ZBR0cHXl5ekJOTw4YNGxAXF4epU6dCQkKC6RBrjIlCuK4ZptEUUhJ0XVMaSuiq+by8vODn5wdZWVkMHjyY6XCqJENDQxw6dAhTpkxB/fr1cefOHUydOhW7d+9Genp6qfsL8/oxLi6uyAqbrVu3xogRI8DhcLB582bk5+cXO0Z+fj4uXryIyZMnl7gdIYQQQqoueidDCCGEEEIIIaTMOBwOtm3bhjZt2sDd3R3S0tJwc3PDy5cvMXz4cKbDq3WE0TrIjtoqlhmbzcbkyZPx6tUrHD16FBoaGnjw4AEGDBgAIyMj+Pv7U2KQiMXGxsLa2hoWFhaIjIxEp06dcO/ePZw8eZKvAldZfg70Myufjgr1IdlAukJjUEJp+XTp0gWBgYGIiIjA4MGD8fnzZyxevBiqqqpwc3PDjx8/mA6x2ouOjoaJiQkcHR2RmZmJuXPnIiEhAYsWLYKMjBCqYhI+Q4VQ4W8YXdeUCSV01UwnT57EqVOnIC0tDVdXV2hoaDAdUpXFZrNhY2MDT09PjBgxAmw2G5cuXcKkSZNw9OhRZGRkiDyG0NBQLFiwANu3by9yvb29PVq3bo24uDj4+Pjwln/9+pVvjBkzZsDd3R2fPn1CUlKSyOMmhBBCiPDRnRFCCCGEEEIIIWUSEBCAtm3bYv78+fj+/TsmTpyIuLg4ODk5QVq6Yg/xK6I2J18YKMpVLIFCUhwW6tR+qLwkJCQwduxYxMTE4MSJE9DV1UV4eDgGDx4MfX19nDlzplafn6Lw7ds3zJs3D23btsWFCxegpKQEb29vPHjwAF27dhXJMelnWDqHCiZfUFvFiunYsSPOnTuHZ8+eYdiwYfj69SucnZ2hoqKCFStW8D3oJYLJzc3FypUroa+vjwcPHkBPTw8PHjzAtm3b0KBBA6bDq7F0m9aFUpMKtFeUEEP/1k2EF1AtQgldNceZM2fg7e0NKSkprF69Gtra2kyHVCE/f/6slOPIyspizJgx8PT0hJWVFfLy8uDj44OJEyfizJkzyM3N5dteGNWl/2jTpg3q1auH4OBg+Pn5FdpWXFwc//33HyQkJHDy5EnEx8cjMjIS06dPx82bN7Fo0SKsXr0aKSkpvH3i4uIqFB8hhBBCmEH1ngkhhBBCCCGECOTFixeYO3cugoKCAABmZmbYtm0b9PX1yz0mJUYIzxzd5th070259h2u2RSS1H6owsTExDBixAjY29vj7NmzWLNmDZ4+fQo7Ozvo6Ohg2bJlsLe3F6jlqKC4XC5YLJbQxqvqCgoKsHfvXqxYsQJfvnyBrKwsli1bhoULFxZbFac880xt+74Ky1DtZth6/035dpaWQG91aqsoDO3atYOPjw9iYmLg5uaGU6dOYfXq1di2bRscHBwwf/58NG4s3OTdmvg7c//+fUyZMgXR0dG8yqMLFy4Em81mOrRaYZJuc6wKji/XvoPaNIGspLiQI6pd/iR02dnZwdfXF66urryELiMjI6xYsQKWlpZCP25NnEuYY8pCZwAAIABJREFUcP78eXh6eoLNZmPFihVo27Yt0yFVyO3bt7Fr1y7MnDkTPXv2rJRjNmjQADNmzICNjQ28vLwQHBwMDw8P+Pv7Y/To0ejVqxfExUufZ8pyHSovLw8XFxcsWrQIhw8fhqqqKgwMDPi2UVZWxrhx4+Dh4YGVK1fiy5cvAIDNmzcXOWZcXBz69u0rcAwVlZubi5SUFGRmZiI7OxsFBQWVduzqQFxcHDIyMqhbty6UlJSE+r6QMOvr169IS0tDZmYmcnNz6V7XPyQlJSErK4tGjRoJ/X0IITUVJXIRQgghhBBCCClRTk4OXF1dsXHjRnA4HKirq2PTpk0YMmQI06GRvwyrQCLXMB1qPyRMLBYLtra2sLW1xYULF3gPP0eNGoVVq1bByckJo0aNgoRExW/LbNiwAXXq1MHs2bOFEHnVFh0djYkTJ+Lhw4dgsVgYM2YM1q1bB0VFRQCUGFoVdGkpB9STAn7mlHnfsVpNwaa2ikKlra2NY8eOYeXKlVi7di28vb2xbt067Ny5E9OnT8fChQvRrFnFq6AlJCRg5syZOHv2LGRlZYUQObN+/vyJJUuWYO/eveByuejevTsOHjxILckqmZ1O+RO56LpGeCozoSs9PR1DhgzBmTNn6CFvBVy8eBEHDhyAhIQEXFxc0KFDB6ZDqpDc3Fx4enoiMzMTcnJylX785s2bY9GiRRg6dCgOHz6M8PBwbN++HX5+fhg3blyRlWArck2qpaUFBwcH7NixA+vXr8eOHTugoKDAt82gQYNw9uxZXhJXSURdkevHjx+4d+8e7t69i5CQEISFhSE7O1ukx6wp5OTk0K1bN5iamsLExASdOnWClJQU02ERASUmJiIkJIT39erVK6ZDqjbU1dVhYmICU1NTmJqaQlNTk+mQCKmS6O4IIYQQQgghhJBi3b59G3p6enBzc4O4uDjc3NwQHR0tlCSu0m5wl7eKTm1lqCgH8fK0V5QQQz9qPyQyVlZWePToES5fvoxu3bohNjYW48ePh6amJg4ePAgOh1PusX/9+oVNmzbB0dGx2E/h1wQcDgeurq7Q19fHw4cP0aFDB4SGhsLLy4uXxEWqBhaLhZna5UsMGqZLbRVFpXXr1vD09ERcXBymTZsGDoeDLVu2QE1NDXPmzMG7d+8qNP7atWtx9epVWFhYVFrrK1EJDAyEjo4O9uzZg/r162P//v0IDg6mJC4G6DarC4XytFcUY8FSgxK5hK0yWi7u3LkTt2/fRrdu3fDhwwchRF37XLt2De7u7hAXF8eyZctgaGjIdEgVdvbsWXz+/BmGhoaFqlNVJnV1daxevRobNmyAlpYWkpOTsWbNGsyfPx/Pnz8vdj9DQ0Ps37+f9zVw4MBC2/z7HrZPnz6wsrJCRkYGVq1ahczMTN66nz9/wsHBQeB2yYmJiRV6v1Gc6OhojBw5Eo0aNcKAAQOwdu1ahISEUBJXGXz//h2XL1+Gk5MTunfvDnl5eSxYsAAfP35kOjRSjIKCAvj6+qJDhw5QV1fHuHHjcOjQIUriKqOEhAR4eXlhypQp0NLSgoaGBg4fPoy8vDymQ6swSUlJHDx4kPfl5OTEdEikGqNELkIIIYQQQgghhXz79g1TpkxBjx49EBsbi549eyIyMhJOTk6V8inR4hKyanOiliDm6jQv8z6Dqf1QpejXrx/u3buHGzduwMzMDAkJCZg6dSpat26NPXv2ICen7BWM3N3dkZ6eDgBYuHAh3NzchB024yIiImBoaIjly5eDxWJhzZo1CAsLQ6dOnfi2E8WcUdS+NAeVzq48lXCkxGGhTtVXRE1VVRX79u1DfHw8HBwcwOVysXPnTrRq1QozZsxAUlJSmcd88+YNPDw8APxuRdinTx98//5d2KGL3KdPnzB8+HBYWVkhJSUFgwcPRnR0NKZOnUpt3hg0SafsCZ4WrRtDTpoakYiKqBK6fv78ie3btwP4XUHIxMQEb968EWLkNd+tW7ewY8cOiImJYfHixejcuTPTIVVYeno6Tp8+DTExMUyZMoXpcAD8bl+8detWuLi4QFlZGa9evcLSpUvh4uKC1NTUQteKqqqqmDRpEu+rffv2Ah1n6tSpaN++PZKTk7Fp0yYUFBQgICAAI0eOxNu3bwWONy8vr1x/34vz7NkzDB06FG3btsXJkyeRn58vtLFru4yMDGzduhVqamqYPXs2UlJSmA6J/L/8/HycOHEC7dq1w7Bhw/Ds2TOmQ6pR4uLiMHHiRGhoaODAgQPIzc1lOqRyExcXx+TJk3lf1tbWTIdEqjEWl+5AEUIIIaQSDR48GP7+/njw4AG6dOnCdDiEEEKK4OvrC0dHR3z8+BGNGjXC5s2bMWHCBKEfp6S3o3/WBQcHIyYmhrfczs4OjRs3LvGham1+4Poo5Rs6H3xYpn1ODG2HEe0USt+QCFVISAhcXV1x/fp1AICCggIWLlyIadOmQUZGptT9MzIyoKqqirS0NL7lLi4uWL16tVBiTF61AWm+gWi1ay3q9zAVypiCys7OxooVK7Blyxbk5+eja9eu8PDwgLa2dpHbC5LIFRsbixs3bvBed+3alfcwrbh5o6jltXmOEUR+ARcSm24BmYJXfxjdXgHeQ9qJMCpSlA8fPmDz5s3Yt28fMjMzwWazMXbsWCxduhStWrUSaIxp06bhwIEDfMs6duyI69evo1GjRhWO8Vf4E8SNd0TDfuZQ3exa4fGKcvz4cTg6OiI9PR0tWrTArl27YGtrK5JjkbKJ/PgTenvvl2kfj0G6mNhRSUQRkX/9qU7i6uqKFy9eAEC5Wi5u2LABS5Ys4VumoKCAGzduQEtLq8Jxfti5Hx8PHIPK6kVoNMSqwuNVNSEhIdiwYQO4XC4WLlwIc3NzpkMSim3btuH69euwsrLCjBkzmA6n0PVmfn4+bty4gePHjyMzMxMeHh6oW7duqeMIcn3JYrHw48cPzJkzB6mpqZCXlxeolWJRZs+ejf79+5dr3z8yMzMxc+ZMeHl5lfg+vnHjxjAyMkLDhg0hKytL183/yM/PR2ZmJlJTU/Ho0SNkZGQUu62kpCRcXFywbNky+j4yKDIyEsOHD+e7L1UULS0t6Orqok6dOtQiswi5ubnIyMhAbGwsoqKiUFBQUOy2ysrKOHHiBIyNjSsxQkKqHkrkIoQQQkilokQuQgipupKTkzFr1ixcuHABAGBvb48dO3agaVPRtKepSAUdSuQqHmvrbeC7gC0txFn4sbgn6klR5QqmhIaGYs2aNbwKFs2aNcOCBQswY8aMEh8Ebd68GQsXLixy3aJFi7Bhw4YKx8ZUIldISAgmT56M2NhY1KlTB25ubpg9ezbExIovLF/W+eTf5ZTIJVyT/F/A87HgVQQCRurDSpNaoTHl8+fP2LJlC9zd3fHr1y+Ii4tj5MiRWLZsGTQ1NYvd7+3bt2jdunWRLZv09PQQFBSEJk0q1rpXlIlcmZmZmDVrFo4cOQIWi4VJkyZh06ZNaNCggVCPQyqm2c4QfPqSWfqGAMACPi/qgcaykqINihRSkYSurKwsqKio4PPnz4XWNW7cGNeuXYO+vn6F4qvJiVwPHjyAm5sbCgoKMH/+fPTu3ZvpkIQiPj4ejo6OqFOnDjw8PFCvXj2mQyp0/fjnNYfDQWJiIjQ0NMr9XraoRK5jx44hNDQUCQkJFYj6d2VgR0fHcu8fFxcHW1tbREZGFlrHZrNha2uLnj17wtTUVCiJl7VFXl4enj59ipCQEFy+fJn3AZ9/DRw4EN7e3nR9woBjx45h2rRpfO1N/2jRogVGjhwJExMTmJiYoHFjqi4sqG/fvuH+/fu4e/cuTp06hcTExELbsNlsbNq0CXPmzGEgQkKqBmqtSAghhBBCCCG1XEFBAXbt2gVdXV1cuHABysrKuHjxIk6ePCmyJC4iOvPK0IaoX+vGlMTFsC5duiAwMBAREREYPHgwPn36hEWLFkFVVRVubm748eNHoX2ysrKwadOmYsfcuHEj5s6dK8qwReLXr1+YNWsWzMzMEBsbi169eiEyMhJz5swpMYmLVD12umX42yEpjj6t6MEHk5o0aYL169cjKSkJLi4uqFu3Lry9vaGjowN7e3teUsa/1q9fX2QSFwA8f/4c5ubm+PjxoyhDL7eYmBh06tQJR44cQdOmTXHlyhUcPHiQHpJWQVN0BW8bbaomT0lcDKlIy8WDBw8WmcQFAGlpaTA3N8f9+2WrzFZbhIWFYd26dSgoKMDs2bNrTBIXAOzfvx9cLhcjR46sEklcRfmTfMVms6GhocG3rLR9ShMQEIATJ05UOIkL+F2Vtrz8/f1hZGRUKIlLUlIS06dPR1xcHE6ePIkpU6ZQElcZSUhIwNDQEPPmzcO1a9cQFhaGQYMGFTpHAgMDYWhoSO38KhGHw8GsWbMwZsyYQklcLVu2xO7du5GQkIDNmzdj8ODBlMRVRg0aNMCAAQOwdu1axMbG4siRI7w59A8Oh4O5c+dixIgRJVauI6Qmo7tghBBCCCGEEFKLRUVFwdjYGI6OjsjIyMCcOXPw4sULDBgwgOnQSDkNK8MDz+Hagid9EdHq2LEjzp07h2fPnmHYsGH4+vUrnJ2doaKigpUrV+Lr16+8bffv349Pnz6VON6OHTswY8YMgaoCVAVXr16Frq4u9uzZg/r16+PQoUMICgqCmpoa06GRcuilJg8ImCQ6QqsppCToFmVV0KhRI6xevRpJSUlwdXVFgwYN4OPjg3bt2sHW1hZPnz7lbZuSkoJDhw6VOF50dDTMzMzw7t07UYdeJl5eXjAyMsKLFy9gbm6Op0+fok+fPkyHRYphpyP4dY29Dn0AgWllTejKzc3Fxo0bSxzzx48fsLCwQFBQkChDr3YeP36MNWvWIC8vDzNmzKhw67yq5N69e4iKioKioiIGDhzIdDgiV1RyV1hYmNDGT0pKKjbxuiQ7duyAjY0Nvn//zrd81KhRiI+Px969e6GioiKsMGs9Q0NDnD9/Hk+ePCnUUi4+Ph5du3alebASZGdnw8LCAnv27OFbLisri927d+P169eYNWsWpKWlGYqwZpGQkMC4ceMQHR0Nb29vNGzYkG/9qVOnYGxsjG/fvjEUISHMobskhBBCCCGEEFIL5ebmwtnZGR07dkRoaCj09PQQGhqK7du3l9jOjVR9nZXkgPpSpW/IArUyq4LatWsHHx8fREVFYdSoUfj58ydWrVoFVVVVODk5ISUlReC2ifv27cPkyZNRUFAg4qjL7+vXrxg/fjz69euHt2/fwtraGtHR0Zg0aRLToZEKYIuLYZSA84u9LiWUVjVycnJwdnbGmzdvsH79ejRu3Bhnz56Fvr4+rK2tERYWhg0bNgj0UDg2Nhbdu3fH27dvKyHykmVmZmLChAkYN24csrKy4OzsjKCgILRo0YLp0EgJ2reoh0aNZATadogWzSdVhaAJXUePHhUo2TMzMxOWlpbw9/cXdejVQmRkJFavXg0Oh4PJkyfDyqrmtIvkcDjw8PAAAEyePBkSEtWvejCLxeJ9/fH9+3fcunVLoP2vXr2KiIgIocWTn59f5spefn5+mDdvHt+HQiQlJbFnzx4cO3YMSkpKQouP8Gvfvj1u3bpVqB1mVlYWbG1tERUVxVBkNR+Xy8XYsWNx+/ZtvuWtW7dGaGgoZs2aBUlJqvwpCuLi4hg9ejQiIiLQoUMHvnXPnj2Dra1tuRJSCanOKJGLEEIIIYQQQmqZhIQEGBsbw83NDeLi4li3bh0iIiJgZGTEdGhECFgsFuYIUL3CTF0e8rLsSoiIlIe2tjaOHTuGly9fYsKECcjMzMS6deswYcKEMrUq8/T0xNixY5Gfny/CaMvn3r170NPTw9GjR9GkSROcPHkS/v7+UFBQYDo0IgQCtVeUFEff1k1EHwwpl3r16mHx4sV48+YNtm7dihYtWuDChQuwsbHBgQMHBB4nISEBpqamQmkPVV5FtVJ0dXWFuLg4YzERwU0VoNqokXJDNK8nQCI7qVTFJXTZ2dnh48ePWL9+vcBj5ebmwtbWFsePHxdhxNVDWloa8vLyMHbsWAwZMoTpcITK398fHz9+RIcOHdC5c2emw6mwPwldHh4e2LJlC5ycnEpMXnz58iV27dol9Dji4uIE3jY0NBRjxozhS+JSUlLCnTt3MGPGDKHHRgpjs9nYsWMHTp48iTp16vCW//jxA5aWlvjw4QOD0dVcS5Ysga+vL98ya2trhIeHo127dgxFVbuoqanh/v37GD9+PN/ymzdvYsqUKcwERQhDKJGLEEIIIYQQQmoRPz8/dOzYEeHh4TAyMsLz58+xZMmSKvVJ56JaS5RlPRGsvaK9DlWtqA5at24NT09PxMXFYerUqeX6BPbx48cxcuRI5OXliSDCsuNyuVi/fj3Mzc2RkpKCoUOHIjo6Gvb29pUWw9+VEmhOEY2+rRoD7JKTZIZrUlvF6kBWVhbz5s1DQkICdu/ejXbt2iE3N7dMY7x9+xampqaIjY0VUZTFK6qVooWFRaXHQcpvmAAJ6iOprWKV9iehKzIyEj4+Pli7di1u3LhR5gTP/Px8jBkzBvv37xdRpNVDjx49sHv37kq9dqoM379/x8mTJyEmJlYlH9gXdc0o6HVk7969oaioiGfPnmHmzJk4ceIEOBwO3/7h4eGYP3++SFqjC5rIlZCQAGtra2RlZfGWKSkpITQ0tEYk1lU39vb2uHbtGl8bv7dv32LgwIHIyMhgMLKaZ//+/YVa/Y4aNQrnz5+HnJwcQ1HVTjIyMjh8+DAWLFjAt/zo0aNYtWoVQ1ERUvnoTgkhhBBCCCGE1AI5OTlwcHDA0KFD8f37d8ydOxd3795FmzZtmA6tSMXdEKeEC8F0bVl6e0UbLXrgWZ2oqqqiffv2ZarG9bfTp0/Dzs6uzMkXwvblyxdYWlpi6dKlEBcXh7u7O3x9fdG4ceMKjSuKOaMiD+sIIM0WxxCNkqttUVvF6kVaWhq2trYIDg4u1/7v379H9+7dER0dLdzAikGtFGsOfYX6qNuw5PaKQ7RpPqkOWCwWhg0bhjlz5sDNza1cY3C5XEyfPh2bNm0ScnTVi6qqKtMhCJ2XlxeysrLQp08fqKmpMR2OULVv3x7u7u4YMWIEgN8ftHBwcEBkZCQAID4+HitXrhTZ8QVJ5MrPz4etrS0+f/7MW1avXj1cvHgRioqKIouNlKxbt27w8vLiex/y+PFjzJ49m8GoapanT5/CwcGBb5mZmRk8PT3p/R+DNm3aBFtbW75lK1euxPXr1xmKiJDKRYlchBBCCCGEEFLDxcfHo1u3bnB3d0fDhg1x/vx5bNu2DZKSkozGVdbKW4LcQKObbL+xWCzMLqF6RWeVhmhWl9oPVSe5ublYt25dhcY4f/48bGxskJ2dLaSoyubevXvo0KEDLl++jFatWuHBgweYOXMmI7GQymFfUoUcSXH0a12xBD5S+TZt2lShOSQ1NRVmZmZ4/vy5EKMqjFop1jyzSqg22lZRDsoNSk70IlXLuXPnEBMTU6ExFi1aBGdnZyFFRJj25s0bXL16FTIyMhg7dizT4ZSJoO9B2Ww2Ro8eDXd3d7Rr1w4pKSlYvHgxtm3bhkaNGqFZM9ElpL59+xY5OTklbnPgwAE8ffqU91pCQgK+vr7Q09MTWVxEMHZ2doXeCx45cgSPHj1iKKKaxdHRka96tZaWFs6dO8f4PbPajsViwdvbG126dOFbPmfOnCpTbZwQUaJELkIIIYQQQgipwU6fPo2OHTvi8ePH6Ny5M548eYJBgwYxHZbAqPVZ+Q0rIYFihDZV46puDh8+jJSUlAqPc+nSJVhbWyMzM1MIUQnm31aKw4YNw+PHj6Gvr19pMRBm9G/TBBAv+vajnUYTSJfSepFULZ8+fcLevXsrPE5aWhrMzc0REREhhKgKo1aKNZNdCS2hR9N1TbVT3mpcRY0zZ84ckbSiI5XrwIEDKCgogL29PRo0aMB0OMUSRhVYJSUlrF+/HvPmzUO9evVw/fp1bN++HR8+fBDZ+96CgoISW5mmp6fDxcWFb9mGDRvQt29fkcRDym7x4sUYOXIk7zWXy8Xs2bNp/qugkydPIiQkhPdaVlYWFy9eRMOGDRmMivwhIyODgIAANGnyv0rPMTEx2LVrF4NREVI5KJGLEEIIIYQQQmqgnJwczJgxA8OHD8fPnz+xYMEChISEQEVFhenQSCUxVm4I1Cv6E6RDSqjWRaoeDoeDtWvXCm2869evY8CAAfj165fQxixOWlpaoVaKPj4+qF+/vsiP/Ud5HohR8qhw1JWSwMA2RVfdoraK1c/mzZuRlZUllLG+fv2Knj174uHDh0IZD6BWijWdgaIcJBtIF7nOlq5rqpXLly/j8ePHQhtv586dmDRpEvLz84U2JqlcDx8+xNOnT9G8eXMMHjyY6XAqBYvFgoWFBQ4cOABLS0uRV6oESm6v6OLigi9fvvBet2vXDnPmzBF5TKRstmzZwvc+6tGjRzhy5AhzAVVzGRkZWLRoEd+ypUuXQl1dnaGISFGaNGlS6H7IqlWr8OnTJ4YiIqRyUCIXIYQQQgghhNQwcXFx6NKlC/bt24dGjRrB398fmzdvBpvNZjq0QoSZLEGJF/xYLBZmaRd+sNlOUQ4t5Yp+EEqqpqNHj+Lt27dCHfP27dvo27cvfvz4IdRx/3bv3j3o6+tXWitFUc8BNMeUT5HVAdliv6t1kWojLS0N7u7uQh3zx48f6N27N+7evVvhsaiVYu0wp4j2imrN6qK1vCwD0ZDyElY1rr8dPnwYI0aMAIfDEfrYRLTy8vJw6NAhAMDEiROr5HtWQQl6rfj3dnJycvj06VOpbQ+FobhErhcvXmD//v18y3bu3El/Q6ug5s2bF6qctnTp0kr5gE5NtHHjRr6q12pqavjvv/8YjIgUZ+LEiTAwMOC9/v79O7VXJjUeJXIRQgghhBBCSA1y6tQpGBgY4OnTp+jatSuePHkCKysrpsMiDBleRMWb0SW0JiJVT15enkgeeALA/fv30atXL3z9+lWo41bFVoplScKihC3hGqjRFBDj/54O1WgKGWqrWK1s2bJFJC1Zf/36hb59++LmzZvlHoNaKdYew4pI5BpH1zXVSnBwMO7duyeSsX19fTF48GChVQ5k2ocPH+Do6Cj0ZP6qJj4+HmlpaWjbti1MTEyYDkcgwrxWvHv3LsLCwoQ2XkliY2OLXL53716+inZDhw6Fubl5pcREym7OnDnQ1NTkvU5NTcWZM2cYjKh6ysvLw759+/iWbdmyBdLS9KG3qkhMTAw7d+7km3+9vb3x/ft3BqMiRLQokYsQQgghhBBCaoDs7GxMmzYNI0aMwK9fv7Bw4ULcuXMHysrKTIdWqoreCGexWJR4UQxj5YZAXf72irb0wLNa8fb2xps3b0Q2fnh4OHr27MnXSqUiqkIrxeIIMk/QXCJ8DWXY6Kkuz7esqCRTUnV9+fIF69evF9n4mZmZsLS0xNWrV8u839+tFF1cXKiVYg1nqCgH8X/aK9oWUX2UVF2iSk7/49KlS+jfvz9+/vwp0uOIWmpqKpYsWYLXr1/j4sWLTIcjUpqamjh06FCNaeP377Xkq1evsG7dOt7XgwcPeOsyMzP5EklEfR2akpKC7OxsvmUcDgenTp3ii2HDhg0ijYNUDJvNxpo1a/iWeXl5MRRN9XXlyhW+1nxdunSBjY0NgxGR0nTr1o2v/W52djZ8fX0ZjKiwvLw8vjmffjdJRVAiFyGEEEIIIYRUQVwuV+Cvly9fonPnzjhw4ADk5eURGBiIjRs3QkJCgul/BmHIn3ODxQJmaP8vYaJVs7pQbyjDd/6Qqis/Px9r164V+XGePn0KMzMzpKam/jO//F5fAMHOl8pupVgUUT0AowSv8vlz3thr/9VeUUIM/Vs3pnmoGtm2bZvIj5GdnQ1ra2tcuHCh0HUOAHAL+Jf920rx6tWrWL16NbWBqqH+/tnP/SshvZG8DHSb1qG5pJp49OgRgoKCRH6c27dv85LUeefG/58eRc0vVU1aWhqWLFmCz58/w8TEBFOnTmU6JJGTl5eHoqIi02EIzd/XjZGRkXB2duZ9/V2B8siRI0hPT6+0uLhcLl6/fs237NKlS3wf6OjevTvU1dUrLSZSPtbW1mjUqBHvdXBwMJKTkxmMqPrx9vbmez1hwgSGIiFlMX78eL7X//4cmcbhcODk5MT72rNnD9MhkWqMErkIIYQQQgghhGFFJWcJ6vjx4zA0NMTz58/RrVs3PHnyBP379xdhtKJRXJJEeHg4Tp8+zfsSdgu4mqC08+fvyjdji6jGVR0eaNVmZ8+ehZ+fHzZs2IApU6bA3NwcSkpKQj/OixcvYGZmhvfv35e4XXEJpVWtlWJRkpKS4Ovry5tP/m0vQ8laFVPSXDRI63+JXFatG0NWUlzgfQnzpk+fjmvXrmHPnj2YP38+rKysoK2tDUlJydJ3LoPc3FwMGTIEZ8+eLXE7Ly8vGBoa8uatJ0+eoFevXkKNhTCntPnATud/Fbim/FONi+aSqk1TUxP37t3D0aNH4eLighEjRsDIyAgNGzYU+rHCw8NhZmaGjx8/lrhdVTtn0tPTsWTJEqSmpqJLly5YvHgxJajWUK9evWKk2lpcXBzf63+TIMaMGVOZ4ZBykpSUxPDhw3mvuVwujh07xmBEguNyuVi2bBkuX77MWFu879+/IyAggPdaSkoKw4YNYyQWUjb9+/dHkyZNeK9DQkJEWsG8Nnn8+DG2bt2KR48egcPhMB0OAUAfzyaEEEIIIYQQBpTlIUFR22ZlZcHR0RGenp4AgMWLF8PV1ZVXhevvfapzcsLevXtx+PBh3uuwsDAYGBjwXlfnf1tFlOX8MVH+38OxodqltzOrKedOTSAuLo527dqhXbt2hdZlZWUhPj4er1+/xuvXrxEXF8f7b3k/jf3q1SuYmZnh5s2baNmypUD7pKWlYezYsbicYzBwAAAgAElEQVRy5QokJSXh7u5e6VW4BHXnzh2MGzeO93rjxo1YsGABADrXy0vQuahJHUlAhg1kcTBIs3GZx6afD7OUlJSgpKQECwsLvuUFBQVITk7mm4P+/H98fDxycnLKfKy8vDwMHz4c3t7esLe351uXmZmJWbNm4ejRo2CxWHB2dsaKFSt4SQ50zlRfZbmu6aQkx/v/IX8liQoyNp0XzJKTk0O3bt3QrVu3Quu+fv3Km0P+fMXHxyMuLo6v9VVZvHjxAqampggKCoJUGfZj6pz59u0bli5divfv38PQ0JDXpprUPPn5+di5cycjiYN/J3J9//4dgYGBvNcyMjKws7Or9JhI+YwdOxZ79+7lvfb29sbSpUsZjEgwXC4XT548wZMnT7B7927o6enBxMQE3bp1E0lib1HOnDnD12bUysoKDRo0qJRjk4phs9mwt7fHrl27APwvidHZ2ZnhyKq/zMxMBAUFISgoCLKysujcuTNMTExgaGgINpvNdHi1EiVyEUIIIYQQQkglKe1GraA3cl++fInhw4cjKioKjRs3xtGjR9GvXz+BxqUHWNVbSedIcetYLGCaoRLOvUmHVpM6JWxX+Nygc6fqkpGRQdu2bdG2bdtC67Kzs5GQkIC4uDi+BK/Xr18jOTm5xPMoPj4eKioqeP36Ndi8PkRFn1/37t3DiBEj8O7dO6irq8PX1xf6+vr/39az+pwv1SnWqqC8f8t2mKtjzvU4DNNpXuIYJc1F9LOqWsTExKCiogIVFZVCFbG4XC5SUlKKnIfi4+P5Hp79Kz8/HyNHjkROTg5sdX4nsr5OT8PATp0QHR2NJk2a4NixY+jdu3exY9A5Uz2U57oGAOZ2Ucaul59gqFi/XNc1dF5UPQ0bNoSRkRGMjIwKrfv161ehOeRPste7d+9KHDc+Ph5qamq447QCdQFwUXzVLSbPmR8/fsDJyQnJycnQ19eHs7MzPTStwc6cOYPExERGjv13ItejR4/4kq6tra1Rv359kR2by+Xi/v37OHfuHD5//owfP35AV1cXvXr1gomJiUDn/JcvX+Dn54fAwECkpaUhKysLUlJS0NDQQOfOnTFy5MgSk4FOnjyJz58/AwCmTZsGKSkp5Obmws/PD9euXcPLly+RnZ2NevXqwd7eHqNHjy70PfHy8sK3b98AAL169YKurm6pcSclJcHf3x/A70SU6dOnV3he6dKlC9q0acP7mcbExCA9PZ2v5WJVx+Vy8ezZMzx79gx79uyBrq4uTExMYGxsDHl5eZEdNyQkhO+1qCvRZWZmwtfXFw8fPsTXr19RUFAAU1NTWFhYQFNTU6AxoqKicPLkSYSGhuLnz5/Izc2FvLw8tLW1MWDAAPTt27fY5N/09HRexTZVVVVYW1sDAJKTk3H69GkEBwfj/fv34HK5aNWqFaZMmYLevXtDTOx/jd1SU1Ph4+PDez1jxgyBfmf//F4B4P2+V9SYMWN4iVwAcPfu3QqPSfhlZmbi1q1buHXrFqSlpfmSuqSkypIeTyqCErkIIYQQQgghRMTK+5CqqHWnT5/GsmXLkJiYCFNTUxw/fhyKiorF7vPvzUFKzKmeijtPBD1/huo0RSNpiRLPD0HPHTpvqj5paWloa2tDW1u70Lo/SV7/Vr14/fo1X0sCc3NznBwzCY112gDiYnzJWVwuFzt27MCiRYuQn58POzs7HDx4kO8hB801NU955qG/19toNkXwm2+QZYuVeH4Iso7OqaqPxWLxKnn16NGDb92fJK+i5qHXr18jKysLADBx4kT8dHJGbj1x7PQ/jbffvsLMzAzHjx9HixYt+Mb7+7j/Hqu4dYQ5Fb2usfv/CqM0l9QOderUQfv27dG+fXu+5VwuF9nZ2YiPj+cld/2d5PX3dc0Q9+3wMLVAi8/pyE39DHbTxlXmnPn16xecnJzw5s0btG3bFsuXLxd661pSdRQUFODixYtgsViMVOR69+4dMjMzISsri8jISL51JiYmIjtucHAwZs6ciZiYGL7l58+fh5ubGzp06IDjx49DR0enyP3z8vKwZs0arF27tsiWXw8fPoS3tzcWLlyIzZs3Y8aMGUX+vm7evBmPHz8GAIwbNw4xMTEYOXJkobiA38k+y5cvR0REBFRUVHjL7927hwMHDgAAJkyYwKuSXpL9+/dj3bp1AH4nzM2YMaPUfQRhbGzMl5wXGRkJMzMzoYxd2bhcLqKiohAVFYV9+/ZBW1sbpqamMDY25mulJwyVde4XFBRgzZo12Lp1a6E2kqdPnwYAzJw5E5s2bYKsrGyRYyQnJ2PixIkICgoqcv3Nmzfh7u4OLS0tnDp1qtDfSgD49OkT5syZAwAYMGAArK2t4e7ujkWLFiEzM5Nv2ydPnuDMmTMYPHgw/Pz8eMlcDRs2xKpVq5Ceng4A0NHRKfFDFcDvn+m0adN4f49PnTpV4vaCMjAwgIyMDO89w78/TyJc2dnZuH37Nm7fvg0pKSkYGRnBxMQEnTp1grS0NNPh1WiUyEUIIYQQQgghIlLWJK3itv/x8xdeJyTimLcX7t+7BzX1VjDtbg6X5cvRokULgZJzivuEOT3AqtrK8qDz32UFBQVIepuCz2npYOXkQLeAi0cRv9C8WVO0VGxR5MMDQc4dOm+qttLOGSkpKWhra0NLS6vQNrm5uUhMTERYeASePn2G4/fvYrS8Ar6/eYv6zZtBXVUZEhISuHXrFvbt24c6derAzc2N10qRzpmaS9C/WUUtS0//hsS3ycjKysYgOQ4ePHoMufp10VpdFZKSkkXu83fS4N+v/z0OnVdVV2lz0Z8kL3Nz80Lbvn//HlEvXuD+/VB4nTsLzsdUaDZXgMXQodiwfj0aNJArNuGitOue4taRylGRuYTL5SIp+R0+fUpDTk4ODFhcPAp/imZNG0O5pWKh7QGaS2qC0s4PaWlp6OrqFpn8weFwkJiYiNCHjxAZGYWAJ08hF3AZ0hLikO/ZHSotFfmqjQh6HVzU8vLKzMyEs7MzEhISoK2tjVWrVlGlixqgpPsAYmJi6NmzJy+J4w8WiwUxMTEUFBSIOjy8fv0aenp6eP78Od/yotq5C8OePXvg6OiI/Px8AL+rWLVq1QocDgfXr19HSkoKnj59CgMDA5w4cQI2NjaFxnBwcMD+/ft5r7W0tGBlZYW6devix48f8PPzw5s3b5CVlYVZs2YhMzMT//33X6Fx/v6dj46OxuDBg/Hp0yfo6Oigc+fOkJWVxZMnT3D//n0AvyuADR06FPfv3+dVHxozZgwvkcvf3x8cDqfEykRcLpevktH48ePL8N0r2b8/s+fPn1fbRK5/xcTEICYmBgcOHICmpiZMTExgYmKCZs2aVWjc/Px8REdH814rKCiIpIrZz58/MWrUKFy4cAEAULduXV4Lxw8fPuDKlSvIzs7Gnj17cOPGDdy+fbvQv+3Lly8wNTVFUlISb5m1tTXatWsHcXFxpKSk4Pjx48jJycHLly9hbGyMx48fQ0NDg2+cv8/7jIwMHD58GA4ODmCxWOjbty9UVVWRn5+PS5cu4f379wB+J1lu2bIFCxcuBABISkpi+PDhvHaefn5+pSZyPXr0iJfEJScnh0GDBpXjO1mYmJgYdHR0EBERAeD3e4cvX76ItIob+S0nJwd3797F3bt3ISkpCQMDA5iamqJTp07FJiOS8qNELkIIIYQQQggRgfI+qOJyufj0OQ0379xHTOxrxL5OwMfUz7ztpBspIRdAwsfvmDBzAWRkpNFGXQ0ardXQyaADjDryf/qOHmBVX4KeL39LepuCG7fvIuplHF6+eo3M//+E4r/q1q0DLY3WaKvVBr3MTKBURGJXSecOnTdVU3nOGS6Xi6/fvuP6zTt4Hv0K0S9j8fXb/39aWFoOSzIygJN+wEk/SEqy0aaVOrQ1W6O3RV9MnjQB7du3L3Q+VLdzhhLNileecyojIxPXb4Xg2YsYRMfEIvVzWpFji4uLo5WaMrQ126CLoT46G+rzHjIIck79WU4/u6qlvNc/eXn5uPvgEcKePMOLl3FIepuCgoICSCm0hrRiG2QBeJX8BYNHTUFLxRbQ1moD/Xa66GVmzFe5prRz5s86Om8qnyB/j/6VnPIeQcF38eJlLGJexSEjs+jrmjp1ZKHVphV0tTTQy8wYKspKfGPSXFL9lPeaBgDeffiIG8F3ERUTi5jY1/j1K+P3BpL14VyQD5zxB874Q1ZGBpoardBWSwM9zYyhptKSN1ZlnDNZWVlYvnw5YmNj0aZNG7i6ukJGRqZCY1ZlGRkZkJSUrPEtIwWpVNq4ceMil/+bxMVms1FQUMBLgBKWV69eQU9Pr1AVGz09PaEeBwACAwMxa9YsAICamho8PDz4KnUWFBRg5cqVcHV1RXZ2NsaMGYMXL17wVcA6cuQIL4lLUlIS+/btw5gxYyAh8b9H3G5ubnB3d8eCBQsAAE5OTjA3N4ehoSFfPH//3o4cORKZmZk4ffo0bG1t+ZJdLl26BEtLSwBAeHg47t+/z0uQMjY2hpqaGhITE5Geno7g4GBYWFgU+z14/PgxEhISAADy8vK8cYXh30SumlqZ6NWrV3j16hU8PDzQunVrmJiYwNTUlK9Cq6Bev37N1+L7/9g77/AoqvWPf2Y3vRdIIyQBEkJHaiogCiJKDQkoggj36u/a0Ktgu1YE7CiCijQFpUgRRQEFC6J0MPReQk1ogSSkkGR3fn/EnexsNskm2c0Gcj7Pk2fnnDkz5+zOu2dO9v3O+9rC7gH+85//KCKu5ORkpk6dSkhIiLL/0qVL9OvXj23btnH48GFGjRrFTz/9pOyXZZn7779fEXG1b9+eOXPm0KlTJ1U/r732Go8++iirV68mNzeX++67j82bN6tEwcZ2v2/fPp588kni4uKYPXu2SghdXFzMI488whdffAHARx99xLhx45TjR44cqQi5vv32W6ZPn15uOkdAJVi9//77rRq9qW3btoqQC0ps//bbb7fa+QWVU1hYyObNm9m8eTOOjo507NiRxMREYmNjcXd3t/fwbgmEkEsgEAgEAoFAIBAIrIwljinjOlmWkWWZHX/v5rvV69iyfSd6fcVp7gzk5xewZ/9B9uw/yLLvVxMSHMiAvr3p27sn3l6e5TqwTM8pHFh1i4psyNy+PfsOsvjbH9i8bSeWcP16Ljv+3s2Ov3czb9Fy4rp24r6k/rRtXRKlyVjUVZk4R9iNfbEkalt59nT2XDrfrPiRn3/9w2yKElMKC4vYf/Aw+w8eBuCzLxczdHAu3eNjcHDQ3tQ2U1fHZU+qIrqQZZkrmVdZ+v1qflyzrlyxhTE6nY4jx05y5NhJvl+1lsahIQwd1I+77uiGk5OTWXFpRSJB03pB7VOd9U9+QQFr1v7Oku9WceHiJVXb8q7nmXPpnDmXztpfNzBj7tcM7teHgff2wddMpC6x7rE/ltiB6b79B4+w+NuVbNyyo1JRBJQISHfu2svOXXuZv3g5MZ07cF9Sf25r17rKc4mwC/tSVQGX8fahI8dYtGwlf27eZpHd5OXnk7p7H6m79/HVN9/SpWN7hiX1o3OH9hbbTHXt5caNG7z++uscOHCApk2bMnHixFs+ksWsWbPYtWsXL730UploMTcblthXRZQXNcbUpkzX55Ik4eLiQlFREcXFxdXu/9tvvyUpKUkVlahRo0b4+vpW+5zmyM3NVURcADNmzCiTblmj0TBhwgQ2bNjAH3/8QW5uLp9//jmTJ09W2rz//vvK9vjx4xk9enSZvpydnXnmmWfYvHkzy5Yto6ioiA8++IBFixap2hl/vmlpacydO5eUlJQy57vnnnu4//77leNXr16tCLkkSWLEiBG8+eabACxbtqxCIZdxNK7hw4dbNXVqfRFyGWNIn/vll1/StGlTJVJXaGioRcebfka2iES3du1aFi5cCJR8t+bPn19GqNuwYUOWLFlCZGQkxcXF/Pzzz+zdu1cZz549e1i3bh1Q8j2ZP3++WdFZWFgYX375JZGRkWRnZ5OamsratWvp37+/0sZYpHjlyhX8/PxYtmyZSlgG4ODgwLRp0/jmm2/Iy8vj/Pnz7NmzR0nXGBsbS2RkJMeOHePixYv89ddf5UaA0+v1KiHXqFGjLP78LMGc7Qshl/0oKipi69atbN26FQcHB2677TYSExOJi4vD09PT3sO7aRFCLoFAIBAIBAKBQCCwElWNNGAo791/iA+mz+TUmXM1HsP59AvMmPs1c776hvuHDGDkfUk4Ojpa5HywpwOruj+G30qO2Ko4rgyvFy9d5q0pn7Br74Eyx1al301bd7Bp6w5ua9uKF/77OIEBJU+Jlyfoqit2U9+pjs1AiQB0+qx5rFn3e40cUYeOHGfCOx8REhzIi888TpuW0RbbTF20l7o6LntgqdNclmV0Oj3zFy1l4bLvKS6ufsSIM2fP88H0mcz9+hueefzfJMZ1VfYZ7EpE1am7VFWADPDrHxuZ+tkccgzRcqpBVnYOXy5cxsJl3zPq/mTuGzIAjUZTxmaM+7Ukapeg5lRFwGXYvnwlk7c/+oydqXvKHFsVtu5IZeuOVNq0iualZ54gOCgAsGwuEXZhP6pjMwBXMq/y/rSZbNn+d4363/73brb/vZsWzSP537NP0Dg0ROmrPJupjr0UFRUxYcIE9u7dS3h4OJMnT77lnZzHjx9n3bp1uLm5ERQUZO/h2AxL19XVTf8lyzL5JhGXXVxccHJyIj8/36IHMwCuX7/O+fPnVVGJWrduXa0xVcS8efM4ffo0AHFxcRWKnZ588kkuXbpEYGCgStR45swZ9u/fD4CPjw8vvvhihX2+8cYbLFu2DIDff/+9zFrAWNASFRVVYZrDbt26KUIu48g/UBKZyCDkWrFiBZ988okqQpgBWZZVYhZrplUECAoKwt/fnytXrgAlIqf6xIkTJzhx4gTz588nPDxcEXUZR3Qz5fjx46qyLWx/woQJyvYLL7xQbrTF8PBwhg8fzr59+wgKCiIzM1PZt2bNGmV75MiRFUYOa9iwIU8++SSTJk0C4LffflMJuUzvUU8++WQZEZcBd3d3OnbsyF9//QWU2L5ByGUQMb7++utAiYixPCHX5s2bOXv2LADR0dHExMSUO/7q0KZNG1W5vtl+Xaa4uJgdO3awY8cOpk2bRrt27UhMTCQ+Ph5vb297D++mQgi5BAKBQCAQCAQCgcAKVMeBmZ9fwKz5i/jux5/L/dFXq9EQHt6Y6MimNPT3w9HREZ1OR25+PifSTnP46PHSdCFGFBUVMX/xcv7cvI3nn36U6KhmVYpIIJxXtUt1HFcbNm3lvamfcz3XvANcK2vwwR1P2Q0HueQH62JJR7aURxZ56CR9mWN27T3Av54cz7gnH6FHQqxSb2orwm7sT3VsRpZljp88xYR3pnLm3Hmz55VkCW9c8ZLdccQBSQadpCdXKuAauRRKZZ/+P59+gaeef41R9yfzwNAktNqyIoraEgDWNEJCReetD/ZdkV2Zs6mMCxd5871pHDh0pJwTghcueMseOOGAJGvQS3rypAKukssNqazD8eq1LF6Z9AH9+/bisX89iIuLs/L5VyYSLK9OYFssEVoYl/Py85n62VzW/rah3HO64oyv3h0XnNDIGmRJTwFFZGlyuS4XgMklLiwsYta8RWxP3cP/nn2CBv5+FaYMFvcv22LpPcq4bvO2nbz90WdkZ+eYPWfJusbtn3VNSRqfYklHjpTHtXLWNfsOHObfTz7H04/9i949u4m5pA5TFQGx8euOv3cz+cNPuWZIC22CVtbgjRteZewmn2vkmrWbQ0eO8fBTzzP2/0bTt3dPqz7YUFRUxMSJE0lNTSU0NJS33noLLy+vCo+5FZg5cyayXJIm7FZ9v1VZg5pLrVhdCgoKVIIsSZLw9vZGq9WSk5NDYWGhss/FxQVHR0dycnLYtWuX6jw+Pj5WG5OBTZs2KduJiYkVfj+GDBnCkCFDytQbi1k6duxYabquli1b4uHhwfXr17lw4QIHDx5UpY4zHkOfPn0qHFOTJk2U7Zwc9b0pKiqKmJgYtm7dyqVLl/jzzz/LRBsD2Lp1q5Iar23btnTo0KHC8VcHb29vRchlKvSrT5w6dYpTp06xYMECGjduTEJCAt26dVNdR4C8vDxV2dq2f+PGDbZv366UExMTK2w/b948s/XGaRa7detWab/GQqnff/9dtc/Uzvv06VPhuZo0aaIIuUxt31jItXz5cqZOnaoSSBowjkT30EMPWX1NZSoIqs+2X5fR6XSkpqaSmprK9OnTFVFXQkKCTe47txpCyCUQCAQCgUAgEAgENcQSx4Np3clTZ3j5zfdIv3CxzPmcnZ3odXs37undk+iopjg7O1fY//n0DDZu2cGKVT9z5qxanHHy1Bkee/Z/jH5gKCOGJSn1logsast5VVPhxc0eOaGqzk5Zlvlk1jyWr1xT5jiNrCFU9qex3BBPXJFMPd1yyZ+MTDZ5nJEucU66gl4q7S83N4833v6I/n178/SjY1Sfa12ym/pMdWwG4Odf/2DK9FkUmaZikSEQb8L1gfjIHmglTZn9/HP6XLmA81ImpzUXVaIuvV7miwVL2bFrL5NffR4Pd7c6JwC0ZK6pzzZc1XvZ9r9388Y7H5Gbq3aGAPjJHkToA/GTvXCUtCYdodhTvlzIBekqadoL5FOoavbDml/Ys/8Qb7/2ghIlECqfh8qrE9gGS8UWhu3zGRd44bW3OXs+vcy53HChqT6QAL0PzpKjSUf/vOqgUC7miiabNCmDa5La/nbt2c+/nhjPGy8+w23tSiMsiPtX7VFVQagsy8z8ciGLl68sc5xGlmj0z7rGC7cK1zU55CvrGmNxTl5+PpM/mM7fu/cx7slH0GpL5iQxl9QdLBGAmrOhLxcsZf7i5WXOJ8kSjWQ/GssBeFtgN2ely5yVLqvspqDgBu9OncHOXXt58ZnH0Wq1NbYZnU7H22+/zfbt2wkODuatt96qF07MTZs2sXfvXkJCQhgwYIC9h1NjrPHQgI+PDw4ODmbTI9Z03pFlmWvXrqnqJElCq9Xi7e2NXq+nefPmSso2Ay4uLtXuszy2bNmibLdo0aJa5zCISYAKIxIZkCSJFi1asGPHDqDE/soTclUmqjLcLwCys7PL7B85ciRbt24FSgQt5oRcxmKWUaNG2eSeYrh23t7eNGvWjPHjx1u9D2syceJEm/dx5swZFi9ezOLFiwEYOnQoiYmJREZGcuPGDVVba9v+rl27FAGlJEnVSiUryzIbN25UypbYvvF3bPfu3Vy/fh0PDw+AMkKrmth+s2bNiI+PZ9OmTaSnp7Nlyxbi4+NVbXQ6HUuXLgVKo3hZG8N1kySJkJAQLl26VCds3zQy4unTp+vEuCojOjq62vO0pciyzO7du9m9ezeffPIJrVq1onv37iQmJuLn52fTvm9WhJBLIBAIBAKBQCAQCGpAZc4Gc3VHj59k/CuTyTZ5ss3Dw53RDwylb++eeHpU/KSpMSHBQaQM7kfK4H7s3LWXeQuXkbpnn7Jfr5eZ89U3XM/N4z9jRihjMSeyEE7N2qU6zs7P5nxVVsQlQ1M5iAh9EM5S5f/qS0h444637E6UvhFpmguclDKQjS73D2vWIUkSTz86RmUvlaUlEnZjW6oitjF+/eOvLbw7dUaZ44P0PkTpQ/GQ/vkBu5JL5y65EEUITfRBpHOZQ5pzFEul6fT27j/Ei2+8zbtvvISra+mPq/YWUFTF6VYfbbiqdrXvwGFemfg+NwrV4is/2YMW+sZ48889rJKP0VVyIoJAwnUBZEhXOaQ5S4GRoOvU6bOMf2UiH739On6+5p3ddUEkWF+xVMRleL2SeZXxL08qI2J3xZmWulAC8CkRXFRy2ZwkB4JlP4JlPzLJ4bDmLNek0uiU2TnXeXHCO3ww6RVaRUcpYxDrHttjybrG9HXuV9+UFXHJECEH0kQfhIupqM8MEhJeuNFaDlfWNSekDGQjofpPv6wHCcY/+X9mr7M5mzCMU9iF7aiOiEuWZRYu+a6siEuGcDmApvrgKtlNKzmMSH0Ip6SLHNekq+zm1z82IkkSLz37RMkxlcwjhvGZs5nZs2ezefNmAgICePvtt6udXu9moqioiDlz5gDwr3/9y2zquVuBqoq7JEnCz8+PixfLPtRlC2RZpri4mAsXLtCwYUO6du2Kl5cX06ZNU9qYi6hTU4zfX3lp3CrDOBpQ48aNLTqmUaNGipDLVIRi/D4rS/Fl/D02J+QaNmwYTz/9NMXFxSxfvpyPP/5YdX69Xq+IWbRaLQ888IBF468qhj7z8/MpLCxUUlHWVXS66qdiry7r1q0jNzcXFxcX9Hp1JEZr276x3fv4+KhShVpKQUGBSuhpie03atRIVTYWchnbspOTU6Xitcpsf+TIkUrEvWXLlpURcv35559kZGQA0Lt3b0JDQysdf1UxXDdZlsnLy0OW5Tph+6b2nZeXVyfGVRlOTk42F3KZcuDAASWq5IABA2wiKL7ZuTVXTQKBQCAQCAQCgUBQC1TF2WB4PXT0OM+/+laZdHjd4rvy7BOP4O/nW6MxdbqtLR3bt2Hl6nV8Omc+eXml4cW/+fYHiouKefyRUapjyhPoGMZ8szivbqaxQvUEOQu+WcHS71apjnPBidv0TfDFs1LntzmcJUei5VACZB92a0+oouKsXL0Wd3dX/j3yPtUxldnKzXYtbhaqK+LatnMXk6dMV7XXyhpayWGEyg2qZTcOaGhMAA10PuzRniBTuq7s23fgMK9O/oBJrzyHk5NjrQkArREhobLz34p2XVUn+rETabw44R2ViEuSIUofQlOCy0Y+sQAJiWDZD/9iL/Zr08iQSiNJnD2fwXOvTubDya/i6elRqbD0Zr2H3WxYIuIy3s7Ouc64V8qKuEL0frTSh5eN3GYhfngSq2/BUek8x6V0ZT4rKLjBC6+9xUdvv06TcLXz61ZZ99Q1qnOPWiKN7cIAACAASURBVLLiR75eskJ1nLPsSHt9E/zxqtb9yUlyoLnciEDZh13aE+RRGnnjp3XrcXd14/GHHyx3LhHC0NrD0vuPqd2sXL2WOV9/o2rvJDvQXt+EBnhX226iCCFQX7Ievk5pqrpf1v+Fq6sr/33sX0pdRfOIYaymdYMGDeL48eM8++yzNGzYsOqDvAlZuXIl6enptG/fnri4OHsPp85QVFSEv79/rQm5ACUCWK9evejfvz8HDx5U7TeNUmQNvL29FSFWdUV8xmkjHR0rF2ia9mX6voy/l5UJuYwxTccHJSky77nnHlauXElGRgabNm1SpdHbuHEj586dA6Bv374EBQVZ3F9VMLxHjUbD+fPnVeko6yKVRZy3Fv7+/sTHxxMXF0e7du0U4Y9p/9a2feNIi9awe7DM9o2jaIH6fVVFwGiKOdsfOnQoTz31FIWFhSxbtowPPvhA9d0yTatoCwzvT5Ikbty4gbe3N5MmTbJJX1UhPz9fFYWtSZMmzJw5044jsgwXFxcOHTpUK31FRUURFxdH9+7dqy3yrS8IIZdAIBAIBAKBQCAQVIOqiLgM2+fOZ/DcK5PINRJXabVaXnrmce66s4fVxiZJEgPvvYvYrh156Y13OHLshLJv+Q9rcHNzZfSIoRU6sOpSqrP6gCXOzt/+2FjGaeUru9NRF4WTBVG4KsMXDxKKW5OqPcoVI1HOoqXfExIYwD133VGlaCbC6WlbTO3DtM6wnXb6LK+/9SHFxaVPhjrLjnTVNcdDcq3xOFwlJ7rqozkoneGUptQZtSN1Dx9+OpvnnvqPUmcvm6nOPFNf7LeqTvRrWVk8/9pbqnSKWllDZ30UfnjWeDxOkgMd9JGkkcFB7Vml/kTaaV59awofTHxZuS7lCS/qy7WzJ1UVcen1el6d/AGnTp9Vnae1PowwOaBaogtjJCSay43wlz3ZoTmG/p/0aDnXcxn/yiTmTH8Pby/POrHuuVWpqohLlmU2btnOjLlfq47zlt3opGtuUXTRyvDGnfjiVuzWHueSVBpNYvnK1YQEBTCoXx+gfEGOsAvbUtk8Ytg2tZvtf+/m48+/VJ3LS3alk665RVG4KsMLN+J0rditOc5FKUup/2HNOkICGzJsyACLBYCmBAYG8u6779Z4jDcLWVlZLFq0CEmSeOSRR+w9nFojJyeHzZs3k5WVRVZWlio1IMDixYvZvHkz0dHRNh+Ls7MzRUVF6PV6iouL8fPz47777lP2GZOfn2/uFDUiNDSUs2dL7v3m0khagnGEFksjORm3MxXSGH9HPT0tX7uWF1Vp5MiRrFxZElVy2bJlKiHXkiVLlG1biVmgVPRTUFCAi4uLTaIfWRPTiFjWpGHDhiQkJJCYmEjLli3Nzsm2tn3jyFjWsHuwzPZNP1dj26+u3YN52/fz8+Pee+9lxYoVnDlzhu3bt9O1a1cAJUIdgJeXF4MGDapSf5ZisHtZLonI5eHhUSds39SenJ2d68S47E10dDSJiYkkJiYSGBho7+HcNAghl0AgEAgEAoFAIBDUEEtEXMXFOt7+6FOViMvR0YGJL48nPqazTcYV2LABU995nWdeepODh48q9QuWrqBLp/a0blHylFhlTk3hyLI+FTk4TetkWSYrO4dpM79UncNLdrWaiMuAo6Slgy6KHdrDXJNKhRqfzf2aLh1vo2EDP7OiCcO2YczCdqxPVW0GSn7MnfLJLFXkJEdZSxcribgMSEi0ksPQ6XWc1VxR6n/6ZT13dI+nc4d2FgtthM3UHhUJ3MoT5Hw6+yuuXit1bEuyRCd9pFVEXMZEEIROp+eI9rxSt3vvAVb9/Cv97u5Vrj2Je5jtqaqIS5ZlVq5ex9796ie8W+hCCSPAqmPzx4uO+mbs1BxTUqNlXr3GZ3O+4vmnH1XaiXWPbbHkHnX9ei4ffTZXdZyH7ExnXXOrr2tu00eyQ3OYq0bpN2fNW0Rc144EBpRERbJEGCrswnpUV8SVl5/PlE9mq9q54Ww1EZcBBzTcpmvGTu0R1cMNcxcsJSG2C6GNgi2aP+q7zXz11Vfk5eVx991306RJE3sPp0rk5+dz7do1rl69qnrt0aNHmfRlpuuprKwspk6dqpSvXbtGcHCwUnZ3d8ff379CMUVNbUej0aDX68tEGnr++eeVyD4BAQGqOe/48ePV7q88mjZtypYtWwBIT0+v1jmMowddvnzZomMyMzOVbScnJ9U+48hEVUnx5+7ubra+X79+eHt7k5WVxfLly5kyZQoajQadTseyZcsA8PX1pV+/fhb3VRXy8/OVqF9AvRRIBAYGKgIRSwSSpp+RtW2/cePGSgS8a9eukZ+fj6tr1f73dnFxwdHRkaKiIqDE9gMCKl43G9s9qG3feD6pamrL8mx/5MiRrFhRElV12bJlipBr/fr1XLp0CShJP1rV924px44dU5Xriu07ODhw//33K+WoqCg7jsa+tGrVisTERBISEupNJFJrI4RcAoFAIBAIBAKBQFBFKosyYHg1dkB8s3wlBw6ViqkcHBx467UXiOncwaZj9XB356O3XuOZ/01g/8EjAOj1Mm9N+YRZH7+D6z9P+tnDqVlZlJxbNYpOdQQ5n835iqzsHKWN6z9OK2s6Ow04Slo66Zqz1eGQklYmLy+faTO/5I0X/wtULuASWBdL5hzDtrHdrPr5N+V7D6CRJTrro/C0oojLmDZyBMWyTpUW78NPZzNn2nu4uDiXKwAUtmN/KrInw/bO1D38st4oqoQMHfRNS9Kf2YBmhFCoLybNKNLbzHmLiOvaCT/f0pQl5dmTcKbXHhWJuC5fyWTOV+pokk30gTTBNumFGuJNe30TdmlLo5Gu/W0DvXt2o2P7NhXairCZ6lHVdY0sy8yav4jMq6X3Cmec6KyLtsm6xgFNybpGe5Ac6Z+oJTdu8NFnc5n86nNKOyEMtS+W/E/1xYKlXLpcKhh3kh3oYmURlwGtpKGDLort2kNkSSUP4hQVFTHl09l8MPFlQAgAK+LUqVP89NNPuLq6MnLkSHsPByhJD2YqzDK8GrYN5fJSrTVu3LiMkMsUPz8/kpOT8fLywsvLC29vb9WfQdTw3XffsWPHDqu9P2M7NBfxqH///rRt21Ype3h4EBERwcmTJwE4dOgQRUVFFqcvtIQePXqwcOFCgDKpHC0lNjaWefPmAbB3716Ljjlw4ICy3alTJ9U+4+9jbm4uluLh4WG23sXFhaFDhzJr1izOnj3L9u3biYmJYcOGDWRkZAAwfPhwm6UTPHDggOp6G1/jW5mQkBASEhLo1q0bkZGRVTq2TZs2qvKePXusOTQcHR2Jj49nw4YNyLLM4cOHue2226p0DkmSiI2N5c8//wRKbL9Vq1YVHmNs92FhYTRo0EApGwsYq2L3UL7t33PPPfj6+nL16lWWL1/OO++8gyRJtZJWEcrOB3XF9h0dHZV5r74hSRKtW7dWxFv+/v72HtJNj6byJgKBQCAQCAQCgUBwc5KXl8fvv/9e5SfOKqI6Iq4TaaeZt3i56jz/fvA+m4u4DLi5ufLm/8bh4VH6JF16xkVmfrlQNV7DdnlUR1hVXcrrqzbHALBz507lKUhbY84BDrBr737W/f6nqm1bXbhNnFYGnCQH2uoiwOjj3rhlO5u37TQ7zsrERfWNnTt3KmlEakpFn2VFIq5rWdnMmrdI1T5SH4wP5n+ItQYSEq11ETjJpY749IyLzF+83KxtG7+HmtqMLeYMc8faw54PHz5c5gnrmmLJNTCuLyoq4sPP5qjOESr7E4ivVcdlSnN9KJ5yaWqR3Nw8Ppk9XzXGqnxH6hOXLl1i8+bNVn3fla2BzN0bPp3zFXlGKUY8ZReiZNumFwnGj0Z6P1Xdh5/MVu7nlq57bkX+/vtvCo2iNNaU6tjEgcNH+fGnX1XnaaMLw1VSR02xJo6Slnb6pkhGl3vbzl38sXGL2bWMmEtKycvL49dff7Xa/1OW2Iy58rETaXy/6mfVuVrrw3CTbCOOgBK7aatriiSXCj927dnP2t821Ot5xBJmzpyJXq9n2LBh+Pradq1QGa+//jqDBg0iOTmZhx9+mPHjxzN58mQ+/fRTFi1axJo1a9i8eTOHDh3iwoULAAQFBdGiRQvi4uLo27cv999/P4899phFghE3NzdGjx5NUlISvXr1okuXLjRv3pzAwEBVZBpjkYU1qMgOGzZsyOjRo8vUGwsfioqKOHToUJk2NaFPnz7K9tatWytsm5aWRmBgINHR0fTt21epv+uuu5Tt1NTUStPyZWRkKJG7vLy8iImJUe2vrpCrvKhEgEqsaIjCVVtiFlMRUl0Rs9iC0NBQ7rvvPqZPn87s2bMZPXp0lUVcUPIZGduBpQLBqnD33Xcr25XZ/vvvv09oaCht27ZVRfMztv2///670j6N30efPn1U77G6dg/l276zszPDhg0D4MSJE+zatYuioiK+/fZboCQSVVxcXJX6qgr1yfbrMpIk0b59ex5//HG+/vpr3n33XQYMGCBEXFZCROQSCAQCgUAgEAgEtyzZ2dnccccdylOxycnJ9OzZEwcH6/8rVJEDYvG3P6icH+3atOT+5IFWH0NFNGzgz7OPP8wb73yk1K1e+xsjhyXh5+ujeorcMO66ECnn/fff54033lDKAQEBtTqmCRMmoNFoiIuLIzExkc6dO5dJz1AVKhNPGMrG+5Z+t0q1v5Hez2YRcIzxwYMwuSGnpUtK3ZLvVhHbpaNSNn7y27hc36MRLFy4kClTptC1a1eGDRtGcnIyYWFhVju/JSKc1Wt/IzevND2mp+xChBwENr4MTpIDLeXG7JZOKnUr16xj5LAkXFycVTYB6h+VbW0zycnJ3HHHHcr5vb29zY6jrrFmzRrWrVtHixYt6N69OwkJCTVy/lkiSDbeL8syf23eTnpGaWQsJ9mBaH1jm9uTVtLQWh/BFs0hpa8NG7dyYdQlJSWa6RxkqKvL17Q2OHPmDPHx8QQFBTFs2DBSUlKIj4+v9udSHcFOxoVL/Llpm9FJoI0+Aq2tDQdooQ/jopRNkVQMwPmMC2zcsoMeibEVRpW81e9db7/9NoWFhcTGxpKYmEiXLl1sEiWkPKGxLMss+361qm2Q3pcAfExPYXW8cCNCDuSkdEGpW7LiR3okxJqdRwxjv9VsoKrk5OTQq1cv/Pz8GDp0KEOHDqVHjx6q6B6WYuk8Ylw2/C1fuQa9vnR/gOxNEGrBpi3wlFxpKgdxXCpNC7dkxY/cdUf3Ctcyt/pcUhHbtm0jNTWVwMBABg8eXK1z5OTklBs1y/AaFRXFE088Uem5ZFlGq9USHByMr68vPj4++Pj4KNumrxWlAbOmaM/aQq6KGDt2LC4uLmXq27Zty8qVK5Xynj17rCqGCA8Pp0WLFhw6dIgNGzawadMm4uPjzbZduHAhFy9e5OLFi7Ro0UKpb9q0KZGRkRw7dowzZ87w9ddf8+CDD5bb5/vvv69s9+rVq0yEMeO5K8/of6XKKC8qEUBCQgIRERGkpaWxbNkyJk+ezPLlJQ/ytWrVqkxUMGtSV6MSWYvw8HAlbWJ4eLhVzmkaje7w4cMUFhbW6HceU+6++25eeuklAN577z3GjBljNtqdXq9n3rx5nDt3jnPnzhEUVBqxtk+fPrzyyisAfP7554wbN67c9HQFBQVMmzZN1b8xxveg/Px89Hq9xffximx/5MiRzJgxAygRMWZkZCgPII0aNcqm9z5j23d3d6dp06Y260ugRqPR0L59exITE4mPj1elwBVYFyHkEggEAoFAIBAIBLc8mZmZzJw5k5kzZ+Ln58egQYNISUnhzjvvrFLqAEsjmBjvu3wlkw0btyhttFot/3v2iWo5P2pKr57d+HXDJv7aXOJULS7WsXL1OkYNTzYrrrCXI8L4MzX80G4vDNE71q9fz/r163FxcaFr167Vcn5WxXllqLt0+Qrbdu5S6rSyhmh9mM3FEwaa60NJlzIpkkqEiHv3H+LM2fM0Dg1R2giHZ/ls27aNbdu28eyzzxITE0NKSgrJyckW/whdHZuRZRm9Xs+adetV+1rqw9BKtTPvhMj+nJEvkSldB0pSc67/azN97uyhtKlI0GUrXF1dlXQ4N1v0DFmWOXjwIAcPHuTzzz+nRYsWdOvWjcTExHJ/0K9OH8bbxn+r1v6mattcbmSTFGjm8MWDRrI/56QrytjWrFvPqOHJQNk5yFAnnOklZGRkMHXqVKZOnUpISIgibE9ISLDKWqQiu/npl99V+0Nlf5tGBTTGSXIgWm7EPumUUrdq7W90TyiJzFGX1j21SUFBAcXFxWzYsIENGzbg7OxMly5dSExMpGvXrmYd/eVhybrY8GrYzsrOYdPW0lRikizRshbXNZFyI87JVyj8R+B3+OgJjp88RbMm4WXGD/XHLiwhMzOTGTNmMGPGDAICAkhOTmbo0KF069atxnNJRXYjyzI516+zYVNpRBNJhpa62rObZvpgzmmuUEBJNLuTp85w4NBRWrWIKjN+SZJIS0sjIiKiXtqMTqdj9uzZAGaFC1lZWZw8edKsMMvwmpWVRXFxcaV9Wfp//KuvvopWq636m7ExtRWt5I477ihXSNSuXTtVOTU1lQceeMCq/T/99NP85z//AUoEZStWrKBx48aqNps2beLdd99VygYBjIGxY8cyduxYAF544QViYmKIjo4u09fWrVtVEY0efvjhMm2qG5nIzc2t3H0ajYYRI0YwceJE0tLSeP/995WoYA899JBNv/upqamqsuk1vRlp0qSJIt4ytRVr0bZtW0XIVVRUxL59++jYsWMlR1nObbfdRo8ePfjjjz84fvw4//vf/5g0aZJq3pJlmddee419+/YB0Lx5c4YMGaLs79ixI/Hx8WzatImsrCyefPJJ5syZUyZClk6n47XXXiMtLQ2AiIgIevfurWpjep/Oz8+vMMqcMRXZflxcHM2aNeP48eMsXbqUc+fOASXfM1um1U1PT1ciKAK0bt3aLr+x1iccHBy47bbbSExMJC4uDk9PT3sPqV4ghFwCgUAgEAgEAoGgXpGZmcncuXOZO3cuPj4+DBw4kOTkZO66664Kn8CrrqDix59+obi4NBpXz27xhAQHYS9GDB2sCLkAfvjpF+5PHoiTU8kPSvXN2VAZjo6OqtSKBQUFKudn586dSUxMJCYmpkrOT2PKs50SJ/h6VfSBINkX51oST0BJSpkQ2Z9TUmkknlXrfuf/Hhputn19i2xSFbZu3crWrVsZN24cXbp0UURdTZo0qfK5KnN4pu7ZT/qF0mvmgUutRHEzJkwOUIRcUCKgMAi5KoqgJGzGMg4dOsShQ4eYNWsWzZs3V5wdxk9xm6M6wov0jIuk7tmvtHGQtQTLtZsqIVwO4BxXlPJPv67ngaGDcXBQO2aF7VTM+fPn+fjjj/n4448JCgpSRF2VCTGqImQ3/BUX6/jp1w2q84TJAVZ+RxUTLPtzSD5L8T9i5L937yM94wLBQYFKm/pmMy4uLly/Xjo337hxg7/++ou//voLJycnOnXqpKxrKnLcGWPJuthQ//Ovf6jWxUGyj01TRZvigIZQuQEnpAyl7seff2Xs/5WkHDO1gfpiF1Xl4sWLfPrpp3z66afKXDJ06FASExPL/byq80CM4e+3PzZRWFi6Hm+It01TKpqilTSE6vw5pi2NyrVq7W+0jC5J6WX8nn/55RemTp3KmDFjSEpKqrUx1jY6nY6srCyVIKt79+6sXr2as2fP0rp1a7p161bmuJ07d6oiJpnDw8Oj0qhZhm1LqIsiLgA/Pz80Gk2lqQJrgpeXF4888ki5+zt06KAqL1++nPfee8+q897DDz/M4sWLWb9+PTt37qRly5Y888wzREdHU1xczNq1a1m+fDk3btwAYOjQoWXSIT7xxBOsWbOGNWvWkJ6eTuvWrfm///s/7r77bry9vcnJyeHLL79U0hpCiRjMNCoRqL+vxv/nV0Zl4sKRI0cyceJEoCSdJ5QKvGzFhQsX+PPPP5Wyt7f3TRuVKDIyUvl/JiQkpPIDakiHDh1U0eiWLl1qVSGXJEnMnj2bdu3akZ+fz3vvvcfq1at57LHH8PPzIz09nSVLlrBlyxal/dtvv63KHqDValmwYAHt27cnOzubb775hg0bNvD888/TqlUrHBwcSEtL491331XSojo7O7N8+fIyIhvT77S1bF+SJEaMGMEbb7zB0aNHOXWq5AGKO++806oRyU1ZunSpqmw6lwmsg4ODAx07dqRbt27ExsZaLP4TWA8h5BIIBAKBQCAQCAT1lmvXrjFv3jzmzZuHl5cX/fv3Jzk5mbvvvttiUU5ljszVJlFxhg7uZ823UGVat2xOy+goDh4+CsC1rGw2b9tB94RYVTshrqicGzdusHHjRjZu3Fip89NS55XxtizL/LJ+o+o8jWXrRN6pCo31DTmlKRUF/br+Lx4Zdb9ZG6jN6Eo3M9u3b2f79u0899xzdOrUiZSUFFJSUlQ/vNfE4fnL+r9U/YXqai91i4EAvQ9OkoMS9eTAoaOcO59Bo5Ag4Si3MkeOHOHIkSPMnTuXZs2akZiYSLdu3arkBCkvQpksy/z6h9qeQmQ/HKjdJ569ccdLdiVbygfg8pWr7Nq7n063mU8fI+5hlZORkcH06dOZPn06gYGBDBkyhJSUFLp3727xE+3lCXYAdu/dz5XMq0rZW3bFm9r98d8BDSGynypF8C/rNzJiWNlUX8JmoLCwkM2bN7N58+ZKHTcVzRmm26r70x/2X9eE6huWCLn+uaS//bGJx//9oFmxh7CLyjGeS4KDg0lJSWHo0KEWp3KtaB4ptRv1faix3g52IzfkmJyu2M3vf27mqf+MVkVXOXjwIJ988gmSJClRZG4mmykuLi4TKau8qFk5OTllrltERAQLFiwAKFc81LhxY7p3716uMMvHx6dKEbPrMpVda61Wi4+Pj5KGzBY8+uijeHmV/zBHVFQU7dq1Y8+ePQCkpaXx559/0r17d6uNQaPRsGTJEgYNGsSmTZvIzc3lzTffNNt2zJgxfPbZZ2XqJUli0aJFPPjgg6xcuRKdTqeISc21fe6555gwYUK54zFQlQexKove1bx5c7p27cq2bdsoLCyJ3tenTx+Cg4Mt7qOqLFiwAJ2uVBw9ZMiQOj3HmNK8eXMlwnBgYGDlB1iRlJQU3njjDaW8YMECJk+ebNXPLzIykh9++IEhQ4aQlZXF/v37efzxx8u0c3V15euvvzabijYiIoK1a9eSnJzM2bNnSU9P5+mnnzbbX2BgIF999ZVZQZrp2t6atm8QcgGK7Y8aNcri81eH+fPnq8opKSk27a8+Ud0HOwS2QQi5BAKBQCAQCAQCgQDIzs5mwYIFLFiwAHd3d0XUdc8996h+5LA0EoUsy5xPv8DVa1lKu+aRTZUnt+3J4H59FCEXwN6DR+gWH1MmzRAIQY6lmHN+GkKOV/TUWkVRK3KuX+dcemnECDdc8K2llFTGeEqueMtuZEl5QIn478LFywQFlnWimUuzeDM5sOzBzp072blzJy+88AIdOnRQRF3NmjUz295UwGVaJ8syh48eNzqgJNVhbaUfMqCVNATLfqpoboeOHCMkuOyP9BU5ygVV4/jx4xw/fpx58+YpaUm6detGaGioql1VBKUHjxxTHdtIrn1hIECovgEHtGeU8qEjx+jYvo1SFvew6nPhwgXFIRoQEEBSUhIpKSn06NFD5fgxZzeGsunfIeN5CGikt4/dNJIbcJpSIdfBI8fMzjFCiKymuLhYSQ9cUSqVqgiN8wsKOHW69DvsglOtR4sEcJec8cODTEoik+Xm5XH2XDphjRspbQz3ItOysI+KSU9PV6L+NWrUSInUFRsbW2aOrmgeMd5fVFTE8ZOlKVKdZAca4l0bb0eFq+REAzy5TA5Q8kBH2umzRDUriax68eJFJk+eTHFxMWPGjKFz5861Psbq8OKLL5KZmcnVq1dV0foqQqPR4O3trYivDAKsH374gevXr3PnnXcSFRVl9tioqCheeOEFa76FWsV0biivzlLKE3JZY67p2rUrPXr0qLTdgw8+yLhx45Ty/PnzrSrkAmjYsCHr16/nm2++Ye7cuezcuZPs7GygJIrUwIEDGTZsGH379i33vXt7e/Pdd9+xdu1aZs6cybZt2zh79ixQ8nmFh4dz//33M2LECFq1alXuWBISEnB1dQVKhDaVjdsgrmnRokWl7/O5555TxIxQIqSzJaZiFlumsrMmGo2GL7/8koCA2o3Uakzr1q3p0KGDkpryzJkz/P7779xxxx1W7efOO+/k8OHDTJ06lVWrVrF//35FfBcdHU1KSgojR46kefPm5Z4jJiaG/fv38+WXX7JkyRJ2796tzNeOjo7ExcXxwAMPMHToUHx8fMyew8nJSSUUc3auOKplx44duXbtGkClYsTIyEjGjh3LmTMlazyNRmNWlGYtDhw4wM6dO5VyaGgoPXv2tFl/9Yn27duzePHiamcbEFgfIeQSCAQCgUAgEAgEAhNyc3NZvHgxixcvxs3Njb59+5KcnEy/fv1UTyNVJMIpI6YA2rct/wfF2sR0HIePngDKd2LWtiCnsh/D67oTzZzzMyEhgbi4ODw8SoRY5TmvjPcdP3FK1cZXb78n4Xxkd0XIBXDsRBqBARU75ev6daqLpKamkpqayksvvUT79u2VtGfNmzevcK4xLucXFHD2vJEAUHLGuRbTVhnjgzvGVnz0RBo9u8cDZUV/pnXG+2xhS/XBPk+ePMnJkyf56quvCAsLIzExkYSEBCIiIlTtzN3LjB3qx4zmIo0s4Yl95iLTaE7HTpwqc18C+9/DbnYuXrzIjBkzmDFjBg0aNGDw4MGkpKTQs2fPMqKuiv6OmdzDajsalwFP3NDIEnrpn3vrP4IQc454A0JUqqa4uJgdO3awY8cOpk2bRrt27RRRl3Gkl4rWbrIscyLttCpdtI8d1zXesrsq/e+xVoCMDQAAIABJREFUE2kqIVdF117MJZZx7tw5pk6dytSpUwkNDVVEXcYp08qbP4z3nTpzTpWO0wd3pNpWp/+Dt+zBZSlHKR89fpLIphHk5+czceJEsrOz6dWrF4MGDQJsO5cUFhZWGDUrLi6O22+/vdLzpKWlkZWVhUajwc/Pz6J0hl5eXmWiu5w9e5b//Oc/ODs789BDD1n1vd6q5OTkKMIHa+Pq6soTTzxhUdvhw4fz/PPPK+KSpUuXMn36dKs78h0dHRkxYgQjRoxAr9eTl5eHg4MDzs7OFn8/JEmiT58+9OnTB4D8/Hx0Oh1ubm4WRxN9+eWXLR5z+/bt+fbbby1uP2TIEIYMGWJx+5qwZ88edu/erZTDw8MtEu7VFewp4jLw4IMPKkIuKBHGWVvIBSWRsiZPnszkyZMpKiqioKAAV1dXVRrFyvDy8mLs2LGMHTtW+f44OjpWKsgy4O7uXiVbfuKJJyyeQwCmTp1qcduaYipgfOCBByz+/gsqxjQlp8D+CCGXQCAQCAQCgUAgEFRAXl4ey5cvZ/ny5bi4uNCnTx9F1GUsyjHnfDAVcrWONv9kcm0TEhSIt7cXWVklT8EeP5lGcXFxuT8k2cNBVZ6Y62Zzlpk6P9u3b09CQgKxsbF4eXlV6Lg6dlLtBPeS7efw9MIdjCKaHDuRRnxMJ6DiayKc4dVn9+7d7N69m1deeYW2bdsqjoHo6OgK7eZk2mnVd8dL72qvt4CX3g3jDHzGNl1e1KTq2kxFc0Z1oySY6/9mtePTp0+zcOFCFi5cSGhoKPHx8SQmJtKkSROljTlB19VrWWRevaaUPXFBaycHugduSDLI/3R//OSpcgXVxtys16wucPnyZWbNmsWsWbPw9/dn0KBBDBkyhDvuuMNsGjootSPjCDqSXHL97IEWCU9cyKIkLWfm1WtcvZaFr0/5EX1qU1R6s6HT6RTR8fTp02nXrh3x8fHEx8fj7e1d7v0J4PiJNNW5vGX7iPsAvE3s0VhoXB7i+lefs2fP8tFHH/HRRx8RFhampHLt0qWL2fbGNnTcZD3sacf1sLdsajenuEunY8qUKZw6dYpWrVrx6KOPVlugfuPGDTIzMy1KbZifn1/hWP39/S0Sck2ZMgV3d3c8PT1rZOO7du1ClmVSUlLw9/ev9nnqE56eniQmJvL7779b/dxjxoyhQQPLImEGBwfTq1cvfv75Z6AkUvlnn33Gf//7X6uPy4BGo1F+S6kJhsha9ZF3331XVR4xYoS4T1WR4cOHM378eIqLiwH45ptveP3118s89GJNHB0da5w+1lrfn5uRzMxMZs+erap78MEH7TQagcD2CCGXQCAQCAQCgUAgEFhIQUEB33//Pd9//z1OTk707t2bpKQk+vfvj6enZxnH1fFT6ids60JaRQOtoiPZvO1vAAoLizhzLp0m4Y3LbW8LQY4lkbfKE3tUdExdRa/XK87PTz75hLZt2xIfH09sbCze3qUOZYMNpZ0+qzreXlFwoKyILO30WZVNlIdwhluHvXv3snfvXl5//XVat25NUlKSIuoCtQDn1JlzqmPt6Sh3l1zQyhp0kh6AU//YjYHyIieZImzGupw9e5YlS5awZMkSgoODFRGGIZ2n8b3slOk8ZEcHugMaPHAhhwIAMi5eouDGDVz/iRghRKW25cqVK8yZM4c5c+bg5+fHwIEDGTx4MD179sTBwUE1DxUU3CDjYqn41wMXHLDfk/KeshtZUqno4eSpM/h4ewmbqSGyLCui4xkzZtCqVStFrO7r66u0UYTGp9X3J3uuazz17iqh8akz5yoU3xgQdlFzTp8+zYcffsiHH35IeHi4IlTv2LGj+YhcJvchUzFVbWJ6D0w7fYavv/6aHTt2EBgYyPPPP68SuRYWFpKZmUleXl6lKdwA5s6dyw8//FBpOwcHBxo2bFhh1KyQkBCL3lNlKbMspV+/frRq1YpGjRpV3vgWpLoPDowfP54WLVrw2WefWW0sbdq04Z577qnSMaNHj1aEXABvvPEGDzzwQJ2ImiQoy8aNG1UpHDUaDaNGjbLjiG5OAgICuOeee1i5ciVQ8nvfuHHjWLZsmZ1HJiiPV199lStXrijlrl27VphKVSC42RFCLoFAIBAIBLXKjRs3ADh//jwnT56082gEAsGtTmZmps3OXVhYyKpVq1i1ahWOjo707NmTgQMH0r9/f3x9fZFlmbw89ZPSDRvUnaeTG5g8KZ2fX8CVK1coLi5GkiQ0Go3KQSVJklI2ra8J1Y2UUx43i1NNlmX27NnDnj17+Pzzz2nZsiUxMTHEx8eX2o/Jk/bOshN2CoSDk6x+avTgwf2kpqbi5+en2IaxfZRXZ+BmuU7VxbDesQX79+9n//79vPnmm7Ro0YKkpCQGDhxIq1atkGWZ3Lw8VXsnyRGs+zWzGAkJJ8mRfEo+j/yCArOp8HQ6nfKDbE3mmvLmk5s9Ipch1Y0tSE9PV6JOBgQEEBcXR2xsrJLOM9fkPuaMfdJ0GnDCEf4Rcul0Or744gv63n23kk7DdP4xNwfVl7no0qVLlTeqJpmZmXzxxRd88cUX+Pr60q9fPwYOHMidd96Jg4MD+QUFqvZOdrYbU7vNy8/n2rVrFBYWKrZhybrnVraXmiLLsnJ/mjVrFtHR0cTExJCQkKCsa/IL6s58Ytr3kcMH2L59OwEBAWWut7n5xXi/6fathEajsXp6NWNOnTrFlClTmDJlCuHh4SQlJTFo0CA6deqkCLly8+vOfGJsN1qKuXoxnVVH9+Lo6EibNm2YO3cuV69e5erVq2RmZpKbmwuUpMRauHBhpXYSFhZGdHR0pakN3d3tJ9KviKZNm9p7CLVCTaK9mqN///6cOHFCEVLVZD5xdHRk7NixVT5HSkoK7733Hjt37gQgKyuLl156qUzkG4H90ev1jB07VlU3ZswYoqLqRvT3m41JkyaxatUq5f+t5cuX89tvv9kkxaKgZuzdu5cZM2ao6t5++207jUYgqB0k2dq/mgsEAoFAIBBUQHBwMBkZGfYehkAgqCc0aNCAy5cv12qfWq2W7t27k5yczF87D3DmXLqy78+fltfqWCpi6oy5LPtulVL21OTTrFkzjh8/XsFR9Q9HR0eKiopqtc/o6Gji4uLYsusg2//eo9T3KG6Lm+Rcq2MxUCTr+MUhVSnnX7vAwb2p5JmIhgQldO/enQ0bNtRqn9HR0QwcOJDARhGsWP2LUt9e34QQ2X4i0j+1+7j+j/BGq9WyaskXZYQ2V69e5fnnn7ep8ORmplWrVhw4cKBW+2zQoAExMTH4NAhgxheLlfrmuhCaYVmUD1uwQ3OES1JJWmBZltm07jvc3NwUZ7mglOjoaA4fPlyrfXp7e3PvvfcycOAgps0ttZuGshed9c1rdSzGHOc8R7TnlbKnJp+OHTvy999/221M9sLd3b3Wvy9RUVHExsay69BJNm3dqdR3K26Nh2SftFg6Wc9ah9LrfyP7MntTt1FgIkKs77i5uXHixAmCgoJqtd8mTZowaNAg/v3vf7Pk+5/49Y+Nyr54XasyqTFrkzWaHSCBm1SAVpJp1KgR586dK9NOq9Xi6+uLr68vDRo04KWXXlJEgXDriv/qC9Z4eMDYBoqKinjhhRc4ePBgjWxj1KhRDBs2rFrHbtq0icTEROU9aDQatm7dSufOnas9HoH1mTVrFo888ohS9vHx4ciRIzRs2NCOo7q5efLJJ5k+fbpSbtOmDampqTg4iFg4dYmePXuyfv16pZycnMzSpUvtNyCBoBYQs5BAIBAIBAK7ERsba+8hCASCWxx3d3d+/fXXWu2zV69eDBgwgL59+7J1z1HVPp1Op0q3YU+KiorL1IWFhamiDhioz46GEydO1Gp/rVu3JiYmhi5durBz/zHVPtleYZUAPXpVWVdchEajwcfHh8jIyDIRtwxUZDu3sl15eHjUan+enp4MGTKE/v37c/DYKdU+e9oNgM6of61Wo0pdZdjWarWEh4fj5+dnlzHWdZyda1/AGR8fT0xMDBmXr6rq9Xa2J2N71ut0ODs7c+PGDdq2batEjTGdW+rrPNSgQYNaF3INGjSI/v3707Zde6BUyGXvecic3YaEhJCTkwNUzWZuds6ePVt5IytiiDjapUsX9h9X962X7LmuUfetKy7CwcEBLy8voqKiykRog/o5l7i62kdol5yczIABA2jUqJFK/AQgm6xJaxMZWYmOKyOhdXSkR48e6PV6/Pz8lD9/f3+8vb1VdnSr2ohAjaXRukztwdHRkf/973+MHTuWq1evlnNUxTRt2pTk5ORqHQsla78HHniAr7/+GiiJ/DR8+HA2b96Mv3/diSxen9mzZw/PPvusqu71118XIq4aMmHCBBYvXqw8BLpv3z7++9//Mm3aNDuPTGBg0qRJKhGXq6srH3zwgf0GJBDUEkLIJRAIBAKBwC7MmjWLf//73/YehkAguMXJyMggODjYpn24ubnRp08fBg4cSJ8+ffDw8ECv1yPLMs7OTqq22TnX8fXxtul4LCUn57qqPPntd4lsGqE4HMylGrJVmiFrBYq2hYNk4MCBVj+nMVqtlvbt2ytOTk9PT2RZRq/X4+yktp8iyXbp1SqjGHXfnWPiuXblImlpabRu3ZpPPvlEZS/1Pb3is88+y+rVq23aR6dOnRg4cCADBgygWbNmyrxz4vR5Vbti2X52o/T/z6V2cnJSUisap1j08vLi5ZdfLtdujF8ro6L5xNK5pry+7GWzH330kc37CAsLIyYmhtjYWMLDw5V56FrOHlW7Iklnt1SdoJ6LNFotgwcPZtGiRWRnZ7NixQqCg4PL3L9sfQ+rq/z999/8+OOPNu0jODiYAQMGMHDgQBISEtBoNOj1eq5fV0d8Mr2H1Dam989xz79El47ty9hJfbCZoUOH2vT8Wq2Wtm3bKusab2/vctc19rQL077bduxM9tVLHDt2jObNmzN79uxaXw/XVS5cuGDzPlq2bMngwYMZOHAgLVu2BFDWNU5O6lSK9rSbQqM1Vb7sRHh4FIMGDVJsBChjL6YppW9lW6lPWDu9IoCfnx+vvfYa48ePr3JEaEmSeOqpp2r80Ni7777Ld999x/XrJb8VHD16lEGDBvHLL7/Y5cECQSnnz5/n3nvvVUToUBK19/HHH7fjqG4NfH19efPNN3n00UeVuunTp9O0aVP++9//2nFkAoCFCxfyyiuvqOqee+45wsLC7DQigaD2EEIugUAgEAgEAoFAIKgi3t7e9OvXj0GDBtG7d29FnGBwVEHJD/UhQYEcPFwaVenw0ePEdulor2GrOHS0dFySJBEcFACUdTAYR8+xFTX9Ifxmc4g4OTnRqVMnYmNj6dy5M66urmVsByA4UP1k7XUpDx/ZvdbHC5AjqVMoNo+O5rF/f8tdd93FvHnzCAoK4tVXX63UQSUcWNVHkiTi4+MZPHgwAwYMIDQ0VHFyGmxGlmWCAgNUx2VLeXYT3uTLhRRJpdH/QoICAcyKuUwxNxfV1HYsmWvqk31GRkYSFxdHXFwcwcHB5cxDanvKsaM9ycjkUJr2zNvLk2nTppCdnc2qVasYPHgwP//8Mz4+PiXtja51fbqutiYsLIzBgwczePBgYmJilDnIYD+yLOPm5oqXpwfZ/4jGcyhARkbCPtfB9B4WEhRodh4SNlM9nJyc6NChA7GxsXTp0gU3NzfFFgDFPkKCTOYT8vDD0x5DLmMTUZFRPPv4w/Tu3ZtFixYRHBzMxIkTlf21sR6ub7Rt25bBgweTlJRE8+bNVXOIse2EmN6HyKMB9nkwRm03Eo2CxVwiKIvpejMzM5MjR44o5bCwMBo1amT22ObNm/P4449XWcSflJREVFRU9QZsRHBwMB9//DFjxoxR6v766y8eeughFi5cKOzZTly/fp17771XFVXT1dWVL774QqT/sxKPPPII3333HT///LNSN27cOCIiIhg8eLAdR1a/2bBhA6NHj1bNqV26dOGFF16w46gqRpZltm7dqpQ9PT1p3bq1HUckuJkRM7xAIBAIBAKBQCAQWEBAQAADBgwgKSmJ22+/HQcHB7POBuPX6Mim/PrHRuUcBw8frRNCruycHM6nlz5d37hRCK4uLhaJK0wRwhzLcHNzo2vXrsTHx9OhQweV+M/YhgzIskzTCPUThlnkEVqbgzbu28Th2SwijCZNmrB06VL69evHO++8Q0hICA8//HAZZ6ewkeqj1Wq5/fbbFfFWYGCgWdGo8WuT8Maqc9hTyGXqKG8WEVZmnrG2fdgiQoLhvDc7kiTRqlUr4uPjiYuLo0GDBmbnIbUwsCGuri7k55cIqLLJt9v4c+UCdFJpSq2mEWFotVrmzJnDwIED2bp1K8OHD2fFihVKmsXKEPOTZURFRZGUlMTgwYPp1KmTSnBhwHS7aUQYu/YeAEAn6cmVC/CQ7JOmzdhuXV1dCAxooNov7KDquLi4KOuajh074uzsbHZNY7zdtC7dnyh7fwoLC2P58uX07duXKVOm0KhRIx577DGLzidsyDI6dOhAUlISQ4YMITIyssz/UsaUt67JqmN2Y4ywA4EB4/Xob7/9xrBhw5R9EyZM4OWXXzZ73MmTJ/nuu++qtJ4NDg5mxIgRNR/0P4wePZpjx44xefJkpW7x4sV4eHjwySef4GQSXVFgWy5fvkxKSgq7du1S6iRJ4quvvqJr1652HNmthUajYenSpSQmJrJnT0lEYr1ezwMPPMDChQsZNGiQnUdY/1i/fj1JSUkUFhYqdREREfzwww8W/69nDwoKCoiLi1PKMTExbNmyxY4jEtzMCCGXQCAQCAQCgUAgEJRD48aNlSfFExISlB9UyxPemL5GNY1Qtdl38Ah1gQOHjqrKzSObWCyusJWDwlbiC3vi5eVFXFwc8fHxtG/fXkl1UZ7DyrDPgKmQK1vKrTOCHINTrWPHjnz11VcMHTqUp59+moCAAOVHThG9ono4OjrSu3dvkpKS6N+/P/7+/uUKbQwY13t7eeLv58OVzGtASSQcnaxHK2lq941QIj40xtSmDVQlMldNqWiuuRVt1ZC+NT4+ntjYWHx8fMzaUUXzb9Pwxuz/575RLOnIlQtwl2r/x/NsjXlhoIuLC9988w19+vRh/fr1PPzww8ybN89seiHhZLectm3bMmTIEJKSkmjdunWl4mPjV4AmEY0VIReUXD8PufaFXLnyDYqNUis2CW9cJaGxLaID3qx4enoSGxtLfHw8t912mxIFpDzxlulrXVrXZGlM708l65q2bduycOFCkpKSGDduHIGBgSQnJ5c5vj7bQVXp0qULQ4YMITk5mYiIiCrNI7IslxVyafKxV3bFLEmdNrZZk3CgevZws9vQ5cuXadCgQeUN6zFVub6yLPPtt98yf/58iouLadmyJZIkceDAgUqPfeqpp6ye9nDixImcPHmSRYsWKXWzZ89m3759LFu2rNyIYgLrsn37dpKTkzl9+rSq/t1332XIkCF2GtWti6enJ6tWrSImJobz588DkJ+fT1JSEs899xyTJk2qcfpSgWW89957vPjii+h0pTd8Hx8fVq1aRWBgoB1HJhDULkLIJRAIBAKBQCAQCARGREVFKY7Lzp07A+YdC8aU57AKaxyCk5MjhYVFAOxM3cPFS1cIaOhv0/dQGavW/qYqR0c2MduuLjgYZs2axfr165XypEmTiIiIUMr2Hp8p/v7+JCQkkJCQQMuWLask3jJ9beDvp0pNdY088uQbuEnW/aG+Mm7IRVwmRyk7OToSGhKkjLVXr1589tlnPPzwwzz00EOsWrWKxMTEWh3jzY6rqyt9+/YlKSmJfv364eXlVaHNGCgvgkWT8DBFyCVLMhc11wiW/Wwy9orI0FxRlZs1CVPGaC4Nka2juG3cuJFPP/1UKQ8fPpx77rkHqHtzSU0wpG+Nj48nJiYGd/eSlKwVRU8y3VaLSsMVIRfABekqTQm22fjLI13KVJWbGIlCfH19WbFiBb169WLZsmUEBQXx/vvv1/YQb3oMgoukpCQiIyOBikV+5vYbys0iwlX16VImIXLtr38umNiNYVy2jg54q+Dn50d8fDzx8fG0adMGrVZb4bq4orWOl5cn/n6+XMm8CkC2VMB1uQCPWhaGFsrFXCJbKWs0GsIbhyo2cPvtt/P5558zZswYxowZQ0BAAD169KjVMd7sxMXFkZKSQlJSEmFhYdX+XwrAxcWZ4KAA0jMuApBHAdnk4YWbLd9CGf6fvfMOi+Lq/vh3dmEpCoqgFEFBRFHsioWqooAoNsTeosbE2EsSE6Pxzc8YzZumKUZN0bxJTIwldkiiIsVuEntXVKQjvS67+/tDd9kdZmZntrAL3M/z8OzMnVvOzBzOvTPnzL1ShQzZokLVPkVR8PL0MPnHMKagrKwMCxcuRLt27bB27VpYWlqaWqR6TW5uLj7++GNcvnwZFhYWmD59OmJjY1FWVobFixcjMzOTtWxkZCS6detmcJkoisL333+PtLQ0JCUlqdLPnj2LXr16YdeuXRg8eLDB2yXUsG3bNixatAiVlZUa6a+99hpWrFhhIqkaPu7u7jhy5AiCg4NRUvL8PYxCocDGjRtx8eJF7Nq1Cy1btjSxlA2X4uJivPTSS9i7d69GuqWlJfbt24fOnTubSDICwTSQQC4CgUAgEAgEAoHQ6OnevbtqmQ8/Pz8A2h2X2lAoFBCLxQjo2xsJyc+n0ZbJ5dh/6BhemWW4pQ+EkpWdg8SUc6p9CwsL1XKP2hwMdRFoQefs2bMaXwIvW7ZMI5DLHHB1dVUFb3Xo0EHjGmlDm0Orv38v/HEi8XkiBaSJctFBUbdfQKdTeVBQNXL27d1d5chVnuuECROQmZmJ1atXY9y4cThx4oTqf4nAjJ2dHaKjozF27FgMGzYMtra2Wh2dXMfU0wf498TFf66o9p9QOXUeyJWPYpRQNY6HVk6OaN/OU+vsW4DxgqoePHigYU969uypCuSq79jY2KBv374IDAxEnz59NJab4NIZProW0LcXDsX9pUp/LM6Fl8wFFOrOIV2uqEI2ahzoVlYS9OpW019TFAV3d3f8/vvvGDp0KL744gu4urqyOroakkNdHyiKQmBgoCp4q02bmuA4oeMgekBgr25+sLKSoLLy+XIo2ShEuaIKNlTdLcmkgAKPxbkaaQF9ezWKQAt9cHZ2Vo1rfH19Occ1QoJCFQoFBvTthcNxx1VpaaIc+Co0Z1wyNhlUHuRqy7T26dkVlpY1bhKFQoHY2FhkZmbi7bffRmxsLE6ePIkuXbqojpPlozWhKApBQUEYN24cYmJiVLP16PM8pV62X5+e+P1wvGr/CZUDP0VbpmJGI0v0TGN53x5dO8PGjJd2Mia7du1CYWEh7OzsSBCXniQkJODLL79EaWkp3N3d8cYbb8DHxwfA82eFd999F0uXLkVFRUWtsi1atMDs2bONJpuVlRUOHz6MGTNm4Pfff1elZ2dnIywsDNHR0Vi9ejX8/f2NJkNj5ODBg1i3bh0uXLhQ69ibb76J999/3wRSNS569OiBEydOICYmBk+ePFGlHz9+HO3bt8f8+fOxbNkyMiuhASkqKsIXX3yBTz/9FLm5mmN3Jycn7Nq1C4MGDTKRdASC6SCBXAQCgUAgEAgEAqFR0r9/f4wdOxZjx46Ft7e31vx8gifo+wqFAiMiBqsCuQDgwNE/MX3yOJO9+N9z4Cjk8honREhAXzSzt9PIQxxUNUilUsZ0T09PBAQEIDAwEF5ezDOaGYKIwcE1gVwA0qhc+Cjc6iyAgskJHj44hFEvli5diszMTHz55ZeIjo7GqVOnNIICCM9nDRo9ejRiYmIwZMgQXsugCHWSA0BAv9749sfdKCsrBwDkobjOl8N7QuVo7IcPCm7UtsQY2Nvbo1+/fggMDETPnj2N5kzt7OuD1m4ueJr+fEaIclQiD8Vwgr1R2mMijcqFutkL7NcHtrY2tfqrTp06Yffu3Rg5ciRWrVoFV1dXTJ06VXWc6ODz5TYHDhyImJgYjBkzBi4uLkZpx8bGGgH9euNk4pnnCdSLPgxuRmmPiTwUoRw1AaXurV3R2denzto3R+gzeyjx8PBQBW/xGRerw2cGSSXhA4M0A7moPPjIW9fp8r9PRJr9U8TgEMZ8ixYtQkZGBj7//HOMGDECSUlJZFyjhkgkQmhoKMaNG4exY8fWsiW6BnExjWvCBwZqBHKlU8/QUe4OC6rulriij2siw5j1BmjY/U1GRgYOHDgAS0tLzJo1y9TimDVc/wNSqRSffPIJTp06BQAYMWIEZs+erRGIDwBt27bFihUrsG7dulp1vPbaa6pZV42Fvb099u3bh40bN+Kdd97RWObs0KFDOHToECIiIrB8+XIEBwfXkp/Aj4KCAsTHx2PDhg34999/ax23t7fHjh07MGbMGBNI1zjx9/fH33//jYkTJ+L48ZpxS1FRET744ANs3rwZr776KmbNmqVaCpUgnHv37uGnn37Cpk2bkJ+fX+u4v78/9uzZQ8ZfhEYLCeQiEAgEAoFAIBAIjQKls2Hs2LEYM2aM6ktxY9POsw18O3jj1p37AIDikhJ8uf0HrFg4t07aV+feg1TsOXBUIy06MkxruYbsjNCGpaWlKpirQ4cOCAwMREBAgCD9EeLIojuvvL3awqutBx4+ev4laCUlRSqy4AXjON7ppCEHZaj5AtzFuSW6dOpQS2alfmzcuBHZ2dn47bffEB0djYSEBLRowTwTVGPRq1atWqlm/Bs4cCAsLIz/KsZKIkHwAH/EH6+Zze22OA295O2N3jYAFKIM6WrLmYlEIoQNDGQMEm0semAo1Jc569q1q2r5VkPAFRwYPigY3//0myr9jugJHOWd6ySotFxRhVRxlkZa+KBg1vyBgYHYsWMHpkyZgrlz56JVq1aIiIgwtphmjaWlJcLDwzF27FiMGjUKjo7cSxwaIgADACIGBdcEcgFIFWXBQ9YS1pTxZ3BDwqUzAAAgAElEQVRRQIHbojSNtPCBQToHqzcUW2VlZaUa17Rv3x4BAQEICgqCu7u70dpU1wsPdzf4+njj1t3n42IpVY0HVGadBfg9pXJRTNWMa5wcHdCjK/syPRs2bEBmZiZ+++03jBgxAqdOnYKDg0NdiGqWiMViDB48GOPGjcOYMWMMsrwUH3vj3KolunTuiGs3bgMAqikZ7osy0FFhPL1VJ4N6hgKqTLXfvJk9+vSsWc6uodgHPnz77beorq7GhAkT0KpVK1OLY7Zo02vlM6aDgwOWLl2KPn36sOYNCAjA5MmT8fPPP6vSlM+kdQFFUVi5ciX8/f0xadIk5ORoBjXGx8cjPj5etbR3cHAw+vXrh2bNmsHW1hYiUd0F6tYHZDIZSktLkZOTg5SUFCQlJeH69esaH9up4+fnh3379qFDhw6MxwnGw8nJCfHx8Vi9ejU2bNig8X9dWlqKjz/+GB9//DGcnJwQGBiIoKAg+Pn5wdbWlgQ1MlBZWYny8nLcvn0bycnJSE5ORkZGBmv+uXPnYvPmzbw+fiMQGiokkItAIBAIBAKBQCA0WMRiMaKiolSOS1NNfT5q2FBVIBcAHDgSj+AB/ujXp2edyVBVJcV7Gz9DdXW1Kq2bny+82mpfzqaxOCaY6Nq1K/z9/REYGKiz/iiDVXQlauhAfPnN/1T7d0XpcJY5wJYy7gutCkUVbomfaqRFhoXW0gf1fYqi8M033yA3NxcnT57E6NGjER8fD1tbW6PKam64ublh8eLFiImJQWBgoEkcGJGDQ/DHiSSV7mVRBchCPpxhXAe0AgpcFz2EQk1N+vfpiRYOzc3alpizE1YZDEhf5qwuUNqvQUH9sWvPQVS8mM2nkCqvs6DSm+LHqKZqZoBo59kGHdpzz4Q4atQobNq0CQsXLsSECRPw119/cTpJGyJWVlaqINIRI0bA3t74M6jR+7sO7dtpBCNXUzLcFD9CzzoIKk1FJoqoctW+tZUVBgYP0Lk+c7UPQuncuTO6d++OgIAAODs7m0SGYUNDVYFcAHBflAFXWQs0NfKskZUKKW7SgvsiBodw9tHKcU1OTg4SEhIwZswYxMXFwcbGxqiymhNisRiRkZEYN24cRo8erTUQ1FgMCwtVBXIBwEMqC66KFrCHcceYVYpq3BA91kgbOihYYxbMhmIftHH16lWcPn0aDg4OGD9+vKnFqfcsXrwYCoWCV/88ZcoUPHz4EGfOPA+Ofu2114wtXi3CwsJw+fJlbNiwAdu2bau13GNVVRXOnDmjkpGgH05OTli2bBkWLVpk9JnXCOyIxWKsX78ekZGRWLt2LU6ePFkrT25uLg4cOIADBw6YQMKGR9++fbFmzRoMHz7c1KIQCCaHhEITCASz472NnyE4MgZDRk1CZVWVqcWphVwuR2FRsV7OOAKBQCAQCHVDy5YtceTIEcyePdtoQVzagloAwL9XN/T31wza+uCTL5CVo7lknTHZvPV7lSMVeD5jz7xZU+us/frKunXr6jQIkEl/BgUPQPt2bVXpMkqOa+JUKGC88agCClwXP9IInvBo7YphQwbWkpOORCLB7t270a1bN5w5cwZTpkzRWIZDSUN2ei1fvhyfffYZgoODDRrEJeSaebi7IYK27M910WNUKqpZShiGh8hEoVrwhJWVBDMnj6uVT3kuDVkPDMXkyZMxZ84coy/bwXVP7OyaYvK4kRppd0XpKFVU1MprSDKoZ8iiCjRkfHn6BF5l58yZg7fffhulpaUYOXIk7t27ZywxzRI/Pz/s3bsXkydPFhzEZQg9U9bx8vSJGvVlUgXIxDO2YgahRFGOOyLNL/wnx46EXdMmjP1sY2Lt2rUYM2aMQYO4KIrivJb0Y0H9/dGpY00wn4JS4Lr4IWRGHNcAwE3RY0ipmj7Q1bkVRg4bolUP1Mc1KSkpmDp1KuvMKQ0RJycnHDt2DLNnzzZKEBefZykA6Nu7O7r5+ar2FZQC10WpkCmMey9uiR6jSk1vWjo5IiY60qhtmiNyuRzbtm0DAMyYMaNRBTMKRZvPQHnczs6Od/9MURRWrFiBtm3bYsmSJSabGdDV1RWbNm3Cw4cPsXz5chJgZARcXFzw0UcfITU1FW+99Ra5xmZCSEgITpw4geTk5EY/06+xCAwMRFxcHM6dO0eCuAiEF5AZuQiEBsZ3//sV5//+F3K55gOD8rn3+csVEdxcneHd1gNenm3g7eWJVi1N8zUVHblcjvN/XwYAdPPrBCuJRO86r9+8g207f0ZV1fOp4zdtWAuJRNg0/lVVUhw8+gcOxx/Hk6cZqKqqQtMmtvD28oR/r24YGRUOh+bN9JaVQCAQCARCw2Xu9Im4ffc+8guKAAB5zwqwcMVqbNr4H7i6GHdZis+2fIsDR+I10l6aMg4uzvovh2Io9FnGqaE7YkUiEebNmorX13ygchzmUcW4RqWiq4J7VhpduUE9RjZVqNqnKArzZk2FhQW/Zdzs7Oxw6NAhhIaG4tChQ1iwYAG+/vpro8hKYGdyzEicv3QZz/KfB8JUUlJcEt+Bv6wjLCnDLcmn5CmVi9uU5ixuU8aNQksn5uU1jYE+H9xw2ZPGYGu0ERU+CAkp5/Ag9fnMJDJKjgsWd9Cv2hc2lP7P7nTyUIQr1EONtIiwEPh487d77777LjIzM/Hdd98hKioKycnJcHGpm6VpCc/p0N4LQwcF4Y8TSaq0y6KHkMgt0QJ2Bm+vXFGJCxZ3IUdNcEf7dp6qQGSC6aEoCq/OnIxl77yvCvR+RpXiKvUA3RXtjLJk603qCTJE+Rppr740WWNWJS7s7e1x6NAhBAcH48CBA1i0aBG+/PJLg8tJ4Obl6ROx7J33VcuDFlBluCx+gJ5yb6PozR0qDU9FmoGnr86cDGvrxrfM019//YX79+/D29sbQ4YMMbU4JsdYH3hzjTdtbGywfv16s1jeVRlstHLlSvz0009ISkpCcnIysrKytBcm1MLb2xtBQUEYOHAgJk6cSJblM2OUwUYXL17E77//jqSkJJw/f77WDHUE7VhaWqqWYx0xYgRCQkK0FyIQGhkkkItAaGA8fpqO6zfvaM137cYtjf2+vXtgyvjR6NW9q7FE48X9h49QWFj0QqbuetVVLZPhf7v2YMfPezS+lFMI/FIr9dETvLFmPTKysjXSS0rLcPnaDVy+dgM//rofyxa8jGFDB+klM4FAIBAIBPNF3+Xx7OyaYt6sqVj/yVeqtIysbCx4fTU+2/AuPFq7GUJMDeRyOT75cjsOHPlDI71X9y4YOigYAPNX72xfwjf2IAZjwKZX9HTPNu4YNWwI9qvdyzRRHizkYnRStDGoTLepNDwW5WikhQ8OrrWUmTZ9cHFxwZEjRxAaGort27fD1dUV7777rkFlbaio339tOsKWl6Io2NhYY/bU8fjv59tU5QqpMvwjvote8g6wMOBE7VnIx1UqFep+VG+vtogKH2Q2doSPDScBWzXQ9el5UOkUvLl2o+oZuxxVuCi+jb6yTrCiDPeasQAluCS6BzlVc88cHZpjyriRWvso+v378ssvkZWVhSNHjmDEiBFISEiAnZ3hA4gINdDt1pTYUbjw9xXkFzwPEJZTClwS3UNfeQc0g+FmmqhQSHHe4i4qUDO7u1j8XG/VZ0dk+x8n//v6wWesrMzT2s0FMdER2P37UdWxDFE+LOWP4adoy1GDcO4jHakizeCCwSED4Nepg4Zc9G16mqurK44cOYKQkBB8/fXXaN26Nd5++22DytqQ0PfZiak+51ZOGD9qGH7ac1CVnkUV4BqVii4KT4MGcz1EJu6LMjXSggb4o1ePLhoyscnakCgvL8fOnTsBAHPnzjXJkuH1Bb46r+t40xyCuNRxcnLC4sWLsXjxYgDA3bt3kZSUhAcPHqCsrAzl5eWNagZDPojFYtja2qJJkybw8/NDcHAwXF1dTS0WQSB9+vRRLdteVVWFixcv4vTp08jNzUVZWRmqqqrIij5qUBQFS0tL2NrawsHBAf3790e/fv3I7I4EghZIIBeB0IBxbtUSLq1awsLSAhZiMSiKQkFhIXJyn+FZfoHGQOL8pX9x/tK/WDh3JsaPjTaZzBcu/ava9u+peyBXekYm3vtwE6+gNi6epKVj8cq1qi/YXZxbInxQCMRiMaRSKRKSzyItPQMVlZX4cNPX6NTRB55t3PVqk0AgEAgEgnmgi/OBK6BCoVCgR9fOmDUlFt/99JuqTHZOLmYveB3zZk3D6BERBnvxn/o4DR988gVu3Lqrke7j7YnFr77EK7BCyDI5+qDvCy7161yfYdI55XJFCoUCE8ZGI/VJGv65ckN1PFWUjQpFFfxknpDoGURRpajGddEjZNJmrPDz7YAZE8dqyMMku/qvEh8fHxw4cABDhw7Fe++9Bzc3N8ydO1cvORs7fIL/1Lf9e3VDTHQk9h6KU+XNo0pwTnQT3WXeaErp98W3AgrcQzruiTI0gricWjjgjUU1zj4uvVHfN9b/sRA701iCuZTnqH5t2OyQMq2dZxvMe2kyvvz2R9XxEqoSZyxuoLusHRzQVG+5HlPZuEmlQU7VOP5sbKzxxuJXVDMUCOnDxGIxdu3ahfDwcJw9exYxMTE4cuQI75l4CNwwBZWqH6MoCrY2Nnhz8StYu3ETKioqAQDVlAznRLfhp2iD1gr9ly/OQxEuix+iElKN9Pmzp6GtR2ut/ReBP4YKNo4ZOQwPH6Xhwj9XVOUei3JQoahCF5mX3sGhUoUMN0SPkE6bUalj+3aYNWV8LZ3gMx729fXFwYMHMXToUKxevRqtW7fGzJkz9ZKTwE9flMdHRIbhwaM0nLnwt6p8migPlQopusi8YE3pZ9ulChluiR4jTZSnkd6+XVu8OnOySh512elpDY3du3cjPz8fAQEB6NrVtB+BmzPqtvDp06d49uwZ5/VqiONNHx8f+Pj4mFoMAqFOkUgkCAgIQEBAgKlFIRAIDQwSyEUgNGDGjojA5PFjGI9Vy2TIzy/A5as38L/d+/Hg4SMAwOfbdqCFQ3MMeTFDQ11z/sXLG4fmzdDOS/gXeAqFAsf+PInPvvoW5S+mMx0THYmTiadR8GKmL77IZDKs+r8PVUFc82ZNxfiYkbAQ1yxBMnNKLD76fBvi/kpAdXU1Pv3qG2zasFaw3AQCgUAgEOo3dCeWurOB7piIHBIKsViM7T/8ospfXl6BT77cjhNJp7HstTnw8tR9hqXyigrs2X8E3/+8G1JptcYx3w7eWLVsPmxs2B3hjcEZUVew6QXffHQHloWFGMvnz8G6j77Erbv3VfkzqQIUWNxAN5knHGGvk6x5KMIVi1SNWUwAwNurDd5c/AosLS11dlr17dsXv/76K0aPHo3XXnsNzs7OGDVqlE5yEjTh6/gcP2Y4iktLNZY2K6LKcVp8A50UHnBXOOk0i0WpogJXxQ+RT5VqpNvbNcWaNxbBybEFb70xxEyAxv7qub473LTpCVNepnwDgwegpKwMO3ftU+UvRxXOiW7BR+4GT4ULxJTw2ToqFFLcEKciS21ZVwCQSCyxcvGr8GrrIXg2LiU2NjY4ePAggoOD8ddff2HmzJn48ccf6/X9NFfYdMfbqy3eXPwK1n/8FaTVz8cnMkqOK1QqcuVF8JW30Slwpxpy3KfS8YDKBN2MzZ4ai5DAfrx0hW9QO9EZbvjYGeW2WCzGknkv4YNPt+Ca2oeY2VQhUsTX0VXuiZZoppMcz1CMKxapKEelRrpnG3e8tXSextJ4Qsc3AwYMwM8//4xx48bh5ZdfhrOzM4YNG6aTnARmuJ6lRCIR5s+ZhvKKCvx7tebjhhyqSKU3rdBcp3bzUYIrFqkog+YSWe5urli1fAFsbWtmDhH6IUx9tSXZ2dnYt28fLCwsMHv2bFOLYxZoG29eunQJGzduBEVR2Lx5s0711xf9IBAIBAKBYDzIHKgEQiPFQixGSydHDBkUjK8/XY/uXTurju3ef9gkMlVWVuLKtecP4H16ddfpgeVJWjo++ORLlFdUwLFFc3y07h0sm/8yLC2Fvww8+scJPHz0BAAwMmooJo8foxHEBQBWVlZY+tocNLG1hUQiQVVlFSqrqpiqIxAIBAKB0IBQOhX4BkLR04YMDMS8lybXWpbi3yvXMf3VpVjw+mocP5WM6mrNQCwuUh89wSdfbsfoyXOwbefPtYK4/Hw7YNXy50Fc6rILDZgw9EtlQwZe1Oep6/nqj5WVFVYueR7QoE4FqnBefAdnRTeRhXwowGMJOSiQiWc4I7qB8+I7tYK4PNzd8PbS12BtbcVLr+nH1P+GDRuG7du3Qy6XY9KkSUhJSeF3YRoZugQWsN0T+n15afI4hAT01ahHRslxTfQIp8TX8JjKRjX4LX2SjxL8LbqHRPG1WkFcTZrY4p0VC+Dm6sxLb7jO01DoYhvqsz3RBb73SKlbw8MHI3aUZuCCggLuiNORIL6C+0hHlYJfH1asKMcV6iESxFdqBXFZWlhg+fw58O3gzanfXNtKWrRogbi4OLi5uWHXrl1YsWIFcZQKhG7b1dOZfulpfr4dsGTeLIhp71bSRc+QIL6C69QjlCo0A2/YqFBIcYdKQ4LoMh6IagdxTYqJRuSQgYL7LwJ/hAasMP3vSiQSvL5wLny8PTXKVFJSXBTfxRnRDWTiGe9xTRbycVZ0E+fEt2sFcbm5OuPtZa+hSRPbWnIKDcoZNWoUvvrqK8hkMsTGxuLChQta5SPUoI8doSgKlpYWWDrvJfh28Naot4qqxiXxPZwWXUcGlcdbb7JRgHOiWzgrvlUriMvFuSXWvLEQ9nZNNWTQJnND4bvvvoNUKsWoUaPI8m8cKMeNe/fuxdq1a1FaWopBgwbByYl7xsnGNt4kEAgEAoHAHzIjF4FAgI21NebNmoZXl74FALh55x6epKXDw92tTuW4fO2myuHYr5duyyoqH9BDg/rj9UWvopm9nW71KBTYobbk0cSYkax5bW1tsGPLx3BycqwV6FVXVFdX459//jFJ2wSCUDIzMwEAXbt2JS87CfWCTp06oWlT/ZcJIjQc6DOXMM0wwPUVufI3NKg/PNzd8PX3P+NxWrpGG5ev3sDlqzdga2sDH28vdPTxhq9POzi1aAErKwmqq2UoLSvDg9THuHX3Pm7ffYCMzCxGeS0tLDB+zHBER4bBwsKC1fmga1BFQ3RY1AXK68Y1Exeb/tja2uC9t5di5659+CshWaPefKoU+eL7kCgs4IAmaC5vCjvYwgJiAApUQ44ilKJAVIp8lEJKMQdaDAoegJmTYhiDuLQ5sNh0YsaMGcjMzMTbb7+N6OhopKSkoHPnzox5Cdphs0Vs+iMSiTBv1hS0a+uBH3/7HdXVMlXZclTiuugxbinS0BxN0FzRBM0UTWAJC1CgIIMcJShHAVWCAlFZLee4kk4d22PR3BkaM3EJCb6hnx/BONB1hyldmx2iKAqxo4fD3c0VW3fsQll5uaqeKqoad8TpuKfIQDPYormiKZormkACS1CgIIccZahAPlWKQlEpShQVtYJwAMDdzQWLXpmBth7uvOyP+nmw0aZNGxw9ehShoaH49NNP0bp1ayxfvlzYBWxksOmL8hgAxnEOm+706dkV61Ytw6atO5CZlaOqS07J8ZjKwWMqB/awRjNFUzigKawVEoggghwKVFJVyEcJCqlSFKEcCqq2XHZNm2DerCno07OboP5L/ZyI/WGGSxfY8vLRCxsba6x5fSF++u0g4o6f0qingCrDP+IHkCgs0BxN4MAwrilGGfJFJShAKapYxjXBAX0xe0qsakYlvmMaLl5++WWkp6fjvffew4gRI3D69Gm0b9+e1/VpbDD1L1zH+DxLWVlZYdWy+fhl/2EciT+h0V4hVY5/qYewVDx5MR5uAns0gQVEAChUQ4ZilKFAVIJ8lKGK0lySVUlAv96YO2MimjZpopJPXW6mX+V2Q7AlN2/eRGJiIpo1a4ZJkyaZWhyzpqqqCps2bUJCQgIsLCywcOFChIeHm1osAoFAIBAI9RgSyEUgEAAAnX19YG/XFEXFJQCAJ08z6jyQ6/yly6rt3j11C+SykkjwzoqFCA8L1eth+fbd+8jOzQMA+Hh7waM197VwcW6lc1uGoKCgAH379tWekUAwI/r3729qEQgEXiQnJyMwMNDUYhBMDN1xwHQc4HZkqudT/rbzbIN1q5bjwNE/8fvRPyCTac6GU1ZWrgrq0oUO7b0w76UpaO3mwurMpMvXEJ0Q5gbdYQVAqwOLfsxKIsHL0yegW+cO2LpjF0rLyjXaqKKqkYVCZIk1Z7fRhq2tDebOmIj+fXrW0hEmfWH6ZTtnAFi5ciUyMjLw+eefIzIyEmfOnEHr1q0FydhYUb++XPrCpj/K/YiwEPh28MamrTuQkZmt0YaMkiMPxcijigXJJhKJEDtqGEYPD4dYLNYpeELI+ROEITT4Aqjdn7HpWH//nvD2aoPN23bizr2HGnXJKQXyUVpr1rbajdZOGjooCFPHj4aVRKKSi4/94dOHde3aFQcOHEB4eDhef/11uLq6YvLkybyuT2NGW0CXNhuknredZxtsePcNfPfjb0g8fZ5WGVCEChRRFXiCXEEy+vl2wPw50+Dk6KB1vKP+q55PfZ/AH6F9FP0eSCQSzJwcg25+HfHVdz+hpETTblRR1chGIbIFjmtsbKwxZ+p4BPbvo/OYhms8vHbtWqSnp+Obb75BREQEzpw5g1atTPuO0Jwx9LOUhYUYU8aNRBdfH2z57ifVe20lUh31xspKgjlTxyM0qL8gvaFvaztmzigUCmzbtg0AMG3aNNja2ppYIvMlNzcX//d//4e7d+/CwcEBq1atQqdOnVTHrays4OjoqNpv8iIwkEAgEAgND4qiNGZjdHBwMKE0hPoOCeQiEAgAnncu1lZWqgdeqZT5SyRjcuHvfwE8d2g6OerWubk4tzJIUFXK2ZpZgjr7+uhdH4FAIBAIhMYBk+OBnq7cV2JpaYGYkZEI6t8bfySkIDHlXK2gHKF09fNF+MAg9O7RRbV8kS4OK/q5MW0T+KEtkEKbA4spKAcA+vbugY4+3oj76xT+TEhGSWmZTvI1bdoE4QMDET44BM3s7QQHTXDpC51PP/0UWVlZ2L17NyIjI5GUlITmzZvrJHdDRl1n6PpD1xf1ND73qK1Ha3yw5nUkppzDkT8SkJUjLFhCiYWFGMED/DEiIgzuLwJGdXF4MuUVCtv/lz5L1qhfX660+grTefANKlX/benkiLVvLsaZC3/jUNwJpD5O01ke/17dEB0xGO3beXLqE5fe8OnDQkJC8NNPP2HChAmYOXMmWrZsiaFDh+okd0OFT7+lbVZAtl8AsLaywrxZUxAa2BeH4k7gXx2D1gGgg7cXRkQMgn+v7hCJRIICdbjsDlsfR9CETRfUt/nqRa/uXfDJulWIP56IP04mobhESyAoC01sbRE2MADDwkLh0LyZVp3gSqefK317y5YtyMrKwqFDhxAVFYWEhAQyo/MLtNkRtvx89UX5271LJ3z03lv4KyEFcScSawV08cXWxgaDQwMQNWSgKiBU17Ew/bzqGwkJCbh9+zY8PT0RERFhanHMBro+37x5E+vWrUN+fj58fHzwzjvv1FpOMTo6GtnZmh9P0OtsyONNAoFAaExYW1sjJydHe0YCgQckkItAIAAAKioqUVBYpNpv8mKq8boiNy8fD1IfAwD8e/Wo07aZuHX3gWq7c8fngVxyuRz3Hz7CtRu3UVxSAhtra7R2c0Xf3t1hYUHMKYFAIBAIDRE25wPbS1UmxwLTjBRM286tWmJq7CiMHx2Fsxf+QfK5S3iY+hhl5RW85Gzt5oJufr4YEhoAV+dWvAIqhDgzuc5bV7Q5dnQJwDD3l958HeNCnEXN7O0wfsxwjBw2BAnJZ5Fy/hJSH6dpLJ3HhKWFBTzbuiO4fx8ED/CHldXzZRSZnOB8Aii4dEwdkUiEH374ATk5OTh58iRGjRqF+Ph4WFtb872MjQounaHbGT7OTiVWEgmGDAzCoOABuPjPVZxIOoO7D1JRUcG8bKJ6m+5uLujToyvCBwczOsi16QmTPFznZ0zos+M1dPjYIKC2TqkfY7qHYrEYgf36YIB/L1y7eRt/nEzGrbsPas2qw4RLq5bo1sUXw4aEwrmlk2Ad0rUPi4mJwebNm7FgwQLExMQgISEBvXr10ipvY4euI0qE9l0ikQhyuRydO/qgU4f2SEvPxLG/TuHajdvIyXumVY7mzezRqWN7RA4OQYf2XoL6KDY71FjsgCGg9zVMxwHhegEA9nZNMW7UMERHDkbi6fNIOnsRqY/SIK1mXjZRiYWFGG093BHUrzdCg/rBxtqaly5wjY3Zzl0dsViMX375BWFhYTh79izGjRuHQ4cOwdLSklPexgzb9RWqL+rbTZs2wajhQxEVPgjJZy8g8fQFPHz0RKveiMVitPVojcC+vTA4JAA2NrX1hu8YR9v58T1uaioqKvDdd98BeL6EqPLDIIImf/zxB7744gtUV1dj0KBBWLRoESQvZhIFmJ9lzf3eEwgEAoFAMB9I5AGBQAAApJy7gKoXs3BZWlrAr3PHOm3/0j81yyr69+pWp20zka32RXqH9u1w/0EqPtz8NW7culsrbwuH5ogaOgjTJ4+DjRk5n/q49TG1CAQCgVAvuZh+0dQiEMwUbc5vZR6A2wlB3xaJRFAoFKo/K4kEIQF9ETzAHwCQlZ2L1MdpeJyWjvKKCkil1RCJxZBILNHK0QFebT3Q1qM1JBIJq2OBHpijLgdf5xbX+RoD5bVu27athmO9vi3roc3BSUddd7j0SKk3cnnNcpzW1laICAtB+OBgSKXVSH38BHcfPELeswJUSaWgKEBiaYmWji3Qvl1btPFoDUsLC633n29QBdc1oOezsrLC77//jpCQECQmJmLKlCn47bffIBKJtF/UBg6XraFfa6YZT9TzsdkddcRiMfr27o6+vbtDJpPhaX3b8icAACAASURBVEYW7j1IRXpWDqqqpKCk1bCwsUIzezt4e7WBt2db3k5Ouix87AqXrPri6OioYU9cXFxU2+YeBGpotPVp9PvHJzgQeG6bunb2RZdOHaFQKJCRlY17Dx7haXomyisrIZPJILG0RNMmtmjn2QbeXm1g92LmGl0c52y6zrcPmz9/PtLT07F+/XpERUXh9OnTaNeuncCr2fBRv/d8A0vV09h0RhnMBQAerV3x8vQJUCgUyC8oxL2Hj/DwcRrKysohr6yCyEoCW2trtPFwg087Tzi2cBCsI1zbTMfYzpHAjPr1YVpCmk9fpY6VlRWGDAxCWGggpFIpHj15insPHiEnL19jXOPk6ABvr7bw9GgNS0tLTl3gClZXyiK036IoCra2tjh8+DACAwMRHx+POXPmYOfOnXpe0YaFtn5HmQfgry/08TBFUZBILDEoeAAGBvVHtUyGx0+e4u6DVOTm5aNKWg1AAYlEAsfmzdC+nSc827hDIuHWG759D/23vtqMffv2IS8vD3379kXPnj1NLY7ZIZPJsH37dhw8eBAikQizZs1CTEyMqcUiEAgEAoHQwCCBXAQCAY+fPMVnX32r2u/bu0edBySd/+cKgOdBZN27dK7TtpnIzs2r2c7Jw/99+BlKSstgbWWFNh5uUCiAtKcZKK+owLP8Avy4ez/u3H+IDWtXmsUXd+1aeGNM57GmFoNAIBDqJWKRBc6lnTW1GAQzRt0JwfRyXlsgDls5tnacWznBuZUT+vWpPWspH6cT1xJDujgu68Ihoe7kWb16NVavXl3reH10jOjqCOcKnGC6Dkonlo+3F3y8vTjlUf4KdXqql1Ovj4/DU4m9vT2OHTuGAQMGYN++fViwYAG++uorreUaK0x6w/a/KdTuKOsWi8Vo4+6GNu5uAJ7rXuUv+2E9YUytenX9Uy/PVBebfLpAv15RUVEYNmwYZ356W/XV3tBhsztc56ZrcKA6bi7OcHVupVU2Q+qWkHMEgPfffx8ZGRn4/vvvERERgZSUFLRqxS0zgTuwVLlPvy/qgVtK1APalbRwaA7/5s3g3/P5h34Vuw/AauxwUC9mQ9dl3MOUV5tdZDrPxg6XLWFaAhjQPh5m0gs6EokE7dt5on07T07ZlL/69ktc9bPh6OiIuLg4BAQE4IcffoCrqys2bNjAWaYxoq4r2uyItrGNEiY7QlEULCkK3l5t4e3VFgBQ+WcCrIYOrFUffVvX2WnZzpdNbnPl+vXrEIvFmDNnjqlFMTuKi4uxfv16XL58GU2aNMHKlSvJbJ4EAoFAIBCMAgnkIhAaGXK5HPkFhcjKyUV2Ti7+vXwdh+L+Us3G1bRpEyyZN5t3fdWy58u1iF485OqCQqHAhUv/AgC6dekMa2srneoxJFVVUtX2fzd/DQBYu3Ipgl4sOQMAZeXlOJ6QjM1ff4+Kykqcv/QvPv5iO1Yufc0kMhMIBAKBQDAOfB1W6unatrU5rJgcHGwBHLo4rZjys9XBJD/BcDBdV3rgCNd9oM/opu7EYtNR9XoMETwh1OGpnu7q6or4+HgEBQVhy5YtcHV1rRW415hR3kNtDnL1NDaHubquqNsfur1Rb0d2+y7KD/8J64ljIHqxrA6XzgiZ6YTrnOmya0OXZVj1ob4Hd7HpFVM+QLtOsfVnfNrRx5FOL6e+T78/2vRq27ZtyM7OxpEjRzB8+HCcPHkSTV/MFNaYETIGYrvmXNtcbaq3URGfAAsfL1j26m6QcQ9XeQJ/hNoSvtu6tMNlS/jYEyYd0MWWeHp64ujRowgNDcXGjRvRunVrLFy4kPXcGgO62hG28TCfwD8mnZHn5KJ0+4+wGhQE0Yvl7/jaEzb9oZdV36/PvP/++7h//z7c3d1NLYpZkZqaiv/85z/IzMyEh4cH1qxZg9atW9f5OJRAIBAIBELjgARyEQgNmC3f/Yhv/vcLJJYSSCSWsLAQI7+gCNXV1Yz5XZ1bYfUbi+Ci5WtZJTdu38W8pW9DLpfjvbeXY1BIgE5yPnj4CPkFhQCAvj1Nv6wiAFhJJKiqqgIA5BcU4pvPP6z11Z+tjQ2ihw2FYwsHvPnuBwCAuL8SsGDuDDRt0qSuRSYQCAQCgVAHMDkF2F7U050PdKcDWwAOWyAOUzt0ZxJfJwRbXrZzFpJOqA39PmoLtOJyhvNxXqnXrSzLpEdcTk8+Dk+2smxtsNGxY0ccPnwYgwcPxpo1a+Dm5obZs/l/XNIQ0aYbbEFd9H02faHbH/U21duQnr4IeX4R5LfuQtylE+u9ZtMXXQJxjAkfR1t9D9LSFW1BFEq4ZlSSy+UqPVL+0fUKYHfO8wmw0BYsSJebbx9mYWGB3bt3IywsDGfPnsW4ceNw6NAhs5hx21zQZwxE1yGmAEB13VFvS/YgFbKMbFSdvgirPj016tNn3MN1ntq2Ccxou0Z89ILtT5dxjT5/9PPia0sAoEePHti3bx+ioqKwZMkSuLi4IDY2lvPaNBb42hGmvpjP+IbNjgBARdJZKErKIbv4L8SB/VT5ufSG78zGbOeqbduc8fb2NrUIZsXp06fx0Ucfoby8HH379sUbb7wBW1tbU4tFIBAIBAKhAaPb9DkEAqHeIJVWo7SsDPkFhcjJfVYriIuiKHT188W82dOwc+un6OrXiVe9lZWVWPffzbycSNq48M9l1bZ/79pLBpmCpk1qHsQG9O3NOXV7QL8+6OrnCwCQyWRIOXPB2OIRCAQCgUCoY4Q4b9SP6fonEolUjgP1bSbHgrZ82o7xcWKpOy0IhoF+fYXqB1/dYdMBrjy6zFrBdX580vv164fdu3fDwsICr7zyCg4dOqTfBW5AMP3/cd0PIfqiTWdKfzsMACg/GMeYh0/9XDaF6Ty5tgn6IeQa62Kj2HSDS2f49GFcusYlH/1ctOmSra0tDh8+jI4dOyI+Ph6zZs0is2zAeGMg9T6Hq3+q+CMBAFB+5DgogLfOGGLcQ2CGz/+XIfonLlvCZ1wjxJ4wycd0TkznykRYWBh27NgBhUKBadOm4dSpU7pf8AaAUDuiiw3Rpg8l238GAJT87zfG/Hyev4TaE2JLGgZVVVXYunUrKioqMH78eKxZs4ZXEJc2m0IgEAgEAoHABZmRi0BowPTr0xNdOnVAWUUlqqVS2Fhbw8bGWvVr17QJunftDHs7O8F1f/39T3iSlg4X55bIzMrRS87zl54HcjVrZg9vr7Z61WUo7OyaIiMrGwDQ/8UXn1wE9O2Nq9dvAQAuXb6GiCEDjSkegUAgEAgEE0JR/GekoKNcilrbMlT0+tlmiKE7CYQ6FnR1ZJKX0MJRv59s6UzXle3ec81coV4v172i64L6Nh+nJ5tOMdXPh+HDh2Pbtm2YNWsWJkyYgOPHj2PAgAG8yjYm2HRJeYwJITMAKuuovv9QVV568x7rvdZHX5jqMiZc1049T2OCzVZw2Q++ywOzzcjFlF/5q6uznKteITg6OiI+Ph4DBgzAjz/+CFdXV3z44YeC62mo6DoGUtogPnXT9abqn6uqPNU3bkPStTPvfouMe+oe5bXStvSmEi57YghbQt8XMqufelldmDRpEtLT07FixQqMHj0aSUlJ6NKli051NST0sSN8l1Sk60z14zRVnuq0TCjKKyB+8RGv0D6Ia5lFuiz0cyO2pH4ikUjwzjvvID09HaGhoXUS5E10hUAgEAgEAgnkIhAaML26+WHy+DEGr/fvy1ex5/cjGDo4BAq5Qq9ArsqqKly+egMA4N+zu8q5aWo823rgzr0HAAALC7HW/G4uzqrtktJSo8lFIBAIBALBdLAFAAh1QgDCgnAMHYzDJ2CCLdiCYBjY7i+fZRb51s1VL5NDSdcACibnlT6O8Jdeegnp6el45513EB0djeTkZPj6+gq6Bg0Fum3RNYCUbzt0nalMOa/KI8vOg+zuA1h2bG8wnWHTG10cnmz22VjUZ3toKL3SJThQmy3SV7fo9ai3IUSv2rZti7i4OISEhOC///0v3NzcsGTJEl7XpaFiiDGQuq3hqzfVT55CmvpUVUdVynlYd+8ieOyjTT4y7hGOIfsoocHpdTWu0deWAMDy5cvx9OlTfPrpp4iMjMSZM2fg4eHB+9o0JAxhR7T1PWw6U5mQUlOJtBrS0+dhET5IsC0ROo4hNBx8fHzg4+PDeEzfcSjRGQKBQCAQCEyYR8QEgUCoN5SWlmH9x1+iadMmWDh3pt71Xb12E1VSKQDAv1c3veszFH4dax7MSkq0B2apP3CJKGJaCQQCgUBo6DA5+wzhOGJa4oxraUWmPEzLjAhdGoTtPOnbxr6+uh43V/hcW330hkkHmHSCS390cXTS5Wc7P76sWrUK8+fPR15eHiIjI5Geni64joaOIfWGTWfKE89otFmZfNZgSw6xnYsxrxOf9oTmb2iw/U8bQre02SJD65a+961bt274/fffYWVlhWXLluHXX3/Vq76GBJOdN5ZNqkw+p9F2WZKmHWLrB/XVG0PqUmPElLbEHPuojz/+GBMnTsTTp08RGRmJ/Px8veus72izI+pphtCZshPJGnWXH0/SakvYnqfYZOQ6P/o2gUAgEAgEAoGgDTIjF4FAEMTn23YgKzsHby6ZB4fmzfSu7/w/V1Tb/r26612foejsWxPIdf32Xa35c/KeqbZtbW2MIpMQHjy7j4O3DppaDAKBQKiXnEs7a2oRCGYMRbHPPqB8OS90JgJlefoyIPQ6merjcoLwdVgyHTMHR4P6taanN0To58t1nnSdoZfTZTY35S8f/WHSIa56tZ0PE5s3b0ZWVhb27NmDyMhIJCUloVkz/Z8/6huGtjl03WGrV0qbBQcAyhLPovkrMzXaEqIzXHpT1w5PJvvSUG0LE3z0CtCvP1NvQ1dbpK9N0levBg4ciP/973+YOHEipk+fDicnJ4SFhfEu39Dg0htlGmBYvaEHlMqy8iC9cw9Wvh1UZbXpiHo75jzuqY/UJ1tC3+ejD4ayJRRFYefOncjOzsaJEycwcuRI/Pnnn7C2tuZdR0OBrx0BdB/f0HVGeuceqtMyNfKXX7oCRWERxA7NVeX56Anbcb5yEhouTGNLAoFAIBAIBH0ggVwEAoE3p89dwpH44+jWpROiwgcbpM4Ll/4F8Hwpw5ZOjgap0xB4e7WFpaUFpNJq/H35GmQyGcRi9iUWL/5zWbXds5tfXYioFRKIQCAQCARC3cHmyNTmbNIWhMO3HvovH2cDU162+ujHDIW2F958ryVTGXNFiNOT6VzYrhmTA4vuVFfPS98Wqj9cumMIRCIRfvzxR+Tk5ODUqVMYNWoU4uPjYWVlZbA26guGcJRz2Rum+ipowRMAIH2SgeqHjyDx9tJoh6+jXP0YXTY2uY2JuduKuoSp71GmA8L7MzY9FbIcmvq2tjR6eaZf+jZfYmNjkZWVhYULF2LMmDFITExEjx49BNfT0BHSj9HLsOmNLDsHlbcf1CpXkXgW1p068tIZtjGPqcY9DRGuPko9D8CtE+r6YChbovzVZWzMVh99my8SiQT79+9HSEgIkpOTMXnyZOzZswciEZnVXwmTHQH46w29DuVv+fGk2gXlCpSfSoH92GhVOeWvvmPg+mRLMjMz4eLiYmoxzILy8nLY2NT9x9nmqhsEAoFAIBBMD3lSIBAIvCgsKsbGz76CjY01Vq1YaJAXDc/yC3DvQSoAwL+n+SyrCACWlpaICn/+pW1hYRF27z/Mmjf10RNc+ucqAEAsEiGov3+dyEggEAgEAsE0cL2U5+sEoC/3wrakIv0YPb+2vHyXlWGSWdu51iUN7QU3H2cPl/5w6RPfJYjYdI1LT+tad6ysrHDgwAF07doVp06dwtSpUyGXy3WqqyHCZH/46o02nSljCOQCgPKkszrpDJN8Qs5Nl+uhT11cZRuKPeJ7D3Tpz9h0hM9yaPrYH77nJpQFCxZg5cqVKC4uxrBhw/Dw4UOD1l+f4Oq/dO232PSGPhuXktJTZ/ReTk9dTqZzoG8ThKPtemvrO4TaErYyfPopunzq52BI7O3tcezYMXh6emL//v1YuHChQeuvL/CxI8ptfe0IBaCEKZALQNlfiZw6posdqU+25OrVq5g9eza+//57U4tiUioqKvDBBx/g3XffRXV1Na8ybPfUXO81gUAgEAiE+gkJ5CIQCLz4+ItteJZfgNcXvQI3F2eD1Hnp35plFfv2Nr8vWl+aEgvrF1/8b9vxMxKSz9T66i89IxNvvbcRVVIpAKB/316ws2ta57ISCAQCgUCoWwzlyGRzGmgLxqE7sricD3RHhLrcfJ2ZxsSQ7dT3l+d8HZ589YfJ2ckVBMjlFBPiqDKk86pZs2aIi4tDmzZtsGfPHixevFiv+uorbNeU7d7oozPy7FxU3rrPKEfpqRSD6Ux9c3g2RNj+f7XdK7Z7rS0gmc0OcdkvPjpNl4vr/HTlgw8+wIwZM5CZmYmIiAjk5OToVV99hsvu8+nD+OpN2SnmQC7pk3TIUh/zHvdw6QuT7AThaPtf46MX9HGLUFuiLfBLiC0Ren5CcHV1RVxcHBwdHfHVV19h/fr1etVXX9FmRwzV/1RevgZZbj6jDOVXbkGencNbb7TJxnRu5opcLsfWrVuhUCjQunVrU4tjMrKzs7FixQokJSUhIyOjUfftBAKBQCAQzA+ytCKBQNDK8VPJOJl4GhFhoRg6KMRg9Z6/9Hw5QgsLC3Tv2tkgdcZMewUFBYW10pWBVgAwbNx0UNB8qO7U0QdffPR/GmmOLRww/+Xp+PiL7aiursbqdR/Bt4M3enXrApGFGA8ePsbpcxdV+T3c3bBy6XyDnIdQmjdvjqQk5i/MCARzJCsrC87OhgkKJRCMTbdu5jVrJMF8oCju5c7Ylj8TWofyGJcc9G02ByU9L18nZn1wSNQ3mO49/ZjyuvNZZpFN59iWrtTVEc+Vn75tKNzc3BAfH4/AwEB88cUXcHV1xdtvv23wdswdNp1Rv+ba7A4fnSlNTGGVofLuI1Q/zYDEo7VG24bSGWPZGvr/GN8yjQEuveLTj9HrMbQtEprGVa++fPPNN8jOzsaxY8cwYsQInDhxAk2aNDFI3fUZoWMfPmXl+QUov3KTtVzpqdNw8PbibYOYjqn/MslFEAab/QC0901c9ZnTuMZQetGxY0ccOnQIYWFhWLVqFdzc3DBz5kyD1F1fYXv+0bf/Kf0zgbNs6Z+n4DBjokZdhtAb9frMkfj4eDx48AA+Pj4YOnSoqcUxCdeuXcO6detQVFSETp06YdWqVWjRooXe9QoZc5qrfhAIBAKBQDAPSCAXgUDgJDcvHx9/vh2uzq2w+NVZqJbJNI7LFc+XNpHL5aiWyUABEIvFWutVKBS48PfzQK6unX1hY21tEHmrqqo0graYkEprT5MsrWYuM3pEJGQyOb765gdUSaW4dec+bt2p/XW6h7sbPl2/Bs2b2esmuJ5YWFggKCjIJG0TCELJzMzEt99+2+inbycQCA0PoQ4qtrJszi76C2E2pzUf5xVXPq5tY6FLkAW9fH2ELYBCfZ9+bkxpbPqjPKZNBvq2EGcVl64Y8r74+vri8OHDjd7pqU1nhNbDpDNlCac5y5YmnobV1PEabeqiM8b4v9XXlvCpv6HDFnihTyCGer3a2hXSl7HpkrH6MAsLC/z2228YPHgwzp8/j9jYWBw8eBAWFo3v9SpXn6WPzijrKko8DXD8L5ecTIbjnGmq/Gy/5jruaQywBeRw6QWbDTfEuEaonvBpSx8GDBiAX3/9FWPGjMHLL78MZ2dnDBs2zGjtmSPaxr769j+yqioUn2APTgeAoj9PosXMSaoybL+6jGXM1ZaUlZXhhx9+AADMnTvXbOU0JkePHsWWLVsgk8kQHh6O+fPnw9LSUlAdQsacBw4cwPTp01X777zzDl5//XVB7REIBAKhflBRUYFWrVqp9v39/XH8+HETSkSozzS+Nw0EQgOnb6/usH+xtJ9P+3Z61/fPlasoLilBcUkJomJnsOZbu+FTYMOnGDooGGveXKK13sdpT1FSWgorKwn6+/fUW04lIyLCUFpeLricq3Mr1mMxo6IQNMAfew/F4fLV63j85CkUCsDW1hrdu/hhRGQYenbzg0hEVqslEPiwd+9eHDhwAFKpVPCLEgKBQDA32BwQ6tu6ODHZ6mbLx7XNx3HJxxlOMA5MzklD6BC9Lq486tu6BG4xyWUM3aE7PVu1aoWoqCiDt1Of4BNMIaSu6rxnKLvMPgsO8CKAYtoEwTqjzTlubHsjxOHW2Gwf07URokNsQRpsddPLMu3roldM+4amSZMmOHLkCAICAnDs2DHMmTMHO3bsMGqb5oq+9odLb0oTuIMvKu6mojo9AxL3miXBdO23SBCX4WAbu+rSHwm1JertqW+bcx8VHR2NLVu2YO7cuYiNjcXJkyfh7+9v8HbMGb7PUtrKM+lM5blLkBeXcrZfefcRqh4+grW3l0aduvzS5TJXfv75ZxQWFiIkJAR+fn6mFqdOqa6uxpYtW3Ds2DGIRCK8+uqrGDlypNHblUqlKCkpUe1XVlYavU0CgUAgmAaFQoHi4mLVfmkp91iEQOCCBHIRCA2MqPDBiAofbLD6XJ2dER4Wynr8yrUbyMzKQbcuneDi3ApdO3XkVW9bD3f8dWCXocRU8cqsqQavEwCcW7XEa7OnGaVuAqGxsWHDBuTn5yMuLg7R0dGmFodAIBD0xpjBXHzb59rm66gydRCXkCALern6jr5OLH3apW8LDfira+dVdHQ0tm7dijlz5iA2NhYnTpxAv379jNaeOcLlKFc/rovulGqZBQcAyq/ehiwnF5YvPoYxd52htyMkEKAxwRXMpb5t6H5NaCCXtjSuug2Fk5MT4uPjERAQgJ07d8LV1RUffPCBUdoyd7iCuQCwBuNw3Rt5cQlKLlzW2nbxySQ4TZ+k0TbXLz0f1zZBd0xlS5R107fN2ZYAwMsvv4ynT5/iP//5D4YPH47Tp0+jffv2RmvPHOETAKiLzhT+cZJX+yV/nITNazUfQ+uqO/Rj5kh6ejoOHjwIiUSC2bNnm1qcOqWwsBDvv/8+rl27Bjs7O7z11lvo0aOHqcVixVjPfwQCgUAgEOoPJJCLQCBw0qVzR3TpzB6c9Z8NnyEzKwfjRkZhUEhAHUpGIBDqI9nZ2UhLSwMAHDx4kARyEQiEBoO2QBw6XC9mmcqyBR5oc4CzpbHlYds3JfPnz8fOnTtV+8nJyWb90l1XdHFiCalHW9tc20Kc3nWhO7Nnz0Z6ejrWrFmD4cOHIyUlBR078vugpKEgxFEO8Lc5xSeSebVfcioFLSaMVZVXr4stjb7NlaYPXMFav/zyC+bMmaPaX79+PRYuXMi73oaOUL1Som2JNL7XTlddMoUt8vLywrFjxxAaGooNGzbA1dUVixYtMmqb5oq2MRCb/WHTm6Kk04BMrrXdkhPJaDljsqqceh1Ctpn2CfpBbIkw1q5di/T0dGzfvh0RERE4c+aMxpI8jQFD60x1WRmKT53h1XZh/Em0mj9HVVa9HrY0+jbTvjmyfft2VFdXY9KkSWjZsqWpxakz7t+/j/feew85OTnw9PTEmjVr4OLiYmqxCAQCgUAgEDgh64ARCAQCgUCoM/bv36/aPnjwIORy7S/oCQQCob5AUVStl/tcL/7VnQPa/kQiEeMfPQ9XHfS2mORi2jc1FRUVKCsrU/3JZDJTi2Q06PdGmUbf1qY/2nRB6B9bm0zy1xWrV6/GvHnzkJeXh8jISGRkZNRZ2+YCm75ou2dsOiMvKUHJhX95tV18IllvnWHTI2MilUo17ElVVVUtmRo7uuiVIf7Y7Baf9rXJbyx69OiB/fv3QyKRYOnSpdi9e3edtGuO8NEZvnpTwjOgtOzqbVTn5Aq2P+Y+7mkosI0T6ostYTsHY7FlyxZER0fjwYMHiIqK0liGrbFgSJ0pSzwNRUVVrfqYqErLRMW1m7zHNEyy1gc78s8//+DcuXNwdHREbGysqcWpMxITE7FixQrk5OQgICAAn3zyidkEcWmbJVaX2aoJBAKBQCA0HMiMXAQCgUAgEOqMPXv2qLazs7ORkpKC4OBgE0pEIBAIhoei+M3OJWRWJb7tsu3zdU6Z2gkh9LxNLa+xYNIhgHl2LkO0xZWmzVFlynvwxRdfIDMzE/v378ewYcOQmJgIe3t7k8ljKtR1Qj2NaWY/rvtVkshvFhwAKLl0BfKCQogdmmu0Qd/ms08wT3TRK7qOCbHpQvXGXPRq8ODB+OGHHzBp0iRMnz4dLVu2xKBBg0wii6nRpjP0PEzbsvJyFJ2+yLvN4pNJcJwYo1E3vS22fbY0guGgj3vV0+vKltDTzNWWiMVi/PLLLwgLC8PZs2cRExODw4cPw9LS0iTymApD6UzhsROC2i2MOw7bbn61ZGgodkQmk2Hr1q0AgFmzZsHa2trEEhkfhUKBH374Ab/++isAYMqUKZg8ebJZ3K+4uDj07t0bQI3+WFlZYfDgwbXy8pmR8NGjR7hx40atMkzQ6+nfvz8cHBz4C/+CyspKnDhxQmt7TO16enqiU6dOgtsEgLNnzyI/P19wu9bW1jqPz4RcX3q7AwYMQPPmzQW3WVFRgZMn+S0PS6ddu3Y6z1atfn2FYGNjg4EDB+rU5p07d3D//n2dyoaGhsLW1lZwuZKSEiQlJenUZvv27eHj46NT2ZSUFBQVFQkuZ2tri9DQUJ3aTE1Nxc2bN3UqGxERAZFI+HxAubm5uHDhgk5tdunSBR4eHjqVPX78eK0PtfjQokUL9OvXT6c2b968idTUVI208vJyneoiEJgggVwEAkEvxo8ZgYFB/dGpo26DFwKB0HjIy8ur9RC6b98+EshFIBAaJFyOS7Y0vkFe2toVsq8tnWA6mJyWbE4s9X1lmpB7yscpZY6OK5FIhJ9//hnh4eFISkrC6NGjERcXB4lEYlK5TIU2u6NNZ/guqwgAkCtQfCoFDmNG1GqPbZ8tjWDejhSKhQAAIABJREFUCNEr+v1tLLZowoQJyMzMxJIlSzB69GgkJiaie/fuJpXJlLDpDKBdb8pSzkFRJeXdVvHxJDhNGsfYltA0gnEhtkQ7tra2OHz4MAIDA/HHH39g9uzZ2Llzp8nlMhW66oyssAhFp4U5kAv+SIDrigWgxGKNuulta0szV44ePYrHjx/D19e3UQQbl5WV4cMPP8T58+dhbW2NFStWICAgwNRiqThz5gxGjBihkdayZUtkZWWxluEKHtq3bx+WLVumkywJCQm8A0TUZcjNzUVUVJRObS5atAibNm0SXE6hUGDJkiU4d+6c4LLOzs7IyMjQ6f9Wn+ubkpKCAQMGCG43JydH5+u7YsUK/Pe//9Wp7NKlS3H27FnB5dzd3fHkyROd2vzuu++wceNGncrevn0bHTp0EFwuNTVV5+u7evVqvPfeezqVXbBgAf79l98M2Op4enri4cOHOrW5d+9erFixQqeypaWlOgXKXbx4Uefru3XrVsydO1enshMmTEBeXp7gcsHBwUhMTNSpza1bt+pkzwgEvpClFQkEgl506tgeoUH90aqlo6lFIRAIZs7+/ftrLYe1d+9eE0lDIBAIxoei2Jd8on/pzZUm5E9bfWxyEswTLh2i77PpAF+9oZcTIoMpsba2xsGDB9GlSxecPHkS06ZNa9TLkAjVGSXyigoUpQhzehYeT6yXOkMQjq561Zhs0eLFi/H666+jqKgIw4YNw6NHj0wtkklhuzfa7nXBcWFOlOJLVyArKOSlQ+rHCKaB2BLtODo6Ii4uDq6urvjf//6Ht956y9QimRRddKbwz5O8ZxhVUp2bj7KL//B+hjIXfeFDcXExfvzxRwDAq6++amJpjI9UKsXy5ctx/vx5uLi44JNPPqnTIK76+ByiUCh4/ZmiTUO339jaJRAIBEL9hgRyEQgEAoFAqBPUl1VU8uTJE1y8yH/pDAKBQKiPaHNmanNU8ambLT9XHebktCJww3VvudIN6fhUP2ZuNG/eHMeOHYOHhwd2796NJUuWmFokk8NXZ5T7pclnBc2CAwDFZy5BXlLKWrd6G4SGgT59mSFtkbn2Xxs3bsS0adOQkZGBiIgI5Obmmlokk6NtDKJ+XCGVoijxjLAG5AoUJdQsjUPGPfUDQ9gSXfPwkcXUeHp64ujRo7C3t8fGjRvx+eefm1okkyNEZwqOHdepjXy1ctqe3+oTP/30E4qLixEWFqbTzDX1DUtLSwwePBjdu3fHZ599Bk9PT6O0QwJtCAQCgUAgGBuytCKBQCAQCASjk5+fj+PHmV+m7d27F3369KljiQgEAqHu0XBWMiyZx+eYkDZ0OW5qhL4Q13UJyvqK8ly16YgujoX6rjvu7u6Ii4tDUFAQNm/eDFdXV6xcudLUYpkcPjqjUCgEz4IDAIpqGYqTTqN5VDhn23WNPo61xmZTdIFNp9SPsR0XUr/QY+YARVH49ttvkZ2djfj4eIwYMQInTpzQaUmShoQ2vVAeLz13CfLSCsH1F/2VBMexI7W2TTAv9LUl+o5bzF03evTogf3792PYsGFYsmQJXFxcEBsba2qxTAofnZFmZaPk0lWd6i/86xTc31kOEcMS3eauL2w8fvwYhw8fhpWVFWbOnGlqceqM2NhYjB07FuIXS2WaGl9fX7z55puceZo2barzONTf319r/Wy0adNGp3J2dnY6txkUFKRTOQCYMWMGBg4cKLicvb29zm3qc31bt26tUzl7e3vBbSp1JyQkRKc2Ad2vb/PmzXVuc9CgQTrb2BYtWuhUrmXLljq/K9Dn+s6aNQvp6emCy+l6ngDQr18/nc/V0tJSp3Le3t46t9mzZ0+dygHAkiVLUFpaKricl5eXzm0OHToUNjY2nHl0tbMEAgBQChI6TiAQCAQCwcjs2LEDL730EuMxHx8f3Llzp44lIhAIBPOgLh7H6pPjgel6zJ49G99//71q/8KFC+jdu7dqvz6dn6Extv7Ux2t7+vTp/2fvvsOaut44gH/D3qCICtY9655V3KMqirPaWrXVtiq17mrVWtuqtcO9WrVaq7i3qKiAGxWx4hZxIIIKiBtkk/X7wx/ITUJIQuAyvp/n8Sn33HPPeZPcnN7kvjkHH374IVJTU+Ht7Y3hw4eLHVKhoumcUWRkILR9L4MSKEp1bY/KS37N2i4M54y298XmzZsF58SCBQswZcoUQZ2inEgkFo5FQsnJyejUqRNCQkLg6emJ/fv3w8yMv6VVpXrePP75d7z08de7HYmZKRqcPQQTO9t3ZUXsnKG3OJao27FjB4YMGQILCwsEBASgQ4cOYodUqKieM883bkfMotUGt1dt+a9w6Pz2Jn1RPF9U/fTTT7h8+TKGDRuGTz/9VOxwihVN45WxxrC8nnva4jD2ea3PYzZm3yWt38LcJxER5R8urUhERET5TtOyipnCw8Nx69atAoyGiKjw0LbUS2FsNz8Z+sV3Sf5tUn68zkXx3MmudevW2L59O0xNTTFy5Ej4++ufFFCcaXp9k4JDDEriAoD4sxegTM8oMueMLuNFSR5TDMWxSMjW1haHDx9GjRo1cPjwYXh5eYkdUqGU/TVWyuWIPxVkUDtKmRxvzpwv0ucMvcWxRN2nn36KRYsWIT09Hf369UNoaKjYIRUqqq/v68PH8tTea7/jRfp8yS4kJASXL19G2bJl8dFHH4kdDunBkFmqs//TtW5eGNIG+81//BxDRFS8MJGLiIiI8lVCQgKOHj2qtc7evXsLKBoiosIr+40IfW46GXIMFT/6nj+Gnm9FQd++fbFq1SrIZDIMHDgQISEhYodUKGW+5gkGLKuYSZmWgcSgC0aMKv/wxkbB4Fj0louLCwICAlCuXDls2LABM2fOFDukQi3l8jXI4xMNPj7heKARo6HCQN9xobiOJQAwefJkTJ48GfHx8fDw8MDjx4/FDqlQSn/4GCm37+epjfhTQVAYsCxTYSOTybB27VoAb2c3ttCwXCQRERERFW5M5CIiIqJ8dfDgQUilUq119u3bV0DREBEVPTndmCpON6jE+IVsSVGczxttvLy8MGvWLCQnJ8PT0xPh4eFih1QoKWUyxJ86l6c24o+dNk4wRmCscSCndjjOGK4kjkXVqlXDkSNHYG9vj99//x0rV64UO6RCKz6PiVgJZy5AkZZupGiosCru18PaLFq0CIMHD0ZMTAw8PDzw+vVrsUMqdOKP5G02LgBQpkuRcNLwBPfCIjk5GRUqVED9+vXRrl07scMxquDgYOzevVvsMPSmz6xZ2esboy1dYtKnblGd3aoo9at6rLGeeyIiKlqYyEVERET5StuyipmuX7+OBw8eFEA0REREVFLMnj0bXl5eeP78Obp3746nT5+KHVKhk3Tpap5mwQGAhMDzUEhlRooofxhy04M3SsgYmjZtin379sHc3BwTJkzgTMQaKJVKxOdhZkAAUKQXndkBiQwhkUjg7e2Nzp07IywsDH369EFammHLIhdXr44cN0o7r43UjpgcHR0xe/Zs/PLLL2KHYjQZGRn466+/MHfuXHh7eyM6OlrskAS0XTdq2qfrdWZBJfJo6kfsPvOr/8LSb17q6NIPEREVfUzkIiIionyTmJiIgIAAnerypgYREREZ26pVq9C3b19ERkaiR48eSEzMW9JScZOXZRUzyRNTkPTfJSNEQ1Q8ffjhh/D29oZSqcTQoUMRGMhlALNLuREK6bNXeW4nr7N6ERV2FhYW8PHxQaNGjXDu3DkMGTIECoVC7LAKhdSwO0iPMk5iT8L5EMhexxulLbFZWVmJHYJRPHz4EBMnTsSRI0dgZ2eHH374Ae+9955o8eT3DLBUMuQlcYznDhFRycBELiIiIso3hw4dQnq6bktccHlFIqKSyZhfQvILTVJlamqK7du3o02bNrh69Sr69++PjIwMscMqFJRKJeKPG2f5oITjp43STl7kx3KIeZlBgSi7IUOGYNGiRUhPT0ffvn1x8+ZNsUMqNBKOGScBK+F0UKGfHZAorxwcHODn54cqVarAx8cH48ePFzukQsGos2jJlYg/etJ47VGeHD58GBMnTsTDhw9Rr149rFy5Em3atBE1ppKwnKuhxFoCsKT1m9k3EREVb0zkIiIionyjy7KKmS5cuICYmJh8jIaIiIhKImtra/j6+qJu3bo4ceIEhg8fzi++AaRcvwnp87zPggMAr4+fgbKIzgrCc4EKyuTJkzFlyhQkJCTAw8MDjx49EjukQuG1kWbS4uyAVFK4urrC398fzs7OWLVqFX7//XexQxKVUqHAqyPHjNrm68PGbY/0l5iYiLlz52LlypWQyWT47LPPMG/ePLi4uIgdWpFSUNe5mpKZCiLBqaT1S0REJYuZ2AEQERFR8ZScnIwjR47odYyPjw/GjRuXTxEREVFRNHHiRPTr1y/rl881atQQOSIqikqVKgV/f3+0bt0aO3bsQPny5bF06VKxwxJVgpFm4wIAeXwikq9ch13zJkZrMz907twZBw4cyLrJUrduXQDvbnJxhgXKbwsXLkRcXBy2bt2K7t27IygoCKVLlxY7LNGk3rmHjOg4o7WXcCIQDm1bGa09osKqdu3a8PX1RZcuXTBz5ky4ubnhiy++EDssUSRfuW6U5VmzS7oSioy4p7AoX86o7ZJubt68iQULFuDly5dwcXHBtGnTUK9ePbHD0koikeSYxKNtX37K7JPXuUREREWTRMkUYSIiIsoHu3btwqBBg/Q6plOnTjh5klPYExGVFLp8HM1eR9cvn/klNeXk1q1baNu2LeLj47FgwQJMnTpV7JBEI09NA2TalyGLXbYaLw8dRZW5M2Dv/oHWuhIrS5iYmxszRL3oswyithtthpYR6UoqlcLT0xPHjh2Du7s7Tpw4AWtra7HDEoVCKoMyLU1rneQbt/Bg8o9w6tgaFX/MZcw2NYWpTcl8Lqlk8vX1Rf/+/SGRSHDw4EH06NFD7JAKnEIqhTItPdd6zzZsxdOte1DxuzFw8uiaa30TaytIzDgPQkGSy+XYunUrdu7cCaVSiTZt2mDixImws7MTOzQ1hiy/rbo/v68nC7I/bY+d/eYfbX3x8woRUdHHK1EiIiLKF/osq5gpMDAQL168QJkyZfIhIiIiKmo0LVfALyQpL+rVq4eDBw+iW7dumD59OlxdXfHZZ5+JHZYoTK2tcq+kUECZnAYTc3OY2he+m2iG0HbjRdMYw3GHjM3c3Bz79u1Dx44dERwcjEGDBsHHxwempqZih1bgTMzNAHPtY4uJpQWUyWmAXFFsxiEiY+nduzdWr14NLy8vfPzxxzh16hRatGghdlgFysTcHNAxkVyZnAaJqRnHkkLo6dOnWLBgAW7fvg1LS0t4eXkVu8TEgr6ezJwJjNexRERERZOJ2AEQERFR8ZOamorDhw/rfZxCocCBAwfyISIiIiKit9q1a4dt27ZBIpHgq6++wtGjR8UOiYhKGDs7Oxw5cgTVq1eHr68vRo8eLXZIRFREjRo1CrNmzUJycjI8PT0RHh4udkhUQNLS0vDixQuxw8izM2fOYNy4cbh9+zaqVKmC5cuXF8kkrsKYMFVQMUkkErW+NJWxXyIiIt0xkYuIiIiMzs/PDykpKQYdu2/fPiNHQ0RERCTUv39/rFy5ElKpFAMGDMClS5fEDomISpiyZcvC398fZcuWxbp16/Dzzz+LHRIRFVGzZ8+Gl5cXnj9/Dg8PDzx9+lTskKgA7Nq1C6NGjcLZs2fFDsUgaWlpWLZsGebNm4fk5GT07t0by5cvR6VKlcQOLVdM2NEsM5lJjNnHSlK/mX0TEVHxxkQuIiIiMjpDllXMdOzYMbx588aI0RARUVGl6VeuRMYyevRo/PTTT0hKSoKnpyfu378vdkhUALSNI5r2cdyh/FSjRg0cOXIEdnZ2mDt3LlavXi12SERURK1atQp9+vTBgwcP4OnpiaSkJLFDonz09OlT7N27F3K5HDVq1BA7HL1FRERgwoQJOHr0KBwcHDBr1ix88803MNdxmczCyhjXjdmTgzT9o+IhL68tzwMiopKBiVxERERkVGlpafD19TX4eKlUikOHDhkxIiIiKqx0+QIys46uX1byS03Sxy+//IKRI0fi2bNn6N69O2ewKGY4HlBR0KxZM+zduxfm5uYYN24cfHx8xA6JiIogU1NT7NixA+7u7rh8+TIGDBgAqVQqdliUT9avXw+pVIp+/frB1dVV7HD0tn79ekRHR6NRo0ZYuXIlWrZsKXZIesvpOtPQhCtdj8uPhC4xksdy6zO/+i8s/ealji79EBFR0cdELiIiIjKqgICAPP/yk8srEhFRdvwikvLT33//jd69e+PBgwfo2bMnZ7AoAVRvcPCGB4mtW7duWL9+PZRKJYYMGVJkl8kiInFZW1vD19cXtWvXxtGjRzFixAgolUqxwyIjCw0NxdmzZ+Hk5IRPP/1U7HAMMmnSJIwYMQK//fYbnJ2dxQ7HYLnN9KotKSevSUN5vX7Vp29jJjgZ0kZJ61f1WM7IRkRUMjGRi4iIiIwqL8sqZvLz80NqaqoRoiEiIiLSztTUFDt37oS7uzuuXLmCjz76iDNYlBC8IUKFyWeffYb58+cjLS0Nffr0QWhoqNghEVER5OzsjICAALi6umLz5s2YMWOG2CGRESkUCqxZswYAMHz4cNjY2IgckWFcXFwwYMAAmJgU/VuU+swcnR8JOfq0y2SsotWvvvi5hoioeCn6V0lERERUaGRkZODgwYN5biclJQX+/v5GiIiIiAo7Y3+JTWQIa2trHDp0CHXq1MGxY8fw5ZdfcgaLIsbQ5Uf0bZPjDOWnqVOn4ttvv0V8fDx69OiBx48fix0SERVBlStXhp+fHxwcHDB//nz8+eefYodERnL8+HFERESgWrVq6Nq1q9jh0P8VpuvDglwisbAsU1jc+9U1NiIiKl6YyEVERERGc+zYMbx588YobXF5RSIiIipIpUuXRkBAANzc3LB161ZMnTpV7JCIqARavHgxPv30U0RHR8PDwwOvX78WOyQiKoIaNWoEHx8fWFhYYNKkSdi9e7fYIVEepaSkwNvbGwAwevToYjGbVXHCRBoSA2cYJiIqvnilR0REREZjjGUVMx08eJDLGhEREVGBqlSpEvz9/eHk5ITFixdjyZIlYodERmDIzQ3eECGxSCQSbNy4ER9++CHCwsLQu3dvLjtPRAbp3LkzNm7cCKVSic8//xyBgYFih0R5sGPHDsTHx6Nt27aoX7++2OHkKi0tDZGRkWKHUaCMuYxfYZjliQovnhNERMWfmdgBEBERUfEhkUjQt29frXVevXqF9PR0ODs7w8LCQmvdiIgI1KlTx5ghEhFRISSRSPK0jB2/wCRjatCgAQ4cOIBu3brhu+++Q/ny5TFkyBCxwyIdaBtL9Bln8mOZRiJ9WFhYYN++fejQoQOCgoIwePBg7N27F6ampmKHRkRFzKefforY2FhMmTIF/fr1w9mzZ4tEEhAJPXnyBPv374e5uTlGjBghdji5un//PubPn4+UlBSsWrUKjo6OYodUoHK6Zsx+LZrXHxoYaxl4XeMw5rLzhjx2Y/RflPolIiJiIhcREREZzfr163Ot069fPxw4cADBwcFo1apVAURFREREpJ/27dtj69at+OSTT/DFF1/AxcUFXbt2FTssIipB7O3t4efnh9atW+PAgQMYM2YM1qxZI3ZYRFQETZ48GTExMViyZAk8PDwQHByMihUrih0W6WHdunWQyWQYNGgQypUrJ3Y4OVIqlfDx8YG3tzdkMhmaNGkChUIhdliFhjETenRJFjNm/4b0Z4x+c2qjuPdLRETERC4iIiIiIiISnaGzcvFLUsovAwYMwIoVKzBu3DgMGDAAp0+fRtOmTcUOi/JAl3GGYwoVJuXKlYO/vz/atGmDtWvXwtXVFbNnzxY7LCIqghYtWoQnT55g+/bt8PDwwLlz51CqVCmxwyIdXL9+HcHBwShdujQGDRokdjg5io+Px+LFi3H58mWYmpriyy+/xMCBA3ltVcAK+vkW6/Utaf0a04uXr5CSIly2+70KrjAxMcnaTk5OwctXrwV1Spd2gp2tbYHEmJuY2DjI5fKsbXNzc7iWLytiRERExsdELiIiIiIiIiq0bt++jdjY2KztDz74APb29iJGRCXJ2LFjERsbi99//x09e/bE+fPnUa1aNbHDIi20JWvFxcUhNDQ0a7tmzZqoVKmSzu0SiaFmzZo4fPgwOnXqhDlz5sDNzQ1eXl5ih0VERYxEIoG3tzeePn2KkydPok+fPjh27BisrKzEDo20kMvlWbMxfvnll4X29bpy5QoWLVqE+Ph4uLq6Yvr06ahVq5bYYVEOFAqFIGmHSpY/13jj5JkgQVnAvi2wsbHO2r5w6Qpm/7FUUGfmd+Ph8WHHgggxV+Om/oQXL19lbdeoVgUbVi0WMSIiIuNjIhcREREREREVCpoSMBYtWoQNGzZkbYeEhKBZs2ZZ9Yny22+//YYnT55gw4YN6N69O86fPw8XFxexwyIDHDt2DMOHD8/aXrBgAaZMmZK1zTGFCqsWLVpgz5496N27N8aMGYNy5cqhb9++YodFREWMhYUFfHx80KFDB5w7dw5DhgzBnj17mNBRiPn7+yMqKgq1a9dG586dxQ5HjUwmg7e3N/bt2wcA6Ny5M8aOHQtra+tcjqT8MnTkBCSnJMPc3BwSiQlkMikypDLIpDJIpVJIZTIolUpYW1uhlJNj1r+a1aqirXsL1KxeldfExZzERP31NWR2dCIiyl9M5CIiIiIiIiIi0mLt2rV49uwZDh8+jJ49e+L06dOwLSTLShBRyeDh4YF///0Xw4cPx+DBg3Hs2DG0adNG7LCIqIhxcHCAn58f3N3d4ePjg3HjxmHVqlVih0UaJCUlYfPmzQCAr7/+utAl18TExGD+/Pm4f/8+rK2tMW7cOHTq1EnssEq8N4mJiE94k2u91NQ0pKamIfbJUwDAueAQbNi6C9WrVsacHyajcsX38jtUEomm5F0lCkci14OoR1j979txr4JbeUz6ZoTIEZGuDhw+inMXQgAAPbt2Qqf2rUWOiKjoYyIXEREREREREZEWZmZm2LVrF7p06YILFy5gwIAB8PX1hbm5udihEVEJMmzYMDx58gTff/89evfujXPnzqFu3bpih0VERUz58uUREBCA1q1bY/Xq1ahQoQJmzpwpdlikYtu2bXjz5g06duyIOnXqiB2OwPHjx7Fq1SqkpaWhVq1amD59OlxdXcUOi4wgIvIhRo6bhiW//4QG9d4XOxzKByYakkJVJ+SqX7cO5s78TlBWp1b1/AwLAHDs5BlcCLkCAKhVo2qO9aZOGI2MjIysbTtbm3yPjXKmVCqxfc8BxDyJAwA0qs+xg8gYmMhFRERERERERJQLGxsbHDp0CG3atEFAQAC++uorbNq0qdDNjkBExdv06dPx5MkTLF++HB4eHjh//jzee4+zZhCRfmrVqoVDhw6hS5cu+PHHH+Hm5oYvv/xS7LDo/6Kjo+Hr6wtLS0t89dVXYoeTJSUlBX/99RdOnz4NABg4cCCGDRsGMzPeaizMmjaq//YPiQRyufztbFxpaUhJSUViUrIgIQYA0tLTscZ7G/5aOFeEaCm/aVxOVyWTq5xLGZRzKVNAEWWGoMTJs+d1qtu6ZbN8job0ER4RmZXERUTGw6srIiIiIiIiIiIdODs7IyAgAO7u7tiyZQtcXV2xYMECscMiohJm6dKliIuLw86dO+Hh4YFz587ByclJ7LCIqIhp1aoVduzYgf79+8PLywvlypVDz549xQ6LAKSnp6Ny5cpo3bo1ypQp2GSKnNy5cwcLFixAXFwcSpUqhalTp6Jx48Zih0W5MDExwfL5c3Lcn5Kain82bsee/YcF5ddvhuFueARq18z/WZioYJmaFs6lFcMjIrOW+qSi5fTZYLFDICqWmMhFRERERERERKSjypUrw9/fH+3bt8fChQvh5uaGSZMmiR0WEZUgEokEmzZtwrNnz3Dq1Cn06dMHR48ehZWVldihEVER07t3b/z9998YNWoUPvnkE5w6dQotWrQQO6wSr3r16lixYgXkcrnYoUChUGD37t3YvHkzFAoFWrRogcmTJ8PR0VHs0MgIbKytMXH0V7j/IArXbtwS7IuOeZJrIteLl68RGBSM8/9dRnzCG6Snp8PCwgK2tjZo2rAeOrR1R9XKFTXOYnz77n1cvnZDUFa3Tk00bdRAY19vEhNx8MgxQZm1lRX69/bQPMtUDgLPXcDjmNisbXs7O/T17AYAiIx6hCPHTuHWnXtISEiEubkZalavio5tW6F1y+YaH0d6RgZ2+xwSlLl/0AzVq1bGm8RErNu0A+ERUcjIyMD4r79A4wb11NpISk7GueAQBAZdwPMXr5CamgYzM1NUqVQRDevXQY8PO8HGxjrXxyaTy3H81FlcuHQVkQ8fQyqV4j238mjTsgU8PbrAzNQUEomGRC6VPK6oR9E4F3xR42PS5nFMLK7fDMP9iChEPo6Go4M93nMtj6pVKqJTu9Zqs/elp6dj9/+TCK+H3hbse/kqHlt27sva7tmtM0qXevvDhX0H/ZCSmpq1r5STIzy7d9Ea27PnL3H6XDAuhFxBwptEpKWlw9LSAna2tmjepAE6tG2FyhW1z3K7Y+8ByGTvxuXGDeqhft3aAIBHj2Nw7NRZXLp2E4mJibCwsEDTRg3Qv1d3VHArr7Xd3GzbvR8KhSJru0nD+qj3fi2N/VpavuvXzTX3fpVKJe4/iMLJwCBcv3UbySmpkEllcHS0R52a1dGpnbvGZVbv3X+Ai5evAQACTgQK9l3NNpbY2dqiX6/uBj1uopKOiVxERERERERUKChVvz3UoT6XtSMxNGzYEPv374eHhwcmT54MV1dXDBo0SOywCPqPI6rHckyhosLCwgL79+9H+/btcfbsWQwZMgR79uzR60YmEREAjBw5EjE+zpqUAAAgAElEQVQxMZg9ezY8PT0RFBSEmjVrih1WiWdiYlIoxvT58+fj7NmzMDMzw6hRo9C3b1+xQ6J80OD92mqJXNoSCRPeJGLFmg04qpLAkd21G7ewfssu1K1TE9MmfqOWBFS5YgXMnrdEMAuTnZ0tNq9ZjjLOpdTa+2vtRvgdOyUomzF5rN7vk6OnzuBM0H9Z26VLOaGvZzds2+WDNd7bBAkzABAR+RD+x0+jZfMm+PXHqbCyshTsl0qlWLNhq6DMydEBtjbWmDB9Np7EvXt8j6OfCBK55HI5du49iH8370SGVKoW64OoRzh5JgjrNu3A9InfoGM79xwfV9jdcMxfthoPIh8Kyh9HxyL44hUcDzyH2d9/q/H5Uv0MFREZpfaYyjiXzjGR69nzl1i3aTv8j5/O8fPY+s278PWXQ9Ghbausz1xp6Rlq/WR6+eq1YF/L5k2yErk279yHFy9fZe2rUa1Kjolcr17HY9nqf3HqTM7LNl69EYp/Nm5Ho/p1MW3iaFSqWEFjvXWbdiA9/d1SpCM+H4T6dWvjwOGjWP73v5BKZYL64RGR2LnvIL7/dkyuiWba/Ltph+D8GDV8MOq9Xwv7D/lj+d8bIJMJ+713PxI79h7EjMlj0bNb5xzbfRwdi9+XrERo2B31ndHAzVt3sHv/YXzQrDFmfT8JDvb2WbvD7oTn+NpdvHwtK8nLzbUcE7mIDCT+VSARERERERERURHTsWNHbN68GRKJBMOGDcPJkyfFDonyUV4SxIjyi4ODA/z8/FClShX4+Phg7NixYodEREXUrFmz4OXlhefPn8PDwwNPn3J5K3qrU6dOqFixIpYtW8YkrmIs7tlztbLq1aporPs6PgFeE6drTeLKLuxOOEZNmIYr128Kym1srDHr+29hmi2xKCkpGYv/Wqt27R1y5bpaEteHHduhR9dOOsWQnQTCH24kJLzB8VNnsXr9FrUkruz+u3QVvy/5S63cRMMMV+npGVi9fosgiUuTpav+xer1WzQmcWWXlJSMn35bhBOB5zTuf5OYiO9n/aGWxJXdtRu3MGnGHMjlMvWdefiscy44BIO/Ggu/Y6e0fmaKjn2Cn35bhI3bdhvcl76ePX+JkeOnak3iyu56aBhGjJuK0LC7GvervtapqWk4/98lLPpzjVoSV3YLV6xB2J17ugeuSuXHRqmpaTh7/iIW//WPWhKXar+3797XuC8mNg5fT5qhOYlLxcXL1zDuu5+Qnp6uX9xElCeckYuIiIiIiIhEZ2iSBGfQITF9/PHHiIuLw4QJE9C/f38EBgaicePGYodFOdBlnOGYQkWNq6srAgIC0KZNG/z9999wc3PDTz/9JHZYRFQErVq1CnFxcTh48CA8PT1x+vRp2NnZiR0WiaxVq1Zo0aIFTE1NxQ6F8snZ8xdxUiXRpXrVyhpnX0pNS8O0n38TzKIFAA72dhjYtyfKlXVBaloafP2OIyJbUpFUKsP3s+dh6z8r4FLGOau8bu2aGDHsU6z13pZVdi74Ik4GBqFLx7ZZfS5YvlrQXwXX8pg64WvDrttVDpErFFj051oAQOuWzdG2VQvIZDIcOHJU8BgA4NSZ87jW20O4PKKGEC5fv4mz5y+q78hm/6EAHDgcICirXrUyvhjyMVzLl0VE5ENs3rkP0TFPsvYvXbkOzRo3hJOjg+C4FX9vwOv4BEGZubkZPv2oD96r4Ir4hDfYvf8woh4+VnvtAMDQNK7bd+9j1h9LBIlo2fuNffIUhwKO4+Wr+Kz967fsQuMG9dC4YT2Ym5mha+f2AIDg/y4hKTklq56dnS3cP2iWtW1vZ6tXbEnJyZj60694/uKVoLyUkyMG9OkBlzLOSE5OwYEjR/HwcUzW/rT0dEz9+TdsX/cnnJxUlpBVea0fPHwMv+OnAQDv164J13JlERsXhzv3IgT15HI5duzzxS8/TNHrMWR1q9JvRNQj+AacAPB2OdLyZcsi5kkc7oYL+5XJZNix7yDmzJgsKE9JTcX0Wb8jMSlJUP7JR73RsW0ryOVynD53AfsO+mV9ho58+Bje2/fg6y+GAgAqVnBF187tkZKSgqALlwTt1KhWBVWrVALw9vkmIsMwkYuIiIiIiIiIyEDjx49HbGws5s2bhx49euD8+fOoWrWq2GGRCs6oRcVZrVq1cOjQIXTu3Bk///wzXF1dMXLkSLHDIqIixtTUFDt27ECXLl0QHByMAQMG4NChQzA3Nxc7NBIZk7iKLoVCgX/+nyQlkUggl8uRkpaGtLR0pKWl49mLl2oz8tjZ2mDW9Eka2zt28oxakkr9unWwaO5M2NraZJX17+WBFWs2YO+BI1llqalp+GfjdvwwZZzg+CEf98N/l6/h+s2wrLIlK/9Bk0b1UbqUE/7ZuB1xT9/NGGZqaoo5P0yGjY21ns/GW5qSv5JTUjDe6wt83L9X1v4+PbvijyUrEaAy89jOfb6CRC5N7WUmttR7vxZGDRuMav9PipPL3874pVQqsWnHHsExbq7l8OeCX2Bv/zaBtnbN6ujcvg3Gfvcj7t1/AODtkpbe23Zj0jcjso57+DhaLUZLSwus+3MhqlR6L6usf28PTP/5D1y9EaoWryGflRITkzB91u/IyMgQlK9e8gdq16yWtd2vV3eM/vYHPP3/rG9KpRJz5i/D7o2rYWNjjZ+nTQQADP9mMpKyJc65lS+btc8Qh/1P4EHUI0FZsyYN8ces6bC2ssoq+6hPDyxcsQaH/58YBbydAW391l2YPHaU4HjV1/pCyBWYmppi/pwf4P5BU0gkEiiVShw5ehLzlq4S1A3+7zLS0tLVlubUhepMYMEXL8PMzAwL585EqxZNAbx9Xg/5H8eC5X+r9Zueng5Ly3f9ng36T5C8BgDfjPgcQz7ul7XduEE9NGvUAD/8Mj+rbPuu/ejXsxvKlXVBsyYN0axJQzyOiVVL5OrSoQ0+G/SR3o+TiIS4tCIRERERERGJKq8JFkqlkkkaJKo//vgDw4cPR1xcHLp3747nz9WXJqH8Z6xxIKd2OM5QYdayZUvs3r0bZmZmGD16NHx9fcUOiYiKIGtra/j6+qJ27do4evQoRowYwf//ERVxm3bsxaYde7Fx+x5s2eWDfQf9cOToSZw8E6SWxNWxnTvWLJuXNZuOqhOBQYJtExMTTJvwtSCJK7Pc64shcC7tJCg/deY8MjKEywiamprip2kTYZdtxqU3iUlYsvIfhN25h90+hwT1x44chto1q+v24DXQlHhVt05NQRJXZlzfjPgclpYWgrq374YLtk00tKdQKFC7ZnUs/vUnNGvSEKWcHFHKyRFlnEsBAO7df6A2U9TAPj2zkrgyWVlZYtKYEYKykMvXBds3QtWXxuvdo6sgiQsArK2sMPGbr9TqAoZ9zgn675LaLGDNmjQUJHEBQBnn0hiaLUEIAF68fIWwO8Ln0dhUz1VzczNMHf+1IIkLePs6jxn5OexVZqA8cfoc5HK5oEzTaz14QB+0btks69yRSCTw7N4FLZo2EtRLS0/Hg4eP1I7XiYZZ34YM7JuVxJXZb+8eXdG0cQNBvdS0NEQ+fCwoOxscIti2tbHBwL6ean20a/2BYFY0uUKBK9fVEwGJKH8wkYuIiIiIiIiIKI/WrVuHHj16IDw8HL169UJycrLYIdH/GXJjgjetqSjq2bMn/vnnH8jlcgwaNAjBwcFih0RERZCzszMCAgLg6uqKzZs3Y8aMGWKHRAWA1z5UvWpluJUrq5a8kunlq9e4euOWoKx2zeo5Jn3ZWFujZfOmgrK09HRcDw1Tq1vOpQymT/pGUBZ47gKmzfpDUNa6ZXMM7KeecKIPiYasmE8/6qMxwcu5dCnUe7+2oOzlq3g8f/EyW4Oal3cc9ukAtQS3TOeC1ZddVE38yVS3dk1YWLxLJnsUHYP4bAlUobfvqh3TuX1rjW1Vr1oZlStWUCs35O1/VsNj6O3RRWPdTu3cMWfGZMyfMwPL58/GmmV/oLJKopkxxT6Jw+179wVlDeq9jwpu5TXWd7C3R7MmwgSoN4lJCFNJ2tP0Wvfs1lljm53auauVxce/0RZ2jlRn5NLab1sN/Sa86zc9IwP/Xboq2N+oQV1YWGiefbNZo/qC7Ru3bucaLxEZB5dWJCIiIiIiIiLKIzMzM+zevRudO3fGxYsX8fHHH+PgwYMwM+NXL0RUcL744gvExsZi5syZ6NWrF4KCglCnTh2xwyKiIqZy5crw8/ND+/btMX/+fFSoUAHjx48XO6xiKzo6GuXKlRNlGUuFQoFdu3YhLCwMc+bM0ZjMQiVDRORDREQ+xLY9B9CmVXP8PH0SbKzfLV94+eoNtYS/WjWqqTYjULNaFbWyG6FhGpOWOrZ1Ry+PLjjk/26Ju4RsCSguZUrjhynj8n6Oaji8Qb2cr5WqVq6IK9duCsruP4iCSxnn/zen3qCFuTlaNNOcmAUAN26pz6K1aec+WFlqXnZPLpMJtm+G3UG71i0BAHfCI9Tqa0rWylSlckW1ZfUA/TK5ZHI5Ll66plZev67m59HJyRGdO7TRq4+8uHhZPbY6Opyrp88KfwRxI/Q2GmR7TKozctnb2eG9Cq4a26tSuaJaWXxCgoaauVM95R0d7OHmWk5j3aoa+s0+c9qDyEdIS08X7H8UHaO2JGOmSJXlKa+HMpGLqKDw20QiIiIiIiISjTF/+a1UKnnjgURla2uLw4cPo3Xr1vDz88PIkSPh7e0tdlglQn4sh6hpTOE4Q0XBDz/8gCdPnuCvv/5C9+7dERwcDDc3N7HDIqIiplGjRvDx8UGPHj0wadIklC9fHh9//LHYYRU7UqkUs2fPhlwux+LFi1G6dOkC6/vly5dYuHAhbty4AXNzczx48ADVqxu+ZB0VPhKJBEf2bIQEEkgkEiiVSqSmpSEpOQVJSclITknBy1evsXnnPkTHPMk6LujCJUyaPhurlv4OM1NTAEBikvqMwxVySCbJVK5sGbWypJTUHOtPGP0Vrt0ME8SS+ThmTf8Wjg72WvvTheq1vL2dHco45/y+K6thX0rqu8eg6aNB1SqV1Jbwyy41NU2t7NjJMznWVxX58HFWIldycopgn7WVFRzsc36eND1WfT8zSQC1ZCAAKO3kqFc7+UXTuepaPpdz1UXTuSp8blXPHUcH+xw/G2ZPgsykuhSlztT6dci5Xxvt/WY/dzNFxzxRe8/l5HF0LGRyeda4QET5h0srEhERERERUaFlYmICiUQi+EdUmJUpUwYBAQEoX748Nm7cyOWIChHV8cTExITLCFGxtXz5cgwcOBCPHj2Ch4cHEgycAYCISrbOnTtj06ZNUCqV+PzzzxEYGCh2SMXOwYMHERsbCxcXlwJN4rpw4QLGjBmDGzduoGLFili2bBmTuIohiUQCO1tb2NrawMbGGra2NijjXBpVKr2H+nVro2XzJujZrTO8Vy+B+wfNBMfevncf5y9cytpO15C4k1syh6mG/RkZGTnWt7aywvu1aqiVW1pYwKWMcd4fqjNomZho/45BYqJ+K10uV2jdb2WleWatTBlSqdb9uUlLe/daqC6FaWFpoVpdQKFQqJXp+5HI1NQUlir92NnaiDKroCaazjEzM+3nqqaZtDMyVF4nPb6PUjsW6olhulKdCUzfflOyJU/KVGZ3M0R6mvpYQETGxxm5iIiIiIiIqND6559/sHbtWiZwUZFStWpV+Pn5oUOHDpg3bx7c3Ny4HFEhMHToUAwdOlQteStzm+MMFScmJibYsmULnj9/jsDAQPTt2xcBAQGwzGHJHiKinAwaNAixsbGYPHky+vXrh7Nnz6J+/fpih1UsJCQkYPv27ZBIJPj6668LpM+MjAysW7cOhw4dAgB4eHjg66+/5v8fSjhLCwt8MWQggi9eFpQfOXYS7du8nfnJUkNykjSXpBCpVH2/tnPt1JnzOHbqrFp5Wno65sxfhpWLf8vzTECql/wymVzrrLtpaeqzZ5lnS/rRdJSphuSu7FSToABgxuSxsLWx0Xpcpvfcymf9bWEhTJ5SXYZRlabZwJR6Lq0IAOVcXPAo+t0SjRYW2hPICpKmWHJLYNKUXGetcq6qJVRp+fioKZnM0sBEN30+p2rq19z83flqoSGGpo0b4KNeHjr3UVgS9oiKOyZyERERERERkSh0mQkns46uy5lx2TMqLBo3box9+/ahZ8+emDRpEsqVK4dPPvlE7LBKFM62RSWdpaUlDhw4gPbt2yMwMBBDhw7Frl27YJLLzUUiIlXffvstYmJisHjxYnh4eCA4OBgVK1YUO6wib+PGjUhJSYGHh0eBzIb16NEjzJs3D1FRUbCzs8OECRPQtm3bfO+XigZnDUvuvXoVn/W3vZ2d2v7HuSzHFh2rvt/BzlZj3afPnmPB8tU5thV2Jxwbt+7CiGGDtfaZK5XvC5JTUhD39Dlcy5fVWD3myVO1ModsSzwa8v2DpqUPGzesB7dclv/TxNHBAbHZYkxKTkFiUjLsc3ieo2Pj1AsN+Nz0XgVXQSLXq9fxSE5Oga2tbslo+UnTufool3M1RsPzYmcnfCx6JVRpSAwzNAFKtV9tYWjqN3tim4O9+nPj6GCPDm1bGRQbEeUffmonIiIiIiKiQimnWXOIioouXbpkLUc0bNgwnDp1SudjHzx4gB07duRjdCWTtnFE0z6OO1TUOTo6ws/PD5UrV8bevXsxYcIEnY+VyWRYsGBBPkZHREXJwoULMWTIEMTExMDDwwOvX7/W+djHjx9jy5Yt+Rhd0fPgwQMEBATA2toaw4YNy/f+jhw5ggkTJiAqKgr16tXDypUrmcRFApFRj9TKsieAtGjaSC2h5P6DSK1t3ruvvr9powZqZXK5HHMXrEBS8rul5ypXrIBvx4wU1Nu4fS+u3wzT2mduNCXjXLt5K8f6UY+i1crq1HyXeGlIIlfzxurPgabnXxe1alRTK7t3/4HGukqlElEP1fsx5DNP21bN1cru3o/QWPdxdCy+mTwT037+DXPmLcOiP9fi/H+XNNY1hpbNm6iV3Y+I0nrMvQj150z1XFWbkEvLa69pZixNs2HpIq8zcmWfta1qlUoo5eQo2P/AwHOPiPIXE7mIiIiIiIiIiPLJoEGDsHTpUqSnp6Nfv364fv16rsdcuXIF7u7umDZtWq5LQBAR5cbNzQ3+/v5wdnbGypUr8dtvv+V6TEpKCvr27Yvp06frlYRKRMWXRCLBhg0b0KVLF4SFhaF3794alxxTdePGDbRs2RLfffedxhvMJdWaNWugVCoxZMgQODk55Vs/iYmJ+PXXX/HXX39BJpNhyJAhmDdvHlxcXPKtTyp6EpOSsX7LLrXyiu+5Zf1dyskRTRoJl1W9cy8Cd+7d19jm8xcv1ZJ1HB0dUO/9Wmp1t+3ej+uhwgSt778di/69PdC4Yb2sMqVSiV/mL0NiYlLuD0oP2/ccgEwuVyuPjHqEu+HC5KSqlSsKZp0yJJGrtYYkqKD/LmuoCbxJTMRun0M4fuosLl+9gQdRj5CebSytW7uG2jGnzwVrbOtc8EVBslwmQ3670r5NK7UlJLfu2q+x7rFTZxEadgfBF6/g+OmzOHA4AK/jE7S2n6FhWU5duZYvi/dr1xSUXQ8NQ9TDxxrrx8TGIeTKDUFZWZcyqFm9qqBMItE9rSIjQ/vMWPrQL5FLQ7/ZEshMTEzg/kEzwf5Hj2PwOCZWY3vBFy/D1+8YzgWH4Nbte3j2/GWuMUg1zApGRPpjIhcRERERERERUT6aOHEipk6dijdv3qBHjx54+PBhjnWPHz+ODh064NmzZ3j8+DH27NlTgJESUXFVp04dHDp0CDY2Nvjxxx+xYcOGHOu+ePECnTt3xpEjRwAAS5YsKagwiaiQs7CwgI+PDxo3boygoCAMGTIECoUix/onTpxA27Zt8eTJEzx9+pSzjf5fUFAQbt68CTc3N/Tp0yff+gkNDcXYsWNx/vx5uLi4YP78+fjss89gamqab31S4aJUKhH1KPptokZ0LB7HxCIi8iFu3b6Hy1dvIOhCCNZv3okvx0zB7bvhascP7NtTsP1hhzZqdZasXId4laSc5OQULPpzLdLS0wXl3bt0UDv/bt+9j383CceGj/t5on7d2pBIJJg2cTTMzc2y9j178RIL/1xj8My5mlJiIh8+xobNOwXjWUpqKlau26RWN3timaEqVnATJMkBwCH/47h6PVRQJpVKsW7TDqxYswFz5i/DpBlzMGbyTMiyJTnVf7+2WvsHDh/Frdv3BGUvXr7G2o3bNcajhP7PpaODPVo0aywou3j5Grbu8hG8NuERkdi2R5jg5eTogI5t3QVlqklhUQ8fqyXR6UP1XFUoFFi88h+1JMDExCQs/HON2g+oenbtpNeShqoUSvX/N2Y/j/WhTyKXpveF6kxgqrOpKZVKLPnrH6SqJGdHRj3CH0tWYsHyvzFjzjyM/nYGfA77C+qovm4AcPx0kMaEMiLSj2EjBhERERERERWI3L6cNOTXn0WFRCIRPP7i/Fip+Js/fz7i4uKwefNmdO/eHUFBQXB2dhbU2bZtG4YPHy74EnnJkiX49NNPCyzO7O+5zD8VBnyxX1ipjiuq+3QpIyqqWrVqhZ07d6Jfv37w8vJC2bJl4enpKagTFRWFbt26ITz83c3cQ4cO4d69e6hVS30GjfykVCqz3q9KRfEZh4iKOnt7e/j5+cHd3R0+Pj4YN24cVq1apVZv69at+OKLLwTXNcuXLy+QZQQzZf0/X6myLSKpVIp169YBAEaNGgVzA5fa0kYul2Pbtm3YufNtYkqbNm0wYcIE2NvbG70vKtyUSiU+95po0LHt27RE9aqVBWVdO7WHr/8JQdLX7bvhGDxiHPr38kDpUk54HZ+AA37HkJDwRnCsc+lS+HLox4KylNRUzJm3FPJsCVSu5cth1BdDsrYrVnDDl0M+wdqN27LKTp05j1bNm6Bnt856Py7V63tTExOYmZth0469uB56G+3cWyAjQwpf/+N48vSZoK6dnS2GDx6osU19x5eJo7/C1J9+e3eto1RiwvRZ6Ny+DerVqQmpTIaAE4GIVJlFavLYkYIZwSpVrIAuHdvixOlzWWVKpRJjpsxEj66dUL1KJcQnvMHeg35ITkmBo4M9Et4kCoMxcGz0+mIIrly7iYxsMzD9vX4LzgWHoHmTBnj24hWOnjyjliQ1avhgwWMA3i75Fx4hXIpzzJQf8WHHtrCzs0W7Vi30SqLz7N4Fh4+dwoPIdz+iunbjFgaPGId+nt3h6GCPV6/jsf/IUSQlJQuOdS1fDkM/6a/WpkRjGqBmmhJmzczyP5FLl37dWzZH8yYNcenqu1nILl29gcFfjUNvjw/hYG+HmNg47D9yFPJsM9VVfM8Nnw/6SNCWSxln2NhYIyUlNavsUXQMho/+Fh80bwyFQoFxXl/A0sDZyIhKMs7IRUREREREVAhk3qxU/WfIcUWFLl9GZdbR9YsrJl1QYSWRSPDvv/+ie/fuuHv3Lnr16oWUlHfLWixevBhDhw5V+5I7JCQE586dU20uz/Qdcwwdo8TE8YBIXa9evbBmzdtZBz755BP8999/WfuuXbsGd3d3QRJXpqVLl+ZbTLqOL0VxHCIqrsqXL4+AgAA4Oztj9erVaku2zps3D5999pnadc2VK1dw5swZo8ejz/gg9liyb98+PH36FE2aNEHLli2N3v6zZ88wffp0bN++HWZmZhg/fjxmzpzJJC7Sy0d9emDOjMlq5VZWllgwZwYquJYXlCclp2Dzzn1Y/vd6bNqxVy2Jy9HRAcv+mAU7W1tB+bJV/yLmSZygbPqkb2BtZSUoG/xxX1SrUklQtnTluhyXg9NG7TOCRIIfv5sAiUSC66Fh+OufjVi7cZtaEhcAjPf6As6lS6mVmxjwuaNl8yb4ZsTnauUnzwThz7Xe+Hv9FrUkrr6e3dGtSwe1Y8aOHAYrS0tBmUKhwOGAE1ixZgM27diL5JQUtGjaCL26d1E73tAhsGb1qpg6cbRaeejtu/DetgdHjp5U+/+Ax4cd0VNDDJ7dOqmVZWRk4MjRk9i1zxexcU/1is3W1gaL5s5E2TLCH08lvEnExu17sGLNBmzZ5aOWxOVcuhSW/P4TrKyEzycASDTMPpUTTTNVyTUs36kLfc4vEw39qr4GZqammPPDZLzn5ioof/nqNby37caKNRuw19dPEK+NjTXmzvwONtbWgmPMzc3RrXN7tT6jY59g30E/7D8UoHXmTiLKGRO5iIiIiIiIRJJfNw6K281NJmNQcWFubo49e/agefPmuHDhAj755BNIpVJMmTIF3333XY7HGWtZs/wYG4rieCORSATjiuo2UXE3YsQIzJ07FykpKfD09MTdu3dx8uRJtG/fHnFxcRqP2bhxI16+fGm0GIw5djCxi0gctWrVUluyVaFQYOzYsZgxY0aOxxkrMdTY7/uCGEdevXqFnTt3wsTEBF5eXkZv/9y5cxg7dizCwsJQpUoVrFixAj169DB6P1Q8OTk6oF+v7li56Fd8O2ZkjrMHOTk54p8V8+HZvYvGpBFVndu3gfeqJahSuaKg/NSZ8/A7dkpQ1svjQzRr3ECtDTMzM0z/doygLC09HXPmLYNUmrcl3ORyOVq3bI7Fv/0EB3s7jXVsbKwxcfRX6NFVPdkIgH5r7mUzeGBf/P7zNJQv56K1nqWlBaZN/AZTxo3SuN+ljDPm/vidxiSzTM2aNMQvM6dorGPI0oqZPD7siHmzv4dzaSet9SwsLDBlnBd+mDIOZhpmjWraqAHGjBym0zmlK5cyzvj3r4Xo2qldrp/3TExM0L1LB3ivWqyW4JRJn5dZ0+Mw+FzNYyJXhlSmVuZgb4+1K+ajR9dOuS6327D++9i4eonaDH2ZxowchuZNGuocIxHpRqLkp1siIiIqQP369cOBAwcQHByMVq1aiR0OEZEocpv1xlC5fTFVGBMVjP2RtDA+RiJVz58/R+vWrREbG4sOHWhaGd4AACAASURBVDrAz89Pa32JRIL79++jWrVqBvVn6JgT/csCvNxzGFVX/AaHjm3VYtKmoN+L2mbdyAsut0jF2dixY7Fq1SoMGDAABw8ezPXm0q+//oqZM2ca3F9u70dN+5MvXcX9rybBqVsHVF70i2BfYRuHiEoqX19f9O/fH02aNEGFChVw4MABrfUlEgkiIiJQtWpVvfsyZBwBgLg/1+LpP1tRcc53KN2/t1o82hhzLFmyZAmOHz+OXr16YcyYMbkfoKc1a9bgwIED6N27N0aMGAELLmVV4vgdOwWZTA5TUxOYmJhALpdDKpVBLpdDJpNB9v9tQIlSTk4o41wKZZydUca5FEo5OeqdRJPwJhFnz/+HCyFX8SYxCWnp6bCytISDvR1aNm+MNq1aoHQpzQk+x0+dRaLKbEgfdmoHeztbjfUB4Oz5//Di5WtBWcvmjeGmMkOYNn8sWYkjR08Kynx3boCTowOev3gJ/2OnEXL1BtLS01G6lCMaN6iLrp3aa02SOnD4qGDWoTLOpdGu9Qc6x6RQKHDj1m2cCAzC02fPkfAmESYSE7iWL4tWLZrig6aN4OTkmGs7ScnJOHjkGK7dDEN8fAIsLCxQo3oVtGreBC2bN4FEIkHUw8e4euOW4DjV5/1xTCwuXbkhqNOscQNUqlghx75lcjkuXrqGE2eC8OLlK6SmpsHS0gIVXMujbasWaN6kocZZrlQ9ehyDwHMXcOvuPVhZWcHWxhrVqlTChx3bwdHh7cyCfsdOIS0tPesYRwd7dO7QRmu7r+MTcCboP/x3+SoSk5KRnp4BaytLODo6oFXzJmjdsjmcHB20tnH0RCCSsy0haGdng66d1GejAoC4p88QfPGKoKxh/fdzTIbSJuD4aaSkpmVt29vZ4sNO7TTWfRL3DBdChP02alBXbUa77OIT3uBkYBCuhYYhPv4N0tLSYGVlhUb130fL5k3wfu0auSZ7KRQKXL52E2eDL+L5i5ewsbGBna0NmjdugDatWhg1QY+opGAiFxERERUoJnIRUUllaCKFtn15uelQmG5uGutjaWF6TES5uXHjBkaOHImQkBCd6o8fPx4rVqzQq4+8JjdpS+TKrrCMNcZO5mISFxV3CoUCI0aMgLe3t071y5cvj4cPH+qVGGBo0kUmbYlcqgrLWERUEm3fvh3Lli3DxYsXdao/adIkvWbmyusPYbQlcqnKabzI6zhy7949TJo0CXZ2dli3bh0cHLQnDBhCKpUiNDQUTZo0MXrbRMXFvKWrcDjghKBsp/cquJUvJ1JERERE6pj+SERERERElI9yWp5D09IdqksD6XLzU1t9bW1w+SEi8Tx9+hTDhw/XOYkLANavX4/4+Hid6mobD3Ibc7L/e7nnMADAukFdreONtjGL4wxR4aRUKjFjxgydk7gAIC4uDtu3b9erj5zKdR2PLGtWBwDEHw3kdQ9RIRUZGYlZs2bpnMQFAP/++y8SExN1qpuXz1KZ/5x6ewAAHs9aZPBYktdxZO3atQCAoUOH5ksSF/B2GW8mcRFppyknMz3b7E5ERESFgeYFjomIiIiIiChPdE1oyKnsdXwC7oZH4N79SNy9/wCRDx8hOSUVUqkUJiYmsLSwQPlyLqhVoxpq16iGWjWqoWrlioLpyjN/NZ7Zh6ZfkSuVSs5SQVSAwsPD0b17d0RGRup1XHJyMtauXYtp06blWEeXMSanOvEJb3Dr9j3cvhuO0Dv3EBP7BKm1q0CpUMJy/DSUcnJEnVo1UK9OTdStXRNVKlcUtKFtvNE2BhUEiUSi941XjotUnMlkMowYMQKbNm3S+9ilS5di+PDhWuvoOjue6rZUKsPd8AiE3bmH0Dv3cD8iCkkpKZDWrARzMzPYfDUW1atWyRqH6tSqIViiR3Ucyl6WvU++v4mM5/Lly+jZsyeePXum13GJiYlYv349Jk6cmGOdvFzXPHv+ArfuhOPWnXsIuxOOuKfPkFHr7XJWFkNHoaxLGbxfuwbq1amFenVqwc313Uw8uX2GMmQcCQwMRFhYGCpWrIhevXrpdSwRGZuGWXe57BsRERUyXFqRiIiIChSXViSikkDXGwyq23K5AhdCLmP/4aO4dPWG3v26lHFGb48u8OzeBc6lS2WV67o8mJg3NvPy0ZQ3ZKmoCAkJQc+ePfHixQuDjq9QoQKioqJgZqb+u7zcxp2ctm/fDcf2vb44F3xRr/fhe26u+LifJ7p36aAxkULXbWPL69JLQP4tqURUGCQnJ2PgwIHw9/c3uI3jx4+jS5cuGvfpew2Umby+/5A/9h8+ijeJSTrHYWVpiZ5dO2FgP09BEgag29jD9zRR3vj5+WHgwIFISUkx6Phq1aohPDxc8EOUTPpc12T+rVQq8d+lq9ixzxfXb4bpFUudWtXxSf/e6NCmFUxN38ZjrHEkPT0do0aNwosXLzB37lw0a9ZMr9iIyLgWLP8bvn7HBGVb/1mBShUriBQRERGROiZyERERUYFiIhcRFXe63GRQ3ZbJZNi9/zB8DgXg+YuXeY7B1NQU7dxb4Muhn6BypfeyynW5+SDWTU0mclFx5+/vjwEDBhh8szPTtm3bMHjw4KxtXZIkNP19I/Q21m3agZthd/IUj4ODPQb07oHBA/vCwsI8qzz7+7IgEyiYyEWUs+fPn6Nnz564dOlSntrp2bMnDh8+LCjTd+YcpVKJhDeJ+HfzDvgfD4RUKjU4HhMTCdq6fwCv4UNQwa281vGnsFz3EBV169evh5eXF+RyeZ7a2b9/P/r27QtAt9n8cvr7TNAFbNi6G1GPovMUT7myLhj6cV/07tE1a3zI6ziybds2bNmyBS1atMCcOXMMjk2hUCAiIgI1a9Y0uA0iAhau+BsHjwgTubav/wvvubmKFBEREZE6zhVJRERERERkBEqlUu3GgrabDpnbd8Mj4DXxe6z13qY1icvMzAzOpUvBtVxZlC3jDDtbmxzryuVynD53ASMnTMe23fshk8kF/eqSXFbQDL2RyhuwVBRs2rQJPXr0yHMSF/B2WTNAfYxRLcvpb5lMjg1bduHbH+ZoTeKyUijgJJejtEwGey03ad+8ScSGrbswZspMPHwck2v/mmItKLqMFxxTqLh68OABmjRpkuckLgA4cuQI7tx5N37oMhapbl+5HooR46bC1+94jklcJkolHOVyOMtkcJTLYZrDmKFQKHEm6D+MnDANAccDtY4/heW6h6gomz17NkaMGJHnJC4AWLZsGYCck0G1XVcolUqkpKZi3rJVmPXHUq1JXDYKBUrLZCgtk8FGocix3tNnz7Fk5TpMn/UHXr2OV+tbU6y5jSPOzs4oVaoURo0apbWeNsnJyZg9eza+++473Lt3z+B2iEjz9b4JPwMQEVEhwxm5iIiIqEBxRi4iKo50XeojO6lUivVbd2PXvoNQKNQ/ltnYWKNjW3fUf78WatWojmpVKsLc3FxQ59nzl7gbHoG74RE4dyEEEZEPNcZXq0Y1zJg8FlUqvZfjLBWFYYYKTR9Pt2/fjuDg4KztqVOnomLFilnbTLqgwm7NmjUYPXq0UdsMDAxEu3btBGW5jTtKpRLPX7zE3AUrEHr7rlqbpkolmqWmoma6FFUzpHCRKpD93ZVsCkSZmyPK0gyXra3xQsPyjpaWFhjv9QU8u79dck3TTBYFMTtXTl91hYSEYPPmzVnb/fr1Q6dOnXSKhWMNFWWRkZFwd3fH06dPjdaml5cX1qxZo/c1kFwuh/fW3di6e7/G92rVjAw0TE1HlYwMVEqXwTzbSCSDEtEWZoiyNMdtS0vctrQANLw3O7Vvje/GecHGxlrr9U1uYxMRqfv222+zkq+M5cqVK2jcuLGgTNtYkvnfiMiHmDN/GaJjnqi1aa1QoHlqKqqnSVFFKkNpmTB5K95UgiiLt9c1IdbWSDQ1VWujlJMjZnw7Bi2aNdY4XuhzTSOVStU+y+kqOjoac+bMQUxMDNzc3PDzzz+jUqVKBrVFRMB/l67iXvgDQVm/3h6wt7MVKSIiIiJ1TOQiIiKiAsVELiIqbvS5gZn5X6lUhtnzliL44mW19qpVrYyPenmga+d2sLG21iuW0LC72HfIH6fOnIdMJhPss7W1wYJffkDd2u+W4lC9CVEYk7lGjBiBDRs2ZG2HhISgWbNmAHjTlYqGpKQk/Pbbb5g3b57R2uzbty98fHwA5DwGqf73+YuXmPj9bMQ9fS6ob61QoH1yMtonpcFerttXRAoAN60tcMLeBlEWFmr7v/nqM3zyUW8A4iRz5fRV1+bNmzF8+PCs7QULFmDKlCk6xcDxhoq6ffv24euvv8aLFy+M0p6VlRUePXqEMmXKZJXllESa/e+FK9bA79gptfYapqahS2IyqmboPsPPE3NTnLazxkUba8hV3qP13q+Fhb/MhLW1VVZZbuMR3+dEuYuLi8MPP/wguD7Pq2HDhsHb2xtA7ssoZv43PCISk3/4BckpqYK2HOVydE5KgXtSKqx0vPMlhRKX7Kxw0tYWT82FieomJiaYM+NbtHX/AEDun59yKjNUSEgI5s+fj5SUFDRt2hTff/897OzsjNY+ERERERVOXFqRiIiIiIjIQPomcSmVSqSnp+PHXxeqJXHZ2Fjju/Ffw3vVYvT17KZ3EhcA1K9bGz9Pm4iNq5eg/vu1BfuSk1Mw9cffcPPWnRwTPXJbNoSI9GdnZ4c//vgDERER6Pc/9u47PKoybQP4PS2TSW8kEEISSggBpHeQJk1pIcHK6roWWMtiARu6q65+KggKCCoqTVlAURQpUpQqSO8tEKqUQHqvM+f7A2eYmZyZOdNT7t915ZrT3jKs59kz8z7zvsnJLqnz559/xrlz5yQlcQmCgPyCQrz8n/eqJXEllJdjyo1sDM8vlZzEBdz6Mql9aQVevJmHsfkFUJr147MFS7Bm/W8mSx+ZL4lkjvGGyL1SUlJw5coVvPfee/D3d37GibKyMnz22WeGfUvxx/jv0/nfVEviCtBqMSErF09kF9iVxAUAjSq1eDC3CJNu5iCq0jSB/cSpM/jPezNQUVFp8bnH2jYRiWvYsCEWLFiAffv2oVevXi6pc/ny5bhx44bFz1Lmf39evYZX3ny/WhJXl5JSvJ6RjQGF0pO4AEAFGXoWlePVG9kYVFgEGPVDp9Phv1Nn4cCho9X6Zdw/Y66KJStWrMBbb72FkpISpKSk4O2332YSFxEREVE9wUQuIiIiIiIiF7A22KD/q6ysxJR3PsS+g0dMynbp2A5fz5uJ0cOHuOQX3LFNGmPujHfxrwn/gMroV+UlpaV4+c33cPxkGgc1iTysWbNm+PHHH/Hbb7+hbdu2TtUlCEK1ZY0sxZ2ysnK89vYHuPTnVeOLMbygEM9k5iPYjgQuMX0LyzD5Zg6izGYB/Gjul9i+c4/kxFEicj+1Wo3XXnsNZ8+exSOPPOJ0fXPnzkV5ebnV+1z/umzFKnz/01qT8i3Ky/HqzRy0Kat0qh8xlVq8dDMbPUpKTI7vP3QU707/RDSRlM89RM7p0qULdu7ciaVLlyImJsapuioqKgyJodbuV0EQkJWdg5f+/R7y8gsM5ZWCgIdz8/FITqFdCVzmFJBhVH4Jns3KRaD2dmJpZVUV3nh3Ok6lpYv2S4wzsaS8vBxTp07FwoULoVQqMWnSJDzxxBNQiCz/SERERER1ExO5iIiIiIiIHCBlCUXzhIVFy77HoSPHTeoZ1P9OTH/3DUQ1iIAryeVy3DdmBKa+PQU+RkuflZWV450PZ6GwqFjSLDnWjhOR/QYOHIjDhw/j008/RVhYmMP1LFy4EHl5eRbvY/2xr5f/gNNnzpmUHVNQhKEFpS77Uii6Uot/ZeYi3CiZSxAEfPjJPGTn5DKBgqiGadSoERYvXow9e/Y4tdz9zZs38b///Q+A9Rm5zp67gPlLvjUp26y8Ak9l5iHIyWRSPR9BhodyitCr2DSZa8euPfhl4xbJswNaO05E1T344INIS0vDm2++CV9fX9sFLPj8889RVlZm2Lf0I5lZny/AzczbS8TKBQGPZeeha3G542/CTMvyKjydlQeNTmc4VlZejvc/movyigqrz13GHIklmZmZmDx5MrZt24bw8HB8+OGHuOuuu5x4N0RERERUGzGRi4iIiIiIyE5Sk7iM/46fTMN3K1eb1HP34AH498sT3frr6q6d2uPDd6bAV602HMvMysGszxeIDjxI+YU5ETlHoVDgqaeeQnp6OiZOdCwGlJSUYN68edXuX+O/8xcuYYXZDDhDCwsxoLBUrEqnBGkFPJuVh2CjGSyKi0sw58vFNgc8mcxF5B3dunXDrl278M033yA6OtqhOmbOnGn1/tZqtfho7lfQGSVExFRWYkJWHlRwfhZSc/flFqFTqWmMm7fof8jJzeNzD5Gb+Pn54a233kJaWhruv/9+h+q4efMmli9fLvpco9/+/Y992LXHaHl6QcC4vAK0dXJWPzGNK7X4Z1Yu1Eax68+r1/C/b38U7dvtLjkeS06cOIHnnnsO586dQ6tWrTBr1iy0bNnS4fqIiIiIqPZiIhcREREREZEdrH1Rb2nQoaSkFFNnfQad7vb1XTu1x2svPgO53P0fyzq1vwP/eeV5k2Obt+3Elh1/SB7U9MQAp71tcNCVarvQ0FDMmjULx44dw9ChQ+0uP2fOHFRW3hq8NL+HtVotPvp0vknyROuyMgzPd30Sl154lQ6PZecBRvfmtt93Y/e+g5JmwdFz5t72Vlmi2komk+Fvf/sbzpw5gzfeeANqo8RvKY4fP45NmzZZTCj9ac0GnEk/b7herdPhiex8aNx0u8kB/C27AA0rbyd2FBYVY+5XX1tNEDF+Nd8mImliY2OxfPly7NixA506dbK7/OzZsw3b5rGkuKQEn3yxyOT6/sUlLp2Jy1zTCi3G5heaHFv2wypcvHzFpI9i28bvQ4r169fjtddeQ15eHgYPHoypU6c6NXMrEREREdVuTOQiIiIiIqI6KysrCykpKViyZAny8/Pd1o75l/XmX+j/tGYDrmfcNFwTEOCPKZOehUzm+pkoLLmzVzfcM2SgybF5C5egqqpK0gxj5ttEZNns2bPxyiuvYO/evZKuT0pKwvr167F69Wq0aNFCcjvXrl3Dd999JxqDdu3Zj9Nn0g3HVToB9+UVSX8TDmpaoUWfEtOlzb5cvMykb8Zqy2w4Nb1/ROZOnjyJhx56CD/88ANKzO5JMf7+/njnnXdw+vRpjB071q62Pv74YwCmiRcAUF5ejm++XWly7T2FxQir0lWrw5WUkOGBvAKTpNKtO/7AuQuXqsUhPvcQWZednY3k5GQsWbIEBQUFksr06dMH+/btw1dffYXIyEjJbR05cgRbt26tFksEQcCqNRuRnZNruDZEq8Xw/GL73owDuheXI6H8drJYVZUWC/9X/dkLAK5evWp3LNFqtZg7dy5mz54NnU6HCRMm4IUXXoBKpXL1WyEiIiKiWoSJXEREREREVGdVVVXhxx9/xMMPP4yIiAjcfffd+PLLL5GZmelQfbYG/sSSKaqqqrDql00m9Ux+djwiwj3/C+vnnnoMjaJuD6ZkZuVgx669ooMlnuZomxxkpZro0qVLmDZtGrp3744mTZrg+eefx44dO0xmxxIzYsQInDx5EtOnT0dQUJCktvSzV5jPXLFm4xaT6+4pKnJ78oTeqLxikyUWL16+ghOn0gz7lmKOuxMopNTJmEJ1RVlZGZYtW4axY8ciIiICqampWLp0qc1EjPj4eKxYsQLbtm1D+/btJbW1ceNGnDhxoto9vH3XXhQW3U60iKmsRD83LO0qplm5Fr1LTNtas+G3arFS31ciEldVVYVVq1bh4YcfRoMGDTBy5EgsXrwYeXl5VsvJ5XI8/vjjOHv2LF566SXJiUmzZs2qFkt0Oh3W/Wr6XHNfbgHUHrp1H8wthMKoT7v2HDAs16rva35+PiZOnIhJkybZfN7Ty8/Px5QpU7B27VoEBATg3XffxejRo93yHoiIiIiodmEiFxERERER1QtVVVVYv349xo8fj4YNG6J///6YPXs2rly5Yndd1pK4zAcIt+/cY/Lr8RbN4nFX/z4ueEf289No8MiDqSbHVq5eLzqoCXB2CiJXuHLlCmbNmoW+ffsiOjoaTz31FH799VdUVVWJXq9SqTBp0iScPXsWjz/+uM2Z+w4ePIht27aZ3MMZNzJx8PAxwzW+Oh36eCh5AgB8BeDOYtMZgNZs2Gx1Ri53Y+yi+qy0tBQrV67EuHHjEBERgeHDh2PBggXIzs62WKZv3744ePAg5s2bh4iICJttiCWVrt242eSaQQXFHv0yelBhicmsXL9t24mysnI+9xA5qKKiAmvWrMGjjz6KBg0a4O6778b8+fOtxpKgoCBMmzYNJ0+exMiRI222sXbtWpw7d87kHj14+Bgybtz+IU6jykq0Lau0UotrRVTp0LG0zLCv1WqxcfN2kz6uW7cOFRUViI+PNzy72YolH3/8MY4dO4a4uDjMmjULHTp0cP+bISIiIqJagYlcRERERERU7+h0Omzbtg3PPfccmjRpgu7du2Pq1KlIT0+3WMbSQJ7YLFzGfz+t22hy/b3Jw13zJhw0eGBfBAcFGvZPpp3FmfQLJtfYminH1ZytWyw5hKgmunHjBj7//HMMHjwYUVFReOyxx7B27VpUVFRUu7ZBgwb48ssvsX//fvTpYz35c+bMmSZxZ8Nv20zuic6lpR6btUKve3EZ5EZ92Pb7bhQVF7s1gcJVcUBKvCeqrSorK7Fu3To8/vjjiIqKwqBBg/DZZ58hIyOj2rVyuRzjx4/H2bNn8cILL0CpVFqs93//+x9u3rxpuLf/vHoNx0/enonPX6tDu9Jyi+XdIbxKh8Ty2/G1pKQU23furnadp597iOoC/Y9knnjiCURFRWHw4MGYN2+exZmPmzdvjlWrVmHDhg1ISkqyWK8gCJgzZ45pktSvW02u6VniueR0vV7Fpm0aJ6pWVFRg7dq1AIDk5GTJdU6YMAGDBg3CRx99hEaNGrmmo0RERERUJzCRi4iIiIiI6r29e/fi1VdfRUJCAtq1a4e33noLx44dE73WUrKB2F9RcQlOpd1ODgsJDsKg/ne6983YoPbxQfLwoSbH9h86YjWxwhwHN4mcl5OTg4ULF2LEiBGIiIjAuHHjsHLlSpSUmM5k1bFjR2zbtg3Lli1DbGysaF3r1q1Denq64R4+dPSEyflexZ5NngCAYK2AtmW32y2vqMCp02ctzl6oP+YOjtTLOEf1gVarxW+//Yann34a0dHRuPPOOzFz5kxcvnzZ5Lrg4GDMmDEDx44dwz333CNaV3l5OT7//HPDPX3YLA51Ly2BEtZnGHSH3mbJFwePnbSZUGqO8YDIOq1Wi19//RX//Oc/0bBhQwwcOBCffvqpaILooEGDcPjwYcycORMhISGi9X399dfIz88XjScKQUC34jLRcu7UorwKUUazqV67fgM3bmZBEARs3rwZ+fn56Nq1K2JiYgBIS1Bv1KgRXnzxRWg0Gg+8AyIiIiKqTZjIRUREREREZOTYsWN4++230a5dOyQkJOCVV17B3r17HRrEO5N+3mS/W5eO8PFRuaqrDuvdo6vJ/umz57zUEyICgMLCQixduhSpqamIiIjA2LFjsWzZMhQWFhquue+++3DixAm8+eab1Qb8BEHAJ598AuDWjIPnL91OwvDT6dCkQnwZR3czngkHANIvXPJKP4jINkEQ8Pvvv+OFF15AXFwcunXrVm220pYtW2L16tVYs2YNEhMTq9Uxb948lJffSuA0v98TPbgMmmm7pnHo3PmLXukHUX2h0+mwZcsWPPPMM4iOjkbfvn0xe/ZsXL161XCNUqnEs88+izNnzuCpp56CXG46TFVUVIRFixYBADKzslFYVGw4F1tZCT+dR95KNa3KTBPj089fgCAIWLVqFQD7ZuMiIiIiIrKGiVxEREREREQWpKenY9q0aejevTvi4uIwceJEbNu2DTrdrdEDa8ldgiAgzSxBqnViC7f2V6oWzeKgUt1eHint7DnRGcX03Dlbjivr5IwZVBeUlpbihx9+wLhx49CgQQOMGDECCxcuRHZ2NjQaDd544w2cOHEC999/v0m5L774Ajk5Obh6/QbKjAYaYyq9kzwBADEVpm2f/SuBQuxetTRjhdT72h0z6djqJ1Fdtm/fPsNspe3bt8d///tfnDhxa1acYcOG4fDhw5g+fTqCg4MNZbKysrB48WIIgoB0s4SpmErvJJRqBCDcaBady1euoby8XNJzDxE5RxAE7Nixw7Ccfa9evfDRRx8ZZv0LDQ3F7NmzsX//fgwcONCk7KuvvgqtVisSS7z4XGMWx9LPX8TevXtx7do1JCQkoE2bNh6ZaZSIiIiI6j4mchEREREREUlw5coVzJkzBwMHDkR0dDQmTJiADRs2oKKiwuJgYJrZjFxJiQne6Ho1KpUKLZrGG/ZzcvORmZUtei2XGSLynoqKCqxbtw5PPPEEGjVqhCFDhmDevHlQqVT4+uuvsXXrVnTu3Nlw/VdffYVzZrPgxHpxwDO6UguZUaxIP3/R4lK03sJYRmTbsWPH8NZbb6Fdu3ZISkrC66+/jiNHjmDixIk4efIknnzyScO1U6ZMgVarw8XLt2ffCdZqEaj13r3WxCgO3pq18E+L1zqbVEpElu3evRsvvfQSmjZtih49emD69Ok4f/482rRpg3Xr1uG7775D06ZNDdevXr26+nONl2YZBYDGZm2fPX8RP/30E4Dqs3ExlhARERGRM5jIRUREREREZKfMzEzMnz8fI0aMQHR0NB599FGsWrUKpaWlJokJObl5JuWaxjXxUo+rizfrS05uXo2cnaJTp04YPXq04S80NNQr/SDyNq1Wi82bN+PZZ59FbGwsBg4ciH379mHp0qX44IMPEBkZiXnz5iEzK8ukXIMqrZd6DKgFIER7e/2jvLx8m7HEnbNYPhO6DQAAIABJREFUxMbGmsSTFi1aGNriwCqRNGfOnMHUqVPRo0cPJCQkYNq0aXjooYewe/du3HHHHSgpKcHatWtRaZQ8FVnlvcSLW+2bxkF9LKpJSaVE9c3+/fvx2muvITExEd27d8e0adOQlJSEgwcP4sUXX0RAQADmzp2LnLx8k3INvBhPokxm5BKQcf06Tp8+jaioKHTv3t0kluzevRuTJ09GSUmJ1/pLRERERLWXTOAnVCIiIvKgxMREnDlzBh07djRZhoOIyB38/Pywbt06j7Y5evRojBo1CkOHDsVr//0QF4xmfdj+y/eQyWQe7Y8lM+d+hR9W/2LY/33DSjzxxBNIS0sT7aPUY/Zw18fRmvJvTPVXQEAA1qxZ47H2IiMj8eSTT+LatWtQ+wfh+NnbcefvOXnoXFLhsb6Y+7+GYbihvLWUq1KpxNrvFkImkxnuU5lMhpycHDz99NPIycmxeP/auq89+fUWYwzVBmFhYVi5cqVH2xw/fjwiIiJw8eIlXM4uNRxvU1aGCVkFHu2LsQ1BGqwNCjTs/75hJSZPnow9e/YAqH5POxqHiOoajUaDhQsXomHDhh5rUy6XY8qUKejTpw+WLFmCwLAoHEu7YDj/0s0cNPHSrFwCgOdiIgEAKlQi2N8Xw4bcZUiw1/vuu++wbNkyAMCkSZMwYMAAADB59iEiIiIiskbp7Q4QERFR/VJQcOsL/EOHDnm5J0RUH0RERHi8zZ9//hk3b95Ebm4utDqd7QJeIoiMHxw8eBAHDx70fGeI6pi+fft6tL2bN2/im2++wahRoxAT39QkkcvrBNMdfcKV8SBmeXk5Dh48iMuXL3u2b0R1WGJiosfbXLJkCYYOHYqRo5OxYNkqj7dviVjKxPHjx7Fjxw6P94WoNvHz8/N4mzqdDvPnz0dWVhZeeeUVLP9xHWCUyGX2YOFRMgAQBEAmg0wGVFaUISUlBQEBARAEAWVlZfjkk0/wxx9/wMfHBxMnTkS/fv281l8iIiIiqr2YyEVERERe8frrr2PcuHHe7gYR1XG5ubno3bu329tRKBTo168f7rnnHiQnJyMyMhI6nQ6TXn/X5Lqy8nJofH3d3h8pysrKTfYPHDoKXx8FysrKIJfLTX4xbj57jnEChjO/KJe6xJoxKe3xV+7kbZ988gm2b9/u9nYSEhIwatQojBw5Ep06dYIgCFi1dqPJNRVy794PFUb3o0qlgiAIkMlkJgldkZGRWLt2reGc/rjYqyVi8ULqMWNSZh9kjKHa4Ny5cxg5cqTb2wkKCsI999yD4cOHY+jQofD19UVefoFJIleFl+8Z8/Y3bd6GJtFRKCoqMjzziD33iMUf3v9Un8jlco+1FRMTg+TkZAwfPhy9e/c2LFGo0WhMrqv04i1YiVtJXHrBDRrBz88PgiAgMzMT77//Pi5duoSIiAhMmTLFsISzOePnHSIiIiIiMUzkIiIiIq+Ij49HUlKSt7tBRHVcRkaG2+pWq9UYPHgwRo8ejeHDhyMkJAQ6nQ6CIED310xcISFBJmUuXb6CVi3Fv9D3NOMlHwEgJDgQUZENTAYvLQ1u6reNXx3BRC6qq8wHHV2pbdu2SE5ORnJyMpKSkgzxRh9/goMDTa7PVCjc1hdbKmQC8hS3B4FDgm/FROMBTEEQoFAo0LJlS6sJFEzkIrJPaWmp7YscFB4ejpEjR2LMmDHo378/VCqVyTOQv58GSqUSVVW3lj+7qfTuV9CZStM4GBwchOjoaJPnHONtazGI9z/VNzdu3HBb3U2bNkVycjLGjBmDLl26GOKI/v+rBUEwPDvoZSqVaFaudVufrLmpuh3LKgUFoho2hiAIOHHiBGbMmIHCwkIkJSXhlVdeQWhoqOFaJm4RERERkb2YyEVERERERCRRQEAA7r77bowZMwbDhg2Dv7+/YdDSeMBB/5rQLB579h82lD+Vll4jErmqqqqQfv72EiUhwUFoEBFebckz44QHbww+GM/a460+ENUEXbt2xZgxYzB69Gg0b97cMMipT+IyjjtN42JNyv6pUnm8v3rXVEoIRvdt8/g40Rm5pHDVIKh5XDE/J+UYUX3UqFEjJCcnIyUlBX369IFcLrcYi+RyOeKaNMa5C5cAAPkKBYoUMgRovbMkmnEclMtkiGvSGED1pFLA9BmI9z+R67Vo0QKpqakYM2YMOnbsaPWzFAA0i4sxKX9FpUR3mM4s7ClXfW4PpwmQo0XTWKxfvx6LFi2CTqfD4MGD8eSTT8LHx+f2dWZxhnGFiIiIiKRgIhcREREREZEVoaGhGDVqFMaMGYPBgwcbvpjXD14aJwSYDzwkNG9qUtfJtLMYM3KYh3pu2bkLl1BZWWXYT2je1OFBBmcGJKwlVJhfI7UNDo5QXSCTydC7d2+kpKRgzJgxiImJqRZzzO8d/X6jqAZQq31QXl4BALjixUSuKz6mbTePv5VkJiXeuGKwU0qMISLLYmNjkZqaipSUFPTo0cNwT5n/ASLJF/FNDIlcAHBFpUArbVX1RtysTAZkGc1M2LhxI6h9fEySSo1jjb1JpkRkW6tWrTB27FikpKTgjjvuACDts9StBPUmJnV5M0H9isp4OE3AhbOnseb4USgUCjz55JMYNmwYf4hCRERERC7BRC4iIiIiIiIzDRs2xJgxYwxLBin+GgC0NHipP2f+2qJZvEm9ew8cRmVlJVReHIAAgJ2795vsJ7a4lXAmJXHCG78k5wAI1QcKhQL9+/c3JG9FRUXZTNoS25bL5YiPjUHa2fMAgGKFHFdUCsRUen4ZojNq01jXrGmsaPKEp+OKpRl4iAhISEgwJFx07tzZJA7Z8wzUPD4Om/C74Zo0Xx+0KvN8Ilearw9gdI83i68ehzhLDpHrtW3bFqmpqRg7dixat24NwP44AgANIsIR4O+HouISAMBlHxVKZYDGC3naaepbP+iRQYBGVoETx48iMDAQkyZNQtu2bRlLiIiIiMhlmMhFREREREQEIC4uzjDrRK9evUx+TW1rRhdLSRb+fhokNI/H2XMXAQA5uXnYvG0nhg7q7/L+S1VRUYmf1m4wOdaxXRsOahJ5gUqlwpAhQ5CamopRo0YhLCzMcE7KTFJiMUoQBLRtnWhI5AKAPwJ8cW9usQt7bluBQoajvr6GfR+VCokJzQx9NI8vlhK73BmLGOOIbmnXrh1SUlKQmpqKtm3bApAWg8Toy7Vt3dLk+B6NBsPziqGEZ++7Xf4ak/32rVsBqB5bpM4OyOcjIss6dOiAsWPHYuzYsUhMTJT8WcqceQJp29aJ2L3vEACgSibDvgBf9C0sc23nbTivVuC6SgU5dNDIKiCXCYiLi8NLL72EyMhIk74zlhARERGRs5jIRURERERE9VZSUhJSUlKQkpKCTp06mZxzZABT7FflwwcPwMxzCw3Hv/tprVcTuX7b9jty8/IN+wnNm1ZbAlLP2gCDqwYfXLn0GQdDqDbQaDS4++67kZqaihEjRiAoKMhwzp7kLWvHB/XtjR9W/WLY36/RYHReEXwEz90je/19oTO6J3t17wx/Pz+Ta1y5pJmlWOJMjBHrB+MM1RVdu3Y1JLAnJCQ4XI+lRI2Y6EZoldAcp8+eAwAUKRQ4rlGjQ2mF4522U45SjtN/zaADABqNL3r36GJyDZMpiJzTpUsXQ/JW8+bNHarDUmK63qB+vQ2JXACwy0/j8USuXf4aKFEFX1klZDKgcUws3n77bfj+lbTOWEJERERErsRELiIiIiIiqlc6depkSN5KSkqSXM7RRIBe3btg0bIfkJdfAAA4k34eW3//A/379HSoPmeUlZVj8dLvTY6NHHaXYdvWkmccoCByTGBgIEaOHInU1FQMGzYMfmYJTeYsDWSKXSN2bVRkBNq1aYWjJ04DAErlcuwM8MOAwlKn3odU5TJgu7/pexw6sK+hj4wjRJ4nk8nQu3dvQ/JWbGys29rSx6LBA/oYErkA4LdAP7QrrYDcbS2b+i1QA8Eo3tzZsxt8fdWSZ8whInHdu3fHvffei9TUVMTHx4te46ofagBA+7ZJiAgPQ1Z2DgDgmkqFk74qtC6rdFkb1mQr5Tjmp4RGXglBAMqhwmuvvQofHx/R6xlLiIiIiMhZTOQiIiIiIqI6TS6Xo2fPnobkLUuDDY6ytKyiflupVGDoXf3w7crVhuPTZ8/DHa1bITws1KV9sWXOl4tw9XqGYT8sNAS9u3f2aB+I6ouwsDCMHj0aqampGDRoENRqtdN12jMoOrh/H0MiFwCsC/RHx5IyhGhdN7Bqydpgf+QpFIb9mMaN0LqV6Yw/XMaVyP2USiX69++PlJQUjBkzBg0bNpRUzhUJGIIgoFf3zpi/5DuUlNxKIr3k44PfAz2zJNpFHwV+9zNPKL2TCepEDpDJZOjVqxfGjh2L1NRUNGnSxGV12/osBdz6PDeoXy8sX7nGcO67kEBMuZHtkdlGvwsJRCXkUAhalAo+6NK5M0JDgt3eLhERERHVX0zkIiIiIiKiOissLAxXr16VPHDpLiOGDMDmbTuR+devyPMLCvHBx59i2n+neGwA8Y+9B7Bq7UaTY4/cPwYKo2QLSzjQSWSfF198EVOnToVS6b2vXbp16YCmcU1w4dKfAIByuRzfhQZifFaBW9u95KPENrPZuB6+LxmA92KJI8srMuZRbdeyZUtkZGQgPDzcY22a32dqHx+MHTkMX3/7o+HYmsAAtCspd2tSqRYClocGmczG1aNrRzSLd3wWMj4LUX0VGhqKK1euIDo62qv9uHtQf6z/bbthluMcpRK/BPtjdF6JW9vd76fGKd9byfhFggIKhQIPpY5iUigRERERuZWnZrImIiIiIiLyOB8fH68ncQGARuOLZ558xOQL/t37DuLD2fNcuuyIJUePn8Kb739kcqxXt07o16eHpPLuHphwtn6ZTMbBE6pRGjdu7NUkLgBQyOV46rFxJvfGcV9fbAjSuK3NXKUMC8KCTZInunVuj26dOwAwvdf12668d63VZU87lq5lnKHaJCAgwOEkLkf/WxcrN3zYXYiPjTHsl8nlmB8ejDI33U4CgGVhgbimUhmO+flp8MTD91froyviAlFd5+Pj49YkLqn3lp+fBv8YN9bk2GZ/fxzSiC9v6AqXfJT4LiTQ6IgMyfcMQVxsjMOxhIiIiIhICiZyERERERERSSB1YN/Sl/ptWiVgxJABJteu/mUT3v9oLnQ6nQt7aurQkeOY9Po7KC29vYxRaEgwxj/6kMUyHJggqjks3Y9iiVDm92vTuCa4xyzurA0KxI4AX5f3s1Ahw5yIUOQqb8/yp/H1xfi/P+jytoio5tPHI4Vcjn/+4yGT+HTJxwdfRgSjEq5PZl8Z4o+9Zksq/u3eZC6DRuRhrkgINd/u2bUTOrZrYzgmyGRYHBaMk74quNp1lQKfR4SgTH57CK1hZAPcmzzcZll+fiIiIiIiZzGRi4iIiIiIyA3EBiEeSB2JVi2bm1z3y6YtePWt95GVnevS9gVBwE9r1mPyv/8PZeXlhuM+KhWef+ofCAzwt9hPS9yZ4GWpvpKSEuTm5hr+tFqtpHJEtZkjM0tZSip9YMwIk9lwAGBFcCB+C9S4LIXipkqOOREhyDSbhWzCow8iLDREcuxw9+x6lZWVJvGk3Cg2SukfEVlmLfmiedM43Jd8j8n1Z9VqfNEgBEUK19xzlRDwbag/tv31fKPXpcMdGDygj6Q4xPufqOawdM+O//sDCAu9nZipk8kwPyzEpTNznVMrMDciBMVGSVw+KhUmTngUavXtdhgziIiIiMhdmMhFREREREQkwpHldmzNkKNSqTDlhafRJqmlSfk/9h7EwxOew7qNm53pssG16xl47pW3MGPOl6ioqDAcV/v44PVJz6BNq5a1ZkDzX//6F8LDww1/hw8f9naXiGoMsXvUOBlKJpNBrfbB6y8+jUZRkcYXYVVwID6PCEahk0kUuwLUmNogHNdVprNhPPHw/bizVzfRvtqKlc7EHmtlv/32W0RERBj+5syZ45J6ieozqTOTjh19D4be1dfk2jS1Gh9EhiFN7dxStNdVCsyICsNOf9MkrjZJLTHp2Sesxhx3LPNKRPaR+llKJpMhLDQEr7/4DAL8b8+8VymXYWF4CJaGBaBCZn+aeokC2BWohA7AumANZkeEoUBxe4ZRhUKBlyaOR2JCM4v9sxVLGGOIiIiIyB5M5CIiIiIiIrKT2KwxYl/ii237+qrxynMT0K5tK5PyRUXFeP+juRg/8RX8smkLyo0SsKRKO3seH3z8KR6e8AIOHT1uck7j64s3Jj+LNkktLfZR7FXsvbuLvXVzQITqOntn37I0sBgUFIg3Jj+DsNAQk3KnfNV4LzIcG4I0KLbjGyIBwAlfFWY3CMbykGBUyk3782DKSAwb1M/QvjNxxZP3OWMKkW1SnoHMj+m3Hxt3L/r06GJStkChwNyIUCwOD8SfPgrYI0spx4pQf3wYGYZrZsmkzZvG4dXnJkClUjmVNGotUY2IHGNvHDE/3zi6IV59/p/wVatN6tjt54f3osKxI8BXckLXeV8FZjf0x9pQDd5tFIz1gYEQzNp9/p+PomO7NhafaRyJC4wlRERERGSNcz93IiIiIiIiqmdkMhkEQTBsAzDZFwSh2jX6Y/rr1T4+eOnZJ7H0+5+x/rfthmsB4NSZdJyaMQefzFuEgf16o21SSyS2aIbYJo2hUJgOcObl5SMt/TxOn0nHzj0HcCrtrGifY2Oi8eyTj6BpXBOXz0rBQQgi1zGOHVKvFXsVOxYeFoq3XpmImZ8vwvmLlw31FCvkWBsUiI0BAehaWoqE8grEV1QhvEpn0l6ZDLikVuKijwoHNL7IMEuaAACVUolHHhiDYYP6G/po3F9rr0RU80l5BjLeNj8mk8nw9ON/Q3BwENZu2GxcMQ5oNDig0aBleTnuKCtHfHkVYioqocDtGKEDcE2lwEW1CqfVPjjmqzZJuNDr2qkdnn78b/D19bWavG78vojIM6zFEbHrLD3XtGgWjzcmP4NZ8xYjMyvbUC5HqcSKkCCsCwpA95ISNCuvRHxFFYK0pm0UKGRYHarGSY0SkMlQKShQqDB9tgnw98PTjz+Mrp3aWf0BjHGfiYiIiIhcgYlcRERERERENthKrrCVuCU2+ODj44NHHkhBjy4d8PmiZbiecdOkzsKiIqxauwGr1m4AAKjVPggLCYGPjwpVVVqUlJYiNy/far8VCjmShw9Fyoih8PFRVRvMlDogwUEJIu8zvg8tJUtYij/618gGEXj71eex4qe1+Hn9byb1V8pl2OXvh11/LVXkr9NBo9NBIQAVMhnyFHLRhAm9mMaN8PyERxHbpLGhPVtxxp6ZuBiHiLzD3mcgW/FIqVTi4fuScUdSS3w6fwkKCotM6jujVuPMX7PsKAUBwVotVIKASpkMRXI5yuWWpw/08VHh7w+kYFD/PpDL5ZLij3nCKZ97iFxPahyx57lGEAQ0bxqH9//zEuYv+Q5/7D1oUmexXI7NAQHYHHBrP0irhVoQIAdQKgOqlFVQyAQIAlCmU6HKbKisTasE/Gv83xEeFspYQkREREQex0QuIiIiIiIiiawNQhgPKlgbfDAv07JFM3zwn5exev2v2LR1J/ILCkXrLy+vwPUbN0XPidXbqX0b3Jc8HPGxMaIJXMZ9tjYAYf7+iMjzzGOPtZlvrL0CgEqlxAOpI9G2dUssX7nWZHYuY8VyOYqtJEzo+Wk0GDKgD1JH3w21j4/kOMOkUaLaxdYzkHkyl/Fx8zgkk8nQ4Y7WmPbWK1j6w2rs3HMAWq22Wr1VMhmyldK+vu7Yrg3+du9oxDRuJDn+mD8fmb8nsW0icpyUOCJ2f1qKIwDg76fBv558BJ3uaI0fVm9Axs1M0foL/prZWIkq+MoqoZABWkGGUsEHt9K7bgkNCcaoYXdh2KB+htmQLcUMsWcexhIiIiIicgUmchEREREREVkgJXFLTyypwvg6S4lcwK0ZJFJGDsPIYYOw/9BRbNq2E6fPnLO7v8FBgRjQpwfu6tcLEeFhooMM1v70fbI1yOCuQQipS8oZX88BEaqrzAcuxc4DsBpvrCVNtU1KxDtTWuJUWjrWbNyCw8dO2tW/iPBQ3DOoPwbc2RMaja9T8YaDnEQ1j73PQJYS2q09/4SEBOOpx8bh/jHDse7Xbdi8fRdKS8sk91GlVKJPz64YPqQ/GjdqaDILlz1xyNq/ARE5TmocEVtqUSyOiD376PXu0QU9unbEgSPHsWbDFqSfv2jWogBfWQVUMh0EASgXlKgQlMBfy7fGxkRj+JAB6N29C1Qqpd3xg7GEiIiIiFyJiVxEZHDtegbSz18CAHRo1xpBgYFe7hERERFRzWE+24T5oITYwILYr8fFyuipVEr06NoR3bt0QE5uHs5fvIwLl67gwuUr+PPKNZSWl6OysgoKuRwqHxUahIchPjYGzeKaoFl8E8TGREOptD3wIDYAIZZMYe0X5UTkefYMehqfs3YvJyW2QFJiC+Tk5uFM+gWcPX8J6RcuIePGTVRUVEIn6OCjUiEoMBDNm8YioVkcWjSLR3xsDBQKhd1xRv8qZcDTkZhjb0KoeVnGOaLqHHkGsnYfmx8LCw3BuLGjMHbkUKRfuIT085dw5txFXLx8BaWlZaisqoJKqYSvrxqxMdGGONSiaRz8/f1c+tzDZAwi97AWR8yfb2w9z1i6FxUKBbp2bIcuHe5Axs1MnD13EekXLuHM2fPIzbwGaHUQAAgqf0Q1iELzpnFIaBaPhObxiIluCJlMZldCqL4v5tuMJURERETkLCZyEblISUkpSkpLIQgCjL/PkslMP2QGBwdB+de0zDXNku9+wupfNkEul2PtikVO1ZVfUIhDR46juKQEGl9fNIgIQ8uE5lD7+Lims0REREReYmngwTypwnjb0pf11o6Hh4UiLDQEXTq2szobj35bbBDB0YEIWwldtvpPRI4TS5IQOy426Gl8XOyYnlwuh06nM6k3LDTEkEhqrW+WBivtjTdiddRkTPIisj67jv48YD3+WDoGAGq1Gq0TE9A6McHmLD5S4429zz3W+kdEzpMaR6zFE+PnGEsaRUWiYWQD3NmzK3Q6HYqLi5GbmwsfHx9ERUVB/tfy0c7GD/Ntsfdivk1EREREZAsTuYhcZNn3q7Bo6Qqb16lUSsQ1iUHTuCZo3jQOd/XrjYZRkR7ooXWCIGDfwcMAgNaJCQjw93eonvMXL2PGnC9w9Pipaud8VCq0a5uEEUPvwsB+vfkBloiIiGoFa0kVgHgCl/64rUQusUEI4/b029YSOJwd0DQua+vfwdp7cZajs+gwuYLqMksxQOw6wHLcEbtH5HL5Xz9Eqj6zjrWYZy2Zy5nECUv9tHbcXlLiDGMK0W224oEYsZlJ7SlvLe5ZSwR19BnIWj9s9ZWIbHM2jlh6nrEnjigUCgQFBSEoKEi0HlfFErHYwhhCRERERI5gIheRh1VWViH9/EWkn7+ITVt24ItFSzGgby8898/HEBoS7LV+Xb2egYwbmQCArp3aO1THxt+24YOZn6KysgoA4KtWo3F0QyiVSly9noGiomLsP3QU+w8dxfZde/HScxMcThgjIiIi8gYpiVVA9YFLa1/gGydTGCdV2GrD+JiUpAp7ZsuxZ6CTiDxHauKB+baUmSuMY45xrBOr31WDnWLvyV3JE84suUhE9ieWmm8bsxSTxJ59bM14au9zj61ylvrM5yEi5zkbR6Q820iNI8b7jnx+EitnXC9jCRERERE5g4lcRG6QlJiAIQPuhFKpgEKhhEIhR0FhIW5m5SA7OxeZ2dm4cOlPFBUVQ6fT4betv+P8hUuYPfVthHgpmWv/wSOG7a6d7U/kunDxsiGJy89Pg1effxq9enQxLKVYpdXi8JHjeOfD2cjJzcPm7TvRICIcz47/u8veAxEREZGn6b+ItzZjhPm+TCazO6HCWhvmAwnG21KTuIzrFBtcEEuscOUghLMJFpYGZ4hqK0tJVZbigNg9YD7AaWvA07wNWwlWziRzidXtigFPVyVrWZpFiLN1Ed0m5V6w9vwjlsgulsxuK/HCeNue5x5bMUnqeyQixzkSR4w5GkeMj1mKC1J+BGNexlobRERERET2YCIXkRs0bxqHscnDrV5TXl6On9dtwpwvF0On0+HCpT8x/ZMv8O6/X/JQL03tOXArkcvPT4OkxAS7y8/8fIFhJq53Xp+Mbp07mJxXKhTo0qk9Zk97G3978jkAwI9r1mPcfclenYmMiIiISApbSRW2vpwXS6gQG3RwZgBC/+qKxAprAxJE5F3G96WtGOTIgKdxcpi9CRT2zvxn3kd3DXg6kuDFpC2iW2w9A+mPm7MWQ2y1ZR6LzMu64rlHrG3e80Tu4ao4Ys8Mo7biiPE+YwkRERER1TRyb3eAqL5Sq9W4d8wITHr2ScOx33fvQ0Fhocf7UqXV4uCRYwCATu3vgFKhsKt8UXExDh89AQCICA9Dl47tLF4b1yQGHdu1BQBUVFRg34HDDvaaiGqrjIwMAEDjxo293BMiItewlADlzJ9cLhf9Mz9vnDhhfo21uu1N6hJ7n67iyuXOuHQa1SVS7kNXxhvz+GAeKyxdIxajxOKJrUFPIqp9rCUs2Io3YnHEUlwSi0219bmHiEy5Io5YihXW4gVjCRERERHVZJyRi8jLhg0agM8WLEFRUTG0Wi127TmAYYP6e7QPp9LOoqSkFADQrZMDyype+tPwa6gGEeGQy63niMY2aYxDR48DAAoKi+xuz5N+/PFH7N6929vdIKqTtm7diq1bt3q7G0R1RvPmzTF+/Hhvd6NOk8ks/5Jc/6W8tV+V2/oFub4u8yXOzNs1L2P+KnWAQergAxHVPNbigjH9DFy2llQ03rdWn3n8MN6WOrBpXl6sbvNjUlhK5HQmwVPs34MzdVF9ZO0ZSH/MHuazA4q1Y+1esxR/9MfMEy7EruVzD5FnuTuOiM1mOWv7AAAgAElEQVRobO9zjaN/YnUSERERETmDiVxEXubjo0LzpnE4cuwkACA3L9/jfdh/8Ihhu2tn+xO5Avz8DNuFRbYTsyorKw3barXa7vY8acOGDZg3b563u0FUJ02bNs3bXSCqU/r3789ErlrA2hf75oMYYoli1uoUG0ioj5hkQVSd2D1ha+DUUmKq+XZtv98YM4i8z9qzj1gsEkv81O/zfiaqn6TGEeNrLcUSIiIiIiJvYyIXUU3g5RVg9h64lcjVMKoBGjdqaHf5hg0jERDgj6KiYly5eh1/XrmGJjHRotcKgmBYxhEAOrZv41iniYiIiLzAnl+S25scIGV2HLF2LM3KZbwtZUYu4/qsDW64cqDD2kw5+nOPPfYY+vTpYzgeFxfnsvaJajprMcdakoM9dds7A6B+W8osFtbOi9Xlbr1798b8+fMN+126dAFgO6mNiCxzdEYd4xhk6dlH7FmqNj/3EJE4T8cR43bsiSVSn2UYS4iIiIjIWUzkIvKyiopKnLt4ybAfGRHu0faLiotxMu0sAKBrpw4OfajU+PriwdRR+HLxMgDA3K8W453XJ0OlUlW79pdNW5BxIxMAcEebVoiJbuRE74mIiIhqDkuJV7aerywlcOnLG79aGgywNBBhaUDC2r6lOr2lV69e6NWrl1f7QFTTWEsgNT9mqaytBDFLZc1frQ16ih03r0usflvvQSrzZNFmzZqhWbNmTi23SFRfSU0slRp/xJZUlFqXtece831nnnu8/QxEVNdIjSP689bqcDaOGLchNZbYiiNidVp7L0REREREljCRi8jLNm/fiaKiYgCAXC53aGlDZxw6chw6nQ4A0M2Jtu9PHYWLl69g05Yd2Ll7Pya+/CZGDx+CNq1aQqVSIuNGJrb8/gdW/vwLAKBBRBjefu1Fl7wHT5rQoAnu8Av0djeIiIiQr9Xi9Stp3u5GvWRrAMKeZC6xumzVZ17efFvqoKW1bWvv3RNsJVlwKTSqT8TihKVzetZmn7AVc6TUYf5am5InbM0EaN424w3Rba5I5hKrz3xGQClJHZ547iEi15MSRwDpscSZOGJ8TEosYRwhIiIiIk9hIheRl+h0Omzd8QemzfrMcOzOnt0QFOjZJKE9fy2rKJPJ0Ln9HQ7Xo/bxwb9ffg6tExOw7PtVOH4qDcdPVR9cjggPw/AhA/HQvcnw89M43J639AwMRTtNkLe7QUREhAJtpbe7UK/Zm8wltQ5L9Unpj/mrtYEIa9eKvZpvE5FnuSJ5QkrM0dejP25e3nzb2UQKsbqJqPZxJJlLyrOU/px5OfNte2IOn3uIaiZHkrkcjSPmx6TGEilxxNo2EREREZFUTOQicoPrN25i15798FGp4OPjA6VKiYKCQty4mYWbmVnIyMxC2pl0XPrzqqFMVGQDvPz8Py3WeS3jBvbsO4SbmVkQBAFxsTHo2qk9IsLDnOrr/oO3ErlatWyBwMAAp+oqLilBdl4etH/N8CWmoKAQN7OykV9QWCsTuYiIiIjE2ErmMh+EsDeJS2wGGUsDm/YMZEp5FWuLiDzPWqwwPwbYl0hha7DTvIylV3uTJ6zVT0Q1kz1JoVLuZ+M6xOqxVs78lc89RLWDrTii3wbsTww13pcaR4y37U0MtVSP+TYRERERkT2YyEXkBgcOHcWBQ0clX39nr2548ZnxorNxlZSU4oOZn2LL9l2iZR9/+AE88mAq5HK53f28lnEDV69nAAC6dXJuSceLl6/gXy/9G3n5BQCAgf16Y8CdPREX0xgymQzXb9zEH/sOYv2mrfhl0xZs3Lwdb0z+FwYNuNOpdomIiIi8xdqAgb0zalkqD0j7Jbr5ttQECimDEURUM1mKFVJnr7AncVRfztK2PbHHUhKq2DlnmcdpS8estc94SFSds8lctu5Na/GIzz1EdYM9CVxiscSZOGJ83njbnucYxhEiIiIicicmchF5SZuklujVrTN6duuMFs3iLX7Y+/f/TcfeA4fRMKoBHnkgFYkJzVFSUooNm7djzfpfMf+b5QgPC8XIuwfZ3Qf9bFwA0NWJRK6i4mJMev0d5OUXQC6X4z8vP4e7+vcxuSY+rgl6duuMkcMGYfIb7yInNw/vfDgboSHB6NyxncNtExEREXmTlGQuMfYuE6IvY36N2L6UX5aL1SElycJTrCVa6M8T1UeWYo7xtiPJpGIxx9q15tuODHJ6M9ZYGvwlIumkJnNJqcP81fi8tbLm29aSMGyVkdImEbmWpThivC/lucTROGJ83lZMkXKdWL1ERERERI5gIheRGwzs2xv/+Nt9KCsrQ0VFJdRqNTS+amg0vvD19YXGVw2l0vbtd/jYCew9cBgajS8+//h9hIeFGs51aNcGCoUcq9ZuxPer1jqUyLX3wK1ELo2vL9oktbS7vN72nXtwMzMLANC/T89qSVzGEpo3xcP3p2DW5wug0+nw7Y+rmchFREREtZqtQQcpCRViAw+uGsx05JiUNt3NUjIXB0WovrMUG+yZAVCsLlszVxhfY2lbauJWTYg1jCVEzpOSzOXIkq/6a62dN9+v7c89RPWV1GQue5LU7X2mMd535vnG2jEiIiIiInswkYvIDfz8NIiPjXG6nuzsXLRJaol2bVubJHHpDRnQF6vWbsTlK9fsrlur1eLA4VvLP3Zs31ZSYpklW3/fbdge1L+3zesH9uuNWZ8vAADs2X8YJaWl8NNoHG6fiIiIyNukzpJjb12ODmZa2pZ6vdi+O1hK1jLvg73/fkR1naUZpaTGHSlJo7YSSV2RNGHpmCOsJX9aizO26pRyjKg+k5LMZU7KMmnmsUhqHDLermnPPUQkztrnH0uxxFVxxPy4Pc8yfE4gIiIiIndhIhdRDXZX/z5WZ7fSfy7U+PraXXfa2XMoKi4BAHRzYllFAMjJzTNsh4aE2Lw+KCjQsK3T6VBSUsZELiIiIqr1bCVzAZYHEexZatH4mNh19mxLqacmqGn9IaoJpCRzAeKzAtozm4WU866KPURUO1mLPfpt43P21i31nJRkLlvliMg7rCVhOfLjDvO67TkvJRmdcYSIiIiI3ImJXES12PerfgEA9O3d3e6y+w4eMWx3dTKRy1etNmyXlpXZvL601PQaPz/7E9G85dHzRxGuVHm7G0RERMjXVnm7CyTC0oCl2ACmvQMRUq61Z+YJW9d7ijOz5YjVRVSfWEvydHRWQHvatrRf0wY7HYkzjCdE9pGS0A64JinDuA1X7hORd9mbpG6+7Uy7UvcZR4iIiIjIE5jIRVSLnDx9BlqtFmlnz+OXX7fiTPp5tGzRFE8/8bDdde39K5ErskEEmsREO9Wv6EZROHL8JIBbSyXaSgw7cOioYTs4KNChGcW8Kbuq0ttdICIiohrO1rIe5secSfASa9uefVvHiahmM48f5sdtxRhnYo4j8cadscZawpY9yVyMk0SO8cbzD597iOoWW3HDfL+uPtMQERERUf3GRC6iWmTSG++iqKgYAKBW++DJvz+IcfeNgUKhsKuekpJSnDiZBuDWbFzOfugcefcg/LJpCwBg4+ZteOje0QgLFV9iUavVYtW6jYb95OFD+aGXiIiI6iRrM+UA9s/SJWVpRWvnavJAprOzctWE90BUE0idoUts35k2HT3mDraSuYDq8dT8vL3niOg2a0kYxsfdmcRu6ZiUc0RUM9iT0KU/5qlnGmvHiYiIiIhcgYlcRLXI3YMHoLS0FNk5eTh24hS+XLwM+w8dwxsvTURkg3DJ9Rw6ehxanQ4A0M3JZRUBoG1SIlq2aIoz6ReQm5ePZya/gbdefREtWzQ1+VCbk5uHOV8uxv6/ZuRSKBRIHjHM6fbdKTg4GFFRUd7uBlGdo9PpIJfLvd0NojolLCzM210gEdaSBmwNdNo6J1aXK895mqPJXDXpPRDVBJbijpQELlv3YF2INUDN6w9RXWTp/9ftff6xVLerzxFRzSQlSd34mJ6UzxWOxAvGESIiIiLyBJngzM+eichg/tfLsWjpCgDAiGGD8MrzT7m1vZLSUnz61TdYtXYDYho3wpIvZkmemWvm3K/ww+pfAABrvluE4KBAp/tz6c8reOU/7+Pq9QzDsehGUWjZohkUCgWysnNw9Pgpw4doX7Ua/375OfTt3d3ptomodsnJycE//vEPrFq1yttdISLyOFsfv1zx8czW4EJNH3yw59+gpr8XoprA2j3lbMyp6QkTrvrKqya8F6LaTMq96K6ZOXn/EtUd7v4sxVhCRERERDUFZ+QiqqX8NBpMnPAP/LH3AK5cvY7DR0+gc8d2ksruPXQEAJCY0NwlSVwAENckBl/M/gBTZ36Gnbv3Q6vV4tr1G7h2/Ua1a1u1bI5XX3gGzZvGuaRtIqpdZs2ahfXr13NWLiKql8SWFrJ03pgjy4A5cl1NYP4L+127duHMmTOG8yNHjkRERIRX+kZUG0mZGdAd7dUE5rN4nD9/Htu3bzfsd+3aFW3atLFZBxE5R8pMOa6+13jvEtU9jn6WclWbRERERESewkQuIhe5e8gAdGx/6wvg8LBQl9R5Mu0srl3LQGLL5mjSOLraeR8fFRo3aoibmVm4mZUtqc7SsjK0TkxA68QEdJWY+CVVUGAg/u/fL6OkpBQHDh/FoWMnUVpaCp1OgEatRsuEZujepaPL/n2IqHb64osvUFFRgZ9//hnJycne7g4RkdfYGoiwdK0j9ddG+v4vWLAACxYsMBzfv38/E7mIHGBPzHGm7prGOJFt586deOyxxwznPvzwQ4uJXDX5PRHVdu6KR7xvieqP+vpcQ0RERET1AxO5iFwkumEUohtGubTOxUu/x649+zF86F149YWnq52v0mrx59VrAICw0BBJdWp8ffHGSxNd2k9zfn4a3NmrO+7sxWUTichUXl4eMjJuLcE6Y8YMJnIREf1FbLDA3gEJDjgQkVTOxJzaGmtkMpmkvtfW90dUW0mZrUtKOSKqvxyNI2JliYiIiIhqAq5nRFSD9et9KxFq/aYt+GPvAZNzgiBgyfKVyMrOQcOoBpKXVSQi8qZPPvnEsL1//34v9oSIqObTJx1I/SMickZ9jDV1/f0R1Ub1MRYRkWvxMxQRERER1XackYuoBhsysC927tmP7Tv34OX/vIcO7dqgdcsWKK+sxKEjx3H+4mX4qtV4+bmnoFQovN1dIiKbvv76a8N2WVkZtmzZggEDBnixR0RERERERERERERERERENQMTuYhqMKVSif9OmYQfVq3D96vW4fDREzh89AQAQCGXo1/vHnh03L1o0Szeux0lIpKgoKAA6enpJsc+/vhjJnIREREREREREREREREREYGJXEQ1nkKhwH0pI3HvmBHIzsnF9Ywb8Pf3R8PIBvDz03i7e0REkn366afVjm3ZssULPSEiotrsgw8+QOfOnU2OqVQqTJo0yeE6V65ciTNnzjhUtnXr1hg1apTDbX/wwQcOlx09ejSSkpIcKnv27Fn88MMPDrc9efJkKJWOfaXwyy+/4MiRIw6VjY+PxwMPPOBQWeDWMs/FxcUOlR08eHC1//akunbtmsnMpPaaOHEi/Pz8HCq7atUqnDp1yqGyjRs3xsMPP+xQWQCYPn06qqqqHCrbv39/9OjRw6GyFy9exPLly6sd/+abbxyqj4iIiIiIiIiIyFNkgiAI3u4EERER1X2tWrVCWlpateN79+5F165dvdAjIiKqDR5//HEsWLDA6jW+vr4oLS11uI3Ro0fj559/dqjsAw88gGXLljlUVhAEyOVyh8oCwNKlS/Hggw86VHbVqlVITk52uO3i4mKHE4uk/G9qyV133YVff/3VobIAEBkZiczMTIfKzpo1CxMnTnSo7B9//IFevXo5VBYAbty4gcjISIfK3nfffVixYoVDZbt3747du3c7VBa4dW+Wl5c7VPa9997Da6+95lDZjRs3YujQoTavmz59ulNJoERERERERERERK7m+DfGRERERBIVFRVZnOlkxowZHu4NEREREREREREREREREVHNw0QuIiIicrvPPvsMliYB3bBhg4d7Q0RERERERERERERERERU83BpRSIiInK71q1b49SpUxbPnzhxAq1bt/Zgj4iIqLbYt28fzp07Z/UapVKJsWPHOtzG77//jitXrjhUNi4uDj179nS47eXLlztctlevXoiNjXWo7NWrV7Fjxw6H27733nuhUCgcKivlf1NLGjZsiP79+ztUFgB++uknlJWVOVS2c+fOSEhIcKhsdnY2Nm3a5FBZABgzZgzUarVDZXft2oXLly87VDYiIgKDBg1yqCwArFixAlqt1qGy7du3R1JSkkNlMzIysHXrVpvXdezYEYmJiQ61QURERERERERE5A5M5CIiIiK3KikpQUBAgMUZuQDg0UcfxcKFCz3YKyIiIiIiIiIiIiIiIiKimoVLKxIREZFbzZs3z2oSFwCsWbPGQ70hIiIiIiIiIiIiIiIiIqqZmMhFREREbrVgwQKb12RlZeHSpUse6A0RERERERERERERERERUc3ERC4iIiJym7KyMpw4cULStR9++KGbe0NEREREREREREREREREVHMxkYuIiIjc5ssvv7S5rKLejz/+6ObeEBERERERERERERERERHVXDJB6ugqERERkZ3atWuHY8eOSb7+xo0biIyMdGOPiIiIiIiIiIiIiIiIiIhqJs7IRURERG5RXl4ueVlFvRkzZripN0RERERERERERERERERENRsTuYiIiMgtFi5cCJ1OZ1eZb7/91k29ISIiIiIiIiIiIiIiIiKq2bi0IhEREblFx44dcfjwYbvL5eXlITg42A09IiIiIiIiIiIiIiIiIiKquTgjFxEREblcRUUFjh496lDZ2bNnu7g3REREREREREREREREREQ1HxO5iIiIyOW+/vpru5dV1Pvmm29c3BsiIiIiIiIiIiIiIiIiopqPSysSERGRy3Xu3BkHDx50qKxMJkNxcTE0Go2Le0VEREREREREREREREREVHNxRi4iIiJyqaqqKhw5csTh8oIg4LPPPnNhj4iIiIiIiIiIiIiIiIiIaj4mchEREZFLLVmyBFqt1qk6Fi5c6KLeEBERERERERERERERERHVDlxakYiIiFyqW7du2Ldvn1N1yOVylJWVQaVSuahXREREREREREREREREREQ1G2fkIiIiIpfRarU4ePCg0/XodDosWrTI+Q4REREREREREREREREREdUSTOQiIiIil1m2bJnTyyrqffHFFy6ph4iIiIiIiIiIiIiIiIioNuDSikREROQyPXv2xO7du11Sl1KpRFlZGRQKhUvqIyIiIiIiIiIiIiIiIiKqyTgjFxEREbnM6dOnXVZXVVUVfv31V5fVR0RERERERERERERERERUk3FGLiIiInKZEydOwNajRY8ePVBcXIx33nkHycnJVq+NjY1FUFCQK7tIRERERERERERERERERFQjKb3dASIiIqo72rRpY/ManU4HAOjQoQPatm3r7i4REREREREREREREREREf0/e/cdHlWZvnH8npkkk0oSAgSQXqX3Kr0JiAqiIrq2VVxXWXXXn2VdRV27uBasiAUsqHQEpEgv0qsivYaeQHoyk0z5/QEMGSY9M5lAvp/r4vKc97znPc9MghdJ7rzPFYEgFwAA8IuIiAh/lwAAwBXjaNxxLV+91m2sc4e2atSgnp8qAgAAAAAAAAB4G0EuAAAAAABKaM++A8rKzlZQYKCMRqOybTbZsm3Kzs5Wtu38f202u0JDQxQVGanoqEhFRVZQUFBgodY/fPSYJkz6wW0sOirKI8i1edvvJX4tjRrUU0R4mCQp4ew5HT4SJ6eUa/tkg8EgSTIajapeNVaxVSrJaDQW6jm79+5Xekam21iw2axmTRoVeG+mxaI/d+9zG2vSuIFCQ0IK9eyctmz/XTlfmslkVOsWBe8yCgAAAAAAAADeRpALAAAAAIASGjtuvPbsO1Dk+66pVlXXdemgfj27qUnjBiWu44lnXyrxGh+/86paNm8iSVq/aavefO+TQt8bbDarTu0aqlOrpnp166wuHdvlGex696MJ2rV3v8f45x+8VeB7cfLUGY/XOvHTd1W/bu1C13rRk/95VTabzXUeER6uX6ZNKvI6AAAAAAAAAFBShfs1WQAAAAAA4HXHT57SlBlz9NDjz2jCxMmy2e3+LqlELFardu89oAWLl+vZl97UXaMe04bN24q0xrjPvsp196+cnA5HScoEAAAAAAAAgDKJHbkAAAAAACgDvvlxurLtdj3ywN0e17p37aiV86f5oaqSOXb8pJ558Q29PuZpdenYrlD3/LFrj5auXKO+PbvlOcdOkAsAAAAAAADAVYggFwAAAAAAPvDQfXcqIjxMkkEOh0OZFosyMy3KyMxUYlKKlq1c4xFImvnzfN1121BFVohwGzcYDMWqIdhs1h3DbyzSPVUqx+R7vUG9OmrVrIkCAgMVGBSgpKQUHT9xSsdOnFB8wjmP+TabTc+/MlazfvjywvtRsE+++FbdOneQ2WzO9XpBO3YBAAAAAAAAwJWIIBcAAAAAAD4wqH9vVYqpmOf1v9w+VE/8+2UlJae4xixWq1auWacbB/X3Sg1hYSF64J6RXlnroq4d22nUfXfmes1isWrWvAX6eMI3buNZ2dlasXqthgzsV6hnnIlP0E8z5uiekbfmev1K25HLbrfr5Okzys7KVmBQoKIjIxUWFurvspSdna3EpBRlWjJlkEGxVSrlGZ4ritS0dCUlJeua6lVlNBq9UCkAAAAAAABQPhDkAgAAAADAD+rXq6ObBw/QpB/cWyaeS0zymLtl++8e4aj77rxV3bt28mmNRRUcbNYdw29WenqmJk6e6nZt2ar8g1yNGtTT3v0HXeff/jhDgwf0yTUM57CX/SCX0+nUspW/adGyldr++59KS89wXTMYDGretLG6d+moGwf1U3iY+05l6zdt1ecTJ7uN3TCgj265aVCez0tJTdW/nnvFbbeyls2b6PGH/+pR18Yt27Vw6UqtXrtBGRmZrmtGo1EN6tXW0BsGamC/ngoMDMz1Wc+8+IYSzl7afa1fr24aeevNOnz0mF547R0dPhInSbp16A0ezwcAAAAAAACQN4JcAAAAAAD4SaWYaI8xsznIYywtLcMt5CRJScmpPqurpAb06eER5EpMSs73nltvHqxPv/zWNc9iterziZP13JOjPeY6nWU7yJWamqaxH47XspW/5Xrd6XTq95279fvO3frl12Ua+9/nVDW2iut6i2bXKu74CWVmWlxjNrs93yDX6rUbtWffAbex24be4HaeabHotXc+1IrV63Jdw+FwaO/+Q3r7g0/1y6KleuOlZxUVWcFj3qHDR3Xy9BnXeZPGDXXq9Bk99Pgz7jXbbHnWCwAAAAAAAMAT+9sDAAAAAOAnW3//02OsQ9vWfqjEu8LDwzzGcu4UlRujwaBR97q3bJz/6zKPcJJU9lsrvvPR5x4hLqPRqNgqlT3em8NH4vTIk/9RVla2ayw0JEQD+vRwm3fw0BEdO3Eyz2cuWbHG7TwkOFg9u3V2nTscDr06dpxHiMtkMqlqbGUFBbkHCP/YtUcvvPZO7h83g8HtND09Q59PnOwW4gIAAAAAAABQdAS5AAAAAAAoZWnp6Rr32Vdaeln4plWLpqpft7afqvKeg4eOeIxFR0Xme0+mxaLBA3p7vP5x47/2CBM5HfmHwvxp05btHh/XypUq6rsJH2jaN59p1uQvNOzGgW7X4xPOafnqtW5jNw3q77F2XjtppaSmavPWHW5jvbp3UUhwsOt82qx5Wrlmvducbl06asZ3n2vqpM/0y9SJeuxv9ysw8NLm7dt27NT8X5d5PM94WZBr38FDWrx8tSQpKChI1zaqr/ZtWioizDPQBwAAAAAAACBvtFYEAAAAAMAHHnnyeQUEmGSQQQ6HQ5kWiywWqywWS647StWtXVOvPv+UHyr1LovFqkk/TPcYb9+6Rb73ZVqsMplM+sff7tcTz77kGt/xxy6tWL1Ovbp3cY0ZjWX399LmLFjiMfaf/3tMNa+pLkkyBwVp1L13at6CJcrKvrQL18/zf3XbhatRg3pq3LC+245kK1av0123D/NYf/XajR6fU4MH9HY7/+WyQFa12Cp66dknZDabz9dlNuu2YUMUFBSkdz4c75o3cfI0DR7Qx+1ew2VBriNHj0mSelzXSS89+08FBgZ61AgAAAAAAACgYAS5AAAAAADwgZOnThdqXs0a1XXHLTdq8IA+Cgjw7pfpWVk2bft9Z6HnBwUFqWnjhkV+TkZmpo4cPa4jccc0+5df9cefuz3mdO3cId81LJbzbfnatW6h6zq315p1m1zXPv5ikrp0aifzhfZ/wcHmItdYWu6781YNu/F6Wa1ZslqzZLFa1fRa9/c0IjxM7du20m/rL73GuGMnPNa6efAAvf3Bp67zXXv363R8gmIrV3Kbd3lbxWpVY9WyWRPX+clTZ3Tgsl3SBvTp4Qpx5TRkYF999tW3SkvPuHDvaSWcPadKMRVdcwxGg8d9Ta9tqJf//S+vfw4DAAAAAAAA5QnfXQMAAAAAwI/ijp3QV99N0fETpzRkUD/Xzk3ekJqWpn88NabQ86+pVlU/fv1xvnNmzJmvZavXKjAgQIGBgUpMTNKZhLP53nPH8JtUp1aNfOdYLFbX8aMP3qt1G7a4dpk6dTpeU2fO1V9G3CKpbAe56tapVah5wZeFqC4Gp3Lq2+s6fTj+a2VeCLlJ0srV63TbsCGu89zaKg7u38tt17KcgbGLGjesn2tdJpNJdevU0u87L4XxdvyxS316Xuc6N8gzyDWoX29CXAAAAAAAAEAJ8R02oIxISU3VmfjzP/yoXjVWoaEhfq4IAAAAQEm0aHatgoKCXJEXi8Wq1PR0padnKD09wy2cc/ZcoiZPm60Zcxbolef/T507tPVP0YWQlp6Ra+goL726d9GD944scF5GjvejZjb1N6IAACAASURBVI3qGnbTIE2bNc819s0P0zWof2/FVIxWcHBw0Yr2A5vdrm3b/9CmrTuUmJyi1LQ0paSmKcuapYDAAB29bAcuRy7tNkNDQtS/T3f9/MuvrrHla9yDXBs3b/doq3h9315u50eOHfdY+5W3P8gzeJWe4f7x3b7zsiCXZ45L13Vun+taAAAAAAAAAAqPIBdQRnz/00xNnjZbkjTz+wkEuQAAAIAr3H+fe9KtHd3lsrKy9dP0n/X15CnKzrZJkixWq5558Q19O/591ap5TWmV6hO1alyj24cN0U2D+8uQW/LnMjl35JKk+++6TQsWL1daWrokKdNi0YRJP+jZfz6ikDK8I5fT6dSMn+fr6++nKDkltdD35RbkkqSbBg1wC3Lt+GOXzp5LVEzFaEnSpm3uu3G1adlc1apWcRu7/L2V5BYkLEjC2XNu54Ycu31JUmBggCpXiin0egAAAAAAAAByR5ALV4Vvf5yur7+fIqfTffzizwoMMkgGg6pUilHdOjVVv04t1a1dS507tlVoSNkITK3fvE2SVKd2zXx/2FMch48e08NP/FvZtvM/HHr1+f9Tl47tvPoMAAAAAEUTFBSou0cOV0xMtN5491I7Q4fDoRlzF+iJvz9Q4mcEm826begNhZ5foUJ4gXPM5iAZDUZXECgoKEgVoyMVHRmpqKhIVY2trP69u6t5k8aFCnBd5LjsC7oKERF64C8j9MFnX7nG5i1coltuHKg6tWoWet3SNmHiZH3704xcr1WpFKNKlSrK4XDo2PGTbjubORwOOZ1Oj/esccN6atSgnvbuP+gaW/XbBg0dcr0kadPlbRUH9PbWS3G5PAh2+Uc1KDDI688EAAAAAAAAyiOCXLgq2O0O12+w5+fYiZM6duKkVv22QZIUHhaqYUMG6u6RwxXix9Yc5xKTdODQEUlSh7atvLq2zWbTq2PHubXGyOs3vQEAAACUvn69ursFuSRp7YbNXglyhYWF6KH77yrxOjmNGHajRt13p+x2u2w2m8xm7+yOZcwl9DV0yPWaPme+jh0/6RobN/5rffj2f2U0Gsvc1zZxx094hLiCAgP13P+NVpuWzVUxOso1/vyrY7Vi9Tq3uQ6HQyaTyWPdmwf319hx413ny9es09Ah1+vEyVM6dTreNR4SHKye3Tp73G82ewat/vfaC+rYrnXhX1wOl+/IVYS8HgAAAAAAAIB8EOTCVadWjWvUsX1rBZhMCgwIkNFoVHJqqhLOnlN8wjklnD2rs+eSJElp6Rn69qcZ2vHnbo195T9+C3Pl/A3qjl4Ocn330wzt2XfAq2sCAAAA8B6jyegxZrV6tsIra0wmU66ho+IyGj3fh4CAAI0eda+efelN19j23//UyjXrFRxsVkZGptee7w0bLuy0nNOjo+5V357dPMZPnjrtMZZXkKtfr+766PNJrl3Qtm7/Q8kpqR67cfXq3iXXr2urxVbxGDuXmJT3CykAuS0AAAAAAADANwhy4arTrEkjPf7wX/OdE59wVj9M/1lTZ86VdP4HAe99/IWee3J0aZToYeOW7ZLO/5CiVYumXlt3z74Dmvj9VElSTMUoV4ANAAAAQNmxbsMWj7GQMtICvjTlFuSSpK6d2qtt6xbasu1319jHEyYpwIshMm+Jy7Fz2EX16tbyGEtPz9C+A4c9xvPaYSw0NET9enfXnPm/uuatWbdRm3K8J1LebRW7dmynT7/81m1s74FDGtivV67zC1KUlpkAAAAAAAAACi/375ICV7nKlWL02N/u10P33ukaW7JijdLTM/K5yzecTqc2bjn/W9stm13rtV3BrFlZevWdD2V3ONS4YX1d36enV9YFAAAA4D179h3U+59+6THernVLP1TjX0Zj7uEgg8Ggxx66z23s5OkzSklNK4Wqiia39pApKZ51fvX9FDmdTo9xuz3vVpE3D+7vdr5s1VptzrEjV7WqsWrZrEmu99auVUPVqsa6jS1etkpp6ekeczMyMzX6qRf075ff1Fvvf6oJEyfr8JE4tzl5fawAAAAAAAAAlAw7cqFcu3XYDfr2pxnKtFiUlZWlNes2akDf0g08HToS59opq4MX2yp+9c2POnwkTgaDQU8//rCWrfzNa2sDAAAAKNjUmXMVFhYqgwySQcrOtslqtSrDYpHVmqVDR45q997c26CPGDaklKv1v7x25JKk+vXqaMjAvpq7YInP68jIzNR/Xnm7SPcM6tdb3bp0UPXLwlKSNOGbH9SmZTNFRIQrIyNTU2bO0ZQZc3JdJ/7sWdUOrZHrtcYN66tRg7rau/+QJGndRved3Ab375Xne2gwGNS3R1d9N2WmaywxKVmvvD1OT45+SFUqx0iSkpKSNfbD8dr++5+ueeHhYRox/MbLV8z1OQAAAAAAAABKhiAXyrWQ4GBd26iBtu74Q5KUcPZcqdew6UJbRUnq0La1V9bc8ccuTZ42W5J027AhatSgHkEuAAAAoJRd/Dd5Ud13562qWaO6l6sp+0yG/DcNf/CekVqyfI0yLRaf1mG327Vyzfoi3dOy6bWSpF7du+iDz75yu3b4SJyG3jVKVSrF6Oy5RFf9D913pz6fONlt7uPPvKjuXTupT/euatOqucdzbho0QO98OD7XGq7v2yvfGu+76zZt3LpDe/ZdCg/+tn6TNm3dofp1a8tgkA4djvN4f5//v3+oQkSE2xg7cgEAAAAAAAC+QWtFlHtG06W/Bvm1sfCV9ZvPt1WMrBChhvXrlHi9jMxMvfrOh5KkKpVi9MDdI0q8JgAAAADfCw8L1Uv//qceuGekv0vxC4Mp/29RxFSM1t133FJK1RRPpZiKuuWmQR7jWVlZOnbipCskNerekbr7juFqfiEAdtHZc0maNXehjh47kev6/Xt3V7DZ7DHepmVzVataJd/azGazXh/zjOrWrulR2649+/Tn7n1uIS6j0ahHR92j6zp38FjLwI5cAAAAAAAAgE8Q5EK55nA4dDTumOs8KiqyVJ+flZWtbRdaVrRr0zLfViKF9ckX3+rkqdOSpH8+OkqhISElXhMAAACAd5mMRlWpXElNr22oQf17662Xn9PPP36lvj27+bs0vynM10O333KjYqtULoVqiu+Jvz+gu0fcopDgYI9r4WGhen3M07pn5K2SpP8+9y+1bd3C47UHBJhyXTs0NET9ent+jgwe0LtQtVWpHKMJH76th+67UzWqV8tzXpNGDfTpu6/pjuE35z7BQJALAAAAAAAA8AVaK6Jc27R1h+ITLrVT7NjeO60NC+v3P3crKyvr/LPbtirxehs2b9PseQslSd27dlS3Lp6/OQ0AAADA+957fYyccspkNMloMsput8tmO//HbredP7bb5LA7FBUVqcgKEUX6RY5OHdpo9g9fuo2FhXr+0sblc4wlDNz069VNXTq2cxsLDvbcEao4xr76vOx2+6V1c9lp6nLmoCBN+vRdWS98HZVThYjwYtUx/dvcWxUWRUjIpdCWwWDQQ/ffpXtG3qoNm7fp1Jl4ORx21alVU+1at1BgYKBrbuVKMfrgzZeUnJKq4ydPyRwUpMgKFVQpJjqfp7l/TEOCg9WzW+dC12oOCtLddwzXX0bcot17D+ho3DElp6TK6XQqOjpKHdq2UnQBv+T0zqv/kc126WNnINgFAAAAAAAAeAVBLpRbe/Yd0Mtvvuc6b9HsWsVWrlSqNWzast113L6EQa7U1DS98e7HkqToqEg99djDJVoPAAAAQOFFFDNEVFjmoCCZg4IKnFcxOsq7zzWbZS5EwKo4IitEFOu+sLBQhYWFeq0Ob79nFwUHm9Xjuk6FmhtZIaJQ78e5xCQtXrbKbaxX9y657v5VEIPBoCaNG6hJ4wZFvrdCRPE+dgAAAAAAAADyR5ALV52U1FQdPhKnwMBABQUFKjAwUCkpqToTn6DT8Qk6E5+gXXv3a+2GLa57wsNC9fxTj+W5ptPp1MHDR3Xg4GGlpKapWtUqatWiqcLDwkpU64Yt2yRJtWpcU+IQ2fuffaWEs+d3F3v+qccK/A1qAAAAAMCVw2q16p0Px8titbqND71hgJ8qAgAAAAAAAOBtBLlw1VmzbpPWrNtU6PkN6tXRc0+OVvWqsbleX7pijcZP/F4nTp52Gw8PC9VzT45W966F+w3ryyUlJWvv/kOSpA7tSrYb18o167VoyQpJ0ohbblLHdqXbIhIAiiI2NlaHDx+WyWTydykAAABl2uJlqxQeHqajccc179dlOnjoiNv1IQP7qem1jfxUHQAAAAAAAABvI8iFcik6KlKdO7RV107t1K1zBwUE5P5XYdqsefrgs69kMpk0/MZBatm8iewOu9as36wly1frxTfe1aTP3lPNa6oXuYbN2353HXdsW/zgVWJSst4e95kkqVGDuvrb/XcWe62yZv369dq1a5e/ywDgZYcPH5YkLVq0SPv37/dvMQB8qmfPnqpbt66/ywCAK9bLb72f57WK0VF65MF7SrEaAAAAAAAAAL5GkAtXnY7tWmv4TYNlsViUlZ0tc1CQQkKCFRIcrODgYIWFhah61VgZjcZ810lKTtHnkyZLkl4f84y6dmrnuta/dw/FVIzWlBlzNGfBEj3ywN1FrnP95vNtFU0mk1q3bFrk+6XzLR/f/uAzJSenKNhs1kvP/kuBgYHFWqss+u677/TRRx/5uwwAPvLKK6/4uwQAPvb9998T5AIAH6hXt7ZefvafiggP83cpAAAAAAAAALyIIBeuOjEVo91CV8W178AhhYWGquY11XNd74YBfTRlxhztP3CoyGs7nU5t2rpdktS8aWOFhoQUq8alK9Zo9doNkqQBfXvoXGKSziUm5Tr3dHyC6/jQkTiFh53/hn/FilHF2lEMAAAAAOBbISHBysy0SJJMRqNqXFNNXTq204P3jpQ5KMjP1QEAAAAAAADwNoJcQB46tG2lmd9PkNPpzPV6cLBZkhQUVPQdsI7EHVd8wjlJUse2rYpd4979B13HP//yq37+5ddC3Tf+6+9dxzcN7q+nHnu42DUAAAAAAHxjwfRvdfpMgrKys3RNtaoKCODbOAAAAAAAAMDVjO8AAgUwGAy5jh84eESS1KxxoyKvuWnLdtdxh7ati1cYAAAAAOCqZjQaVa1qFX+XAQAAAAAAAKCUEOQCimnWvIUKDAxQ397dinzvhi3bJEnh4WFq1KBusWv4yx3DdcvNgws197ufZmjW3IWSpGf/+YjaX9gJLOTCzmIAAAAAAAAAAAAAAADwH4JcQDF8+8N0bdi8TX+7/y5VrxpbpHuzs7O1dftOSVL7Ni1lMpmKXUdEeJgiwsMKNTc8NNR1HBVZQbGVKxX7uQAAAAAAAAAAAAAAAPAuglxAETidTv0042d9Pmmy7h5xi+66fViR19i5a68sVqskqeOFXbEAAAAAAAAAAAAAAABQvhHkAgopOSVVY8d9ppVr1uvJ0aM0dMjAYq2zcct213H7NgS5AAAAAAAAAAAAAAAAQJALKJT1m7bqjXc/Umpqul574Wl179qx2Gtt2LJNklTjmmqqVrWKt0oEAAAAAAAAAAAAAADAFYwgF64KXTu1U6WYaEnnA1LetHzVWr3w2juKCA/XB2+9rOZNGxd7LWtWlsxBZrVodq2u69Tei1UWrMd1nXVN9aqSpAb16pTqswEAAAAAAAAAAAAAAJA/gly4KjRqUE+NGtTz+rrbft+p/779vqKjIvX+my+pXp1aJVrPHBSkj955xUvVFU2Txg3UpHEDvzwbAAAAAAAAAAAAAAAA+TP6uwCgrLLb7Xrl7XHKzrbphacfL3GICwAAAAAAAAAAAAAAAMgLO3IBediy/Q+diU+Q2RykeYuWat6ipXnOvXvELapft3YpVlc+jBw5Ui1atPB3GQB8JC0tTYGBgTKbzf4uBYCPdOrUyd8lAAAAAAAAAAAAXDEIcgF52LlrryTJas3SkuWr8517w4A+BLl8oGvXruratau/ywDgIxMmTFBkZKRuv/12f5cCAAAAAAAAAAAAAH5ncDqdTn8XAZRFKampSkvPKNTcmOgodpQBgCIaNGiQwsPDNXXqVH+XAgAAAAAAAAAAAAB+R5ALAACUuuTkZEVFRUmS0tPTFRoa6ueKAAAAAAAAAAAAAMC/jP4uAAAAlD9z5sxxHc+bN8+PlQAAAAAAAAAAAABA2UCQCwAAlLoZM2a4jqdNm+bHSgAAAAAAAAAAAACgbKC1IgAAKFUZGRmKiYmRxWKRJIWGhiohIUEhISF+rgwAAAAAAAAAAAAA/IcduQAAQKmaP3++K8QlnQ92/fLLL36sCAAAAAAAAAAAAAD8jyAXAAAoVTnbKl40depUP1QCAAAAAAAAAAAAAGUHrRUBAECpycrKUqVKlZSamuo2HhYWpoSEBAUHB/upMgAAAAAAAAAAAADwL3bkAgAApWbx4sUeIS5JSk9P1/z58/1QEQAAAAAAAAAAAACUDQS5AABAqcmtreJFtFcEAAAAAAAAAAAAUJ7RWhEAAJQKu92u2NhYnT17Ntfr4eHhio+Pp70iAAAAAAAAAAAAgHKJHbkAAECpWLlyZZ4hLklKS0vTwoULS7EiAAAAAAAAAAAAACg7CHIBAIBSkV9bxYtorwgAAAAAAAAAAACgvKK1IgAA8Dmn06kaNWroxIkT+c6LiIhQfHy8zGZzKVUGAAAAAAAAAAAAAGUDO3IBAACfW79+fYEhLklKTU2lvSIAAAAAAAAAAACAcokgFwAA8LnCtFW8aNq0aT6sBAAAAAAAAAAAAADKJlorAgAAn6tfv74OHjxYqLkVKlRQfHy8goKCfFwVAAAAAAAAAAAAAJQd7MgFAAB8avv27YUOcUlSSkqKFi1a5MOKAAAAAAAAAAAAAKDsIcgFAAB8qihtFS+aOnWqDyoBAAAAAAAAAAAAgLKL1ooAAMCnmjdvrp07dxbpnsjISJ05c4b2igAAAAAAAAAAAADKDXbkAgAAPrN3794ih7gkKTk5WYsXL/ZBRQAAAAAAAAAAAABQNhHkAgAAPlOctooX0V4RAAAAAAAAAAAAQHlCa0UAAOAzHTp00KZNm4p1b1RUlM6cOaPAwEAvVwUAAAAAAAAAAAAAZQ87cgEAAJ+Ii4srdohLkpKSkrRkyRIvVgQAAAAAAAAAAAAAZRdBLgAA4BMlaat4Ee0VAQAAAAAAAAAAAJQXtFYEAAA+0aNHD61atapEa0RHR+vMmTMKCAjwUlUAAAAAAAAAAAAAUDaxIxcAAPC6M2fOaPXq1SVeJzExkfaKAAAAAAAAAAAAAMoFglwAAMDrZs2aJW9t+jlt2jSvrAMAAAAAAAAAAAAAZRlBLgAA4HXTp0/32lozZ86UzWbz2noAAAAAAAAAAAAAUBYF+LsAAABwdbHb7apcubJGjBghk8nk+mM0Gt3Od+7cqbS0NHXq1ElhYWEe13OeJyYmqnLlyv5+aQAAAAAAAAAAAADgMwant/oeAQAAFEHnzp21fv16HTx4UHXr1vV3OQAAAAAAAAAAAADgV7RWBAAAAAAAAAAAAAAAAAA/I8gFAAAAAAAAAAAAAAAAAH5GkAvlWtzxExr5wGiNfGC0Zs9b5O9yAAAAAAAAAAAAAAAAUE4F+LsAXLlOn4nXhEk/yCnJ6XS6XTMYDOf/Kyk8PEz169RSndo1Vbd2TYWHhZV+sXnYuHm7jh0/KUlqUK9Osdex2WzaumOnVqxZpz37Dig+4awyLVZVi62iNq2a64YBfUq0PgAAAAAAAAAAAAAAAK5uBLlQbCmpaVq4ZEWR72vSuKHuvPVmde/aUSaTyQeVFd6GLdskSeFhoWrcqH6x1vht/Wa9/u5HSk5O8bh24NARHTh0RDPnLNCjD96j24YNKVG9AAAAAAAAAAAAAAAAuDoR5ILXREVWUGBggAJMATIFmGS1WnX2XJIcDofbvF179umF195Rh7at9MaLz8hsNvulXpvNpi3b/5AktW3dQgHFCJV9++N0fT5xsus8OipSHdu1VoUKEUpLS9e6jVuUmJQsu92uceO/Vs0a1dW5Q1uvvQYAAAAAAAAAAAAAAABcHQhywSsqRIRrzk9fe4zb7XYlJqXo7LlzOnbipOYuWKJNW3dIkjZu2a7nX31Hb//3OVcrxtL05+59ysy0SJI6tm1d5PsPHT6qCZN+cJ0/8uA9un3YELddxux2u77+boom/TBNkvTux1/op68/9svrBQAAAAAAAAAAAAAAQNll9HcBuLqZTCZViolW44b11bdnN733xot6+K9/cV1ft3GL9h045JfaNl5oqyhJHdq2LPL94yd+L6fTKUm6/ZYbNfLWmz1aRZpMJj1470h1bHc+KHby1GnFHTtRgqoBAAAAAAAAAAAAAABwNSLIhVJ31+3D1KBeHdf5/MXL/VLHhi3bJUnVq8WqerWqRbrXZrNpw+ZLQbCRw2/Kd37/3t1dx7v3HSjSswDgahUSEqLQ0FAZjfxzBAAAAAAAAAAAAABorQi/6HldZ+0/eFiSXP8tTampadq1Z78kqUPbVkVfwGDQhHFv61xiklJSU1UppmK+0yvnuG4ylc3AQlpamlavXu3vMgCUI8uXL5ck7dq1S7t27fJvMQCuWm3atFFsbKy/ywAAAAAAAAAAACgQQS74RUREmOvYarGW+vO3bP/D1RaxY9vWRb4/wGRS/bq1Vb9u7ULNz8zxGsPDwvKZ6T8HDx7UoEGD/F0GgHKI//cA8KUpU6botttu83cZAAAAAAAAAAAABSqbWwPhqpeUlOI6Dg4OLvXnb9hyvi2iwWBQm1bNff68XXv2SZICAwPUoum1Pn8eAAAAAAAAAAAAAAAAriwEueAXK39b7zpu2az0g00bt+yQJDW9tqEiwn27Q5bNZtOcBYslSR3atlZoaIhPnwcAAAAAAAAAAAAAAIArD0EulLrJU2bq4OGjrvPrOnco1ecfP3FKJ0+dliR1aNPK58+bu2CJEpOSZTIa9dB9d/r8eQAAAAAAAAAAAAAAALjyBPi7AFz9bDab4hPOKe74Cc2cu1Cr125wXRvUv7eubVS/VOvZuGW767hjO98GuZavXqv3PvlCknT3Hbeoft3aPn0eAAAAAAAAAAAAAAAArkwEueAVKalpuuvBxxQUFHjhT5Cs1iydiU/Q2XOJud7TpWNb/evRUTIYDHmum3A2UWPHfaaDh4+oT/eu+vuD95S41g1btkmSQkKC1aRxwxKvl5ff1m/WS2+8J4fDoVtuGqS/3n2Hz54FAAAAAAAAAAAAAACAKxtBLnjN0WPHC5xjNBrVotm1Gjp4gPr26pZviGvlmvV66/1PlJKaJklKTE4pcY02u11btv0hSWrXuoUCArz/V8DpdGr2vEUaN/4r2e12PfzXv+jO24bm+1oBAAAAAAAAAAAAAABQvhHkglcEBgaoX6/uslitslgsCggIUHBwsEJDghUSfP5PnVo11KFda0WEh+W7VlZWtt79eILmLVyiqrGV1alDW/26dKVX6ty9Z7/SMzIkSR3btvbKmjklnE3Um+99rPWbtspkMumFpx/XgD49vP4cAAAAAAAAAAAAAAAAXF0IcsErQoKD9dyTo72yVmJysuYtXKLBA/rosb/drwVLlnstyLXxQltFSWrftqVX1rxo9dqNeuPdj5SSmqaQkGC9/sLTat+2lVefAQAAAAAAAAAAAAAAgKsTQS6UOebAQL0+5ml179rJ62tv2LJDklQ1trJqVK/mlTUdDoe++WGavvz2J0lSTMUojX3leTWsX9cr6wMAAAAAAAAAAAAAAODqR5ALZU5UVKRPQlxp6en6c/deSVL7Nq1kMBhKvKbT6dTYcZ9p7oIlkqSaNarr3ddeUNXYKiVeGwAAAAAAAAAAAAAAAOUHQS6UG1u375TD4ZAkdWrX2itrTp01zxXiqlG9mj4a+4oqRkd5ZW0AAAAAAAAAAAAAAACUHwS5UG5s2LLNddy2dfMSr7f/4GF99PlESVKFiHCNffU/V3SIq2rVqnr55Zf9XQaAciI1NVURERH+LgNAOdC8ecn/3QcAAAAAAAAAAFAaCHKh3Ni0ZYckqUmjBqrghfDA7F8Wyel0SpKeHP2QalSvVuI1/alKlSoaM2aMv8sAUA4kJCTo+eef19ixY/1dCgAAAAAAAAAAAACUGQS5UC6cPHVGx06clCR1aNuqxOtZrVYtXrbKdf715Kma9OP0Qt//9sv/VmyVyiWuAwCuRDNnztT06dP18ccfy2Qy+bscAAAAAAAAAAAAACgTCHKhXNi0dbvruEO7kge5Dh6OU1p6huv88JG4It2fbbOVuAYAuFJ98MEHSkhI0OLFi3X99df7uxwAAAAAAAAAAAAAKBMIcqHYQkNCXLtbhYaG+Lma/K3fvE2SFGw2q9m1jUq8ntFoKNHOXuagoBLXAABXonPnzmnnzp2Szu/MRZALAAAAAAAAAAAAAM4zOJ1Op7+LAPIz/edf9P4nX2pQ/9567snRxVojMSlZdrtdJpNJ0VGRXq4QAFBYX331lR544AFJUvXq1XXs2DEZDAY/VwUAAAAAAAAAAAAA/mf0dwFAaYiOilSlmIqEuADAz6ZNm+Y6PnHihNavX+/HagAAAAAAAAAAAACg7KC1Isqc3Xv3Kz7hrOt8/8HDkqTTZ+K16rdLP/CvUrmSGjesX9rlAQCKKSkpSYsXL3YbmzFjhjp37uynigAAAAAAAAAAAACg7KC1Isqc/771vn5dtqrAef379NCYpx8vhYoAAN7wzTff6N5773Ubq1evng4cOOCnigAAAAAAAAAAAACg7GBHLpQ5fXtep/p1axc4r27tmqVQDQDAW3K2Vbzo4MGD2r59u1q1auWHigAAAAAAAAAAAACg7GBHLgAA4HMpKSmqXLmysrKyPK6NGTNGL7/8sh+qAgAAAAAAAAAAAICyw+jvAgAAwNVvzpw5uYa4JGn69OmlXA0AAAAAAAAAAAAAlD0EuQAAgM/l1lbxop07d2rv3r2lOFsWQQAAIABJREFUWA0AAAAAAAAAAAAAlD0EuQAAgE+lpaVp/vz5+c6ZMWNGKVUDAAAAAAAAAAAAAGUTQS4AAOBT8+bNk9VqzXcO7RUBAAAAAAAAAAAAlHcEuQAAgE/l11bxok2bNikuLq4UqgEAAAAAAAAAAACAsokgFwAA8JmMjAzNmzevUHNprwgAAAAAAAAAAACgPCPIBQAAfOaXX35RZmZmoebSXhEAAAAAAAAAAABAeUaQCwAA+Exh2ipetHr1ap05c8aH1QAAAAAAAAAAAABA2UWQCwAA+ITFYtHcuXMLPd/pdGrWrFk+rAgAAAAAAAAAAAAAyi6CXAAAwCcWLFig9PT0It1De0UAAAAAAAAAAAAA5RVBLgAA4BNFaat40dKlS5WUlOSDagAAAAAAAAAAAACgbCPIBQAAvM5qternn38u8n02m01z5szxQUUAAAAAAAAAAAAAULYR5AIAAF63aNEipaamFute2isCAAAAAAAAAAAAKI8IcgEAAK8rTlvFixYuXKj09HQvVgMAAAAAAAAAAAAAZR9BLgAA4FXZ2dmaPXt2se+3WCyaP3++FysCAAAAAAAAAAAAgLKPIBcAAPCqxYsXKzk5uURr0F4RAAAAAAAAAAAAQHlDkAsAAHhVSdoqXjR37lxZrVYvVAMAAAAAAAAAAAAAVwaCXAAAwGtsNptmzpxZ4nXS0tK0ePFiL1QEAAAAAAAAAAAAAFcGglwAAMBrli5dqsTERK+sRXtFAAAAAAAAAAAAAOUJQS4AAOA13mireNHs2bNlt9u9th4AAAAAAAAAAAAAlGUB/i4AAABcPRITE9WjRw85nU4ZDIZc56Snpys7O1vh4eEKCMj9nyIX79+zZ4+aNm3qy5IBAAAAAAAAAAAAoEwwOJ1Op7+LAAAA5cfQoUM1e/ZsrV27Vp07d/Z3OQAAAAAAAAAAAABQJtBaEQAAAAAAAAAAAAAAAAD8jCAXAAAAAAAAAAAAAAAAAPgZQS4AAAAAAAAAAAAAAAAA8LMAfxeAsqv7wOFu56sWTPdTJQAAAAAAAAAAAAAAAMDVjSAXUABrVpZeefsDSdJ/n3tSRiMb2QEAAAAAAAAAAAAAAMC7CHIBBThw8LBWrF7n7zIAAAAAAAAAAAAAAABwFWNrIaAAu/bs93cJAAAAAAAAAAAAAAAAuMoR5AIKsHPPPn+XAAAAAAAAAAAAAAAAgKscrRVRIt/8ME3HTpySJD05epTMZrNOnjqjqbPmatPWHYo7flI2m00R4eG6vl9P3TJkoGrWqO6xTnZ2tsaOGy9JCgsL1eMP/1VOp1MrVq/T/MXL9fvO3UpNS1NoaIgaN6ivPj266Ibr+yowMNBjrZOnzujr76dIkqpWqay/3j0i39cwYeJkxZ89J0n6x0P3KSIiXBmZmXr/ky8lSRs2bXXNfePdj2UwGCRJt948WI0a1CvqWwYAAAAAAAAAAAAAAAB4MDidTqe/i0DZ1H3gcLfzVQume8z5+z+f0x+79kiSFkz/Vms3bNab738iqzUr1zXDQkP1xUdvq0b1am7jVqtV/W6+U5JkNgdp4Yzv9NKb72n5qrV51tewfl29+9oLioqKdBs/fCROd//tCdecrz5+J9/Xecf9j+r4yfNhtBnffa7KlWKUnJKqIbffl+99b7z4rLp16ZDvHACAp4uBWLvdLqORzUEBAAAAAAAAAAAAQGJHLpTUhR/GS9Ka9Zv0+v8+ksFg0I2D+qtJo/oKDAzUzt17tXDJCmVmWpSekaExr/1PX3401vWD/MvXsVqz9OmX32n5qrWKCA/X8JsGqWpsZVmtWVq7cYvWbdwiSdp34JBeGTtO/3vtBbeSgoPNRXoJWdmXQmeOC7nGoKBA3TH8Jjmd0k8zfnZdH3HLTa5Sr6kWW6Tn5Gf37t1q0qSJ19YDgCuByWTydwkAUCpmzpypoUOH+rsMAAAAAAAAAABQxhHkQokYjZcCWO+MG69qsVX0/lsvKbZyJdf4wH69NKh/bz3yz+dkdzi078AhHT4Sp7p1arnmGGRwW3fKzDnq1b2Lnn7874oID3OND7txoJauXKOX3nhPkrRh8zat27hFnTu0dc0JCCjap7XBkGM3mAtBrpDgYD066l45nU63INcjD97tk91j2BgPAADg6sW/9QAAAAAAAAAAQGHQzwglkjOAZbFaNeaZJ9xCXBc1bdxQ1+VoQ7j2wq5arnXcc1wKCQnWk6MfcgtxnZ9nUN+e3dxaGi5autJtjsVqLdJrCDZf2sGLH7IBAAAAAAAAAAAAAADAHwhyoWRyBLCaNWmsJo0b5Dn12ob1XcfHTpxyX+ayJNfNN1yvqMgKea41/MZBruO9Bw65XbNaihbkMpuDXMcOglwAAAAAAAAAAAAAAADwA4JcKBFjjraEndq1ynduVGSk6zg9I8P94mVBrsYN6ua7Vp3aNV3HR+OOy5pjFy6LNSvfey9nzrEjlwhyAQAAAAAAAAAAAAAAwA8IcqFEcuavqlermu9ck+nSp1tmZqb7OpfNrVOzRr5rxVSMVlDQ+Z20nE6nTpw87bpmsVjyvfdywTl25CLHBQAAAAAAAAAAAAAAAH8gyIUSMRgvfQqFhYYU+r70jMuCXJftyFWxYnT+zzUY3J6XlW1zHVusRWutmPPZDqejSPcCAAAAAAAAAAAAAAAA3kCQCyWSM34VFhpa6PtsNpvb+eVBLqOx4E9NozFHAMtxKYCVlZVd6DokKS0t/dIJO3IBAAAAAAAAAAAAAADADwhyoURyBrCCg82Fvi8kxH33rsuDXHa7vcA17PZL4a3AwIAcawcXug5JSk2/FORy0lsRAAAAAAAAAAAAAAAAfkCQCyWSM4BVlJ2wQoLzD1udPZeY73Wb3a6U1DTXeWSFCNdxeFiY67gwwazUHDtyOQhyAQAAAAAAAAAAAAAAwA8IcqFEcga5LFZroe8LKWD3rgOHjuR7PT7+rKudYlRkBVWKqei6Fh52qcWjrYCdvU6cOq3k5JRLAwS5AAAAAAAAAAAAAAAA4AcEuVAiRmOOIJelCEGuXNof5gyFbf9jV77379qzz3XcuGF9t3vDw8NdxydPnc63TeO6DVvczmmtCAAAAAAAAAAAAAAAAH8I8HcBuNLlDHJZCn1XcC6tFQ0GgytINX/RUo0YNkR1atf0mOd0OvXjjDmu81bNmrhdj6kYpaqxlXXqdLys1iz9uXuvWlw2R5ISk5I1cfJUt7GCWitmZWUruIDdxIojNDRUXbp08fq6AAAA8L+YmBh/lwAAAAAAAAAAAK4ABLlQIjl3wsoswo5c5sBAz7VyHIeGhurFN97Vvx4dpVYtmrrGMzIz9cWkH1w7ckVWiNCwmwZ61HRdx/aaPme+JOntceP11sv/VvWqsa45v+/cpXHjv1Z2drZq16qhI0ePnb9wWZDLYDAoPDxMaWnpkqRJP07Tg3ffIZPJJJvdrgCTqdCvOT+1a9fWb7/95pW1AAAAAAAAAAAAAAAAcOUhyIUSydla0WgsfKdOizWX0NeFUJjRaNQzT/xdz786VqOfekHNmjRS1dgqsmXb9OeevYpPOOe65bGH/6rwsDCPpW4bNkQLlqxQekaGDh+J04j7HlGzJo0UHByss+cSdfhInCTpzZee1aKlq1xBrtz24xrUv7emzpwrSfruxxmat2CJzOYgdenYTv96dFShXzMAAAAAAAAAAAAAAACQl8Inb4BcXQpymYM8d9nKS367dzkcDvXs1lkv/fufioysoJ279mrJ8tVasWadK8QVWSFCLzz9uAb06ZHrGtdUr6oxzzyhkBwtHHfu2qvNW3fo8JE4RUVW0GsvPK3rOndQYOClPKPD4fBY68G771DNGtVd54lJyTp1Ol4OR/5tGAEAAAAAAAAAAAAAAIDCMjidTtIoyFX3gcPdzlctmO4x58DBw0pKTpEk1a1TSxWjo/JcL+HsOdfOVxWjo1S3Ti23672HjJDNZpMkrZw/TQaDQWnp6dq4ebu279yltLR0RUSEq0WTxurUvo3CwkILfA0ZGZlavHyVduzcLaekyIhwtWnZTO3atFRoSIgk6dDhozqXmCRJatK4oUJDQzzWsdvtWr9pm7b98afMgYGqEBGuzh3bquY11T3mAgAAAAAAAAAAAAAAAEVFkAtlRp8bRyg7+3yQa9m8KQowmfxcEQAAAAAAAAAAAAAAAFA6aK2IMsOQo02jM5cWhwAAAAAAAAAAAAAAAMDViiAXyg7DpSCXg43iAAAAAAAAAAAAAAAAUI4Q5EKZkSPHJQc7cgEAAAAAAAAAAAAAAKAcIciFMsO9tSI7cgEAAAAAAAAAAAAAAKD8IMiFMsOQY0suOztyAQAAAAAAAAAAAAAAoBwxOJ1Otj5CmbDqtw2yO+ySpOs6tVdgYKCfKwIAAAAAAAAAAAAAAABKB0EuACgHbDabziYmKS01TZJUs8Y1CgrKPSzpcDh0LjFZaWlpsmZlq2aNagoNCZEkpaSmKjEp2W1+lcqVFBIc7JO6rVlZOnX6jNtYZESEoqIiffI8+Fd6eoYSzp1zG4upGK3wsDA/VeQbdrtdx06cdBsLDQlR5UoxRV7LYrHqdHy821hkhQqKiqxQohoBAAAAAAAAAAAAlL4AfxcAAPCd1LR0fT7xey1etkpp6RmucZPRqK8++Z/q1anlGrPZ7fph6izNmrtQZxLOuq3zwlOPaUDfnlrw63J9+PlEt2vvvT5G7du28kn9h48c04P/eMpt7L47b9UD94z0yfPgX+s3bdWLb7zrNvbvfz2qwQP6+Kki37BYrPrLqMfdxvr0uE4vP/evIq+1Z98BjX7qBbexUfeO1D0jby1RjQAAAAAAAAAAAABKH0EuAPCTr779SVNnzyvxOnVq1tCn773uMZ6Wnq6HHn9Gx46f9Lhmdzhks9lc5w6HQ8+9/JbWbtic6zOsWVklrhMF+3P3Xj35/Kse4zcM6KvRD91b4P3vf/qlFi5Z4Tpv07KZXh/zjFdr9CWn2CQUAAAAAAAAAAAAQPlFkAsA/MSSlaW0tPQSr5OekZHr+BcTf8g1xCVJRqPR7XzhkhV5hrgun4viWbJitRYsPh+y6t6lg24aPMBjjt1uz/VzYuqsuRoysK/q1KqR7zMyMjLd7s/ItJSw6oJl/H979x1dRZn/cfyT3uiBJARCgNB7DwEChI69rbquHUUUFCsuWEFFUVfdVVFXkQVUUEGQIr0kEOk9EAKk0UsSEiA9N/n9gVwymbkhF9Fw/L1f53hO7neemee5kxnNOX7O98nJ1evvfKCSEsnNzVXvvD72yi9GjstphN8AAAAAAAAAAACAvw6CXADwF1RcXKwV0esMNRcXFz078lENHtBHbq5ucnG5dGzFmnUq67abhuqR++6Wr6+PCgoLJUkR3ToroE5tw7jGjUKv/hf4TXBQgN546XlDLTSk3h823x9p9s+LFbd3nyQppF5dp84tLi7Wp19O03tvvHTZcX+22I2btX7TNkmSu/vv+7OipIRQktO4ZQAAAAAAAAAAAMBfBkEuALiG+NeqoSp+fk6dE1w30FTLzslV1tlzhlqXju10yw2DLa9x5Jixc1fVKlX09OPD5PJb2svHzU2SFFI/WCH1g51a3+9RtWoV9Y2M+NPm+6OcTku3h7iu1IbN27Rxy3aFd+nocExlBLlWxay/ateiu5TzCL8BAAAAAAAAAAAAfx0EuQDgGvL4w/dp8IC+v/s6+fn5plpYOZ2zyo4PaxxqD3Hh91uz9uqEnT7+Yqo6d2wn99+CdWUV/8mhnuzsHG3csu2qXY9MkvMIvwEAAAAAAAAAAAB/HQS5AOAvZN36zUpOPSSbzWY6tjMuXjNmzbF/btaksfYfTJIkFdmMnZyOHD1uGNu9ayc1DWukvQkHtHX7LsPY/n17KTjI3BWstMSkFO3eu08HklJ05Ohx1axZQ/XrBqlpWCP1iugqNwfBpPSMM/pl2SpDrW3rFurQtrXDuc5nZyt2/Watid2otPR05eTkyd3DXQ1D6qtdmxYaOiBKvr4+lucWFhZq1pz5hlpEt85q0rihJOlgUopWrF6r7bv3KDs7R15eXurRrZNuum6Q6tT2N5x3+MgxrVl3IcC1dGW04Vh8wkH7/fXwcNfdt99suR4/X1/l5uXZO22lHj6q+YuW6babhlqOd7YjV05OrtZt2Kzo2A06dTpNubl5cnN3V2hIPbVr1UJDBva17BA35+dflJObq8NHj6uwsMhet9lshuemZ/euatywQcUXVE6SK7+gQEuWr9HGrdt1+OhxlZSUKKResHp066Shg/o5DLeVtid+v7bu2KVtu/YoLS1dJSWSr6+3mjRuqMiIcHXu2FZenp4VXu7RYye0Y/ceHUhKUcqhI6ri56v6dYPUMDRE/Xr3lKenR4Wv5cjJU6e1bFWMoRYUWEcDo3pLKj/8VmSzacXqtVq/eZuSUw6puLhYdYMC1bVTe918/aAKf9fDR49pTcx6bd6+S+ezs1VQUCAvLy9Vr1ZV3bt2Up+e4QoMqOPw/Ny8PM2et8hQi4zopoahIZKkhANJWrFmrXbGxSsnJ0fe3t7qGd5ZN103SP61al52fcXFxdqzb79WRscqYX+icnJzZbPZ5O9fSy2bhmlgVKTCfnuHAQAAAAAAAAAAgGsZQS4A+AtZtfZXLS8T+rgoPuGA4hMO2D/ffcfNmjX7Z8uxaekZ+u//vrN/rlG9mpqGNVLcnn2GuiS1bNbEYZDr8JFj+nzqN4qJ3ehwzWGNQvXEI/erW+cOFus4Y5rvwXvusAxy2Ww2fT9nvqbM+F4FhYWm40nJqVoVE6sp07/XmNEjLLdsLCwqMs1XvVpVhTUK1TezftKX02eatrLbfzBJ02f9pImvjlHP7l3t9ZRDh03XuiguPkFx8QmSJB8fb4dBrmpVq2hgv0jNW7jUXvtq+iwNjIpU1apVTOMrGuQqLi7W7HmL9MX/vlNBQYHpeFJyqlbH/KopM2bpuVHDNSAq0nB8xvdzlJ6RaTqvpKTE8J0Davs7FeRytE1gyqEjemnCuzp05KihnnroiNat36TZ8xfr9RefViMHc507d17vf/JfrYqOtTy+b3+iFi5ZqZo1qmvS+HFq2bxJuetMSz+jr7/5XguXrHC45ikzZmn4g/eof59ecnV1Lfd6jmRn52jMqxOVlHLIXqtapYo++/CtS4MczH86LV3jJkzSvv2Jhnrq4aPasHmb5sz/RS+/8JTatmrhcP4jx47rvf98oW07djscs3nbTn38xVT17hmuZ554RLX9a5nG5OXlm96FgNr+Cm1QX1/PmKX/fTfbdE7CgURNmzlH773xkrp2au9w/viEg5r4wSdKST1sOpZ6+Ki27ditb3+cp0H9+2jMU4/Jy8vL4bUAAAAAAAAAAACAynZl/2cRAIDLWLB4he4dPrrcEJckJSan6rmX3tCCxSt+13wfTp6iz77+xjLEVdq58+f1ylvva+WadaZjri7m/yzm5ubpl2Wr9N9p3zkM7RQXF2v8pI906PBRy+NXKjcvT8Puu1t+vr722rnz5zX12x8crqMiPvlymj7+7/8sQ1ylnc/O0fhJH2nJijUVXvPvYXV3z507rzGvTjSFuEpLSk7V+EkfqciiE11eXr6Gj/6nwxBXaWcyszTqhVe0tZzg0qatO/T3h0dqweLlDp8HSTpx8rQmTPq3Pp/67WXntVJks+n1dz40hLg8PTw0acJYhYbUt9es1lBQUKh/vv6OKcRV2rHjJ/X6xA+Uk5treTwxKUXDRr1QboirtJjYjRo26gUdOXbcdMxqm9bcvDz9tGCxZYjrIpvNplcn/kvHTpy0PL53336NfP4lyxBXWctWRmvMa2873bUOAAAAAAAAAAAA+DPRkQsA/kLalequU7YzV92gQLVp1dz+uXFoiAb2u7A92/pNW3X+fLb9WBU/X0WEd7F/rhcc5NQ6Nm7Zrvc//sIQmvDx8da9d96qgDq1lZicqoVLVxrm/OizKWrTspnDrkrlmbdwqX5etNRQa9K4oR645w7VDQxUYnKKvvlhrg4fOWY//sGnX6pzx3aqUb2avWaRN9GehANav3GrJKl921byr1lTKYcOGwI20oXA15wFi/XME49IkgLq1NbAfr1VVFSk1TG/GsY2qF9PzZuFSZK8ytl+Ly8vXzWqV9OD/7hDn3453V7/af5i3XL9YDUIqWcYX16w6KJflq3Sj3MXGmqNQkP04D1/U73gukpOPaRvfpir1ENH7Mf//dkUde3U3r7NXd+eETqbna3de+J14uRpw7UuPlPShWfOGVbrnznnZ6VnZKp+cF0NjIqUh7u7du6J18Yt2w3jEpNTNXveQlN3s2mzZpvCRY1CQ/TkYw+perWq+nXjFk2bOUdFRRe2iCwoKNBX02aqc4e2prUkJqfq5TffU15+vr3m5uamO2+7UY0a1NeJk6e0YMkKnU7LuLT+H+epc/s2Cu/S0al78cl//6cNm7fZP7u4uOj1sc+YOmhZ/cYXLl2h9IxM1favpesGRsnb20v7E5O1Zu16w7hTaemaMuN7PTn8QUP95Ok0Pffym8rJMYa8gusG6tYbBqtG9erKyDijOfMX61Rauv14xplMvfDKW5r22YeGbSWtglw74uIVE7tBktSxXRvVrFFdiSmphudOks6fz9b8Rcs0Yth9hnpaeobGjp9k2NrT08ND9//9dnXt3EE52TlasjLasK3pth27tWDxCt18/SCLuwYAAAAAAAAAAABUPoJcAHANOXTkmHbFxVd4vI+Pt5qGNbJ/vuWGwbrlhsGSzEGuju1aa+yzIw21oQOjJEnDRr2g/QeT7PWAgDp6dcxop9cvScdPnNIrb71vCHF5enho2mcfqm5QgL1245ABeuyZsfYwV0FBgSa8+29Nnfwvp+YrKSnR9FnGrj51gwL1n0nj7dsPNm/aWP1699TI51+2f8+z5y50troYvJIkF4uOXKtjfpWvr48+ee8NtW/bStKFzlff/ThPX5TpthS9boNGj3hYrq6uat40TK+OGa3s7BxTkCu8Swc9NeLhy363vPx8lZSU6LYbr9PchUt17PiFzkS24mJ9+tV0TRo/1nQvylNSUqJpM433KqC2v/7z7gR7oK1508aK6t1Do198TXvi90u60Jnrq+mz9OLTj0uSnh554Z5NmPSRIcjl7u5+xc+No/WnZ2Rq6MAoPf/kY/Zw0H2SlqxYo7fe/9gwdvrMn3TXbTfZg0M5Obma+aNx+1D/WjUN37dZk8Y6eTpdC5dc6ggXF5+gxKQUhTVuaK/l5ObqhVfeVG5unuF6H787Xm1bt7R/vuWGIXri2ZcM4bE33vuP5kz/vMLb+s1dsERzfv7FUHt25KOK7BFeofPTMzLVI7yLXvvn0/L18bHXN2/bqWfHTTCMnT1vkYbdd5dh3Mwf5ik944xh3NCBURozeoTc3S/96Xj7Lddr3PhJ2rR1h7125OhxzV2wWHfdfpO95moR5Fq5Zp2qVa2ij999Sa1bNpN0oQPXtO9mmzrOrV63Xo89fK8hELZo6UplnDFu7znu+VHq36eX/XOXTu3VslkTffTZFHtt8pTpGtS/t3y8vU1rAgAAAAAAAAAAACobWysCwDVk+qw5Gvn8yxX+591/f1bZSzZZGb3OFHbp27uHIcQlSQ1C6umOm4YaageTUnT02Amn5tt/MMnQAUmS7rj5OnuI6yJvby89/cQwQ23ztp2Gz1YduSTpkfvutoe4JMnV1VX3/O0WNSkV9JGk9IwzOnU6zan1X05+foE8PT008pEHDPVfN24xrf9yQaGU1MP2MNhFt944xNCVTJK8PD31zBOPGmpl5/qzVPHz1ZPDHzR0eJKkwf37qE2Z7lTnzp/X0eOXnp9z2dm64+brdfP1gzVkQF/1jYzQ7TcONX3f6wZFmebdsXuv4fOmLTtMz1nrls0MIS5Jqlmjuh645w5DLSvrrHZWMKC5cct2Q/BIkh74+x32gGZZVuE3d3d3Pf34MEM4S5K6dmqvvpERhlpxcbEhxGmz2bR6nbFzV80a1fXkYw8ZQlzSb8/JyEfk5uZmqC9bbQyROnqxRjx8rz3EJV3obvbAPXeoQX1jp7ljx08qK+usobZ2/WbD55D6werXu6dpjttuGqqwRqH2zzk5uYpPOGC5HgAAAAAAAAAAAKCy0ZELAHBVrV2/yVS7cUh/y7E3DBmgsEYN5eXlKW8vL3l7e5tCNpezzmK+rh3bWY5t1bypPD09VVBQIEk6fOSYMjOzVKNGdUnWW8BJ0pCB5qCPq6ur+vbqroNJKYZ6ZtZZBQUGmMZfqbz8fHl7eymyRze1b9tKO0sFjD7+Yqq+nvwvuf8WpLlcl6F1ZcIv0oVwj5WmYQ3l5+ur7JwcSdLJU6d18nSaAuvUvtKvcllWoaTIHuGmUJ504XfVp2c3xe3dZ6jv3XdA9YPrSpIC69TWqOEPmM4tKyigjql29tw5w+cYy+d6gOX1eoZ30fixz8rb2+vCP15eCq7ANpPJKYf06sR/GbrZDR0YpWH33+3wHKt71ql9G1Nw8qI+PcJNWyzu3XdAHdq2liTtjNtr6nTVs3tXVa3iZ3m9+sF11TSsofbtT7TX9h9MVlr6GdX2v7AVp9Vr5ebqqkGltuG0193c1KdnuGZ8/5Ohnpl11v6enk5LV8KBRMPxbh3bW76/Li4u6tCutRKTU+21XXH71Km9eetMAAAAAAAAAAAAoLIR5AIAXDWZWWe1d5+5203rFs0sRkuBAXUUaBGiccauvQmm2owf5srbQXcqW1GR8fw9+9S754Ut66yCIKEh9RyGWBqFNjDVzmRmXXbNzsjLy5OqV5OLi4ueHP6QHnnyBfux5NTDWrh4hb1bk5eXZ7nX2h2/z1SbOWe+qXPTRQWFBcbz4+IVGBXp7FeoMKtQUpNS3ZTKCqkXbKodSEy2DAhJFzqmHTp8VOfOZ+vc+fM6d/68JCkvr8A0tqjIZvi8ftNwrBsyAAAS6UlEQVRW05g2rZpbzlO1ahX162PuDlWezKwsjXltonJycu21mjWqa8zoEQ4Dho40bmh+Li8KKdPtSpIOJKbYf96wZYfpePMmjcudr0ljY5BLknbviVdU7x6SJFeLLUvDGoc67CDXMDTEVDuTmWWvX9zy0zBf/D69++/PLa+3Z59x/M64vZbjAAAAAAAAAAAAgMpGkAsAriER3TqpsUU4yJE6dfz/wNU4r6Cg0FSrXq2qPDw8LEZfHbmlgi8XLV8VYzHSWnLqoXKDXNXL6RDm62PugHX1g1z59p+bN22soQOjtHj5anvtq+kz1b9vL1Wt4iffy3TkysnJM9VWrllX4bUkpx6u8NgrYZHjcthZSpL8a9U01XLzjN+xpKRES1as0aKlq5wK8JTuiiVJ+fn5pjFW81+pbTvjTLUzmVlaFfOrw2CaZB1+K6/7l/U9u/QOnTt33ny9uuV3EwusYw5jXuzkJkmyyKFVr1bee2UOFpZ+r3Jyze/8/oPJ2n8wudx1XvRHP8cAAAAAAAAAAADAlSLIBQDXkP69e2rwgL6VvYwr5utrDhJd3A7tj1JYpsOWs0oHpZztfFRQaA6u5VgEy36PsnMMf/AerYqJVX7+hS5SWWfPadp3szVq+APy9rbucHRRYZF5vc4oG5K6+syhJHd3x3+quLqaOz2V7qRVXFysV956XzGxG51eSdkgl6+vr7Kyzto/u7m5yc/X1+nrOuvjL6Yqomsny+0lJesgV/n3zPyMl75n+QXm7mRuv23d6Yi7u/l46VCn0++VxRqyS71XhYVX750HAAAAAAAAAAAAriUEuQAAV42fr698fX0MYSbPP7AblyR5epqvP/bZkRUO2dQPDrL/7HzgxByMutrdx9zKhJVq+9fSvXfeqikzvrfXZv+8SDdfP1A+l+nI5elp3nrx+ScfU41yuo6VVl53rKvBKpRksxVbjLygyCLE5+Fx6U+buQuWmEJc/rVqaOiAKLVo1kRNwxrKw8NDJ0+l6fFnxxnGFdqMWysG1qltCHJ5eno4/bxcjqeHh64f0l9zFyyx1zKzzuqzr7/RmNEjLM+x6mJmszkOOlkFH0s/s1ZbklrdZ8M1LQKNpUOFrhb3qbx7ZxWQ9Cz1e7V65/v17ql+v23leDlWYTYAAAAAAAAAAADgWkCQCwBw1bi4uKh+cF3tP5hkrx05dlzFxcWW3ZOuhmpVq5pqHdq2UnDdIIvRznOx2hfuN1adg6xCJr9rfov7dvftN2v+4uU6nZYhSbLZbJr81XRFdO1U7rWqWXR1at+mpRqGhlydxf5OVqGk02npDscfP3HKVKtR7dLzsGTlGsMxDw93TRo/Ts2bhhnqWWfPma5TtiNX/XrG5zo3N08ZZzJVq2YNh+tzRt3AAL07YZwahoYoIyNT0bEb7McWLF6uoQP7qm2rFhZnmm/ayXLv2UlTrXqpe1atip/p+JGjxxXepaPDax4+etxUMwQpr0JHrtJhM6vnOKCOv/r06u7UPAAAAAAAAAAAAMC15o/5v+oAgP+3IiO6Gj7n5ubpiEXQQ5J274nXyOdf1phXJ2rCpI/0wadfasfuPU7N17VjO1MtKeWwU9cozZkuS/kWHbmudpDLKgDn7e2lEQ/da6itW79Ze/YdKPda1vfq0O9b4FVk1ZErKdXx+lIOmX/PLZs3lXQh3JZwIMlwrFFoiCnEJUlxe/eZamWDXJHdu5rG7NufaLmuk6fT9PizL+mFV97S6+98qPc//q+i122wHFt63RcDdaOGP2Dqnvbevz+37Ixldc9SUo84nMfqWKvmTew/d7cIAyYcTDLVStufmGz47OLiovZtW136XE4Y0opVp7vS96N921amTnXX0nMMAAAAAAAAAAAAXCmCXACAq6pf756m2rc/zrMc+8vy1doVF6/1m7Zq+eq1mrtgifLzzd14ytOjexdTLXbjFsuxZ8+d049zF2rF6rXaun2XEpNTlV+m+4/VNnCOFBRadOSqwNaKBYXlb1VXkfUMiIpUi2bGUNLi5avLvVZEuMW92rTVcmx2do5+mLtQy1fHaMu2nUpMSlF+fn6517fZbCoqsyWhc8yhpLW/brKct8hm04roWFO91W9BrpKSElPIqchim0abzabFK9aY6mWDXD26dzGFq779ca5lkGrlmnWK27tPGzZv08o16/TzoqVKS88wjXMkKDBA9911m6GWnHpYP/y0wDTWqovZ1u27lJmZZTG2RMtWx5jqrVs0s//ctnULU5ex6NgNlteTpJ279yr1kDEc1q5NS8N2nc5uZXi5TndV/PzUvl1rw/G4+ATLzmqStDJ6nRYtXalfN27R3oQDSs8449R6AAAAAAAAAAAAgD8LWysCwDVkd3yCXN2cz9iGNWqoxg0bXP0FXYEGIfXUpHFDHUxKsdd+WbZK7du01HWD+tlrW3fs1qKlqwznhtQPVucObZ2ar35wXTWoX0+Hjhy11xYtXalBUZHq2L6NvVZUVKSvp3+vOQsW22t+vr6aPeNzeZUO6DgR5CopNqdoyoZ9rH6fMbEb9MSw++Tr63PZORw9D66urnpy+EMa+fzLl9ZjleopJbBObdPvZtnKaA2KijRsnVdks2nqtz/q+5/m22veXl6aPeMLeXl5GdZQWklJiZavitHQgVGX/V5WrJafnnFGU7/5QcMf+od9vpKSEs2a/bOp01tI/WDVqFFdkuTu7i5vLy/llQqBpaYeVsKBRHtXrqKiIn01bab2WnQyKxso9PXxUY/wzlqzdr29tisuXl/P+F4P33eXvZNbSuphTZs523BuFT9fDYiKrOhtkCT9/W8365flqw1bIU755gdF9e6pukEB5Z6bm5enyVNm6IWnHjNsSbhsZbR27zF2H6vi52vYWtPNzU19IyP00/xL70lOTq4++vxrjX3mCcPvPy09Qx98+qVp/utLveeSnN5asdjiQSgbkOwV3kXbduw2rPHjL6bqn888IXf3S3/exu1N0MT3P1FB4aUuX88/+Zhuvn6QU2sCAAAAAAAAAAAA/gwEuQDgGvLzomX6edEyp8975P67r5kglySNevQBPTNugiFY9PYHn2rZ6rVq16qFDh89rpXR60zBo1GPPmAIYVTUUyMe0guvvGW/XklJiZ568TX1691TrVs0VWFRkZaujFZyqnErvmeeGKYqfn6GmjNbK7q5uZlq7mVq3l5eCgyoo5OnTttrZzKzdO/wp9Q7IlwlLtL9d98u/1o1Leew2lrxonZtWqpvZIQhXHQ5Tz32kJ4eO97Qcer5l99Un57d1bZVc9lsNi1fs84Q9pIu3OPq1aoaao1LBYAuevuDT7Vh83bVqe2vFk0bOxVgsgqi1aheTd/+OE/bdu1R317d5erqotiNW7Vjl3kLzkfv/7vhc4/wLloVc6lrl624WM+99IYG9ustV1dXrVu/SceOn5Sbq6taNm+quPgE+9jo2A1q07K5QhvUU4e2F7o/PXL/3dq4ebty8/Ls4/733Y/auGW7unXuoPQzmVq2KsbUUeqhf9xpuneX4+XpqdEjHtI/X3/HXisoKNCHk7/UpPHj7M+po3u2ePlq7d23X0MHRsnD3V3bdsUpdoO5U93D995leo7/8bdbFRO70dBF7EKXsQTdMLi//Hx9dPTESS1YvML0Xdu0aqHB/fsYas7FuKzfKzd3Y+2GoQO0cNkqJSWn2mtLV0Zrz779Gjqgr7y9vXUwKcXUpa59m1a6fkh/J1cEAAAAAAAAAAAA/DkIcgEArrrOHdtpxEP/0Gdff2Oob92+S1u377I85+9/u0UR3Tpf0XzhXTpq5CP365Mvpxnqq2JiDUGe0m6+frAGD+hrqjuztaJVyKrs1oIuLi66YXA/TZnxvaF+Oi3D3h3szltucDzHZdbz+LD7tG79ZhUVVWy7xo7t22j0iIf14eSvDPXo2A2Kjt1gec7QgVG6YcgAU71fn576+tsfDN2rSkpK7Pf8rttuci7IZbG14rMjH9U7H01WfMIBxSeYO2ddFN6lo/pGRhhqNw0dYPr9Z509p9nzFtk/u7i4aMzTj8vN1dUQ5MrNzdP7H38hby8vLf/5O0lSaEh9jXt+lF55833DNeP3H1T8/oOW6+rXu6duvXGIw3WXp0d4F3Xv2kkbNm+z19Zv2qbo2A3q2+vidzXfsxEP36uvps9S6uGj+rzMO1hasyaNLdcWUMdf77/5skY+97Kyc3Ls9ZOnTmvKjFkOr9e4YQO9/dqLpveivDCiFTeL8bYi43vl4+2tt197UY+N/qcys87a60eOHteX02ZaXre2fy29PvYZU9gSAAAAAAAAAAAAuFY4v38XAAAVcM+dt+ql559UlSp+5Y7z8/XVhHHP6Ylh9znVDausu26/SRNffVFBgXXKHefl5akxox/Xc6MetR7wO4NcBQWFptq9d92mqN49Knxd4xzlh06CgwJ1162Og2BWbrtpqN6dME51gwLLHefp4aFnRz6qsc+OtPzdBAUG6NUxT8vH29up+R2y2FoxrFGovvjoHYWG1HN4Wt/ICL358gumNXbu2E7jnhvl8Lwqfr56/82Xdd2gfuofFanuXTtddol9e0Xog4mvKqC2f7njPDzc9eTwB/X62GcM2xs6w8XFRU+NeMjUpe6jyVOUnX0hYGW1HWVQYIA+/+httWrR1OG1O3dspw/fftVhB7ywRqGaOvlfFbon7u7uuvO2G/XZhxNVo3o1y+/hDMv3qtD8XgUHBWrq5A/UK6LbZa8Z2aObpk7+l2r713JqLQAAAAAAAAAAAMCfyaXEak8eAMAfLm5vgg4kJl+Va7Vs3kQtmjUx1OYuWGL43CCknjp3aGt5/uqYXw1dbapVq6L+fXqZxh1MStHuPfsMtR7duyiwTm2HaysoKNSvG7do9dr1yszKUl5+vrw8vRTaoJ4iI7qpY7vWDoMumZlZWl1m28DmzcLUqrnjgEpxcbF27YnXyuhYnTx1WmfPnZeLi6vqBgWoe5eO6ta5g2XY5KL5vyyTzXZp28Ha/jUV2SPccmzKoSPavjPOUOvWuYPqBQeZxpaUlGhXXLxi1m/SkaPH5efnKz9fH3Vo20q9e4TLw8NDaelntPbXjYbzhgzse9mgVHZ2jpatijHV69T2V6+Irg7PKykp0e49+7Qiep1OnDyts+fOycXFVUEBddS9Swd169JRNWtUL3duSUpLz1B07AZt3bFb7u7u8vP1UXBQoAb0jVTdoIDLnn9RYlKKdpV5vgYP6CNfHx/l5Obql6WrtGnbDmVmnpW3t7daNgtTZI9wtWnVvNzrnjh5SiujY7V1x26dz86Rt5eXunVur4H9ehue3ZKSEiUcSNT6TVtVUFQkLw9PNQoNUZ9e3U3XtNls2rpjt5avXqvT6RnKzc2Vh4eHgoMC1at7V3Xp1E6+Pj6W6yksLNTCJSsNtXrBQerWuYPl+PWbturEydOGWsf2bdSwQX0dO35CG7fsMBzr06u7atWsocLCQi1ZsUbrN29TWlqGPD091bRJI/Xs1lmdO7arcMDq5KnTil63Qdt2xel8do4KCwvl4+OjWjVrqNdvXcN8fa2/60Vl/30UGFBbPcK7WI5NTE7Vrrh4Qy2iWycFBTp+lk6npWvZqhjF7z+ozKyzKioqkq+Pjzq1b6NunTuoaVij3xUUBQAAAAAAAAAAAP4MBLkAAAAAAAAAAAAAAAAAoJKxtSIAAAAAAAAAAAAAAAAAVDKCXAAAAAAAAAAAAAAAAABQyQhyAQAAAAAAAAAAAAAAAEAlI8gFAAAAAAAAAAAAAAAAAJWMIBcAAAAAAAAAAAAAAAAAVDKCXAAAAAAAAAAAAAAAAABQyQhyAQAAAAAAAAAAAAAAAEAlI8gFAAAAAAAAAAAAAAAAAJWMIBcAAAAAAAAAAAAAAAAAVDKCXAAAAAAAAAAAAAAAAABQyQhyAQAAAAAAAAAAAAAAAEAlI8gFAAAAAAAAAAAAAAAAAJWMIBcAAAAAAAAAAAAAAAAAVDKCXAAAAAAAAAAAAAAAAABQyQhyAQAAAAAAAAAAAAAAAEAlI8gFAAAAAAAAAAAAAAAAAJWMIBcAAAAAAAAAAAAAAAAAVDKCXAAAAAAAAAAAAAAAAABQyQhyAQAAAAAAAAAAAAAAAEAlI8gFAAAAAAAAAAAAAAAAAJWMIBcAAAAAAAAAAAAAAAAAVDKCXAAAAAAAAAAAAAAAAABQyQhyAQAAAAAAAAAAAAAAAEAlI8gFAAAAAAAAAAAAAAAAAJWMIBcAAAAAAAAAAAAAAAAAVDKCXAAAAAAAAAAAAAAAAABQyQhyAQAAAAAAAAAAAAAAAEAlI8gFAAAAAAAAAAAAAAAAAJWMIBcAAAAAAAAAAAAAAAAAVDKCXAAAAAAAAAAAAAAAAABQyQhyAQAAAAAAAAAAAAAAAEAlI8gFAAAAAAAAAAAAAAAAAJWMIBcAAAAAAAAAAAAAAAAAVDKCXAAAAAAAAAAAAAAAAABQyf4Pg/ZBZXPEIMEAAAAASUVORK5CYII=" + } + }, + "cell_type": "markdown", + "id": "370302a7", + "metadata": {}, + "source": [ + "## Model architecture\n", + "\n", + "\n", + "Recently, the Google Brain team released their own ConvNet model called **[EfficientNet](https://arxiv.org/abs/1905.11946)**. EfficientNet forms the backbone of the **EfficientDet**. The model seeks to optimize downstream performance (eg. Object detection) given free range over depth, width, and resolution while staying within the constraints of target memory and target FLOPs. The new model architecture is discovered through neural architecture search where it optimizes for accuracy, given a certain number of FLOPS, and results in the creation of a baseline ConvNet called EfficientNet-D0. This baseline model is scaled up using compound scaling, which jointly scales up all dimensions to create a family of EfficientDet models from baseline EfficientDet-D0 through EfficientDet-D7.\n", + "\n", + "![efficientdet.png](attachment:efficientdet.png)\n", + "\n", + "[EfficientDet](https://arxiv.org/abs/1911.09070) architecture – It employs [EfficientNet](https://arxiv.org/abs/1905.11946) as the backbone network, BiFPN as the feature network, and shared class/box prediction network" + ] + }, + { + "cell_type": "markdown", + "id": "488b3faa", + "metadata": {}, + "source": [ + "## TensorRT model conversion pipeline\n", + "\n", + "TensorRT provides a lot of options for model optimization like reduced precision, batching, layer fusion, etc. In particular, for EfficientDet model, additional optimizations are performed in addition to the ones mentioned previously:\n", + "* Fusion of Convolution+Swish layers (used throughout the EfficientNet backbone)\n", + "* Improved INT8 Global Average Pooling (used in the SE blocks of EfficientNet backbone)\n", + "\n", + "To run a model with TensorRT, we'll follow these steps: \n", + "* Download and save pre-trained TensorFlow model checkpoint in saved model format\n", + "* Export the checkpoint an ONNX model\n", + "* Build TensorRT engine from the ONNX model and serialize to TensorRT plan file\n", + "\n", + "The final TensorRT engine of EfficientDet can then be launched for inference. Note that TensorRT engine is being runtime optimized before serialization. TensorRT tries a vast set of options to find the strategy that performs best on user’s GPU (depends on the type of underlying GPU) - so it takes a few minutes. After the TensorRT plan file is created, it can be reused." + ] + }, + { + "cell_type": "markdown", + "id": "6f77ab56", + "metadata": {}, + "source": [ + "## Requirements\n", + "\n", + "* Nvidia GPU (Check TensorRT 8 for GPU requirements)\n", + "* Nvidia driver 465 with CUDA toolkit \n", + "* TensorRT >= 8.0.1 as per [TensorRT installation](https://docs.nvidia.com/deeplearning/tensorrt/install-guide/index.html) guide\n", + "* TensorFlow >= 2.4.0" + ] + }, + { + "cell_type": "markdown", + "id": "408ac33a", + "metadata": {}, + "source": [ + "## Setup\n", + "\n", + "Before running this notebook, please check whether NVIDIA driver, CUDA, TensorRT are installed using the following commands:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b59a2d16", + "metadata": {}, + "outputs": [], + "source": [ + "# NVIDIA driver and CUDA version\n", + "!nvidia-smi" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f12a26fa", + "metadata": {}, + "outputs": [], + "source": [ + "# TensorRT version\n", + "!python3 -c 'import tensorrt; print(\"TensorRT version: {}\".format(tensorrt.__version__))'" + ] + }, + { + "cell_type": "markdown", + "id": "b6b01d8d", + "metadata": {}, + "source": [ + "You will need to make sure the Python bindings for TensorRT are also installed correctly, these are available by installing the `python3-libnvinfer` and `python3-libnvinfer-dev` packages on your TensorRT download." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "11e4a6b7", + "metadata": {}, + "outputs": [], + "source": [ + "!dpkg -l | grep TensorRT\n", + "\n", + "# Check 'Python 3 bindings for TensorRT'\n", + "# Check 'Python 3 development package for TensorRT'" + ] + }, + { + "cell_type": "markdown", + "id": "34066147", + "metadata": {}, + "source": [ + "## Install dependencies for EfficientDet" + ] + }, + { + "cell_type": "markdown", + "id": "b3dd17c7", + "metadata": {}, + "source": [ + "### 1. Install requirements and dependencies" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cf687a52-d705-403e-bf3f-df2ff3bab596", + "metadata": {}, + "outputs": [], + "source": [ + "# EfficientDet sample is present at this location\n", + "!ls -ltr $TRT_OSSPATH/samples/python/efficientdet" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6a5074ae", + "metadata": {}, + "outputs": [], + "source": [ + "# Install the dependencies\n", + "!pip3 install -r $TRT_OSSPATH/samples/python/efficientdet/requirements.txt" + ] + }, + { + "cell_type": "markdown", + "id": "b16d2c6e", + "metadata": {}, + "source": [ + "### 2. Clone [AutoML github repository](https://github.com/google/automl) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "02343be0", + "metadata": {}, + "outputs": [], + "source": [ + "!git clone https://github.com/google/automl" + ] + }, + { + "cell_type": "markdown", + "id": "a0ad7c98", + "metadata": {}, + "source": [ + "### 3. Install requirements for AutoML" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b05ca377", + "metadata": {}, + "outputs": [], + "source": [ + "!pip3 install matplotlib>=3.0.3 PyYAML>=5.1 tensorflow-model-optimization>=0.5" + ] + }, + { + "cell_type": "markdown", + "id": "855e8150-6ef7-4cc1-b734-8087bfc38bb3", + "metadata": {}, + "source": [ + "The full list of requirements for AutoML is present at `automl/efficientdet/requirements.txt`, but we only need the above for this example." + ] + }, + { + "cell_type": "markdown", + "id": "7c514b30", + "metadata": {}, + "source": [ + "### 4. Install onnx_graphsurgeon \n", + "\n", + "You will also need the latest onnx_graphsurgeon python module. If not already installed by TensorRT, you can install it manually by running:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "41d09474", + "metadata": {}, + "outputs": [], + "source": [ + "!pip3 install 'git+https://github.com/NVIDIA/TensorRT#subdirectory=tools/onnx-graphsurgeon'" + ] + }, + { + "cell_type": "markdown", + "id": "659c26fc", + "metadata": {}, + "source": [ + "# Model conversion\n", + "\n", + "## 1. TensorFlow Saved Model\n", + "\n", + "The first step in TensorRT pipeline for EfficientDet is downloading pre-trained TensorFlow checkpoint and converting it into TensorFlow saved model as follows: " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e3863495", + "metadata": {}, + "outputs": [], + "source": [ + "![ ! -d \"tf_checkpoint\" ] && mkdir tf_checkpoint\n", + "!wget https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/efficientdet-d0.tar.gz -P tf_checkpoint\n", + "!tar -xvf tf_checkpoint/efficientdet-d0.tar.gz -C tf_checkpoint" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "15a8d061", + "metadata": {}, + "outputs": [], + "source": [ + "!ls tf_checkpoint/efficientdet-d0/" + ] + }, + { + "cell_type": "markdown", + "id": "c25a4a77", + "metadata": {}, + "source": [ + "## 2. Export a TensorFlow saved model\n", + "\n", + "The extracted TensorFlow checkpoint now be converted into saved model format using the automl/efficientdet/model_inspect.py script:\n", + "```\n", + "* --runmode is passed as saved_model\n", + "* --model_name supports any one of the model from efficientdet-d0 to efficientdet-d7x\n", + "* --ckpt_path /path/to/tf_checkpoint\n", + "* --saved_model_dir /path/to/tf_model with protobuf graph and other related files inside.\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1286b00a", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Create a directory to store TensorFlow saved model \n", + "![ ! -d \"tf_model\" ] && mkdir tf_model\n", + "\n", + "# Export TF model\n", + "!python3 ./automl/efficientdet/model_inspect.py \\\n", + " --runmode saved_model \\\n", + " --model_name efficientdet-d0 \\\n", + " --ckpt_path ./tf_checkpoint/efficientdet-d0/ \\\n", + " --saved_model_dir ./tf_model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1fb9cd58", + "metadata": {}, + "outputs": [], + "source": [ + "!ls tf_model/" + ] + }, + { + "cell_type": "markdown", + "id": "ab756d32", + "metadata": {}, + "source": [ + "## 3. Create ONNX Graph\n", + "\n", + "To generate an ONNX model file, first find the input shape that corresponds to the model you're converting:\n", + "\n", + "| **Model** | **Input Size** |\n", + "| --------------------|----------------|\n", + "| efficientdet-d0 | 512,512 |\n", + "| efficientdet-d1 | 640,640 |\n", + "| efficientdet-d2 | 768,768 |\n", + "| efficientdet-d3 | 896,896 |\n", + "| efficientdet-d4 | 1024,1024 |\n", + "| efficientdet-d5 | 1280,1280 |\n", + "| efficientdet-d6 | 1280,1280 |\n", + "| efficientdet-d7 | 1536,1536 |\n", + "| efficientdet-d7x | 1536,1536 |\n", + "| efficientdet-lite0 | 320,320 |\n", + "| efficientdet-lite1 | 384,384 |\n", + "| efficientdet-lite2 | 448,448 |\n", + "| efficientdet-lite3 | 512,512 |\n", + "| efficientdet-lite3x | 640,640 |\n", + "| efficientdet-lite4 | 640,640 |\n", + "\n", + "To create the ONNX graph, execute efficientdet/create_onnx.py script which takes the following arguments:\n", + "```\n", + "* --saved_model /path/to/tf_model \n", + "* --onnx /path/to/onnx.model\n", + "* --input_size One of the input shapes corresponding to the model mentioned previously\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d0f0f987", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Create directory for onnx_model\n", + "![ ! -d \"onnx_model\" ] && mkdir onnx_model\n", + "\n", + "# Export TF to ONNX\n", + "!python3 $TRT_OSSPATH/samples/python/efficientdet/create_onnx.py \\\n", + " --saved_model ./tf_model/ \\\n", + " --onnx ./onnx_model/model.onnx \\\n", + " --input_size '512,512'" + ] + }, + { + "cell_type": "markdown", + "id": "fb9ebeca", + "metadata": {}, + "source": [ + "This will create the file `model.onnx` which is ready to be converted to TensorRT. \n", + "\n", + "You can visualize the resulting file with a tool such as [Netron](https://netron.app/).\n", + "\n", + "The script has a few additional arguments:\n", + "\n", + "* `--nms_threshold` allows overriding the NMS score threshold value. The runtime latency of the EfficientNMS plugin is sensitive to the score threshold used, so it's a good practice to set this value as high as possible, while still fulfilling your application requirements, to reduce latency as much as possible.\n", + "* `--preprocessor [imagenet,scale_range]` allows switching between two possible image preprocessing methods. Most EfficientDet models use the `imagenet` method, which this argument defaults to, and corresponds to standard ImageNet mean subtraction and standard deviation normalization. The `scale_range` method instead normalizes the image to a range of [-1,+1]. Please use this method only when converting the **AdvProp** pre-trained checkpoints, as they were created with this preprocessor operation.\n" + ] + }, + { + "cell_type": "markdown", + "id": "07945320", + "metadata": {}, + "source": [ + "## 4. Build TensorRT engine\n", + "\n", + "Final step is to convert the exported ONNX model into TensorRT using the efficientdet/build_engine.py script\n", + "```\n", + "* --onnx /path/to/model.onnx\n", + "* --engine /path/to/trt_output\n", + "* --precision (fp32,fp16,int8)\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5257225c", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Create directory for exported TensorRT engine\n", + "![ ! -d \"trt_engine\" ] && mkdir trt_engine\n", + "\n", + "# Build engine with FP32 precision\n", + "!python3 $TRT_OSSPATH/samples/python/efficientdet/build_engine.py \\\n", + " --onnx ./onnx_model/model.onnx \\\n", + " --engine ./trt_engine/engine.trt \\\n", + " --precision fp32\n", + "\n", + "## To build TensorRT engine with INT8 precision run the following after setting path to 'calib_input' and 'calib_cache':\n", + "# python $TRT_OSSPATH/samples/python/efficientdet/build_engine.py \\\n", + "# --onnx ./onnx_model/model.onnx \\\n", + "# --engine ./trt_engine/engine.trt \\\n", + "# --precision int8 \\\n", + "# --calib_input /path/to/calibration/images \\\n", + "# --calib_cache /path/to/calibration.cache\n", + "\n", + "# Where --calib_input points to a directory with several thousands of images. \n", + "# For example, this could be a subset of the training or validation datasets that were used for the model.\n", + "# It is important that this data represents the runtime data distribution relatively well, therefore,\n", + "# the more images that are used for calibration, the better accuracy that will be achieved in INT8 precision. \n", + "# For models trained for the COCO dataset, we have found that 5,000 images gives a good result.\n", + "\n", + "# The --calib_cache controls where the calibration cache file will be written to.\n", + "# This is useful to keep a cached copy of the calibration results. \n", + "# Next time you need to build the engine for the same network, if this file exists, \n", + "# it will skip the calibration step and use the cached values instead.\n", + "\n", + "# Run python build_engine.py --help for additional calibration options." + ] + }, + { + "cell_type": "markdown", + "id": "59256876", + "metadata": {}, + "source": [ + "The file `engine.trt` will be created, which can now be used to infer with TensorRT.\n", + "For best results, make sure no other processes are using the GPU during engine build, as it may affect the optimal tactic selection process." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "10a9e312", + "metadata": {}, + "outputs": [], + "source": [ + "!ls trt_engine/" + ] + }, + { + "cell_type": "markdown", + "id": "953ff4d4", + "metadata": {}, + "source": [ + "# Benchmarking TensorRT Engine\n", + "\n", + "Optionally, you can obtain execution timing information for the built engine by using the trtexec utility, as:\n", + "\n", + "`NOTE:` After a succesful TensorRT OSS build, the `trtexec` binary should have been created in the `out/` directory." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8763b4f9", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "!/workspace/TensorRT/build/out/trtexec \\\n", + " --loadEngine=trt_engine/engine.trt \\\n", + " --useCudaGraph --noDataTransfers \\\n", + " --iterations=100 --avgRuns=100" + ] + }, + { + "cell_type": "markdown", + "id": "05af5387", + "metadata": {}, + "source": [ + "The step above should generate a Performance summary. For instance:
\n", + "```\n", + "GPU Compute Time: min = 3.58606 ms, max = 4.67763 ms, mean = 3.71858 ms, median = 3.6167 ms, percentile(99%) = 4.56601 ms\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "ea40c5da", + "metadata": {}, + "source": [ + "# Inference\n", + "\n", + "Now let's check inference of our TensorRT engine and compare with TensorFlow predictions and ground truth on COCO validation 2017 dataset as follows: " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "04e6c30a", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Download the validation dataset images\n", + "!wget http://images.cocodataset.org/zips/val2017.zip\n", + "\n", + "# Unzip the archive\n", + "!unzip val2017.zip " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7421869b", + "metadata": {}, + "outputs": [], + "source": [ + "# Download the annotations (Ground truth)\n", + "!wget http://images.cocodataset.org/annotations/annotations_trainval2017.zip\n", + " \n", + "# Unzip the annotations\n", + "!unzip annotations_trainval2017.zip" + ] + }, + { + "cell_type": "markdown", + "id": "43abe1a2", + "metadata": {}, + "source": [ + "To check how the TensorRT results look in comparison to the original TensorFlow model and ground truth, you can run efficientdet/compare_tf.py:\n", + "\n", + "```\n", + "* --engine /path/to/trt_engine\n", + "* --saved_model /path/to/tf_saved_model\n", + "* --input /path/to/input_image\n", + "* --annotations /path/to/downloaded_annotations/annotations.json\n", + "* --labels /path/to/labels\n", + "* --output /path/to/output directory\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d025d09f", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Create directory for exported TensorRT engine\n", + "![ ! -d \"output_imgs\" ] && mkdir output_imgs\n", + "\n", + "# Run inference and compare the outputs\n", + "!python3 $TRT_OSSPATH/samples/python/efficientdet/compare_tf.py \\\n", + " --engine ./trt_engine/engine.trt \\\n", + " --saved_model ./tf_model/ \\\n", + " --input ./val2017 \\\n", + " --annotations ./annotations/instances_val2017.json \\\n", + " --labels $TRT_OSSPATH/samples/python/efficientdet/labels_coco.txt \\\n", + " --output ./output_imgs" + ] + }, + { + "attachments": { + "000000002153.compare.png": { + "image/png": "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" + } + }, + "cell_type": "markdown", + "id": "37c32c51", + "metadata": {}, + "source": [ + "![000000002153.compare.png](attachment:000000002153.compare.png)\n", + "\n", + "The inference on validation image from [COCO dataset](http://images.cocodataset.org/zips/val2017.zip) using TensorRT engine of EfficientDet shows detection of people and baseball bat \n", + "\n", + "The predictions on left are by TensorFlow saved model, in the center are TensorRT predictions and the ones on the right are ground truth. As we can see, the accuracy of EfficientDet TensorRT engine predictions remain same as the original TensorRT model. \n", + "\n", + "The TensorRT engine built with this process can also be deployed at scale with either [Triton Inference Server](https://developer.nvidia.com/nvidia-triton-inference-server) or [DeepStream SDK](https://developer.nvidia.com/deepstream-sdk)." + ] + }, + { + "cell_type": "markdown", + "id": "cf70d4ba", + "metadata": {}, + "source": [ + "## Validation on entire dataset\n", + "\n", + "Given a validation dataset (such as [COCO val2017 data](http://images.cocodataset.org/zips/val2017.zip)) and ground truth annotations (such as [COCO instances_val2017.json](http://images.cocodataset.org/annotations/annotations_trainval2017.zip)), you can get the mAP metrics for the built TensorRT engine. This will use the mAP metrics calculation script from the AutoML EfficientDet repository on `https://github.com/google/automl`.\n", + "\n", + "```\n", + "python eval_coco.py \\\n", + " --engine /path/to/engine.trt \\\n", + " --input /path/to/coco/val2017 \\\n", + " --annotations /path/to/coco/annotations/instances_val2017.json \\\n", + " --automl_path /path/to/automl\n", + "```\n", + "\n", + "Where the `--automl_path` argument points to the root of the AutoML repository.\n", + "\n", + "**NOTE:** mAP metrics are highly sensitive to NMS threshold. Using a high threshold will obviously reduce the mAP value. Ideally, this should run with a threshold of 0.00 or 0.01, but such a low threshold will impact the runtime performance of the EfficientNMS plugin. So you may need to build separate TensorRT engines for different purposes, one with a low threshold (like 0.01) dedicated for validation, and one with your application specific threshold (like 0.4) for deployment inference to minimimze latency. This is why we keep the NMS threshold as a configurable parameter in the TensorRT conversion script.\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/Notebook Tutorials/object_counting.ipynb b/examples/Notebook Tutorials/object_counting.ipynb new file mode 100644 index 0000000..8c3d0ba --- /dev/null +++ b/examples/Notebook Tutorials/object_counting.ipynb @@ -0,0 +1,210 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "PN1cAxdvd61e" + }, + "source": [ + "
\n", + "\n", + " \n", + " \n", + "\n", + " [中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es/) | [Português](https://docs.ultralytics.com/pt/) | [Türkçe](https://docs.ultralytics.com/tr/) | [Tiếng Việt](https://docs.ultralytics.com/vi/) | [العربية](https://docs.ultralytics.com/ar/)\n", + "\n", + " \"Ultralytics\n", + " \"Run\n", + " \"Open\n", + " \"Open\n", + " \"Discord\"\n", + "\n", + "Welcome to the Ultralytics YOLOv8 🚀 notebook! YOLOv8 is the latest version of the YOLO (You Only Look Once) AI models developed by Ultralytics. This notebook serves as the starting point for exploring the various resources available to help you get started with YOLOv8 and understand its features and capabilities.\n", + "\n", + "YOLOv8 models are fast, accurate, and easy to use, making them ideal for various object detection and image segmentation tasks. They can be trained on large datasets and run on diverse hardware platforms, from CPUs to GPUs.\n", + "\n", + "We hope that the resources in this notebook will help you get the most out of YOLOv8. Please browse the YOLOv8 Object Counting Docs for details, raise an issue on GitHub for support, and join our Discord community for questions and discussions!\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "o68Sg1oOeZm2" + }, + "source": [ + "# Setup\n", + "\n", + "Pip install `ultralytics` and [dependencies](https://github.com/ultralytics/ultralytics/blob/main/pyproject.toml) and check software and hardware.\n", + "\n", + "[![PyPI - Version](https://img.shields.io/pypi/v/ultralytics?logo=pypi&logoColor=white)](https://pypi.org/project/ultralytics/) [![Downloads](https://static.pepy.tech/badge/ultralytics)](https://pepy.tech/project/ultralytics) [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/ultralytics?logo=python&logoColor=gold)](https://pypi.org/project/ultralytics/)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "9dSwz_uOReMI", + "outputId": "fd3bab88-2f25-46c0-cae9-04d2beedc0c1" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ultralytics YOLOv8.2.18 🚀 Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (Tesla T4, 15102MiB)\n", + "Setup complete ✅ (2 CPUs, 12.7 GB RAM, 29.8/78.2 GB disk)\n" + ] + } + ], + "source": [ + "%pip install ultralytics\n", + "import ultralytics\n", + "\n", + "ultralytics.checks()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "m7VkxQ2aeg7k" + }, + "source": [ + "# Object Counting using Ultralytics YOLOv8 🚀\n", + "\n", + "## What is Object Counting?\n", + "\n", + "Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics/) involves accurate identification and counting of specific objects in videos and camera streams. YOLOv8 excels in real-time applications, providing efficient and precise object counting for various scenarios like crowd analysis and surveillance, thanks to its state-of-the-art algorithms and deep learning capabilities.\n", + "\n", + "## Advantages of Object Counting?\n", + "\n", + "- **Resource Optimization:** Object counting facilitates efficient resource management by providing accurate counts, and optimizing resource allocation in applications like inventory management.\n", + "- **Enhanced Security:** Object counting enhances security and surveillance by accurately tracking and counting entities, aiding in proactive threat detection.\n", + "- **Informed Decision-Making:** Object counting offers valuable insights for decision-making, optimizing processes in retail, traffic management, and various other domains.\n", + "\n", + "## Real World Applications\n", + "\n", + "| Logistics | Aquaculture |\n", + "|:-------------------------------------------------------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------------------------------------------:|\n", + "| ![Conveyor Belt Packets Counting Using Ultralytics YOLOv8](https://github.com/RizwanMunawar/ultralytics/assets/62513924/70e2d106-510c-4c6c-a57a-d34a765aa757) | ![Fish Counting in Sea using Ultralytics YOLOv8](https://github.com/RizwanMunawar/ultralytics/assets/62513924/c60d047b-3837-435f-8d29-bb9fc95d2191) |\n", + "| Conveyor Belt Packets Counting Using Ultralytics YOLOv8 | Fish Counting in Sea using Ultralytics YOLOv8 |\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Cx-u59HQdu2o" + }, + "outputs": [], + "source": [ + "import cv2\n", + "\n", + "from ultralytics import YOLO, solutions\n", + "\n", + "# Load the pre-trained YOLOv8 model\n", + "model = YOLO(\"yolov8n.pt\")\n", + "\n", + "# Open the video file\n", + "cap = cv2.VideoCapture(\"path/to/video/file.mp4\")\n", + "assert cap.isOpened(), \"Error reading video file\"\n", + "\n", + "# Get video properties: width, height, and frames per second (fps)\n", + "w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))\n", + "\n", + "# Define points for a line or region of interest in the video frame\n", + "line_points = [(20, 400), (1080, 400)] # Line coordinates\n", + "\n", + "# Specify classes to count, for example: person (0) and car (2)\n", + "classes_to_count = [0, 2] # Class IDs for person and car\n", + "\n", + "# Initialize the video writer to save the output video\n", + "video_writer = cv2.VideoWriter(\"object_counting_output.avi\", cv2.VideoWriter_fourcc(*\"mp4v\"), fps, (w, h))\n", + "\n", + "# Initialize the Object Counter with visualization options and other parameters\n", + "counter = solutions.ObjectCounter(\n", + " view_img=True, # Display the image during processing\n", + " reg_pts=line_points, # Region of interest points\n", + " names=model.names, # Class names from the YOLO model\n", + " draw_tracks=True, # Draw tracking lines for objects\n", + " line_thickness=2, # Thickness of the lines drawn\n", + ")\n", + "\n", + "# Process video frames in a loop\n", + "while cap.isOpened():\n", + " success, im0 = cap.read()\n", + " if not success:\n", + " print(\"Video frame is empty or video processing has been successfully completed.\")\n", + " break\n", + "\n", + " # Perform object tracking on the current frame, filtering by specified classes\n", + " tracks = model.track(im0, persist=True, show=False, classes=classes_to_count)\n", + "\n", + " # Use the Object Counter to count objects in the frame and get the annotated image\n", + " im0 = counter.start_counting(im0, tracks)\n", + "\n", + " # Write the annotated frame to the output video\n", + " video_writer.write(im0)\n", + "\n", + "# Release the video capture and writer objects\n", + "cap.release()\n", + "video_writer.release()\n", + "\n", + "# Close all OpenCV windows\n", + "cv2.destroyAllWindows()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QrlKg-y3fEyD" + }, + "source": [ + "# Additional Resources\n", + "\n", + "## Community Support\n", + "\n", + "For more information on counting objects with Ultralytics, you can explore the comprehensive [Ultralytics Object Counting Docs](https://docs.ultralytics.com/guides/object-counting/). This guide covers everything from basic concepts to advanced techniques, ensuring you get the most out of counting and visualization.\n", + "\n", + "## Ultralytics ⚡ Resources\n", + "\n", + "At Ultralytics, we are committed to providing cutting-edge AI solutions. Here are some key resources to learn more about our company and get involved with our community:\n", + "\n", + "- [Ultralytics HUB](https://ultralytics.com/hub): Simplify your AI projects with Ultralytics HUB, our no-code tool for effortless YOLO training and deployment.\n", + "- [Ultralytics Licensing](https://ultralytics.com/license): Review our licensing terms to understand how you can use our software in your projects.\n", + "- [About Us](https://ultralytics.com/about): Discover our mission, vision, and the story behind Ultralytics.\n", + "- [Join Our Team](https://ultralytics.com/work): Explore career opportunities and join our team of talented professionals.\n", + "\n", + "## YOLOv8 🚀 Resources\n", + "\n", + "YOLOv8 is the latest evolution in the YOLO series, offering state-of-the-art performance in object detection and image segmentation. Here are some essential resources to help you get started with YOLOv8:\n", + "\n", + "- [GitHub](https://github.com/ultralytics/ultralytics): Access the YOLOv8 repository on GitHub, where you can find the source code, contribute to the project, and report issues.\n", + "- [Docs](https://docs.ultralytics.com/): Explore the official documentation for YOLOv8, including installation guides, tutorials, and detailed API references.\n", + "- [Discord](https://ultralytics.com/discord): Join our Discord community to connect with other users, share your projects, and get help from the Ultralytics team.\n", + "\n", + "These resources are designed to help you leverage the full potential of Ultralytics' offerings and YOLOv8. Whether you're a beginner or an experienced developer, you'll find the information and support you need to succeed." + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/examples/Notebook Tutorials/object_tracking.ipynb b/examples/Notebook Tutorials/object_tracking.ipynb new file mode 100644 index 0000000..17c27c0 --- /dev/null +++ b/examples/Notebook Tutorials/object_tracking.ipynb @@ -0,0 +1,245 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "PN1cAxdvd61e" + }, + "source": [ + "
\n", + "\n", + " \n", + " \n", + "\n", + " [中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es/) | [Português](https://docs.ultralytics.com/pt/) | [Türkçe](https://docs.ultralytics.com/tr/) | [Tiếng Việt](https://docs.ultralytics.com/vi/) | [العربية](https://docs.ultralytics.com/ar/)\n", + "\n", + " \"Ultralytics\n", + " \"Run\n", + " \"Open\n", + " \"Open\n", + " \"Discord\"\n", + "\n", + "Welcome to the Ultralytics YOLOv8 🚀 notebook! YOLOv8 is the latest version of the YOLO (You Only Look Once) AI models developed by Ultralytics. This notebook serves as the starting point for exploring the various resources available to help you get started with YOLOv8 and understand its features and capabilities.\n", + "\n", + "YOLOv8 models are fast, accurate, and easy to use, making them ideal for various object detection and image segmentation tasks. They can be trained on large datasets and run on diverse hardware platforms, from CPUs to GPUs.\n", + "\n", + "We hope that the resources in this notebook will help you get the most out of YOLOv8. Please browse the YOLOv8 Tracking Docs for details, raise an issue on GitHub for support, and join our Discord community for questions and discussions!\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "o68Sg1oOeZm2" + }, + "source": [ + "# Setup\n", + "\n", + "Pip install `ultralytics` and [dependencies](https://github.com/ultralytics/ultralytics/blob/main/pyproject.toml) and check software and hardware.\n", + "\n", + "[![PyPI - Version](https://img.shields.io/pypi/v/ultralytics?logo=pypi&logoColor=white)](https://pypi.org/project/ultralytics/) [![Downloads](https://static.pepy.tech/badge/ultralytics)](https://pepy.tech/project/ultralytics) [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/ultralytics?logo=python&logoColor=gold)](https://pypi.org/project/ultralytics/)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "9dSwz_uOReMI", + "outputId": "ed8c2370-8fc7-4e4e-f669-d0bae4d944e9" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ultralytics YOLOv8.2.17 🚀 Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (Tesla T4, 15102MiB)\n", + "Setup complete ✅ (2 CPUs, 12.7 GB RAM, 29.8/78.2 GB disk)\n" + ] + } + ], + "source": [ + "%pip install ultralytics\n", + "import ultralytics\n", + "\n", + "ultralytics.checks()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "m7VkxQ2aeg7k" + }, + "source": [ + "# Ultralytics Object Tracking\n", + "\n", + "[Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics/) instance segmentation involves identifying and outlining individual objects in an image, providing a detailed understanding of spatial distribution. Unlike semantic segmentation, it uniquely labels and precisely delineates each object, crucial for tasks like object detection and medical imaging.\n", + "\n", + "There are two types of instance segmentation tracking available in the Ultralytics package:\n", + "\n", + "- **Instance Segmentation with Class Objects:** Each class object is assigned a unique color for clear visual separation.\n", + "\n", + "- **Instance Segmentation with Object Tracks:** Every track is represented by a distinct color, facilitating easy identification and tracking.\n", + "\n", + "## Samples\n", + "\n", + "| Instance Segmentation | Instance Segmentation + Object Tracking |\n", + "|:---------------------------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------------------------------------------------:|\n", + "| ![Ultralytics Instance Segmentation](https://github.com/RizwanMunawar/ultralytics/assets/62513924/d4ad3499-1f33-4871-8fbc-1be0b2643aa2) | ![Ultralytics Instance Segmentation with Object Tracking](https://github.com/RizwanMunawar/ultralytics/assets/62513924/2e5c38cc-fd5c-4145-9682-fa94ae2010a0) |\n", + "| Ultralytics Instance Segmentation 😍 | Ultralytics Instance Segmentation with Object Tracking 🔥 |" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-ZF9DM6e6gz0" + }, + "source": [ + "## CLI\n", + "\n", + "Command-Line Interface (CLI) example." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "-XJqhOwo6iqT" + }, + "outputs": [], + "source": [ + "!yolo track source=\"/path/to/video/file.mp4\" save=True" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XRcw0vIE6oNb" + }, + "source": [ + "## Python\n", + "\n", + "Python Instance Segmentation and Object tracking example." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Cx-u59HQdu2o" + }, + "outputs": [], + "source": [ + "from collections import defaultdict\n", + "\n", + "import cv2\n", + "\n", + "from ultralytics import YOLO\n", + "from ultralytics.utils.plotting import Annotator, colors\n", + "\n", + "# Dictionary to store tracking history with default empty lists\n", + "track_history = defaultdict(lambda: [])\n", + "\n", + "# Load the YOLO model with segmentation capabilities\n", + "model = YOLO(\"yolov8n-seg.pt\")\n", + "\n", + "# Open the video file\n", + "cap = cv2.VideoCapture(\"path/to/video/file.mp4\")\n", + "\n", + "# Retrieve video properties: width, height, and frames per second\n", + "w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))\n", + "\n", + "# Initialize video writer to save the output video with the specified properties\n", + "out = cv2.VideoWriter(\"instance-segmentation-object-tracking.avi\", cv2.VideoWriter_fourcc(*\"MJPG\"), fps, (w, h))\n", + "\n", + "while True:\n", + " # Read a frame from the video\n", + " ret, im0 = cap.read()\n", + " if not ret:\n", + " print(\"Video frame is empty or video processing has been successfully completed.\")\n", + " break\n", + "\n", + " # Create an annotator object to draw on the frame\n", + " annotator = Annotator(im0, line_width=2)\n", + "\n", + " # Perform object tracking on the current frame\n", + " results = model.track(im0, persist=True)\n", + "\n", + " # Check if tracking IDs and masks are present in the results\n", + " if results[0].boxes.id is not None and results[0].masks is not None:\n", + " # Extract masks and tracking IDs\n", + " masks = results[0].masks.xy\n", + " track_ids = results[0].boxes.id.int().cpu().tolist()\n", + "\n", + " # Annotate each mask with its corresponding tracking ID and color\n", + " for mask, track_id in zip(masks, track_ids):\n", + " annotator.seg_bbox(mask=mask, mask_color=colors(track_id, True), track_label=str(track_id))\n", + "\n", + " # Write the annotated frame to the output video\n", + " out.write(im0)\n", + " # Display the annotated frame\n", + " cv2.imshow(\"instance-segmentation-object-tracking\", im0)\n", + "\n", + " # Exit the loop if 'q' is pressed\n", + " if cv2.waitKey(1) & 0xFF == ord(\"q\"):\n", + " break\n", + "\n", + "# Release the video writer and capture objects, and close all OpenCV windows\n", + "out.release()\n", + "cap.release()\n", + "cv2.destroyAllWindows()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QrlKg-y3fEyD" + }, + "source": [ + "# Additional Resources\n", + "\n", + "## Community Support\n", + "\n", + "For more information on using tracking with Ultralytics, you can explore the comprehensive [Ultralytics Tracking Docs](https://docs.ultralytics.com/modes/track/). This guide covers everything from basic concepts to advanced techniques, ensuring you get the most out of tracking and visualization.\n", + "\n", + "## Ultralytics ⚡ Resources\n", + "\n", + "At Ultralytics, we are committed to providing cutting-edge AI solutions. Here are some key resources to learn more about our company and get involved with our community:\n", + "\n", + "- [Ultralytics HUB](https://ultralytics.com/hub): Simplify your AI projects with Ultralytics HUB, our no-code tool for effortless YOLO training and deployment.\n", + "- [Ultralytics Licensing](https://ultralytics.com/license): Review our licensing terms to understand how you can use our software in your projects.\n", + "- [About Us](https://ultralytics.com/about): Discover our mission, vision, and the story behind Ultralytics.\n", + "- [Join Our Team](https://ultralytics.com/work): Explore career opportunities and join our team of talented professionals.\n", + "\n", + "## YOLOv8 🚀 Resources\n", + "\n", + "YOLOv8 is the latest evolution in the YOLO series, offering state-of-the-art performance in object detection and image segmentation. Here are some essential resources to help you get started with YOLOv8:\n", + "\n", + "- [GitHub](https://github.com/ultralytics/ultralytics): Access the YOLOv8 repository on GitHub, where you can find the source code, contribute to the project, and report issues.\n", + "- [Docs](https://docs.ultralytics.com/): Explore the official documentation for YOLOv8, including installation guides, tutorials, and detailed API references.\n", + "- [Discord](https://ultralytics.com/discord): Join our Discord community to connect with other users, share your projects, and get help from the Ultralytics team.\n", + "\n", + "These resources are designed to help you leverage the full potential of Ultralytics' offerings and YOLOv8. Whether you're a beginner or an experienced developer, you'll find the information and support you need to succeed." + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/examples/Notebook Tutorials/qat-ptq-workflow.ipynb b/examples/Notebook Tutorials/qat-ptq-workflow.ipynb new file mode 100644 index 0000000..cadd4de --- /dev/null +++ b/examples/Notebook Tutorials/qat-ptq-workflow.ipynb @@ -0,0 +1,1732 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "b861c182", + "metadata": {}, + "outputs": [], + "source": [ + "# Copyright 2022 NVIDIA Corporation. All Rights Reserved.\n", + "#\n", + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# http://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License.\n", + "# ==============================================================================" + ] + }, + { + "cell_type": "markdown", + "id": "c6384192", + "metadata": {}, + "source": [ + "\n", + "\n", + "# Accelerate Deep Learning Models using TensorRT " + ] + }, + { + "attachments": { + "img1.JPG": { + "image/jpeg": "/9j/4AAQSkZJRgABAQEAeAB4AAD/4RDuRXhpZgAATU0AKgAAAAgABAE7AAIAAAAMAAAISodpAAQAAAABAAAIVpydAAEAAAAYAAAQzuocAAcAAAgMAAAAPgAAAAAc6gAAAAgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAEFubmllIFN1cmxhAAAFkAMAAgAAABQAABCkkAQAAgAAABQAABC4kpEAAgAAAAM5NgAAkpIAAgAAAAM5NgAA6hwABwAACAwAAAiYAAAAABzqAAAACAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAMjAyMjowNjowNiAxNDo0MTozOQAyMDIyOjA2OjA2IDE0OjQxOjM5AAAAQQBuAG4AaQBlACAAUwB1AHIAbABhAAAA/+ELHmh0dHA6Ly9ucy5hZG9iZS5jb20veGFwLzEuMC8APD94cGFja2V0IGJlZ2luPSfvu78nIGlkPSdXNU0wTXBDZWhpSHpyZVN6TlRjemtjOWQnPz4NCjx4OnhtcG1ldGEgeG1sbnM6eD0iYWRvYmU6bnM6bWV0YS8iPjxyZGY6UkRGIHhtbG5zOnJkZj0iaHR0cDovL3d3dy53My5vcmcvMTk5OS8wMi8yMi1yZGYtc3ludGF4LW5zIyI+PHJkZjpEZXNjcmlwdGlvbiByZGY6YWJvdXQ9InV1aWQ6ZmFmNWJkZDUtYmEzZC0xMWRhLWFkMzEtZDMzZDc1MTgyZjFiIiB4bWxuczpkYz0iaHR0cDovL3B1cmwub3JnL2RjL2VsZW1lbnRzLzEuMS8iLz48cmRmOkRlc2NyaXB0aW9uIHJkZjphYm91dD0idXVpZDpmYWY1YmRkNS1iYTNkLTExZGEtYWQzMS1kMzNkNzUxODJmMWIiIHhtbG5zOnhtcD0iaHR0cDovL25zLmFkb2JlLmNvbS94YXAvMS4wLyI+PHhtcDpDcmVhdGVEYXRlPjIwMjItMDYtMDZUMTQ6NDE6MzkuOTYzPC94bXA6Q3JlYXRlRGF0ZT48L3JkZjpEZXNjcmlwdGlvbj48cmRmOkRlc2NyaXB0aW9uIHJkZjphYm91dD0idXVpZDpmYWY1YmRkNS1iYTNkLTExZGEtYWQzMS1kMzNkNzUxODJmMWIiIHhtbG5zOmRjPSJodHRwOi8vcHVybC5vcmcvZGMvZWxlbWVudHMvMS4xLyI+PGRjOmNyZWF0b3I+PHJkZjpTZXEgeG1sbnM6cmRmPSJodHRwOi8vd3d3LnczLm9yZy8xOTk5LzAyLzIyLXJkZi1zeW50YXgtbnMjIj48cmRmOmxpPkFubmllIFN1cmxhPC9yZGY6bGk+PC9yZGY6U2VxPg0KCQkJPC9kYzpjcmVhdG9yPjwvcmRmOkRlc2NyaXB0aW9uPjwvcmRmOlJERj48L3g6eG1wbWV0YT4NCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgPD94cGFja2V0IGVuZD0ndyc/Pv/bAEMABwUFBgUEBwYFBggHBwgKEQsKCQkKFQ8QDBEYFRoZGBUYFxseJyEbHSUdFxgiLiIlKCkrLCsaIC8zLyoyJyorKv/bAEMBBwgICgkKFAsLFCocGBwqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKv/AABEIALgCEwMBIgACEQEDEQH/xAAfAAABBQEBAQEBAQAAAAAAAAAAAQIDBAUGBwgJCgv/xAC1EAACAQMDAgQDBQUEBAAAAX0BAgMABBEFEiExQQYTUWEHInEUMoGRoQgjQrHBFVLR8CQzYnKCCQoWFxgZGiUmJygpKjQ1Njc4OTpDREVGR0hJSlNUVVZXWFlaY2RlZmdoaWpzdHV2d3h5eoOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4eLj5OXm5+jp6vHy8/T19vf4+fr/xAAfAQADAQEBAQEBAQEBAAAAAAAAAQIDBAUGBwgJCgv/xAC1EQACAQIEBAMEBwUEBAABAncAAQIDEQQFITEGEkFRB2FxEyIygQgUQpGhscEJIzNS8BVictEKFiQ04SXxFxgZGiYnKCkqNTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqCg4SFhoeIiYqSk5SVlpeYmZqio6Slpqeoqaqys7S1tre4ubrCw8TFxsfIycrS09TV1tfY2dri4+Tl5ufo6ery8/T19vf4+fr/2gAMAwEAAhEDEQA/APpGisPxl4gXwz4UvdTKSs8cZ8sRRGQ7sHGQO3vXDeAvG9paeE4db8Ta9qU7X8iRt9tgKRxSMCcJx933oWt/IHoeq0Vx1v8AFTwrcTTwm9lglhQSCOeBo2lUnAKAj5+fSr+i+M9G8UWN82lXUqSWikTpJEY5YuOu080dAOiorjNL8Z6LpHhHSLrUtanu4b6R4or25iKtIwLE7h2wFI/CruhfEHQPEOrPpljPNHeKnmLDcwNE0if3l3D5hz1FO2tgeh01FFFIAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigCvqGoWul6fNe6hMsFtApeSRuigVzWj/EnQ9Y1WCwSO+s5brP2V7y2MSXOBn5D345wcVn/GBmPg62gwTFcajbxTD1QuMg034uRpb+E9NuYAEntNYsWtyByCZ0Ugf8BJFC3172/L/MHt8rnf0Ui8qPpS0AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUE4Ga8hTxrL4g+MV1pg1PV7Cw05Y/Lt4LRlSZwXL+axHCkKAD3o62Dpc9eorh1+L/g94beZL6ZoJsZmFu2yInoHbGFP1q7H8SvDMutwaWl6/m3D+XDMYm8mRv7ok6E+1HkB1dFYHiHxpovhieG31KeRrqZS0dtbxNLKyjq21ece9N0vxxomtaLd6nps8s0Vln7REIm82MgZIKdc0dLgdDRXOy+OtBj0Cy1gXTSWt/IsdsI0LPI7HAUKOc56+ldEDkZoAKKKKAMfxd/yJmr/9ecv/AKCa8y1OCO6+G/w4hnUPG+o2QZSMgjNexTwRXMDwXEayxSKVdHGQwPUEVUbRdMa2tbdtPtjDZur20ZiG2Fl+6VHYjtinHR380/uYf8H8TjfE1vFL8bPBzSxq7JZ3rKSOh/d8/qfzqBRt+L/ikLxu0WInHc88130un2c9/BfTWsL3VurLDOyAvGGxuAPUA4GfpSf2ZY/bprz7HB9pnjEUs3lje6DopPUj2pdLev43HfV/L8LHi2n20N34O+GcVxGskZ1okqwyCQ0pH8q7fxUo/wCFueDGwN3l3gzjnG1OK6yPQdJihtIY9NtVjspPMtkEKgQvz8yjseTyPWs7Xdc8L6Tq9q+vz2sN/DGXt3ljLOiscEqcHGdv6VTd3fzb+9A3d/13bOhormP+FkeEf+g3B/3y3+FH/CyPCP8A0G4P++W/wqRGzq2tadodp9p1a7jtYs4Bc8sfQAcn8KwbP4meFr26ECaiYmY4RponRW/EjA/HFeaa5qyeK/Ft5fmUT2ls/k2mB8u0fxY9+tRTQR3ETRyoGVuoIrx8TmkaFX2aje25hOsoytY97BDKCpBBGQR3pa8v8CePNN0rSJtL8Q6kkD2cmyAyBiWjPIGQD0rd1X4m+HIdJupNO1aCW6WM+Um1uW7dq9aMlOKktmbJ3VzW1zxpoPh6UQ6nfqs5GRDGpd/xCg4/GjQvGmheIpjDpl8r3AGTBIpR8fQgZ/CvGLCIuhvLgmS5uD5kkjckk0XsbQ7dQtG8q7tD5sUi9QV5x9K8j+1oe29ny6bXMPbrmsfQlFcfp3xN8MzaZbS3urQw3DxK0sZVvkbHI6etWf8AhZHhH/oNwf8AfLf4V7J0HT0VzH/CyPCP/Qbg/wC+W/wo/wCFkeEf+g3B/wB8t/hQB09Fcx/wsjwj/wBBuD/vlv8ACmv8SfCSoxXWoWIGQNrc/pQBp654o0fw5GravepAX+4mCzt9FGTVHRvH3h3XbpbWyvttyxwsMyNGzfTIwfwryCG4k1q+n1m/bzbm5kJBPRF7AegxS39lHd25DDDryjjqprxqmawhW9ny6LS5zuslKx9BUVwXhf4maJL4Zsjr2rRRagEKzqytnIJAJwO4AP41o3XxL8Kx2krwaxDJKqEooVuTjgdK9k6DS13xdonhsqmrXqxSuMrEql3I+gBNQaH458P+ILgW+n3w+0kZ8iVDG5+mRz+Ga8dtmfUJ5dWvT5t3dsXZz2HYD8KL+2EkBmj/AHc8P7yKVeGVhyMGvGlm0I1vZ8um1znddKVj6DorhtA+Jmgy6BaNrGrQw33lgTIVbhu/atL/AIWR4R/6DcH/AHy3+FeydB09Fcx/wsjwj/0G4P8Avlv8KP8AhZHhH/oNwf8AfLf4UAdPRXMf8LI8I/8AQbg/75b/AAo/4WR4R/6DcH/fLf4UAXvFnh2LxV4budKllaAygNHMoyY3ByrY9jXLr4S8Xa9f6ZH401DSm03S50uVjsFk33cifcL7gAoBw2BnJHWtn/hZHhH/AKDcH/fL/wCFc/4P+Iejw2eojXNczI2ozNB5xdj5PG3HHA68ULR3DdWPSKK5j/hZHhH/AKDcH/fLf4Uf8LI8I/8AQbg/75b/AAoA6esjXPFOjeHEU6vepAzjKR4LO30UZNZr/EnwksbFdahYgEgbW5/SvJILiXWbyfWdQbzbq6ctuPRF6AD0GK5MXiY4anztXInNQVz2HRfHvh7XrpbWyvtty33YZkaNm+mRg/hXR18+31lHdQHIxIvKOOqmvRfDHxM0WXw3aHXtVih1BVKzqytnIJGeB3GDWeDxkcVFu1mhU6imjvaztY8QaX4ftxPq95HbIxwu7JZvoByfwrJ/4WR4R/6DcH/fLf4V5bqWpL4m8TXerOwliR/KtvRVHcCtsTiI4em6jHOSgrnp+n/ErwvqN0tvHqHkyO2EE8TRhvoSMfnXVA5GRyK8CuLaK6haKZAyn9K7LwP8QNKsfDy6f4h1KOC4s3MS7wxLIOh4BrnwWOWKurWaJp1Oc9LormP+FkeEf+g3B/3y3+FH/CyPCP8A0G4P++W/wr0TU376/tNMs3utQuI7eCMfNJIcAVzEXxS8Jy3Xk/2iyAnCyvA6oT9cfzrgvGXiCDxf4mWK0uFudLskDR7QQHc9Sc1QaKN4zG6KyEYKkcV5OKzKOHqezUb9zCdZRdj3mKWOeJZYXWSNwGV0OQwPcHvTq8h+H/i6y8NS3+ka1frBZIVltPMBO3dksoxnj/69dv8A8LI8I/8AQbg/75b/AAr06c1Ugpx2ZsndXOnormP+FkeEf+g3B/3y3+FH/CyPCP8A0G4P++W/wqxnT0VzH/CyPCP/AEG4P++W/wAKa/xK8JLGzLrMLEAkAK3P6UAaWueKdG8OKp1e9SBnGUjALO3/AAEZP41T0Xx94e126W1sr7bct92GZGjZvpkYP4V49bTy6xdTazqB827unLFj/COgA9BinX1nHdQ5I2yJ8yOOqmvGqZrCFb2fLotLnO6yUrH0FRXB+GviZokvh20Ou6rFDqCrtnVlbOQSM8DuMGtX/hZHhH/oNwf98t/hXsnQdPXnukf8lb8bf9g6z/lNW3/wsjwj/wBBuD/vlv8ACqqeNvAkd7cXkeoWS3NyqpPMIWDSqucBjt5AyfzpNXGnb+vM4rRrSCL9lqZUiUCSyd3G0fMxbJNafjKGOH4V+FliRUCXenlQoxt+dOlbyeLvh9HpH9lR3enrp+3Z9lEBEe3027cYrX0678NeKrAQWAtNQtbN0IjMWViYcpgEcYxx6Yq27yv5p/cT9nl9fxOL1jWJZPi1qVhpd5o/h+7t9Ph87UdSQyS3CMWIWNC6qFU5yc9TVX4Yalb/APCVeNbu61yHVoo3jaW/EaxxyAIMkAEjb75OfU16TqvhrRNdlil1nSbK/khOY2uYFkKfTI4pZPDmiyrcLJpVmwuY1jnBgX96g6K3HIHpULRfeU7N/ceL+Fxb6X8QLbxRf2MkHhfU7qSLSTLJlLSZ+PNK4wocggHPGfeveqp3Oj6be6WNNu7G3nsQoUW0kQaPA6DaeOKtqqogVAFVRgAdhVdLdhPV3FooopAFFFFABRRRQAVQ1ZIo7Ce7bT0vZoYyVjKAs2OcAmr9FAGLocuj69pMV9aWVvtcYZDCuUbuDx1rQ/suw/58bb/vyv8AhWdpXhuPSNcv720uHWC8IY2v8Cv3YfWtugDxXxjo0/hrxVd3RgP9mX7+akqL8sbHqpx0rGfUrYL+6kErnhUTksfpX0BNDFcRNFcRpLG3VHUMD+Bqha+HdGsbgz2mmWsMuc71iAI+npXl4jLaVer7Ru3cxlRjJ3Ob+H3hU6doclxrNrGbu9k81o5EBMa9AOa6DVvD1jqOkXVmlrbxNNEUVxEvynsa1qK9OMVFJLobbHz5tn0SVtN1lGtp4TtBcYDjsQaFS4164XStFQ3E8/yu6jKxKeCxNe832l2GpxhNQs4blR082MNj6elJY6XYaZGU06zgtlPXyowufr615f8AZdH23tb+djH2Mea5X07QNPsNMtrT7Jbv5ESx7jEuTgYz0qz/AGXYf8+Nt/35X/CrVFeqbFX+y7D/AJ8bb/vyv+FH9l2H/Pjbf9+V/wAKtUUAVf7LsP8Anxtv+/K/4U19J090ZTY2+GGD+6X/AAq5RQB4FfabceE9Rm07U0ZIRITb3BX5JFJ9agkumu2FrpKNeXcvypHENx+te+3dla38PlXttFcR/wB2VAw/WoLDRdM0rJ06wt7Ynq0cYBP49a8upllGdb2rfyMXRi5XMzwr4WttE8L2On3dvbzTxIfNcxg5YksefbOK0rnRdPubSWE2duvmIVyIl4yPpV+ivUNj5/uLW48M30mlaujQ+UxEMzD5ZU7EGmNLLqb/AGDR42u7ub5VWMZCg9ST2Fe9XunWWpReXqFpDcp2EqBsfnTLDR9O0pdunWMFtkYJjjAJ+p6mvKlldGVb2l/Oxi6MXK5n+H/DNppHh+zsZ7eCaWGIK8hiBLHvWl/Zdh/z423/AH5X/CrVFeqbFX+y7D/nxtv+/K/4Uf2XYf8APjbf9+V/wq1RQBV/suw/58bb/vyv+FH9l2H/AD423/flf8KtUUAVf7LsP+fG2/78r/hXI/DzTbZtP1j7TZRE/wBsXAXzIh93K4xkdK7iigCr/Zdh/wA+Nt/35X/Cj+y7D/nxtv8Avyv+FWqKAKb6Tp7xspsbfDDB/dL/AIV4beadceE9Ql0zVFZIlcm3uCvySKT6179UF3Y2t/D5V9bRXEf92VAw/WubE4eGIp8kiJxU1ZngT3T3jC00lGu7uX5UjiG7Hua9j8LeFrbRPDNlYXVvBNPGmZXaMHLEknn8cfhWnYaLpmlZ/s6wt7Ynq0cYBP49avVGFwkMLFqOtxQgoLQq/wBl2H/Pjbf9+V/wrxrxRpUvhfxLdebCy6ddyeZDMF+VSeqn0r2+o7i2gu4TFdQxzRt1SRQwP4GtcRQjXpunIqUVJWZ4BJqUGAts32iZjiOKPksT0r1PwF4WXS/DKf2taxNeXDmaRZIwSme3Nb9l4f0jTpjLY6bawSE53pEAR9D2rRrDCYKGFvyu7ZMKahsVf7LsP+fG2/78r/hR/Zdh/wA+Nt/35X/CrVFdxoeSfEPw/PpHiAa3Y2pawnjCTiFP9UR3IHauXbVLMR7hOregXkn8K+gnRZEKuoZWGCCMg1mxeGtEgujcxaTZpN13CFePp6V5uJy6niJ87dmYzpKbucX8NfDEyre6zrVoqm8KrBBNGCVRc4bB6Zz+ld7/AGXYf8+Nt/35X/CrVFehCChFRjsjVKysir/Zdh/z423/AH5X/Cj+y7D/AJ8bb/vyv+FWqKoZV/suw/58bb/vyv8AhTX0jT5I2Q2Nvhhg/ul/wq5RQB4Bd6dc+E76XS9VVkjRz9nuCMJKmeDmomumvJBaaSjXd3L8qRxDdj3Ne+3djaahD5V9bRXEf92VAw/WoLDRNM0vJ06wt7Ynq0cYBP49a8qeV0Z1vaN/IxdGLlczfDHhe10bw3Z2N1bW808afvXMYOWJJPP44rW/suw/58bb/vyv+FWqK9U2Kv8AZdh/z423/flf8KP7LsP+fG2/78r/AIVaooAo3FnpdrbyT3FpaxxRqWZmiXAH5VS8L6pb6xYy3dlYfZLZpCsThQvnKP4sCpdd0aLxJp0Vs93IlsXDuISMTKP4SfStOCCO2gSGBFjjjUKqqMACgB9FFFABRRRQAUUUUAFFFFABRRRQAUUUUARXNut1ayQSFgsilSVOCM+9cl4RudQ0fVrjwvqqyzLCDLZ3RBIePPQn1FdlTZMqrOib3CnA6E+2aAHUVi+G/EkPiG1mIia3ubeVop7d/vRkHvW1QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVznjG61X7HBpmgwv8AatQYxG5x8tumPmYn1x0qz4m8Rw+HdOEpQz3MrbLe3X70jnoK1LKSeWxhku4hDOyAyRg52tjkUAVNC0eDQdGg0+1yUiXBZjkse5P41o0UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAGL/wjkUXiz+3LWZoHki8u4hUfLMezH3FbVFcdphvPDPit9KlE9zpmoMZbaUgt5D9SpPpQB2NFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVDdXdvZQGa7mSGIEAu7YGTU1cdq2iaj4n8WRxanF5OhaeRIiZz9qk9T7CgDdl0GyuvEEGsy7pZoYikSk5Rc/wAQHrWpSKoVQqgAAYAHaloAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigApCO+MkdKWigDB0TxMuqavqGmXNs1neWb8ROcl4+zCt6sbUfDcF74gsNYika3u7RiGdB/rYyDlD+NbAZWZgrAlTggHpQAtFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFU5tVsYNTg06a5jS7uFLRxE8sB1oAxL/wAR3c/iy20TQYkmaJhJfzNysUf93/eNdPVPT9JstLe5exgEbXUvmytnJZvrVygAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK426ivPC/jL+0LdZbjStWkC3MagsYZsYDgehArsqCAeozQAA5GRRWHH4kQeLJNDvIGt3ZA9tIx4mHfHvW5QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUVUm1XTreUxz39rFIvVXmUEfgTU1vdQXcfmWs8c6ZxujcMM/UUAS0UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUVHcXEVrbyT3DrHFGpZ2Y4AAoAWUyCFzCqtJg7QxwCfc1zPhbw9e2+oXWueIWSXVLolQF5WCPsq03wtqureItTutVY+Rozfu7SFl+aUA/fz2zXV0AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAY/iDw5BryWzNK9vcW0okinj+8uDyK114AUnJA/OlzjrXkHjrxnbaB4yTUPDd6Li9MDQXcRJaHH8PfqD6UAevbhu25G7GcUteMfCrxZfaj41vE1e7eeS+iyGc8AqegHbr0HpXs9ABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAeZfELTPD0+qJpth4fstS8T6rnY0kefJXvLIewH6113grwjY+CfDMGkacMhSZJZMY8yQ/ebHb6elYM3wrVvEmoa3Z+LfEFjdX7Zl8iWHAA6KN0ZIUema6fw/ok2h2ckFxrOoauzvuE1+6M6jHQbVUY/CiOkQe5rUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB5/wDEbx5ceEtQ02KxCSMxMk8bfxJ0x7Gtnw54u0bxxZzwQRsxWMefBMnGD29DXiPxF1n+2vG97KjbooW8mP6LxWn8P/BfiS+1KHU9Pmk0qCM5F0w5cdwF/iH14oA9/hhjt4UhgRY40AVUUYAA7U+moGWNQ7b2AwWIxn3p1ABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFACEBlIIyDwa8u8a/COG+Ml/4b2w3Byz2zH5XPt6GvUqKAPl/Qpbvwx40spLyF7eWCcB0cYIB4P86+n0cSRq6nKsMg+1YXifwdpXim1KX0QScD93cIMOn+NWdupaZ4bSO0gS/vreMIiGQRiTHHU9OKANWuL8aeOzoF0mm6Xbi61CRN53H5Ih7+9O/tzx1/0KFt/4Mkrza7lvbjxVq02r262160o3whw4QY4AYda5MZXdCi6kdyKkuWNzci+Iniq1l865S0u4s5aFU2kD0Br0vw7r9r4k0WHUbLKrJkNG33o2BwVNeN1reAr3xFZyaunh3Sob+2NwpYyXAi2Pt5xnrXBluNq4iThU9TKlUcnZnfeMfGEHhazjCxfab64OIIAevufauCPxB8XCTzv9DKYz5Hl/1rO8T3Gq3XjcP4gso7K4FsBHEkokGPXIqGs8fmFWjV9nT6Cq1ZRlZHq/hDxbb+K9NeVYzb3UDbJ7djypx1Hsa6GvEfClxrFr40nbw7Zx3krWf76OSURqBu4OfWu8/tTx7/0L1j/4GrXr4er7alGo1ubxlzRTOqv7+30zT5ry9kEcEKF3Y9gK8ru/iX4g1KRpNHhgsbXP7szJvdh6n60/4gX3iybwnImtaVbWlkZY/MkiuQ5+8MDH1rn1wFG3pjjFcGZYyph1FU92ZVqjhax2/hT4iXF7qyaV4hhjhnm/1M8Zwjn09jXfzTR28DzTuEjjUszMcAAd6+fNULLHA8H/AB8LOhix3bNdv4xvfGr+Db9dR0uzgtjFiaSO43MF78V0YHESxFHnluXTm5xuyrf/ABO1nU53bw/BFaWYOI5bhdzP747Vo+GviPdtqkWn+JYokE52xXUXC7vQjtXFwBBbxiP7gUbfpVbVv+Qe5H3wQU/3s8V5FPNK0q6TXut2sYRrScj6Forh7O88ffYYNumacR5a4LXHJ471N9r8f/8AQM0z/v8A19KdZv6trlvpF3p1vcI7NqFx5EZXs2M81p15R4qn8Xtq3h46hZ2McovwbYRy5DPjo3oK6X7T8QP+fDS/+/xoA7KiuN+0fED/AJ8tK/7+mjz/AIg/8+mk/wDf00Aa/irxPa+FtIN3cqZZHbZBCp+aRj2rzp/iF4smlE0a2cEfXyCmfzNVPG769J4j0geJo7aPCSmAW7EgnjOc9+lUq8PMcdVoVFCn6nNVqOLsj07wZ41j8TpLb3MP2XULcAyRZ4Yf3l9q0PFPia18LaQby5BkkdtkMKn5pGPYV5HoZ1NfHVn/AGAITetDJkTkhCuO+KueOG19vEGjr4mFqo2ymAWxJUnjOc9+ld9PEuWF9u1rZv7jVTvDmLT/ABC8WTSiaJbOCPqICmc/U123gzxrH4nWW2uYfsuoW4Bkizww/vL7V5jS6OdTXxxYf8I/5P25o5B++zs245zivMwOYVa1bkns/wADGnVlKVme71keJvEVt4Z0d766Bc52xRL1kY9BWJj4h/3tF/8AH64zxz/wkf8AaWlL4mazMW5jELXON3vmvcqz9nTlPsjpk7K5K/xC8WTyCaJbO2jzkQsm449Ca7PwX42XxJ5tnfQC11G3ALpn5ZAf4l9q80pdM/tL/hNNL/sFoVvmEgBmB2bcfxY5xXhYLMa1auoT2f4HNTqylKzPd6y/EXiC08NaNLqF8SVThI1+9Ix6KKwfJ+In/Pzof/fElcb47TxIt7pC+J5bGSAysY/sisAGxxnNe7Vn7Om59kdLdlckf4h+K7mXzoUtLWPJIgZNxx2BNdd4L8djxDM+n6lALXUYl3YB+WUeq15zTLNb4+LtK/sV4UvzIQjTAlAMd8c4rwsFmNarXUJ7P8Dmp1ZSlZnvdU9Xe4TR7n7DGZLloysSjux4H+NZWiQeLo7/AHeILvS5bXafltY3D7u3XjFdDX0J1HnXhT4UWWnSC/18re3rHf5Z+4h6/jXoiIqIFRQqgYAAwBS0UAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUVHcTrbW0k8gYrGpYhRk4HoKNgJK4Pxt4Cn1bUBrOhSpFfhAssMg+ScDpk9jXY6TqUOsaTbajarKkNzGJEWZCjgH1B6GrdTOCmnGS0Fo0eLw+C/F95N5DWENipOGuHmDAD1AFen+FfDdt4W0OPT7ZjK+4vNMwwZXPUn9B9BWzRWVHD0qCapq1xRjGOxynjXwUnieKK6tJhbalbZ8qUrkMP7re1cB/wh/jEyeQNKgB/57+eNn5da9qoqa2Fo12nUjewpQjLdHMeCvBsfhWzmeeYXWoXTbp7jbj6Ko7Af57AdPRRXQkkrIsp6tpVtrWk3Gn3yb4J0KsB1HuPcV5PeeAfE+jyeRYQx6tbLxHIJBG4HYEHjivZKKyrUKdePLUVyZRUlZnmnhT4eXv9rQ6r4mMa/Zzuhs0O4Bv7zHv9K9Fu7SC/s5bW7jEsEyFJEbowIwRU1FXTpxpxUIKyGkkrI8dvvh54h0SUw6OiatZD/VbnEciD0OeDj1rQ8O/DrUb3UorzxQscFvbsHSzjbcZG/wBo+ntXqVFYLCUI1PaqOpPJG97ABgYHAooorqLMnWtBj1m90u4kmaM6dci4UAZ3nGMGtaiigAooooA57xh4Sg8WaWkLym3urd/Mt7hRko3uO4PpXnEng7xhBN5A02C45wLhZwE+pB5r2iiuethqVe3tI3sTKEZbnHeCfA58OyS6hqUy3OpTrtLKvyxL/dX/ABrR8YeE7fxZpSQPIbe5gfzLe4UZKN9O4Pp9K6CitlGKjypaDsrWPF5PB3jCCbyBpsFx2Fwk4C/Ug8123gnwOfD0suo6nMtzqU67SVX5YV/ur/jXZUVhSwtGjJypxsyYwjF3SCsLxb4Xg8VaP9klkaCaNt8EyjJRvp3FbtFdO5Z4vJ4O8YW83kDTYLoZwLhJgqn3IPNdp4I8DvoU8mqavKlxqUyBVCD5Ldf7q+p9T/k9pRXNSwlCjLmhGzIjCMXdIKxfFXhm18VaK9jdMYnB3wzqPmicdDW1RXSWeLSeDPGFrN5A0+G8AJAuEmCgj1IPT8K6/wAE+ApNHvTq+uSJPqDLtjjQfJAPY9z713VFc1LCUaUnOEbMiMIxd0gooorpLCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAqK5nW1tJZ3+7Ehc/QDNS02WNZoXjkGUdSrD1BpO9tBrfU8u0l/GPiXw1N4stfEX2RpFkktNNWFTCEGcBj1JOOtVLvxP4nf4eeBLmwv9uqatfx29xLIoYOGWTO4dwMA4GOlaLeGvGfhrQr7RtEv9LbRAkjQ3Nwjm5t0OSUCj5W9iSPoag8M+Hb3Xfhz8PpbSSIDTLqO7nMrEFkAkU4wDk5YelUrN+V4/rcNr/P/gC3KeLtJ8eWXhyLxRJdwatavM1xcQLvtih52Acc5HXpVzQ9T1zS9X8T6BqWryan9gtFuba7kjCyLuB4OODg10Oo+Hby7+I2ka9E8ItLK0mhkVmO8s5BGBjGOPWqreFL8+L/ABDqm+DyNSsEtoRuO4MAc7hjgc+9Rry/J/rYatf7v+CcgfEvie68N/Dj7BqZS81osl3NIgYOPJY7iO5GMgccgVuabPr3hv4k2Wh6jrkus2Wp2skym4jVXhdCOAR1BzRp/gPVbWx8BQyS2pbw6zG82u2GzCyfJ8vPLDrit3U/Dt5efETR9dieEWtlazQyqzHeWYjGBjGOPWtHbn+b+63+ZC+Fen43OmoooqRhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBx3j3XNSs59H0TQp0tb7WLgxC5dd3koBlmA7msSQeKfDfxA8MaZc+IZdT0u/llErTRqshZYmIU46rxntjFdP4y8MXOvwWV1pN0lnq2mzefaSyLuQnoVYehFcVfJ4pf4qeDJ/FUunRgzXCx2unh2QHyWy5d+SfbAxz1pw3+Y3t8ifTz4q8U+I/F1pD4lm0yz0zUTFbGGFWf8A1aHbk/wjPTryaz7C/wDGmt/DiXxPL4k+x3FirlLeCAGOfyzgl88/Njt0ru/C/hu90bVPE1zdvCyarqDXUAjYkhDGi4bIGDlT0zVDRvB2o6f8Lbvw5PJbm9mSZVZXJj+ckjJxnv6VGqhpvZffYrTm17/gU9f8S6lP4W8PXi6vZ+H7TUYkkvtQndAYgUztjViMsTx3xWT4H8Xy3HxFbw/aeJ5fEmmz2L3C3M0IVonVgpUMBhhz+FX9Q8B69CvhTUNIbTLrUNCtDbPaahuMEmVALqwGVYY4OO9WbDwr4tb4i2vinWrvTZFjsJLU2dsHRYcsGXaxyXyQck47YFae7zvtr+Tt+hH2TD1DxT4p0PXp/BHnPd6pqUu/S9RkxhISfmLY7p29a9VsLeW006C3uLmS6ljjCvPJjdIe5OK88k+GV9q1lqOqa1dwr4puJhNaXcDEpZ7D+7RCQDt9eOa7/STqH9kW39tLAt+IwJ/s7Foy/cqSAcfhUr4bPf8Ar+mD30/r+uhcooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAbJGk0bRyqGRwQykcEVFY2NrptlFZ2EEdvbQrtjijXCqPQCiigCeiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAqtcabZXV7bXdzaxS3FqS0ErLloiRgkHtkHFFFAFmiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigD//2Q==" + } + }, + "cell_type": "markdown", + "id": "f5454823", + "metadata": {}, + "source": [ + "## Overview\n", + "\n", + "Deep Learning has touched almost every industry and has transformed the way industries operate and provide services. We perform or experience real-time analytics all the time around us, for example, an advertisement that you saw while swiping through the stories on Instagram, or the video recommendation that floated on your youtube home screen. To cater to these real-time inferences, deep learning practitioners need to maximise model throughput while having highly accurate predictions. Among many techniques, quantization can be used to accelerate models.\n", + "\n", + "Model Quantization is a popular way of optimization which reduces the size of models thereby accelerating inference, while also opening up the possibilities of deployments on devices with lower computation power such as Jetson. Simply put, quantization is a process of mapping input values from a larger set to output values in a smaller set. In the context of deep learning, we often train deep learning models using floating-point 32 bit arithmetic (FP32) as we can take advantage of a wider range of numbers, resulting in more accurate models. The model data (network parameters and activations) are converted from this floating point representation to a lower precision representation, typically using 8-bit integers (int8). In the case of int8, the range [qmin, qmax] would be [-128, 127].\n", + "\n", + "![img1.JPG](attachment:img1.JPG)\n", + "\n", + "A quick rationale of how higher throughput is achieved through quantization can be shown through the following thought experiment: Imagine the complexity of multiplying 3.999x2.999 versus 4x3. The latter is easier to perform than the former. This is the simplicity in calculation seen by quantizing the numbers to lower precision. However, the challenge here is that round errors can result in a lower accuracy model. To address this loss of accuracy, different quantization techniques have been developed. These techniques can be classified into two categories, post-training quantization (PTQ) and quantization-aware training (QAT).\n", + "\n", + "In this notebook, we illustrate the workflow that you can adopt in order to quantize a deep learning model using TensorRT. The notebook takes you through an example of Mobilenetv2 for a classification task on a subset of Imagenet Dataset called Imagenette which has 10 classes. \n", + "\n", + "1. [Requirements](#1)\n", + "2. [Setup a baseline Mobilenetv2 model](#2)\n", + "3. [Convert to TensorRT](#3)\n", + "4. [Post Training Quantization (PTQ)](#4)\n", + "5. [Quantization Aware Training (QAT)](#5)\n", + "6. [Evaluation and Benchmarking](#6)\n", + "7. [Conclusion](#7)\n", + "8. [References](#8)\n", + "\n", + "This notebook is implemented using the NGC pytorch container nvcr.io/nvidia/pytorch:22.04-py3. Follow instructions here https://ngc.nvidia.com/setup/api-key to setup your own API key to use the NGC service through the Docker client. " + ] + }, + { + "cell_type": "markdown", + "id": "06b37d07", + "metadata": {}, + "source": [ + "\n", + "## 1. Requirements\n", + "Please install the required dependencies and import these libraries accordingly" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "0a068b12", + "metadata": {}, + "outputs": [], + "source": [ + "!pip install ipywidgets --trusted-host pypi.org --trusted-host pypi.python.org --trusted-host=files.pythonhosted.org\n", + "!pip install wget\n", + "!pip install pycuda" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "4e2e58b2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.1.2\n" + ] + } + ], + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "import torch.optim as optim\n", + "import torch.utils.data as data\n", + "import torchvision.transforms as transforms\n", + "from torchvision import models, datasets\n", + "\n", + "import pytorch_quantization\n", + "from pytorch_quantization import nn as quant_nn\n", + "from pytorch_quantization import quant_modules\n", + "from pytorch_quantization import calib\n", + "from tqdm import tqdm\n", + "\n", + "print(pytorch_quantization.__version__)\n", + "\n", + "import os\n", + "import tensorrt as trt\n", + "import numpy as np\n", + "import time\n", + "import wget\n", + "import tarfile\n", + "import shutil" + ] + }, + { + "cell_type": "markdown", + "id": "0575e590", + "metadata": {}, + "source": [ + "\n", + "## 2. Setup a baseline Mobilenetv2 Model" + ] + }, + { + "cell_type": "markdown", + "id": "a83b886f", + "metadata": {}, + "source": [ + "#### Preparing the Dataset\n", + "\n", + "Imagenette is a subset of ImageNet and has 10 classes. The classes are as follows in the order of their labels : 'tench', 'English springer', 'cassette player', 'chain saw', 'church', 'French horn', 'garbage truck', 'gas pump', 'golf ball' and 'parachute'. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "50d60fbe", + "metadata": {}, + "outputs": [], + "source": [ + "def download_data(DATA_DIR):\n", + " if os.path.exists(DATA_DIR):\n", + " if not os.path.exists(os.path.join(DATA_DIR, 'imagenette2-320')):\n", + " url = 'https://s3.amazonaws.com/fast-ai-imageclas/imagenette2-320.tgz'\n", + " wget.download(url)\n", + " # open file\n", + " file = tarfile.open('imagenette2-320.tgz')\n", + " # extracting file\n", + " file.extractall(DATA_DIR)\n", + " file.close()\n", + " else:\n", + " print(\"This directory doesn't exist. Create the directory and run again\")" + ] + }, + { + "cell_type": "markdown", + "id": "2e25dc45", + "metadata": {}, + "source": [ + "Let's create the data directory if it doesn't exist." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "4a4d8949", + "metadata": {}, + "outputs": [], + "source": [ + "if not os.path.exists(\"./data\"):\n", + " os.mkdir(\"./data\")\n", + "download_data(\"./data\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "07d1fc63", + "metadata": {}, + "outputs": [], + "source": [ + "# Define main data directory\n", + "DATA_DIR = './data/imagenette2-320' \n", + "# Define training and validation data paths\n", + "TRAIN_DIR = os.path.join(DATA_DIR, 'train') \n", + "VAL_DIR = os.path.join(DATA_DIR, 'val')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "acd3cd99", + "metadata": {}, + "outputs": [], + "source": [ + "# Performing Transformations on the dataset and defining training and validation dataloaders\n", + "transform = transforms.Compose([\n", + " transforms.Resize(256),\n", + " transforms.CenterCrop(224),\n", + " transforms.ToTensor(),\n", + " ])\n", + "train_dataset = datasets.ImageFolder(TRAIN_DIR, transform=transform)\n", + "val_dataset = datasets.ImageFolder(VAL_DIR, transform=transform)\n", + "calib_dataset = torch.utils.data.random_split(val_dataset, [2901, 1024])[1]\n", + "\n", + "train_dataloader = data.DataLoader(train_dataset, batch_size=64, shuffle=True, drop_last=True)\n", + "val_dataloader = data.DataLoader(val_dataset, batch_size=64, shuffle=False, drop_last=True)\n", + "calib_dataloader = data.DataLoader(calib_dataset, batch_size=64, shuffle=False, drop_last=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "a2f8914c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(0)\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Visualising an image from the validation set\n", + "import matplotlib.pyplot as plt\n", + "for images, labels in val_dataloader:\n", + " print(labels[0])\n", + " image = images[0]\n", + " img = image.swapaxes(0, 1)\n", + " img = img.swapaxes(1, 2)\n", + " plt.imshow(img)\n", + " break" + ] + }, + { + "cell_type": "markdown", + "id": "4b7441e6", + "metadata": {}, + "source": [ + "#### Setting up Mobilenetv2\n", + "\n", + "Mobilenetv2 available in Torchvision is pretrained on the ImageNet that has 1000 classes. The Imagenette dataset has 10 classes. \n", + "We set up this model by freezing the weights excpet for the last classification layer and train only the last classification layer to be able to predict the 10 classes of the dataset. " + ] + }, + { + "cell_type": "markdown", + "id": "b9577f2a", + "metadata": {}, + "source": [ + "*Define the Mobilenetv2 model*" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "c29ae7b8", + "metadata": {}, + "outputs": [], + "source": [ + "# This function allows you to set the all the parameters to not have gradients, \n", + "# allowing you to freeze the model and not undergo training during the train step. \n", + "def set_parameter_requires_grad(model, feature_extracting):\n", + " if feature_extracting:\n", + " for param in model.parameters():\n", + " param.requires_grad = False\n", + " \n", + "feature_extract = True #This varaible can be set False if you want to finetune the model by updating all the parameters. \n", + "model = models.mobilenet_v2(pretrained=True)\n", + "set_parameter_requires_grad(model, feature_extract)\n", + "#Define a classification head for 10 classes.\n", + "model.classifier[1] = nn.Linear(1280, 10)\n", + "model = model.cuda()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "5c03df98", + "metadata": {}, + "outputs": [], + "source": [ + "# Declare Learning rate\n", + "lr = 0.0001\n", + "\n", + "# Use cross entropy loss for classification and SGD optimizer\n", + "criterion = nn.CrossEntropyLoss()\n", + "optimizer = optim.SGD(model.parameters(), lr=lr)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7095a995", + "metadata": {}, + "outputs": [], + "source": [ + "# Define functions for training, evalution, saving checkpoint and train parameter setting function\n", + "def train(model, dataloader, crit, opt, epoch):\n", + " model.train()\n", + " running_loss = 0.0\n", + " for batch, (data, labels) in enumerate(dataloader):\n", + " data, labels = data.cuda(), labels.cuda(non_blocking=True)\n", + " opt.zero_grad()\n", + " out = model(data)\n", + " loss = crit(out, labels)\n", + " loss.backward()\n", + " opt.step()\n", + " running_loss += loss.item()\n", + " if batch % 100 == 99:\n", + " print(\"Batch: [%5d | %5d] loss: %.3f\" % (batch + 1, len(dataloader), running_loss / 100))\n", + " running_loss = 0.0\n", + " \n", + "def evaluate(model, dataloader, crit, epoch):\n", + " total = 0\n", + " correct = 0\n", + " loss = 0.0\n", + " class_probs = []\n", + " class_preds = []\n", + " model.eval()\n", + " with torch.no_grad():\n", + " for data, labels in dataloader:\n", + " data, labels = data.cuda(), labels.cuda(non_blocking=True)\n", + " out = model(data)\n", + " loss += crit(out, labels)\n", + " preds = torch.max(out, 1)[1]\n", + " class_preds.append(preds)\n", + " total += labels.size(0)\n", + " correct += (preds == labels).sum().item()\n", + " return correct / total\n", + "\n", + "def save_checkpoint(state, ckpt_path=\"checkpoint.pth\"):\n", + " torch.save(state, ckpt_path)\n", + " print(\"Checkpoint saved\")\n", + " \n", + "# Helper function to benchmark the model\n", + "cudnn.benchmark = True\n", + "def benchmark(model, input_shape=(1024, 1, 32, 32), dtype='fp32', nwarmup=50, nruns=1000):\n", + " input_data = torch.randn(input_shape)\n", + " input_data = input_data.to(\"cuda\")\n", + " if dtype=='fp16':\n", + " input_data = input_data.half()\n", + " \n", + " with torch.no_grad():\n", + " for _ in range(nwarmup):\n", + " features = model(input_data)\n", + " torch.cuda.synchronize()\n", + " \n", + " timings = []\n", + " with torch.no_grad():\n", + " for i in range(1, nruns+1):\n", + " start_time = time.time()\n", + " output = model(input_data)\n", + " torch.cuda.synchronize()\n", + " end_time = time.time()\n", + " timings.append(end_time - start_time)\n", + "\n", + " print('Average batch time: %.2f ms'%(np.mean(timings)*1000))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "02a625c9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: [ 1 / 5] LR: 0.000100\n", + "Batch: [ 100 | 147] loss: 2.315\n", + "Test Acc: 22.93%\n", + "Epoch: [ 2 / 5] LR: 0.000100\n", + "Batch: [ 100 | 147] loss: 2.177\n", + "Test Acc: 35.09%\n", + "Epoch: [ 3 / 5] LR: 0.000100\n", + "Batch: [ 100 | 147] loss: 2.053\n", + "Test Acc: 49.33%\n", + "Epoch: [ 4 / 5] LR: 0.000100\n", + "Batch: [ 100 | 147] loss: 1.935\n", + "Test Acc: 61.50%\n", + "Epoch: [ 5 / 5] LR: 0.000100\n", + "Batch: [ 100 | 147] loss: 1.836\n", + "Test Acc: 71.11%\n", + "Checkpoint saved\n" + ] + } + ], + "source": [ + "# Train the model for 5 epochs to attain an acceptable accuracy.\n", + "num_epochs=5\n", + "for epoch in range(num_epochs):\n", + " print('Epoch: [%5d / %5d] LR: %f' % (epoch + 1, num_epochs, lr))\n", + "\n", + " train(model, train_dataloader, criterion, optimizer, epoch)\n", + " test_acc = evaluate(model, val_dataloader, criterion, epoch)\n", + "\n", + " print(\"Test Acc: {:.2f}%\".format(100 * test_acc))\n", + " \n", + "save_checkpoint({'epoch': epoch + 1,\n", + " 'model_state_dict': model.state_dict(),\n", + " 'acc': test_acc,\n", + " 'opt_state_dict': optimizer.state_dict()\n", + " },\n", + " ckpt_path=\"models/mobilenetv2_base_ckpt\")" + ] + }, + { + "cell_type": "markdown", + "id": "b829681d", + "metadata": {}, + "source": [ + "We will first generate and evaluate our models and then finally look at the performance to the end of the notebook." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "411d0ebc", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mobilenetv2 Baseline accuracy: 71.11%\n" + ] + } + ], + "source": [ + "# Evaluate the baseline model\n", + "test_acc = evaluate(model, val_dataloader, criterion, 0)\n", + "print(\"Mobilenetv2 Baseline accuracy: {:.2f}%\".format(100 * test_acc))" + ] + }, + { + "cell_type": "markdown", + "id": "71fdd581", + "metadata": {}, + "source": [ + "\n", + "### Convert to TensorRT\n", + "\n", + "TensorRT is an SDK facilitating high-performance deep learning inference, optimized to run on NVIDIA GPUs. It accelerates models through graph optimization and quantization. This notebook uses the trtexec CLI tool to build TensorRT engine. " + ] + }, + { + "cell_type": "markdown", + "id": "f75ab9fd", + "metadata": {}, + "source": [ + "Let us convert the above FP32 Mobilenetv2 into a TensorRT engine. Before we do that, we need to first export our model into ONNX format. ONNX is a standard for representing deep learning models enabling them to be transferred between frameworks. The average run time of the TRT model would be the 'GPU Compute Time' printed in the logs." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "e24451cf", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "&&&& RUNNING TensorRT.trtexec [TensorRT v8205] # trtexec --onnx=models/mobilenetv2_base.onnx --saveEngine=models/mobilenetv2_base.trt\n", + "[07/25/2022-16:42:22] [I] === Model Options ===\n", + "[07/25/2022-16:42:22] [I] Format: ONNX\n", + "[07/25/2022-16:42:22] [I] Model: models/mobilenetv2_base.onnx\n", + "[07/25/2022-16:42:22] [I] Output:\n", + "[07/25/2022-16:42:22] [I] === Build Options ===\n", + "[07/25/2022-16:42:22] [I] Max batch: explicit batch\n", + "[07/25/2022-16:42:22] [I] Workspace: 16 MiB\n", + "[07/25/2022-16:42:22] [I] minTiming: 1\n", + "[07/25/2022-16:42:22] [I] avgTiming: 8\n", + "[07/25/2022-16:42:22] [I] Precision: FP32\n", + "[07/25/2022-16:42:22] [I] Calibration: \n", + "[07/25/2022-16:42:22] [I] Refit: Disabled\n", + "[07/25/2022-16:42:22] [I] Sparsity: Disabled\n", + "[07/25/2022-16:42:22] [I] Safe mode: Disabled\n", + "[07/25/2022-16:42:22] [I] DirectIO mode: Disabled\n", + "[07/25/2022-16:42:22] [I] Restricted mode: Disabled\n", + "[07/25/2022-16:42:22] [I] Save engine: models/mobilenetv2_base.trt\n", + "[07/25/2022-16:42:22] [I] Load engine: \n", + "[07/25/2022-16:42:22] [I] Profiling verbosity: 0\n", + "[07/25/2022-16:42:22] [I] Tactic sources: Using default tactic sources\n", + "[07/25/2022-16:42:22] [I] timingCacheMode: local\n", + "[07/25/2022-16:42:22] [I] timingCacheFile: \n", + "[07/25/2022-16:42:22] [I] Input(s)s format: fp32:CHW\n", + "[07/25/2022-16:42:22] [I] Output(s)s format: fp32:CHW\n", + "[07/25/2022-16:42:22] [I] Input build shapes: model\n", + "[07/25/2022-16:42:22] [I] Input calibration shapes: model\n", + "[07/25/2022-16:42:22] [I] === System Options ===\n", + "[07/25/2022-16:42:22] [I] Device: 0\n", + "[07/25/2022-16:42:22] [I] DLACore: \n", + "[07/25/2022-16:42:22] [I] Plugins:\n", + "[07/25/2022-16:42:22] [I] === Inference Options ===\n", + "[07/25/2022-16:42:22] [I] Batch: Explicit\n", + "[07/25/2022-16:42:22] [I] Input inference shapes: model\n", + "[07/25/2022-16:42:22] [I] Iterations: 10\n", + "[07/25/2022-16:42:22] [I] Duration: 3s (+ 200ms warm up)\n", + "[07/25/2022-16:42:22] [I] Sleep time: 0ms\n", + "[07/25/2022-16:42:22] [I] Idle time: 0ms\n", + "[07/25/2022-16:42:22] [I] Streams: 1\n", + "[07/25/2022-16:42:22] [I] ExposeDMA: Disabled\n", + "[07/25/2022-16:42:22] [I] Data transfers: Enabled\n", + "[07/25/2022-16:42:22] [I] Spin-wait: Disabled\n", + "[07/25/2022-16:42:22] [I] Multithreading: Disabled\n", + "[07/25/2022-16:42:22] [I] CUDA Graph: Disabled\n", + "[07/25/2022-16:42:22] [I] Separate profiling: Disabled\n", + "[07/25/2022-16:42:22] [I] Time Deserialize: Disabled\n", + "[07/25/2022-16:42:22] [I] Time Refit: Disabled\n", + "[07/25/2022-16:42:22] [I] Skip inference: Disabled\n", + "[07/25/2022-16:42:22] [I] Inputs:\n", + "[07/25/2022-16:42:22] [I] === Reporting Options ===\n", + "[07/25/2022-16:42:22] [I] Verbose: Disabled\n", + "[07/25/2022-16:42:22] [I] Averages: 10 inferences\n", + "[07/25/2022-16:42:22] [I] Percentile: 99\n", + "[07/25/2022-16:42:22] [I] Dump refittable layers:Disabled\n", + "[07/25/2022-16:42:22] [I] Dump output: Disabled\n", + "[07/25/2022-16:42:22] [I] Profile: Disabled\n", + "[07/25/2022-16:42:22] [I] Export timing to JSON file: \n", + "[07/25/2022-16:42:22] [I] Export output to JSON file: \n", + "[07/25/2022-16:42:22] [I] Export profile to JSON file: \n", + "[07/25/2022-16:42:22] [I] \n", + "[07/25/2022-16:42:22] [I] === Device Information ===\n", + "[07/25/2022-16:42:22] [I] Selected Device: NVIDIA Graphics Device\n", + "[07/25/2022-16:42:22] [I] Compute Capability: 8.0\n", + "[07/25/2022-16:42:22] [I] SMs: 124\n", + "[07/25/2022-16:42:22] [I] Compute Clock Rate: 1.005 GHz\n", + "[07/25/2022-16:42:22] [I] Device Global Memory: 47681 MiB\n", + "[07/25/2022-16:42:22] [I] Shared Memory per SM: 164 KiB\n", + "[07/25/2022-16:42:22] [I] Memory Bus Width: 6144 bits (ECC enabled)\n", + "[07/25/2022-16:42:22] [I] Memory Clock Rate: 1.215 GHz\n", + "[07/25/2022-16:42:22] [I] \n", + "[07/25/2022-16:42:22] [I] TensorRT version: 8.2.5\n", + "[07/25/2022-16:42:23] [I] [TRT] [MemUsageChange] Init CUDA: CPU +440, GPU +0, now: CPU 452, GPU 5848 (MiB)\n", + "[07/25/2022-16:42:23] [I] [TRT] [MemUsageSnapshot] Begin constructing builder kernel library: CPU 452 MiB, GPU 5848 MiB\n", + "[07/25/2022-16:42:23] [I] [TRT] [MemUsageSnapshot] End constructing builder kernel library: CPU 669 MiB, GPU 5920 MiB\n", + "[07/25/2022-16:42:23] [I] Start parsing network model\n", + "[07/25/2022-16:42:23] [I] [TRT] ----------------------------------------------------------------\n", + "[07/25/2022-16:42:23] [I] [TRT] Input filename: models/mobilenetv2_base.onnx\n", + "[07/25/2022-16:42:23] [I] [TRT] ONNX IR version: 0.0.7\n", + "[07/25/2022-16:42:23] [I] [TRT] Opset version: 13\n", + "[07/25/2022-16:42:23] [I] [TRT] Producer name: pytorch\n", + "[07/25/2022-16:42:23] [I] [TRT] Producer version: 1.13.0\n", + "[07/25/2022-16:42:23] [I] [TRT] Domain: \n", + "[07/25/2022-16:42:23] [I] [TRT] Model version: 0\n", + "[07/25/2022-16:42:23] [I] [TRT] Doc string: \n", + "[07/25/2022-16:42:23] [I] [TRT] ----------------------------------------------------------------\n", + "[07/25/2022-16:42:23] [I] Finish parsing network model\n", + "[07/25/2022-16:42:24] [I] [TRT] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +839, GPU +362, now: CPU 1532, GPU 6290 (MiB)\n", + "[07/25/2022-16:42:24] [I] [TRT] [MemUsageChange] Init cuDNN: CPU +128, GPU +58, now: CPU 1660, GPU 6348 (MiB)\n", + "[07/25/2022-16:42:24] [I] [TRT] Local timing cache in use. Profiling results in this builder pass will not be stored.\n", + "[07/25/2022-16:42:28] [I] [TRT] Some tactics do not have sufficient workspace memory to run. Increasing workspace size may increase performance, please check verbose output.\n", + "[07/25/2022-16:43:21] [I] [TRT] Detected 1 inputs and 1 output network tensors.\n", + "[07/25/2022-16:43:21] [I] [TRT] Total Host Persistent Memory: 82528\n", + "[07/25/2022-16:43:21] [I] [TRT] Total Device Persistent Memory: 8861184\n", + "[07/25/2022-16:43:21] [I] [TRT] Total Scratch Memory: 4194304\n", + "[07/25/2022-16:43:21] [I] [TRT] [MemUsageStats] Peak memory usage of TRT CPU/GPU memory allocators: CPU 8 MiB, GPU 624 MiB\n", + "[07/25/2022-16:43:21] [I] [TRT] [BlockAssignment] Algorithm ShiftNTopDown took 1.67234ms to assign 4 blocks to 59 nodes requiring 449576960 bytes.\n", + "[07/25/2022-16:43:21] [I] [TRT] Total Activation Memory: 449576960\n", + "[07/25/2022-16:43:21] [I] [TRT] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +0, GPU +8, now: CPU 2512, GPU 6760 (MiB)\n", + "[07/25/2022-16:43:21] [I] [TRT] [MemUsageChange] Init cuDNN: CPU +1, GPU +10, now: CPU 2513, GPU 6770 (MiB)\n", + "[07/25/2022-16:43:21] [I] [TRT] [MemUsageChange] TensorRT-managed allocation in building engine: CPU +0, GPU +9, now: CPU 0, GPU 9 (MiB)\n", + "[07/25/2022-16:43:21] [I] [TRT] [MemUsageChange] Init CUDA: CPU +0, GPU +0, now: CPU 2521, GPU 6724 (MiB)\n", + "[07/25/2022-16:43:21] [I] [TRT] Loaded engine size: 10 MiB\n", + "[07/25/2022-16:43:21] [I] [TRT] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +0, GPU +10, now: CPU 2522, GPU 6746 (MiB)\n", + "[07/25/2022-16:43:21] [I] [TRT] [MemUsageChange] Init cuDNN: CPU +1, GPU +8, now: CPU 2523, GPU 6754 (MiB)\n", + "[07/25/2022-16:43:21] [I] [TRT] [MemUsageChange] TensorRT-managed allocation in engine deserialization: CPU +0, GPU +8, now: CPU 0, GPU 8 (MiB)\n", + "[07/25/2022-16:43:22] [I] Engine built in 59.1433 sec.\n", + "[07/25/2022-16:43:22] [I] [TRT] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +0, GPU +10, now: CPU 2289, GPU 6696 (MiB)\n", + "[07/25/2022-16:43:22] [I] [TRT] [MemUsageChange] Init cuDNN: CPU +1, GPU +8, now: CPU 2290, GPU 6704 (MiB)\n", + "[07/25/2022-16:43:22] [I] [TRT] [MemUsageChange] TensorRT-managed allocation in IExecutionContext creation: CPU +0, GPU +438, now: CPU 0, GPU 446 (MiB)\n", + "[07/25/2022-16:43:22] [I] Using random values for input input.1\n", + "[07/25/2022-16:43:22] [I] Created input binding for input.1 with dimensions 64x3x224x224\n", + "[07/25/2022-16:43:22] [I] Using random values for output 536\n", + "[07/25/2022-16:43:22] [I] Created output binding for 536 with dimensions 64x10\n", + "[07/25/2022-16:43:22] [I] Starting inference\n", + "[07/25/2022-16:43:25] [I] Warmup completed 34 queries over 200 ms\n", + "[07/25/2022-16:43:25] [I] Timing trace has 501 queries over 3.01732 s\n", + "[07/25/2022-16:43:25] [I] \n", + "[07/25/2022-16:43:25] [I] === Trace details ===\n", + "[07/25/2022-16:43:25] [I] Trace averages of 10 runs:\n", + "[07/25/2022-16:43:25] [I] Average on 10 runs - GPU latency: 5.88872 ms - Host latency: 8.93236 ms (end to end 11.4268 ms, enqueue 2.05089 ms)\n", + "[07/25/2022-16:43:25] [I] Average on 10 runs - GPU latency: 5.88841 ms - Host latency: 8.92661 ms (end to end 11.4266 ms, enqueue 2.07079 ms)\n", + "[07/25/2022-16:43:25] [I] Average on 10 runs - GPU latency: 5.88647 ms - Host latency: 8.93378 ms (end to end 11.4245 ms, enqueue 2.07513 ms)\n", + "[07/25/2022-16:43:25] [I] Average on 10 runs - GPU latency: 5.8879 ms - Host latency: 8.93474 ms (end to end 11.4218 ms, enqueue 2.04516 ms)\n", + "[07/25/2022-16:43:25] [I] Average on 10 runs - GPU latency: 5.88708 ms - Host latency: 8.92472 ms (end to end 11.2913 ms, enqueue 2.04477 ms)\n", + "[07/25/2022-16:43:25] [I] Average on 10 runs - GPU latency: 5.88964 ms - Host latency: 8.93073 ms (end to end 11.4241 ms, enqueue 2.04273 ms)\n", + "[07/25/2022-16:43:25] [I] Average on 10 runs - GPU latency: 5.89016 ms - Host latency: 8.92474 ms (end to end 11.4283 ms, enqueue 2.04633 ms)\n", + "[07/25/2022-16:43:25] [I] Average on 10 runs - GPU latency: 5.88841 ms - Host latency: 8.92583 ms (end to end 11.4307 ms, enqueue 2.05944 ms)\n", + "[07/25/2022-16:43:25] [I] Average on 10 runs - GPU latency: 5.88973 ms - Host latency: 8.92712 ms (end to end 11.4225 ms, enqueue 2.06941 ms)\n", + "[07/25/2022-16:43:25] [I] Average on 10 runs - GPU latency: 5.88892 ms - Host latency: 8.92521 ms (end to end 11.4224 ms, enqueue 2.05708 ms)\n", + "[07/25/2022-16:43:25] [I] Average on 10 runs - GPU latency: 5.92097 ms - Host latency: 8.96465 ms (end to end 11.4841 ms, enqueue 2.04125 ms)\n", + "[07/25/2022-16:43:25] [I] Average on 10 runs - GPU latency: 6.09852 ms - Host latency: 9.13358 ms (end to end 11.7906 ms, enqueue 2.04748 ms)\n", + "[07/25/2022-16:43:25] [I] Average on 10 runs - GPU latency: 6.44015 ms - Host latency: 9.47874 ms (end to end 12.5498 ms, enqueue 2.05565 ms)\n", + "[07/25/2022-16:43:25] [I] Average on 10 runs - GPU latency: 6.25358 ms - Host latency: 9.28981 ms (end to end 12.1605 ms, enqueue 2.05262 ms)\n", + "[07/25/2022-16:43:25] [I] Average on 10 runs - GPU latency: 6.14546 ms - Host latency: 9.18715 ms (end to end 11.9508 ms, enqueue 2.06964 ms)\n", + "[07/25/2022-16:43:25] [I] Average on 10 runs - GPU latency: 6.02576 ms - Host latency: 9.06241 ms (end to end 11.7147 ms, enqueue 2.04923 ms)\n", + "[07/25/2022-16:43:25] [I] Average on 10 runs - GPU latency: 5.92704 ms - Host latency: 8.96814 ms (end to end 11.5024 ms, enqueue 2.04821 ms)\n", + "[07/25/2022-16:43:25] [I] Average on 10 runs - GPU latency: 6.01957 ms - Host latency: 9.05573 ms (end to end 11.6706 ms, enqueue 2.04988 ms)\n", + "[07/25/2022-16:43:25] [I] Average on 10 runs - GPU latency: 5.94579 ms - Host latency: 8.98406 ms (end to end 11.5354 ms, enqueue 2.13973 ms)\n", + "[07/25/2022-16:43:25] [I] Average on 10 runs - GPU latency: 5.89835 ms - Host latency: 8.94883 ms (end to end 11.4496 ms, enqueue 2.08344 ms)\n", + "[07/25/2022-16:43:25] [I] Average on 10 runs - GPU latency: 5.99513 ms - Host latency: 9.03672 ms (end to end 11.6076 ms, enqueue 2.0929 ms)\n", + "[07/25/2022-16:43:25] [I] Average on 10 runs - GPU latency: 6.08859 ms - Host latency: 9.12035 ms (end to end 11.8224 ms, enqueue 2.06177 ms)\n", + "[07/25/2022-16:43:25] [I] Average on 10 runs - GPU latency: 5.9467 ms - Host latency: 8.987 ms (end to end 11.5444 ms, enqueue 2.06372 ms)\n", + "[07/25/2022-16:43:25] [I] Average on 10 runs - GPU latency: 5.89579 ms - Host latency: 8.93199 ms (end to end 11.4334 ms, enqueue 2.04498 ms)\n", + "[07/25/2022-16:43:25] [I] Average on 10 runs - GPU latency: 5.91914 ms - Host latency: 8.95847 ms (end to end 11.4744 ms, enqueue 2.06753 ms)\n", + "[07/25/2022-16:43:25] [I] Average on 10 runs - GPU latency: 5.92528 ms - Host latency: 8.96528 ms (end to end 11.4935 ms, enqueue 2.05543 ms)\n", + "[07/25/2022-16:43:25] [I] Average on 10 runs - GPU latency: 5.92607 ms - Host latency: 8.96593 ms (end to end 11.4996 ms, enqueue 2.05464 ms)\n", + "[07/25/2022-16:43:25] [I] Average on 10 runs - GPU latency: 6.11134 ms - Host latency: 9.14991 ms (end to end 11.8276 ms, enqueue 2.06058 ms)\n", + "[07/25/2022-16:43:25] [I] Average on 10 runs - GPU latency: 6.24971 ms - Host latency: 9.2879 ms (end to end 12.1685 ms, enqueue 2.05168 ms)\n", + "[07/25/2022-16:43:25] [I] Average on 10 runs - GPU latency: 6.1583 ms - Host latency: 9.19552 ms (end to end 11.9784 ms, enqueue 2.05416 ms)\n", + "[07/25/2022-16:43:25] [I] Average on 10 runs - GPU latency: 6.03793 ms - Host latency: 9.07539 ms (end to end 11.7194 ms, enqueue 2.04376 ms)\n", + "[07/25/2022-16:43:25] [I] Average on 10 runs - GPU latency: 6.03723 ms - Host latency: 9.07742 ms (end to end 11.7207 ms, enqueue 2.04446 ms)\n", + "[07/25/2022-16:43:25] [I] Average on 10 runs - GPU latency: 6.16055 ms - Host latency: 9.1936 ms (end to end 11.9269 ms, enqueue 2.06987 ms)\n", + "[07/25/2022-16:43:25] [I] Average on 10 runs - GPU latency: 6.24443 ms - Host latency: 9.28486 ms (end to end 12.1531 ms, enqueue 2.04836 ms)\n", + "[07/25/2022-16:43:25] [I] Average on 10 runs - GPU latency: 5.94749 ms - Host latency: 8.98728 ms (end to end 11.5623 ms, enqueue 2.05354 ms)\n", + "[07/25/2022-16:43:25] [I] Average on 10 runs - GPU latency: 5.91284 ms - Host latency: 8.95781 ms (end to end 11.4716 ms, enqueue 2.04207 ms)\n", + "[07/25/2022-16:43:25] [I] Average on 10 runs - GPU latency: 5.98567 ms - Host latency: 9.02083 ms (end to end 11.6108 ms, enqueue 2.04358 ms)\n", + "[07/25/2022-16:43:25] [I] Average on 10 runs - GPU latency: 5.93113 ms - Host latency: 8.97266 ms (end to end 11.533 ms, enqueue 2.06318 ms)\n", + "[07/25/2022-16:43:25] [I] Average on 10 runs - GPU latency: 5.89543 ms - Host latency: 8.92844 ms (end to end 11.4434 ms, enqueue 2.05273 ms)\n", + "[07/25/2022-16:43:25] [I] Average on 10 runs - GPU latency: 5.95349 ms - Host latency: 8.99211 ms (end to end 11.5469 ms, enqueue 2.07312 ms)\n", + "[07/25/2022-16:43:25] [I] Average on 10 runs - GPU latency: 5.94853 ms - Host latency: 8.98025 ms (end to end 11.5569 ms, enqueue 2.04573 ms)\n", + "[07/25/2022-16:43:25] [I] Average on 10 runs - GPU latency: 5.97844 ms - Host latency: 9.01548 ms (end to end 11.6017 ms, enqueue 2.05762 ms)\n", + "[07/25/2022-16:43:25] [I] Average on 10 runs - GPU latency: 5.96038 ms - Host latency: 9.00027 ms (end to end 11.5838 ms, enqueue 2.04302 ms)\n", + "[07/25/2022-16:43:25] [I] Average on 10 runs - GPU latency: 6.03623 ms - Host latency: 9.07041 ms (end to end 11.7005 ms, enqueue 2.05886 ms)\n", + "[07/25/2022-16:43:25] [I] Average on 10 runs - GPU latency: 6.08901 ms - Host latency: 9.12502 ms (end to end 11.8232 ms, enqueue 2.06831 ms)\n", + "[07/25/2022-16:43:25] [I] Average on 10 runs - GPU latency: 6.07283 ms - Host latency: 9.11433 ms (end to end 11.8008 ms, enqueue 2.07654 ms)\n", + "[07/25/2022-16:43:25] [I] Average on 10 runs - GPU latency: 6.10923 ms - Host latency: 9.14961 ms (end to end 11.8509 ms, enqueue 2.05337 ms)\n", + "[07/25/2022-16:43:25] [I] Average on 10 runs - GPU latency: 6.05793 ms - Host latency: 9.09639 ms (end to end 11.776 ms, enqueue 2.06641 ms)\n", + "[07/25/2022-16:43:25] [I] Average on 10 runs - GPU latency: 6.1678 ms - Host latency: 9.20984 ms (end to end 11.9751 ms, enqueue 2.05627 ms)\n", + "[07/25/2022-16:43:25] [I] Average on 10 runs - GPU latency: 6.05857 ms - Host latency: 9.0998 ms (end to end 11.7799 ms, enqueue 2.06199 ms)\n", + "[07/25/2022-16:43:25] [I] \n", + "[07/25/2022-16:43:25] [I] === Performance summary ===\n", + "[07/25/2022-16:43:25] [I] Throughput: 166.041 qps\n", + "[07/25/2022-16:43:25] [I] Latency: min = 8.90146 ms, max = 9.52582 ms, mean = 9.04623 ms, median = 8.99896 ms, percentile(99%) = 9.50714 ms\n", + "[07/25/2022-16:43:25] [I] End-to-End Host Latency: min = 10.1021 ms, max = 12.6202 ms, mean = 11.6584 ms, median = 11.5563 ms, percentile(99%) = 12.5932 ms\n", + "[07/25/2022-16:43:25] [I] Enqueue Time: min = 1.93103 ms, max = 2.48816 ms, mean = 2.05872 ms, median = 2.05432 ms, percentile(99%) = 2.24194 ms\n", + "[07/25/2022-16:43:25] [I] H2D Latency: min = 3.00195 ms, max = 3.14062 ms, mean = 3.03002 ms, median = 3.02588 ms, percentile(99%) = 3.08609 ms\n", + "[07/25/2022-16:43:25] [I] GPU Compute Time: min = 5.87982 ms, max = 6.47681 ms, mean = 6.00728 ms, median = 5.94946 ms, percentile(99%) = 6.47375 ms\n", + "[07/25/2022-16:43:25] [I] D2H Latency: min = 0.00708008 ms, max = 0.0134277 ms, mean = 0.00893093 ms, median = 0.00878906 ms, percentile(99%) = 0.0117188 ms\n", + "[07/25/2022-16:43:25] [I] Total Host Walltime: 3.01732 s\n", + "[07/25/2022-16:43:25] [I] Total GPU Compute Time: 3.00965 s\n", + "[07/25/2022-16:43:25] [I] Explanations of the performance metrics are printed in the verbose logs.\n", + "[07/25/2022-16:43:25] [I] \n", + "&&&& PASSED TensorRT.trtexec [TensorRT v8205] # trtexec --onnx=models/mobilenetv2_base.onnx --saveEngine=models/mobilenetv2_base.trt\n" + ] + } + ], + "source": [ + "# Exporting to Onnx\n", + "dummy_input = torch.randn(64, 3, 224, 224, device='cuda')\n", + "input_names = [ \"actual_input_1\" ]\n", + "output_names = [ \"output1\" ]\n", + "torch.onnx.export(\n", + " model,\n", + " dummy_input,\n", + " \"models/mobilenetv2_base.onnx\",\n", + " verbose=False,\n", + " opset_version=13,\n", + " do_constant_folding = False)\n", + "\n", + "# Converting ONNX model to TRT\n", + "!trtexec --onnx=models/mobilenetv2_base.onnx --saveEngine=models/mobilenetv2_base.trt" + ] + }, + { + "cell_type": "markdown", + "id": "0a079b97", + "metadata": {}, + "source": [ + "\n", + "## 4. Post Training Quantization (PTQ)" + ] + }, + { + "attachments": { + "img4.JPG": { + "image/jpeg": "/9j/4AAQSkZJRgABAQEAeAB4AAD/4RDuRXhpZgAATU0AKgAAAAgABAE7AAIAAAAMAAAISodpAAQAAAABAAAIVpydAAEAAAAYAAAQzuocAAcAAAgMAAAAPgAAAAAc6gAAAAgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAEFubmllIFN1cmxhAAAFkAMAAgAAABQAABCkkAQAAgAAABQAABC4kpEAAgAAAAM4NAAAkpIAAgAAAAM4NAAA6hwABwAACAwAAAiYAAAAABzqAAAACAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAMjAyMjowNjoxMyAxMDo0MDowNgAyMDIyOjA2OjEzIDEwOjQwOjA2AAAAQQBuAG4AaQBlACAAUwB1AHIAbABhAAAA/+ELHmh0dHA6Ly9ucy5hZG9iZS5jb20veGFwLzEuMC8APD94cGFja2V0IGJlZ2luPSfvu78nIGlkPSdXNU0wTXBDZWhpSHpyZVN6TlRjemtjOWQnPz4NCjx4OnhtcG1ldGEgeG1sbnM6eD0iYWRvYmU6bnM6bWV0YS8iPjxyZGY6UkRGIHhtbG5zOnJkZj0iaHR0cDovL3d3dy53My5vcmcvMTk5OS8wMi8yMi1yZGYtc3ludGF4LW5zIyI+PHJkZjpEZXNjcmlwdGlvbiByZGY6YWJvdXQ9InV1aWQ6ZmFmNWJkZDUtYmEzZC0xMWRhLWFkMzEtZDMzZDc1MTgyZjFiIiB4bWxuczpkYz0iaHR0cDovL3B1cmwub3JnL2RjL2VsZW1lbnRzLzEuMS8iLz48cmRmOkRlc2NyaXB0aW9uIHJkZjphYm91dD0idXVpZDpmYWY1YmRkNS1iYTNkLTExZGEtYWQzMS1kMzNkNzUxODJmMWIiIHhtbG5zOnhtcD0iaHR0cDovL25zLmFkb2JlLmNvbS94YXAvMS4wLyI+PHhtcDpDcmVhdGVEYXRlPjIwMjItMDYtMTNUMTA6NDA6MDYuODQxPC94bXA6Q3JlYXRlRGF0ZT48L3JkZjpEZXNjcmlwdGlvbj48cmRmOkRlc2NyaXB0aW9uIHJkZjphYm91dD0idXVpZDpmYWY1YmRkNS1iYTNkLTExZGEtYWQzMS1kMzNkNzUxODJmMWIiIHhtbG5zOmRjPSJodHRwOi8vcHVybC5vcmcvZGMvZWxlbWVudHMvMS4xLyI+PGRjOmNyZWF0b3I+PHJkZjpTZXEgeG1sbnM6cmRmPSJodHRwOi8vd3d3LnczLm9yZy8xOTk5LzAyLzIyLXJkZi1zeW50YXgtbnMjIj48cmRmOmxpPkFubmllIFN1cmxhPC9yZGY6bGk+PC9yZGY6U2VxPg0KCQkJPC9kYzpjcmVhdG9yPjwvcmRmOkRlc2NyaXB0aW9uPjwvcmRmOlJERj48L3g6eG1wbWV0YT4NCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgPD94cGFja2V0IGVuZD0ndyc/Pv/bAEMABwUFBgUEBwYFBggHBwgKEQsKCQkKFQ8QDBEYFRoZGBUYFxseJyEbHSUdFxgiLiIlKCkrLCsaIC8zLyoyJyorKv/bAEMBBwgICgkKFAsLFCocGBwqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKv/AABEIALYCnAMBIgACEQEDEQH/xAAfAAABBQEBAQEBAQAAAAAAAAAAAQIDBAUGBwgJCgv/xAC1EAACAQMDAgQDBQUEBAAAAX0BAgMABBEFEiExQQYTUWEHInEUMoGRoQgjQrHBFVLR8CQzYnKCCQoWFxgZGiUmJygpKjQ1Njc4OTpDREVGR0hJSlNUVVZXWFlaY2RlZmdoaWpzdHV2d3h5eoOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4eLj5OXm5+jp6vHy8/T19vf4+fr/xAAfAQADAQEBAQEBAQEBAAAAAAAAAQIDBAUGBwgJCgv/xAC1EQACAQIEBAMEBwUEBAABAncAAQIDEQQFITEGEkFRB2FxEyIygQgUQpGhscEJIzNS8BVictEKFiQ04SXxFxgZGiYnKCkqNTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqCg4SFhoeIiYqSk5SVlpeYmZqio6Slpqeoqaqys7S1tre4ubrCw8TFxsfIycrS09TV1tfY2dri4+Tl5ufo6ery8/T19vf4+fr/2gAMAwEAAhEDEQA/APpGiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACq9/dGy0+e5C7zEhbbnGcVYqnq8Mlxo91DCu+R4yFUdzWVZyVOTjvZl00nNJ7FGHWroXFol7ZJFHd8Rsku45xnkYqe01pL3V3s4Y28tY94lbjdzjgenvSaXodlZJFMtqEuQgDMWLYPfqcD8KUWsw8UG58v9z9kEe//a3E4rmisRHl5n19dLd7Lr5HTL2Lb5V0/H73+Zp1V1DU7PS4Fm1C4S3jZggZ+mTVqsDxZps+pW+npbwecI72KSQZHCg8nmvSpRjKaUnZHFJtJtEsfjHw9KIjHq1ufOfy05PLf060HxJbW95qKahLBbwWTonmbyTlhn5hjj9a5rU/DV9LB4k8iwy93eRSW+No3qMZI54703XfDuqXjeIfJs2kF3cW7Q/MvzqoG7vXfGhh2/i381/d/wA39xk5TV/67/5HZabrmmaw0q6ZeRXLQnEgQ9Kv1z1npk8Hjy9vRb7LSS0jjVxgAsCeMV0NcNWMIyXJtZGkb9QooorIoKKKKACiiigAqjq11fWlp5unWkd0y5LrJLswAOvQ5q9UV0jSWcyIMs0bAD1OKuDSkm1cum0pJtXOcsfFlzLpA1K/04RW8q/6OsMhkeVsn5duOOhrZ0TUjq+i21+0YiM67igOcckdfwqn4csbiz8H21pdRGO4SJlKEgkEk/41L4XtJ7DwxZW13GY5o0IdCc4OT6V111RtPkVmpWWvTX/gHbiFQtPkSTUrLXpr/wAA1qKKK4TzwooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKRmCqWY4AGST2oAWivPx8Q9TbTh4hXRI/wDhGDNsF0bj9+Y9+zztmMbc89c45rv1YMoZeQRkGgBaKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigBshcRsY1DOAdqk4ya5iDxHrcmrT2L6NAJLdFkm23WcKfT5eT7V1NYNvZ3UHi7Vb4wMYZLaNYiCPnYDkV1UHC0udJ6aX9V5nXh3T5Z88U9NL33uvNEdj4lu5tUtIL7TDaw32/wCzuZMv8v8AeXHHBroq4/Q49Um8RNf63pVwJ3ykUhkTy7eP0AznJ7muwp4qMITSilt0d1+pWMhCE0oJba2d1+bCiiiuQ4gooooAKztV1KaxltYreBZpLhygDPtwfyrRrH1zTm1G609TCZYElJmw2MLgfjXPiHUVP93vdfmvJ9PI2oqDqLn21/Iauuzqt1HNYn7VAVAiibeG3dOccVa0zUZLyW4guIBDNbsAyq+4c9OfwqKez/snTJRodmDM5wAGz+JJPak0CCS3t5FntZYZWIaSSVgxlY9Tx6VhTdVVYwm76a6affbf7vTU2kqTpuUV6d+nS+33mtRRRXecZlXvijRdOvHtb3UYYZ0Kho3JyCwyP0p9p4h0i+kuEtNQgla2BM21uEHrn0965nWPD97d33iqaOy8z7ZbQpatlfnIUbgOeORRf6FqSXssmm2Mf/IGW3TeqlTIG+6QeDx68V6CoUHFe9r6rsn+tjPmlc6C38WaFd29xPbalFJHbLulKg/IPXGOlXLbVrG8umt7W5SaVY1kZU5wrDIOenIrhdK0vW7O/vdQuNLurkz2HlLDcSxtufOCuBgKncKO1a3gXRL7w2LjT7y1QpIFmW6jIwWIGUPOeD0oq4ejCMnGWqtbVfP7v62JjOTtdHYUUUV55sFFFFABRWH4uvr3S9AlvtPnWKSErkPGGDAkD8OtSwpqJuFQavHNmMll8hQVz0Ix79jUc2trG6oN01Uura9+lvLzNeiuF/4SPWrTw7e6xPdQTiyvHgaAwhPMVX28HPB5zW34gv8AU7OyF5p81uh+Xy7WRMtcMf4Ac8H6VKqpq/8AWptLBVIyUbrVtfNW8vNG/RWdqKatOqppk0Nqdu5pJU8zJ/u4yPzpvh/UZ9T0dJ7yNY5w7xuF+6SrEEj2OKvm1sc/sn7PnujToooqjIKKQsFGWIA9TXO6TqlxP4nv7ee43QRhvLU4wPmAFYVK0acoxf2nY1hSc4ykuh0dFM86P/non/fQrg/iJ4ovLO4ttG0ecwTXCGWadMZSPOMD3JrSpONOLnLZGLaSuzv6K+edO1XWYNQnlstWu1njJI8yYurkHowJwa9r8KeIk8ReG7XUHCxTONsyE/dccNj2zWk+WFV0r6pJ/eroinUU1obdFM82P/nov/fQo82P/nov/fVI0H0UzzY/+ei/99UebH/z0X/vqgB9FN82P++v51xHjLx1Npd+NJ0FYpLwIHnmk5SAHoMd2PWonONOLlJ2SE2krs7mivJLH4h69pl0kmrtDfWW797sj2PGP7w9celeqw3cFxAk0MqPHIoZWDdQaijXp1481N3QoyUldE1FN82P++v50ebH/fX862KHUVynhTxNdazqmoW975SpbNhCoxn5iP6V1PmJ/fX862rUZUZ8k9zavRnQn7Oe46im+Yn99fzo8xP76/nWJiOopvmJ/fX86PMT++v50AOorkvHXi2Tw/Z29tpmyTUb0kRbuVjUY3OfpkYFefp4i8TwTfaI9cmllzkxyopjb2xjiuSvjKNCSjUerIlUjF2Z7bRWL4V8RReI9AivsCKXJjnjJ+5IvUfTv+NYfj3xdc6Q0GmaKyi+uFMjSsMiKPOM/UmuiVSMIc8noU2krnbUV4nB4m8TWE/2mLV5btgctBcKCj+3A4/CvWdA1221/Q7bUrb5FmXJRjyjA4Kn6EGscPiqWITdN7ExnGWxp0Vj+JvEMPhzQJ9Qcea64SGIH78h4UfnXlT+I/E91N9om1uWCQnIihVQie2COfxoxGKpYe3tHuEpxjue20Vx3gPxbca5Dc2WrbBf2m0l1GBKh6N9eOa6/en95fzreMozipR2ZSaauh1FN3p/eX86N6f3l/OqGOqG8iaexnhQ4aSNlB9CRipd6/3h+dG9f7w/Ok1dWGtGeNprmnH4Ajw59qhOsiD+yTYFx5wuA2zbs6/7XTpzXsFrGYbOGNzlkjVSfcCsOOTSJfHlxD/ZNoNQhtEm/tAxJ5hDErt3Yz29a8+8UeKNQ17XLy2s7ye002zlaBVgfY0zLwzEjnGegrKvXhQg6kyJSUV5I9iorxDSPEuoeFb6G4+2XFxpu8LcW80hfap43KT0xXp3jHxL/wAI5oRuLZUmu5nEVtGx4LHufYDmpoYmnXp+0i9BRmpK6OhorxFvEPid5PPbXplm/uqi+WPbGOlej+CPFL+ItLkW+VI7+1by51X7rejD61FDGUa7cab1QRqRlsdPRSb1H8Q/OvJdf8catq+qXEOjXbWGnwOYleNR5kxHBOT0Ga1rVoUIc83oOUlFXZ63RXkvh7xxq2latbwa1dm/0+4cRGSRR5kLHgHI6jNes7l9R+dFGtCtDng7oIyUldC0Um5fUfnRuX1H51sULRSbh6j86Nw9R+dAC0Um4eo/OjcPUfnQAtFeUeJvG2q6jrVzZ6JdmysbVzE0sagvK44PJ6AHiodC8b6vo2pW6ateNf6bK4jkaVR5kOeAwI6jOM1yPGUFV9jfUj2keblPXaKTcPUfnWJ4wupbTwreTWsrRSoAVdDgjkV304OpNQXV2N6VN1akYLq7G5RWH4PupbvwpZz3UrSyurFnc5J+Y1t5HqKKkHTm4Po7BVpulUlB9HYWijI9aMj1rMzCijI9aMj1oAKKMisXxHq8+kxW7WwRjI5U7hWVarGjB1J7I0p05VJqEd2bVFNjbdGrE8kAmnZrVamYUUZozQAUUZrzn4keIb1NSt9B06d7YPD59zLGcMVJICg9uhrOpUjSg5y2Qm0ldno1FfOGlSXtrezz6bez29yjblcSE7j/ALQPX8a928Ja7/wkfhez1J08uWVSJUHZ1ODj2yK0naNWVK+qt+KuiKdRTWhs0UUUGgUUUUAc746EkvhW4toLa4uZZioWOCFpCcMCc4BxwO9aGn2+n2lobyysvswaP51S1aNyB6pgNn8M1pUVHL7zkdHtn7JUvNvfe9v8jjPCek2l2l4+pabcrML+W4jW7hkRSrNlWAb5Sf1rR8QJaavHdabcaTczXCxkW8ptztLEcFZei4PXJHTvXRUVPs1ychrLFylW9rr5a7GBfajPpNjZ2L29/dymFVmuLW1eXGAATkDqTWjo86XGnKYbO4s4kOxI7iPY+B3weavUVaTvdsxlUjKFra9/+AFFFFUYFXUtMs9YsXs9St1uLdyC0bZwcHI/UVxOm+GdGv8AX7zTbywjls7YN5MJJwmGAGOfQ16BWS+ieVfNeadO1vNIcyAjcr881xYmnOUoTir8rudNCcYxnF9UZ/8Awrrwn/0BLf8ANv8AGuJ8deErfw3e2uqaLYeVYGMxXKwqW8s5yHPXjtXqd1qFvZNGt1Js8zgMRx+J7VP+7mi/hkRh9QRW9SNOtGVJv1OaUG46rRnzdZ3KpeTmMGV5CREiDcXOeAMV6V4H8F+G9Q8NQyajbW97qBLNcfMwaNic7CMjkdK7y20bTbO4M9rYW0MpOS6RAGmf2HYDWV1RIdl0FKsyHAfPqO9dUlSnVlVaabSX3K34mFOm6exk/wDCuvCn/QHh/wC+m/xo/wCFdeFf+gPF/wB9N/jVmXxRDY601jq1tLYxscW91Jjy5fbI6H61ug5GR0pSpyhZtbmyaexzP/CuvCv/AECIv++2/wAaP+FdeFf+gTH/AN9t/jXTUVAzmf8AhXXhb/oEx/8Afbf415prujxeHPGF9Yww+RbzhZrb0ZcYIBPoa9xrN1vQNO8Q2YttVtxKinKMDhkPqCORXNiaCxFJ027XInHmjY8RvpAlo6kbmkGxEAyWY8ACvS9E+HGhR6DZLqWnK92IE85i7cvjnofWr2j/AA90HRr5byGGWedPuPcSF9n0B4/GunrHA4P6rFpu7ZNOnyI5n/hXfhf/AKBi/wDfxv8AGj/hXfhj/oGL/wB/G/xrpqbJIkUbSSMFRRlmPQCu81PK/BvhnSdX1fU4NQtfNjtm2xDcRtG4jsfauv8A+Fd+GP8AoHf+RW/xrS0XTdJt1a+0dVK3Q3NIrE7+f8a1a68ZVVas5pW2/BHbjqyr13NK22/krHMf8K78M/8AQO/8it/jR/wrvwz/ANA//wAiv/jXT0VyHEcx/wAK78M/9A8/9/X/AMaP+Fd+Gv8AnwP/AH+f/GunooA8l8eeEbXw7NY6tpNqy2i7obohi3l5xtbnt1H5Vzj3UEcXmNKuzGc56173LEk0TRTIro4wysMgisODwP4atrsXMOjWqyqdynbnB9cV5mLy6OJqKfNYxnSU3c5XwT4Bsbvw/wDbtdtH+0XkzTqnmMpRDjaCB34z+NY3jjwzb+GNZtr6whaPT7iLyZGLFvLkBJBJPQEH9K9hqK6tYL22e3u4kmhkGHRxkEV2VKMalJ0ntaxo4px5Twaa7hhhLs4PHyqDksfQV33hP4daePDdtJrtpIL6bMsieay7NxyFwD1AxXSWPgzw7pt4t1ZaTbxTqcq4XJU+3pW5XNg8FHC31u2RTpqB5z4z8AWNv4de80K0ka6tJFn2eazF1U/MAD3xz+FcPFdwTQiRJF245ycY+te/Vg3Xgjw3eXjXVzo9s8zHczbcbj6mjGYGOKs72aCpTUzgPAnha28SX19qOoQu1mirDAyuV3t1Y8dR0Fdt/wAK88Pf8+03/gQ/+NdHBBFawJDbxrFEgwqIMACpK66VNUqahHZGkVyqxzH/AArzw9/z7zf+BD/40f8ACvPD3/PCf/wIf/GunorUZzH/AArzw/8A88J//Al/8aP+FeeH/wDnjcf+BL/4109FAHm9v4L0d/iJe2Jim8iPT45VHntncXIPOfauR1jSH8Ja7d2N0rR2ckrS2k7Z2ujc7d3qOhr3MQxiYyiNRIRtL7RuI9M0y6tLa9hMV5BHPH/dkQMP1rnxOHjiKfs5ETipqzPBorKXxLeRaRpamZp3AmkQZWFM5LMe3Suw8deCrTSNHg1TSIZ2+xyhp1MrOfLIwSAfSvRrLTrPTovLsLWG3TuIkC5/KrJAYEEZB4INZ0MHCjRdJa33FGmox5TwIXUBi8wTJsxndu4rqPAfg+31y3u9W1SKdIZ3C22yVoyyr1bA7E12r+BfDEl0bh9FtTITknZxn6VvoixoERQqqMAAYAFYYTL44abne7Jp0lB3OYPw80IqQFux/wBvT/415S1o+h6jdaTer5MsErbA5++hOQwPfg179Wfq2gaVriIurWMN0E+4ZF5X6GujFYaOJp8jdi5wU1Y8Rjs213VLXSLMGWSaVTJsP+rQHJYnt0r1b/hXuh/9Pn/gW/8AjWxpWg6XocbppNjDah/veWuC31NaFPC4aOGp8idwhBQVjl/+Fe6H/wBPv/gXJ/jR/wAK+0T1vf8AwMk/xrqKK6izl/8AhXuif3r7/wADJP8AGj/hXuif3r7/AMDJP8a6iigDl/8AhX2i/wB+/wD/AAMk/wAaP+FfaL/z0v8A/wADZP8AGuoooA8EurBvD+tXmk3QMZSZngZz/rIycgg9/emG2bWby30iyHmz3UiqQp+4uclj6AAV7dquhaZrkSx6tZQ3Sp93zFyV+hpuk+HtJ0MONJsIbUuMMyLy31NeU8tg8R7a/W9vMx9iufmMgfD3RgoHnahx/wBPsn+NZPinwZpuneHLq6tpL5pIwCokundTz3BPNd/UYaG5RgCkqg4YcEAivaoz9nUjO2zTOujP2dSNS2zTOE8L+DNN1Hw3a3VzLfLLICWEd26KOT0APFa//CvtI/576l/4HSf4106IsahY1CqOgUYFLSqz9pUlPu7hWqe1qyqd22cv/wAK+0j/AJ+NS/8AA6T/ABo/4V9pH/Pzqf8A4Hyf411FFZmRy/8Awr/Sf+fnU/8AwPk/xo/4V/pP/P1qn/gfJ/jXUUUAcv8A8K/0r/n61T/wPk/xrH8QeGrPRI4HtJryQysVb7RctIAPbJ4rtdQu5LSFfs9u9xNIdqKo4z6k9hUC6V9stUGslbiUMXAXgJnsK4cXF16cqEN/wOrDyVKaqy2/Ex08A6W0asbvVQSAeL+T/Gl/4V/pf/P5q3/gwk/xrqFAVQB0AwKWu1aI5Wct/wAK/wBL/wCf3Vv/AAYSf40f8K/0v/n+1f8A8GEn+NdTWdrOsLo9sj/Zbi6llbZFDAmS7emeg/GrjFyfLETaSuzGbwDpSKWa/wBWUDkk6jJx+ted+JtOsrPXftXh6W91O3ii2XshLziIg8Hee3t2r1rTF1G/02YeIba3iM5O23jYtsQjG1j3P09avWtnbWVqtvaQRwwqMBEXApVaVKUJU6mt9NHoS05q3Q+c9Okle6ljsoJLm4mOIoo1JLHP8q9X8P8Aw1t7PQraLUL3UUuyu6dba9dEDnkgAHHHTPfFdpDY2ltIXt7WGJz1aOMKT+VT06qhOtKtFauy+5WJpU1TRy3/AAgGnf8AQR1n/wAGMn+NdTRRUmoUUUUAFFFFABRRRQAUUUUAFFFFABRRRQA2SJJoykqK6nqGGQarx2i2Nk8enRqDyyKzHbn/AAq1RUOEW79e5Sk0rdDOs9Uea4FreWsltcY6feVvcNWjRjms27s79LprrTrvlvvW8wyh+npWd50o6+9+dv1LtGctNC7cW0F3CYrqFJoz1V1yKzNcutXsBBcaTZR3tvHn7RAG2yEeqduPStKW6ito4zdyJEXIUZPGfSpgQRkHI9RXTTqJS726GUouxT0rU4dXsEurdJY1YkFJUKspHUEVcqtqFtLdadNb2ty1pLIuFmRQSh9cGs7QrnWfMlstdtlMkKgreRH5Jh9Oxq+RSTlH7uv/AASb2dmbVFAOelFZFBRRRQAUjKGUqwyCMEHvS0UAYnhzSbnRfttoxU2PnF7X5ssqnkgj2NbdY/iaW9tNKF7pztutXEskQ/5axj7w/KtO1uY72ziuYDujlQOp9jXRV5pr20uv5/8ABOmtz1F7eXX8139dyWiiiuc5gooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAKupSXMWmXD2MRluBGfLQHGW7VU8N6WdJ0SKGXmd8yTMepduTUVtqlzfeKbi1tSv2Gzj2zMRktKegB9hW1XRPmpw9m+tn/AJHTNzpQ9k+tn/lf8/mFFFFc5zBRRRQAVnajJfyzLaaenlbhl7luQg9h3NNkiv7zUPnY2tpC+QEPzTEfyFadYO9ZNapfmaq1Np6MZChjhRGdpCoALt1PvT6KiubqCzt2nupUhiUZZ3OAK3iuiMm+pLUbTxJMkLSKJHztQnlsdcCqVjqkGu6fNJpksiLyiTGPHP8AeAPUVV0jwxbabcfbbmWS/wBRI+a7nOW56hR0UfSteRRvzuzXQm7exFqFrr+q6lJbJPHpmmIR++iO+af6dlFbyJsjRSS20Y3N1NOopSqOSStZIaVtQooorMYUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAR3FtDdRGK5iWVD1VhmoLLT49OjdLdpChOVR3JC+wq3RUOnFy57a9yuaSXLfQzrXWIprgW1zG9rcnpHIPvfQ960aaUVmVmUEqcqSOlUb0anHP51k8cseMG3cYP1DetZ3nTjefvei1/r0+4u0Zy93Qz4tF1LSdU83SL3zrGeTM9pdMW2Z6sjdR9DW39qgN0bbzk88IHMe75tpzzj04NEMxe3SSdPJZhyrHofSs7WvD1prOyVi9teRf6m7hO2SM/XuPY12KpGq05v52/P8Aq5g4uOxrUVm3GoRaDpMMmsXTPt2xyT7OpPcgdBV+KWOeJZYXWSNxlWU5BH1qHFpX6DuPoooqRiMoZSrAEEYIPes+LVoTrsmkmJopI4hIhOMOvt9K0az73SIrzVLK/wDMaKa0JwU/jUjlT7VpT5NVPt+JrT5NVPt+PT/I0KKRHWRQyMGU9CDkGlrMyCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAqpqep2+k2LXV4xEYYLhRkkk4AA71brK1TRjqmp6fNNMPs1o5laDH33x8p/CtKSg5++9DWioOa9o9P60+ZftreCFWe3hWLzj5j4XGSe596mooqG23dmbbbuwooqvdNcPak6eYzIeAznIHvUSlyq4JXdiZ2IRtgDMBwM9az7Gyu3uftupTHzcYSCNsJGP6mp9PsBYxuWleaaU7pZXPLH+gq3WXJ7S0p6W6F83JdR+8KKinmMdvLJEhmeNSRGp5Y4zj61j6UviC7vhe6rJFZ2wUhLGMBic92b1+ldcYXi5XtYyb1sJqfiRob5tO0ixlv75fvKBtjjz3ZjV+70mz1aO2bVbRJXhO9UYkqrY9Oh/GrwUAkgDJ6mlpuaVuRWa69RW7iIixqFRQqgYAAwBS0UVkUFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBDdWkN7btBcpvjbqKhsLF7Euv2qSaHA2JJyU/HvVyis3Tg5qdtS1OSjy9DPTU7G9mkspxtk5VoZ1xvH0PUUzTdFtNBhuRpkcojkYyC38wlVOOig8DNXZ7S3uWQ3EKSFDlSw5BqG+kvoWSSzhSeMA74ycMfoaI1KtKDU3deX6objCcly6epU0fxJZ6vK9th7W+i/wBZaTjbIvuPUe4rXqhbxWmovBqMlnsuI8qjSph07EVQ1PX7jRNRLalZsdLcgJdw5YxHvvHpnuK6YqNZ3ordf1b+rmLvDSZvUjKroVcBlYYII4IoVgyhhnBGRkYpayKMPw1aXelrdadPG32WCUm1kJzlDzj8K3KyfEl/eaXpYvbJFkWCRWnQjJMefmxUieIdLlvrazivI5Li5G6ONDk42lufTgd66ZxqVf3tt+3lvf8AM6qkKtb99a973t5bt/maVFFFcxyhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFZuua7a6BaxXN8H8uSUR5QZIyCc4/CqhCU5KMVdsuEJVJKMFdssandtY6XcXMcTzPFGWWNFJLHsMCqvhyzuLPRYheyO9zKTLLuJO1m5wPTFV9O8SRaxrTW2mbJ7SOASSTgkYYnhcVuVtNSpR9nJWb1/yNqinRh7KSs3r5+XoFNd0jQvIwVR1LHAFNluYYHjWaVUaRtqBj94+1UrnSjfXwe8mMlqmCluBgE+p9a4pzklaCu/63MYxTfvOyJNRtbi9SOKC48mFj+9K/eYegParFraw2dskFumyNBwKdJJFbQNJKyxRRrksxwFArH0zxKms6kY9LtJprFAQ98RtjLDsufvfWtIYe7dVL5/p/W5MqmigXtT1ex0a18/UbhIU6KCeWPoB1JqHVba+1K0iTS9Q+xJIcySqmWK4/hPY+9Ivh3Tv7ak1WaIz3TEbGlYsIuP4QenrWpW/NCFnDfz/AMiLN7mfo+i2uiWhgs/MYu2+SSVyzSN/eJPetCiis5ScnzSeo0klZBRRRUjCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAjuIFubd4XLKrjBKnBH41Us7S7tpGinuVubbHy+Yvzj2PrV+is3Ti5KfVFqbUeXoYWsXF/PIP8AhHr23+1WrHzbSbgS8dM9vrV7R9Qn1HTxNeWE1hMCVeKXHBHUg9x71JeaZa3pDTR4kH3ZF4YfjVTxEzrpEieVI1uyFZmhk2Oi46g1XtnGDVVKy6q97eaDkUpLk3fRmL4/t9cbTGl0udjaBSLiGNfmx3Oe49a878KTGDxbpjjjNwq/99HH9a7TQ/Es9hciO+nlubdsKWk5YDsf8aZr/hSNb6HXfDu2SOOZZJoYjnGCDuX+or1smzjD4ilPD7bq+33+v/APqMHW+r0nha6S5lo/VbPzPQ6KMjOM80V5Z8oFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFZ2tXmqWVmsmi6UmpzlsGJ7oQAD13EH8q0a5T4hS+Jv7BS28IabLeT3Mnl3EkNxFFJBFj5mXzGUFj0HPGc0mNDPCPjHUfEms6nY3mgrYx6cRHJcxXouI2lPJjBCjJA64zjp1rrq5PwVJf2lrHpMng660Gyt4vklmvLebe2ec+W7EsSSSTXWVTEFFFFIAooooAKKKKACiiigAooooAKKKKACuB+KsuNP0+L+9KzH8AP8a76uF+IGmvf3dpJK2y1hjJJ7uxPCj8vyrrwdalh6yrVXaMbt/cellbjHFwlLZX/I5TwTompapqnnWNxJZwQkebOhwf90ep/SvXYru3a4azScPPGoLDOT+NeZ6RfNpMhW3Zo4nXa4Q4OPUe9d9plxpNuYrWzuIzPcKZApbMjjuTXBLOZZtWvTjZR++39b9EdmcTdSpzz26f8Fk9rpUcN093cObi4JOJH/gHoB2qPWtSvNPhiGnadLfXE7bECkBEOM5c9hVPUNG1PV9SdbzUfs+mKRsgtcq8vH8bduewreRQkaoM4UYGTW0KdKgly691r+LPBlOU3qUNKg1H7A665LDPNKxJWJcKikfd56/Wr0caRRrHEioijCqowAKdRRKTk7iSsFFFFSMKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAKd/q+n6Xs/tG7itt/C+Y2M0ltrOnXl0ba1vIZJ1XeY1b5seuKyPHAB0ezyM/8TC3/wDQxRr2inU9WWazk+z6ha24ktpx2bceD6qRwRWLnJSaX9aXO6nRpSpxlJtXvr00t/nqbEur6fBfCzlu40uSMiIn5setVj4n0UMqnUoNzZ2jd1x6Vn6TqqazrFt58QivILWeK6gPWNt8X6HqKfqahPHXh1VACiC7AA7fKlPmdk11KWHgp8k072b3XRN9utjWbV9PSzS6a7iEEhwj7uG+lWIJ4rmFZreRZI2GVZTkGuf8Qxzwajp82irG9/EJWS1cYWVDjec9jnGD7471c8LyW8uihrcOrGaQzpIu1klLEuuO2CenpinGTcmmZToxVFVY9f8Ag/5b9fkbFFFFaHIFUdW046pYm2E5hVjliFzn2q9RUThGpFwlsyoycJKS3Rxc3gW4H+ovY3/30K/yzUWn+GNZ0nVEuYLtYIdwM+xgyOnfKnHOO9dzVXU7CPVNNmsppJY45l2u0TbWx9a5cPgaNCqqlNuP46ejvc6auMq1Yck7M8i134h3R8bR32muTZ2ZMaR5wJV/iz9cfoK9b0nVLbWdMhvrJ90Uq5Hqp7g+9ed6h8G1OW0rVSPRLmPP/jy/4VJ4Y0jxX4JnmjNiupWMoJ8uGYcPjgjPI9Dx/KvqsVHB1qK9hJKUV6X/AOCeVB1Iy95aM9Mrk/EfxB07QL42EUE2oXyjLwwYAj9NzHgfTmnHxF4nKnb4OfOOM6gn/wATXlFm8k3nz3OftMs7tPuOTu3Hg/SvlcdiXhqXOld7G1SfJG56x4b8e6d4hu/sTQTWN7jcsM4HzjvtYcH9DXSzzxWtvJPcSLHFGpZ3Y4CgdTXgs0s1vdWU9kpa6juYzCoOCx3fdz79K6/x7rPiG58KtDqHh86dayTRrLOL1ZPl3dNoA4NLB4p4ij7SS1QU580bmhP8WbAXJFjpV9d2ynBuF2ru91UnJH1xXW6F4g0/xHp/2zTJS6A7XVl2sjehHY14woCqAowAMAVr+Cb/AFaw8Raguh6WNSWSFGmiNyIQjZOGyQcmuTBZjLEVXCUbGdOq5ysz2KsbxJ4p07wvZpNqDO8kpxDBEu55D7D+prP/ALd8Xf8AQmx/+DZP/iK888UXeo3/AI2d9bsRYTR2yCK3E4mCgk5IYADmvRxNb2FKVS17G05csbnYWPxW06W6WPU9Ou9OjdsLO+HQem7HI/Wu7VldAyEFWGQR3FeCTqj28iyYKFTnPpXX6RrXi1Ph3H9n0JJYltGEd418FcqAcNs25yB2zXJl+MlilLmVmjOlUc9zY1j4m6fp9/LZ2Flc6lJC22R4iFjU9xuPU/hWt4Z8Y6d4o82O1EtvdQjMltOAHUeoxwR7ivHdPC/YIShzuXcT6k9TV3S7i9s/FulzaPAs96zsnkmTYJE28gt2Fc9DMpVMT7Jx0/EmNZufLY90qOeeO2t5J53CRRqWdj0AHU1zP9r+M/8AoV7P/wAGQ/8Aiaoa7qni5/D9+tx4ctIojbuHcagGKjacnG3mvbOg7W3uIrq2juLdxJFKgdHHRlIyDUlcJ4e1LxcnhnTEtfD9nLCtpEI5Gv8AaWXaMEjbxx2rR/tTxr/0Llh/4MP/ALGgDqq5vxL4403w3MlrKk13eONwt7cAkD1JPAFQ/wBp+Nv+he0//wAD/wD7GvMbuW7ufEeqzarGsd99oKyRq24IAOAD3GK5MZiHh6LmldkVJckbno2g/EjTtX1GKwvLWfTbqY4iExBSQ+gYd/rXXXNzDaWslxcyLFDEpd3Y4CgdTXgGqELp8kmdrx4eNh1Dg5GPxrq/G2oeKZvBlums2NrbWc0sKzSw3BZ2B5wRjoT1rHBYx4ik5yWqJp1OaN2bMvxasftH+h6TfXNqDgzjaufdVJyR+Vddoeu2HiLTVvdMlLxk7WDDDI3oR2NeLqAqgKMAdAK1fA9zrlt4h1OLw5b21yrRRvNHcSlFVskAjHeufBZjLEVXCSsRTqucrM9krlPEfxA07QL1rCKCbUL1Rl4YMAR56bmPA+nJoN7462nGk6RnH/Py/wDhXlVm8swnmujm6knczknnfuOa68diXhqXPFXexpUnyRuer+G/H2neIbz7C0M1jfbdywTgfOO+1hwf0NdTXgEzzw3VlNY4+2Jcp5GTjLE4x9D3r1T7V48P/MO0Uf8AbeSngsS8TS52rMKc+eNzY17xDp/hzT/tepyFUJ2oiDc0jegHeuTg+LNh9oxf6VfWdsTgTsFcL7sAcj8M1zHjSfW5/FFjH4jitYikDPAls7MhOcE896zGAZSGAKkYINceMzGWHrKEY37mdSq4Ssj3aCeK5t457eRZIpFDI6nIYHoRXAfEG/axUXF5Kij7trbA5Z/Vj6CqPgK48XHwrGmjRaZNYxzSJC128gfaG9uMV1l34I0rV777frSTXVwyjKvKQieygY4r2I0cPiLLE35N7Lr5HVGrUpe9S3/I8Tm8QX8zgrIIgDnCDFe0+B7LSDoVvqGnQfvpk/eyyfNJu7jJ960bTwtodjj7LpdqhHfywT+ZrUjjSJdsSKi+ijFd9aphFT9nhaXJ6dfXv95lerOXNVlcdRRRXCWFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAZWuaCmuxRRT3t1bxxSLIFtygyynIJ3KTxT4tIkj1Rb5tUvZGWMRmJxFsYe4CA5zzwR+XFaVFTyK9zZVpqPJ09F1Kg0y1XVzqSR7bpovJZh/EuQefpiqd54fW81221VtRvY5bUERRxmPYoYAMMFCTnHc/TFa9FHKmKNapF3T6W+RRv9Kjvp4bhZ5rW5gDCOeAruAbGRhgQQcDqO1S2NjFp9uYoSzFnMju5yzserH3qzRTsk7kupJx5L6BRRRTICiiigAoorK8Q69F4f00XD21xdzSP5UFvbRl3lcgkD2HByTwKANWiuP0Txhf3/wAMz4kudNMl6EkYWVsCSzKxUIOvoMmqqeIfFWi6toqeJ49LltNYuBaqlkrrLbSFCwzuJDj5SCQB9KOtg6XO6rgPEnw4lvNVm1Lw9dx20twd09vOpMbN/eGOQT3qHUfGniJm8QX+lrpMGnaBM0U1veb/AD59qhiQQQEBz8uQc13emXv9o6Va3ojaL7REsuxuq5GcGonTjVjyyV0KST0Zxvhj4dy6fqkep69dx3U8HMEECkRof7xzyTXZanpttq+mT2F8m+CdCrjv9R71aoohTjTjywVkCSSsjyyb4X61bz+Tp2q2slpnCPco3mIPQ44Ndr4T8KW/hbT3iSZrm6nbfcXDgAu3sOw9q3qKinQpU5OUIpNiUUndIK5fxh4Li8TpDcW9wbPUbcYimC5Vh3Vh3FdRRWsoqSs9itzy60+FuqXdwqa5qVulmG+dLNW3yj0y3SvTIbaG3tEtoY1WGNAipjgKBjFS0VnTpU6S5YKwlFR2PNdV+GN5HeySeHL23S2lYv8AZ7sE+WSedpXt7Gtvwh4ETw/dvqWo3AvNSdPLDKMRwr6KOuT3J/xz19FKNClGbqKOrFypO9gqG9tI76xntJ9wjnjMb7Tg4IwcVNRWxRXsLOPTtOt7KAsYreJYkLHJwowM/lViiigArivFfgBtZ1I6ppF2tpeuoWVJVzHKB0JxyD712tFTOEZx5ZK6E0mrM850T4ZXH9pQ3fiW7hnjt3EkdrbAhGYdC5PJHtXdarpdrrOlT6ffJvt502MB1HoR6EdRVyipp04U48sFZAkkrI8sl+F+twz+TY6raPa5wslwjeYo98cE123hXwra+FtPeGB3nuJm33FxJjdI39AOwrdoqadClTblCNmxKKWyCuA8SfDiW81SXUvD93HbS3B3T286kxs394Y5BPeu/oq6lOFSPLNXQ2k1ZnB+GPh1Jp+qx6nr13HdTwcwQQKRHGf7xzyTXeUVR1jVoNF0uS9uUmkVMARwRl3djwFAHUmiEIU48sVZAklojN8WeErXxTZRrJK1td25LW9wgGVPoR3B9K42H4X6zcTeTqWq2sdpnDtao3mOvtu4FdL4N8W3viDw/qupapp5tJbG+uIBaxjc4WPoD1y30rHk8W+L9N0rT/EOtWWmw6XeTQo+nKsgurdZWCrlicMw3DK7R35qJ4elVkpTim9Px2FKKerW36Hd6Zp1tpGmQWFjH5cFugRF9h3PqatVwvjLUvGWgwX+rW2qeH7fSbddyJc2c0kx4AC5WQAsW4AA7iui8KT63c+GLOfxTFbQ6pKm+aK2UqiZ6DBJOQOvPWtlqVsbFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAGV4ii16bSwvha5sba+8wHffRNJHs5yMKQc9KdpEerxaGq+I57S41ABvMks42SM+mAxJ/WtOigDzzwl4gsvC3wbbWtTbbbWZnd8dT+9YAD3JIH41jeD/ABl4X8UeK7PVdb8R2V3rcmY9M0yEsY7MMOccfNIRwWPToK9cooWjDpY8O1CDwrPda4/j9Jh4tNzILLIkEpjB/cfZwvBHTp3zmvXPC7ai3hTTG1sEagbZPtAIwd+Oc+9atFC0VgeruFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAcJ4GvrfTNG8W317IIre212/llc/wqrZJ/IVyWkfEfwn4z8QWep+IvENpBDDOG0vRvmJEmcLJLxgvzwvQZ9a9oooWlvJL8Byd7+dzivFa/2x4+8M6FIN1tE0mpzoRw5iwIwfo7A/hXa0UUdLCCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAP/Z" + } + }, + "cell_type": "markdown", + "id": "bf3d4397", + "metadata": {}, + "source": [ + "As the name suggests, PTQ is performed on a trained model that has achieved acceptable accuracy. It is effective and also quick to implement because it does not require any retraining of the network. Now that we have the trained checkpoint ready, let's start quantizing the model. \n", + "\n", + "To perform PTQ, we perform inference in FP32 on calibration data, a subset of training or validation data, to determine the range of representable FP32 values to be quantized. This gives us the scale that can be used to map the values to the quantized range. We call this process of choosing the input range \"Calibration\". The three popular techniques used to calibrate are:\n", + "\n", + "- Min-Max: Use the minimum and maximum of the FP32 values seen during calibration. The disadvantage with this method is that, if there is an outlier, our mapping can induce a larger rounding error. \n", + "\n", + "- Entropy: Not all values in the FP32 tensor may be equally important. Hence using cross entropy with different range values [T1, T2], we try to minimize the information loss between the original FP32 tensor and quantized tensor. \n", + "\n", + "- Percentile: Use the percentile of the distribution of absolute values seen during calibration. Say, at 99% calibration, we clip 1% of the largest magnitude values, and determine [P1, P2] as the representable range to be quantized\n", + "\n", + "\n", + "![img4.JPG](attachment:img4.JPG)\n", + "\n", + "\n", + "We will be using the Pytorch Quantization toolkit, a toolkit built for training and evaluating PyTorch Models with simulated quantization. \n", + "\n", + "`quant_modules.initialize()` will ensure quantized modules are called instead of original modules. For example, when you define a model with convolution, linear snd pooling layers, you will make a call to `QuantConv2d`, `QuantLinear` and `QuantPooling` respectively. `QuantConv2d` basically wraps quantizer nodes around inputs and weights of regular `Conv2d`. Please refer to all the quantized modules in pytorch-quantization toolkit for more information. " + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "f1520afc", + "metadata": {}, + "outputs": [], + "source": [ + "quant_modules.initialize()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "ee09402f", + "metadata": {}, + "outputs": [], + "source": [ + "# We define Mobilenetv2 again just like we did above\n", + "# All the regular conv, FC layers will be converted to their quantized counterparts due to quant_modules.initialize()\n", + "feature_extract = True\n", + "q_model = models.mobilenet_v2(pretrained=True)\n", + "set_parameter_requires_grad(q_model, feature_extract)\n", + "q_model.classifier[1] = nn.Linear(1280, 10)\n", + "q_model = q_model.cuda()\n", + "\n", + "# mobilenetv2_base_ckpt is the checkpoint generated from Step 2 : Training a baseline Mobilenetv2 model.\n", + "ckpt = torch.load(\"./models/mobilenetv2_base_ckpt\")\n", + "modified_state_dict={}\n", + "for key, val in ckpt[\"model_state_dict\"].items():\n", + " # Remove 'module.' from the key names\n", + " if key.startswith('module'):\n", + " modified_state_dict[key[7:]] = val\n", + " else:\n", + " modified_state_dict[key] = val\n", + "\n", + "# Load the pre-trained checkpoint\n", + "q_model.load_state_dict(modified_state_dict)\n", + "optimizer.load_state_dict(ckpt[\"opt_state_dict\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "b8726956", + "metadata": {}, + "outputs": [], + "source": [ + "def compute_amax(model, **kwargs):\n", + " # Load calib result\n", + " for name, module in model.named_modules():\n", + " if isinstance(module, quant_nn.TensorQuantizer):\n", + " if module._calibrator is not None:\n", + " if isinstance(module._calibrator, calib.MaxCalibrator):\n", + " module.load_calib_amax()\n", + " else:\n", + " module.load_calib_amax(**kwargs)\n", + " model.cuda()\n", + "\n", + "def collect_stats(model, data_loader, num_batches):\n", + " \"\"\"Feed data to the network and collect statistics\"\"\"\n", + " # Enable calibrators\n", + " for name, module in model.named_modules():\n", + " if isinstance(module, quant_nn.TensorQuantizer):\n", + " if module._calibrator is not None:\n", + " module.disable_quant()\n", + " module.enable_calib()\n", + " else:\n", + " module.disable()\n", + "\n", + " # Feed data to the network for collecting stats\n", + " for i, (image, _) in tqdm(enumerate(data_loader), total=num_batches):\n", + " model(image.cuda())\n", + " if i >= num_batches:\n", + " break\n", + "\n", + " # Disable calibrators\n", + " for name, module in model.named_modules():\n", + " if isinstance(module, quant_nn.TensorQuantizer):\n", + " if module._calibrator is not None:\n", + " module.enable_quant()\n", + " module.disable_calib()\n", + " else:\n", + " module.enable()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "da627181", + "metadata": {}, + "outputs": [], + "source": [ + "# Calibrate the model using max calibration technique.\n", + "with torch.no_grad():\n", + " collect_stats(q_model, train_dataloader, num_batches=16)\n", + " compute_amax(q_model, method=\"max\")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "73e6d51c", + "metadata": {}, + "outputs": [], + "source": [ + "# Save the PTQ model\n", + "torch.save(q_model.state_dict(), \"./models/mobilenetv2_ptq.pth\")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "c7dadbf2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mobilenetv2 PTQ accuracy: 68.11%\n" + ] + } + ], + "source": [ + "# Evaluate the PTQ Model \n", + "test_acc = evaluate(q_model, val_dataloader, criterion, 0)\n", + "print(\"Mobilenetv2 PTQ accuracy: {:.2f}%\".format(100 * test_acc))" + ] + }, + { + "cell_type": "markdown", + "id": "efd5ff11", + "metadata": {}, + "source": [ + "Let us now prepare this model to export into ONNX. Setting `quant_nn.TensorQuantizer.use_fb_fake_quant = True` enables the quantized model to use `torch.fake_quantize_per_tensor_affine` and `torch.fake_quantize_per_channel_affine` operators instead of `tensor_quant` function to export quantization operators. " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "3f10f707", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "W0725 16:43:50.537823 139848660895552 tensor_quantizer.py:280] Use Pytorch's native experimental fake quantization.\n", + "/opt/conda/lib/python3.8/site-packages/pytorch_quantization/nn/modules/tensor_quantizer.py:283: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n", + " if amax.numel() == 1:\n", + "/opt/conda/lib/python3.8/site-packages/pytorch_quantization/nn/modules/tensor_quantizer.py:285: TracerWarning: Converting a tensor to a Python number might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n", + " inputs, amax.item() / bound, 0,\n", + "/opt/conda/lib/python3.8/site-packages/pytorch_quantization/nn/modules/tensor_quantizer.py:291: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n", + " quant_dim = list(amax.shape).index(list(amax_sequeeze.shape)[0])\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "&&&& RUNNING TensorRT.trtexec [TensorRT v8205] # trtexec --onnx=models/mobilenetv2_ptq.onnx --int8 --saveEngine=models/mobilenetv2_ptq.trt\n", + "[07/25/2022-16:43:56] [I] === Model Options ===\n", + "[07/25/2022-16:43:56] [I] Format: ONNX\n", + "[07/25/2022-16:43:56] [I] Model: models/mobilenetv2_ptq.onnx\n", + "[07/25/2022-16:43:56] [I] Output:\n", + "[07/25/2022-16:43:56] [I] === Build Options ===\n", + "[07/25/2022-16:43:56] [I] Max batch: explicit batch\n", + "[07/25/2022-16:43:56] [I] Workspace: 16 MiB\n", + "[07/25/2022-16:43:56] [I] minTiming: 1\n", + "[07/25/2022-16:43:56] [I] avgTiming: 8\n", + "[07/25/2022-16:43:56] [I] Precision: FP32+INT8\n", + "[07/25/2022-16:43:56] [I] Calibration: Dynamic\n", + "[07/25/2022-16:43:56] [I] Refit: Disabled\n", + "[07/25/2022-16:43:56] [I] Sparsity: Disabled\n", + "[07/25/2022-16:43:56] [I] Safe mode: Disabled\n", + "[07/25/2022-16:43:56] [I] DirectIO mode: Disabled\n", + "[07/25/2022-16:43:56] [I] Restricted mode: Disabled\n", + "[07/25/2022-16:43:56] [I] Save engine: models/mobilenetv2_ptq.trt\n", + "[07/25/2022-16:43:56] [I] Load engine: \n", + "[07/25/2022-16:43:56] [I] Profiling verbosity: 0\n", + "[07/25/2022-16:43:56] [I] Tactic sources: Using default tactic sources\n", + "[07/25/2022-16:43:56] [I] timingCacheMode: local\n", + "[07/25/2022-16:43:56] [I] timingCacheFile: \n", + "[07/25/2022-16:43:56] [I] Input(s)s format: fp32:CHW\n", + "[07/25/2022-16:43:56] [I] Output(s)s format: fp32:CHW\n", + "[07/25/2022-16:43:56] [I] Input build shapes: model\n", + "[07/25/2022-16:43:56] [I] Input calibration shapes: model\n", + "[07/25/2022-16:43:56] [I] === System Options ===\n", + "[07/25/2022-16:43:56] [I] Device: 0\n", + "[07/25/2022-16:43:56] [I] DLACore: \n", + "[07/25/2022-16:43:56] [I] Plugins:\n", + "[07/25/2022-16:43:56] [I] === Inference Options ===\n", + "[07/25/2022-16:43:56] [I] Batch: Explicit\n", + "[07/25/2022-16:43:56] [I] Input inference shapes: model\n", + "[07/25/2022-16:43:56] [I] Iterations: 10\n", + "[07/25/2022-16:43:56] [I] Duration: 3s (+ 200ms warm up)\n", + "[07/25/2022-16:43:56] [I] Sleep time: 0ms\n", + "[07/25/2022-16:43:56] [I] Idle time: 0ms\n", + "[07/25/2022-16:43:56] [I] Streams: 1\n", + "[07/25/2022-16:43:56] [I] ExposeDMA: Disabled\n", + "[07/25/2022-16:43:56] [I] Data transfers: Enabled\n", + "[07/25/2022-16:43:56] [I] Spin-wait: Disabled\n", + "[07/25/2022-16:43:56] [I] Multithreading: Disabled\n", + "[07/25/2022-16:43:56] [I] CUDA Graph: Disabled\n", + "[07/25/2022-16:43:56] [I] Separate profiling: Disabled\n", + "[07/25/2022-16:43:56] [I] Time Deserialize: Disabled\n", + "[07/25/2022-16:43:56] [I] Time Refit: Disabled\n", + "[07/25/2022-16:43:56] [I] Skip inference: Disabled\n", + "[07/25/2022-16:43:56] [I] Inputs:\n", + "[07/25/2022-16:43:56] [I] === Reporting Options ===\n", + "[07/25/2022-16:43:56] [I] Verbose: Disabled\n", + "[07/25/2022-16:43:56] [I] Averages: 10 inferences\n", + "[07/25/2022-16:43:56] [I] Percentile: 99\n", + "[07/25/2022-16:43:56] [I] Dump refittable layers:Disabled\n", + "[07/25/2022-16:43:56] [I] Dump output: Disabled\n", + "[07/25/2022-16:43:56] [I] Profile: Disabled\n", + "[07/25/2022-16:43:56] [I] Export timing to JSON file: \n", + "[07/25/2022-16:43:56] [I] Export output to JSON file: \n", + "[07/25/2022-16:43:56] [I] Export profile to JSON file: \n", + "[07/25/2022-16:43:56] [I] \n", + "[07/25/2022-16:43:56] [I] === Device Information ===\n", + "[07/25/2022-16:43:56] [I] Selected Device: NVIDIA Graphics Device\n", + "[07/25/2022-16:43:56] [I] Compute Capability: 8.0\n", + "[07/25/2022-16:43:56] [I] SMs: 124\n", + "[07/25/2022-16:43:56] [I] Compute Clock Rate: 1.005 GHz\n", + "[07/25/2022-16:43:56] [I] Device Global Memory: 47681 MiB\n", + "[07/25/2022-16:43:56] [I] Shared Memory per SM: 164 KiB\n", + "[07/25/2022-16:43:56] [I] Memory Bus Width: 6144 bits (ECC enabled)\n", + "[07/25/2022-16:43:56] [I] Memory Clock Rate: 1.215 GHz\n", + "[07/25/2022-16:43:56] [I] \n", + "[07/25/2022-16:43:56] [I] TensorRT version: 8.2.5\n", + "[07/25/2022-16:43:57] [I] [TRT] [MemUsageChange] Init CUDA: CPU +440, GPU +0, now: CPU 452, GPU 5862 (MiB)\n", + "[07/25/2022-16:43:57] [I] [TRT] [MemUsageSnapshot] Begin constructing builder kernel library: CPU 452 MiB, GPU 5862 MiB\n", + "[07/25/2022-16:43:57] [I] [TRT] [MemUsageSnapshot] End constructing builder kernel library: CPU 669 MiB, GPU 5934 MiB\n", + "[07/25/2022-16:43:57] [I] Start parsing network model\n", + "[07/25/2022-16:43:57] [I] [TRT] ----------------------------------------------------------------\n", + "[07/25/2022-16:43:57] [I] [TRT] Input filename: models/mobilenetv2_ptq.onnx\n", + "[07/25/2022-16:43:57] [I] [TRT] ONNX IR version: 0.0.7\n", + "[07/25/2022-16:43:57] [I] [TRT] Opset version: 13\n", + "[07/25/2022-16:43:57] [I] [TRT] Producer name: pytorch\n", + "[07/25/2022-16:43:57] [I] [TRT] Producer version: 1.13.0\n", + "[07/25/2022-16:43:57] [I] [TRT] Domain: \n", + "[07/25/2022-16:43:57] [I] [TRT] Model version: 0\n", + "[07/25/2022-16:43:57] [I] [TRT] Doc string: \n", + "[07/25/2022-16:43:57] [I] [TRT] ----------------------------------------------------------------\n", + "[07/25/2022-16:43:57] [W] [TRT] parsers/onnx/onnx2trt_utils.cpp:506: Your ONNX model has been generated with double-typed weights, while TensorRT does not natively support double. Attempting to cast down to float.\n", + "[07/25/2022-16:43:57] [W] [TRT] parsers/onnx/onnx2trt_utils.cpp:368: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32.\n", + "[07/25/2022-16:43:57] [I] Finish parsing network model\n", + "[07/25/2022-16:43:57] [I] FP32 and INT8 precisions have been specified - more performance might be enabled by additionally specifying --fp16 or --best\n", + "[07/25/2022-16:43:58] [W] [TRT] Calibrator won't be used in explicit precision mode. Use quantization aware training to generate network with Quantize/Dequantize nodes.\n", + "[07/25/2022-16:43:59] [I] [TRT] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +838, GPU +362, now: CPU 1543, GPU 6342 (MiB)\n", + "[07/25/2022-16:43:59] [I] [TRT] [MemUsageChange] Init cuDNN: CPU +128, GPU +58, now: CPU 1671, GPU 6400 (MiB)\n", + "[07/25/2022-16:43:59] [I] [TRT] Local timing cache in use. Profiling results in this builder pass will not be stored.\n", + "[07/25/2022-16:44:20] [I] [TRT] Detected 1 inputs and 1 output network tensors.\n", + "[07/25/2022-16:44:21] [I] [TRT] Total Host Persistent Memory: 75056\n", + "[07/25/2022-16:44:21] [I] [TRT] Total Device Persistent Memory: 2367488\n", + "[07/25/2022-16:44:21] [I] [TRT] Total Scratch Memory: 0\n", + "[07/25/2022-16:44:21] [I] [TRT] [MemUsageStats] Peak memory usage of TRT CPU/GPU memory allocators: CPU 11 MiB, GPU 184 MiB\n", + "[07/25/2022-16:44:21] [I] [TRT] [BlockAssignment] Algorithm ShiftNTopDown took 3.69334ms to assign 4 blocks to 87 nodes requiring 131661824 bytes.\n", + "[07/25/2022-16:44:21] [I] [TRT] Total Activation Memory: 131661824\n", + "[07/25/2022-16:44:21] [I] [TRT] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +0, GPU +8, now: CPU 1674, GPU 6412 (MiB)\n", + "[07/25/2022-16:44:21] [I] [TRT] [MemUsageChange] Init cuDNN: CPU +0, GPU +10, now: CPU 1674, GPU 6422 (MiB)\n", + "[07/25/2022-16:44:21] [I] [TRT] [MemUsageChange] TensorRT-managed allocation in building engine: CPU +2, GPU +4, now: CPU 2, GPU 4 (MiB)\n", + "[07/25/2022-16:44:21] [I] [TRT] [MemUsageChange] Init CUDA: CPU +0, GPU +0, now: CPU 1665, GPU 6384 (MiB)\n", + "[07/25/2022-16:44:21] [I] [TRT] Loaded engine size: 2 MiB\n", + "[07/25/2022-16:44:21] [I] [TRT] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +0, GPU +10, now: CPU 1666, GPU 6398 (MiB)\n", + "[07/25/2022-16:44:21] [I] [TRT] [MemUsageChange] Init cuDNN: CPU +0, GPU +8, now: CPU 1666, GPU 6406 (MiB)\n", + "[07/25/2022-16:44:21] [I] [TRT] [MemUsageChange] TensorRT-managed allocation in engine deserialization: CPU +0, GPU +2, now: CPU 0, GPU 2 (MiB)\n", + "[07/25/2022-16:44:21] [I] Engine built in 24.535 sec.\n", + "[07/25/2022-16:44:21] [I] [TRT] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +0, GPU +10, now: CPU 1435, GPU 6312 (MiB)\n", + "[07/25/2022-16:44:21] [I] [TRT] [MemUsageChange] Init cuDNN: CPU +1, GPU +8, now: CPU 1436, GPU 6320 (MiB)\n", + "[07/25/2022-16:44:21] [I] [TRT] [MemUsageChange] TensorRT-managed allocation in IExecutionContext creation: CPU +0, GPU +128, now: CPU 0, GPU 130 (MiB)\n", + "[07/25/2022-16:44:21] [I] Using random values for input inputs.1\n", + "[07/25/2022-16:44:21] [I] Created input binding for inputs.1 with dimensions 64x3x224x224\n", + "[07/25/2022-16:44:21] [I] Using random values for output 1225\n", + "[07/25/2022-16:44:21] [I] Created output binding for 1225 with dimensions 64x10\n", + "[07/25/2022-16:44:21] [I] Starting inference\n", + "[07/25/2022-16:44:24] [I] Warmup completed 64 queries over 200 ms\n", + "[07/25/2022-16:44:24] [I] Timing trace has 967 queries over 3.00851 s\n", + "[07/25/2022-16:44:24] [I] \n", + "[07/25/2022-16:44:24] [I] === Trace details ===\n", + "[07/25/2022-16:44:24] [I] Trace averages of 10 runs:\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.62079 ms - Host latency: 4.67811 ms (end to end 4.69463 ms, enqueue 1.64643 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.58894 ms - Host latency: 4.64457 ms (end to end 4.66023 ms, enqueue 1.64765 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.58884 ms - Host latency: 4.68113 ms (end to end 4.69763 ms, enqueue 1.4498 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.59182 ms - Host latency: 4.74732 ms (end to end 4.76547 ms, enqueue 1.01564 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.57819 ms - Host latency: 4.72507 ms (end to end 4.74366 ms, enqueue 1.02484 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.57656 ms - Host latency: 4.72242 ms (end to end 4.74165 ms, enqueue 1.02861 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.57644 ms - Host latency: 4.71519 ms (end to end 4.7332 ms, enqueue 1.01613 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.58525 ms - Host latency: 4.71598 ms (end to end 4.73434 ms, enqueue 1.02659 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.58402 ms - Host latency: 4.73148 ms (end to end 4.74992 ms, enqueue 1.01769 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.5875 ms - Host latency: 4.73852 ms (end to end 4.75818 ms, enqueue 1.01811 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.58987 ms - Host latency: 4.73746 ms (end to end 4.75689 ms, enqueue 1.03277 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.58997 ms - Host latency: 4.7413 ms (end to end 4.75951 ms, enqueue 1.01619 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.58946 ms - Host latency: 4.72262 ms (end to end 4.74041 ms, enqueue 1.02238 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.58925 ms - Host latency: 4.73135 ms (end to end 4.74933 ms, enqueue 1.01594 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.59028 ms - Host latency: 4.73451 ms (end to end 4.75285 ms, enqueue 1.02201 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.58268 ms - Host latency: 4.73112 ms (end to end 4.74874 ms, enqueue 1.02508 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.58301 ms - Host latency: 4.72178 ms (end to end 4.74047 ms, enqueue 1.01762 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.58886 ms - Host latency: 4.65172 ms (end to end 4.66926 ms, enqueue 1.51528 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.58914 ms - Host latency: 4.64406 ms (end to end 4.65896 ms, enqueue 1.63688 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.58699 ms - Host latency: 4.64383 ms (end to end 4.65996 ms, enqueue 1.65472 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.58555 ms - Host latency: 4.64166 ms (end to end 4.65729 ms, enqueue 1.63208 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.5912 ms - Host latency: 4.70112 ms (end to end 4.71844 ms, enqueue 1.32826 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.59344 ms - Host latency: 4.73959 ms (end to end 4.75857 ms, enqueue 1.02899 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.58987 ms - Host latency: 4.73836 ms (end to end 4.75505 ms, enqueue 1.01709 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.59006 ms - Host latency: 4.73572 ms (end to end 4.75276 ms, enqueue 1.02136 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.59048 ms - Host latency: 4.71992 ms (end to end 4.73885 ms, enqueue 1.02228 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.59038 ms - Host latency: 4.70565 ms (end to end 4.72057 ms, enqueue 1.07745 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.59613 ms - Host latency: 4.654 ms (end to end 4.66982 ms, enqueue 1.64631 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.60891 ms - Host latency: 4.6658 ms (end to end 4.68058 ms, enqueue 1.64453 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.63901 ms - Host latency: 4.72241 ms (end to end 4.74214 ms, enqueue 1.34059 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.62897 ms - Host latency: 4.68709 ms (end to end 4.69999 ms, enqueue 1.66216 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.63082 ms - Host latency: 4.70751 ms (end to end 4.7218 ms, enqueue 1.45334 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.62992 ms - Host latency: 4.6874 ms (end to end 4.70267 ms, enqueue 1.64911 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.622 ms - Host latency: 4.73571 ms (end to end 4.75325 ms, enqueue 1.20652 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.59867 ms - Host latency: 4.6564 ms (end to end 4.67043 ms, enqueue 1.59722 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.60093 ms - Host latency: 4.65856 ms (end to end 4.67501 ms, enqueue 1.66334 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.60172 ms - Host latency: 4.72034 ms (end to end 4.73595 ms, enqueue 1.27314 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.60275 ms - Host latency: 4.7422 ms (end to end 4.76001 ms, enqueue 1.03055 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.60154 ms - Host latency: 4.75237 ms (end to end 4.76968 ms, enqueue 1.01521 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.60103 ms - Host latency: 4.65785 ms (end to end 4.67402 ms, enqueue 1.57283 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.59702 ms - Host latency: 4.65447 ms (end to end 4.66899 ms, enqueue 1.6537 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.60101 ms - Host latency: 4.66719 ms (end to end 4.68365 ms, enqueue 1.57606 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.60033 ms - Host latency: 4.66338 ms (end to end 4.67982 ms, enqueue 1.52695 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.61044 ms - Host latency: 4.6709 ms (end to end 4.68477 ms, enqueue 1.65308 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.61915 ms - Host latency: 4.75122 ms (end to end 4.76687 ms, enqueue 1.15017 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.60728 ms - Host latency: 4.74371 ms (end to end 4.76132 ms, enqueue 1.03044 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.59255 ms - Host latency: 4.72791 ms (end to end 4.74779 ms, enqueue 1.03347 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.59315 ms - Host latency: 4.74182 ms (end to end 4.75947 ms, enqueue 1.01835 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.59058 ms - Host latency: 4.73859 ms (end to end 4.75806 ms, enqueue 1.01575 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.59283 ms - Host latency: 4.73408 ms (end to end 4.75116 ms, enqueue 1.02853 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.59253 ms - Host latency: 4.73284 ms (end to end 4.7496 ms, enqueue 1.0173 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.59139 ms - Host latency: 4.73563 ms (end to end 4.7526 ms, enqueue 1.01703 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.59039 ms - Host latency: 4.68552 ms (end to end 4.70142 ms, enqueue 1.15013 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.58688 ms - Host latency: 4.64351 ms (end to end 4.65852 ms, enqueue 1.65355 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.59246 ms - Host latency: 4.78259 ms (end to end 4.79854 ms, enqueue 0.765063 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.58976 ms - Host latency: 4.79293 ms (end to end 4.80812 ms, enqueue 0.447778 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.59243 ms - Host latency: 4.72633 ms (end to end 4.74291 ms, enqueue 1.14955 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.58927 ms - Host latency: 4.70409 ms (end to end 4.71877 ms, enqueue 1.46211 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.58922 ms - Host latency: 4.69727 ms (end to end 4.71404 ms, enqueue 1.4674 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.60126 ms - Host latency: 4.71378 ms (end to end 4.72882 ms, enqueue 1.4665 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.6146 ms - Host latency: 4.72861 ms (end to end 4.74229 ms, enqueue 1.46687 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.61904 ms - Host latency: 4.73428 ms (end to end 4.75139 ms, enqueue 1.45776 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.6167 ms - Host latency: 4.72507 ms (end to end 4.7394 ms, enqueue 1.46343 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.6165 ms - Host latency: 4.72825 ms (end to end 4.74551 ms, enqueue 1.48093 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.61758 ms - Host latency: 4.72815 ms (end to end 4.74431 ms, enqueue 1.47295 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.60859 ms - Host latency: 4.727 ms (end to end 4.74077 ms, enqueue 1.45435 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.6021 ms - Host latency: 4.71687 ms (end to end 4.73274 ms, enqueue 1.45869 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.61111 ms - Host latency: 4.72588 ms (end to end 4.73958 ms, enqueue 1.46362 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.6085 ms - Host latency: 4.71299 ms (end to end 4.72961 ms, enqueue 1.4863 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.62021 ms - Host latency: 4.73657 ms (end to end 4.75117 ms, enqueue 1.46689 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.61001 ms - Host latency: 4.7217 ms (end to end 4.73774 ms, enqueue 1.47329 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.60894 ms - Host latency: 4.72175 ms (end to end 4.73774 ms, enqueue 1.45996 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.59602 ms - Host latency: 4.69124 ms (end to end 4.70601 ms, enqueue 1.48582 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.58879 ms - Host latency: 4.7061 ms (end to end 4.72107 ms, enqueue 1.45811 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.59341 ms - Host latency: 4.7093 ms (end to end 4.72632 ms, enqueue 1.46155 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.59861 ms - Host latency: 4.67756 ms (end to end 4.69421 ms, enqueue 1.54897 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.59929 ms - Host latency: 4.65381 ms (end to end 4.66875 ms, enqueue 1.64392 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.60144 ms - Host latency: 4.71389 ms (end to end 4.73044 ms, enqueue 1.3313 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.59775 ms - Host latency: 4.71812 ms (end to end 4.73245 ms, enqueue 1.02263 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.57788 ms - Host latency: 4.66929 ms (end to end 4.68704 ms, enqueue 1.26707 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.58318 ms - Host latency: 4.64211 ms (end to end 4.6571 ms, enqueue 1.6553 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.59839 ms - Host latency: 4.6543 ms (end to end 4.66938 ms, enqueue 1.65542 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.59526 ms - Host latency: 4.66873 ms (end to end 4.68474 ms, enqueue 1.57432 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.60022 ms - Host latency: 4.74575 ms (end to end 4.76467 ms, enqueue 1.02512 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.59861 ms - Host latency: 4.725 ms (end to end 4.74438 ms, enqueue 1.03474 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.60442 ms - Host latency: 4.74048 ms (end to end 4.75903 ms, enqueue 1.02407 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.60613 ms - Host latency: 4.74568 ms (end to end 4.76294 ms, enqueue 1.02964 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.60151 ms - Host latency: 4.74846 ms (end to end 4.76499 ms, enqueue 1.01465 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.60273 ms - Host latency: 4.7436 ms (end to end 4.76155 ms, enqueue 1.02131 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.59956 ms - Host latency: 4.73704 ms (end to end 4.75496 ms, enqueue 1.02078 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.60122 ms - Host latency: 4.74536 ms (end to end 4.76064 ms, enqueue 1.02913 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.60396 ms - Host latency: 4.75247 ms (end to end 4.77131 ms, enqueue 1.0165 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.60598 ms - Host latency: 4.74436 ms (end to end 4.76189 ms, enqueue 1.01392 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.59995 ms - Host latency: 4.71816 ms (end to end 4.73706 ms, enqueue 1.02988 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.60974 ms - Host latency: 4.7179 ms (end to end 4.73477 ms, enqueue 1.07554 ms)\n", + "[07/25/2022-16:44:24] [I] Average on 10 runs - GPU latency: 1.61443 ms - Host latency: 4.66958 ms (end to end 4.68594 ms, enqueue 1.65239 ms)\n", + "[07/25/2022-16:44:24] [I] \n", + "[07/25/2022-16:44:24] [I] === Performance summary ===\n", + "[07/25/2022-16:44:24] [I] Throughput: 321.422 qps\n", + "[07/25/2022-16:44:24] [I] Latency: min = 4.61383 ms, max = 5.11646 ms, mean = 4.71056 ms, median = 4.71863 ms, percentile(99%) = 4.80322 ms\n", + "[07/25/2022-16:44:24] [I] End-to-End Host Latency: min = 4.62366 ms, max = 5.13928 ms, mean = 4.72723 ms, median = 4.73462 ms, percentile(99%) = 4.81934 ms\n", + "[07/25/2022-16:44:24] [I] Enqueue Time: min = 0.337158 ms, max = 1.83459 ms, mean = 1.28084 ms, median = 1.0896 ms, percentile(99%) = 1.71924 ms\n", + "[07/25/2022-16:44:24] [I] H2D Latency: min = 3.01642 ms, max = 3.51599 ms, mean = 3.09767 ms, median = 3.10742 ms, percentile(99%) = 3.1925 ms\n", + "[07/25/2022-16:44:24] [I] GPU Compute Time: min = 1.56671 ms, max = 1.6599 ms, mean = 1.59911 ms, median = 1.59741 ms, percentile(99%) = 1.63635 ms\n", + "[07/25/2022-16:44:24] [I] D2H Latency: min = 0.00561523 ms, max = 0.0314941 ms, mean = 0.0137833 ms, median = 0.0134277 ms, percentile(99%) = 0.0292969 ms\n", + "[07/25/2022-16:44:24] [I] Total Host Walltime: 3.00851 s\n", + "[07/25/2022-16:44:24] [I] Total GPU Compute Time: 1.54634 s\n", + "[07/25/2022-16:44:24] [W] * Throughput may be bound by host-to-device transfers for the inputs rather than GPU Compute and the GPU may be under-utilized.\n", + "[07/25/2022-16:44:24] [W] Add --noDataTransfers flag to disable data transfers.\n", + "[07/25/2022-16:44:24] [I] Explanations of the performance metrics are printed in the verbose logs.\n", + "[07/25/2022-16:44:24] [I] \n", + "&&&& PASSED TensorRT.trtexec [TensorRT v8205] # trtexec --onnx=models/mobilenetv2_ptq.onnx --int8 --saveEngine=models/mobilenetv2_ptq.trt\n" + ] + } + ], + "source": [ + "# Set static member of TensorQuantizer to use Pytorch’s own fake quantization functions\n", + "quant_nn.TensorQuantizer.use_fb_fake_quant = True\n", + "\n", + "# Exporting to ONNX\n", + "dummy_input = torch.randn(64, 3, 224, 224, device='cuda')\n", + "input_names = [ \"actual_input_1\" ]\n", + "output_names = [ \"output1\" ]\n", + "torch.onnx.export(\n", + " q_model,\n", + " dummy_input,\n", + " \"models/mobilenetv2_ptq.onnx\",\n", + " verbose=False,\n", + " opset_version=13,\n", + " do_constant_folding = False)\n", + "\n", + "# Converting ONNX model to TRT\n", + "!trtexec --onnx=models/mobilenetv2_ptq.onnx --int8 --saveEngine=models/mobilenetv2_ptq.trt" + ] + }, + { + "attachments": { + "img5.JPG": { + "image/jpeg": "/9j/4AAQSkZJRgABAQEAeAB4AAD/4RDuRXhpZgAATU0AKgAAAAgABAE7AAIAAAAMAAAISodpAAQAAAABAAAIVpydAAEAAAAYAAAQzuocAAcAAAgMAAAAPgAAAAAc6gAAAAgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAEFubmllIFN1cmxhAAAFkAMAAgAAABQAABCkkAQAAgAAABQAABC4kpEAAgAAAAM5NwAAkpIAAgAAAAM5NwAA6hwABwAACAwAAAiYAAAAABzqAAAACAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAMjAyMjowNjoxMyAxMTowMToxNAAyMDIyOjA2OjEzIDExOjAxOjE0AAAAQQBuAG4AaQBlACAAUwB1AHIAbABhAAAA/+ELHmh0dHA6Ly9ucy5hZG9iZS5jb20veGFwLzEuMC8APD94cGFja2V0IGJlZ2luPSfvu78nIGlkPSdXNU0wTXBDZWhpSHpyZVN6TlRjemtjOWQnPz4NCjx4OnhtcG1ldGEgeG1sbnM6eD0iYWRvYmU6bnM6bWV0YS8iPjxyZGY6UkRGIHhtbG5zOnJkZj0iaHR0cDovL3d3dy53My5vcmcvMTk5OS8wMi8yMi1yZGYtc3ludGF4LW5zIyI+PHJkZjpEZXNjcmlwdGlvbiByZGY6YWJvdXQ9InV1aWQ6ZmFmNWJkZDUtYmEzZC0xMWRhLWFkMzEtZDMzZDc1MTgyZjFiIiB4bWxuczpkYz0iaHR0cDovL3B1cmwub3JnL2RjL2VsZW1lbnRzLzEuMS8iLz48cmRmOkRlc2NyaXB0aW9uIHJkZjphYm91dD0idXVpZDpmYWY1YmRkNS1iYTNkLTExZGEtYWQzMS1kMzNkNzUxODJmMWIiIHhtbG5zOnhtcD0iaHR0cDovL25zLmFkb2JlLmNvbS94YXAvMS4wLyI+PHhtcDpDcmVhdGVEYXRlPjIwMjItMDYtMTNUMTE6MDE6MTQuOTY3PC94bXA6Q3JlYXRlRGF0ZT48L3JkZjpEZXNjcmlwdGlvbj48cmRmOkRlc2NyaXB0aW9uIHJkZjphYm91dD0idXVpZDpmYWY1YmRkNS1iYTNkLTExZGEtYWQzMS1kMzNkNzUxODJmMWIiIHhtbG5zOmRjPSJodHRwOi8vcHVybC5vcmcvZGMvZWxlbWVudHMvMS4xLyI+PGRjOmNyZWF0b3I+PHJkZjpTZXEgeG1sbnM6cmRmPSJodHRwOi8vd3d3LnczLm9yZy8xOTk5LzAyLzIyLXJkZi1zeW50YXgtbnMjIj48cmRmOmxpPkFubmllIFN1cmxhPC9yZGY6bGk+PC9yZGY6U2VxPg0KCQkJPC9kYzpjcmVhdG9yPjwvcmRmOkRlc2NyaXB0aW9uPjwvcmRmOlJERj48L3g6eG1wbWV0YT4NCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgPD94cGFja2V0IGVuZD0ndyc/Pv/bAEMABwUFBgUEBwYFBggHBwgKEQsKCQkKFQ8QDBEYFRoZGBUYFxseJyEbHSUdFxgiLiIlKCkrLCsaIC8zLyoyJyorKv/bAEMBBwgICgkKFAsLFCocGBwqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKv/AABEIAKoCGgMBIgACEQEDEQH/xAAfAAABBQEBAQEBAQAAAAAAAAAAAQIDBAUGBwgJCgv/xAC1EAACAQMDAgQDBQUEBAAAAX0BAgMABBEFEiExQQYTUWEHInEUMoGRoQgjQrHBFVLR8CQzYnKCCQoWFxgZGiUmJygpKjQ1Njc4OTpDREVGR0hJSlNUVVZXWFlaY2RlZmdoaWpzdHV2d3h5eoOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4eLj5OXm5+jp6vHy8/T19vf4+fr/xAAfAQADAQEBAQEBAQEBAAAAAAAAAQIDBAUGBwgJCgv/xAC1EQACAQIEBAMEBwUEBAABAncAAQIDEQQFITEGEkFRB2FxEyIygQgUQpGhscEJIzNS8BVictEKFiQ04SXxFxgZGiYnKCkqNTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqCg4SFhoeIiYqSk5SVlpeYmZqio6Slpqeoqaqys7S1tre4ubrCw8TFxsfIycrS09TV1tfY2dri4+Tl5ufo6ery8/T19vf4+fr/2gAMAwEAAhEDEQA/APpGiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKMj1oyPWgAooyPWjI9aACijI9aMj1oAKKMj1oyPWgAooyPWjI9aACijI9aMj1oAKKMj1oyPWgAooyPWjI9aACijI9aMj1oAKKMj1oyPWgAooyPWjI9aACijI9aMj1oAKKMj1oyPWgAooyPWjI9aACijI9aMj1oAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKzfEP/ACALr/dH/oQrSrN8Q/8AIAuv90f+hCgDz+iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAO48Kf8AIDH/AF0atqsXwp/yAx/10atqgAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAopCwXqab5qeooAfRTPNT1FHmp6igB9FM81PUUeanqKAH0UzzU9RR5qeooAfRTPNT1FHmp6igB9FNDqehp1ABRRRQAUUUUAFZviH/kAXX+6P/QhWlWb4h/5AF1/uj/0IUAef0UUUAFFFFABRRRQBqWkdsdDuLiS0jeWF1UMzPyCe4DCoHhN0yRW9mIZVUs4VjtK4BB+YnHFWrCVoNBvPKnWKV3QoBKFYgde+ai0+T7VeTvdXGHMBA3ybBKQAApPvTAdptiY9Ztob6BHSbkZbII9QQcGqj2U3ktcBVEO8qCXUEkdgM5NbqPEdQ0qUz2qrCm2QLIAFPNVbo28kttfo8Y8mQLLbiReinOVGeQaQGZJp9zFC0rx4RCA+GBK56ZAOR+NSaZYi9mk81isMMZkkK9cDsPetC+kaEXTxX1q9vcA4WJU3vnoDxnj1qpo13HBJcQXDbIrmIxlz/CexNABaCyv7pbU2ot/MO1JUdmIPbIJwf0qCfTbm3BaRV2eaYd3mLjd6Hnj8at2Wny2V5Fd3u2O2iYOJNwIkxyAuOuafJMupaXcASxRyteGYpI4X5SMd+tAFBtNukneFowGjUM+XXaoPTJzilGmXhuGgEBMiruIBHTGc571sXQgmubt0uoXzFGEjMwVJMAZzyOnpTzJEdRjlW5ttv2DyyVkAAbaRj2oAxRpV15sCSKsYnbajM4xn04PX261K1lJbSXqLBHcJGpBcuDsGcZ4PX2qzbOkWmWQkljBS9DsPMBIXjnGaeqotxqxM8GJo22YlX5stkDr6UMDNi0u7nZFiSNndQyr5yAkYznGc9KI9Mu5f9XEDkkKN65bHXHPP4VejIs9NCwTRvd3Q2u/mr+6T+716mreneRaz2LtdQSKhYO8ko/d8nhVzwD6/wAqAMgaa50sXm9Duk2hN4z0+vX260XNtO91FCLRYpDGpCIeox9488VZdEbQ2gE8PmR3JcjzByNvb1544q601v8AbGjeaIefYCFZA4IVsdCR0oGYrafcq0Q8vd5pwhRgwY+mQcU6bS7y3ikklh2rEcP8wJX8M5/GrtrKsGnR2sjxmRrtZB+8BCAdTnOBVh3jNxrR86EiZf3f71fm5zxzQI5+iiigAooooAKKKKAO48Kf8gMf9dGrarF8Kf8AIDH/AF0atqgAooooAKKY0qr1NJ56f3h+dAElFR+en94fnR56f3h+dAElFR+en94fnR56f3h+dAElFR+en94fnR56f3h+dAElFR+en94fnR56f3h+dAElFNWRW6GnUAFFFFABSE4FLSN900Acl4lvnae3tA7ostxGrFGKnBYZ5FT/ANgWn/PS6/8AAl/8ayvEX/Icsf8Ar5j/APQhXU1y+zhOrLmV9F+pFk3qZf8AYFp/z0uv/Al/8aP7AtP+el1/4Ev/AI1qUVf1el/Kh8sexl/2Baf89Lr/AMCX/wAaP7AtP+el1/4Ev/jWpRR9XpfyoOWPYy/7AtP+el1/4Ev/AI0f2Baf89Lr/wACX/xrUoo+r0v5UHLHsZf9gWn/AD0uv/Al/wDGj+wLT/npdf8AgS/+NalFH1el/Kg5Y9jFutNhsI4ri3luQ6XEP3rhyCDIoIIJ54Jrqom3Rg1gax/yDx/13g/9GrW9B/qV+lOEIwm1FW0X6gkk9CSiiitigooooAKzfEP/ACALr/dH/oQrSrN8Q/8AIAuv90f+hCgDz+iiigAooooAKTIqtPcCKeJW5DOqn8TXa/8ACO6b/wA8D/323+NAHJ5FGRXWf8I7pv8AzwP/AH23+NH/AAjum/8APA/99t/jQByeRRkV1n/CO6b/AM8D/wB9t/jR/wAI7pv/ADwP/fbf40AcnkUZFdZ/wjum/wDPA/8Afbf40f8ACO6b/wA8D/323+NAHPQajNDbtb/LLA3WKQZGfUdx+FVmOWJACgnoO1a2vafa6fbwtbR7C8mD8xPGKyKACiiigApM0McKTVnw1Db6rfXcN0m8RKpHzEYyT6UAVsijIrrP+Ed03/ngf++2/wAaP+Ed03/ngf8Avtv8aAOTyKMius/4R3Tf+eB/77b/ABo/4R3Tf+eB/wC+2/xoA5PIoyK6z/hHdN/54H/vtv8AGj/hHdN/54H/AL7b/GgDk8ilrrB4e00f8sD/AN9t/jXL3apFqFxFGMJHIVAz0ANAEdFFFABRRRQB3HhT/kBj/ro1bVYvhT/kBj/ro1bVABTZDtjJp1Rz/wCpb6UAcdeSLqXia3tLgFogrkruIzx7Vof8I/pn/Puf+/r/AONZEH/I8Rf7j/yrqa5I0qc5zcop69vJEJJt3M3/AIR/TP8An3P/AH9f/Gj/AIR/TP8An3P/AH9f/GtKitfq9H+Rfch8q7Gb/wAI/pn/AD7n/v6/+NH/AAj+mf8APuf+/r/41pUUfV6P8i+5ByrsZv8Awj+mf8+5/wC/r/40f8I/pn/Puf8Av6/+NaVFH1ej/IvuQcq7Gb/wj+mf8+5/7+v/AI0f8I/pn/Puf+/r/wCNaVFH1ej/ACL7kHKuxjrZW+m67pxtEaPzDIG+djn5fc11SnKiubvf+Q3pf+/J/wCg10ifdFTRiouairK/6IFpcWiiiugoKRvumlpG+6aAOE8Rf8h2x/6+Y/8A0IV1Nct4i/5Dtj/18x/+hCuprCP8WXy/UlbsKKr30L3FhPFC7RyMhCOpwQe1YDa5Orm95Ns0Hkhf+m23d/P5aKlaNN+9/X9fqDkludPRXODVrqy/0WGI3BtVVZSySO0jYBOCoIHXvU82qz2018xjjyphCMzMFUPnlvTHfFT9Zp/d/X6C50blFc0+pX121qYngMgvGiBjZvLcCMnOe4z/ACqWbUdRmS0EHkxTfa2glBJKsVB/HHH8qSxUHsn/AFb/ADDnR0FFZsd9cnWDbTLDFF0Tdu3S8Zyvb8OtaVbxmpbFJ3KGsf8AIPH/AF3h/wDRq1vQf6lfpWDrH/IPH/XeH/0atb0H+pX6Ul/Efov1DqSUUUVoMKKKKACs3xD/AMgC6/3R/wChCtKs3xD/AMgC6/3R/wChCgDz+iiigAopCwHU4oyD0NAGVqR/0q3/AOuqfzFeq15VqX/H1b/9dU/mK9VoAKKKKACiqlzqljZyeXdXcUT4ztZwDViKWOaNZIXWRGGQynINSpRbsmK62H0UUVQzn/Fn/Hpbf9df6GufHSug8Wf8elt/11/oa58dKAFoopNy5xkUANk+4aseBP8AkN6l/wBc0/maryH92aseBP8AkN6n/wBc0/maAO6ooqOeeK2haWd1jjX7zMcAUm0ldgSUVTt9W0+6lEdveQyOeiq4yasTTxW8TSzyLHGvVmOAKSnFrmT0FdMkoqrbalZXrlbW6ilYDJVGBNWqcZKSumF09grhL3/kMXn/AF2b+dd3XCXv/IYvP+uzfzpjI6KKKACiiigDuPCn/IDH/XRq2qxfCn/IDH/XRq2qACo5/wDUt9KJZ4oI2kmcIijLMxwAPWo2nimt98Tq6sMhlOQadna4HFwf8jxF/uP/ACrqa5aD/kd4v9x/5V1Nc9L4p+v6IlbsKKKK3KCiiigAooooAKKKKAM29/5Del/78n/oNdIn3RXN3v8AyG9L/wB+T/0GukT7orGn8U/X9EJbsWiiithhSN900tI33TQBwniL/kO2P/XzH/6EK6muW8Rf8h2x/wCvmP8A9CFdTWEf4svl+pK3YVUGm2otxB5f7sS+dj/a3bv51borZxT3Q7XKM2kwTXZuA80TtjeIpSofHTIHWmahpYuYbnyR+9uNm7dIVGFPqBxWfpl1PdxmSW7vvMUuceSoiIBOBu2f1pui6jcXclptu5rnzIyblXiAWI44IYAd+3NcCq0aiS5fi9P66kcy+8u6dpDwRxi7ct5MvmQIJC+z5cY3EDPU9qsS6TbSwtGTIuZjOHRyrK57giqdtJdtqF3ZLfSMywgq88QBDZIyoAGVqxpj3BuruOS4a5giZVSRwoO7HzDgAccVpT9m0o8u/p0/4b8gVtrEq6XELuO4eW4kaP7iySkqDjGceuKu0UV1Rio7FpJGL4lvfsmkNJtLlJYm2r1OJFOKpx+PnVAP7Gvj9Fq/r9sJdPGf+e8P/o1avw6MhjFa0qlKMmpwvt1a7mcoyctHYw/+FgP/ANAW+/75o/4WA/8A0Bb7/vmt/wDsVKP7FSun21D/AJ9fixclT+b8DA/4WA//AEBb7/vmj/hYD/8AQFvv++a3/wCxUo/sVKPbUP8An1+LDkqfzfgYH/CwH/6At9/3zVTVfHD3WlzQnSbyIOAN7rwORXVf2KlZ+vaMi6Hcn/ZH8xR7ah/z6/FhyVP5vwPO/wC3P+mD0f25/wBMHqx/Zi0f2YtHtqH/AD6/FhyVP5vwMq91U3CGPGwN2JqGDULmD7khI9G5qzfaQxcNGOQc0yHR5pGzIcD0Fejh8ZhIUHGcOu2/5nNUo1pTTUvmK2oNdXFvvXaRKmSOnUV7IOa8fnsxb3FsB/z1T+Yr2AcV5FadOc7048q7XudkFJK0ncWo55DFbSSAZKIWA+gqSkIDKQRkEYIrCV2mkWcz4Y061v8ATHvr6GO5nuJGLtIobHPTnpT/AA8n2LXNU06JibeNg8ak5257f59KZHYa1oXnRaRHDdWrsXRZD8ye3UZpnhi6s45pTc3JbU7x/wB5GyEFTzx0+teRStCdKDXK1vfS+ltO93qc60snudVSE4FLSEZFewdByfjW/EFrbYUsfN6D6GuT/tz/AKYPXXeMbMSW9sT/AM9f6GuZ/sxa6KdSlGNpQu/Vmcozb0lYrPrhKn9yw+tZc1688/mK5UgY+U9K2pdLUoayG0mZJGEfRjnOK78JisNTq80oWXq3+BhWpVJRspXJodYuEG2TEi+/Brp/h7L5ur6i2MZiTj8TXPQaOVG6Qkmuh+H8ezWdSH/TNP5mufF18PVf7qny+f8AwNi6NOpBe/K531ZHin/kWbv/AHV/9CFa9Z2u2k19olxb2yhpXA2gnGeQa8jFRcqE0t7P8jd7GLe6VZt4Pjuo4UhuIoEkWVBtbOB1I61C8zazq+i2l780LW4ndT/G2D1/L9TU/wDZmu6hYw6deCC0tI1VXZG3M4H4n09qvapocjG0udJZY7myULGH6Ovof8968+VOUnzxh7vu3W17PXT+rmHK2tEUvE1jb6ZFa6hYRJbzxTqv7tdoYHPUD6V1AOQDXGahPdz6lap4nCWdpGfMURKWWRh2JGa662uYby3Se2ffE/3WAxmunCyi6lTl0206+bt0Li1zOxNXl+rasYNfv4/Ic7Z2GR35r1CvN9TsVl1y+Y952/nXqU5Qi7zjf8C5KT2djO/tw/8APu9H9uN/z7N+dWf7NWl/s1K29tQ/59fizPkn/N+CKn9tv/z7N+dH9tyf8+zfn/8AWq3/AGalH9mpR7aj/wA+l97/AMw5J/zfgjY0PxffWmmiKHRprhd5O9XwP5Vo/wDCb6p28PzfjN/9jVzwtpMb6KCR/wAtGrZ/seP0o9tR/wCfS++X+Yck/wCb8jhNa8W6jc2M0MulPbrIhUsZc4BH0rj7bVL7TvmsbqWHHOFbg/UdDXq2seG1u4GReNwIyO1Y9n4FggG513kd25r0MNj6FGlKLp79N/vu2c9XD1JyTUv6+RgeCNXv9W8WBr9BhY22SBdu7jmvTa4zSrNbPxlEiDH7t/5V2deFzqdWpKKsr7fJHVTTirN3CqWr3ElppFzNAdsip8rEZ29s/hV2myRpLG0cih0YYZSMgiiacotI0MxreDRbOW+Rp5mSL5t8zNvPrycD8KgudWv7COVbuO3eXyvOjMe7bjcAQc/XrV2HRrOEMuJZEZDHsklZlCnqACeKauiWaxSxkSP5qhGaSVmIUHIAJPArllTq2tCy0/HXy9DOz6ED6jf2/wBpS4S3Z4FSY+UGwYySGHJ6jBq7ZXbXjzum026vsiYDlsD5j9M8fhU32aL7Q8xXLyIEbPQgZ7fiaLe3itLdIIF2RxjCgdq2hCaer0/r9CknclooorYozb3/AJDel/78n/oNdIn3RXN3v/Ib0v8A35P/AEGukT7orGn8U/X9EJbsWiiithhSN900tI33TQBwniL/AJDtj/18x/8AoQrqa5bxF/yHbH/r5j/9CFdTWEf4svl+pK3YUUUVuUVbOyW0sfsyuWGW+Yj+8Sf606ztBZ6fFao5IjQIHxz9asUVCpxja3RWFYzYtLmSSSaW/kluGiMSSMijyweeg6mn6Zp82nx+W9408QGFQxquDnrkcmr9FTGlCLTX5sOVBRRRWoyhrH/IPH/XeH/0atb0H+pX6Vg6x/yDx/13h/8ARq1vQf6lfpWa/iP0X6i6klFFFaDCiiigArN8Q/8AIAuv90f+hCtKs3xD/wAgC6/3R/6EKAPP6KKKAEKg9RRtA6ClooAydS/4+rf/AK6p/MV6rXlWpf8AH1b/APXVP5ivVaACiiigAqMwQtKJWiQyL0cqMj8akooAKKKKAOf8Wf8AHpbf9df6GufHSug8Wf8AHpbf9df6GufHSgBaTYvpS0UAMkGIzVjwJ/yG9T/65p/M1Xk+4aseBP8AkN6l/wBc0/maAO6ooooAKKKKAGuiSKVkVXU9QwyDRHGkSBIkVFHRVGAKdRRbqAVwl7/yGLz/AK7N/Ou7rhL3/kMXn/XZv50AR0UUUAFFFFAHceFP+QGP+ujVtVi+FP8AkBj/AK6NW1QAhAPUVHMAIWwO1S1HP/qW+lAHDwf8jxF/uP8Ayrqa5aD/AJHiL/cf+VdTWFL4p+v6IlbsKKKK3KCiiigAooooAKKKKAM29/5Del/78n/oNdIn3RXN3v8AyG9L/wB+T/0GukT7orGn8U/X9EJbsWiiithhSN900tIeRQBwniL/AJDlj/18x/8AoQrqayPEGjy3cqSwMVeNg6sOxByKyTbeIv8An/l/75X/AArD3o1HJK97E63Otorkvs3iP/n/AJf++V/wo+zeI/8An/l/75X/AAquef8AL+Q7vsdbRXJfZvEf/P8Ay/8AfK/4UfZvEf8Az/y/98r/AIUc8/5fyC77HW0VyX2bxH/z/wAv/fK/4UfZvEf/AD/y/wDfK/4Uc8/5fyC77HW0VyX2bxH/AM/8v/fK/wCFH2bxH/z/AMv/AHyv+FHPP+X8gu+xvax/yDx/13g/9GrW9B/qV+lchZaZqdzIg1C9mkjV1cphQCVII7eoFdjEu2MCiHM5OTVtv1BbjqKKK1GFFFFABWb4h/5AF1/uj/0IVpVm+If+QBdf7o/9CFAHn9FFFABRRRQBSvbZpdrRnDKQVPoRTG1bxNu41WQD/rmn+FaFJgUAZ/8Aavif/oKyf9+0/wDiaP7V8T/9BWT/AL9p/wDE1oYFGBQBn/2r4n/6Csn/AH7T/wCJo/tXxP8A9BWT/v2n/wATWhgUYFAGf/avif8A6Csn/ftP/iaUar4nzzqsn/ftP/iav4FGBQBGt5qN5Gq6jctOFORlQMH8BUtGKKACiiigBGGVIrOU6jYTyS6XctbtIAHIUHIH1FaVJigDP/tXxP8A9BWT/v2n/wATR/avif8A6Csn/ftP/ia0MCjAoAz/AO1fE/8A0FZP+/af/E0f2r4n/wCgrJ/37T/4mtDAowKAM/8AtXxP/wBBWT/v2n/xNH9q+J/+grJ/37T/AOJrQwKMCgCpDq3iQOPM1OQj/rmn+FWVMkjtJM26Rzlmx1NOwKWgAooooAKKKKAO48Kf8gMf9dGrarF8Kf8AIDH/AF0atqgAqOf/AFLfSpKbIu5CKAOFh/5HeL/cf+VdTXP6xoNxLei4tZZIZBnDxsVP5is7+xtb/wCglef9/wBv8a50qkZSaV7vv5JfoTqmdjRXHf2Nrf8A0Erz/v8At/jR/Y2t/wDQSvP+/wC3+NVzVf5fx/4AXfY7GiuO/sbW/wDoJXn/AH/b/Gj+xtb/AOglef8Af9v8aOar/L+P/AC77HY0Vx39ja3/ANBK8/7/ALf40f2Nrf8A0Erz/v8At/jRzVf5fx/4AXfY7GiuO/sbW/8AoJXn/f8Ab/GgaNrf/QSvP+/7f40c1X+X8f8AgBd9jfvf+Q3pf+/J/wCg10ifdFcro2iTx3Udxe3FxO8edvmyswGRg8GuqAwKKcZJycur/RIav1FooorYYUUUUAIUB6im+UnpT6KAGeUnpR5SelPooAZ5SelHlJ6U+igBnlJ6UeUnpT6KAGeUnpR5SelPooAaI1HQU6iigAooooAKKKKACs3xD/yALr/dH/oQrSpGUMuGAI9CKAPLqK9O8iH/AJ5J/wB8ijyIf+eSf98igDzGivTvIh/55J/3yKPIh/55J/3yKAPMaK9O8iH/AJ5J/wB8ijyIf+eSf98igDzGivTvIh/55J/3yKPIh/55J/3yKAPMaK9O8iH/AJ5J/wB8ijyIf+eSf98igDzGivTvIh/55J/3yKPIh/55J/3yKAPMaK9O8iH/AJ5J/wB8ijyIf+eSf98igDzGivTvIh/55J/3yKPIh/55J/3yKAPMaK9O8iH/AJ5J/wB8ijyIf+eSf98igDzGivTvIh/55J/3yKPIh/55J/3yKAPMaK9O8iH/AJ5J/wB8ijyIf+eSf98igDzGivTvIh/55J/3yKPIh/55J/3yKAPMaK9O8iH/AJ5J/wB8ijyIf+eSf98igDzGivTvIh/55J/3yKPIh/55J/3yKAPMaK9O8iH/AJ5J/wB8ijyIf+eSf98igDJ8Kf8AIDH/AF0atqkVFQYRQo9AMUtABRRRQAhRT1FJ5a+lOooAb5a+lHlr6U6igBvlr6UeWvpTqKAG+WvpR5a+lOooAb5a+lHlr6U6igBAoHQUtFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAf/9k=" + } + }, + "cell_type": "markdown", + "id": "d3e676e7", + "metadata": {}, + "source": [ + "\n", + "## 5. Quantization Aware Training (QAT)\n", + "\n", + "PTQ resulted in a ~3% accuracy drop. After PTQ is performed, sometimes the model may perform poorly by not retaining the accuracy as the process is not able to mitigate the large quantization error induced by low-bit quantization. This could happen if there are sensitive layers in the network, like the Depth wise convolutional networks, in MobileNets which are more susceptible to producing larger quantization error. \n", + "\n", + "This is when we might want to consider using QAT. The idea behind QAT is simple: you can improve the lost accuracy of the quantized model, if you had trained the model with quantization error. There are many ways of doing this, starting the training of the model from scratch or fine-tuning a pre-trained model. Whatever method you choose, the quantization error is induced in the training loss by inserting fake-quantization operations. The operation is called “fake” because we quantize the data and immediately perform a dequantize operation producing an approximate version of the data where both input and output still remain as floating point values. We are here trying to simulate the effects of quantization without changing much in the model. \n", + "In the forward-pass, we fake-quantize the weights and activations and use these fake-quantized outputs to perform the layer operations.\n", + "\n", + "![img5.JPG](attachment:img5.JPG)\n", + "\n", + "In the backward pass, while calculating gradient, the quantization operation’s derivative is undefined at the step boundaries, and zero everywhere else. To handle this, QAT uses Straight-through Estimator by approximating the derivative to be 1 for inputs in the representable range. This estimator is essentially letting gradients pass as is through this operator in the backward pass. When the QAT process is done, the scales that were used to quantize the weights and activations are stored in the model and can be used for inference. " + ] + }, + { + "cell_type": "markdown", + "id": "bcc10e0f", + "metadata": {}, + "source": [ + "Usually the finetuning of QAT model should be quick compared to the full training of the original model. For this Mobilenetv2 model, it is enough to finetune for 2 epochs to get acceptable accuracy. \n", + "\n", + "tensor_quant function in `pytorch_quantization` toolkit is responsible for the above tensor quantization. Usually, per channel quantization is recommended for weights, while per tensor quantization is recommended for activations in a network.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "dc144132", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: [ 1 / 2] LR: 0.000100\n", + "Batch: [ 100 | 147] loss: 1.806\n", + "Test Acc: 69.88%\n", + "Epoch: [ 2 / 2] LR: 0.000100\n", + "Batch: [ 100 | 147] loss: 1.800\n", + "Test Acc: 69.49%\n", + "Checkpoint saved\n" + ] + } + ], + "source": [ + "# Finetune the QAT model for 2 epochs\n", + "num_epochs=2\n", + "\n", + "for epoch in range(num_epochs):\n", + " print('Epoch: [%5d / %5d] LR: %f' % (epoch + 1, num_epochs, lr))\n", + "\n", + " train(q_model, train_dataloader, criterion, optimizer, epoch)\n", + " test_acc = evaluate(q_model, val_dataloader, criterion, epoch)\n", + "\n", + " print(\"Test Acc: {:.2f}%\".format(100 * test_acc))\n", + " \n", + "save_checkpoint({'epoch': epoch + 1,\n", + " 'model_state_dict': q_model.state_dict(),\n", + " 'acc': test_acc,\n", + " 'opt_state_dict': optimizer.state_dict()\n", + " },\n", + " ckpt_path=\"models/mobilenetv2_qat_ckpt\")" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "0d801c67", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mobilenetv2 QAT accuracy: 69.49%\n" + ] + } + ], + "source": [ + "# Evaluate the QAT model\n", + "test_acc = evaluate(q_model, val_dataloader, criterion, 0)\n", + "print(\"Mobilenetv2 QAT accuracy: {:.2f}%\".format(100 * test_acc))" + ] + }, + { + "cell_type": "markdown", + "id": "70bdaeed", + "metadata": {}, + "source": [ + "As you can see, accuracy recovered by ~1.3%. Fine-tuning for more epochs with learning rate annealing can improve accuracy further. It should be noted that the same fine-tuning schedule will improve the accuracy of the unquantized model as well. Please refer to Achieving FP32 Accuracy for INT8 Inference Using Quantization Aware Training with NVIDIA TensorRT for detailed recommendations.\n", + "\n", + "During inference, we use `torch.fake_quantize_per_tensor_affine` and `torch.fake_quantize_per_channel_affine` to perform quantization as this is easier to convert into corresponding TensorRT operators. \n", + "\n", + "Let us now prepare this model to export into ONNX. " + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "176a6bfd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "&&&& RUNNING TensorRT.trtexec [TensorRT v8205] # trtexec --onnx=models/mobilenetv2_qat.onnx --int8 --saveEngine=models/mobilenetv2_qat.trt\n", + "[07/25/2022-16:46:43] [I] === Model Options ===\n", + "[07/25/2022-16:46:43] [I] Format: ONNX\n", + "[07/25/2022-16:46:43] [I] Model: models/mobilenetv2_qat.onnx\n", + "[07/25/2022-16:46:43] [I] Output:\n", + "[07/25/2022-16:46:43] [I] === Build Options ===\n", + "[07/25/2022-16:46:43] [I] Max batch: explicit batch\n", + "[07/25/2022-16:46:43] [I] Workspace: 16 MiB\n", + "[07/25/2022-16:46:43] [I] minTiming: 1\n", + "[07/25/2022-16:46:43] [I] avgTiming: 8\n", + "[07/25/2022-16:46:43] [I] Precision: FP32+INT8\n", + "[07/25/2022-16:46:43] [I] Calibration: Dynamic\n", + "[07/25/2022-16:46:43] [I] Refit: Disabled\n", + "[07/25/2022-16:46:43] [I] Sparsity: Disabled\n", + "[07/25/2022-16:46:43] [I] Safe mode: Disabled\n", + "[07/25/2022-16:46:43] [I] DirectIO mode: Disabled\n", + "[07/25/2022-16:46:43] [I] Restricted mode: Disabled\n", + "[07/25/2022-16:46:43] [I] Save engine: models/mobilenetv2_qat.trt\n", + "[07/25/2022-16:46:43] [I] Load engine: \n", + "[07/25/2022-16:46:43] [I] Profiling verbosity: 0\n", + "[07/25/2022-16:46:43] [I] Tactic sources: Using default tactic sources\n", + "[07/25/2022-16:46:43] [I] timingCacheMode: local\n", + "[07/25/2022-16:46:43] [I] timingCacheFile: \n", + "[07/25/2022-16:46:43] [I] Input(s)s format: fp32:CHW\n", + "[07/25/2022-16:46:43] [I] Output(s)s format: fp32:CHW\n", + "[07/25/2022-16:46:43] [I] Input build shapes: model\n", + "[07/25/2022-16:46:43] [I] Input calibration shapes: model\n", + "[07/25/2022-16:46:43] [I] === System Options ===\n", + "[07/25/2022-16:46:43] [I] Device: 0\n", + "[07/25/2022-16:46:43] [I] DLACore: \n", + "[07/25/2022-16:46:43] [I] Plugins:\n", + "[07/25/2022-16:46:43] [I] === Inference Options ===\n", + "[07/25/2022-16:46:43] [I] Batch: Explicit\n", + "[07/25/2022-16:46:43] [I] Input inference shapes: model\n", + "[07/25/2022-16:46:43] [I] Iterations: 10\n", + "[07/25/2022-16:46:43] [I] Duration: 3s (+ 200ms warm up)\n", + "[07/25/2022-16:46:43] [I] Sleep time: 0ms\n", + "[07/25/2022-16:46:43] [I] Idle time: 0ms\n", + "[07/25/2022-16:46:43] [I] Streams: 1\n", + "[07/25/2022-16:46:43] [I] ExposeDMA: Disabled\n", + "[07/25/2022-16:46:43] [I] Data transfers: Enabled\n", + "[07/25/2022-16:46:43] [I] Spin-wait: Disabled\n", + "[07/25/2022-16:46:43] [I] Multithreading: Disabled\n", + "[07/25/2022-16:46:43] [I] CUDA Graph: Disabled\n", + "[07/25/2022-16:46:43] [I] Separate profiling: Disabled\n", + "[07/25/2022-16:46:43] [I] Time Deserialize: Disabled\n", + "[07/25/2022-16:46:43] [I] Time Refit: Disabled\n", + "[07/25/2022-16:46:43] [I] Skip inference: Disabled\n", + "[07/25/2022-16:46:43] [I] Inputs:\n", + "[07/25/2022-16:46:43] [I] === Reporting Options ===\n", + "[07/25/2022-16:46:43] [I] Verbose: Disabled\n", + "[07/25/2022-16:46:43] [I] Averages: 10 inferences\n", + "[07/25/2022-16:46:43] [I] Percentile: 99\n", + "[07/25/2022-16:46:43] [I] Dump refittable layers:Disabled\n", + "[07/25/2022-16:46:43] [I] Dump output: Disabled\n", + "[07/25/2022-16:46:43] [I] Profile: Disabled\n", + "[07/25/2022-16:46:43] [I] Export timing to JSON file: \n", + "[07/25/2022-16:46:43] [I] Export output to JSON file: \n", + "[07/25/2022-16:46:43] [I] Export profile to JSON file: \n", + "[07/25/2022-16:46:43] [I] \n", + "[07/25/2022-16:46:43] [I] === Device Information ===\n", + "[07/25/2022-16:46:43] [I] Selected Device: NVIDIA Graphics Device\n", + "[07/25/2022-16:46:43] [I] Compute Capability: 8.0\n", + "[07/25/2022-16:46:43] [I] SMs: 124\n", + "[07/25/2022-16:46:43] [I] Compute Clock Rate: 1.005 GHz\n", + "[07/25/2022-16:46:43] [I] Device Global Memory: 47681 MiB\n", + "[07/25/2022-16:46:43] [I] Shared Memory per SM: 164 KiB\n", + "[07/25/2022-16:46:43] [I] Memory Bus Width: 6144 bits (ECC enabled)\n", + "[07/25/2022-16:46:43] [I] Memory Clock Rate: 1.215 GHz\n", + "[07/25/2022-16:46:43] [I] \n", + "[07/25/2022-16:46:43] [I] TensorRT version: 8.2.5\n", + "[07/25/2022-16:46:44] [I] [TRT] [MemUsageChange] Init CUDA: CPU +440, GPU +0, now: CPU 452, GPU 5862 (MiB)\n", + "[07/25/2022-16:46:44] [I] [TRT] [MemUsageSnapshot] Begin constructing builder kernel library: CPU 452 MiB, GPU 5862 MiB\n", + "[07/25/2022-16:46:44] [I] [TRT] [MemUsageSnapshot] End constructing builder kernel library: CPU 669 MiB, GPU 5934 MiB\n", + "[07/25/2022-16:46:44] [I] Start parsing network model\n", + "[07/25/2022-16:46:44] [I] [TRT] ----------------------------------------------------------------\n", + "[07/25/2022-16:46:44] [I] [TRT] Input filename: models/mobilenetv2_qat.onnx\n", + "[07/25/2022-16:46:44] [I] [TRT] ONNX IR version: 0.0.7\n", + "[07/25/2022-16:46:44] [I] [TRT] Opset version: 13\n", + "[07/25/2022-16:46:44] [I] [TRT] Producer name: pytorch\n", + "[07/25/2022-16:46:44] [I] [TRT] Producer version: 1.13.0\n", + "[07/25/2022-16:46:44] [I] [TRT] Domain: \n", + "[07/25/2022-16:46:44] [I] [TRT] Model version: 0\n", + "[07/25/2022-16:46:44] [I] [TRT] Doc string: \n", + "[07/25/2022-16:46:44] [I] [TRT] ----------------------------------------------------------------\n", + "[07/25/2022-16:46:44] [W] [TRT] parsers/onnx/onnx2trt_utils.cpp:506: Your ONNX model has been generated with double-typed weights, while TensorRT does not natively support double. Attempting to cast down to float.\n", + "[07/25/2022-16:46:44] [W] [TRT] parsers/onnx/onnx2trt_utils.cpp:368: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32.\n", + "[07/25/2022-16:46:45] [I] Finish parsing network model\n", + "[07/25/2022-16:46:45] [I] FP32 and INT8 precisions have been specified - more performance might be enabled by additionally specifying --fp16 or --best\n", + "[07/25/2022-16:46:45] [W] [TRT] Calibrator won't be used in explicit precision mode. Use quantization aware training to generate network with Quantize/Dequantize nodes.\n", + "[07/25/2022-16:46:47] [I] [TRT] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +838, GPU +362, now: CPU 1543, GPU 6342 (MiB)\n", + "[07/25/2022-16:46:47] [I] [TRT] [MemUsageChange] Init cuDNN: CPU +128, GPU +58, now: CPU 1671, GPU 6400 (MiB)\n", + "[07/25/2022-16:46:47] [I] [TRT] Local timing cache in use. Profiling results in this builder pass will not be stored.\n", + "[07/25/2022-16:47:09] [I] [TRT] Detected 1 inputs and 1 output network tensors.\n", + "[07/25/2022-16:47:09] [I] [TRT] Total Host Persistent Memory: 82480\n", + "[07/25/2022-16:47:09] [I] [TRT] Total Device Persistent Memory: 2413056\n", + "[07/25/2022-16:47:09] [I] [TRT] Total Scratch Memory: 0\n", + "[07/25/2022-16:47:09] [I] [TRT] [MemUsageStats] Peak memory usage of TRT CPU/GPU memory allocators: CPU 11 MiB, GPU 184 MiB\n", + "[07/25/2022-16:47:09] [I] [TRT] [BlockAssignment] Algorithm ShiftNTopDown took 3.32319ms to assign 4 blocks to 84 nodes requiring 130056192 bytes.\n", + "[07/25/2022-16:47:09] [I] [TRT] Total Activation Memory: 130056192\n", + "[07/25/2022-16:47:09] [I] [TRT] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +0, GPU +8, now: CPU 1674, GPU 6412 (MiB)\n", + "[07/25/2022-16:47:09] [I] [TRT] [MemUsageChange] Init cuDNN: CPU +0, GPU +10, now: CPU 1674, GPU 6422 (MiB)\n", + "[07/25/2022-16:47:09] [I] [TRT] [MemUsageChange] TensorRT-managed allocation in building engine: CPU +2, GPU +4, now: CPU 2, GPU 4 (MiB)\n", + "[07/25/2022-16:47:09] [I] [TRT] [MemUsageChange] Init CUDA: CPU +0, GPU +0, now: CPU 1665, GPU 6384 (MiB)\n", + "[07/25/2022-16:47:09] [I] [TRT] Loaded engine size: 2 MiB\n", + "[07/25/2022-16:47:09] [I] [TRT] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +0, GPU +10, now: CPU 1666, GPU 6398 (MiB)\n", + "[07/25/2022-16:47:09] [I] [TRT] [MemUsageChange] Init cuDNN: CPU +0, GPU +8, now: CPU 1666, GPU 6406 (MiB)\n", + "[07/25/2022-16:47:09] [I] [TRT] [MemUsageChange] TensorRT-managed allocation in engine deserialization: CPU +0, GPU +2, now: CPU 0, GPU 2 (MiB)\n", + "[07/25/2022-16:47:09] [I] Engine built in 25.2523 sec.\n", + "[07/25/2022-16:47:09] [I] [TRT] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +0, GPU +10, now: CPU 1435, GPU 6322 (MiB)\n", + "[07/25/2022-16:47:09] [I] [TRT] [MemUsageChange] Init cuDNN: CPU +1, GPU +8, now: CPU 1436, GPU 6330 (MiB)\n", + "[07/25/2022-16:47:09] [I] [TRT] [MemUsageChange] TensorRT-managed allocation in IExecutionContext creation: CPU +0, GPU +126, now: CPU 0, GPU 128 (MiB)\n", + "[07/25/2022-16:47:09] [I] Using random values for input inputs.1\n", + "[07/25/2022-16:47:09] [I] Created input binding for inputs.1 with dimensions 64x3x224x224\n", + "[07/25/2022-16:47:09] [I] Using random values for output 1225\n", + "[07/25/2022-16:47:09] [I] Created output binding for 1225 with dimensions 64x10\n", + "[07/25/2022-16:47:09] [I] Starting inference\n", + "[07/25/2022-16:47:12] [I] Warmup completed 63 queries over 200 ms\n", + "[07/25/2022-16:47:12] [I] Timing trace has 976 queries over 3.0073 s\n", + "[07/25/2022-16:47:12] [I] \n", + "[07/25/2022-16:47:12] [I] === Trace details ===\n", + "[07/25/2022-16:47:12] [I] Trace averages of 10 runs:\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.92225 ms - Host latency: 5.03344 ms (end to end 5.05219 ms, enqueue 1.40172 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.66963 ms - Host latency: 4.78574 ms (end to end 4.80028 ms, enqueue 1.39754 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.61669 ms - Host latency: 4.73438 ms (end to end 4.75002 ms, enqueue 1.40104 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.59776 ms - Host latency: 4.70923 ms (end to end 4.72325 ms, enqueue 1.40551 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.59918 ms - Host latency: 4.715 ms (end to end 4.72859 ms, enqueue 1.39258 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.59212 ms - Host latency: 4.70311 ms (end to end 4.71815 ms, enqueue 1.40127 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.59171 ms - Host latency: 4.70111 ms (end to end 4.71709 ms, enqueue 1.3924 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.58884 ms - Host latency: 4.69999 ms (end to end 4.71507 ms, enqueue 1.38793 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.59119 ms - Host latency: 4.70641 ms (end to end 4.72385 ms, enqueue 1.39411 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.58546 ms - Host latency: 4.70263 ms (end to end 4.7179 ms, enqueue 1.39454 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.58618 ms - Host latency: 4.69799 ms (end to end 4.71401 ms, enqueue 1.38189 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.59365 ms - Host latency: 4.70694 ms (end to end 4.72247 ms, enqueue 1.40284 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.59426 ms - Host latency: 4.70533 ms (end to end 4.71981 ms, enqueue 1.40167 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.59406 ms - Host latency: 4.70507 ms (end to end 4.72038 ms, enqueue 1.39868 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.59302 ms - Host latency: 4.70604 ms (end to end 4.72096 ms, enqueue 1.39022 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.5956 ms - Host latency: 4.70856 ms (end to end 4.72499 ms, enqueue 1.39016 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.59622 ms - Host latency: 4.71029 ms (end to end 4.72501 ms, enqueue 1.39351 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.59784 ms - Host latency: 4.70826 ms (end to end 4.72278 ms, enqueue 1.39263 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.59805 ms - Host latency: 4.71088 ms (end to end 4.72592 ms, enqueue 1.39367 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.59795 ms - Host latency: 4.71144 ms (end to end 4.72837 ms, enqueue 1.3975 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.59713 ms - Host latency: 4.70555 ms (end to end 4.72311 ms, enqueue 1.40206 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.59601 ms - Host latency: 4.68881 ms (end to end 4.70304 ms, enqueue 1.3645 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.58742 ms - Host latency: 4.69799 ms (end to end 4.71174 ms, enqueue 1.39108 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.59344 ms - Host latency: 4.70665 ms (end to end 4.72214 ms, enqueue 1.39278 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.59734 ms - Host latency: 4.70482 ms (end to end 4.71854 ms, enqueue 1.39332 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.59714 ms - Host latency: 4.70997 ms (end to end 4.72628 ms, enqueue 1.40047 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.61176 ms - Host latency: 4.72535 ms (end to end 4.7418 ms, enqueue 1.39706 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.61494 ms - Host latency: 4.72816 ms (end to end 4.7448 ms, enqueue 1.39434 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.61383 ms - Host latency: 4.72913 ms (end to end 4.7439 ms, enqueue 1.40642 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.61697 ms - Host latency: 4.73928 ms (end to end 4.75625 ms, enqueue 1.41578 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.61782 ms - Host latency: 4.83635 ms (end to end 4.85382 ms, enqueue 0.316187 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.61688 ms - Host latency: 4.81012 ms (end to end 4.82694 ms, enqueue 0.524707 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.62682 ms - Host latency: 4.69824 ms (end to end 4.71261 ms, enqueue 1.44248 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.62582 ms - Host latency: 4.68247 ms (end to end 4.69834 ms, enqueue 1.57075 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.62538 ms - Host latency: 4.68074 ms (end to end 4.69913 ms, enqueue 1.56764 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.62548 ms - Host latency: 4.68276 ms (end to end 4.69795 ms, enqueue 1.58025 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.62765 ms - Host latency: 4.68287 ms (end to end 4.70229 ms, enqueue 1.56355 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.62581 ms - Host latency: 4.68279 ms (end to end 4.69857 ms, enqueue 1.57596 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.62439 ms - Host latency: 4.68186 ms (end to end 4.69902 ms, enqueue 1.56841 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.62468 ms - Host latency: 4.6818 ms (end to end 4.69666 ms, enqueue 1.57666 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.62562 ms - Host latency: 4.68257 ms (end to end 4.6985 ms, enqueue 1.57379 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.61575 ms - Host latency: 4.67201 ms (end to end 4.68948 ms, enqueue 1.58751 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.61467 ms - Host latency: 4.67125 ms (end to end 4.68734 ms, enqueue 1.57214 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.6139 ms - Host latency: 4.66783 ms (end to end 4.6828 ms, enqueue 1.56377 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.61342 ms - Host latency: 4.67017 ms (end to end 4.68673 ms, enqueue 1.57308 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.61005 ms - Host latency: 4.66664 ms (end to end 4.68411 ms, enqueue 1.55513 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.59465 ms - Host latency: 4.65076 ms (end to end 4.66672 ms, enqueue 1.56719 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.5959 ms - Host latency: 4.65466 ms (end to end 4.66882 ms, enqueue 1.5709 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.60471 ms - Host latency: 4.66272 ms (end to end 4.68046 ms, enqueue 1.58149 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.61157 ms - Host latency: 4.66888 ms (end to end 4.68478 ms, enqueue 1.62261 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.61403 ms - Host latency: 4.66865 ms (end to end 4.68436 ms, enqueue 1.61089 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.61339 ms - Host latency: 4.66898 ms (end to end 4.6855 ms, enqueue 1.59581 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.61229 ms - Host latency: 4.66919 ms (end to end 4.68688 ms, enqueue 1.57114 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.61361 ms - Host latency: 4.67148 ms (end to end 4.68864 ms, enqueue 1.57201 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.61329 ms - Host latency: 4.66671 ms (end to end 4.6823 ms, enqueue 1.56505 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.61117 ms - Host latency: 4.66793 ms (end to end 4.68323 ms, enqueue 1.58344 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.61322 ms - Host latency: 4.67312 ms (end to end 4.68901 ms, enqueue 1.57474 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.61351 ms - Host latency: 4.6689 ms (end to end 4.68566 ms, enqueue 1.57411 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.6125 ms - Host latency: 4.67083 ms (end to end 4.68839 ms, enqueue 1.56761 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.61216 ms - Host latency: 4.66829 ms (end to end 4.68427 ms, enqueue 1.57145 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.61221 ms - Host latency: 4.66812 ms (end to end 4.68464 ms, enqueue 1.57742 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.61724 ms - Host latency: 4.67236 ms (end to end 4.69009 ms, enqueue 1.58645 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.6342 ms - Host latency: 4.69334 ms (end to end 4.70886 ms, enqueue 1.58391 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.64475 ms - Host latency: 4.70205 ms (end to end 4.71633 ms, enqueue 1.57148 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.64463 ms - Host latency: 4.70203 ms (end to end 4.71699 ms, enqueue 1.56494 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.64092 ms - Host latency: 4.69741 ms (end to end 4.71147 ms, enqueue 1.57456 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.62642 ms - Host latency: 4.68474 ms (end to end 4.70034 ms, enqueue 1.56938 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.62737 ms - Host latency: 4.68528 ms (end to end 4.70254 ms, enqueue 1.57288 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.62422 ms - Host latency: 4.68096 ms (end to end 4.69629 ms, enqueue 1.58088 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.62236 ms - Host latency: 4.67939 ms (end to end 4.69592 ms, enqueue 1.56531 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.61946 ms - Host latency: 4.67705 ms (end to end 4.69207 ms, enqueue 1.57915 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.62383 ms - Host latency: 4.68113 ms (end to end 4.69565 ms, enqueue 1.56628 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.62493 ms - Host latency: 4.68076 ms (end to end 4.69827 ms, enqueue 1.57712 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.62881 ms - Host latency: 4.68533 ms (end to end 4.70332 ms, enqueue 1.59106 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.62705 ms - Host latency: 4.77595 ms (end to end 4.79063 ms, enqueue 1.23335 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.63042 ms - Host latency: 4.83225 ms (end to end 4.84863 ms, enqueue 0.584692 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.62356 ms - Host latency: 4.80049 ms (end to end 4.81941 ms, enqueue 0.722852 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.61289 ms - Host latency: 4.70488 ms (end to end 4.72126 ms, enqueue 1.16353 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.61414 ms - Host latency: 4.67012 ms (end to end 4.6865 ms, enqueue 1.55625 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.61272 ms - Host latency: 4.66924 ms (end to end 4.68572 ms, enqueue 1.57039 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.61147 ms - Host latency: 4.66743 ms (end to end 4.6821 ms, enqueue 1.57139 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.61204 ms - Host latency: 4.66624 ms (end to end 4.68369 ms, enqueue 1.57068 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.61245 ms - Host latency: 4.67002 ms (end to end 4.68525 ms, enqueue 1.56729 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.61497 ms - Host latency: 4.67256 ms (end to end 4.68835 ms, enqueue 1.5822 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.61396 ms - Host latency: 4.6707 ms (end to end 4.6873 ms, enqueue 1.56724 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.61487 ms - Host latency: 4.67173 ms (end to end 4.68682 ms, enqueue 1.57334 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.61299 ms - Host latency: 4.66936 ms (end to end 4.68381 ms, enqueue 1.57117 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.61013 ms - Host latency: 4.66755 ms (end to end 4.68381 ms, enqueue 1.57551 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.61992 ms - Host latency: 4.67517 ms (end to end 4.69097 ms, enqueue 1.58848 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.62769 ms - Host latency: 4.6877 ms (end to end 4.70227 ms, enqueue 1.57029 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.62732 ms - Host latency: 4.68355 ms (end to end 4.70088 ms, enqueue 1.56836 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.62974 ms - Host latency: 4.6852 ms (end to end 4.69971 ms, enqueue 1.56511 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.61794 ms - Host latency: 4.67524 ms (end to end 4.68911 ms, enqueue 1.57212 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.61436 ms - Host latency: 4.6708 ms (end to end 4.68591 ms, enqueue 1.5667 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.60833 ms - Host latency: 4.66543 ms (end to end 4.68132 ms, enqueue 1.57961 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.6125 ms - Host latency: 4.66885 ms (end to end 4.68545 ms, enqueue 1.56494 ms)\n", + "[07/25/2022-16:47:12] [I] Average on 10 runs - GPU latency: 1.63328 ms - Host latency: 4.69219 ms (end to end 4.70671 ms, enqueue 1.57573 ms)\n", + "[07/25/2022-16:47:12] [I] \n", + "[07/25/2022-16:47:12] [I] === Performance summary ===\n", + "[07/25/2022-16:47:12] [I] Throughput: 324.544 qps\n", + "[07/25/2022-16:47:12] [I] Latency: min = 4.63513 ms, max = 5.62218 ms, mean = 4.69772 ms, median = 4.68481 ms, percentile(99%) = 4.86353 ms\n", + "[07/25/2022-16:47:12] [I] End-to-End Host Latency: min = 4.64392 ms, max = 5.64146 ms, mean = 4.71364 ms, median = 4.70197 ms, percentile(99%) = 4.88013 ms\n", + "[07/25/2022-16:47:12] [I] Enqueue Time: min = 0.310181 ms, max = 4.23633 ms, mean = 1.46804 ms, median = 1.5567 ms, percentile(99%) = 1.67847 ms\n", + "[07/25/2022-16:47:12] [I] H2D Latency: min = 3.01538 ms, max = 3.23657 ms, mean = 3.06713 ms, median = 3.05371 ms, percentile(99%) = 3.20923 ms\n", + "[07/25/2022-16:47:12] [I] GPU Compute Time: min = 1.578 ms, max = 2.49139 ms, mean = 1.61667 ms, median = 1.61377 ms, percentile(99%) = 1.69678 ms\n", + "[07/25/2022-16:47:12] [I] D2H Latency: min = 0.00561523 ms, max = 0.0319824 ms, mean = 0.0139259 ms, median = 0.0134277 ms, percentile(99%) = 0.0289307 ms\n", + "[07/25/2022-16:47:12] [I] Total Host Walltime: 3.0073 s\n", + "[07/25/2022-16:47:12] [I] Total GPU Compute Time: 1.57787 s\n", + "[07/25/2022-16:47:12] [W] * Throughput may be bound by Enqueue Time rather than GPU Compute and the GPU may be under-utilized.\n", + "[07/25/2022-16:47:12] [W] If not already in use, --useCudaGraph (utilize CUDA graphs where possible) may increase the throughput.\n", + "[07/25/2022-16:47:12] [W] * Throughput may be bound by host-to-device transfers for the inputs rather than GPU Compute and the GPU may be under-utilized.\n", + "[07/25/2022-16:47:12] [W] Add --noDataTransfers flag to disable data transfers.\n", + "[07/25/2022-16:47:12] [I] Explanations of the performance metrics are printed in the verbose logs.\n", + "[07/25/2022-16:47:12] [I] \n", + "&&&& PASSED TensorRT.trtexec [TensorRT v8205] # trtexec --onnx=models/mobilenetv2_qat.onnx --int8 --saveEngine=models/mobilenetv2_qat.trt\n" + ] + } + ], + "source": [ + "# Set static member of TensorQuantizer to use Pytorch’s own fake quantization functions\n", + "quant_nn.TensorQuantizer.use_fb_fake_quant = True\n", + "\n", + "# Exporting to ONNX\n", + "dummy_input = torch.randn(64, 3, 224, 224, device='cuda')\n", + "input_names = [ \"actual_input_1\" ]\n", + "output_names = [ \"output1\" ]\n", + "torch.onnx.export(\n", + " q_model,\n", + " dummy_input,\n", + " \"models/mobilenetv2_qat.onnx\",\n", + " verbose=False,\n", + " opset_version=13,\n", + " do_constant_folding = False)\n", + "\n", + "# Converting ONNX model to TRT\n", + "!trtexec --onnx=models/mobilenetv2_qat.onnx --int8 --saveEngine=models/mobilenetv2_qat.trt" + ] + }, + { + "cell_type": "markdown", + "id": "b5108ef4", + "metadata": {}, + "source": [ + "\n", + "### 6. Evaluation and Benchmarking" + ] + }, + { + "cell_type": "markdown", + "id": "2e5362ca", + "metadata": {}, + "source": [ + "Now, we have converted our model to a TensorRT engine. Great! That means we are ready to load it into the native Python TensorRT runtime to perform inference and evaluate our models." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "790d73a6", + "metadata": {}, + "outputs": [], + "source": [ + "# Import needed libraries and define the evaluate function\n", + "\n", + "import pycuda.driver as cuda\n", + "import pycuda.autoinit\n", + "import time \n", + "\n", + "def evaluate_trt(engine_path, dataloader, batch_size):\n", + " \n", + " def predict(batch): # result gets copied into output\n", + " # transfer input data to device\n", + " cuda.memcpy_htod_async(d_input, batch, stream)\n", + " # execute model\n", + " context.execute_async_v2(bindings, stream.handle, None)\n", + " # transfer predictions back\n", + " cuda.memcpy_dtoh_async(output, d_output, stream)\n", + " # syncronize threads\n", + " stream.synchronize()\n", + " return output\n", + " \n", + " with open(engine_path, 'rb') as f, trt.Runtime(trt.Logger(trt.Logger.WARNING)) as runtime, runtime.deserialize_cuda_engine(f.read()) as engine, engine.create_execution_context() as context:\n", + " total = 0\n", + " correct = 0\n", + " for images, labels in val_dataloader:\n", + " input_batch = images.numpy()\n", + " labels = labels.numpy()\n", + " output = np.empty([batch_size, 10], dtype = np.float32) \n", + "\n", + " # Now allocate input and output memory, give TRT pointers (bindings) to it:\n", + " d_input = cuda.mem_alloc(1 * input_batch.nbytes)\n", + " d_output = cuda.mem_alloc(1 * output.nbytes)\n", + " bindings = [int(d_input), int(d_output)]\n", + "\n", + " stream = cuda.Stream()\n", + " preds = predict(input_batch)\n", + " pred_labels = []\n", + " for pred in preds:\n", + " pred_label = (-pred).argsort()[0]\n", + " pred_labels.append(pred_label)\n", + "\n", + " total += len(labels)\n", + " correct += (pred_labels == labels).sum()\n", + " \n", + " return correct/total" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "f3fd416f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mobilenetv2 TRT Baseline accuracy: 71.13%\n" + ] + } + ], + "source": [ + "# Evaluate and benchmark the performance of the baseline TRT model (TRT FP32 Model)\n", + "batch_size = 64\n", + "test_acc = evaluate_trt(\"models/mobilenetv2_base.trt\", val_dataloader, batch_size)\n", + "print(\"Mobilenetv2 TRT Baseline accuracy: {:.2f}%\".format(100 * test_acc))" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "a5ec3a81", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mobilenetv2 TRT PTQ accuracy: 68.11%\n" + ] + } + ], + "source": [ + "# Evaluate the PTQ model\n", + "batch_size = 64\n", + "test_acc = evaluate_trt(\"models/mobilenetv2_ptq.trt\", val_dataloader, batch_size)\n", + "print(\"Mobilenetv2 TRT PTQ accuracy: {:.2f}%\".format(100 * test_acc))" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "eb95977d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mobilenetv2 TRT PTQ accuracy: 70.31%\n" + ] + } + ], + "source": [ + "# Evaluate the QAT model\n", + "batch_size = 64\n", + "test_acc = evaluate_trt(\"models/mobilenetv2_qat.trt\", val_dataloader, batch_size)\n", + "print(\"Mobilenetv2 TRT PTQ accuracy: {:.2f}%\".format(100 * test_acc))" + ] + }, + { + "cell_type": "markdown", + "id": "20c82807", + "metadata": {}, + "source": [ + "Compared to the TRT FP32 model, we observe a speedup of ~3.7x with only a ~0.8% loss in accuracy. " + ] + }, + { + "cell_type": "markdown", + "id": "52f311fb", + "metadata": {}, + "source": [ + "\n", + "## 7. Conclusion\n", + "We put together all the observations that were made in this notebook. Note that, these numbers can vary with every run due to the stochastic nature of the training process, but a similar pattern can still be noticed.\n", + "\n", + "| Model | Accuracy | Performance |\n", + "| ------------------------ | -------- | ----------- |\n", + "| Baseline MobileNetv2 | 71.11% | 11.92ms |\n", + "| Base + TRT
(TRT FP32) | 71.13% | 5.95ms |\n", + "| PTQ + TRT
(TRT int8) | 68.11% | 1.59ms |\n", + "| QAT+TRT
(TRT INT8) | 70.31% | 1.61ms |" + ] + }, + { + "cell_type": "markdown", + "id": "91dfc2c1", + "metadata": {}, + "source": [ + "\n", + "## 8. References\n", + "* Very Deep Convolution Networks for large scale Image Recognition\n", + "* Achieving FP32 Accuracy for INT8 Inference Using Quantization Aware Training with NVIDIA TensorRT\n", + "* Pytorch-quantization toolkit from NVIDIA\n", + "* Pytorch quantization toolkit userguide\n", + "* Quantization basics\n", + "* TensorRT Developer Guide" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.13" + }, + "vscode": { + "interpreter": { + "hash": "b8290132a159428f0004735847c0b4016c8a5153e62fd80cc71ad5cd485f05b0" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/Notebook Tutorials/tutorial.ipynb b/examples/Notebook Tutorials/tutorial.ipynb new file mode 100644 index 0000000..8fbe02d --- /dev/null +++ b/examples/Notebook Tutorials/tutorial.ipynb @@ -0,0 +1,658 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "YOLOv8 Tutorial", + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "accelerator": "GPU" + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "t6MPjfT5NrKQ" + }, + "source": [ + "
\n", + "\n", + " \n", + " \n", + "\n", + " [中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es/) | [Português](https://docs.ultralytics.com/pt/) | [Türkçe](https://docs.ultralytics.com/tr/) | [Tiếng Việt](https://docs.ultralytics.com/vi/) | [العربية](https://docs.ultralytics.com/ar/)\n", + "\n", + " \"Ultralytics\n", + " \"Run\n", + " \"Open\n", + " \"Open\n", + "\n", + " \"Discord\"\n", + " \"Ultralytics\n", + " \"Ultralytics\n", + "\n", + "Welcome to the Ultralytics YOLOv8 🚀 notebook! YOLOv8 is the latest version of the YOLO (You Only Look Once) AI models developed by Ultralytics. This notebook serves as the starting point for exploring the various resources available to help you get started with YOLOv8 and understand its features and capabilities.\n", + "\n", + "YOLOv8 models are fast, accurate, and easy to use, making them ideal for various object detection and image segmentation tasks. They can be trained on large datasets and run on diverse hardware platforms, from CPUs to GPUs.\n", + "\n", + "We hope that the resources in this notebook will help you get the most out of YOLOv8. Please browse the YOLOv8 Docs for details, raise an issue on GitHub for support, and join our Discord community for questions and discussions!\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7mGmQbAO5pQb" + }, + "source": [ + "# Setup\n", + "\n", + "Pip install `ultralytics` and [dependencies](https://github.com/ultralytics/ultralytics/blob/main/pyproject.toml) and check software and hardware.\n", + "\n", + "[![PyPI - Version](https://img.shields.io/pypi/v/ultralytics?logo=pypi&logoColor=white)](https://pypi.org/project/ultralytics/) [![Downloads](https://static.pepy.tech/badge/ultralytics)](https://pepy.tech/project/ultralytics) [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/ultralytics?logo=python&logoColor=gold)](https://pypi.org/project/ultralytics/)" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "wbvMlHd_QwMG", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "96335d4c-20a9-4864-f7a4-bb2eb0077a9d" + }, + "source": [ + "%pip install ultralytics\n", + "import ultralytics\n", + "ultralytics.checks()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Ultralytics YOLOv8.2.3 🚀 Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (Tesla T4, 15102MiB)\n", + "Setup complete ✅ (2 CPUs, 12.7 GB RAM, 28.8/78.2 GB disk)\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4JnkELT0cIJg" + }, + "source": [ + "# 1. Predict\n", + "\n", + "YOLOv8 may be used directly in the Command Line Interface (CLI) with a `yolo` command for a variety of tasks and modes and accepts additional arguments, i.e. `imgsz=640`. See a full list of available `yolo` [arguments](https://docs.ultralytics.com/usage/cfg/) and other details in the [YOLOv8 Predict Docs](https://docs.ultralytics.com/modes/train/).\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "zR9ZbuQCH7FX", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "84f32db2-80b0-4f35-9a2a-a56d11f7863f" + }, + "source": [ + "# Run inference on an image with YOLOv8n\n", + "!yolo predict model=yolov8n.pt source='https://ultralytics.com/images/zidane.jpg'" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Downloading https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n.pt to 'yolov8n.pt'...\n", + "100% 6.23M/6.23M [00:00<00:00, 83.2MB/s]\n", + "Ultralytics YOLOv8.2.3 🚀 Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (Tesla T4, 15102MiB)\n", + "YOLOv8n summary (fused): 168 layers, 3151904 parameters, 0 gradients, 8.7 GFLOPs\n", + "\n", + "Downloading https://ultralytics.com/images/zidane.jpg to 'zidane.jpg'...\n", + "100% 165k/165k [00:00<00:00, 11.1MB/s]\n", + "image 1/1 /content/zidane.jpg: 384x640 2 persons, 1 tie, 21.4ms\n", + "Speed: 1.9ms preprocess, 21.4ms inference, 6.2ms postprocess per image at shape (1, 3, 384, 640)\n", + "Results saved to \u001b[1mruns/detect/predict\u001b[0m\n", + "💡 Learn more at https://docs.ultralytics.com/modes/predict\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hkAzDWJ7cWTr" + }, + "source": [ + "        \n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0eq1SMWl6Sfn" + }, + "source": [ + "# 2. Val\n", + "Validate a model's accuracy on the [COCO](https://docs.ultralytics.com/datasets/detect/coco/) dataset's `val` or `test` splits. The latest YOLOv8 [models](https://github.com/ultralytics/ultralytics#models) are downloaded automatically the first time they are used. See [YOLOv8 Val Docs](https://docs.ultralytics.com/modes/val/) for more information." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "WQPtK1QYVaD_" + }, + "source": [ + "# Download COCO val\n", + "import torch\n", + "torch.hub.download_url_to_file('https://ultralytics.com/assets/coco2017val.zip', 'tmp.zip') # download (780M - 5000 images)\n", + "!unzip -q tmp.zip -d datasets && rm tmp.zip # unzip" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "X58w8JLpMnjH", + "outputId": "bed10d45-ceb6-4b6f-86b7-9428208b142a", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "source": [ + "# Validate YOLOv8n on COCO8 val\n", + "!yolo val model=yolov8n.pt data=coco8.yaml" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Ultralytics YOLOv8.2.3 🚀 Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (Tesla T4, 15102MiB)\n", + "YOLOv8n summary (fused): 168 layers, 3151904 parameters, 0 gradients, 8.7 GFLOPs\n", + "\n", + "Dataset 'coco8.yaml' images not found ⚠️, missing path '/content/datasets/coco8/images/val'\n", + "Downloading https://ultralytics.com/assets/coco8.zip to '/content/datasets/coco8.zip'...\n", + "100% 433k/433k [00:00<00:00, 14.2MB/s]\n", + "Unzipping /content/datasets/coco8.zip to /content/datasets/coco8...: 100% 25/25 [00:00<00:00, 1093.93file/s]\n", + "Dataset download success ✅ (1.3s), saved to \u001b[1m/content/datasets\u001b[0m\n", + "\n", + "Downloading https://ultralytics.com/assets/Arial.ttf to '/root/.config/Ultralytics/Arial.ttf'...\n", + "100% 755k/755k [00:00<00:00, 17.4MB/s]\n", + "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco8/labels/val... 4 images, 0 backgrounds, 0 corrupt: 100% 4/4 [00:00<00:00, 157.00it/s]\n", + "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/datasets/coco8/labels/val.cache\n", + " Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:06<00:00, 6.89s/it]\n", + " all 4 17 0.621 0.833 0.888 0.63\n", + " person 4 10 0.721 0.5 0.519 0.269\n", + " dog 4 1 0.37 1 0.995 0.597\n", + " horse 4 2 0.751 1 0.995 0.631\n", + " elephant 4 2 0.505 0.5 0.828 0.394\n", + " umbrella 4 1 0.564 1 0.995 0.995\n", + " potted plant 4 1 0.814 1 0.995 0.895\n", + "Speed: 0.3ms preprocess, 4.9ms inference, 0.0ms loss, 1.3ms postprocess per image\n", + "Results saved to \u001b[1mruns/detect/val\u001b[0m\n", + "💡 Learn more at https://docs.ultralytics.com/modes/val\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ZY2VXXXu74w5" + }, + "source": [ + "# 3. Train\n", + "\n", + "

\n", + "\n", + "Train YOLOv8 on [Detect](https://docs.ultralytics.com/tasks/detect/), [Segment](https://docs.ultralytics.com/tasks/segment/), [Classify](https://docs.ultralytics.com/tasks/classify/) and [Pose](https://docs.ultralytics.com/tasks/pose/) datasets. See [YOLOv8 Train Docs](https://docs.ultralytics.com/modes/train/) for more information." + ] + }, + { + "cell_type": "code", + "source": [ + "#@title Select YOLOv8 🚀 logger {run: 'auto'}\n", + "logger = 'Comet' #@param ['Comet', 'TensorBoard']\n", + "\n", + "if logger == 'Comet':\n", + " %pip install -q comet_ml\n", + " import comet_ml; comet_ml.init()\n", + "elif logger == 'TensorBoard':\n", + " %load_ext tensorboard\n", + " %tensorboard --logdir ." + ], + "metadata": { + "id": "ktegpM42AooT" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "1NcFxRcFdJ_O", + "outputId": "9f60c6cb-fa9c-4785-cb7a-71d40abeaf38", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "source": [ + "# Train YOLOv8n on COCO8 for 3 epochs\n", + "!yolo train model=yolov8n.pt data=coco8.yaml epochs=3 imgsz=640" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Ultralytics YOLOv8.2.3 🚀 Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (Tesla T4, 15102MiB)\n", + "\u001b[34m\u001b[1mengine/trainer: \u001b[0mtask=detect, mode=train, model=yolov8n.pt, data=coco8.yaml, epochs=3, time=None, patience=100, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, bgr=0.0, mosaic=1.0, mixup=0.0, copy_paste=0.0, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs/detect/train\n", + "\n", + " from n params module arguments \n", + " 0 -1 1 464 ultralytics.nn.modules.conv.Conv [3, 16, 3, 2] \n", + " 1 -1 1 4672 ultralytics.nn.modules.conv.Conv [16, 32, 3, 2] \n", + " 2 -1 1 7360 ultralytics.nn.modules.block.C2f [32, 32, 1, True] \n", + " 3 -1 1 18560 ultralytics.nn.modules.conv.Conv [32, 64, 3, 2] \n", + " 4 -1 2 49664 ultralytics.nn.modules.block.C2f [64, 64, 2, True] \n", + " 5 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2] \n", + " 6 -1 2 197632 ultralytics.nn.modules.block.C2f [128, 128, 2, True] \n", + " 7 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2] \n", + " 8 -1 1 460288 ultralytics.nn.modules.block.C2f [256, 256, 1, True] \n", + " 9 -1 1 164608 ultralytics.nn.modules.block.SPPF [256, 256, 5] \n", + " 10 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", + " 11 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", + " 12 -1 1 148224 ultralytics.nn.modules.block.C2f [384, 128, 1] \n", + " 13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", + " 14 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", + " 15 -1 1 37248 ultralytics.nn.modules.block.C2f [192, 64, 1] \n", + " 16 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2] \n", + " 17 [-1, 12] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", + " 18 -1 1 123648 ultralytics.nn.modules.block.C2f [192, 128, 1] \n", + " 19 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2] \n", + " 20 [-1, 9] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", + " 21 -1 1 493056 ultralytics.nn.modules.block.C2f [384, 256, 1] \n", + " 22 [15, 18, 21] 1 897664 ultralytics.nn.modules.head.Detect [80, [64, 128, 256]] \n", + "Model summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPs\n", + "\n", + "Transferred 355/355 items from pretrained weights\n", + "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/detect/train', view at http://localhost:6006/\n", + "Freezing layer 'model.22.dfl.conv.weight'\n", + "\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks with YOLOv8n...\n", + "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n", + "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/datasets/coco8/labels/train... 4 images, 0 backgrounds, 0 corrupt: 100% 4/4 [00:00<00:00, 837.19it/s]\n", + "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/datasets/coco8/labels/train.cache\n", + "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n", + "/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n", + " self.pid = os.fork()\n", + "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco8/labels/val.cache... 4 images, 0 backgrounds, 0 corrupt: 100% 4/4 [00:00\n" + ], + "metadata": { + "id": "Phm9ccmOKye5" + } + }, + { + "cell_type": "markdown", + "source": [ + "## 1. Detection\n", + "\n", + "YOLOv8 _detection_ models have no suffix and are the default YOLOv8 models, i.e. `yolov8n.pt` and are pretrained on COCO. See [Detection Docs](https://docs.ultralytics.com/tasks/detect/) for full details.\n" + ], + "metadata": { + "id": "yq26lwpYK1lq" + } + }, + { + "cell_type": "code", + "source": [ + "# Load YOLOv8n, train it on COCO128 for 3 epochs and predict an image with it\n", + "from ultralytics import YOLO\n", + "\n", + "model = YOLO('yolov8n.pt') # load a pretrained YOLOv8n detection model\n", + "model.train(data='coco8.yaml', epochs=3) # train the model\n", + "model('https://ultralytics.com/images/bus.jpg') # predict on an image" + ], + "metadata": { + "id": "8Go5qqS9LbC5" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## 2. Segmentation\n", + "\n", + "YOLOv8 _segmentation_ models use the `-seg` suffix, i.e. `yolov8n-seg.pt` and are pretrained on COCO. See [Segmentation Docs](https://docs.ultralytics.com/tasks/segment/) for full details.\n" + ], + "metadata": { + "id": "7ZW58jUzK66B" + } + }, + { + "cell_type": "code", + "source": [ + "# Load YOLOv8n-seg, train it on COCO128-seg for 3 epochs and predict an image with it\n", + "from ultralytics import YOLO\n", + "\n", + "model = YOLO('yolov8n-seg.pt') # load a pretrained YOLOv8n segmentation model\n", + "model.train(data='coco8-seg.yaml', epochs=3) # train the model\n", + "model('https://ultralytics.com/images/bus.jpg') # predict on an image" + ], + "metadata": { + "id": "WFPJIQl_L5HT" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## 3. Classification\n", + "\n", + "YOLOv8 _classification_ models use the `-cls` suffix, i.e. `yolov8n-cls.pt` and are pretrained on ImageNet. See [Classification Docs](https://docs.ultralytics.com/tasks/classify/) for full details.\n" + ], + "metadata": { + "id": "ax3p94VNK9zR" + } + }, + { + "cell_type": "code", + "source": [ + "# Load YOLOv8n-cls, train it on mnist160 for 3 epochs and predict an image with it\n", + "from ultralytics import YOLO\n", + "\n", + "model = YOLO('yolov8n-cls.pt') # load a pretrained YOLOv8n classification model\n", + "model.train(data='mnist160', epochs=3) # train the model\n", + "model('https://ultralytics.com/images/bus.jpg') # predict on an image" + ], + "metadata": { + "id": "5q9Zu6zlL5rS" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## 4. Pose\n", + "\n", + "YOLOv8 _pose_ models use the `-pose` suffix, i.e. `yolov8n-pose.pt` and are pretrained on COCO Keypoints. See [Pose Docs](https://docs.ultralytics.com/tasks/pose/) for full details." + ], + "metadata": { + "id": "SpIaFLiO11TG" + } + }, + { + "cell_type": "code", + "source": [ + "# Load YOLOv8n-pose, train it on COCO8-pose for 3 epochs and predict an image with it\n", + "from ultralytics import YOLO\n", + "\n", + "model = YOLO('yolov8n-pose.pt') # load a pretrained YOLOv8n pose model\n", + "model.train(data='coco8-pose.yaml', epochs=3) # train the model\n", + "model('https://ultralytics.com/images/bus.jpg') # predict on an image" + ], + "metadata": { + "id": "si4aKFNg19vX" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## 4. Oriented Bounding Boxes (OBB)\n", + "\n", + "YOLOv8 _OBB_ models use the `-obb` suffix, i.e. `yolov8n-obb.pt` and are pretrained on the DOTA dataset. See [OBB Docs](https://docs.ultralytics.com/tasks/obb/) for full details." + ], + "metadata": { + "id": "cf5j_T9-B5F0" + } + }, + { + "cell_type": "code", + "source": [ + "# Load YOLOv8n-obb, train it on DOTA8 for 3 epochs and predict an image with it\n", + "from ultralytics import YOLO\n", + "\n", + "model = YOLO('yolov8n-obb.pt') # load a pretrained YOLOv8n OBB model\n", + "model.train(data='coco8-dota.yaml', epochs=3) # train the model\n", + "model('https://ultralytics.com/images/bus.jpg') # predict on an image" + ], + "metadata": { + "id": "IJNKClOOB5YS" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IEijrePND_2I" + }, + "source": [ + "# Appendix\n", + "\n", + "Additional content below." + ] + }, + { + "cell_type": "code", + "source": [ + "# Pip install from source\n", + "!pip install git+https://github.com/ultralytics/ultralytics@main" + ], + "metadata": { + "id": "pIdE6i8C3LYp" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Git clone and run tests on updates branch\n", + "!git clone https://github.com/ultralytics/ultralytics -b main\n", + "%pip install -qe ultralytics" + ], + "metadata": { + "id": "uRKlwxSJdhd1" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Run tests (Git clone only)\n", + "!pytest ultralytics/tests" + ], + "metadata": { + "id": "GtPlh7mcCGZX" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Validate multiple models\n", + "for x in 'nsmlx':\n", + " !yolo val model=yolov8{x}.pt data=coco.yaml" + ], + "metadata": { + "id": "Wdc6t_bfzDDk" + }, + "execution_count": null, + "outputs": [] + } + ] +} diff --git a/examples/Nvidia TRT Clean/classify-trt.py b/examples/Nvidia TRT Clean/classify-trt.py new file mode 100644 index 0000000..e09d581 --- /dev/null +++ b/examples/Nvidia TRT Clean/classify-trt.py @@ -0,0 +1,78 @@ +#!/usr/bin/env python3 +import argparse +import time + +import inferlib.ops as ops +import inferlib.ops.classify as classify +# import inferlib.ops.imaging as imaging +import inferlib.ops.utils as utils + +import trtops.tensorrt as trt_ops + + +def build_pipeline(engine, dataspec, rate, limit): + + # get the shape of the input + image_shape = trt_ops.get_binding_shape(engine, "image") + preds_shape = trt_ops.get_binding_shape(engine, "preds") + + print(f"- input shape: {image_shape}") + print(f"- output shape: {preds_shape}") + + batch_size, nchans, height, width = image_shape + + pipe = ops.datasource(dataspec, resize=(width, height), silent=True) + if rate > 0: + pipe = utils.rate_limiter(pipe, rate=rate) + if limit > 0: + pipe = utils.limiter(pipe, limit=limit) + pipe = utils.worker(pipe) + + # pipe = imaging.resize(pipe, width=width, height=height) + pipe = classify.preprocess(pipe) + pipe = trt_ops.classify(pipe, engine=engine) + pipe = classify.postprocess(pipe) + + return pipe + + +def run(pipe): + start = time.time() + + for idx, item in enumerate(pipe): + image_id = item['image_id'] + image_size = item['image_size'] + tops = item['top'] + + print(f"{idx:02d} {image_id} {image_size}") + for top, prob in tops: + print(f" {top} @ {prob*100.0:0.2f}") + + duration = time.time() - start + + if item.get('jpeg', None): + with open("image.jpg", "wb") as f: + f.write(item['jpeg']) + + return duration, idx+1 + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument('-l', '--limit', help='maximum number of images to process', type=int, default=0) + parser.add_argument('-r', '--rate', help='requests per second', type=int, default=0) + parser.add_argument('engine', help='path to the tensorrt engine file', type=str) + parser.add_argument('dataspec', help='the data source specification', type=str) + args = parser.parse_args() + + engine = trt_ops.load_engine(args.engine) + + pipe = build_pipeline(engine, args.dataspec, args.rate, args.limit) + duration, count = run(pipe) + + print(f"runtime: {int(duration)} seconds") + print(f" fps: {count/duration:0.2f}") + + +if __name__ == "__main__": + main() diff --git a/examples/Nvidia TRT Clean/convert_te_onnx_to_trt_onnx.py b/examples/Nvidia TRT Clean/convert_te_onnx_to_trt_onnx.py new file mode 100644 index 0000000..e82f82b --- /dev/null +++ b/examples/Nvidia TRT Clean/convert_te_onnx_to_trt_onnx.py @@ -0,0 +1,261 @@ +#!/usr/bin/env python3 +# +# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import argparse +import onnx +import logging +import os +import numpy as np +import onnx_graphsurgeon as gs +from onnx import helper, TensorProto, numpy_helper, version_converter + +''' +This script is converting TE ONNX models (cast + CustomOp Q) and (CustomOp DQ + cast) pairs to Opset19 ONNX Q/DQ +usage: +python3 convert_te_onnx_to_trt_onnx.py --onnx_model_path + +This script requires onnx 1.14 and above +''' + +def find_node_by_tensor(graph, search_tensor, is_node_input, search_node_type=None): + for idx, node in enumerate(graph.node): + search_container = node.output + if is_node_input: + search_container = node.input + for node_tensor in search_container: + if search_node_type and node.op_type != search_node_type: + continue + if node_tensor == search_tensor: + return node, idx + + return None, None + +def redirect_quantize_input(graph, q_node): + assert(q_node.op_type == 'QuantizeLinear') + q_input = q_node.input[0] + cast_node, cast_node_idx = find_node_by_tensor(graph, q_input, False, 'Cast') + if cast_node: + q_node.input[0] = cast_node.input[0] + return [cast_node_idx] + return [] + +def redirect_dequantize_output(graph, dq_node): + assert(dq_node.op_type == 'DequantizeLinear') + dq_output = dq_node.output[0] + cast_node, cast_node_idx = find_node_by_tensor(graph, dq_output, True, 'Cast') + if cast_node: + dq_node.output[0] = cast_node.output[0] + return [cast_node_idx] + return [] + +def get_attr_numpy_tensor(attr): + assert(attr.type == onnx.AttributeProto.TENSOR) + return numpy_helper.to_array(attr.t) + +def get_attr(node, search_attr_name): + for idx, attr in enumerate(node.attribute): + if attr.name == search_attr_name: + return attr, idx + + return None, None + +def cast_scale(graph, qdq_node, cast_to): + assert(cast_to in ['fp32', 'fp16']) + assert(qdq_node.op_type in ['QuantizeLinear', 'DequantizeLinear']) + constant_node_idx = None + scale_tensor = qdq_node.input[1] + constant_node, constant_node_idx = find_node_by_tensor(graph, scale_tensor, False, 'Constant') + scale_cast_to_dtype = None + onnx_cast_to_dtype = None + if cast_to == 'fp16': + scale_cast_to_dtype = np.dtype(np.float32) + onnx_cast_to_dtype = onnx.TensorProto.FLOAT16 + elif cast_to == 'fp32': + scale_cast_to_dtype = np.dtype(np.float32) + onnx_cast_to_dtype = onnx.TensorProto.FLOAT + + if constant_node: + scale_attr, _ = get_attr(constant_node, 'value') + assert(scale_attr) + numpy_scale = get_attr_numpy_tensor(scale_attr) + logging.info(type(numpy_scale.dtype)) + logging.info(type(scale_cast_to_dtype)) + if numpy_scale.dtype != scale_cast_to_dtype: + logging.debug(f'Change {qdq_node.name} scale from {numpy_scale.dtype} to {scale_cast_to_dtype}') + numpy_scale = numpy_scale.astype(scale_cast_to_dtype) + tensor_name = constant_node.name + '_casted' + create_constant_tensor(graph, tensor_name, onnx_cast_to_dtype, numpy_scale) + qdq_node.input[1] = tensor_name + else: + logging.warning(f'No constant node connected to {qdq_node} as scale') + + if constant_node_idx: + return [constant_node_idx] + return [] + +def create_constant_tensor(graph, name, dtype, np_tensor): + tensor_value_info = helper.make_tensor_value_info(name, dtype, np_tensor.shape) + graph.input.append(tensor_value_info) + helper.make_tensor(name, data_type=dtype, dims=(), vals=[0]) + + tensor_initializer = helper.make_tensor(name, dtype, np_tensor.shape, np_tensor.flatten().tolist()) + graph.initializer.append(tensor_initializer) + +''' +Convert custom operators to opset19 +''' +def custom_op_to_opset19(graph, node, use_int32_quantization, remove_cast_before_q, remove_cast_after_dq, change_qdq_scale_precision): + assert(node.op_type in ['TRT_FP8QuantizeLinear', 'TRT_FP8DequantizeLinear']) + is_dq = node.op_type == 'TRT_FP8DequantizeLinear' + logging.debug(f'Convert {node.name} to Opset19') + orig_node_name = node.name + new_node_name = orig_node_name + '_converted' + + quant_to = TensorProto.FLOAT8E4M3FN + if use_int32_quantization: + quant_to = TensorProto.INT32 + + #add zero point to the node + tensor_name = new_node_name + '_zero_point' + create_constant_tensor(graph, tensor_name, quant_to, np.array([0])) + node.input.append(tensor_name) + + node.domain = "" + node.op_type = "QuantizeLinear" + + node_idxs_to_delete = [] + if is_dq: + node.op_type = "DequantizeLinear" + if remove_cast_after_dq: + node_idxs_to_delete += redirect_dequantize_output(graph, node) + if change_qdq_scale_precision: + node_idxs_to_delete += cast_scale(graph, node, change_qdq_scale_precision) + else: + if remove_cast_before_q: + node_idxs_to_delete += redirect_quantize_input(graph, node) + if change_qdq_scale_precision: + node_idxs_to_delete += cast_scale(graph, node, change_qdq_scale_precision) + + node.name = new_node_name + logging.debug(f'Convert Done\n') + return node_idxs_to_delete + +def check_model(graph): + converted_qdq_ops = ['TRT_FP8QuantizeLinear', 'TRT_FP8DequantizeLinear'] + passed_check = True + for node in graph.node: + if node.op_type in converted_qdq_ops: + logging.error(f'Node \"{node.name}\" of type {node.op_type} should have been removed') + passed_check = False + return passed_check + +def update_quantize_node_type(model): + graph = gs.import_onnx(model) + for node in graph.nodes: + if node.op == "TRT_FP8QuantizeLinear": + for out in node.outputs: + out.dtype = TensorProto.FLOAT8E4M3FN + return gs.export_onnx(graph) + +''' +Converts onnx files from TE to TRT +''' +def replace_customop_qdq_with_onnx_qdq(te_onnx_files, results_path, create_netron_compatible_model, remove_cast_before_q, remove_cast_after_dq, change_qdq_scale_precision): + # store mappings from original ONNX name to new ONNX name. + file_mappings = {} + for te_onnx_file in te_onnx_files: + logging.debug('Loading model') + model = onnx.load(te_onnx_file, load_external_data=False) + # update QuantizeLinear output dtype + model = update_quantize_node_type(model) + # change model opset to 19 + model.opset_import[0].version = 19 + graph = model.graph + logging.debug('Loading model finished') + converted_qdq_ops = ['TRT_FP8QuantizeLinear', 'TRT_FP8DequantizeLinear'] + + try: + node_idxs_to_delete = [] + converted = False + for node in graph.node: + if node.op_type in converted_qdq_ops: + converted = True + node_idxs_to_delete += custom_op_to_opset19(graph, node, create_netron_compatible_model, remove_cast_before_q, remove_cast_after_dq, change_qdq_scale_precision) + + if converted: + assert(check_model(graph)) + node_idxs_to_delete = reversed(sorted(node_idxs_to_delete)) + for node_idx in node_idxs_to_delete: + del(graph.node[node_idx]) + suffix = '.opset19' + if create_netron_compatible_model: + suffix += '.netron' + suffix += '.onnx' + new_model_filename = os.path.join(results_path, os.path.splitext(os.path.split(te_onnx_file)[1])[0] + suffix) + onnx.save_model(model, new_model_filename) + logging.info(f'The converted model is saved at {new_model_filename}!') + file_mappings[te_onnx_file] = new_model_filename + else: + logging.info(f'No conversion was done with {te_onnx_file}!') + file_mappings[te_onnx_file] = te_onnx_file + except Exception as ex: + logging.error(f'Failed: {ex}') + file_mappings[te_onnx_file] = None + return file_mappings + +if __name__ == "__main__": + logging.getLogger().setLevel(logging.INFO) + parser = argparse.ArgumentParser() + parser.add_argument('--onnx_model_path', required=True, help="Path of model or a folder of models. When using a folder, this script will convert all \'.onnx\' files") + parser.add_argument('--results_path', required=False, help="Path for generated models, when not set, the generated model(s) will be next ot the origianl model(s)") + parser.add_argument('--create_netron_compatible_model', action='store_true', required=False, help="When set, the script will use int32 quantization. " + "This enables the user to view the graph with Netron, until it adds support for opset19. The generated model isn't TRT compatible.") + parser.add_argument('--remove_casts', required=False, help="Controls whether to remove casts around q/dq nodes. " + "For example, when set to \'dq\', remove casts only after dq. Default is \'keep_all\'", choices=['q', 'dq', 'qdq', 'keep_all'], default='keep_all') + parser.add_argument('--change_qdq_scale_precision', required=False, help="When set controls q/dq nodes scales data type.", choices=['fp32', 'fp16']) + args = parser.parse_args() + + results_path = args.results_path + if results_path and os.path.isdir(results_path) == False: + logging.error(f'\'--results_path\' set to \'{results_path}\', but the folder doesn\'t exist, exiting') + exit(-1) + + if results_path is None: + results_path = args.onnx_model_path + if os.path.isfile(results_path): + results_path = os.path.split(results_path)[0] + + remove_cast_after_dq = False + remove_cast_before_q = False + if args.remove_casts == 'q': + remove_cast_before_q = True + elif args.remove_casts == 'dq': + remove_cast_after_dq = True + elif args.remove_casts == 'qdq': + remove_cast_after_dq = True + remove_cast_before_q = True + + onnx_files = [] + if os.path.isdir(args.onnx_model_path): + logging.info(f"Got folder: {args.onnx_model_path}") + onnx_files = [os.path.join(args.onnx_model_path, filename) for filename in os.listdir(args.onnx_model_path) if filename.endswith('.onnx')==True and filename.endswith('.opset19.onnx')==False] + + else: + logging.info(f"Got file: {args.onnx_model_path}") + onnx_files = [args.onnx_model_path] + + replace_customop_qdq_with_onnx_qdq(onnx_files, results_path, args.create_netron_compatible_model, remove_cast_before_q, remove_cast_after_dq, args.change_qdq_scale_precision) diff --git a/examples/Nvidia TRT Clean/helper.py b/examples/Nvidia TRT Clean/helper.py new file mode 100644 index 0000000..c00ed98 --- /dev/null +++ b/examples/Nvidia TRT Clean/helper.py @@ -0,0 +1,111 @@ +# +# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from tensorflow.python.compiler.tensorrt import trt_convert as tf_trt +from tensorflow.python.saved_model import tag_constants +import tensorflow as tf +import tensorrt as trt + +import numpy as np + +precision_dict = { + "FP32": tf_trt.TrtPrecisionMode.FP32, + "FP16": tf_trt.TrtPrecisionMode.FP16, + "INT8": tf_trt.TrtPrecisionMode.INT8, +} + +# For TF-TRT: + +class OptimizedModel(): + def __init__(self, saved_model_dir = None): + self.loaded_model_fn = None + + if not saved_model_dir is None: + self.load_model(saved_model_dir) + + + def predict(self, input_data): + if self.loaded_model_fn is None: + raise(Exception("Haven't loaded a model")) + x = tf.constant(input_data.astype('float32')) + labeling = self.loaded_model_fn(x) + try: + preds = labeling['predictions'].numpy() + except: + try: + preds = labeling['probs'].numpy() + except: + try: + preds = labeling[next(iter(labeling.keys()))] + except: + raise(Exception("Failed to get predictions from saved model object")) + return preds + + def load_model(self, saved_model_dir): + saved_model_loaded = tf.saved_model.load(saved_model_dir, tags=[tag_constants.SERVING]) + wrapper_fp32 = saved_model_loaded.signatures['serving_default'] + + self.loaded_model_fn = wrapper_fp32 + +class ModelOptimizer(): + def __init__(self, input_saved_model_dir, calibration_data=None): + self.input_saved_model_dir = input_saved_model_dir + self.calibration_data = None + self.loaded_model = None + + if not calibration_data is None: + self.set_calibration_data(calibration_data) + + + def set_calibration_data(self, calibration_data): + + def calibration_input_fn(): + yield (tf.constant(calibration_data.astype('float32')), ) + + self.calibration_data = calibration_input_fn + + + def convert(self, output_saved_model_dir, precision="FP32", max_workspace_size_bytes=8000000000, **kwargs): + + if precision == "INT8" and self.calibration_data is None: + raise(Exception("No calibration data set!")) + + trt_precision = precision_dict[precision] + conversion_params = tf_trt.DEFAULT_TRT_CONVERSION_PARAMS._replace(precision_mode=trt_precision, + max_workspace_size_bytes=max_workspace_size_bytes, + use_calibration= precision == "INT8") + converter = tf_trt.TrtGraphConverterV2(input_saved_model_dir=self.input_saved_model_dir, + conversion_params=conversion_params) + + if precision == "INT8": + converter.convert(calibration_input_fn=self.calibration_data) + else: + converter.convert() + + converter.save(output_saved_model_dir=output_saved_model_dir) + + return OptimizedModel(output_saved_model_dir) + + def predict(self, input_data): + if self.loaded_model is None: + self.load_default_model() + + return self.loaded_model.predict(input_data) + + def load_default_model(self): + self.loaded_model = tf.keras.models.load_model('resnet50_saved_model') + diff --git a/examples/Nvidia TRT Clean/onnx_helper.py b/examples/Nvidia TRT Clean/onnx_helper.py new file mode 100644 index 0000000..2f3d676 --- /dev/null +++ b/examples/Nvidia TRT Clean/onnx_helper.py @@ -0,0 +1,89 @@ +# +# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import numpy as np +import tensorflow as tf +import tensorrt as trt + +import pycuda.driver as cuda +import pycuda.autoinit + +# For ONNX: + +class ONNXClassifierWrapper(): + def __init__(self, file, num_classes, target_dtype = np.float32): + + self.target_dtype = target_dtype + self.num_classes = num_classes + self.load(file) + + self.stream = None + + def load(self, file): + f = open(file, "rb") + runtime = trt.Runtime(trt.Logger(trt.Logger.WARNING)) + + engine = runtime.deserialize_cuda_engine(f.read()) + self.context = engine.create_execution_context() + + def allocate_memory(self, batch): + self.output = np.empty(self.num_classes, dtype = self.target_dtype) # Need to set both input and output precisions to FP16 to fully enable FP16 + + # Allocate device memory + self.d_input = cuda.mem_alloc(1 * batch.nbytes) + self.d_output = cuda.mem_alloc(1 * self.output.nbytes) + + self.bindings = [int(self.d_input), int(self.d_output)] + + self.stream = cuda.Stream() + + def predict(self, batch): # result gets copied into output + if self.stream is None: + self.allocate_memory(batch) + + # Transfer input data to device + cuda.memcpy_htod_async(self.d_input, batch, self.stream) + # Execute model + self.context.execute_async_v2(self.bindings, self.stream.handle, None) + # Transfer predictions back + cuda.memcpy_dtoh_async(self.output, self.d_output, self.stream) + # Syncronize threads + self.stream.synchronize() + + return self.output + +def convert_onnx_to_engine(onnx_filename, engine_filename = None, max_batch_size = 32, max_workspace_size = 1 << 30, fp16_mode = True): + logger = trt.Logger(trt.Logger.WARNING) + with trt.Builder(logger) as builder, builder.create_network() as network, trt.OnnxParser(network, logger) as parser: + builder.max_workspace_size = max_workspace_size + builder.fp16_mode = fp16_mode + builder.max_batch_size = max_batch_size + + print("Parsing ONNX file.") + with open(onnx_filename, 'rb') as model: + if not parser.parse(model.read()): + for error in range(parser.num_errors): + print(parser.get_error(error)) + + print("Building TensorRT engine. This may take a few minutes.") + engine = builder.build_cuda_engine(network) + + if engine_filename: + with open(engine_filename, 'wb') as f: + f.write(engine.serialize()) + + return engine, logger diff --git a/examples/Nvidia TRT Clean/trt_classify.py b/examples/Nvidia TRT Clean/trt_classify.py new file mode 100644 index 0000000..939f1e9 --- /dev/null +++ b/examples/Nvidia TRT Clean/trt_classify.py @@ -0,0 +1,49 @@ +import time +import numpy as np +import tensorrt as trt + +import pycuda.autoinit +import pycuda.driver as cuda + + +def classify(pipe, *, engine, batch_size=1): + + # prepare the bindings + # h_input, h_output - host input and output + # d_input, d_output - device input and output + ishape = engine.get_binding_shape("image") + itype = trt.nptype(engine.get_binding_dtype("image")) + h_input = np.empty(shape=ishape, dtype=itype) + d_input = cuda.mem_alloc(h_input.nbytes) + + oshape = engine.get_binding_shape("preds") + otype = trt.nptype(engine.get_binding_dtype("preds")) + h_output = np.empty(shape=oshape, dtype=otype) + d_output = cuda.mem_alloc(h_output.nbytes) + + bindings = [int(d_input), int(d_output)] + + # create the execution context for the engine + context = engine.create_execution_context() + stream = cuda.Stream() + + # run the loop + total_time = 0 + + for item in pipe: + start = time.time() + + image = item['image'] + + cuda.memcpy_htod_async(d_input, image, stream) + context.execute_async_v2(bindings, stream.handle, None) + cuda.memcpy_dtoh_async(h_output, d_output, stream) + stream.synchronize() + + item['preds'] = np.copy(h_output) + + total_time += (time.time() - start) + item['inference_time'] = total_time + + yield item + diff --git a/examples/Nvidia TRT Clean/trt_engine.py b/examples/Nvidia TRT Clean/trt_engine.py new file mode 100644 index 0000000..9ba2065 --- /dev/null +++ b/examples/Nvidia TRT Clean/trt_engine.py @@ -0,0 +1,20 @@ +import tensorrt as trt + + +def load_engine(engine_path): + print("loading engine...", flush=True) + + # load the engine archive + with open(engine_path, "rb") as f: + runtime = trt.Runtime(trt.Logger(trt.Logger.WARNING)) + engine = runtime.deserialize_cuda_engine(f.read()) + + return engine + + +def get_binding_shape(engine, name): + return list(engine.get_binding_shape(name)) + +def get_binding_dtype(engine, name): + return trt.nptype(engine.get_binding_dtype("image")) + diff --git a/examples/ONNX Runtime/YOLOv8-ONNXRuntime/README.md b/examples/ONNX Runtime/YOLOv8-ONNXRuntime/README.md new file mode 100644 index 0000000..b206b2e --- /dev/null +++ b/examples/ONNX Runtime/YOLOv8-ONNXRuntime/README.md @@ -0,0 +1,43 @@ +# YOLOv8 - ONNX Runtime + +This project implements YOLOv8 using ONNX Runtime. + +## Installation + +To run this project, you need to install the required dependencies. The following instructions will guide you through the installation process. + +### Installing Required Dependencies + +You can install the required dependencies by running the following command: + +```bash +pip install -r requirements.txt +``` + +### Installing `onnxruntime-gpu` + +If you have an NVIDIA GPU and want to leverage GPU acceleration, you can install the onnxruntime-gpu package using the following command: + +```bash +pip install onnxruntime-gpu +``` + +Note: Make sure you have the appropriate GPU drivers installed on your system. + +### Installing `onnxruntime` (CPU version) + +If you don't have an NVIDIA GPU or prefer to use the CPU version of onnxruntime, you can install the onnxruntime package using the following command: + +```bash +pip install onnxruntime +``` + +### Usage + +After successfully installing the required packages, you can run the YOLOv8 implementation using the following command: + +```bash +python main.py --model yolov8n.onnx --img image.jpg --conf-thres 0.5 --iou-thres 0.5 +``` + +Make sure to replace yolov8n.onnx with the path to your YOLOv8 ONNX model file, image.jpg with the path to your input image, and adjust the confidence threshold (conf-thres) and IoU threshold (iou-thres) values as needed. diff --git a/examples/ONNX Runtime/YOLOv8-ONNXRuntime/main.py b/examples/ONNX Runtime/YOLOv8-ONNXRuntime/main.py new file mode 100644 index 0000000..71b251d --- /dev/null +++ b/examples/ONNX Runtime/YOLOv8-ONNXRuntime/main.py @@ -0,0 +1,229 @@ +# Ultralytics YOLO 🚀, AGPL-3.0 license + +import argparse + +import cv2 +import numpy as np +import onnxruntime as ort +import torch + +from ultralytics.utils import ASSETS, yaml_load +from ultralytics.utils.checks import check_requirements, check_yaml + + +class YOLOv8: + """YOLOv8 object detection model class for handling inference and visualization.""" + + def __init__(self, onnx_model, input_image, confidence_thres, iou_thres): + """ + Initializes an instance of the YOLOv8 class. + + Args: + onnx_model: Path to the ONNX model. + input_image: Path to the input image. + confidence_thres: Confidence threshold for filtering detections. + iou_thres: IoU (Intersection over Union) threshold for non-maximum suppression. + """ + self.onnx_model = onnx_model + self.input_image = input_image + self.confidence_thres = confidence_thres + self.iou_thres = iou_thres + + # Load the class names from the COCO dataset + self.classes = yaml_load(check_yaml("coco8.yaml"))["names"] + + # Generate a color palette for the classes + self.color_palette = np.random.uniform(0, 255, size=(len(self.classes), 3)) + + def draw_detections(self, img, box, score, class_id): + """ + Draws bounding boxes and labels on the input image based on the detected objects. + + Args: + img: The input image to draw detections on. + box: Detected bounding box. + score: Corresponding detection score. + class_id: Class ID for the detected object. + + Returns: + None + """ + # Extract the coordinates of the bounding box + x1, y1, w, h = box + + # Retrieve the color for the class ID + color = self.color_palette[class_id] + + # Draw the bounding box on the image + cv2.rectangle(img, (int(x1), int(y1)), (int(x1 + w), int(y1 + h)), color, 2) + + # Create the label text with class name and score + label = f"{self.classes[class_id]}: {score:.2f}" + + # Calculate the dimensions of the label text + (label_width, label_height), _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1) + + # Calculate the position of the label text + label_x = x1 + label_y = y1 - 10 if y1 - 10 > label_height else y1 + 10 + + # Draw a filled rectangle as the background for the label text + cv2.rectangle( + img, (label_x, label_y - label_height), (label_x + label_width, label_y + label_height), color, cv2.FILLED + ) + + # Draw the label text on the image + cv2.putText(img, label, (label_x, label_y), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA) + + def preprocess(self): + """ + Preprocesses the input image before performing inference. + + Returns: + image_data: Preprocessed image data ready for inference. + """ + # Read the input image using OpenCV + self.img = cv2.imread(self.input_image) + + # Get the height and width of the input image + self.img_height, self.img_width = self.img.shape[:2] + + # Convert the image color space from BGR to RGB + img = cv2.cvtColor(self.img, cv2.COLOR_BGR2RGB) + + # Resize the image to match the input shape + img = cv2.resize(img, (self.input_width, self.input_height)) + + # Normalize the image data by dividing it by 255.0 + image_data = np.array(img) / 255.0 + + # Transpose the image to have the channel dimension as the first dimension + image_data = np.transpose(image_data, (2, 0, 1)) # Channel first + + # Expand the dimensions of the image data to match the expected input shape + image_data = np.expand_dims(image_data, axis=0).astype(np.float32) + + # Return the preprocessed image data + return image_data + + def postprocess(self, input_image, output): + """ + Performs post-processing on the model's output to extract bounding boxes, scores, and class IDs. + + Args: + input_image (numpy.ndarray): The input image. + output (numpy.ndarray): The output of the model. + + Returns: + numpy.ndarray: The input image with detections drawn on it. + """ + # Transpose and squeeze the output to match the expected shape + outputs = np.transpose(np.squeeze(output[0])) + + # Get the number of rows in the outputs array + rows = outputs.shape[0] + + # Lists to store the bounding boxes, scores, and class IDs of the detections + boxes = [] + scores = [] + class_ids = [] + + # Calculate the scaling factors for the bounding box coordinates + x_factor = self.img_width / self.input_width + y_factor = self.img_height / self.input_height + + # Iterate over each row in the outputs array + for i in range(rows): + # Extract the class scores from the current row + classes_scores = outputs[i][4:] + + # Find the maximum score among the class scores + max_score = np.amax(classes_scores) + + # If the maximum score is above the confidence threshold + if max_score >= self.confidence_thres: + # Get the class ID with the highest score + class_id = np.argmax(classes_scores) + + # Extract the bounding box coordinates from the current row + x, y, w, h = outputs[i][0], outputs[i][1], outputs[i][2], outputs[i][3] + + # Calculate the scaled coordinates of the bounding box + left = int((x - w / 2) * x_factor) + top = int((y - h / 2) * y_factor) + width = int(w * x_factor) + height = int(h * y_factor) + + # Add the class ID, score, and box coordinates to the respective lists + class_ids.append(class_id) + scores.append(max_score) + boxes.append([left, top, width, height]) + + # Apply non-maximum suppression to filter out overlapping bounding boxes + indices = cv2.dnn.NMSBoxes(boxes, scores, self.confidence_thres, self.iou_thres) + + # Iterate over the selected indices after non-maximum suppression + for i in indices: + # Get the box, score, and class ID corresponding to the index + box = boxes[i] + score = scores[i] + class_id = class_ids[i] + + # Draw the detection on the input image + self.draw_detections(input_image, box, score, class_id) + + # Return the modified input image + return input_image + + def main(self): + """ + Performs inference using an ONNX model and returns the output image with drawn detections. + + Returns: + output_img: The output image with drawn detections. + """ + # Create an inference session using the ONNX model and specify execution providers + session = ort.InferenceSession(self.onnx_model, providers=["CUDAExecutionProvider", "CPUExecutionProvider"]) + + # Get the model inputs + model_inputs = session.get_inputs() + + # Store the shape of the input for later use + input_shape = model_inputs[0].shape + self.input_width = input_shape[2] + self.input_height = input_shape[3] + + # Preprocess the image data + img_data = self.preprocess() + + # Run inference using the preprocessed image data + outputs = session.run(None, {model_inputs[0].name: img_data}) + + # Perform post-processing on the outputs to obtain output image. + return self.postprocess(self.img, outputs) # output image + + +if __name__ == "__main__": + # Create an argument parser to handle command-line arguments + parser = argparse.ArgumentParser() + parser.add_argument("--model", type=str, default="yolov8n.onnx", help="Input your ONNX model.") + parser.add_argument("--img", type=str, default=str(ASSETS / "bus.jpg"), help="Path to input image.") + parser.add_argument("--conf-thres", type=float, default=0.5, help="Confidence threshold") + parser.add_argument("--iou-thres", type=float, default=0.5, help="NMS IoU threshold") + args = parser.parse_args() + + # Check the requirements and select the appropriate backend (CPU or GPU) + check_requirements("onnxruntime-gpu" if torch.cuda.is_available() else "onnxruntime") + + # Create an instance of the YOLOv8 class with the specified arguments + detection = YOLOv8(args.model, args.img, args.conf_thres, args.iou_thres) + + # Perform object detection and obtain the output image + output_image = detection.main() + + # Display the output image in a window + cv2.namedWindow("Output", cv2.WINDOW_NORMAL) + cv2.imshow("Output", output_image) + + # Wait for a key press to exit + cv2.waitKey(0) diff --git a/examples/ONNX Runtime/YOLOv8-OpenCV-ONNX-Python/README.md b/examples/ONNX Runtime/YOLOv8-OpenCV-ONNX-Python/README.md new file mode 100644 index 0000000..c9076fa --- /dev/null +++ b/examples/ONNX Runtime/YOLOv8-OpenCV-ONNX-Python/README.md @@ -0,0 +1,19 @@ +# YOLOv8 - OpenCV + +Implementation YOLOv8 on OpenCV using ONNX Format. + +Just simply clone and run + +```bash +pip install -r requirements.txt +python main.py --model yolov8n.onnx --img image.jpg +``` + +If you start from scratch: + +```bash +pip install ultralytics +yolo export model=yolov8n.pt imgsz=640 format=onnx opset=12 +``` + +_\*Make sure to include "opset=12"_ diff --git a/examples/ONNX Runtime/YOLOv8-OpenCV-ONNX-Python/main.py b/examples/ONNX Runtime/YOLOv8-OpenCV-ONNX-Python/main.py new file mode 100644 index 0000000..c58b9ce --- /dev/null +++ b/examples/ONNX Runtime/YOLOv8-OpenCV-ONNX-Python/main.py @@ -0,0 +1,130 @@ +# Ultralytics YOLO 🚀, AGPL-3.0 license + +import argparse + +import cv2.dnn +import numpy as np + +from ultralytics.utils import ASSETS, yaml_load +from ultralytics.utils.checks import check_yaml + +CLASSES = yaml_load(check_yaml("coco8.yaml"))["names"] +colors = np.random.uniform(0, 255, size=(len(CLASSES), 3)) + + +def draw_bounding_box(img, class_id, confidence, x, y, x_plus_w, y_plus_h): + """ + Draws bounding boxes on the input image based on the provided arguments. + + Args: + img (numpy.ndarray): The input image to draw the bounding box on. + class_id (int): Class ID of the detected object. + confidence (float): Confidence score of the detected object. + x (int): X-coordinate of the top-left corner of the bounding box. + y (int): Y-coordinate of the top-left corner of the bounding box. + x_plus_w (int): X-coordinate of the bottom-right corner of the bounding box. + y_plus_h (int): Y-coordinate of the bottom-right corner of the bounding box. + """ + label = f"{CLASSES[class_id]} ({confidence:.2f})" + color = colors[class_id] + cv2.rectangle(img, (x, y), (x_plus_w, y_plus_h), color, 2) + cv2.putText(img, label, (x - 10, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2) + + +def main(onnx_model, input_image): + """ + Main function to load ONNX model, perform inference, draw bounding boxes, and display the output image. + + Args: + onnx_model (str): Path to the ONNX model. + input_image (str): Path to the input image. + + Returns: + list: List of dictionaries containing detection information such as class_id, class_name, confidence, etc. + """ + # Load the ONNX model + model: cv2.dnn.Net = cv2.dnn.readNetFromONNX(onnx_model) + + # Read the input image + original_image: np.ndarray = cv2.imread(input_image) + [height, width, _] = original_image.shape + + # Prepare a square image for inference + length = max((height, width)) + image = np.zeros((length, length, 3), np.uint8) + image[0:height, 0:width] = original_image + + # Calculate scale factor + scale = length / 640 + + # Preprocess the image and prepare blob for model + blob = cv2.dnn.blobFromImage(image, scalefactor=1 / 255, size=(640, 640), swapRB=True) + model.setInput(blob) + + # Perform inference + outputs = model.forward() + + # Prepare output array + outputs = np.array([cv2.transpose(outputs[0])]) + rows = outputs.shape[1] + + boxes = [] + scores = [] + class_ids = [] + + # Iterate through output to collect bounding boxes, confidence scores, and class IDs + for i in range(rows): + classes_scores = outputs[0][i][4:] + (minScore, maxScore, minClassLoc, (x, maxClassIndex)) = cv2.minMaxLoc(classes_scores) + if maxScore >= 0.25: + box = [ + outputs[0][i][0] - (0.5 * outputs[0][i][2]), + outputs[0][i][1] - (0.5 * outputs[0][i][3]), + outputs[0][i][2], + outputs[0][i][3], + ] + boxes.append(box) + scores.append(maxScore) + class_ids.append(maxClassIndex) + + # Apply NMS (Non-maximum suppression) + result_boxes = cv2.dnn.NMSBoxes(boxes, scores, 0.25, 0.45, 0.5) + + detections = [] + + # Iterate through NMS results to draw bounding boxes and labels + for i in range(len(result_boxes)): + index = result_boxes[i] + box = boxes[index] + detection = { + "class_id": class_ids[index], + "class_name": CLASSES[class_ids[index]], + "confidence": scores[index], + "box": box, + "scale": scale, + } + detections.append(detection) + draw_bounding_box( + original_image, + class_ids[index], + scores[index], + round(box[0] * scale), + round(box[1] * scale), + round((box[0] + box[2]) * scale), + round((box[1] + box[3]) * scale), + ) + + # Display the image with bounding boxes + cv2.imshow("image", original_image) + cv2.waitKey(0) + cv2.destroyAllWindows() + + return detections + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--model", default="yolov8n.onnx", help="Input your ONNX model.") + parser.add_argument("--img", default=str(ASSETS / "bus.jpg"), help="Path to input image.") + args = parser.parse_args() + main(args.model, args.img) diff --git a/examples/ONNX Runtime/YOLOv8-Segmentation-ONNXRuntime-Python/README.md b/examples/ONNX Runtime/YOLOv8-Segmentation-ONNXRuntime-Python/README.md new file mode 100644 index 0000000..b647700 --- /dev/null +++ b/examples/ONNX Runtime/YOLOv8-Segmentation-ONNXRuntime-Python/README.md @@ -0,0 +1,63 @@ +# YOLOv8-Segmentation-ONNXRuntime-Python Demo + +This repository provides a Python demo for performing segmentation with YOLOv8 using ONNX Runtime, highlighting the interoperability of YOLOv8 models without the need for the full PyTorch stack. + +## Features + +- **Framework Agnostic**: Runs segmentation inference purely on ONNX Runtime without importing PyTorch. +- **Efficient Inference**: Supports both FP32 and FP16 precision for ONNX models, catering to different computational needs. +- **Ease of Use**: Utilizes simple command-line arguments for model execution. +- **Broad Compatibility**: Leverages Numpy and OpenCV for image processing, ensuring broad compatibility with various environments. + +## Installation + +Install the required packages using pip. You will need `ultralytics` for exporting YOLOv8-seg ONNX model and using some utility functions, `onnxruntime-gpu` for GPU-accelerated inference, and `opencv-python` for image processing. + +```bash +pip install ultralytics +pip install onnxruntime-gpu # For GPU support +# pip install onnxruntime # Use this instead if you don't have an NVIDIA GPU +pip install numpy +pip install opencv-python +``` + +## Getting Started + +### 1. Export the YOLOv8 ONNX Model + +Export the YOLOv8 segmentation model to ONNX format using the provided `ultralytics` package. + +```bash +yolo export model=yolov8s-seg.pt imgsz=640 format=onnx opset=12 simplify +``` + +### 2. Run Inference + +Perform inference with the exported ONNX model on your images. + +```bash +python main.py --model --source +``` + +### Example Output + +After running the command, you should see segmentation results similar to this: + +Segmentation Demo + +## Advanced Usage + +For more advanced usage, including real-time video processing, please refer to the `main.py` script's command-line arguments. + +## Contributing + +We welcome contributions to improve this demo! Please submit issues and pull requests for bug reports, feature requests, or submitting a new algorithm enhancement. + +## License + +This project is licensed under the AGPL-3.0 License - see the [LICENSE](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) file for details. + +## Acknowledgments + +- The YOLOv8-Segmentation-ONNXRuntime-Python demo is contributed by GitHub user [jamjamjon](https://github.com/jamjamjon). +- Thanks to the ONNX Runtime community for providing a robust and efficient inference engine. diff --git a/examples/ONNX Runtime/YOLOv8-Segmentation-ONNXRuntime-Python/main.py b/examples/ONNX Runtime/YOLOv8-Segmentation-ONNXRuntime-Python/main.py new file mode 100644 index 0000000..c1779de --- /dev/null +++ b/examples/ONNX Runtime/YOLOv8-Segmentation-ONNXRuntime-Python/main.py @@ -0,0 +1,338 @@ +# Ultralytics YOLO 🚀, AGPL-3.0 license + +import argparse + +import cv2 +import numpy as np +import onnxruntime as ort + +from ultralytics.utils import ASSETS, yaml_load +from ultralytics.utils.checks import check_yaml +from ultralytics.utils.plotting import Colors + + +class YOLOv8Seg: + """YOLOv8 segmentation model.""" + + def __init__(self, onnx_model): + """ + Initialization. + + Args: + onnx_model (str): Path to the ONNX model. + """ + # Build Ort session + self.session = ort.InferenceSession( + onnx_model, + providers=["CUDAExecutionProvider", "CPUExecutionProvider"] + if ort.get_device() == "GPU" + else ["CPUExecutionProvider"], + ) + + # Numpy dtype: support both FP32 and FP16 onnx model + self.ndtype = np.half if self.session.get_inputs()[0].type == "tensor(float16)" else np.single + + # Get model width and height(YOLOv8-seg only has one input) + self.model_height, self.model_width = [x.shape for x in self.session.get_inputs()][0][-2:] + + # Load COCO class names + self.classes = yaml_load(check_yaml("coco8.yaml"))["names"] + + # Create color palette + self.color_palette = Colors() + + def __call__(self, im0, conf_threshold=0.4, iou_threshold=0.45, nm=32): + """ + The whole pipeline: pre-process -> inference -> post-process. + + Args: + im0 (Numpy.ndarray): original input image. + conf_threshold (float): confidence threshold for filtering predictions. + iou_threshold (float): iou threshold for NMS. + nm (int): the number of masks. + + Returns: + boxes (List): list of bounding boxes. + segments (List): list of segments. + masks (np.ndarray): [N, H, W], output masks. + """ + # Pre-process + im, ratio, (pad_w, pad_h) = self.preprocess(im0) + + # Ort inference + preds = self.session.run(None, {self.session.get_inputs()[0].name: im}) + + # Post-process + boxes, segments, masks = self.postprocess( + preds, + im0=im0, + ratio=ratio, + pad_w=pad_w, + pad_h=pad_h, + conf_threshold=conf_threshold, + iou_threshold=iou_threshold, + nm=nm, + ) + return boxes, segments, masks + + def preprocess(self, img): + """ + Pre-processes the input image. + + Args: + img (Numpy.ndarray): image about to be processed. + + Returns: + img_process (Numpy.ndarray): image preprocessed for inference. + ratio (tuple): width, height ratios in letterbox. + pad_w (float): width padding in letterbox. + pad_h (float): height padding in letterbox. + """ + # Resize and pad input image using letterbox() (Borrowed from Ultralytics) + shape = img.shape[:2] # original image shape + new_shape = (self.model_height, self.model_width) + r = min(new_shape[0] / shape[0], new_shape[1] / shape[1]) + ratio = r, r + new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r)) + pad_w, pad_h = (new_shape[1] - new_unpad[0]) / 2, (new_shape[0] - new_unpad[1]) / 2 # wh padding + if shape[::-1] != new_unpad: # resize + img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR) + top, bottom = int(round(pad_h - 0.1)), int(round(pad_h + 0.1)) + left, right = int(round(pad_w - 0.1)), int(round(pad_w + 0.1)) + img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=(114, 114, 114)) + + # Transforms: HWC to CHW -> BGR to RGB -> div(255) -> contiguous -> add axis(optional) + img = np.ascontiguousarray(np.einsum("HWC->CHW", img)[::-1], dtype=self.ndtype) / 255.0 + img_process = img[None] if len(img.shape) == 3 else img + return img_process, ratio, (pad_w, pad_h) + + def postprocess(self, preds, im0, ratio, pad_w, pad_h, conf_threshold, iou_threshold, nm=32): + """ + Post-process the prediction. + + Args: + preds (Numpy.ndarray): predictions come from ort.session.run(). + im0 (Numpy.ndarray): [h, w, c] original input image. + ratio (tuple): width, height ratios in letterbox. + pad_w (float): width padding in letterbox. + pad_h (float): height padding in letterbox. + conf_threshold (float): conf threshold. + iou_threshold (float): iou threshold. + nm (int): the number of masks. + + Returns: + boxes (List): list of bounding boxes. + segments (List): list of segments. + masks (np.ndarray): [N, H, W], output masks. + """ + x, protos = preds[0], preds[1] # Two outputs: predictions and protos + + # Transpose dim 1: (Batch_size, xywh_conf_cls_nm, Num_anchors) -> (Batch_size, Num_anchors, xywh_conf_cls_nm) + x = np.einsum("bcn->bnc", x) + + # Predictions filtering by conf-threshold + x = x[np.amax(x[..., 4:-nm], axis=-1) > conf_threshold] + + # Create a new matrix which merge these(box, score, cls, nm) into one + # For more details about `numpy.c_()`: https://numpy.org/doc/1.26/reference/generated/numpy.c_.html + x = np.c_[x[..., :4], np.amax(x[..., 4:-nm], axis=-1), np.argmax(x[..., 4:-nm], axis=-1), x[..., -nm:]] + + # NMS filtering + x = x[cv2.dnn.NMSBoxes(x[:, :4], x[:, 4], conf_threshold, iou_threshold)] + + # Decode and return + if len(x) > 0: + # Bounding boxes format change: cxcywh -> xyxy + x[..., [0, 1]] -= x[..., [2, 3]] / 2 + x[..., [2, 3]] += x[..., [0, 1]] + + # Rescales bounding boxes from model shape(model_height, model_width) to the shape of original image + x[..., :4] -= [pad_w, pad_h, pad_w, pad_h] + x[..., :4] /= min(ratio) + + # Bounding boxes boundary clamp + x[..., [0, 2]] = x[:, [0, 2]].clip(0, im0.shape[1]) + x[..., [1, 3]] = x[:, [1, 3]].clip(0, im0.shape[0]) + + # Process masks + masks = self.process_mask(protos[0], x[:, 6:], x[:, :4], im0.shape) + + # Masks -> Segments(contours) + segments = self.masks2segments(masks) + return x[..., :6], segments, masks # boxes, segments, masks + else: + return [], [], [] + + @staticmethod + def masks2segments(masks): + """ + Takes a list of masks(n,h,w) and returns a list of segments(n,xy), from + https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/ops.py. + + Args: + masks (numpy.ndarray): the output of the model, which is a tensor of shape (batch_size, 160, 160). + + Returns: + segments (List): list of segment masks. + """ + segments = [] + for x in masks.astype("uint8"): + c = cv2.findContours(x, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)[0] # CHAIN_APPROX_SIMPLE + if c: + c = np.array(c[np.array([len(x) for x in c]).argmax()]).reshape(-1, 2) + else: + c = np.zeros((0, 2)) # no segments found + segments.append(c.astype("float32")) + return segments + + @staticmethod + def crop_mask(masks, boxes): + """ + Takes a mask and a bounding box, and returns a mask that is cropped to the bounding box, from + https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/ops.py. + + Args: + masks (Numpy.ndarray): [n, h, w] tensor of masks. + boxes (Numpy.ndarray): [n, 4] tensor of bbox coordinates in relative point form. + + Returns: + (Numpy.ndarray): The masks are being cropped to the bounding box. + """ + n, h, w = masks.shape + x1, y1, x2, y2 = np.split(boxes[:, :, None], 4, 1) + r = np.arange(w, dtype=x1.dtype)[None, None, :] + c = np.arange(h, dtype=x1.dtype)[None, :, None] + return masks * ((r >= x1) * (r < x2) * (c >= y1) * (c < y2)) + + def process_mask(self, protos, masks_in, bboxes, im0_shape): + """ + Takes the output of the mask head, and applies the mask to the bounding boxes. This produces masks of higher + quality but is slower, from https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/ops.py. + + Args: + protos (numpy.ndarray): [mask_dim, mask_h, mask_w]. + masks_in (numpy.ndarray): [n, mask_dim], n is number of masks after nms. + bboxes (numpy.ndarray): bboxes re-scaled to original image shape. + im0_shape (tuple): the size of the input image (h,w,c). + + Returns: + (numpy.ndarray): The upsampled masks. + """ + c, mh, mw = protos.shape + masks = np.matmul(masks_in, protos.reshape((c, -1))).reshape((-1, mh, mw)).transpose(1, 2, 0) # HWN + masks = np.ascontiguousarray(masks) + masks = self.scale_mask(masks, im0_shape) # re-scale mask from P3 shape to original input image shape + masks = np.einsum("HWN -> NHW", masks) # HWN -> NHW + masks = self.crop_mask(masks, bboxes) + return np.greater(masks, 0.5) + + @staticmethod + def scale_mask(masks, im0_shape, ratio_pad=None): + """ + Takes a mask, and resizes it to the original image size, from + https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/ops.py. + + Args: + masks (np.ndarray): resized and padded masks/images, [h, w, num]/[h, w, 3]. + im0_shape (tuple): the original image shape. + ratio_pad (tuple): the ratio of the padding to the original image. + + Returns: + masks (np.ndarray): The masks that are being returned. + """ + im1_shape = masks.shape[:2] + if ratio_pad is None: # calculate from im0_shape + gain = min(im1_shape[0] / im0_shape[0], im1_shape[1] / im0_shape[1]) # gain = old / new + pad = (im1_shape[1] - im0_shape[1] * gain) / 2, (im1_shape[0] - im0_shape[0] * gain) / 2 # wh padding + else: + pad = ratio_pad[1] + + # Calculate tlbr of mask + top, left = int(round(pad[1] - 0.1)), int(round(pad[0] - 0.1)) # y, x + bottom, right = int(round(im1_shape[0] - pad[1] + 0.1)), int(round(im1_shape[1] - pad[0] + 0.1)) + if len(masks.shape) < 2: + raise ValueError(f'"len of masks shape" should be 2 or 3, but got {len(masks.shape)}') + masks = masks[top:bottom, left:right] + masks = cv2.resize( + masks, (im0_shape[1], im0_shape[0]), interpolation=cv2.INTER_LINEAR + ) # INTER_CUBIC would be better + if len(masks.shape) == 2: + masks = masks[:, :, None] + return masks + + def draw_and_visualize(self, im, bboxes, segments, vis=False, save=True): + """ + Draw and visualize results. + + Args: + im (np.ndarray): original image, shape [h, w, c]. + bboxes (numpy.ndarray): [n, 4], n is number of bboxes. + segments (List): list of segment masks. + vis (bool): imshow using OpenCV. + save (bool): save image annotated. + + Returns: + None + """ + # Draw rectangles and polygons + im_canvas = im.copy() + for (*box, conf, cls_), segment in zip(bboxes, segments): + # draw contour and fill mask + cv2.polylines(im, np.int32([segment]), True, (255, 255, 255), 2) # white borderline + cv2.fillPoly(im_canvas, np.int32([segment]), self.color_palette(int(cls_), bgr=True)) + + # draw bbox rectangle + cv2.rectangle( + im, + (int(box[0]), int(box[1])), + (int(box[2]), int(box[3])), + self.color_palette(int(cls_), bgr=True), + 1, + cv2.LINE_AA, + ) + cv2.putText( + im, + f"{self.classes[cls_]}: {conf:.3f}", + (int(box[0]), int(box[1] - 9)), + cv2.FONT_HERSHEY_SIMPLEX, + 0.7, + self.color_palette(int(cls_), bgr=True), + 2, + cv2.LINE_AA, + ) + + # Mix image + im = cv2.addWeighted(im_canvas, 0.3, im, 0.7, 0) + + # Show image + if vis: + cv2.imshow("demo", im) + cv2.waitKey(0) + cv2.destroyAllWindows() + + # Save image + if save: + cv2.imwrite("demo.jpg", im) + + +if __name__ == "__main__": + # Create an argument parser to handle command-line arguments + parser = argparse.ArgumentParser() + parser.add_argument("--model", type=str, required=True, help="Path to ONNX model") + parser.add_argument("--source", type=str, default=str(ASSETS / "bus.jpg"), help="Path to input image") + parser.add_argument("--conf", type=float, default=0.25, help="Confidence threshold") + parser.add_argument("--iou", type=float, default=0.45, help="NMS IoU threshold") + args = parser.parse_args() + + # Build model + model = YOLOv8Seg(args.model) + + # Read image by OpenCV + img = cv2.imread(args.source) + + # Inference + boxes, segments, _ = model(img, conf_threshold=args.conf, iou_threshold=args.iou) + + # Draw bboxes and polygons + if len(boxes) > 0: + model.draw_and_visualize(img, boxes, segments, vis=False, save=True) diff --git a/examples/Old Example Conversion/README.md b/examples/Old Example Conversion/README.md new file mode 100644 index 0000000..7876faa --- /dev/null +++ b/examples/Old Example Conversion/README.md @@ -0,0 +1,483 @@ +# TensorRT Conversion + +

PyTorch -> ONNX -> TensorRT

+ +This repo includes installation guide for TensorRT, how to convert PyTorch models to ONNX format and run inference with TensoRT Python API. + +The following table compares the speed gain got from using TensorRT running [YOLOv5](https://github.com/ultralytics/yolov5). + +Device/ Env | PyTorch (FP16) | TensorRT (FP16) +--- | --- | --- +RTX 2060 | 60-61 | 96-97 +Jetson Xavier | 17-18 | 38-39 + +*Notes: YOLO model in comparison is using YOLOv5-L with image size of 352x416. Units are in FPS.* + +Example conversion of YOLOv5 PyTorch Model to TensorRT is described in `examples` folder. + + +## Installation + +Recommended CUDA version is + +* cuda-10.2 + cuDNN-7.6 + + +Tested environments: + +* CUDA 10.2 + cuDNN 7.6 +* TensorRT 7.0.0.11 +* ONNX 1.7 +* ONNXRuntime 1.3 +* Protobuf >= 3.12.3 +* CMake 3.15.2/ CMake 3.17.3 +* PyTorch 1.5 + CUDA 10.2 + +### Protobuf + +Only Protobuf version >= 3.12.3 is supported in ONNX_TENSORRT package. So, you need to build the latest version from source. + +To build protobuf from source, the following tools are needed: + +```bash +sudo apt install autoconf automake libtool curl make g++ unzip +``` + +Clone protobuf repository and make sure to also clone submodules and generated the configure script. + +```bash +git clone --recursive https://github.com/protocolbuffers/protobuf.git +cd protobuf +./autogen.sh +./configure --prefix=/usr +make -j$(nproc) +sudo make install +sudo ldconfig # refresh shared library cache +``` + +Verify the installation: + +```bash +protoc --version +``` + +You should see the installed libprotoc version. + +### NVIDIA Driver + +First detect your graphics card model and recommended driver. + +```bash +ubuntu-drivers devices +``` + +If you don't find your desired driver version, you can enable Nvidia beta driver repository. + +```bash +sudo add-apt-repository ppa:graphics-drivers/ppa +``` + +Then install the desired driver version using: + +```bash +sudo apt install nvidia-driver-440 +sudo reboot +``` + +### CUDA + +Go to [CUDA toolkit archive](https://developer.nvidia.com/cuda-toolkit-archive) and download your desired CUDA version and installation method. + +Below is the sample installation method for CUDA 10.2 deb file. + +```bash +wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-ubuntu1804.pin +sudo mv cuda-ubuntu1804.pin /etc/apt/preferences.d/cuda-repository-pin-600 +wget http://developer.download.nvidia.com/compute/cuda/10.2/Prod/local_installers/cuda-repo-ubuntu1804-10-2-local-10.2.89-440.33.01_1.0-1_amd64.deb +sudo dpkg -i cuda-repo-ubuntu1804-10-2-local-10.2.89-440.33.01_1.0-1_amd64.deb +sudo apt-key add /var/cuda-repo-10-2-local-10.2.89-440.33.01/7fa2af80.pub +sudo apt-get update +sudo apt-get -y install cuda +``` + +Check using: + +```bash +nvcc -V +``` + +### cuDNN + +Go to [NVIDIA cuDNN](https://developer.nvidia.com/cudnn) and download your desired cuDNN version. + +You need to download `cuDNN Runtime Library` and `Developer Library`. `Code Samples and User Guide` is not essential. + +Then install step by step: + +```bash +sudo dpkg -i libcudnn8_x.x.x-1+cudax.x_amd64.deb +sudo dpkg -i libcudnn8-dev_8.x.x.x-1+cudax.x_amd64.deb +``` + + +### TensorRT + +Download TensorRT from the following link: + +https://developer.nvidia.com/tensorrt + +Be careful to download to match with your CUDA install method. For example, if you installed CUDA with deb file, download TensorRT deb file also. Otherwise, it won't work. + +The following example will install TensorRT deb file method. For other version of TensoRT installation, please check [official documentation](https://docs.nvidia.com/deeplearning/tensorrt/archives/tensorrt-713/install-guide/index.html#installing). + +```bash +os="ubuntu1x04" +tag="cudax.x-trt7.x.x.x-ga-yyyymmdd" +sudo dpkg -i nv-tensorrt-repo-${os}-${tag}_1-1_amd64.deb + +sudo apt-key add /var/nv-tensorrt-repo-${tag}/7fa2af80.pub + +sudo apt-get update +sudo apt-get install tensorrt cuda-nvrtc-x-y +``` + +Where x-y for cuda-nvrtc is 10-2 or 11-0 depending on your CUD version. + +If you plan to use TensorRT with TensorFlow, install this: + +```bash +sudo apt install uff-converter-tf +``` + +Verify the installation with + +```bash +dpkg -l | grep TensorRT +``` + +You should see libnvinfer, tensorrt and other related packages installed. + + +### PyCUDA + +PyCUDA is used within Python wrappers to access NVIDIA’s CUDA APIs. + +Install PyCUDA with the following command: + +```bash +pip3 install pycuda +``` + +If you faced this `error: command 'aarch64-linux-gnu-gcc' failed with exit status 1`, install like this: `pip3 install pycuda --user`. + +If you cannot access cuda driver with PyCUDA, please uninstall PyCUDA, clean pip cache and install PyCUDA again. + +```bash +pip3 cache purge +``` + +To use the above command `pip3 cache purge`, you need to have pip version >= 20.x.x. + + +### CMake + +CMake >= 3.13 is required but on Ubuntu 18.04, installed version is 3.10.2. So, upgrade CMake. + +Download latest CMake from [here](https://github.com/Kitware/CMake/releases). + +Install OpenSSL: + +```bash +sudo apt install libssl-dev +``` + +Then, install: + +```bash +tar -xvzf cmake-3.x.x.tar.gz +cd cmake-3.x.x +./bootstrap +make -j$(nproc) +sudo make install +``` + +Verify the installation: + +```bash +cmake --version +``` + +### ONNX_TensorRT + +Parses ONNX models for execution with TensorRT. + +Install Pre-requisities: + +```bash +sudo apt install swig +``` + +Install ONNX_TRT: + +```bash +git clone https://github.com/onnx/onnx-tensorrt +cd onnx-tensorrt +git submodule update --init --recursive +mkdir -p build && cd build +cmake .. -DTENSORRT_ROOT=/usr/src/tensorrt +make -j$(nproc) +sudo make install +cd .. +sudo python3 setup.py build +sudo python3 setup.py install +``` + +Possible errors when running `setup.py`: +* `error: command 'swig' failed with exit status 1`. To fix this, do the following: Add `#define TENSORRTAPI` at the top of `NvOnnxParser.h`. +* `error: command 'aarch64-linux-gnu-gcc' failed with exit status 1`. This error will be occurred on Jetson platforms. To fix: Delete `'-m64,'` line in `setup.py` and try to re-build. + + +### trtexec + +A command line wrapper tool to serve two main purposes: benchmarking networks on random data and generating serialized engines from models. + +`trtexec` can build engines from models in Caffe, UFF (TensorFlow), or ONNX format. + +`trtexec` is included when you installed TensorRT but not enabled. You need to build to use it. + +Switch to this `trtexec` directory and build it: + +```bash +cd /usr/src/tensorrt/samples/trtexec/ +sudo make +``` + +Then, the binary named `trtexec` will be created in `/bin`. Add this path in `.bashrc`. + +```bash +gedit ~/.bashrc + +export PATH=$PATH:/usr/src/tensorrt/bin + +source ~/.bashrc +``` + +### ONNX + +```bash +pip3 install onnx +``` + +### ONNXRuntime + +CPU: + +```bash +pip3 install onnxruntime +``` + +GPU + +```bash +pip3 install onnxruntime-gpu +``` + +### ONNX Simplifier + + +```bash +pip3 install onnx-simplifier +``` + +## Conversion + +### PyTorch to ONNX + +Run `onnx_export.py`. + +Detail steps are as follows: + +Load the PyTorch Model. + +```python +device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') +model = Model() +model.load_state_dict(torch.load(model_path, map_location=device)) +model.to(device).eval() +``` + +Prepare the input: + +```python +img = torch.zeros((1, 3, height, width)).to(device) +``` + +Note that height and width is fixed. Dynamic input shape is still not available in PyTorch >> ONNX >> TensorRT. + +Export to ONNX format: + +```python +torch.onnx.export( + model, # PyTorch Model + img, # Input tensor + f, # Output file (eg. 'output_model.onnx') + opset_version=12, # Operator support version + input_names=['image'] # Input tensor name (arbitary) + output_names=['output'] # Output tensor name (arbitary) +) +``` + +`opset_version` is very important. Some PyTorch operators are still not supported in ONNX even if `opset_version=12`. Default `opset_version` in PyTorch is 12. Please check official ONNX repo for supported PyTorch operators. If your model includes unsupported operators, convert to supported operators. For example, `torch.repeat_interleave()` is not supported, it can be converted into supported `torch.repeat() + torch.view()` to achieve the same function. + +### ONNX Simplifier + +`onnxsim` will be used to simplify the exported ONNX model. This `onnxsim` will strip some unnecessary operations and will reduce the number of layers. Moreover, it will get rid of unsupported operators when converting to TensorRT. + +An example before and after simplification from official repo is shown below: + +![comparison](imgs/comparison.png) + +It includues optimizers from `onnx.optimizer`, eliminate constant nodes and can run with 3 versions: + +#### Web Version + +Open official published https://convertmodel.com page and choose ONNX as the output format and convert it. + +#### Commandline Version + +If the web version won't work well, run the following command to simplify the ONNX model: + +```bash +python3 -m onnxsim +``` + +For more available functions this command can do like skipping optimization and others: + +```bash +python3 -m onnxsim -h +``` + +#### Python In-Script Version + +```python +import onnx +from onnxsim import simplify + +onnx_model = onnx.load(f) +simplified_model, check = simplify(onnx_model) + +assert check, "Simplified ONNX model could not be validated." + +onnx.save(simplified_model, 'onnx_model_simplified.onnx') +``` + +After all, check the exported ONNX model: + +```python +onnx.checker.check_model(simplified_model) +print(onnx.helper.printable_graph(simplified_model.graph)) # print a human readable representation of the graph +``` + +You can view the ONNX model with this tool [Netron](https://github.com/lutzroeder/netron). + +*Note*: Don't convert PyTorch to ONNX on Jetson; it will take more GPU memory usage. Try to do this on host PC. Sometimes, commandline method won't work, so recommended method is In-script version. + + +### ONNX to TensorRT with onnx-tensorrt + +**ONNX-TensorRT** package installed above will be used to convert the ONNX model (`.onnx`) to Tensort model (`.trt`). + +You can also run `.onnx` model directly with TensorRT Python API but converting to `.trt` will be more convenient. + +To convert, run the following command in your terminal: + +```bash +onnx2trt model.onnx -o model.trt -b 1 -d 16 +``` + +* `-o`: To output TensorRT engine file +* `-b`: Set batch size (default: 32) +* `-d`: Set Model data type (16 for FP16, 32 for FP32) + +Please see other available options and their usage on official [repo](https://github.com/onnx/onnx-tensorrt). + +*Note*: Converted TRT model on one device will not result the same output on other device. This is more obvious if you use other optimization passes option. Try to run this on each device. + +### ONNX to TensorRT with trtexec + +`trtexec` commandline tool can be used to convert the ONNX model instead of `onnx2trt`. + +To convert ONNX model, run the following: + +```bash +trtexec --onnx=model.onnx --saveEngine=model.trt --workspace=1024 --fp16 +``` + +It also includes model benchmarking and profiling. To see other available options and use cases, check out official [Documentation](https://github.com/NVIDIA/TensorRT/tree/master/samples/opensource/trtexec). + + +## Run TRT Model + +First implement a logging interface through which TensorRT reports errors, warnings and informational messages. + +```python +import tensorrt as trt + +TRT_LOGGER = trt.Logger(trt.Logger.WARNING) +``` + +Then, read the TRT model and deserialize it. + +```python +with open('trt_model.trt', 'rb) as f, trt.Runtime(TRT_LOGGER) as runtime: + engine = runtime.deserialize_cuda_engine(f.read()) +``` + +Allocate some host and device buffers for inputs and outputs. + +```python +import pycuda.driver as cuda +import pycuda.autoinit + +h_input = cuda.pagelocked_empty(trt.volume(engine.get_binding_shape(0)), dtype=np.float32) +h_output = cuda.pagelocked_empty(trt.volume(engine.get_binding_shape(1)), dtype=np.float32) +# Allocate device memory for inputs and outputs. +d_input = cuda.mem_alloc(h_input.nbytes) +d_output = cuda.mem_alloc(h_output.nbytes) +# Create a stream in which to copy inputs/outputs and run inference. +stream = cuda.Stream() +``` + +Finally, run inference with created engine: + +```python +with engine.create_execution_context() as context: + # Transfer input data to the GPU. + cuda.memcpy_htod_async(d_input, h_input, stream) + # Run inference. + context.execute_async(bindings=[int(d_input), int(d_output)], stream_handle=stream.handle) + # Transfer predictions back from the GPU. + cuda.memcpy_dtoh_async(h_output, d_output, stream) + # Synchronize the stream + stream.synchronize() + # Return the host output. + return h_output +``` + +There is also an option to run ONNX model directly with TensorRT Python API, but it is not recommended. + +## Examples + +Example conversion of YOLOv5 model into TRT model can be seen in [conversion](conversion). + +You can see the example converted models in [examples](examples). + +## References + +* [YOLOv5](https://github.com/ultralytics/yolov5) +* [TensorRT Documentation](https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html) +* [ONNX](https://github.com/onnx/onnx) +* [ONNX-Runtime](https://github.com/microsoft/onnxruntime) +* [ONNX-TensorRT](https://github.com/onnx/onnx-tensorrt) +* [ONNX Simplifier](https://github.com/daquexian/onnx-simplifier) +* [trtexec](https://github.com/NVIDIA/TensorRT/tree/master/samples/opensource/trtexec) \ No newline at end of file diff --git a/examples/Old Example Conversion/common.py b/examples/Old Example Conversion/common.py new file mode 100644 index 0000000..2ad55b0 --- /dev/null +++ b/examples/Old Example Conversion/common.py @@ -0,0 +1,143 @@ +from itertools import chain +import argparse +import os +import pycuda.driver as cuda +import pycuda.autoinit +import numpy as np +import tensorrt as trt + + +EXPLICIT_BATCH = 1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH) + +def GiB(val): + return val * 1 << 30 + + +def add_help(description): + parser = argparse.ArgumentParser(description=description, formatter_class=argparse.ArgumentDefaultsHelpFormatter) + args, _ = parser.parse_known_args() + + +def find_sample_data(description="Runs a TensorRT Python sample", subfolder="", find_files=[]): + ''' + Parses sample arguments. + + Args: + description (str): Description of the sample. + subfolder (str): The subfolder containing data relevant to this sample + find_files (str): A list of filenames to find. Each filename will be replaced with an absolute path. + + Returns: + str: Path of data directory. + ''' + + # Standard command-line arguments for all samples. + kDEFAULT_DATA_ROOT = os.path.join(os.sep, "usr", "src", "tensorrt", "data") + parser = argparse.ArgumentParser(description=description, formatter_class=argparse.ArgumentDefaultsHelpFormatter) + parser.add_argument("-d", "--datadir", help="Location of the TensorRT sample data directory, and any additional data directories.", action="append", default=[kDEFAULT_DATA_ROOT]) + args, _ = parser.parse_known_args() + + def get_data_path(data_dir): + # If the subfolder exists, append it to the path, otherwise use the provided path as-is. + data_path = os.path.join(data_dir, subfolder) + if not os.path.exists(data_path): + print("WARNING: " + data_path + " does not exist. Trying " + data_dir + " instead.") + data_path = data_dir + # Make sure data directory exists. + if not (os.path.exists(data_path)): + print("WARNING: {:} does not exist. Please provide the correct data path with the -d option.".format(data_path)) + return data_path + + data_paths = [get_data_path(data_dir) for data_dir in args.datadir] + return data_paths, locate_files(data_paths, find_files) + +def locate_files(data_paths, filenames): + """ + Locates the specified files in the specified data directories. + If a file exists in multiple data directories, the first directory is used. + + Args: + data_paths (List[str]): The data directories. + filename (List[str]): The names of the files to find. + + Returns: + List[str]: The absolute paths of the files. + + Raises: + FileNotFoundError if a file could not be located. + """ + found_files = [None] * len(filenames) + for data_path in data_paths: + # Find all requested files. + for index, (found, filename) in enumerate(zip(found_files, filenames)): + if not found: + file_path = os.path.abspath(os.path.join(data_path, filename)) + if os.path.exists(file_path): + found_files[index] = file_path + + # Check that all files were found + for f, filename in zip(found_files, filenames): + if not f or not os.path.exists(f): + raise FileNotFoundError("Could not find {:}. Searched in data paths: {:}".format(filename, data_paths)) + return found_files + +# Simple helper data class that's a little nicer to use than a 2-tuple. +class HostDeviceMem(object): + def __init__(self, host_mem, device_mem): + self.host = host_mem + self.device = device_mem + + def __str__(self): + return "Host:\n" + str(self.host) + "\nDevice:\n" + str(self.device) + + def __repr__(self): + return self.__str__() + +# Allocates all buffers required for an engine, i.e. host/device inputs/outputs. +def allocate_buffers(engine): + inputs = [] + outputs = [] + bindings = [] + stream = cuda.Stream() + for binding in engine: + size = trt.volume(engine.get_binding_shape(binding)) * engine.max_batch_size + dtype = trt.nptype(engine.get_binding_dtype(binding)) + # Allocate host and device buffers + host_mem = cuda.pagelocked_empty(size, dtype) + device_mem = cuda.mem_alloc(host_mem.nbytes) + # Append the device buffer to device bindings. + bindings.append(int(device_mem)) + # Append to the appropriate list. + if engine.binding_is_input(binding): + inputs.append(HostDeviceMem(host_mem, device_mem)) + else: + outputs.append(HostDeviceMem(host_mem, device_mem)) + return inputs, outputs, bindings, stream + +# This function is generalized for multiple inputs/outputs. +# inputs and outputs are expected to be lists of HostDeviceMem objects. +def do_inference(context, bindings, inputs, outputs, stream, batch_size=1): + # Transfer input data to the GPU. + [cuda.memcpy_htod_async(inp.device, inp.host, stream) for inp in inputs] + # Run inference. + context.execute_async(batch_size=batch_size, bindings=bindings, stream_handle=stream.handle) + # Transfer predictions back from the GPU. + [cuda.memcpy_dtoh_async(out.host, out.device, stream) for out in outputs] + # Synchronize the stream + stream.synchronize() + # Return only the host outputs. + return [out.host for out in outputs] + +# This function is generalized for multiple inputs/outputs for full dimension networks. +# inputs and outputs are expected to be lists of HostDeviceMem objects. +def do_inference_v2(context, bindings, inputs, outputs, stream): + # Transfer input data to the GPU. + [cuda.memcpy_htod_async(inp.device, inp.host, stream) for inp in inputs] + # Run inference. + context.execute_async_v2(bindings=bindings, stream_handle=stream.handle) + # Transfer predictions back from the GPU. + [cuda.memcpy_dtoh_async(out.host, out.device, stream) for out in outputs] + # Synchronize the stream + stream.synchronize() + # Return only the host outputs. + return [out.host for out in outputs] \ No newline at end of file diff --git a/examples/Old Example Conversion/onnx_export.py b/examples/Old Example Conversion/onnx_export.py new file mode 100644 index 0000000..765ffc2 --- /dev/null +++ b/examples/Old Example Conversion/onnx_export.py @@ -0,0 +1,61 @@ +import argparse +import onnx +import os +from onnxsim import simplify +import torch +from models.common import * +from models.yolo import Model + + +def load_yolo_model(args): + # although yolov5 weights contain model codes, some operations didn't support in TensorRT + # so we need to reload the model with modified model file `yolo.py` + model = Model('models/yolov5l.yaml') + ckpt = torch.load(args.model_path, map_location=torch.device('cpu')) + ckpt = {k: v for k, v in ckpt.state_dict().items() if model.state_dict()[k].numel() == v.numel()} + model.load_state_dict(ckpt, strict=False) + model.eval() + model.fuse() + + return model + + +def argument_parser(): + parser = argparse.ArgumentParser() + parser.add_argument('--height', type=int, default=352, help='inference size (pixels)') + parser.add_argument('--width', type=int, default=416, help='inference size (pixels)') + parser.add_argument('--model-path', type=str, default='weights/yolov5l.pt', help='PyTorch Model Path') + return parser.parse_args() + + +if __name__ == '__main__': + args = argument_parser() + + # load the model (you can export the model to cuda or not) + model = load_yolo_model(args) + + # output filename + f = args.model_path.replace('.pt', '.onnx') + + # dummy input (if your model is in cuda, send to cuda if not, leave it as original) + img = torch.zeros((1, 3, args.height, args.width)) + + #out = model(img)[0] # dry run + # Export to onnx + torch.onnx.export(model, img, f, verbose=False, opset_version=11, input_names=['image'], output_names=['output']) + + # simplify it + onnx_model = onnx.load(f) # load onnx model + + # dummy input shape + input_shapes = {None: [1, 3, args.height, args.width]} + + # simplify it using onnx simplifier + simplified_model, check = simplify(onnx_model, skip_fuse_bn=True, input_shapes=input_shapes) + assert check, "Simplified ONNX model could not be validated." + onnx.save(simplified_model, os.path.splitext(f)[0]+f'_{args.height}_{args.width}.onnx') + + # Check onnx model + onnx.checker.check_model(simplified_model) # check onnx model + #print(onnx.helper.printable_graph(simplified_model.graph)) # print a human readable representation of the graph + print('Export complete. ONNX model saved to %s\nView with https://github.com/lutzroeder/netron' % f) diff --git a/examples/Old Example Conversion/run_trt.py b/examples/Old Example Conversion/run_trt.py new file mode 100644 index 0000000..ff4559a --- /dev/null +++ b/examples/Old Example Conversion/run_trt.py @@ -0,0 +1,164 @@ +import tensorrt as trt +import numpy as np +import pycuda.driver as cuda +import pycuda.autoinit +import common +import os +import cv2 +from PIL import Image +import time +from threading import Thread + +TRT_LOGGER = trt.Logger(trt.Logger.VERBOSE) + +def preprocess(img, input_resolution): + image = cv2.resize(img[..., ::-1], input_resolution).transpose(2, 0, 1).astype(np.float32) + + image /= 255.0 + mean = np.array([0.485, 0.456, 0.406], dtype=np.float32) + std = np.array([0.229, 0.224, 0.225], dtype=np.float32) + mean = mean[:, np.newaxis, np.newaxis] + std = std[:, np.newaxis, np.newaxis] + image = (image - mean) / std + + image = np.expand_dims(image, axis=0) + return np.array(image, dtype=np.float32, order='C') + + +def postprocess(pred, input_resolution): + depth = pred.reshape(input_resolution) + depth = normalize_depth(depth) + return depth + +def normalize_depth(depth): + depth *= 1000.0 + depth = depth - depth.min() + depth = (depth / depth.max()) * 255 + #depth = ((depth - depth.min()) / (depth.max() - depth.min())) * 255 + return depth.astype(np.uint8) + +class WebcamVideoStream: + """From PyImageSearch + Webcam reading with multi-threading + """ + def __init__(self, src=0, name='WebcamVideoStream'): + self.stream = cv2.VideoCapture(src) + self.grabbed, self.frame = self.stream.read() + self.name = name + self.stopped = False + + def start(self): + t = Thread(target=self.update, name=self.name, args=()) + t.daemon = True + t.start() + return self + + def update(self): + while True: + if self.stopped: + return + + self.grabbed, self.frame = self.stream.read() + + def read(self): + return self.frame + + def stop(self): + self.stopped = True + + +def build_engine(onnx_file_path): + """ + Takes an ONNX file and creates a TensorRT engine to run inference with. + """ + with trt.Builder(TRT_LOGGER) as builder, builder.create_network(common.EXPLICIT_BATCH) as network, trt.OnnxParser(network, TRT_LOGGER) as parser: + builder.max_workspace_size = 1 << 28 # 256MB + builder.max_batch_size = 1 + + # Parser model file + print(f"Loading ONNX file from path {onnx_file_path} ...") + with open(onnx_file_path, 'rb') as model: + print("Beginning ONNX file parsing") + if not parser.parse(model.read()): + print(f"ERROR: Failed to parse the ONNX file") + for error in range(parser.num_errors): + print(parser.get_error(error)) + return None + print(f"Completed parsing of ONNX file.") + print(f"Building an engine form file {onnx_file_path}; this may take a while ...") + engine = builder.build_cuda_engine(network) + print("Completed creating Engine") + + with open(onnx_file_path.replace('.onnx', '.trt'), 'wb') as f: + f.write(engine.serialize()) + + return engine + + +def get_engine(model_path: str): + """ + Attempts to load a serialized engine if available, otherwise builds a new TensorRT engine and saves it. + """ + if os.path.exists(model_path): + if model_path.endswith('trt'): + print(f"Reading engine from file {model_path}") + with open(model_path, 'rb') as f, trt.Runtime(TRT_LOGGER) as runtime: + return runtime.deserialize_cuda_engine(f.read()) + + elif model_path.endswith('onnx'): + build_engine(model_path) + + else: + print("Invalid File: Only .onnx and .trt are supported.") + else: + print(f"FILE: {model_path} not found.") + + +def main(): + model_path = 'weights/bts_nyu_320_mem.trt' + input_image_path = 'images/NYU0937.jpg' + input_resolution = (320, 320) + + vs = WebcamVideoStream().start() + accum_time = 0 + curr_fps = 0 + fps = "FPS: ??" + + with get_engine(model_path) as engine, engine.create_execution_context() as context: + inputs, outputs, bindings, stream = common.allocate_buffers(engine) + + while True: + prev_time = time.time() + + frame = vs.read() + image = preprocess(frame, input_resolution) + + inputs[0].host = image + + trt_outputs = common.do_inference_v2(context, bindings, inputs, outputs, stream)[-1] + + vis = postprocess(trt_outputs, input_resolution) + + curr_time = time.time() + exec_time = curr_time - prev_time + prev_time = curr_time + accum_time = accum_time + exec_time + curr_fps = curr_fps + 1 + if accum_time > 1: + accum_time = accum_time - 1 + fps = "FPS: " + str(curr_fps) + print(fps) + curr_fps = 0 + + cv2.imshow('frame', vis) + + if cv2.waitKey(1) == ord('q'): + break + + cv2.destroyAllWindows() + vs.stop() + + #cv2.imwrite('images/trt_output.jpg', depth_image) + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/examples/Old Example Conversion/yolo.py b/examples/Old Example Conversion/yolo.py new file mode 100644 index 0000000..aa9d8e9 --- /dev/null +++ b/examples/Old Example Conversion/yolo.py @@ -0,0 +1,238 @@ +import argparse + +import yaml + +from models.experimental import * + + +class Detect(nn.Module): + def __init__(self, nc=80, anchors=()): # detection layer + super(Detect, self).__init__() + self.stride = None # strides computed during build + self.nc = nc # number of classes + self.no = nc + 5 # number of outputs per anchor + self.nl = len(anchors) # number of detection layers + self.na = len(anchors[0]) // 2 # number of anchors + self.grid = [torch.zeros(1)] * self.nl # init grid + a = torch.tensor(anchors).float().view(self.nl, -1, 2) + self.register_buffer('anchors', a) # shape(nl,na,2) + self.register_buffer('anchor_grid', a.clone().view(self.nl, 1, -1, 1, 1, 2)) # shape(nl,1,na,1,1,2) + self.export = False # onnx export + + def forward(self, x): + # x = x.copy() # for profiling + z = [] # inference output + self.training |= self.export + for i in range(self.nl): + bs, _, ny, nx = x[i].shape # x(bs,255,20,20) to x(bs,3,20,20,85) + x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous() + + if not self.training: # inference + if self.grid[i].shape[2:4] != x[i].shape[2:4]: + self.grid[i] = self._make_grid(nx, ny).to(x[i].device) + + y = x[i].sigmoid() + + #y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i].to(x[i].device)) * self.stride[i] # xy + #y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh + + t0 = (y[..., 0:2] * 2. - 0.5 + self.grid[i].to(x[i].device)) * self.stride[i] + t1 = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] + y = torch.cat([t0.float(), t1.float(), y[...,4:].float()], -1) + + z.append(y.view(bs, -1, self.no)) + + return x if self.training else (torch.cat(z, 1), x) + + @staticmethod + def _make_grid(nx=20, ny=20): + yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)]) + return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float() + + +class Model(nn.Module): + def __init__(self, model_cfg='yolov5s.yaml', ch=4, nc=None): # model, input channels, number of classes + super(Model, self).__init__() + if type(model_cfg) is dict: + self.md = model_cfg # model dict + else: # is *.yaml + with open(model_cfg) as f: + self.md = yaml.load(f, Loader=yaml.FullLoader) # model dict + + # Define model + if nc: + self.md['nc'] = nc # override yaml value + self.model, self.save = parse_model(self.md, ch=[ch]) # model, savelist, ch_out + # print([x.shape for x in self.forward(torch.zeros(1, ch, 64, 64))]) + + # Build strides, anchors + m = self.model[-1] # Detect() + m.stride = torch.tensor([64 / x.shape[-2] for x in self.forward(torch.zeros(1, ch, 64, 64))]) # forward + m.anchors /= m.stride.view(-1, 1, 1) + self.stride = m.stride + + # Init weights, biases + torch_utils.initialize_weights(self) + self._initialize_biases() # only run once + torch_utils.model_info(self) + print('') + + def forward(self, x, augment=False, profile=False): + if augment: + img_size = x.shape[-2:] # height, width + s = [0.83, 0.67] # scales + y = [] + for i, xi in enumerate((x, + torch_utils.scale_img(x.flip(3), s[0]), # flip-lr and scale + torch_utils.scale_img(x, s[1]), # scale + )): + # cv2.imwrite('img%g.jpg' % i, 255 * xi[0].numpy().transpose((1, 2, 0))[:, :, ::-1]) + y.append(self.forward_once(xi)[0]) + + y[1][..., :4] /= s[0] # scale + y[1][..., 0] = img_size[1] - y[1][..., 0] # flip lr + y[2][..., :4] /= s[1] # scale + return torch.cat(y, 1), None # augmented inference, train + else: + return self.forward_once(x, profile) # single-scale inference, train + + def forward_once(self, x, profile=False): + y, dt = [], [] # outputs + for m in self.model: + if m.f != -1: # if not from previous layer + x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f] # from earlier layers + + if profile: + import thop + o = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2 # FLOPS + t = torch_utils.time_synchronized() + for _ in range(10): + _ = m(x) + dt.append((torch_utils.time_synchronized() - t) * 100) + print('%10.1f%10.0f%10.1fms %-40s' % (o, m.np, dt[-1], m.type)) + + x = m(x) # run + y.append(x if m.i in self.save else None) # save output + + if profile: + print('%.1fms total' % sum(dt)) + return x + + def _initialize_biases(self, cf=None): # initialize biases into Detect(), cf is class frequency + # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1. + m = self.model[-1] # Detect() module + for f, s in zip(m.f, m.stride): #  from + mi = self.model[f % m.i] + b = mi.bias.view(m.na, -1) # conv.bias(255) to (3,85) + b[:, 4] += math.log(8 / (640 / s) ** 2) # obj (8 objects per 640 image) + b[:, 5:] += math.log(0.6 / (m.nc - 0.99)) if cf is None else torch.log(cf / cf.sum()) # cls + mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True) + + def _print_biases(self): + m = self.model[-1] # Detect() module + for f in sorted([x % m.i for x in m.f]): #  from + b = self.model[f].bias.detach().view(m.na, -1).T # conv.bias(255) to (3,85) + print(('%g Conv2d.bias:' + '%10.3g' * 6) % (f, *b[:5].mean(1).tolist(), b[5:].mean())) + + # def _print_weights(self): + # for m in self.model.modules(): + # if type(m) is Bottleneck: + # print('%10.3g' % (m.w.detach().sigmoid() * 2)) # shortcut weights + + def fuse(self): # fuse model Conv2d() + BatchNorm2d() layers + print('Fusing layers...') + for m in self.model.modules(): + if type(m) is Conv: + m.conv = torch_utils.fuse_conv_and_bn(m.conv, m.bn) # update conv + m.bn = None # remove batchnorm + m.forward = m.fuseforward # update forward + torch_utils.model_info(self) + + +def parse_model(md, ch): # model_dict, input_channels(3) + print('\n%3s%15s%3s%10s %-40s%-30s' % ('', 'from', 'n', 'params', 'module', 'arguments')) + anchors, nc, gd, gw = md['anchors'], md['nc'], md['depth_multiple'], md['width_multiple'] + na = (len(anchors[0]) // 2) # number of anchors + no = na * (nc + 5) # number of outputs = anchors * (classes + 5) + + layers, save, c2 = [], [], ch[-1] # layers, savelist, ch out + for i, (f, n, m, args) in enumerate(md['backbone'] + md['head']): # from, number, module, args + m = eval(m) if isinstance(m, str) else m # eval strings + for j, a in enumerate(args): + try: + args[j] = eval(a) if isinstance(a, str) else a # eval strings + except: + pass + + n = max(round(n * gd), 1) if n > 1 else n # depth gain + if m in [nn.Conv2d, Conv, Bottleneck, SPP, DWConv, MixConv2d, Focus, ConvPlus, BottleneckCSP]: + c1, c2 = ch[f], args[0] + + # Normal + # if i > 0 and args[0] != no: # channel expansion factor + # ex = 1.75 # exponential (default 2.0) + # e = math.log(c2 / ch[1]) / math.log(2) + # c2 = int(ch[1] * ex ** e) + # if m != Focus: + c2 = make_divisible(c2 * gw, 8) if c2 != no else c2 + + # Experimental + # if i > 0 and args[0] != no: # channel expansion factor + # ex = 1 + gw # exponential (default 2.0) + # ch1 = 32 # ch[1] + # e = math.log(c2 / ch1) / math.log(2) # level 1-n + # c2 = int(ch1 * ex ** e) + # if m != Focus: + # c2 = make_divisible(c2, 8) if c2 != no else c2 + + args = [c1, c2, *args[1:]] + if m is BottleneckCSP: + args.insert(2, n) + n = 1 + elif m is nn.BatchNorm2d: + args = [ch[f]] + elif m is Concat: + c2 = sum([ch[-1 if x == -1 else x + 1] for x in f]) + elif m is Detect: + f = f or list(reversed([(-1 if j == i else j - 1) for j, x in enumerate(ch) if x == no])) + else: + c2 = ch[f] + + m_ = nn.Sequential(*[m(*args) for _ in range(n)]) if n > 1 else m(*args) # module + t = str(m)[8:-2].replace('__main__.', '') # module type + np = sum([x.numel() for x in m_.parameters()]) # number params + m_.i, m_.f, m_.type, m_.np = i, f, t, np # attach index, 'from' index, type, number params + print('%3s%15s%3s%10.0f %-40s%-30s' % (i, f, n, np, t, args)) # print + save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist + layers.append(m_) + ch.append(c2) + return nn.Sequential(*layers), sorted(save) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--cfg', type=str, default='yolov5s.yaml', help='model.yaml') + parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') + opt = parser.parse_args() + opt.cfg = glob.glob('./**/' + opt.cfg, recursive=True)[0] # find file + device = torch_utils.select_device(opt.device) + + # Create model + model = Model(opt.cfg).to(device) + model.train() + + # Profile + # img = torch.rand(8 if torch.cuda.is_available() else 1, 3, 640, 640).to(device) + # y = model(img, profile=True) + # print([y[0].shape] + [x.shape for x in y[1]]) + + # ONNX export + # model.model[-1].export = True + # torch.onnx.export(model, img, f.replace('.yaml', '.onnx'), verbose=True, opset_version=11) + + # Tensorboard + # from torch.utils.tensorboard import SummaryWriter + # tb_writer = SummaryWriter() + # print("Run 'tensorboard --logdir=models/runs' to view tensorboard at http://localhost:6006/") + # tb_writer.add_graph(model.model, img) # add model to tensorboard + # tb_writer.add_image('test', img[0], dataformats='CWH') # add model to tensorboard diff --git a/examples/ROS2 Yolo Nodes/README.md b/examples/ROS2 Yolo Nodes/README.md new file mode 100644 index 0000000..97691d4 --- /dev/null +++ b/examples/ROS2 Yolo Nodes/README.md @@ -0,0 +1,5 @@ +These are the ROS2 Nodes of popular open source projects running converted TensorRT models. + +To see only TensorRT inference, please look into `project_name/project_name/scripts/infer.py`. + +For example, in YOLOv5, please see `yolov5/yolov5/scripts/infer.py`. \ No newline at end of file diff --git a/examples/ROS2 Yolo Nodes/efficientdet/config/cam2image.yaml b/examples/ROS2 Yolo Nodes/efficientdet/config/cam2image.yaml new file mode 100644 index 0000000..e5ed3d7 --- /dev/null +++ b/examples/ROS2 Yolo Nodes/efficientdet/config/cam2image.yaml @@ -0,0 +1,11 @@ +cam2image: + ros__parameters: + burger_mode: false + depth: 10 + frequency: 30.0 + height: 480 + history: keep_all + reliability: reliable + show_camera: false + use_sim_time: false + width: 640 diff --git a/examples/ROS2 Yolo Nodes/efficientdet/efficientdet/__init__.py b/examples/ROS2 Yolo Nodes/efficientdet/efficientdet/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/examples/ROS2 Yolo Nodes/efficientdet/efficientdet/efficientdet_node.py b/examples/ROS2 Yolo Nodes/efficientdet/efficientdet/efficientdet_node.py new file mode 100644 index 0000000..923dd11 --- /dev/null +++ b/examples/ROS2 Yolo Nodes/efficientdet/efficientdet/efficientdet_node.py @@ -0,0 +1,61 @@ +from std_msgs.msg import String +from sensor_msgs.msg import Image +from cv_bridge import CvBridge, CvBridgeError +import rclpy +import yaml +import cv2 +import torch +import os +import time +from rclpy.node import Node +from threading import Thread, Event +from efficientdet.scripts.infer import EFFICIENTDET, FPS +from ament_index_python.packages import get_package_prefix + +BASE_PATH = os.path.join(get_package_prefix('efficientdet').replace('install', 'src'), 'efficientdet') +WEIGHTS_PATH = os.path.join(BASE_PATH, 'scripts/cfg', 'efficientdet-d3.trt') + +class EfficientDetNode(Node): + def __init__(self): + super().__init__('efficientdet_node') + self.bridge = CvBridge() + self.current_frame = None + + self.get_logger().info('Model Initializing...') + self.efficientdet = EFFICIENTDET(WEIGHTS_PATH) + self.get_logger().info('Model Loaded...') + + self.subscriber = self.create_subscription(Image, '/image', self.callback_image, 5) + self.subscriber + self.publisher = self.create_publisher(Image, '/efficientdet/vis', 5) + self.fps = FPS() + timer_period = 0.01 + timer = self.create_timer(timer_period, self.process_image) + + def process_image(self): + if self.current_frame is not None: + self.fps.start() + image = self.efficientdet.predict(self.current_frame) + image_msg = self.bridge.cv2_to_imgmsg(image, 'rgb8') + self.publisher.publish(image_msg) + self.fps.stop() + curr_fps = self.fps.get_fps() + self.get_logger().info(f'Current {curr_fps}') + + def callback_image(self, data): + try: + cv_image = self.bridge.imgmsg_to_cv2(data, 'rgb8') + except CvBridgeError as e: + raise e + self.current_frame = cv_image + + +def main(args=None): + rclpy.init(args=args) + main_node = EfficientDetNode() + rclpy.spin(main_node) + main_node.destroy_node() + rclpy.shutdown() + +if __name__ == '__main__': + main() diff --git a/examples/ROS2 Yolo Nodes/efficientdet/efficientdet/scripts/__init__.py b/examples/ROS2 Yolo Nodes/efficientdet/efficientdet/scripts/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/examples/ROS2 Yolo Nodes/efficientdet/efficientdet/scripts/infer.py b/examples/ROS2 Yolo Nodes/efficientdet/efficientdet/scripts/infer.py new file mode 100644 index 0000000..1a757d6 --- /dev/null +++ b/examples/ROS2 Yolo Nodes/efficientdet/efficientdet/scripts/infer.py @@ -0,0 +1,106 @@ +import tensorrt as trt +import numpy as np +import os +import cv2 +import torch +from efficientdet.scripts.utils import * +#from utils import * +import re + +TRT_LOGGER = trt.Logger(trt.Logger.VERBOSE) + +def get_engine(model_path: str): + if os.path.exists(model_path) and model_path.endswith('trt'): + print(f"Reading engine from file {model_path}") + with open(model_path, 'rb') as f, trt.Runtime(TRT_LOGGER) as runtime: + return runtime.deserialize_cuda_engine(f.read()) + else: + print(f"FILE: {model_path} not found or extension not supported.") + + +def preprocess(img, img_size, mean=(0.406, 0.456, 0.485), std=(0.225, 0.224, 0.229)): + normalized_img = (img / 255 - mean) / std + framed_img, *framed_meta = aspectaware_resize_padding(normalized_img, img_size, img_size) + framed_img = framed_img.transpose(2, 0, 1) + + return np.ascontiguousarray(framed_img[np.newaxis, ...]), framed_meta + + +def postprocess_outputs(pred, anchors, img_size, image, original_img, regressBoxes, clipBoxes, threshold, iou_threshold, framed_meta): + regression = torch.from_numpy(pred[0].reshape(1, -1, 4)) + classification = torch.from_numpy(pred[1].reshape(1, -1, 90)) + + out = postprocess(image, anchors, regression, classification, + regressBoxes, clipBoxes, + threshold, iou_threshold)[0] + + out = scale_coords(framed_meta, out) + vis = plot_bbox(out, original_img) + + return vis + + +class EFFICIENTDET: + def __init__(self, model_path='cfg/efficientdet-d0.trt'): + model_type = int(re.search(r'\d+', model_path).group()) + self.img_size = 512 + self.threshold = 0.2 + self.iou_threshold = 0.2 + anchor_scale = [4., 4., 4., 4., 4., 4., 4., 5.] + self.regressBoxes = BBoxTransform() + self.clipBoxes = ClipBoxes() + self.anchors = anchors_def(anchor_scale=anchor_scale[model_type]) + + engine = get_engine(model_path) + self.context = engine.create_execution_context() + self.inputs, self.outputs, self.bindings, self.stream = allocate_buffers(engine) + + def predict(self, frame): + #frame = cv2.flip(frame, 0) + image, framed_meta = preprocess(frame, self.img_size) + self.inputs[0].host = image + trt_outputs = do_inference_v2(self.context, self.bindings, self.inputs, self.outputs, self.stream) + vis = postprocess_outputs(trt_outputs, self.anchors, self.img_size, image, frame, self.regressBoxes, self.clipBoxes, self.threshold, self.iou_threshold, framed_meta) + + return vis + + +def main(): + model_type = 0 + model_path = f'cfg/efficientdet-d{model_type}.trt' + img_size = 512 + threshold = 0.2 + iou_threshold = 0.2 + anchor_scale = [4., 4., 4., 4., 4., 4., 4., 5.] + + webcam = WebcamStream() + fps = FPS() + regressBoxes = BBoxTransform() + clipBoxes = ClipBoxes() + anchors = anchors_def(anchor_scale=anchor_scale[model_type]) + + with get_engine(model_path) as engine, engine.create_execution_context() as context: + inputs, outputs, bindings, stream = allocate_buffers(engine) + + while True: + fps.start() + frame = webcam.read() + + image, framed_meta = preprocess(frame, img_size) + inputs[0].host = image + + trt_outputs = do_inference_v2(context, bindings, inputs, outputs, stream) + + vis = postprocess_outputs(trt_outputs, anchors, img_size, image, frame, regressBoxes, clipBoxes, threshold, iou_threshold, framed_meta) + + fps.stop() + print(fps.get_fps()) + + cv2.imshow('frame', vis) + + if cv2.waitKey(1) == ord("q"): + webcam.stop() + + +if __name__ == '__main__': + main() diff --git a/examples/ROS2 Yolo Nodes/efficientdet/efficientdet/scripts/utils.py b/examples/ROS2 Yolo Nodes/efficientdet/efficientdet/scripts/utils.py new file mode 100644 index 0000000..d6624fe --- /dev/null +++ b/examples/ROS2 Yolo Nodes/efficientdet/efficientdet/scripts/utils.py @@ -0,0 +1,281 @@ +import itertools +import torch +import torch.nn as nn +import numpy as np +from torchvision.ops import nms +import os +import pycuda.driver as cuda +import pycuda.autoinit +import tensorrt as trt +from threading import Thread +import time +import cv2 +import random + +EXPLICIT_BATCH = 1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH) + +obj_list = ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', + 'fire hydrant', '', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', + 'cow', 'elephant', 'bear', 'zebra', 'giraffe', '', 'backpack', 'umbrella', '', '', 'handbag', 'tie', + 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', + 'skateboard', 'surfboard', 'tennis racket', 'bottle', '', 'wine glass', 'cup', 'fork', 'knife', 'spoon', + 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', + 'cake', 'chair', 'couch', 'potted plant', 'bed', '', 'dining table', '', '', 'toilet', '', 'tv', + 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', + 'refrigerator', '', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', + 'toothbrush'] + +colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(len(obj_list))] + + +class BBoxTransform(nn.Module): + def forward(self, anchors, regression): + y_centers_a = (anchors[..., 0] + anchors[..., 2]) / 2 + x_centers_a = (anchors[..., 1] + anchors[..., 3]) / 2 + ha = anchors[..., 2] - anchors[..., 0] + wa = anchors[..., 3] - anchors[..., 1] + + w = regression[..., 3].exp() * wa + h = regression[..., 2].exp() * ha + + y_centers = regression[..., 0] * ha + y_centers_a + x_centers = regression[..., 1] * wa + x_centers_a + + ymin = y_centers - h / 2. + xmin = x_centers - w / 2. + ymax = y_centers + h / 2. + xmax = x_centers + w / 2. + + return torch.stack([xmin, ymin, xmax, ymax], dim=2) + + +class ClipBoxes(nn.Module): + + def __init__(self): + super(ClipBoxes, self).__init__() + + def forward(self, boxes, img): + batch_size, num_channels, height, width = img.shape + + boxes[:, :, 0] = torch.clamp(boxes[:, :, 0], min=0) + boxes[:, :, 1] = torch.clamp(boxes[:, :, 1], min=0) + + boxes[:, :, 2] = torch.clamp(boxes[:, :, 2], max=width - 1) + boxes[:, :, 3] = torch.clamp(boxes[:, :, 3], max=height - 1) + + return boxes + + +def postprocess(x, anchors, regression, classification, regressBoxes, clipBoxes, threshold, iou_threshold): + transformed_anchors = regressBoxes(anchors, regression) + transformed_anchors = clipBoxes(transformed_anchors, x) + + scores = torch.max(classification, dim=2, keepdim=True)[0] + scores_over_thresh = (scores > threshold)[:, :, 0] + + out = [] + for i in range(x.shape[0]): + if scores_over_thresh.sum() == 0: + out.append({ + 'rois': np.array(()), + 'class_ids': np.array(()), + 'scores': np.array(()), + }) + + classification_per = classification[i, scores_over_thresh[i, :], ...].permute(1, 0) + transformed_anchors_per = transformed_anchors[i, scores_over_thresh[i, :], ...] + scores_per = scores[i, scores_over_thresh[i, :], ...] + anchors_nms_idx = nms(transformed_anchors_per, scores_per[:, 0], iou_threshold=iou_threshold) + + if anchors_nms_idx.shape[0] != 0: + scores_, classes_ = classification_per[:, anchors_nms_idx].max(dim=0) + boxes_ = transformed_anchors_per[anchors_nms_idx, :] + + out.append({ + 'rois': boxes_.cpu().numpy(), + 'class_ids': classes_.cpu().numpy(), + 'scores': scores_.cpu().numpy(), + }) + else: + out.append({ + 'rois': np.array(()), + 'class_ids': np.array(()), + 'scores': np.array(()), + }) + + return out + + +def anchors_def(anchor_scale, image_shape=(512, 512), dtype=torch.float32): + pyramid_levels = [3, 4, 5, 6, 7] + strides = [2 ** x for x in pyramid_levels] + scales = [2 ** 0, 2 ** (1.0 / 3.0), 2 ** (2.0 / 3.0)] + ratios = [(1.0, 1.0), (1.4, 0.7), (0.7, 1.4)] + + boxes_all = [] + for stride in strides: + boxes_level = [] + for scale, ratio in itertools.product(scales, ratios): + base_anchor_size = anchor_scale * stride * scale + anchor_size_x_2 = base_anchor_size * ratio[0] / 2.0 + anchor_size_y_2 = base_anchor_size * ratio[1] / 2.0 + + x = torch.arange(stride / 2, image_shape[1], stride) + y = torch.arange(stride / 2, image_shape[0], stride) + xv, yv = torch.meshgrid(x, y) + xv, yv = xv.t().reshape(-1), yv.t().reshape(-1) + + # y1,x1,y2,x2 + boxes = torch.stack((yv - anchor_size_y_2, xv - anchor_size_x_2, yv + anchor_size_y_2, xv + anchor_size_x_2)) + + boxes_level.append(boxes.transpose(0, 1).unsqueeze(1)) + + # concat anchors on the same level to the reshape NxAx4 + boxes_level = torch.cat(boxes_level, dim=1) + boxes_all.append(boxes_level.reshape(-1, 4)) + + anchor_boxes = torch.cat(boxes_all, dim=0).type(dtype).unsqueeze(0) + + return anchor_boxes + + +def aspectaware_resize_padding(image, width, height): + old_h, old_w, c = image.shape + + if old_w > old_h: + new_w, new_h = width, int(width / old_w * old_h) + else: + new_w, new_h = int(height / old_h * old_w), height + + canvas = np.zeros((height, height, c), np.float32) + + if new_w != old_w or new_h != old_h: + image = cv2.resize(image, (new_w, new_h)) + + padding_h = height - new_h + padding_w = width - new_w + + canvas[:new_h, :new_w] = image + + return canvas, new_w, new_h, old_w, old_h, padding_w, padding_h, + + +def scale_coords(metas, preds): + if len(preds['rois']) == 0: + return preds + new_w, new_h, old_w, old_h, padding_w, padding_h = metas + preds['rois'][:, [0, 2]] = preds['rois'][:, [0, 2]] / (new_w / old_w) + preds['rois'][:, [1, 3]] = preds['rois'][:, [1, 3]] / (new_h / old_h) + + return preds + + +def plot_bbox(preds, img): + if len(preds['rois']) == 0: + return img + + for j in range(len(preds['rois'])): + (x1, y1, x2, y2) = preds['rois'][j].astype(np.int) + color = colors[int(preds['class_ids'][j])] + cv2.rectangle(img, (x1, y1), (x2, y2), color, 2, lineType=cv2.LINE_AA) + obj = obj_list[preds['class_ids'][j]] + score = float(preds['scores'][j]) + label = f'{obj}, {score:.3f}' + t_size = cv2.getTextSize(label, 0, 2/3, 1)[0] + cv2.rectangle(img, (x1, y1), (x1+t_size[0], y1-t_size[1]-3), color, -1, cv2.LINE_AA) + cv2.putText(img, label, (x1, y1-2), 0, 2/3, (255, 255, 255), 1, cv2.LINE_AA) + + return img + + +class WebcamStream: + def __init__(self, src=1): + cap = cv2.VideoCapture(src, cv2.CAP_V4L2) + cap.set(3, 640) + cap.set(4, 480) + assert cap.isOpened(), f"Failed to open {src}" + _, self.frame = cap.read() + + Thread(target=self.update, args=([cap]), daemon=True).start() + + def update(self, cap): + while cap.isOpened(): + cap.grab() + _, self.frame = cap.retrieve() + + def read(self): + return self.frame.copy() + + def stop(self): + cv2.destroyAllWindows() + raise StopIteration + + +class FPS: + def __init__(self): + self.accum_time = 0 + self.curr_fps = 0 + self.fps = "FPS: ??" + + def start(self): + self.prev_time = time.time() + + def stop(self): + self.curr_time = time.time() + exec_time = self.curr_time - self.prev_time + self.prev_time = self.curr_time + self.accum_time += exec_time + + def get_fps(self): + self.curr_fps += 1 + if self.accum_time > 1: + self.accum_time -= 1 + self.fps = "FPS: " + str(self.curr_fps) + self.curr_fps = 0 + return self.fps + + +class HostDeviceMem(object): + def __init__(self, host_mem, device_mem): + self.host = host_mem + self.device = device_mem + + def __str__(self): + return "Host:\n" + str(self.host) + "\nDevice:\n" + str(self.device) + + def __repr__(self): + return self.__str__() + + +def allocate_buffers(engine): + inputs = [] + outputs = [] + bindings = [] + stream = cuda.Stream() + for binding in engine: + size = trt.volume(engine.get_binding_shape(binding)) * engine.max_batch_size + dtype = trt.nptype(engine.get_binding_dtype(binding)) + # Allocate host and device buffers + host_mem = cuda.pagelocked_empty(size, dtype) + device_mem = cuda.mem_alloc(host_mem.nbytes) + # Append the device buffer to device bindings. + bindings.append(int(device_mem)) + # Append to the appropriate list. + if engine.binding_is_input(binding): + inputs.append(HostDeviceMem(host_mem, device_mem)) + else: + outputs.append(HostDeviceMem(host_mem, device_mem)) + return inputs, outputs, bindings, stream + + +def do_inference_v2(context, bindings, inputs, outputs, stream): + # Transfer input data to the GPU. + [cuda.memcpy_htod_async(inp.device, inp.host, stream) for inp in inputs] + # Run inference. + context.execute_async_v2(bindings=bindings, stream_handle=stream.handle) + # Transfer predictions back from the GPU. + [cuda.memcpy_dtoh_async(out.host, out.device, stream) for out in outputs] + # Synchronize the stream + stream.synchronize() + # Return only the host outputs. + return [out.host for out in outputs] diff --git a/examples/ROS2 Yolo Nodes/efficientdet/launch/efficientdet_launch.py b/examples/ROS2 Yolo Nodes/efficientdet/launch/efficientdet_launch.py new file mode 100644 index 0000000..11d4951 --- /dev/null +++ b/examples/ROS2 Yolo Nodes/efficientdet/launch/efficientdet_launch.py @@ -0,0 +1,25 @@ +from launch import LaunchDescription +from launch_ros.actions import Node +from ament_index_python.packages import get_package_share_directory +import os + +def generate_launch_description(): + config = os.path.join('/home/teama/dev_ws/src/efficientdet', 'config', 'cam2image.yaml') + + return LaunchDescription([ + Node( + package='image_tools', + node_executable='cam2image', + parameters=[config] + ), + Node( + package='efficientdet', + node_executable='efficientdet_node', + output='screen' + ), + Node( + package='rqt_image_view', + node_executable='rqt_image_view', + output='screen' + ) + ]) diff --git a/examples/ROS2 Yolo Nodes/efficientdet/package.xml b/examples/ROS2 Yolo Nodes/efficientdet/package.xml new file mode 100644 index 0000000..4b5a591 --- /dev/null +++ b/examples/ROS2 Yolo Nodes/efficientdet/package.xml @@ -0,0 +1,20 @@ + + + + efficientdet + 0.0.0 + TODO: Package description + teama + TODO: License declaration + + ament_python + + ament_copyright + ament_flake8 + ament_pep257 + python3-pytest + + + ament_python + + diff --git a/examples/ROS2 Yolo Nodes/efficientdet/resource/efficientdet b/examples/ROS2 Yolo Nodes/efficientdet/resource/efficientdet new file mode 100644 index 0000000..e69de29 diff --git a/examples/ROS2 Yolo Nodes/efficientdet/setup.cfg b/examples/ROS2 Yolo Nodes/efficientdet/setup.cfg new file mode 100644 index 0000000..f516fdc --- /dev/null +++ b/examples/ROS2 Yolo Nodes/efficientdet/setup.cfg @@ -0,0 +1,4 @@ +[develop] +script-dir=$base/lib/efficientdet +[install] +install-scripts=$base/lib/efficientdet diff --git a/examples/ROS2 Yolo Nodes/efficientdet/setup.py b/examples/ROS2 Yolo Nodes/efficientdet/setup.py new file mode 100644 index 0000000..fec20f1 --- /dev/null +++ b/examples/ROS2 Yolo Nodes/efficientdet/setup.py @@ -0,0 +1,32 @@ +from setuptools import setup, find_packages +from glob import glob +import os + +package_name = 'efficientdet' + +setup( + name=package_name, + version='0.0.0', + packages=find_packages(exclude=['test']), + package_data={ + package_name: ['msg/*'] + }, + data_files=[ + ('share/ament_index/resource_index/packages', ['resource/' + package_name]), + ('share/' + package_name, ['package.xml']), + ('share/' + package_name, ['launch/efficientdet_launch.py']), + ('share/' + package_name, ['config/cam2image.yaml']), + ], + install_requires=['setuptools'], + zip_safe=True, + maintainer='sithu', + maintainer_email='sithuaung@globalwalkers.co.jp', + description='TODO: Package description', + license='TODO: License declaration', + tests_require=['pytest'], + entry_points={ + 'console_scripts': [ + 'efficientdet_node = efficientdet.efficientdet_node:main' + ], + }, +) diff --git a/examples/ROS2 Yolo Nodes/efficientdet/test/test_copyright.py b/examples/ROS2 Yolo Nodes/efficientdet/test/test_copyright.py new file mode 100644 index 0000000..cc8ff03 --- /dev/null +++ b/examples/ROS2 Yolo Nodes/efficientdet/test/test_copyright.py @@ -0,0 +1,23 @@ +# Copyright 2015 Open Source Robotics Foundation, Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from ament_copyright.main import main +import pytest + + +@pytest.mark.copyright +@pytest.mark.linter +def test_copyright(): + rc = main(argv=['.', 'test']) + assert rc == 0, 'Found errors' diff --git a/examples/ROS2 Yolo Nodes/efficientdet/test/test_flake8.py b/examples/ROS2 Yolo Nodes/efficientdet/test/test_flake8.py new file mode 100644 index 0000000..eff8299 --- /dev/null +++ b/examples/ROS2 Yolo Nodes/efficientdet/test/test_flake8.py @@ -0,0 +1,23 @@ +# Copyright 2017 Open Source Robotics Foundation, Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from ament_flake8.main import main +import pytest + + +@pytest.mark.flake8 +@pytest.mark.linter +def test_flake8(): + rc = main(argv=[]) + assert rc == 0, 'Found errors' diff --git a/examples/ROS2 Yolo Nodes/efficientdet/test/test_pep257.py b/examples/ROS2 Yolo Nodes/efficientdet/test/test_pep257.py new file mode 100644 index 0000000..b234a38 --- /dev/null +++ b/examples/ROS2 Yolo Nodes/efficientdet/test/test_pep257.py @@ -0,0 +1,23 @@ +# Copyright 2015 Open Source Robotics Foundation, Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from ament_pep257.main import main +import pytest + + +@pytest.mark.linter +@pytest.mark.pep257 +def test_pep257(): + rc = main(argv=['.', 'test']) + assert rc == 0, 'Found code style errors / warnings' diff --git a/examples/ROS2 Yolo Nodes/yolov4/config/cam2image.yaml b/examples/ROS2 Yolo Nodes/yolov4/config/cam2image.yaml new file mode 100644 index 0000000..e5ed3d7 --- /dev/null +++ b/examples/ROS2 Yolo Nodes/yolov4/config/cam2image.yaml @@ -0,0 +1,11 @@ +cam2image: + ros__parameters: + burger_mode: false + depth: 10 + frequency: 30.0 + height: 480 + history: keep_all + reliability: reliable + show_camera: false + use_sim_time: false + width: 640 diff --git a/examples/ROS2 Yolo Nodes/yolov4/launch/yolov4_launch.py b/examples/ROS2 Yolo Nodes/yolov4/launch/yolov4_launch.py new file mode 100644 index 0000000..008fede --- /dev/null +++ b/examples/ROS2 Yolo Nodes/yolov4/launch/yolov4_launch.py @@ -0,0 +1,25 @@ +from launch import LaunchDescription +from launch_ros.actions import Node +from ament_index_python.packages import get_package_share_directory +import os + +def generate_launch_description(): + config = os.path.join('/home/teama/dev_ws/src/yolov4', 'config', 'cam2image.yaml') + + return LaunchDescription([ + Node( + package='image_tools', + node_executable='cam2image', + parameters=[config] + ), + Node( + package='yolov4', + node_executable='yolov4_node', + output='screen' + ), + Node( + package='rqt_image_view', + node_executable='rqt_image_view', + output='screen' + ) + ]) diff --git a/examples/ROS2 Yolo Nodes/yolov4/package.xml b/examples/ROS2 Yolo Nodes/yolov4/package.xml new file mode 100644 index 0000000..f9366a1 --- /dev/null +++ b/examples/ROS2 Yolo Nodes/yolov4/package.xml @@ -0,0 +1,20 @@ + + + + yolov4 + 0.0.0 + TODO: Package description + teama + TODO: License declaration + + ament_python + + ament_copyright + ament_flake8 + ament_pep257 + python3-pytest + + + ament_python + + diff --git a/examples/ROS2 Yolo Nodes/yolov4/resource/yolov4 b/examples/ROS2 Yolo Nodes/yolov4/resource/yolov4 new file mode 100644 index 0000000..e69de29 diff --git a/examples/ROS2 Yolo Nodes/yolov4/setup.cfg b/examples/ROS2 Yolo Nodes/yolov4/setup.cfg new file mode 100644 index 0000000..70f31ec --- /dev/null +++ b/examples/ROS2 Yolo Nodes/yolov4/setup.cfg @@ -0,0 +1,4 @@ +[develop] +script-dir=$base/lib/yolov4 +[install] +install-scripts=$base/lib/yolov4 diff --git a/examples/ROS2 Yolo Nodes/yolov4/setup.py b/examples/ROS2 Yolo Nodes/yolov4/setup.py new file mode 100644 index 0000000..08278b4 --- /dev/null +++ b/examples/ROS2 Yolo Nodes/yolov4/setup.py @@ -0,0 +1,32 @@ +from setuptools import setup, find_packages +from glob import glob +import os + +package_name = 'yolov4' + +setup( + name=package_name, + version='0.0.0', + packages=find_packages(exclude=['test']), + package_data={ + package_name: ['msg/*'] + }, + data_files=[ + ('share/ament_index/resource_index/packages', ['resource/' + package_name]), + ('share/' + package_name, ['package.xml']), + ('share/' + package_name, ['launch/yolov4_launch.py']), + ('share/' + package_name, ['config/cam2image.yaml']), + ], + install_requires=['setuptools'], + zip_safe=True, + maintainer='sithu', + maintainer_email='sithuaung@globalwalkers.co.jp', + description='TODO: Package description', + license='TODO: License declaration', + tests_require=['pytest'], + entry_points={ + 'console_scripts': [ + 'yolov4_node = yolov4.yolov4_node:main' + ], + }, +) diff --git a/examples/ROS2 Yolo Nodes/yolov4/test/test_copyright.py b/examples/ROS2 Yolo Nodes/yolov4/test/test_copyright.py new file mode 100644 index 0000000..cc8ff03 --- /dev/null +++ b/examples/ROS2 Yolo Nodes/yolov4/test/test_copyright.py @@ -0,0 +1,23 @@ +# Copyright 2015 Open Source Robotics Foundation, Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from ament_copyright.main import main +import pytest + + +@pytest.mark.copyright +@pytest.mark.linter +def test_copyright(): + rc = main(argv=['.', 'test']) + assert rc == 0, 'Found errors' diff --git a/examples/ROS2 Yolo Nodes/yolov4/test/test_flake8.py b/examples/ROS2 Yolo Nodes/yolov4/test/test_flake8.py new file mode 100644 index 0000000..eff8299 --- /dev/null +++ b/examples/ROS2 Yolo Nodes/yolov4/test/test_flake8.py @@ -0,0 +1,23 @@ +# Copyright 2017 Open Source Robotics Foundation, Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from ament_flake8.main import main +import pytest + + +@pytest.mark.flake8 +@pytest.mark.linter +def test_flake8(): + rc = main(argv=[]) + assert rc == 0, 'Found errors' diff --git a/examples/ROS2 Yolo Nodes/yolov4/test/test_pep257.py b/examples/ROS2 Yolo Nodes/yolov4/test/test_pep257.py new file mode 100644 index 0000000..b234a38 --- /dev/null +++ b/examples/ROS2 Yolo Nodes/yolov4/test/test_pep257.py @@ -0,0 +1,23 @@ +# Copyright 2015 Open Source Robotics Foundation, Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from ament_pep257.main import main +import pytest + + +@pytest.mark.linter +@pytest.mark.pep257 +def test_pep257(): + rc = main(argv=['.', 'test']) + assert rc == 0, 'Found code style errors / warnings' diff --git a/examples/ROS2 Yolo Nodes/yolov4/yolov4/__init__.py b/examples/ROS2 Yolo Nodes/yolov4/yolov4/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/examples/ROS2 Yolo Nodes/yolov4/yolov4/scripts/__init__.py b/examples/ROS2 Yolo Nodes/yolov4/yolov4/scripts/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/examples/ROS2 Yolo Nodes/yolov4/yolov4/scripts/infer.py b/examples/ROS2 Yolo Nodes/yolov4/yolov4/scripts/infer.py new file mode 100644 index 0000000..e8da0ed --- /dev/null +++ b/examples/ROS2 Yolo Nodes/yolov4/yolov4/scripts/infer.py @@ -0,0 +1,90 @@ +import tensorrt as trt +import numpy as np +import os +import cv2 +import torch +from yolov4.scripts.utils import * +#from utils import * +import re + +TRT_LOGGER = trt.Logger(trt.Logger.VERBOSE) + +def get_engine(model_path: str): + if os.path.exists(model_path) and model_path.endswith('trt'): + print(f"Reading engine from file {model_path}") + with open(model_path, 'rb') as f, trt.Runtime(TRT_LOGGER) as runtime: + return runtime.deserialize_cuda_engine(f.read()) + else: + print(f"FILE: {model_path} not found or extension not supported.") + + +def preprocess(img, img_size): + image = letterbox(img, new_shape=img_size[-1])[0] + image = image.transpose(2, 0, 1).astype(np.float32) + image = image[np.newaxis, ...] + image /= 255.0 + return np.ascontiguousarray(image) + + +def postprocess(pred, img_size, original_img): + pred = pred.reshape(1, -1, 85) + output = non_max_suppression(torch.from_numpy(pred), conf_thres=0.2, iou_thres=0.2)[0] + + #for det in output: + if output is not None and len(output): + output[:, :4] = scale_coords(img_size, output[:, :4], original_img.shape[:2]).round() + + for *xyxy, conf, cls in output: + label = f'{names[int(cls)]} {conf:.2f}' + plot_one_box(xyxy, original_img, label=label, color=colors[int(cls)], line_thickness=2) + + return original_img + + +class YOLOV4: + def __init__(self, model_path='cfg/yolov4_512_640.trt'): + self.img_size = (352, 416) + engine = get_engine(model_path) + self.context = engine.create_execution_context() + self.inputs, self.outputs, self.bindings, self.stream = allocate_buffers(engine) + + def predict(self, frame): + image = preprocess(frame, self.img_size) + self.inputs[0].host = image + trt_outputs = do_inference_v2(self.context, self.bindings, self.inputs, self.outputs, self.stream)[-1] + vis = postprocess(trt_outputs, self.img_size, frame) + + return vis + + +def main(): + model_path = 'cfg/yolov4_512_640.trt' + img_size = (512, 640) + + webcam = WebcamStream() + fps = FPS() + + engine = get_engine(model_path) + context = engine.create_execution_context() + inputs, outputs, bindings, stream = allocate_buffers(engine) + + while True: + fps.start() + frame = webcam.read() + + image = preprocess(frame[..., ::-1], img_size) + inputs[0].host = image + + trt_outputs = do_inference_v2(context, bindings, inputs, outputs, stream)[-1] + vis = postprocess(trt_outputs, img_size, frame) + + fps.stop() + print(fps.get_fps()) + + cv2.imshow('frame', vis) + + if cv2.waitKey(1) == ord("q"): + webcam.stop() + +if __name__ == '__main__': + main() diff --git a/examples/ROS2 Yolo Nodes/yolov4/yolov4/scripts/utils.py b/examples/ROS2 Yolo Nodes/yolov4/yolov4/scripts/utils.py new file mode 100644 index 0000000..d2089d8 --- /dev/null +++ b/examples/ROS2 Yolo Nodes/yolov4/yolov4/scripts/utils.py @@ -0,0 +1,287 @@ +import itertools +import torch +import torch.nn as nn +import numpy as np +import os +import pycuda.driver as cuda +import pycuda.autoinit +import tensorrt as trt +from threading import Thread +import time +import cv2 +import random +import torchvision + + +names = ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', + 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', + 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', + 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', + 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', + 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', + 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', + 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', + 'hair drier', 'toothbrush'] + +colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(len(names))] + + +EXPLICIT_BATCH = 1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH) + +class WebcamStream: + def __init__(self, src=0): + cap = cv2.VideoCapture(src, cv2.CAP_V4L2) + cap.set(3, 640) + cap.set(4, 480) + assert cap.isOpened(), f"Failed to open {src}" + _, self.frame = cap.read() + + Thread(target=self.update, args=([cap]), daemon=True).start() + + def update(self, cap): + while cap.isOpened(): + cap.grab() + _, self.frame = cap.retrieve() + + def read(self): + return self.frame.copy() + + def stop(self): + cv2.destroyAllWindows() + raise StopIteration + + +class FPS: + def __init__(self): + self.accum_time = 0 + self.curr_fps = 0 + self.fps = "FPS: ??" + + def start(self): + self.prev_time = time.time() + + def stop(self): + self.curr_time = time.time() + exec_time = self.curr_time - self.prev_time + self.prev_time = self.curr_time + self.accum_time += exec_time + + def get_fps(self): + self.curr_fps += 1 + if self.accum_time > 1: + self.accum_time -= 1 + self.fps = "FPS: " + str(self.curr_fps) + self.curr_fps = 0 + return self.fps + + +class HostDeviceMem(object): + def __init__(self, host_mem, device_mem): + self.host = host_mem + self.device = device_mem + + def __str__(self): + return "Host:\n" + str(self.host) + "\nDevice:\n" + str(self.device) + + def __repr__(self): + return self.__str__() + + +def allocate_buffers(engine): + inputs = [] + outputs = [] + bindings = [] + stream = cuda.Stream() + for binding in engine: + size = trt.volume(engine.get_binding_shape(binding)) * engine.max_batch_size + dtype = trt.nptype(engine.get_binding_dtype(binding)) + # Allocate host and device buffers + host_mem = cuda.pagelocked_empty(size, dtype) + device_mem = cuda.mem_alloc(host_mem.nbytes) + # Append the device buffer to device bindings. + bindings.append(int(device_mem)) + # Append to the appropriate list. + if engine.binding_is_input(binding): + inputs.append(HostDeviceMem(host_mem, device_mem)) + else: + outputs.append(HostDeviceMem(host_mem, device_mem)) + return inputs, outputs, bindings, stream + + +def do_inference_v2(context, bindings, inputs, outputs, stream): + # Transfer input data to the GPU. + [cuda.memcpy_htod_async(inp.device, inp.host, stream) for inp in inputs] + # Run inference. + context.execute_async_v2(bindings=bindings, stream_handle=stream.handle) + # Transfer predictions back from the GPU. + [cuda.memcpy_dtoh_async(out.host, out.device, stream) for out in outputs] + # Synchronize the stream + stream.synchronize() + # Return only the host outputs. + return [out.host for out in outputs] + + +def letterbox(img, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True): + # Resize image to a 32-pixel-multiple rectangle https://github.com/ultralytics/yolov3/issues/232 + shape = img.shape[:2] # current shape [height, width] + if isinstance(new_shape, int): + new_shape = (new_shape, new_shape) + + # Scale ratio (new / old) + r = min(new_shape[0] / shape[0], new_shape[1] / shape[1]) + if not scaleup: # only scale down, do not scale up (for better test mAP) + r = min(r, 1.0) + + # Compute padding + ratio = r, r # width, height ratios + new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r)) + dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding + if auto: # minimum rectangle + dw, dh = np.mod(dw, 64), np.mod(dh, 64) # wh padding + elif scaleFill: # stretch + dw, dh = 0.0, 0.0 + new_unpad = (new_shape[1], new_shape[0]) + ratio = new_shape[1] / shape[1], new_shape[0] / shape[0] # width, height ratios + + dw /= 2 # divide padding into 2 sides + dh /= 2 + + if shape[::-1] != new_unpad: # resize + img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR) + top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1)) + left, right = int(round(dw - 0.1)), int(round(dw + 0.1)) + img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border + return img, ratio, (dw, dh) + + +def non_max_suppression(prediction, conf_thres=0.1, iou_thres=0.6, merge=False, classes=None, agnostic=False): + """Performs Non-Maximum Suppression (NMS) on inference results + + Returns: + detections with shape: nx6 (x1, y1, x2, y2, conf, cls) + """ + if prediction.dtype is torch.float16: + prediction = prediction.float() # to FP32 + + nc = prediction[0].shape[1] - 5 # number of classes + xc = prediction[..., 4] > conf_thres # candidates + + # Settings + min_wh, max_wh = 2, 4096 # (pixels) minimum and maximum box width and height + max_det = 300 # maximum number of detections per image + time_limit = 10.0 # seconds to quit after + redundant = True # require redundant detections + multi_label = nc > 1 # multiple labels per box (adds 0.5ms/img) + + t = time.time() + output = [None] * prediction.shape[0] + for xi, x in enumerate(prediction): # image index, image inference + # Apply constraints + # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # width-height + x = x[xc[xi]] # confidence + + # If none remain process next image + if not x.shape[0]: + continue + + # Compute conf + x[:, 5:] *= x[:, 4:5] # conf = obj_conf * cls_conf + + # Box (center x, center y, width, height) to (x1, y1, x2, y2) + box = xywh2xyxy(x[:, :4]) + + # Detections matrix nx6 (xyxy, conf, cls) + if multi_label: + i, j = (x[:, 5:] > conf_thres).nonzero().t() + x = torch.cat((box[i], x[i, j + 5, None], j[:, None].float()), 1) + else: # best class only + conf, j = x[:, 5:].max(1, keepdim=True) + x = torch.cat((box, conf, j.float()), 1)[conf.view(-1) > conf_thres] + + # Filter by class + if classes: + x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)] + + # Apply finite constraint + # if not torch.isfinite(x).all(): + # x = x[torch.isfinite(x).all(1)] + + # If none remain process next image + n = x.shape[0] # number of boxes + if not n: + continue + + # Sort by confidence + # x = x[x[:, 4].argsort(descending=True)] + + # Batched NMS + c = x[:, 5:6] * (0 if agnostic else max_wh) # classes + boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores + i = torchvision.ops.boxes.nms(boxes, scores, iou_thres) + if i.shape[0] > max_det: # limit detections + i = i[:max_det] + if merge and (1 < n < 3E3): # Merge NMS (boxes merged using weighted mean) + try: # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4) + iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix + weights = iou * scores[None] # box weights + x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True) # merged boxes + if redundant: + i = i[iou.sum(1) > 1] # require redundancy + except: # possible CUDA error https://github.com/ultralytics/yolov3/issues/1139 + print(x, i, x.shape, i.shape) + pass + + output[xi] = x[i] + if (time.time() - t) > time_limit: + break # time limit exceeded + + return output + + +def xywh2xyxy(x): + # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right + y = torch.zeros_like(x) if isinstance(x, torch.Tensor) else np.zeros_like(x) + y[:, 0] = x[:, 0] - x[:, 2] / 2 # top left x + y[:, 1] = x[:, 1] - x[:, 3] / 2 # top left y + y[:, 2] = x[:, 0] + x[:, 2] / 2 # bottom right x + y[:, 3] = x[:, 1] + x[:, 3] / 2 # bottom right y + return y + + +def scale_coords(img1_shape, coords, img0_shape, ratio_pad=None): + # Rescale coords (xyxy) from img1_shape to img0_shape + if ratio_pad is None: # calculate from img0_shape + gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new + pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding + else: + gain = ratio_pad[0][0] + pad = ratio_pad[1] + + coords[:, [0, 2]] -= pad[0] # x padding + coords[:, [1, 3]] -= pad[1] # y padding + coords[:, :4] /= gain + clip_coords(coords, img0_shape) + return coords + + +def clip_coords(boxes, img_shape): + # Clip bounding xyxy bounding boxes to image shape (height, width) + boxes[:, 0].clamp_(0, img_shape[1]) # x1 + boxes[:, 1].clamp_(0, img_shape[0]) # y1 + boxes[:, 2].clamp_(0, img_shape[1]) # x2 + boxes[:, 3].clamp_(0, img_shape[0]) # y2 + + +def plot_one_box(x, img, color=None, label=None, line_thickness=None): + # Plots one bounding box on image img + tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1 # line/font thickness + color = color or [random.randint(0, 255) for _ in range(3)] + c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3])) + cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA) + if label: + tf = max(tl - 1, 1) # font thickness + t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0] + c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3 + cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA) # filled + cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA) + diff --git a/examples/ROS2 Yolo Nodes/yolov4/yolov4/yolov4_node.py b/examples/ROS2 Yolo Nodes/yolov4/yolov4/yolov4_node.py new file mode 100644 index 0000000..db830e1 --- /dev/null +++ b/examples/ROS2 Yolo Nodes/yolov4/yolov4/yolov4_node.py @@ -0,0 +1,62 @@ +from std_msgs.msg import String +from sensor_msgs.msg import Image +from cv_bridge import CvBridge, CvBridgeError +import rclpy +import yaml +import cv2 +import torch +import os +import time +from rclpy.node import Node +from threading import Thread, Event +from yolov4.scripts.infer import YOLOV4, FPS +from ament_index_python.packages import get_package_prefix + +BASE_PATH = os.path.join(get_package_prefix('yolov4').replace('install', 'src'), 'yolov4') +WEIGHTS_PATH = os.path.join(BASE_PATH, 'scripts/cfg', 'yolov4_352_416.trt') + + +class YOLOv4Node(Node): + def __init__(self): + super().__init__('yolov4_node') + self.bridge = CvBridge() + self.current_frame = None + + self.get_logger().info('Model Initializing...') + self.yolo = YOLOV4(WEIGHTS_PATH) + self.get_logger().info('Model Loaded...') + + self.subscriber = self.create_subscription(Image, '/image', self.callback_image, 5) + self.subscriber + self.publisher = self.create_publisher(Image, '/yolov4/vis', 5) + self.fps = FPS() + timer_period = 0.01 + timer = self.create_timer(timer_period, self.process_image) + + def process_image(self): + if self.current_frame is not None: + self.fps.start() + image = self.yolo.predict(self.current_frame) + image_msg = self.bridge.cv2_to_imgmsg(image, 'rgb8') + self.publisher.publish(image_msg) + self.fps.stop() + curr_fps = self.fps.get_fps() + self.get_logger().info(f'Current {curr_fps}') + + def callback_image(self, data): + try: + cv_image = self.bridge.imgmsg_to_cv2(data, 'rgb8') + except CvBridgeError as e: + raise e + self.current_frame = cv_image + + +def main(args=None): + rclpy.init(args=args) + main_node = YOLOv4Node() + rclpy.spin(main_node) + main_node.destroy_node() + rclpy.shutdown() + +if __name__ == '__main__': + main() diff --git a/examples/ROS2 Yolo Nodes/yolov5/config/cam2image.yaml b/examples/ROS2 Yolo Nodes/yolov5/config/cam2image.yaml new file mode 100644 index 0000000..e5ed3d7 --- /dev/null +++ b/examples/ROS2 Yolo Nodes/yolov5/config/cam2image.yaml @@ -0,0 +1,11 @@ +cam2image: + ros__parameters: + burger_mode: false + depth: 10 + frequency: 30.0 + height: 480 + history: keep_all + reliability: reliable + show_camera: false + use_sim_time: false + width: 640 diff --git a/examples/ROS2 Yolo Nodes/yolov5/launch/yolov5_launch.py b/examples/ROS2 Yolo Nodes/yolov5/launch/yolov5_launch.py new file mode 100644 index 0000000..63a23db --- /dev/null +++ b/examples/ROS2 Yolo Nodes/yolov5/launch/yolov5_launch.py @@ -0,0 +1,25 @@ +from launch import LaunchDescription +from launch_ros.actions import Node +from ament_index_python.packages import get_package_share_directory +import os + +def generate_launch_description(): + config = os.path.join('/home/teama/dev_ws/src/yolov4', 'config', 'cam2image.yaml') + + return LaunchDescription([ + Node( + package='image_tools', + node_executable='cam2image', + parameters=[config] + ), + Node( + package='yolov5', + node_executable='yolov5_node', + output='screen' + ), + Node( + package='rqt_image_view', + node_executable='rqt_image_view', + output='screen' + ) + ]) diff --git a/examples/ROS2 Yolo Nodes/yolov5/package.xml b/examples/ROS2 Yolo Nodes/yolov5/package.xml new file mode 100644 index 0000000..fa76986 --- /dev/null +++ b/examples/ROS2 Yolo Nodes/yolov5/package.xml @@ -0,0 +1,20 @@ + + + + yolov5 + 0.0.0 + TODO: Package description + teama + TODO: License declaration + + ament_python + + ament_copyright + ament_flake8 + ament_pep257 + python3-pytest + + + ament_python + + diff --git a/examples/ROS2 Yolo Nodes/yolov5/resource/yolov5 b/examples/ROS2 Yolo Nodes/yolov5/resource/yolov5 new file mode 100644 index 0000000..e69de29 diff --git a/examples/ROS2 Yolo Nodes/yolov5/setup.cfg b/examples/ROS2 Yolo Nodes/yolov5/setup.cfg new file mode 100644 index 0000000..6678ebb --- /dev/null +++ b/examples/ROS2 Yolo Nodes/yolov5/setup.cfg @@ -0,0 +1,4 @@ +[develop] +script-dir=$base/lib/yolov5 +[install] +install-scripts=$base/lib/yolov5 diff --git a/examples/ROS2 Yolo Nodes/yolov5/setup.py b/examples/ROS2 Yolo Nodes/yolov5/setup.py new file mode 100644 index 0000000..63e7598 --- /dev/null +++ b/examples/ROS2 Yolo Nodes/yolov5/setup.py @@ -0,0 +1,32 @@ +from setuptools import setup, find_packages +from glob import glob +import os + +package_name = 'yolov5' + +setup( + name=package_name, + version='0.0.0', + packages=find_packages(exclude=['test']), + package_data={ + package_name: ['msg/*'] + }, + data_files=[ + ('share/ament_index/resource_index/packages', ['resource/' + package_name]), + ('share/' + package_name, ['package.xml']), + ('share/' + package_name, ['launch/yolov5_launch.py']), + ('share/' + package_name, ['config/cam2image.yaml']), + ], + install_requires=['setuptools'], + zip_safe=True, + maintainer='sithu', + maintainer_email='sithuaung@globalwalkers.co.jp', + description='TODO: Package description', + license='TODO: License declaration', + tests_require=['pytest'], + entry_points={ + 'console_scripts': [ + 'yolov5_node = yolov5.yolov5_node:main' + ], + }, +) diff --git a/examples/ROS2 Yolo Nodes/yolov5/test/test_copyright.py b/examples/ROS2 Yolo Nodes/yolov5/test/test_copyright.py new file mode 100644 index 0000000..cc8ff03 --- /dev/null +++ b/examples/ROS2 Yolo Nodes/yolov5/test/test_copyright.py @@ -0,0 +1,23 @@ +# Copyright 2015 Open Source Robotics Foundation, Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from ament_copyright.main import main +import pytest + + +@pytest.mark.copyright +@pytest.mark.linter +def test_copyright(): + rc = main(argv=['.', 'test']) + assert rc == 0, 'Found errors' diff --git a/examples/ROS2 Yolo Nodes/yolov5/test/test_flake8.py b/examples/ROS2 Yolo Nodes/yolov5/test/test_flake8.py new file mode 100644 index 0000000..eff8299 --- /dev/null +++ b/examples/ROS2 Yolo Nodes/yolov5/test/test_flake8.py @@ -0,0 +1,23 @@ +# Copyright 2017 Open Source Robotics Foundation, Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from ament_flake8.main import main +import pytest + + +@pytest.mark.flake8 +@pytest.mark.linter +def test_flake8(): + rc = main(argv=[]) + assert rc == 0, 'Found errors' diff --git a/examples/ROS2 Yolo Nodes/yolov5/test/test_pep257.py b/examples/ROS2 Yolo Nodes/yolov5/test/test_pep257.py new file mode 100644 index 0000000..b234a38 --- /dev/null +++ b/examples/ROS2 Yolo Nodes/yolov5/test/test_pep257.py @@ -0,0 +1,23 @@ +# Copyright 2015 Open Source Robotics Foundation, Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from ament_pep257.main import main +import pytest + + +@pytest.mark.linter +@pytest.mark.pep257 +def test_pep257(): + rc = main(argv=['.', 'test']) + assert rc == 0, 'Found code style errors / warnings' diff --git a/examples/ROS2 Yolo Nodes/yolov5/yolov5/__init__.py b/examples/ROS2 Yolo Nodes/yolov5/yolov5/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/examples/ROS2 Yolo Nodes/yolov5/yolov5/scripts/__init__.py b/examples/ROS2 Yolo Nodes/yolov5/yolov5/scripts/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/examples/ROS2 Yolo Nodes/yolov5/yolov5/scripts/infer.py b/examples/ROS2 Yolo Nodes/yolov5/yolov5/scripts/infer.py new file mode 100644 index 0000000..870f6ab --- /dev/null +++ b/examples/ROS2 Yolo Nodes/yolov5/yolov5/scripts/infer.py @@ -0,0 +1,90 @@ +import tensorrt as trt +import numpy as np +import os +import cv2 +import torch +from yolov5.scripts.utils import * +#from utils import * +import re + +TRT_LOGGER = trt.Logger(trt.Logger.VERBOSE) + +def get_engine(model_path: str): + if os.path.exists(model_path) and model_path.endswith('trt'): + print(f"Reading engine from file {model_path}") + with open(model_path, 'rb') as f, trt.Runtime(TRT_LOGGER) as runtime: + return runtime.deserialize_cuda_engine(f.read()) + else: + print(f"FILE: {model_path} not found or extension not supported.") + + +def preprocess(img, img_size): + image = letterbox(img, new_shape=img_size[-1])[0] + image = image.transpose(2, 0, 1).astype(np.float32) + image = image[np.newaxis, ...] + image /= 255.0 + return np.ascontiguousarray(image) + + +def postprocess(pred, img_size, original_img): + pred = pred.reshape(1, -1, 85) + output = non_max_suppression(torch.from_numpy(pred), conf_thres=0.2, iou_thres=0.2)[0] + + #for det in output: + if output is not None and len(output): + output[:, :4] = scale_coords(img_size, output[:, :4], original_img.shape[:2]).round() + + for *xyxy, conf, cls in output: + label = f'{names[int(cls)]} {conf:.2f}' + plot_one_box(xyxy, original_img, label=label, color=colors[int(cls)], line_thickness=2) + + return original_img + + +class YOLOV5: + def __init__(self, model_path='cfg/yolov5_512_640.trt'): + self.img_size = (352, 416) + engine = get_engine(model_path) + self.context = engine.create_execution_context() + self.inputs, self.outputs, self.bindings, self.stream = allocate_buffers(engine) + + def predict(self, frame): + image = preprocess(frame, self.img_size) + self.inputs[0].host = image + trt_outputs = do_inference_v2(self.context, self.bindings, self.inputs, self.outputs, self.stream)[-1] + vis = postprocess(trt_outputs, self.img_size, frame) + + return vis + + +def main(): + model_path = 'cfg/yolov5_512_640.trt' + img_size = (512, 640) + + webcam = WebcamStream() + fps = FPS() + + engine = get_engine(model_path) + context = engine.create_execution_context() + inputs, outputs, bindings, stream = allocate_buffers(engine) + + while True: + fps.start() + frame = webcam.read() + + image = preprocess(frame[..., ::-1], img_size) + inputs[0].host = image + + trt_outputs = do_inference_v2(context, bindings, inputs, outputs, stream)[-1] + vis = postprocess(trt_outputs, img_size, frame) + + fps.stop() + print(fps.get_fps()) + + cv2.imshow('frame', vis) + + if cv2.waitKey(1) == ord("q"): + webcam.stop() + +if __name__ == '__main__': + main() diff --git a/examples/ROS2 Yolo Nodes/yolov5/yolov5/scripts/utils.py b/examples/ROS2 Yolo Nodes/yolov5/yolov5/scripts/utils.py new file mode 100644 index 0000000..d2089d8 --- /dev/null +++ b/examples/ROS2 Yolo Nodes/yolov5/yolov5/scripts/utils.py @@ -0,0 +1,287 @@ +import itertools +import torch +import torch.nn as nn +import numpy as np +import os +import pycuda.driver as cuda +import pycuda.autoinit +import tensorrt as trt +from threading import Thread +import time +import cv2 +import random +import torchvision + + +names = ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', + 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', + 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', + 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', + 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', + 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', + 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', + 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', + 'hair drier', 'toothbrush'] + +colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(len(names))] + + +EXPLICIT_BATCH = 1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH) + +class WebcamStream: + def __init__(self, src=0): + cap = cv2.VideoCapture(src, cv2.CAP_V4L2) + cap.set(3, 640) + cap.set(4, 480) + assert cap.isOpened(), f"Failed to open {src}" + _, self.frame = cap.read() + + Thread(target=self.update, args=([cap]), daemon=True).start() + + def update(self, cap): + while cap.isOpened(): + cap.grab() + _, self.frame = cap.retrieve() + + def read(self): + return self.frame.copy() + + def stop(self): + cv2.destroyAllWindows() + raise StopIteration + + +class FPS: + def __init__(self): + self.accum_time = 0 + self.curr_fps = 0 + self.fps = "FPS: ??" + + def start(self): + self.prev_time = time.time() + + def stop(self): + self.curr_time = time.time() + exec_time = self.curr_time - self.prev_time + self.prev_time = self.curr_time + self.accum_time += exec_time + + def get_fps(self): + self.curr_fps += 1 + if self.accum_time > 1: + self.accum_time -= 1 + self.fps = "FPS: " + str(self.curr_fps) + self.curr_fps = 0 + return self.fps + + +class HostDeviceMem(object): + def __init__(self, host_mem, device_mem): + self.host = host_mem + self.device = device_mem + + def __str__(self): + return "Host:\n" + str(self.host) + "\nDevice:\n" + str(self.device) + + def __repr__(self): + return self.__str__() + + +def allocate_buffers(engine): + inputs = [] + outputs = [] + bindings = [] + stream = cuda.Stream() + for binding in engine: + size = trt.volume(engine.get_binding_shape(binding)) * engine.max_batch_size + dtype = trt.nptype(engine.get_binding_dtype(binding)) + # Allocate host and device buffers + host_mem = cuda.pagelocked_empty(size, dtype) + device_mem = cuda.mem_alloc(host_mem.nbytes) + # Append the device buffer to device bindings. + bindings.append(int(device_mem)) + # Append to the appropriate list. + if engine.binding_is_input(binding): + inputs.append(HostDeviceMem(host_mem, device_mem)) + else: + outputs.append(HostDeviceMem(host_mem, device_mem)) + return inputs, outputs, bindings, stream + + +def do_inference_v2(context, bindings, inputs, outputs, stream): + # Transfer input data to the GPU. + [cuda.memcpy_htod_async(inp.device, inp.host, stream) for inp in inputs] + # Run inference. + context.execute_async_v2(bindings=bindings, stream_handle=stream.handle) + # Transfer predictions back from the GPU. + [cuda.memcpy_dtoh_async(out.host, out.device, stream) for out in outputs] + # Synchronize the stream + stream.synchronize() + # Return only the host outputs. + return [out.host for out in outputs] + + +def letterbox(img, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True): + # Resize image to a 32-pixel-multiple rectangle https://github.com/ultralytics/yolov3/issues/232 + shape = img.shape[:2] # current shape [height, width] + if isinstance(new_shape, int): + new_shape = (new_shape, new_shape) + + # Scale ratio (new / old) + r = min(new_shape[0] / shape[0], new_shape[1] / shape[1]) + if not scaleup: # only scale down, do not scale up (for better test mAP) + r = min(r, 1.0) + + # Compute padding + ratio = r, r # width, height ratios + new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r)) + dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding + if auto: # minimum rectangle + dw, dh = np.mod(dw, 64), np.mod(dh, 64) # wh padding + elif scaleFill: # stretch + dw, dh = 0.0, 0.0 + new_unpad = (new_shape[1], new_shape[0]) + ratio = new_shape[1] / shape[1], new_shape[0] / shape[0] # width, height ratios + + dw /= 2 # divide padding into 2 sides + dh /= 2 + + if shape[::-1] != new_unpad: # resize + img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR) + top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1)) + left, right = int(round(dw - 0.1)), int(round(dw + 0.1)) + img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border + return img, ratio, (dw, dh) + + +def non_max_suppression(prediction, conf_thres=0.1, iou_thres=0.6, merge=False, classes=None, agnostic=False): + """Performs Non-Maximum Suppression (NMS) on inference results + + Returns: + detections with shape: nx6 (x1, y1, x2, y2, conf, cls) + """ + if prediction.dtype is torch.float16: + prediction = prediction.float() # to FP32 + + nc = prediction[0].shape[1] - 5 # number of classes + xc = prediction[..., 4] > conf_thres # candidates + + # Settings + min_wh, max_wh = 2, 4096 # (pixels) minimum and maximum box width and height + max_det = 300 # maximum number of detections per image + time_limit = 10.0 # seconds to quit after + redundant = True # require redundant detections + multi_label = nc > 1 # multiple labels per box (adds 0.5ms/img) + + t = time.time() + output = [None] * prediction.shape[0] + for xi, x in enumerate(prediction): # image index, image inference + # Apply constraints + # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # width-height + x = x[xc[xi]] # confidence + + # If none remain process next image + if not x.shape[0]: + continue + + # Compute conf + x[:, 5:] *= x[:, 4:5] # conf = obj_conf * cls_conf + + # Box (center x, center y, width, height) to (x1, y1, x2, y2) + box = xywh2xyxy(x[:, :4]) + + # Detections matrix nx6 (xyxy, conf, cls) + if multi_label: + i, j = (x[:, 5:] > conf_thres).nonzero().t() + x = torch.cat((box[i], x[i, j + 5, None], j[:, None].float()), 1) + else: # best class only + conf, j = x[:, 5:].max(1, keepdim=True) + x = torch.cat((box, conf, j.float()), 1)[conf.view(-1) > conf_thres] + + # Filter by class + if classes: + x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)] + + # Apply finite constraint + # if not torch.isfinite(x).all(): + # x = x[torch.isfinite(x).all(1)] + + # If none remain process next image + n = x.shape[0] # number of boxes + if not n: + continue + + # Sort by confidence + # x = x[x[:, 4].argsort(descending=True)] + + # Batched NMS + c = x[:, 5:6] * (0 if agnostic else max_wh) # classes + boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores + i = torchvision.ops.boxes.nms(boxes, scores, iou_thres) + if i.shape[0] > max_det: # limit detections + i = i[:max_det] + if merge and (1 < n < 3E3): # Merge NMS (boxes merged using weighted mean) + try: # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4) + iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix + weights = iou * scores[None] # box weights + x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True) # merged boxes + if redundant: + i = i[iou.sum(1) > 1] # require redundancy + except: # possible CUDA error https://github.com/ultralytics/yolov3/issues/1139 + print(x, i, x.shape, i.shape) + pass + + output[xi] = x[i] + if (time.time() - t) > time_limit: + break # time limit exceeded + + return output + + +def xywh2xyxy(x): + # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right + y = torch.zeros_like(x) if isinstance(x, torch.Tensor) else np.zeros_like(x) + y[:, 0] = x[:, 0] - x[:, 2] / 2 # top left x + y[:, 1] = x[:, 1] - x[:, 3] / 2 # top left y + y[:, 2] = x[:, 0] + x[:, 2] / 2 # bottom right x + y[:, 3] = x[:, 1] + x[:, 3] / 2 # bottom right y + return y + + +def scale_coords(img1_shape, coords, img0_shape, ratio_pad=None): + # Rescale coords (xyxy) from img1_shape to img0_shape + if ratio_pad is None: # calculate from img0_shape + gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new + pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding + else: + gain = ratio_pad[0][0] + pad = ratio_pad[1] + + coords[:, [0, 2]] -= pad[0] # x padding + coords[:, [1, 3]] -= pad[1] # y padding + coords[:, :4] /= gain + clip_coords(coords, img0_shape) + return coords + + +def clip_coords(boxes, img_shape): + # Clip bounding xyxy bounding boxes to image shape (height, width) + boxes[:, 0].clamp_(0, img_shape[1]) # x1 + boxes[:, 1].clamp_(0, img_shape[0]) # y1 + boxes[:, 2].clamp_(0, img_shape[1]) # x2 + boxes[:, 3].clamp_(0, img_shape[0]) # y2 + + +def plot_one_box(x, img, color=None, label=None, line_thickness=None): + # Plots one bounding box on image img + tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1 # line/font thickness + color = color or [random.randint(0, 255) for _ in range(3)] + c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3])) + cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA) + if label: + tf = max(tl - 1, 1) # font thickness + t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0] + c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3 + cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA) # filled + cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA) + diff --git a/examples/ROS2 Yolo Nodes/yolov5/yolov5/yolov5_node.py b/examples/ROS2 Yolo Nodes/yolov5/yolov5/yolov5_node.py new file mode 100644 index 0000000..e1048bb --- /dev/null +++ b/examples/ROS2 Yolo Nodes/yolov5/yolov5/yolov5_node.py @@ -0,0 +1,62 @@ +from std_msgs.msg import String +from sensor_msgs.msg import Image +from cv_bridge import CvBridge, CvBridgeError +import rclpy +import yaml +import cv2 +import torch +import os +import time +from rclpy.node import Node +from threading import Thread, Event +from yolov5.scripts.infer import YOLOV5, FPS +from ament_index_python.packages import get_package_prefix + +BASE_PATH = os.path.join(get_package_prefix('yolov5').replace('install', 'src'), 'yolov5') +WEIGHTS_PATH = os.path.join(BASE_PATH, 'scripts/cfg', 'yolov5_352_416.trt') + + +class YOLOv5Node(Node): + def __init__(self): + super().__init__('yolov5_node') + self.bridge = CvBridge() + self.current_frame = None + + self.get_logger().info('Model Initializing...') + self.yolo = YOLOV5(WEIGHTS_PATH) + self.get_logger().info('Model Loaded...') + + self.subscriber = self.create_subscription(Image, '/image', self.callback_image, 5) + self.subscriber + self.publisher = self.create_publisher(Image, '/yolov5/vis', 5) + self.fps = FPS() + timer_period = 0.01 + timer = self.create_timer(timer_period, self.process_image) + + def process_image(self): + if self.current_frame is not None: + self.fps.start() + image = self.yolo.predict(self.current_frame) + image_msg = self.bridge.cv2_to_imgmsg(image, 'rgb8') + self.publisher.publish(image_msg) + self.fps.stop() + curr_fps = self.fps.get_fps() + self.get_logger().info(f'Current {curr_fps}') + + def callback_image(self, data): + try: + cv_image = self.bridge.imgmsg_to_cv2(data, 'rgb8') + except CvBridgeError as e: + raise e + self.current_frame = cv_image + + +def main(args=None): + rclpy.init(args=args) + main_node = YOLOv5Node() + rclpy.spin(main_node) + main_node.destroy_node() + rclpy.shutdown() + +if __name__ == '__main__': + main() diff --git a/examples/Ultralytics Module/autobackend.py b/examples/Ultralytics Module/autobackend.py new file mode 100644 index 0000000..a0e2e43 --- /dev/null +++ b/examples/Ultralytics Module/autobackend.py @@ -0,0 +1,671 @@ +# Ultralytics YOLO 🚀, AGPL-3.0 license + +import ast +import contextlib +import json +import platform +import zipfile +from collections import OrderedDict, namedtuple +from pathlib import Path + +import cv2 +import numpy as np +import torch +import torch.nn as nn +from PIL import Image + +from ultralytics.utils import ARM64, IS_JETSON, IS_RASPBERRYPI, LINUX, LOGGER, ROOT, yaml_load +from ultralytics.utils.checks import check_requirements, check_suffix, check_version, check_yaml +from ultralytics.utils.downloads import attempt_download_asset, is_url + + +def check_class_names(names): + """ + Check class names. + + Map imagenet class codes to human-readable names if required. Convert lists to dicts. + """ + if isinstance(names, list): # names is a list + names = dict(enumerate(names)) # convert to dict + if isinstance(names, dict): + # Convert 1) string keys to int, i.e. '0' to 0, and non-string values to strings, i.e. True to 'True' + names = {int(k): str(v) for k, v in names.items()} + n = len(names) + if max(names.keys()) >= n: + raise KeyError( + f"{n}-class dataset requires class indices 0-{n - 1}, but you have invalid class indices " + f"{min(names.keys())}-{max(names.keys())} defined in your dataset YAML." + ) + if isinstance(names[0], str) and names[0].startswith("n0"): # imagenet class codes, i.e. 'n01440764' + names_map = yaml_load(ROOT / "cfg/datasets/ImageNet.yaml")["map"] # human-readable names + names = {k: names_map[v] for k, v in names.items()} + return names + + +def default_class_names(data=None): + """Applies default class names to an input YAML file or returns numerical class names.""" + if data: + with contextlib.suppress(Exception): + return yaml_load(check_yaml(data))["names"] + return {i: f"class{i}" for i in range(999)} # return default if above errors + + +class AutoBackend(nn.Module): + """ + Handles dynamic backend selection for running inference using Ultralytics YOLO models. + + The AutoBackend class is designed to provide an abstraction layer for various inference engines. It supports a wide + range of formats, each with specific naming conventions as outlined below: + + Supported Formats and Naming Conventions: + | Format | File Suffix | + |-----------------------|------------------| + | PyTorch | *.pt | + | TorchScript | *.torchscript | + | ONNX Runtime | *.onnx | + | ONNX OpenCV DNN | *.onnx (dnn=True)| + | OpenVINO | *openvino_model/ | + | CoreML | *.mlpackage | + | TensorRT | *.engine | + | TensorFlow SavedModel | *_saved_model | + | TensorFlow GraphDef | *.pb | + | TensorFlow Lite | *.tflite | + | TensorFlow Edge TPU | *_edgetpu.tflite | + | PaddlePaddle | *_paddle_model | + | NCNN | *_ncnn_model | + + This class offers dynamic backend switching capabilities based on the input model format, making it easier to deploy + models across various platforms. + """ + + @torch.no_grad() + def __init__( + self, + weights="yolov8n.pt", + device=torch.device("cpu"), + dnn=False, + data=None, + fp16=False, + batch=1, + fuse=True, + verbose=True, + ): + """ + Initialize the AutoBackend for inference. + + Args: + weights (str): Path to the model weights file. Defaults to 'yolov8n.pt'. + device (torch.device): Device to run the model on. Defaults to CPU. + dnn (bool): Use OpenCV DNN module for ONNX inference. Defaults to False. + data (str | Path | optional): Path to the additional data.yaml file containing class names. Optional. + fp16 (bool): Enable half-precision inference. Supported only on specific backends. Defaults to False. + batch (int): Batch-size to assume for inference. + fuse (bool): Fuse Conv2D + BatchNorm layers for optimization. Defaults to True. + verbose (bool): Enable verbose logging. Defaults to True. + """ + super().__init__() + w = str(weights[0] if isinstance(weights, list) else weights) + nn_module = isinstance(weights, torch.nn.Module) + ( + pt, + jit, + onnx, + xml, + engine, + coreml, + saved_model, + pb, + tflite, + edgetpu, + tfjs, + paddle, + ncnn, + triton, + ) = self._model_type(w) + fp16 &= pt or jit or onnx or xml or engine or nn_module or triton # FP16 + nhwc = coreml or saved_model or pb or tflite or edgetpu # BHWC formats (vs torch BCWH) + stride = 32 # default stride + model, metadata = None, None + + # Set device + cuda = torch.cuda.is_available() and device.type != "cpu" # use CUDA + if cuda and not any([nn_module, pt, jit, engine, onnx]): # GPU dataloader formats + device = torch.device("cpu") + cuda = False + + # Download if not local + if not (pt or triton or nn_module): + w = attempt_download_asset(w) + + # In-memory PyTorch model + if nn_module: + model = weights.to(device) + if fuse: + model = model.fuse(verbose=verbose) + if hasattr(model, "kpt_shape"): + kpt_shape = model.kpt_shape # pose-only + stride = max(int(model.stride.max()), 32) # model stride + names = model.module.names if hasattr(model, "module") else model.names # get class names + model.half() if fp16 else model.float() + self.model = model # explicitly assign for to(), cpu(), cuda(), half() + pt = True + + # PyTorch + elif pt: + from ultralytics.nn.tasks import attempt_load_weights + + model = attempt_load_weights( + weights if isinstance(weights, list) else w, device=device, inplace=True, fuse=fuse + ) + if hasattr(model, "kpt_shape"): + kpt_shape = model.kpt_shape # pose-only + stride = max(int(model.stride.max()), 32) # model stride + names = model.module.names if hasattr(model, "module") else model.names # get class names + model.half() if fp16 else model.float() + self.model = model # explicitly assign for to(), cpu(), cuda(), half() + + # TorchScript + elif jit: + LOGGER.info(f"Loading {w} for TorchScript inference...") + extra_files = {"config.txt": ""} # model metadata + model = torch.jit.load(w, _extra_files=extra_files, map_location=device) + model.half() if fp16 else model.float() + if extra_files["config.txt"]: # load metadata dict + metadata = json.loads(extra_files["config.txt"], object_hook=lambda x: dict(x.items())) + + # ONNX OpenCV DNN + elif dnn: + LOGGER.info(f"Loading {w} for ONNX OpenCV DNN inference...") + check_requirements("opencv-python>=4.5.4") + net = cv2.dnn.readNetFromONNX(w) + + # ONNX Runtime + elif onnx: + LOGGER.info(f"Loading {w} for ONNX Runtime inference...") + check_requirements(("onnx", "onnxruntime-gpu" if cuda else "onnxruntime")) + if IS_RASPBERRYPI or IS_JETSON: + # Fix 'numpy.linalg._umath_linalg' has no attribute '_ilp64' for TF SavedModel on RPi and Jetson + check_requirements("numpy==1.23.5") + import onnxruntime + + providers = ["CUDAExecutionProvider", "CPUExecutionProvider"] if cuda else ["CPUExecutionProvider"] + session = onnxruntime.InferenceSession(w, providers=providers) + output_names = [x.name for x in session.get_outputs()] + metadata = session.get_modelmeta().custom_metadata_map + + # OpenVINO + elif xml: + LOGGER.info(f"Loading {w} for OpenVINO inference...") + check_requirements("openvino>=2024.0.0") + import openvino as ov + + core = ov.Core() + w = Path(w) + if not w.is_file(): # if not *.xml + w = next(w.glob("*.xml")) # get *.xml file from *_openvino_model dir + ov_model = core.read_model(model=str(w), weights=w.with_suffix(".bin")) + if ov_model.get_parameters()[0].get_layout().empty: + ov_model.get_parameters()[0].set_layout(ov.Layout("NCHW")) + + # OpenVINO inference modes are 'LATENCY', 'THROUGHPUT' (not recommended), or 'CUMULATIVE_THROUGHPUT' + inference_mode = "CUMULATIVE_THROUGHPUT" if batch > 1 else "LATENCY" + LOGGER.info(f"Using OpenVINO {inference_mode} mode for batch={batch} inference...") + ov_compiled_model = core.compile_model( + ov_model, + device_name="AUTO", # AUTO selects best available device, do not modify + config={"PERFORMANCE_HINT": inference_mode}, + ) + input_name = ov_compiled_model.input().get_any_name() + metadata = w.parent / "metadata.yaml" + + # TensorRT + elif engine: + LOGGER.info(f"Loading {w} for TensorRT inference...") + try: + import tensorrt as trt # noqa https://developer.nvidia.com/nvidia-tensorrt-download + except ImportError: + if LINUX: + check_requirements("tensorrt>7.0.0,<=10.1.0") + import tensorrt as trt # noqa + check_version(trt.__version__, ">=7.0.0", hard=True) + check_version(trt.__version__, "<=10.1.0", msg="https://github.com/ultralytics/ultralytics/pull/14239") + if device.type == "cpu": + device = torch.device("cuda:0") + Binding = namedtuple("Binding", ("name", "dtype", "shape", "data", "ptr")) + logger = trt.Logger(trt.Logger.INFO) + # Read file + with open(w, "rb") as f, trt.Runtime(logger) as runtime: + try: + meta_len = int.from_bytes(f.read(4), byteorder="little") # read metadata length + metadata = json.loads(f.read(meta_len).decode("utf-8")) # read metadata + except UnicodeDecodeError: + f.seek(0) # engine file may lack embedded Ultralytics metadata + model = runtime.deserialize_cuda_engine(f.read()) # read engine + + # Model context + try: + context = model.create_execution_context() + except Exception as e: # model is None + LOGGER.error(f"ERROR: TensorRT model exported with a different version than {trt.__version__}\n") + raise e + + bindings = OrderedDict() + output_names = [] + fp16 = False # default updated below + dynamic = False + is_trt10 = not hasattr(model, "num_bindings") + num = range(model.num_io_tensors) if is_trt10 else range(model.num_bindings) + for i in num: + if is_trt10: + name = model.get_tensor_name(i) + dtype = trt.nptype(model.get_tensor_dtype(name)) + is_input = model.get_tensor_mode(name) == trt.TensorIOMode.INPUT + if is_input: + if -1 in tuple(model.get_tensor_shape(name)): + dynamic = True + context.set_input_shape(name, tuple(model.get_tensor_profile_shape(name, 0)[1])) + if dtype == np.float16: + fp16 = True + else: + output_names.append(name) + shape = tuple(context.get_tensor_shape(name)) + else: # TensorRT < 10.0 + name = model.get_binding_name(i) + dtype = trt.nptype(model.get_binding_dtype(i)) + is_input = model.binding_is_input(i) + if model.binding_is_input(i): + if -1 in tuple(model.get_binding_shape(i)): # dynamic + dynamic = True + context.set_binding_shape(i, tuple(model.get_profile_shape(0, i)[1])) + if dtype == np.float16: + fp16 = True + else: + output_names.append(name) + shape = tuple(context.get_binding_shape(i)) + im = torch.from_numpy(np.empty(shape, dtype=dtype)).to(device) + bindings[name] = Binding(name, dtype, shape, im, int(im.data_ptr())) + binding_addrs = OrderedDict((n, d.ptr) for n, d in bindings.items()) + batch_size = bindings["images"].shape[0] # if dynamic, this is instead max batch size + + # CoreML + elif coreml: + LOGGER.info(f"Loading {w} for CoreML inference...") + import coremltools as ct + + model = ct.models.MLModel(w) + metadata = dict(model.user_defined_metadata) + + # TF SavedModel + elif saved_model: + LOGGER.info(f"Loading {w} for TensorFlow SavedModel inference...") + import tensorflow as tf + + keras = False # assume TF1 saved_model + model = tf.keras.models.load_model(w) if keras else tf.saved_model.load(w) + metadata = Path(w) / "metadata.yaml" + + # TF GraphDef + elif pb: # https://www.tensorflow.org/guide/migrate#a_graphpb_or_graphpbtxt + LOGGER.info(f"Loading {w} for TensorFlow GraphDef inference...") + import tensorflow as tf + + from ultralytics.engine.exporter import gd_outputs + + def wrap_frozen_graph(gd, inputs, outputs): + """Wrap frozen graphs for deployment.""" + x = tf.compat.v1.wrap_function(lambda: tf.compat.v1.import_graph_def(gd, name=""), []) # wrapped + ge = x.graph.as_graph_element + return x.prune(tf.nest.map_structure(ge, inputs), tf.nest.map_structure(ge, outputs)) + + gd = tf.Graph().as_graph_def() # TF GraphDef + with open(w, "rb") as f: + gd.ParseFromString(f.read()) + frozen_func = wrap_frozen_graph(gd, inputs="x:0", outputs=gd_outputs(gd)) + with contextlib.suppress(StopIteration): # find metadata in SavedModel alongside GraphDef + metadata = next(Path(w).resolve().parent.rglob(f"{Path(w).stem}_saved_model*/metadata.yaml")) + + # TFLite or TFLite Edge TPU + elif tflite or edgetpu: # https://www.tensorflow.org/lite/guide/python#install_tensorflow_lite_for_python + try: # https://coral.ai/docs/edgetpu/tflite-python/#update-existing-tf-lite-code-for-the-edge-tpu + from tflite_runtime.interpreter import Interpreter, load_delegate + except ImportError: + import tensorflow as tf + + Interpreter, load_delegate = tf.lite.Interpreter, tf.lite.experimental.load_delegate + if edgetpu: # TF Edge TPU https://coral.ai/software/#edgetpu-runtime + LOGGER.info(f"Loading {w} for TensorFlow Lite Edge TPU inference...") + delegate = {"Linux": "libedgetpu.so.1", "Darwin": "libedgetpu.1.dylib", "Windows": "edgetpu.dll"}[ + platform.system() + ] + interpreter = Interpreter(model_path=w, experimental_delegates=[load_delegate(delegate)]) + else: # TFLite + LOGGER.info(f"Loading {w} for TensorFlow Lite inference...") + interpreter = Interpreter(model_path=w) # load TFLite model + interpreter.allocate_tensors() # allocate + input_details = interpreter.get_input_details() # inputs + output_details = interpreter.get_output_details() # outputs + # Load metadata + with contextlib.suppress(zipfile.BadZipFile): + with zipfile.ZipFile(w, "r") as model: + meta_file = model.namelist()[0] + metadata = ast.literal_eval(model.read(meta_file).decode("utf-8")) + + # TF.js + elif tfjs: + raise NotImplementedError("YOLOv8 TF.js inference is not currently supported.") + + # PaddlePaddle + elif paddle: + LOGGER.info(f"Loading {w} for PaddlePaddle inference...") + check_requirements("paddlepaddle-gpu" if cuda else "paddlepaddle") + import paddle.inference as pdi # noqa + + w = Path(w) + if not w.is_file(): # if not *.pdmodel + w = next(w.rglob("*.pdmodel")) # get *.pdmodel file from *_paddle_model dir + config = pdi.Config(str(w), str(w.with_suffix(".pdiparams"))) + if cuda: + config.enable_use_gpu(memory_pool_init_size_mb=2048, device_id=0) + predictor = pdi.create_predictor(config) + input_handle = predictor.get_input_handle(predictor.get_input_names()[0]) + output_names = predictor.get_output_names() + metadata = w.parents[1] / "metadata.yaml" + + # NCNN + elif ncnn: + LOGGER.info(f"Loading {w} for NCNN inference...") + check_requirements("git+https://github.com/Tencent/ncnn.git" if ARM64 else "ncnn") # requires NCNN + import ncnn as pyncnn + + net = pyncnn.Net() + net.opt.use_vulkan_compute = cuda + w = Path(w) + if not w.is_file(): # if not *.param + w = next(w.glob("*.param")) # get *.param file from *_ncnn_model dir + net.load_param(str(w)) + net.load_model(str(w.with_suffix(".bin"))) + metadata = w.parent / "metadata.yaml" + + # NVIDIA Triton Inference Server + elif triton: + check_requirements("tritonclient[all]") + from ultralytics.utils.triton import TritonRemoteModel + + model = TritonRemoteModel(w) + + # Any other format (unsupported) + else: + from ultralytics.engine.exporter import export_formats + + raise TypeError( + f"model='{w}' is not a supported model format. Ultralytics supports: {export_formats()['Format']}\n" + f"See https://docs.ultralytics.com/modes/predict for help." + ) + + # Load external metadata YAML + if isinstance(metadata, (str, Path)) and Path(metadata).exists(): + metadata = yaml_load(metadata) + if metadata and isinstance(metadata, dict): + for k, v in metadata.items(): + if k in {"stride", "batch"}: + metadata[k] = int(v) + elif k in {"imgsz", "names", "kpt_shape"} and isinstance(v, str): + metadata[k] = eval(v) + stride = metadata["stride"] + task = metadata["task"] + batch = metadata["batch"] + imgsz = metadata["imgsz"] + names = metadata["names"] + kpt_shape = metadata.get("kpt_shape") + elif not (pt or triton or nn_module): + LOGGER.warning(f"WARNING ⚠️ Metadata not found for 'model={weights}'") + + # Check names + if "names" not in locals(): # names missing + names = default_class_names(data) + names = check_class_names(names) + + # Disable gradients + if pt: + for p in model.parameters(): + p.requires_grad = False + + self.__dict__.update(locals()) # assign all variables to self + + def forward(self, im, augment=False, visualize=False, embed=None): + """ + Runs inference on the YOLOv8 MultiBackend model. + + Args: + im (torch.Tensor): The image tensor to perform inference on. + augment (bool): whether to perform data augmentation during inference, defaults to False + visualize (bool): whether to visualize the output predictions, defaults to False + embed (list, optional): A list of feature vectors/embeddings to return. + + Returns: + (tuple): Tuple containing the raw output tensor, and processed output for visualization (if visualize=True) + """ + b, ch, h, w = im.shape # batch, channel, height, width + if self.fp16 and im.dtype != torch.float16: + im = im.half() # to FP16 + if self.nhwc: + im = im.permute(0, 2, 3, 1) # torch BCHW to numpy BHWC shape(1,320,192,3) + + # PyTorch + if self.pt or self.nn_module: + y = self.model(im, augment=augment, visualize=visualize, embed=embed) + + # TorchScript + elif self.jit: + y = self.model(im) + + # ONNX OpenCV DNN + elif self.dnn: + im = im.cpu().numpy() # torch to numpy + self.net.setInput(im) + y = self.net.forward() + + # ONNX Runtime + elif self.onnx: + im = im.cpu().numpy() # torch to numpy + y = self.session.run(self.output_names, {self.session.get_inputs()[0].name: im}) + + # OpenVINO + elif self.xml: + im = im.cpu().numpy() # FP32 + + if self.inference_mode in {"THROUGHPUT", "CUMULATIVE_THROUGHPUT"}: # optimized for larger batch-sizes + n = im.shape[0] # number of images in batch + results = [None] * n # preallocate list with None to match the number of images + + def callback(request, userdata): + """Places result in preallocated list using userdata index.""" + results[userdata] = request.results + + # Create AsyncInferQueue, set the callback and start asynchronous inference for each input image + async_queue = self.ov.runtime.AsyncInferQueue(self.ov_compiled_model) + async_queue.set_callback(callback) + for i in range(n): + # Start async inference with userdata=i to specify the position in results list + async_queue.start_async(inputs={self.input_name: im[i : i + 1]}, userdata=i) # keep image as BCHW + async_queue.wait_all() # wait for all inference requests to complete + y = np.concatenate([list(r.values())[0] for r in results]) + + else: # inference_mode = "LATENCY", optimized for fastest first result at batch-size 1 + y = list(self.ov_compiled_model(im).values()) + + # TensorRT + elif self.engine: + if self.dynamic or im.shape != self.bindings["images"].shape: + if self.is_trt10: + self.context.set_input_shape("images", im.shape) + self.bindings["images"] = self.bindings["images"]._replace(shape=im.shape) + for name in self.output_names: + self.bindings[name].data.resize_(tuple(self.context.get_tensor_shape(name))) + else: + i = self.model.get_binding_index("images") + self.context.set_binding_shape(i, im.shape) + self.bindings["images"] = self.bindings["images"]._replace(shape=im.shape) + for name in self.output_names: + i = self.model.get_binding_index(name) + self.bindings[name].data.resize_(tuple(self.context.get_binding_shape(i))) + + s = self.bindings["images"].shape + assert im.shape == s, f"input size {im.shape} {'>' if self.dynamic else 'not equal to'} max model size {s}" + self.binding_addrs["images"] = int(im.data_ptr()) + self.context.execute_v2(list(self.binding_addrs.values())) + y = [self.bindings[x].data for x in sorted(self.output_names)] + + # CoreML + elif self.coreml: + im = im[0].cpu().numpy() + im_pil = Image.fromarray((im * 255).astype("uint8")) + # im = im.resize((192, 320), Image.BILINEAR) + y = self.model.predict({"image": im_pil}) # coordinates are xywh normalized + if "confidence" in y: + raise TypeError( + "Ultralytics only supports inference of non-pipelined CoreML models exported with " + f"'nms=False', but 'model={w}' has an NMS pipeline created by an 'nms=True' export." + ) + # TODO: CoreML NMS inference handling + # from ultralytics.utils.ops import xywh2xyxy + # box = xywh2xyxy(y['coordinates'] * [[w, h, w, h]]) # xyxy pixels + # conf, cls = y['confidence'].max(1), y['confidence'].argmax(1).astype(np.float32) + # y = np.concatenate((box, conf.reshape(-1, 1), cls.reshape(-1, 1)), 1) + elif len(y) == 1: # classification model + y = list(y.values()) + elif len(y) == 2: # segmentation model + y = list(reversed(y.values())) # reversed for segmentation models (pred, proto) + + # PaddlePaddle + elif self.paddle: + im = im.cpu().numpy().astype(np.float32) + self.input_handle.copy_from_cpu(im) + self.predictor.run() + y = [self.predictor.get_output_handle(x).copy_to_cpu() for x in self.output_names] + + # NCNN + elif self.ncnn: + mat_in = self.pyncnn.Mat(im[0].cpu().numpy()) + with self.net.create_extractor() as ex: + ex.input(self.net.input_names()[0], mat_in) + # WARNING: 'output_names' sorted as a temporary fix for https://github.com/pnnx/pnnx/issues/130 + y = [np.array(ex.extract(x)[1])[None] for x in sorted(self.net.output_names())] + + # NVIDIA Triton Inference Server + elif self.triton: + im = im.cpu().numpy() # torch to numpy + y = self.model(im) + + # TensorFlow (SavedModel, GraphDef, Lite, Edge TPU) + else: + im = im.cpu().numpy() + if self.saved_model: # SavedModel + y = self.model(im, training=False) if self.keras else self.model(im) + if not isinstance(y, list): + y = [y] + elif self.pb: # GraphDef + y = self.frozen_func(x=self.tf.constant(im)) + else: # Lite or Edge TPU + details = self.input_details[0] + is_int = details["dtype"] in {np.int8, np.int16} # is TFLite quantized int8 or int16 model + if is_int: + scale, zero_point = details["quantization"] + im = (im / scale + zero_point).astype(details["dtype"]) # de-scale + self.interpreter.set_tensor(details["index"], im) + self.interpreter.invoke() + y = [] + for output in self.output_details: + x = self.interpreter.get_tensor(output["index"]) + if is_int: + scale, zero_point = output["quantization"] + x = (x.astype(np.float32) - zero_point) * scale # re-scale + if x.ndim == 3: # if task is not classification, excluding masks (ndim=4) as well + # Denormalize xywh by image size. See https://github.com/ultralytics/ultralytics/pull/1695 + # xywh are normalized in TFLite/EdgeTPU to mitigate quantization error of integer models + if x.shape[-1] == 6: # end-to-end model + x[:, :, [0, 2]] *= w + x[:, :, [1, 3]] *= h + else: + x[:, [0, 2]] *= w + x[:, [1, 3]] *= h + y.append(x) + # TF segment fixes: export is reversed vs ONNX export and protos are transposed + if len(y) == 2: # segment with (det, proto) output order reversed + if len(y[1].shape) != 4: + y = list(reversed(y)) # should be y = (1, 116, 8400), (1, 160, 160, 32) + if y[1].shape[-1] == 6: # end-to-end model + y = [y[1]] + else: + y[1] = np.transpose(y[1], (0, 3, 1, 2)) # should be y = (1, 116, 8400), (1, 32, 160, 160) + y = [x if isinstance(x, np.ndarray) else x.numpy() for x in y] + + # for x in y: + # print(type(x), len(x)) if isinstance(x, (list, tuple)) else print(type(x), x.shape) # debug shapes + if isinstance(y, (list, tuple)): + if len(self.names) == 999 and (self.task == "segment" or len(y) == 2): # segments and names not defined + ip, ib = (0, 1) if len(y[0].shape) == 4 else (1, 0) # index of protos, boxes + nc = y[ib].shape[1] - y[ip].shape[3] - 4 # y = (1, 160, 160, 32), (1, 116, 8400) + self.names = {i: f"class{i}" for i in range(nc)} + return self.from_numpy(y[0]) if len(y) == 1 else [self.from_numpy(x) for x in y] + else: + return self.from_numpy(y) + + def from_numpy(self, x): + """ + Convert a numpy array to a tensor. + + Args: + x (np.ndarray): The array to be converted. + + Returns: + (torch.Tensor): The converted tensor + """ + return torch.tensor(x).to(self.device) if isinstance(x, np.ndarray) else x + + def warmup(self, imgsz=(1, 3, 640, 640)): + """ + Warm up the model by running one forward pass with a dummy input. + + Args: + imgsz (tuple): The shape of the dummy input tensor in the format (batch_size, channels, height, width) + """ + import torchvision # noqa (import here so torchvision import time not recorded in postprocess time) + + warmup_types = self.pt, self.jit, self.onnx, self.engine, self.saved_model, self.pb, self.triton, self.nn_module + if any(warmup_types) and (self.device.type != "cpu" or self.triton): + im = torch.empty(*imgsz, dtype=torch.half if self.fp16 else torch.float, device=self.device) # input + for _ in range(2 if self.jit else 1): + self.forward(im) # warmup + + @staticmethod + def _model_type(p="path/to/model.pt"): + """ + Takes a path to a model file and returns the model type. Possibles types are pt, jit, onnx, xml, engine, coreml, + saved_model, pb, tflite, edgetpu, tfjs, ncnn or paddle. + + Args: + p: path to the model file. Defaults to path/to/model.pt + + Examples: + >>> model = AutoBackend(weights="path/to/model.onnx") + >>> model_type = model._model_type() # returns "onnx" + """ + from ultralytics.engine.exporter import export_formats + + sf = export_formats()["Suffix"] # export suffixes + if not is_url(p) and not isinstance(p, str): + check_suffix(p, sf) # checks + name = Path(p).name + types = [s in name for s in sf] + types[5] |= name.endswith(".mlmodel") # retain support for older Apple CoreML *.mlmodel formats + types[8] &= not types[9] # tflite &= not edgetpu + if any(types): + triton = False + else: + from urllib.parse import urlsplit + + url = urlsplit(p) + triton = bool(url.netloc) and bool(url.path) and url.scheme in {"http", "grpc"} + + return types + [triton] diff --git a/examples/Ultralytics Module/exporter.py b/examples/Ultralytics Module/exporter.py new file mode 100644 index 0000000..d4987f9 --- /dev/null +++ b/examples/Ultralytics Module/exporter.py @@ -0,0 +1,1196 @@ +# Ultralytics YOLO 🚀, AGPL-3.0 license +""" +Export a YOLOv8 PyTorch model to other formats. TensorFlow exports authored by https://github.com/zldrobit. + +Format | `format=argument` | Model +--- | --- | --- +PyTorch | - | yolov8n.pt +TorchScript | `torchscript` | yolov8n.torchscript +ONNX | `onnx` | yolov8n.onnx +OpenVINO | `openvino` | yolov8n_openvino_model/ +TensorRT | `engine` | yolov8n.engine +CoreML | `coreml` | yolov8n.mlpackage +TensorFlow SavedModel | `saved_model` | yolov8n_saved_model/ +TensorFlow GraphDef | `pb` | yolov8n.pb +TensorFlow Lite | `tflite` | yolov8n.tflite +TensorFlow Edge TPU | `edgetpu` | yolov8n_edgetpu.tflite +TensorFlow.js | `tfjs` | yolov8n_web_model/ +PaddlePaddle | `paddle` | yolov8n_paddle_model/ +NCNN | `ncnn` | yolov8n_ncnn_model/ + +Requirements: + $ pip install "ultralytics[export]" + +Python: + from ultralytics import YOLO + model = YOLO('yolov8n.pt') + results = model.export(format='onnx') + +CLI: + $ yolo mode=export model=yolov8n.pt format=onnx + +Inference: + $ yolo predict model=yolov8n.pt # PyTorch + yolov8n.torchscript # TorchScript + yolov8n.onnx # ONNX Runtime or OpenCV DNN with dnn=True + yolov8n_openvino_model # OpenVINO + yolov8n.engine # TensorRT + yolov8n.mlpackage # CoreML (macOS-only) + yolov8n_saved_model # TensorFlow SavedModel + yolov8n.pb # TensorFlow GraphDef + yolov8n.tflite # TensorFlow Lite + yolov8n_edgetpu.tflite # TensorFlow Edge TPU + yolov8n_paddle_model # PaddlePaddle + yolov8n_ncnn_model # NCNN + +TensorFlow.js: + $ cd .. && git clone https://github.com/zldrobit/tfjs-yolov5-example.git && cd tfjs-yolov5-example + $ npm install + $ ln -s ../../yolov5/yolov8n_web_model public/yolov8n_web_model + $ npm start +""" + +import gc +import json +import os +import shutil +import subprocess +import time +import warnings +from copy import deepcopy +from datetime import datetime +from pathlib import Path + +import numpy as np +import torch + +from ultralytics.cfg import TASK2DATA, get_cfg +from ultralytics.data import build_dataloader +from ultralytics.data.dataset import YOLODataset +from ultralytics.data.utils import check_cls_dataset, check_det_dataset +from ultralytics.nn.autobackend import check_class_names, default_class_names +from ultralytics.nn.modules import C2f, Detect, RTDETRDecoder +from ultralytics.nn.tasks import DetectionModel, SegmentationModel, WorldModel +from ultralytics.utils import ( + ARM64, + DEFAULT_CFG, + IS_JETSON, + LINUX, + LOGGER, + MACOS, + PYTHON_VERSION, + ROOT, + WINDOWS, + __version__, + callbacks, + colorstr, + get_default_args, + yaml_save, +) +from ultralytics.utils.checks import check_imgsz, check_is_path_safe, check_requirements, check_version +from ultralytics.utils.downloads import attempt_download_asset, get_github_assets, safe_download +from ultralytics.utils.files import file_size, spaces_in_path +from ultralytics.utils.ops import Profile +from ultralytics.utils.torch_utils import TORCH_1_13, get_latest_opset, select_device, smart_inference_mode + + +def export_formats(): + """Ultralytics YOLO export formats.""" + x = [ + ["PyTorch", "-", ".pt", True, True], + ["TorchScript", "torchscript", ".torchscript", True, True], + ["ONNX", "onnx", ".onnx", True, True], + ["OpenVINO", "openvino", "_openvino_model", True, False], + ["TensorRT", "engine", ".engine", False, True], + ["CoreML", "coreml", ".mlpackage", True, False], + ["TensorFlow SavedModel", "saved_model", "_saved_model", True, True], + ["TensorFlow GraphDef", "pb", ".pb", True, True], + ["TensorFlow Lite", "tflite", ".tflite", True, False], + ["TensorFlow Edge TPU", "edgetpu", "_edgetpu.tflite", True, False], + ["TensorFlow.js", "tfjs", "_web_model", True, False], + ["PaddlePaddle", "paddle", "_paddle_model", True, True], + ["NCNN", "ncnn", "_ncnn_model", True, True], + ] + return dict(zip(["Format", "Argument", "Suffix", "CPU", "GPU"], zip(*x))) + + +def gd_outputs(gd): + """TensorFlow GraphDef model output node names.""" + name_list, input_list = [], [] + for node in gd.node: # tensorflow.core.framework.node_def_pb2.NodeDef + name_list.append(node.name) + input_list.extend(node.input) + return sorted(f"{x}:0" for x in list(set(name_list) - set(input_list)) if not x.startswith("NoOp")) + + +def try_export(inner_func): + """YOLOv8 export decorator, i.e. @try_export.""" + inner_args = get_default_args(inner_func) + + def outer_func(*args, **kwargs): + """Export a model.""" + prefix = inner_args["prefix"] + try: + with Profile() as dt: + f, model = inner_func(*args, **kwargs) + LOGGER.info(f"{prefix} export success ✅ {dt.t:.1f}s, saved as '{f}' ({file_size(f):.1f} MB)") + return f, model + except Exception as e: + LOGGER.error(f"{prefix} export failure ❌ {dt.t:.1f}s: {e}") + raise e + + return outer_func + + +class Exporter: + """ + A class for exporting a model. + + Attributes: + args (SimpleNamespace): Configuration for the exporter. + callbacks (list, optional): List of callback functions. Defaults to None. + """ + + def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None): + """ + Initializes the Exporter class. + + Args: + cfg (str, optional): Path to a configuration file. Defaults to DEFAULT_CFG. + overrides (dict, optional): Configuration overrides. Defaults to None. + _callbacks (dict, optional): Dictionary of callback functions. Defaults to None. + """ + self.args = get_cfg(cfg, overrides) + if self.args.format.lower() in {"coreml", "mlmodel"}: # fix attempt for protobuf<3.20.x errors + os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python" # must run before TensorBoard callback + + self.callbacks = _callbacks or callbacks.get_default_callbacks() + callbacks.add_integration_callbacks(self) + + @smart_inference_mode() + def __call__(self, model=None) -> str: + """Returns list of exported files/dirs after running callbacks.""" + self.run_callbacks("on_export_start") + t = time.time() + fmt = self.args.format.lower() # to lowercase + if fmt in {"tensorrt", "trt"}: # 'engine' aliases + fmt = "engine" + if fmt in {"mlmodel", "mlpackage", "mlprogram", "apple", "ios", "coreml"}: # 'coreml' aliases + fmt = "coreml" + fmts = tuple(export_formats()["Argument"][1:]) # available export formats + flags = [x == fmt for x in fmts] + if sum(flags) != 1: + raise ValueError(f"Invalid export format='{fmt}'. Valid formats are {fmts}") + jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle, ncnn = flags # export booleans + is_tf_format = any((saved_model, pb, tflite, edgetpu, tfjs)) + + # Device + if fmt == "engine" and self.args.device is None: + LOGGER.warning("WARNING ⚠️ TensorRT requires GPU export, automatically assigning device=0") + self.args.device = "0" + self.device = select_device("cpu" if self.args.device is None else self.args.device) + + # Checks + if not hasattr(model, "names"): + model.names = default_class_names() + model.names = check_class_names(model.names) + if self.args.half and self.args.int8: + LOGGER.warning("WARNING ⚠️ half=True and int8=True are mutually exclusive, setting half=False.") + self.args.half = False + if self.args.half and onnx and self.device.type == "cpu": + LOGGER.warning("WARNING ⚠️ half=True only compatible with GPU export, i.e. use device=0") + self.args.half = False + assert not self.args.dynamic, "half=True not compatible with dynamic=True, i.e. use only one." + self.imgsz = check_imgsz(self.args.imgsz, stride=model.stride, min_dim=2) # check image size + if self.args.int8 and engine: + self.args.dynamic = True # enforce dynamic to export TensorRT INT8 + if self.args.optimize: + assert not ncnn, "optimize=True not compatible with format='ncnn', i.e. use optimize=False" + assert self.device.type == "cpu", "optimize=True not compatible with cuda devices, i.e. use device='cpu'" + if edgetpu: + if not LINUX: + raise SystemError("Edge TPU export only supported on Linux. See https://coral.ai/docs/edgetpu/compiler") + elif self.args.batch != 1: # see github.com/ultralytics/ultralytics/pull/13420 + LOGGER.warning("WARNING ⚠️ Edge TPU export requires batch size 1, setting batch=1.") + self.args.batch = 1 + if isinstance(model, WorldModel): + LOGGER.warning( + "WARNING ⚠️ YOLOWorld (original version) export is not supported to any format.\n" + "WARNING ⚠️ YOLOWorldv2 models (i.e. 'yolov8s-worldv2.pt') only support export to " + "(torchscript, onnx, openvino, engine, coreml) formats. " + "See https://docs.ultralytics.com/models/yolo-world for details." + ) + if self.args.int8 and not self.args.data: + self.args.data = DEFAULT_CFG.data or TASK2DATA[getattr(model, "task", "detect")] # assign default data + LOGGER.warning( + "WARNING ⚠️ INT8 export requires a missing 'data' arg for calibration. " + f"Using default 'data={self.args.data}'." + ) + # Input + im = torch.zeros(self.args.batch, 3, *self.imgsz).to(self.device) + file = Path( + getattr(model, "pt_path", None) or getattr(model, "yaml_file", None) or model.yaml.get("yaml_file", "") + ) + if file.suffix in {".yaml", ".yml"}: + file = Path(file.name) + + # Update model + model = deepcopy(model).to(self.device) + for p in model.parameters(): + p.requires_grad = False + model.eval() + model.float() + model = model.fuse() + for m in model.modules(): + if isinstance(m, (Detect, RTDETRDecoder)): # includes all Detect subclasses like Segment, Pose, OBB + m.dynamic = self.args.dynamic + m.export = True + m.format = self.args.format + m.max_det = self.args.max_det + elif isinstance(m, C2f) and not is_tf_format: + # EdgeTPU does not support FlexSplitV while split provides cleaner ONNX graph + m.forward = m.forward_split + + y = None + for _ in range(2): + y = model(im) # dry runs + if self.args.half and onnx and self.device.type != "cpu": + im, model = im.half(), model.half() # to FP16 + + # Filter warnings + warnings.filterwarnings("ignore", category=torch.jit.TracerWarning) # suppress TracerWarning + warnings.filterwarnings("ignore", category=UserWarning) # suppress shape prim::Constant missing ONNX warning + warnings.filterwarnings("ignore", category=DeprecationWarning) # suppress CoreML np.bool deprecation warning + + # Assign + self.im = im + self.model = model + self.file = file + self.output_shape = ( + tuple(y.shape) + if isinstance(y, torch.Tensor) + else tuple(tuple(x.shape if isinstance(x, torch.Tensor) else []) for x in y) + ) + self.pretty_name = Path(self.model.yaml.get("yaml_file", self.file)).stem.replace("yolo", "YOLO") + data = model.args["data"] if hasattr(model, "args") and isinstance(model.args, dict) else "" + description = f'Ultralytics {self.pretty_name} model {f"trained on {data}" if data else ""}' + self.metadata = { + "description": description, + "author": "Ultralytics", + "date": datetime.now().isoformat(), + "version": __version__, + "license": "AGPL-3.0 License (https://ultralytics.com/license)", + "docs": "https://docs.ultralytics.com", + "stride": int(max(model.stride)), + "task": model.task, + "batch": self.args.batch, + "imgsz": self.imgsz, + "names": model.names, + } # model metadata + if model.task == "pose": + self.metadata["kpt_shape"] = model.model[-1].kpt_shape + + LOGGER.info( + f"\n{colorstr('PyTorch:')} starting from '{file}' with input shape {tuple(im.shape)} BCHW and " + f'output shape(s) {self.output_shape} ({file_size(file):.1f} MB)' + ) + + # Exports + f = [""] * len(fmts) # exported filenames + if jit or ncnn: # TorchScript + f[0], _ = self.export_torchscript() + if engine: # TensorRT required before ONNX + f[1], _ = self.export_engine() + if onnx: # ONNX + f[2], _ = self.export_onnx() + if xml: # OpenVINO + f[3], _ = self.export_openvino() + if coreml: # CoreML + f[4], _ = self.export_coreml() + if is_tf_format: # TensorFlow formats + self.args.int8 |= edgetpu + f[5], keras_model = self.export_saved_model() + if pb or tfjs: # pb prerequisite to tfjs + f[6], _ = self.export_pb(keras_model=keras_model) + if tflite: + f[7], _ = self.export_tflite(keras_model=keras_model, nms=False, agnostic_nms=self.args.agnostic_nms) + if edgetpu: + f[8], _ = self.export_edgetpu(tflite_model=Path(f[5]) / f"{self.file.stem}_full_integer_quant.tflite") + if tfjs: + f[9], _ = self.export_tfjs() + if paddle: # PaddlePaddle + f[10], _ = self.export_paddle() + if ncnn: # NCNN + f[11], _ = self.export_ncnn() + + # Finish + f = [str(x) for x in f if x] # filter out '' and None + if any(f): + f = str(Path(f[-1])) + square = self.imgsz[0] == self.imgsz[1] + s = ( + "" + if square + else f"WARNING ⚠️ non-PyTorch val requires square images, 'imgsz={self.imgsz}' will not " + f"work. Use export 'imgsz={max(self.imgsz)}' if val is required." + ) + imgsz = self.imgsz[0] if square else str(self.imgsz)[1:-1].replace(" ", "") + predict_data = f"data={data}" if model.task == "segment" and fmt == "pb" else "" + q = "int8" if self.args.int8 else "half" if self.args.half else "" # quantization + LOGGER.info( + f'\nExport complete ({time.time() - t:.1f}s)' + f"\nResults saved to {colorstr('bold', file.parent.resolve())}" + f'\nPredict: yolo predict task={model.task} model={f} imgsz={imgsz} {q} {predict_data}' + f'\nValidate: yolo val task={model.task} model={f} imgsz={imgsz} data={data} {q} {s}' + f'\nVisualize: https://netron.app' + ) + + self.run_callbacks("on_export_end") + return f # return list of exported files/dirs + + def get_int8_calibration_dataloader(self, prefix=""): + """Build and return a dataloader suitable for calibration of INT8 models.""" + LOGGER.info(f"{prefix} collecting INT8 calibration images from 'data={self.args.data}'") + data = (check_cls_dataset if self.model.task == "classify" else check_det_dataset)(self.args.data) + # TensorRT INT8 calibration should use 2x batch size + batch = self.args.batch * (2 if self.args.format == "engine" else 1) + dataset = YOLODataset( + data[self.args.split or "val"], + data=data, + task=self.model.task, + imgsz=self.imgsz[0], + augment=False, + batch_size=batch, + ) + n = len(dataset) + if n < 300: + LOGGER.warning(f"{prefix} WARNING ⚠️ >300 images recommended for INT8 calibration, found {n} images.") + return build_dataloader(dataset, batch=batch, workers=0) # required for batch loading + + @try_export + def export_torchscript(self, prefix=colorstr("TorchScript:")): + """YOLOv8 TorchScript model export.""" + LOGGER.info(f"\n{prefix} starting export with torch {torch.__version__}...") + f = self.file.with_suffix(".torchscript") + + ts = torch.jit.trace(self.model, self.im, strict=False) + extra_files = {"config.txt": json.dumps(self.metadata)} # torch._C.ExtraFilesMap() + if self.args.optimize: # https://pytorch.org/tutorials/recipes/mobile_interpreter.html + LOGGER.info(f"{prefix} optimizing for mobile...") + from torch.utils.mobile_optimizer import optimize_for_mobile + + optimize_for_mobile(ts)._save_for_lite_interpreter(str(f), _extra_files=extra_files) + else: + ts.save(str(f), _extra_files=extra_files) + return f, None + + @try_export + def export_onnx(self, prefix=colorstr("ONNX:")): + """YOLOv8 ONNX export.""" + requirements = ["onnx>=1.12.0"] + if self.args.simplify: + requirements += ["onnxslim==0.1.34", "onnxruntime" + ("-gpu" if torch.cuda.is_available() else "")] + check_requirements(requirements) + import onnx # noqa + + opset_version = self.args.opset or get_latest_opset() + LOGGER.info(f"\n{prefix} starting export with onnx {onnx.__version__} opset {opset_version}...") + f = str(self.file.with_suffix(".onnx")) + + output_names = ["output0", "output1"] if isinstance(self.model, SegmentationModel) else ["output0"] + dynamic = self.args.dynamic + if dynamic: + dynamic = {"images": {0: "batch", 2: "height", 3: "width"}} # shape(1,3,640,640) + if isinstance(self.model, SegmentationModel): + dynamic["output0"] = {0: "batch", 2: "anchors"} # shape(1, 116, 8400) + dynamic["output1"] = {0: "batch", 2: "mask_height", 3: "mask_width"} # shape(1,32,160,160) + elif isinstance(self.model, DetectionModel): + dynamic["output0"] = {0: "batch", 2: "anchors"} # shape(1, 84, 8400) + + torch.onnx.export( + self.model.cpu() if dynamic else self.model, # dynamic=True only compatible with cpu + self.im.cpu() if dynamic else self.im, + f, + verbose=False, + opset_version=opset_version, + do_constant_folding=True, # WARNING: DNN inference with torch>=1.12 may require do_constant_folding=False + input_names=["images"], + output_names=output_names, + dynamic_axes=dynamic or None, + ) + + # Checks + model_onnx = onnx.load(f) # load onnx model + + # Simplify + if self.args.simplify: + try: + import onnxslim + + LOGGER.info(f"{prefix} slimming with onnxslim {onnxslim.__version__}...") + model_onnx = onnxslim.slim(model_onnx) + + except Exception as e: + LOGGER.warning(f"{prefix} simplifier failure: {e}") + + # Metadata + for k, v in self.metadata.items(): + meta = model_onnx.metadata_props.add() + meta.key, meta.value = k, str(v) + + onnx.save(model_onnx, f) + return f, model_onnx + + @try_export + def export_openvino(self, prefix=colorstr("OpenVINO:")): + """YOLOv8 OpenVINO export.""" + check_requirements(f'openvino{"<=2024.0.0" if ARM64 else ">=2024.0.0"}') # fix OpenVINO issue on ARM64 + import openvino as ov + + LOGGER.info(f"\n{prefix} starting export with openvino {ov.__version__}...") + assert TORCH_1_13, f"OpenVINO export requires torch>=1.13.0 but torch=={torch.__version__} is installed" + ov_model = ov.convert_model( + self.model, + input=None if self.args.dynamic else [self.im.shape], + example_input=self.im, + ) + + def serialize(ov_model, file): + """Set RT info, serialize and save metadata YAML.""" + ov_model.set_rt_info("YOLOv8", ["model_info", "model_type"]) + ov_model.set_rt_info(True, ["model_info", "reverse_input_channels"]) + ov_model.set_rt_info(114, ["model_info", "pad_value"]) + ov_model.set_rt_info([255.0], ["model_info", "scale_values"]) + ov_model.set_rt_info(self.args.iou, ["model_info", "iou_threshold"]) + ov_model.set_rt_info([v.replace(" ", "_") for v in self.model.names.values()], ["model_info", "labels"]) + if self.model.task != "classify": + ov_model.set_rt_info("fit_to_window_letterbox", ["model_info", "resize_type"]) + + ov.runtime.save_model(ov_model, file, compress_to_fp16=self.args.half) + yaml_save(Path(file).parent / "metadata.yaml", self.metadata) # add metadata.yaml + + if self.args.int8: + fq = str(self.file).replace(self.file.suffix, f"_int8_openvino_model{os.sep}") + fq_ov = str(Path(fq) / self.file.with_suffix(".xml").name) + check_requirements("nncf>=2.8.0") + import nncf + + def transform_fn(data_item) -> np.ndarray: + """Quantization transform function.""" + data_item: torch.Tensor = data_item["img"] if isinstance(data_item, dict) else data_item + assert data_item.dtype == torch.uint8, "Input image must be uint8 for the quantization preprocessing" + im = data_item.numpy().astype(np.float32) / 255.0 # uint8 to fp16/32 and 0 - 255 to 0.0 - 1.0 + return np.expand_dims(im, 0) if im.ndim == 3 else im + + # Generate calibration data for integer quantization + ignored_scope = None + if isinstance(self.model.model[-1], Detect): + # Includes all Detect subclasses like Segment, Pose, OBB, WorldDetect + head_module_name = ".".join(list(self.model.named_modules())[-1][0].split(".")[:2]) + ignored_scope = nncf.IgnoredScope( # ignore operations + patterns=[ + f".*{head_module_name}/.*/Add", + f".*{head_module_name}/.*/Sub*", + f".*{head_module_name}/.*/Mul*", + f".*{head_module_name}/.*/Div*", + f".*{head_module_name}\\.dfl.*", + ], + types=["Sigmoid"], + ) + + quantized_ov_model = nncf.quantize( + model=ov_model, + calibration_dataset=nncf.Dataset(self.get_int8_calibration_dataloader(prefix), transform_fn), + preset=nncf.QuantizationPreset.MIXED, + ignored_scope=ignored_scope, + ) + serialize(quantized_ov_model, fq_ov) + return fq, None + + f = str(self.file).replace(self.file.suffix, f"_openvino_model{os.sep}") + f_ov = str(Path(f) / self.file.with_suffix(".xml").name) + + serialize(ov_model, f_ov) + return f, None + + @try_export + def export_paddle(self, prefix=colorstr("PaddlePaddle:")): + """YOLOv8 Paddle export.""" + check_requirements(("paddlepaddle", "x2paddle")) + import x2paddle # noqa + from x2paddle.convert import pytorch2paddle # noqa + + LOGGER.info(f"\n{prefix} starting export with X2Paddle {x2paddle.__version__}...") + f = str(self.file).replace(self.file.suffix, f"_paddle_model{os.sep}") + + pytorch2paddle(module=self.model, save_dir=f, jit_type="trace", input_examples=[self.im]) # export + yaml_save(Path(f) / "metadata.yaml", self.metadata) # add metadata.yaml + return f, None + + @try_export + def export_ncnn(self, prefix=colorstr("NCNN:")): + """YOLOv8 NCNN export using PNNX https://github.com/pnnx/pnnx.""" + check_requirements("ncnn") + import ncnn # noqa + + LOGGER.info(f"\n{prefix} starting export with NCNN {ncnn.__version__}...") + f = Path(str(self.file).replace(self.file.suffix, f"_ncnn_model{os.sep}")) + f_ts = self.file.with_suffix(".torchscript") + + name = Path("pnnx.exe" if WINDOWS else "pnnx") # PNNX filename + pnnx = name if name.is_file() else (ROOT / name) + if not pnnx.is_file(): + LOGGER.warning( + f"{prefix} WARNING ⚠️ PNNX not found. Attempting to download binary file from " + "https://github.com/pnnx/pnnx/.\nNote PNNX Binary file must be placed in current working directory " + f"or in {ROOT}. See PNNX repo for full installation instructions." + ) + system = "macos" if MACOS else "windows" if WINDOWS else "linux-aarch64" if ARM64 else "linux" + try: + release, assets = get_github_assets(repo="pnnx/pnnx") + asset = [x for x in assets if f"{system}.zip" in x][0] + assert isinstance(asset, str), "Unable to retrieve PNNX repo assets" # i.e. pnnx-20240410-macos.zip + LOGGER.info(f"{prefix} successfully found latest PNNX asset file {asset}") + except Exception as e: + release = "20240410" + asset = f"pnnx-{release}-{system}.zip" + LOGGER.warning(f"{prefix} WARNING ⚠️ PNNX GitHub assets not found: {e}, using default {asset}") + unzip_dir = safe_download(f"https://github.com/pnnx/pnnx/releases/download/{release}/{asset}", delete=True) + if check_is_path_safe(Path.cwd(), unzip_dir): # avoid path traversal security vulnerability + shutil.move(src=unzip_dir / name, dst=pnnx) # move binary to ROOT + pnnx.chmod(0o777) # set read, write, and execute permissions for everyone + shutil.rmtree(unzip_dir) # delete unzip dir + + ncnn_args = [ + f'ncnnparam={f / "model.ncnn.param"}', + f'ncnnbin={f / "model.ncnn.bin"}', + f'ncnnpy={f / "model_ncnn.py"}', + ] + + pnnx_args = [ + f'pnnxparam={f / "model.pnnx.param"}', + f'pnnxbin={f / "model.pnnx.bin"}', + f'pnnxpy={f / "model_pnnx.py"}', + f'pnnxonnx={f / "model.pnnx.onnx"}', + ] + + cmd = [ + str(pnnx), + str(f_ts), + *ncnn_args, + *pnnx_args, + f"fp16={int(self.args.half)}", + f"device={self.device.type}", + f'inputshape="{[self.args.batch, 3, *self.imgsz]}"', + ] + f.mkdir(exist_ok=True) # make ncnn_model directory + LOGGER.info(f"{prefix} running '{' '.join(cmd)}'") + subprocess.run(cmd, check=True) + + # Remove debug files + pnnx_files = [x.split("=")[-1] for x in pnnx_args] + for f_debug in ("debug.bin", "debug.param", "debug2.bin", "debug2.param", *pnnx_files): + Path(f_debug).unlink(missing_ok=True) + + yaml_save(f / "metadata.yaml", self.metadata) # add metadata.yaml + return str(f), None + + @try_export + def export_coreml(self, prefix=colorstr("CoreML:")): + """YOLOv8 CoreML export.""" + mlmodel = self.args.format.lower() == "mlmodel" # legacy *.mlmodel export format requested + check_requirements("coremltools>=6.0,<=6.2" if mlmodel else "coremltools>=7.0") + import coremltools as ct # noqa + + LOGGER.info(f"\n{prefix} starting export with coremltools {ct.__version__}...") + assert not WINDOWS, "CoreML export is not supported on Windows, please run on macOS or Linux." + assert self.args.batch == 1, "CoreML batch sizes > 1 are not supported. Please retry at 'batch=1'." + f = self.file.with_suffix(".mlmodel" if mlmodel else ".mlpackage") + if f.is_dir(): + shutil.rmtree(f) + if self.args.nms and getattr(self.model, "end2end", False): + LOGGER.warning(f"{prefix} WARNING ⚠️ 'nms=True' is not available for end2end models. Forcing 'nms=False'.") + self.args.nms = False + + bias = [0.0, 0.0, 0.0] + scale = 1 / 255 + classifier_config = None + if self.model.task == "classify": + classifier_config = ct.ClassifierConfig(list(self.model.names.values())) if self.args.nms else None + model = self.model + elif self.model.task == "detect": + model = IOSDetectModel(self.model, self.im) if self.args.nms else self.model + else: + if self.args.nms: + LOGGER.warning(f"{prefix} WARNING ⚠️ 'nms=True' is only available for Detect models like 'yolov8n.pt'.") + # TODO CoreML Segment and Pose model pipelining + model = self.model + + ts = torch.jit.trace(model.eval(), self.im, strict=False) # TorchScript model + ct_model = ct.convert( + ts, + inputs=[ct.ImageType("image", shape=self.im.shape, scale=scale, bias=bias)], + classifier_config=classifier_config, + convert_to="neuralnetwork" if mlmodel else "mlprogram", + ) + bits, mode = (8, "kmeans") if self.args.int8 else (16, "linear") if self.args.half else (32, None) + if bits < 32: + if "kmeans" in mode: + check_requirements("scikit-learn") # scikit-learn package required for k-means quantization + if mlmodel: + ct_model = ct.models.neural_network.quantization_utils.quantize_weights(ct_model, bits, mode) + elif bits == 8: # mlprogram already quantized to FP16 + import coremltools.optimize.coreml as cto + + op_config = cto.OpPalettizerConfig(mode="kmeans", nbits=bits, weight_threshold=512) + config = cto.OptimizationConfig(global_config=op_config) + ct_model = cto.palettize_weights(ct_model, config=config) + if self.args.nms and self.model.task == "detect": + if mlmodel: + # coremltools<=6.2 NMS export requires Python<3.11 + check_version(PYTHON_VERSION, "<3.11", name="Python ", hard=True) + weights_dir = None + else: + ct_model.save(str(f)) # save otherwise weights_dir does not exist + weights_dir = str(f / "Data/com.apple.CoreML/weights") + ct_model = self._pipeline_coreml(ct_model, weights_dir=weights_dir) + + m = self.metadata # metadata dict + ct_model.short_description = m.pop("description") + ct_model.author = m.pop("author") + ct_model.license = m.pop("license") + ct_model.version = m.pop("version") + ct_model.user_defined_metadata.update({k: str(v) for k, v in m.items()}) + try: + ct_model.save(str(f)) # save *.mlpackage + except Exception as e: + LOGGER.warning( + f"{prefix} WARNING ⚠️ CoreML export to *.mlpackage failed ({e}), reverting to *.mlmodel export. " + f"Known coremltools Python 3.11 and Windows bugs https://github.com/apple/coremltools/issues/1928." + ) + f = f.with_suffix(".mlmodel") + ct_model.save(str(f)) + return f, ct_model + + @try_export + def export_engine(self, prefix=colorstr("TensorRT:")): + """YOLOv8 TensorRT export https://developer.nvidia.com/tensorrt.""" + assert self.im.device.type != "cpu", "export running on CPU but must be on GPU, i.e. use 'device=0'" + f_onnx, _ = self.export_onnx() # run before TRT import https://github.com/ultralytics/ultralytics/issues/7016 + + try: + import tensorrt as trt # noqa + except ImportError: + if LINUX: + check_requirements("tensorrt>7.0.0,<=10.1.0") + import tensorrt as trt # noqa + check_version(trt.__version__, ">=7.0.0", hard=True) + check_version(trt.__version__, "<=10.1.0", msg="https://github.com/ultralytics/ultralytics/pull/14239") + + # Setup and checks + LOGGER.info(f"\n{prefix} starting export with TensorRT {trt.__version__}...") + is_trt10 = int(trt.__version__.split(".")[0]) >= 10 # is TensorRT >= 10 + assert Path(f_onnx).exists(), f"failed to export ONNX file: {f_onnx}" + f = self.file.with_suffix(".engine") # TensorRT engine file + logger = trt.Logger(trt.Logger.INFO) + if self.args.verbose: + logger.min_severity = trt.Logger.Severity.VERBOSE + + # Engine builder + builder = trt.Builder(logger) + config = builder.create_builder_config() + workspace = int(self.args.workspace * (1 << 30)) + if is_trt10: + config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, workspace) + else: # TensorRT versions 7, 8 + config.max_workspace_size = workspace + flag = 1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH) + network = builder.create_network(flag) + half = builder.platform_has_fast_fp16 and self.args.half + int8 = builder.platform_has_fast_int8 and self.args.int8 + # Read ONNX file + parser = trt.OnnxParser(network, logger) + if not parser.parse_from_file(f_onnx): + raise RuntimeError(f"failed to load ONNX file: {f_onnx}") + + # Network inputs + inputs = [network.get_input(i) for i in range(network.num_inputs)] + outputs = [network.get_output(i) for i in range(network.num_outputs)] + for inp in inputs: + LOGGER.info(f'{prefix} input "{inp.name}" with shape{inp.shape} {inp.dtype}') + for out in outputs: + LOGGER.info(f'{prefix} output "{out.name}" with shape{out.shape} {out.dtype}') + + if self.args.dynamic: + shape = self.im.shape + if shape[0] <= 1: + LOGGER.warning(f"{prefix} WARNING ⚠️ 'dynamic=True' model requires max batch size, i.e. 'batch=16'") + profile = builder.create_optimization_profile() + min_shape = (1, shape[1], 32, 32) # minimum input shape + max_shape = (*shape[:2], *(max(1, self.args.workspace) * d for d in shape[2:])) # max input shape + for inp in inputs: + profile.set_shape(inp.name, min=min_shape, opt=shape, max=max_shape) + config.add_optimization_profile(profile) + + LOGGER.info(f"{prefix} building {'INT8' if int8 else 'FP' + ('16' if half else '32')} engine as {f}") + if int8: + config.set_flag(trt.BuilderFlag.INT8) + config.set_calibration_profile(profile) + config.profiling_verbosity = trt.ProfilingVerbosity.DETAILED + + class EngineCalibrator(trt.IInt8Calibrator): + def __init__( + self, + dataset, # ultralytics.data.build.InfiniteDataLoader + batch: int, + cache: str = "", + ) -> None: + trt.IInt8Calibrator.__init__(self) + self.dataset = dataset + self.data_iter = iter(dataset) + self.algo = trt.CalibrationAlgoType.ENTROPY_CALIBRATION_2 + self.batch = batch + self.cache = Path(cache) + + def get_algorithm(self) -> trt.CalibrationAlgoType: + """Get the calibration algorithm to use.""" + return self.algo + + def get_batch_size(self) -> int: + """Get the batch size to use for calibration.""" + return self.batch or 1 + + def get_batch(self, names) -> list: + """Get the next batch to use for calibration, as a list of device memory pointers.""" + try: + im0s = next(self.data_iter)["img"] / 255.0 + im0s = im0s.to("cuda") if im0s.device.type == "cpu" else im0s + return [int(im0s.data_ptr())] + except StopIteration: + # Return [] or None, signal to TensorRT there is no calibration data remaining + return None + + def read_calibration_cache(self) -> bytes: + """Use existing cache instead of calibrating again, otherwise, implicitly return None.""" + if self.cache.exists() and self.cache.suffix == ".cache": + return self.cache.read_bytes() + + def write_calibration_cache(self, cache) -> None: + """Write calibration cache to disk.""" + _ = self.cache.write_bytes(cache) + + # Load dataset w/ builder (for batching) and calibrate + config.int8_calibrator = EngineCalibrator( + dataset=self.get_int8_calibration_dataloader(prefix), + batch=2 * self.args.batch, # TensorRT INT8 calibration should use 2x batch size + cache=str(self.file.with_suffix(".cache")), + ) + + elif half: + config.set_flag(trt.BuilderFlag.FP16) + + # Free CUDA memory + del self.model + gc.collect() + torch.cuda.empty_cache() + + # Write file + build = builder.build_serialized_network if is_trt10 else builder.build_engine + with build(network, config) as engine, open(f, "wb") as t: + # Metadata + meta = json.dumps(self.metadata) + t.write(len(meta).to_bytes(4, byteorder="little", signed=True)) + t.write(meta.encode()) + # Model + t.write(engine if is_trt10 else engine.serialize()) + + return f, None + + @try_export + def export_saved_model(self, prefix=colorstr("TensorFlow SavedModel:")): + """YOLOv8 TensorFlow SavedModel export.""" + cuda = torch.cuda.is_available() + try: + import tensorflow as tf # noqa + except ImportError: + suffix = "-macos" if MACOS else "-aarch64" if ARM64 else "" if cuda else "-cpu" + version = ">=2.0.0" + check_requirements(f"tensorflow{suffix}{version}") + import tensorflow as tf # noqa + check_requirements( + ( + "keras", # required by 'onnx2tf' package + "tf_keras", # required by 'onnx2tf' package + "sng4onnx>=1.0.1", # required by 'onnx2tf' package + "onnx_graphsurgeon>=0.3.26", # required by 'onnx2tf' package + "onnx>=1.12.0", + "onnx2tf>1.17.5,<=1.22.3", + "onnxslim>=0.1.31", + "tflite_support<=0.4.3" if IS_JETSON else "tflite_support", # fix ImportError 'GLIBCXX_3.4.29' + "flatbuffers>=23.5.26,<100", # update old 'flatbuffers' included inside tensorflow package + "onnxruntime-gpu" if cuda else "onnxruntime", + ), + cmds="--extra-index-url https://pypi.ngc.nvidia.com", # onnx_graphsurgeon only on NVIDIA + ) + + LOGGER.info(f"\n{prefix} starting export with tensorflow {tf.__version__}...") + check_version( + tf.__version__, + ">=2.0.0", + name="tensorflow", + verbose=True, + msg="https://github.com/ultralytics/ultralytics/issues/5161", + ) + import onnx2tf + + f = Path(str(self.file).replace(self.file.suffix, "_saved_model")) + if f.is_dir(): + shutil.rmtree(f) # delete output folder + + # Pre-download calibration file to fix https://github.com/PINTO0309/onnx2tf/issues/545 + onnx2tf_file = Path("calibration_image_sample_data_20x128x128x3_float32.npy") + if not onnx2tf_file.exists(): + attempt_download_asset(f"{onnx2tf_file}.zip", unzip=True, delete=True) + + # Export to ONNX + self.args.simplify = True + f_onnx, _ = self.export_onnx() + + # Export to TF + np_data = None + if self.args.int8: + tmp_file = f / "tmp_tflite_int8_calibration_images.npy" # int8 calibration images file + verbosity = "info" + if self.args.data: + f.mkdir() + images = [batch["img"].permute(0, 2, 3, 1) for batch in self.get_int8_calibration_dataloader(prefix)] + images = torch.cat(images, 0).float() + np.save(str(tmp_file), images.numpy().astype(np.float32)) # BHWC + np_data = [["images", tmp_file, [[[[0, 0, 0]]]], [[[[255, 255, 255]]]]]] + else: + verbosity = "error" + + LOGGER.info(f"{prefix} starting TFLite export with onnx2tf {onnx2tf.__version__}...") + onnx2tf.convert( + input_onnx_file_path=f_onnx, + output_folder_path=str(f), + not_use_onnxsim=True, + verbosity=verbosity, + output_integer_quantized_tflite=self.args.int8, + quant_type="per-tensor", # "per-tensor" (faster) or "per-channel" (slower but more accurate) + custom_input_op_name_np_data_path=np_data, + disable_group_convolution=True, # for end-to-end model compatibility + enable_batchmatmul_unfold=True, # for end-to-end model compatibility + ) + yaml_save(f / "metadata.yaml", self.metadata) # add metadata.yaml + + # Remove/rename TFLite models + if self.args.int8: + tmp_file.unlink(missing_ok=True) + for file in f.rglob("*_dynamic_range_quant.tflite"): + file.rename(file.with_name(file.stem.replace("_dynamic_range_quant", "_int8") + file.suffix)) + for file in f.rglob("*_integer_quant_with_int16_act.tflite"): + file.unlink() # delete extra fp16 activation TFLite files + + # Add TFLite metadata + for file in f.rglob("*.tflite"): + f.unlink() if "quant_with_int16_act.tflite" in str(f) else self._add_tflite_metadata(file) + + return str(f), tf.saved_model.load(f, tags=None, options=None) # load saved_model as Keras model + + @try_export + def export_pb(self, keras_model, prefix=colorstr("TensorFlow GraphDef:")): + """YOLOv8 TensorFlow GraphDef *.pb export https://github.com/leimao/Frozen_Graph_TensorFlow.""" + import tensorflow as tf # noqa + from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2 # noqa + + LOGGER.info(f"\n{prefix} starting export with tensorflow {tf.__version__}...") + f = self.file.with_suffix(".pb") + + m = tf.function(lambda x: keras_model(x)) # full model + m = m.get_concrete_function(tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype)) + frozen_func = convert_variables_to_constants_v2(m) + frozen_func.graph.as_graph_def() + tf.io.write_graph(graph_or_graph_def=frozen_func.graph, logdir=str(f.parent), name=f.name, as_text=False) + return f, None + + @try_export + def export_tflite(self, keras_model, nms, agnostic_nms, prefix=colorstr("TensorFlow Lite:")): + """YOLOv8 TensorFlow Lite export.""" + # BUG https://github.com/ultralytics/ultralytics/issues/13436 + import tensorflow as tf # noqa + + LOGGER.info(f"\n{prefix} starting export with tensorflow {tf.__version__}...") + saved_model = Path(str(self.file).replace(self.file.suffix, "_saved_model")) + if self.args.int8: + f = saved_model / f"{self.file.stem}_int8.tflite" # fp32 in/out + elif self.args.half: + f = saved_model / f"{self.file.stem}_float16.tflite" # fp32 in/out + else: + f = saved_model / f"{self.file.stem}_float32.tflite" + return str(f), None + + @try_export + def export_edgetpu(self, tflite_model="", prefix=colorstr("Edge TPU:")): + """YOLOv8 Edge TPU export https://coral.ai/docs/edgetpu/models-intro/.""" + LOGGER.warning(f"{prefix} WARNING ⚠️ Edge TPU known bug https://github.com/ultralytics/ultralytics/issues/1185") + + cmd = "edgetpu_compiler --version" + help_url = "https://coral.ai/docs/edgetpu/compiler/" + assert LINUX, f"export only supported on Linux. See {help_url}" + if subprocess.run(cmd, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL, shell=True).returncode != 0: + LOGGER.info(f"\n{prefix} export requires Edge TPU compiler. Attempting install from {help_url}") + sudo = subprocess.run("sudo --version >/dev/null", shell=True).returncode == 0 # sudo installed on system + for c in ( + "curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add -", + 'echo "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main" | ' + "sudo tee /etc/apt/sources.list.d/coral-edgetpu.list", + "sudo apt-get update", + "sudo apt-get install edgetpu-compiler", + ): + subprocess.run(c if sudo else c.replace("sudo ", ""), shell=True, check=True) + ver = subprocess.run(cmd, shell=True, capture_output=True, check=True).stdout.decode().split()[-1] + + LOGGER.info(f"\n{prefix} starting export with Edge TPU compiler {ver}...") + f = str(tflite_model).replace(".tflite", "_edgetpu.tflite") # Edge TPU model + + cmd = f'edgetpu_compiler -s -d -k 10 --out_dir "{Path(f).parent}" "{tflite_model}"' + LOGGER.info(f"{prefix} running '{cmd}'") + subprocess.run(cmd, shell=True) + self._add_tflite_metadata(f) + return f, None + + @try_export + def export_tfjs(self, prefix=colorstr("TensorFlow.js:")): + """YOLOv8 TensorFlow.js export.""" + check_requirements("tensorflowjs") + if ARM64: + # Fix error: `np.object` was a deprecated alias for the builtin `object` when exporting to TF.js on ARM64 + check_requirements("numpy==1.23.5") + import tensorflow as tf + import tensorflowjs as tfjs # noqa + + LOGGER.info(f"\n{prefix} starting export with tensorflowjs {tfjs.__version__}...") + f = str(self.file).replace(self.file.suffix, "_web_model") # js dir + f_pb = str(self.file.with_suffix(".pb")) # *.pb path + + gd = tf.Graph().as_graph_def() # TF GraphDef + with open(f_pb, "rb") as file: + gd.ParseFromString(file.read()) + outputs = ",".join(gd_outputs(gd)) + LOGGER.info(f"\n{prefix} output node names: {outputs}") + + quantization = "--quantize_float16" if self.args.half else "--quantize_uint8" if self.args.int8 else "" + with spaces_in_path(f_pb) as fpb_, spaces_in_path(f) as f_: # exporter can not handle spaces in path + cmd = ( + "tensorflowjs_converter " + f'--input_format=tf_frozen_model {quantization} --output_node_names={outputs} "{fpb_}" "{f_}"' + ) + LOGGER.info(f"{prefix} running '{cmd}'") + subprocess.run(cmd, shell=True) + + if " " in f: + LOGGER.warning(f"{prefix} WARNING ⚠️ your model may not work correctly with spaces in path '{f}'.") + + # Add metadata + yaml_save(Path(f) / "metadata.yaml", self.metadata) # add metadata.yaml + return f, None + + def _add_tflite_metadata(self, file): + """Add metadata to *.tflite models per https://www.tensorflow.org/lite/models/convert/metadata.""" + import flatbuffers + + try: + # TFLite Support bug https://github.com/tensorflow/tflite-support/issues/954#issuecomment-2108570845 + from tensorflow_lite_support.metadata import metadata_schema_py_generated as schema # noqa + from tensorflow_lite_support.metadata.python import metadata # noqa + except ImportError: # ARM64 systems may not have the 'tensorflow_lite_support' package available + from tflite_support import metadata # noqa + from tflite_support import metadata_schema_py_generated as schema # noqa + + # Create model info + model_meta = schema.ModelMetadataT() + model_meta.name = self.metadata["description"] + model_meta.version = self.metadata["version"] + model_meta.author = self.metadata["author"] + model_meta.license = self.metadata["license"] + + # Label file + tmp_file = Path(file).parent / "temp_meta.txt" + with open(tmp_file, "w") as f: + f.write(str(self.metadata)) + + label_file = schema.AssociatedFileT() + label_file.name = tmp_file.name + label_file.type = schema.AssociatedFileType.TENSOR_AXIS_LABELS + + # Create input info + input_meta = schema.TensorMetadataT() + input_meta.name = "image" + input_meta.description = "Input image to be detected." + input_meta.content = schema.ContentT() + input_meta.content.contentProperties = schema.ImagePropertiesT() + input_meta.content.contentProperties.colorSpace = schema.ColorSpaceType.RGB + input_meta.content.contentPropertiesType = schema.ContentProperties.ImageProperties + + # Create output info + output1 = schema.TensorMetadataT() + output1.name = "output" + output1.description = "Coordinates of detected objects, class labels, and confidence score" + output1.associatedFiles = [label_file] + if self.model.task == "segment": + output2 = schema.TensorMetadataT() + output2.name = "output" + output2.description = "Mask protos" + output2.associatedFiles = [label_file] + + # Create subgraph info + subgraph = schema.SubGraphMetadataT() + subgraph.inputTensorMetadata = [input_meta] + subgraph.outputTensorMetadata = [output1, output2] if self.model.task == "segment" else [output1] + model_meta.subgraphMetadata = [subgraph] + + b = flatbuffers.Builder(0) + b.Finish(model_meta.Pack(b), metadata.MetadataPopulator.METADATA_FILE_IDENTIFIER) + metadata_buf = b.Output() + + populator = metadata.MetadataPopulator.with_model_file(str(file)) + populator.load_metadata_buffer(metadata_buf) + populator.load_associated_files([str(tmp_file)]) + populator.populate() + tmp_file.unlink() + + def _pipeline_coreml(self, model, weights_dir=None, prefix=colorstr("CoreML Pipeline:")): + """YOLOv8 CoreML pipeline.""" + import coremltools as ct # noqa + + LOGGER.info(f"{prefix} starting pipeline with coremltools {ct.__version__}...") + _, _, h, w = list(self.im.shape) # BCHW + + # Output shapes + spec = model.get_spec() + out0, out1 = iter(spec.description.output) + if MACOS: + from PIL import Image + + img = Image.new("RGB", (w, h)) # w=192, h=320 + out = model.predict({"image": img}) + out0_shape = out[out0.name].shape # (3780, 80) + out1_shape = out[out1.name].shape # (3780, 4) + else: # linux and windows can not run model.predict(), get sizes from PyTorch model output y + out0_shape = self.output_shape[2], self.output_shape[1] - 4 # (3780, 80) + out1_shape = self.output_shape[2], 4 # (3780, 4) + + # Checks + names = self.metadata["names"] + nx, ny = spec.description.input[0].type.imageType.width, spec.description.input[0].type.imageType.height + _, nc = out0_shape # number of anchors, number of classes + assert len(names) == nc, f"{len(names)} names found for nc={nc}" # check + + # Define output shapes (missing) + out0.type.multiArrayType.shape[:] = out0_shape # (3780, 80) + out1.type.multiArrayType.shape[:] = out1_shape # (3780, 4) + + # Model from spec + model = ct.models.MLModel(spec, weights_dir=weights_dir) + + # 3. Create NMS protobuf + nms_spec = ct.proto.Model_pb2.Model() + nms_spec.specificationVersion = 5 + for i in range(2): + decoder_output = model._spec.description.output[i].SerializeToString() + nms_spec.description.input.add() + nms_spec.description.input[i].ParseFromString(decoder_output) + nms_spec.description.output.add() + nms_spec.description.output[i].ParseFromString(decoder_output) + + nms_spec.description.output[0].name = "confidence" + nms_spec.description.output[1].name = "coordinates" + + output_sizes = [nc, 4] + for i in range(2): + ma_type = nms_spec.description.output[i].type.multiArrayType + ma_type.shapeRange.sizeRanges.add() + ma_type.shapeRange.sizeRanges[0].lowerBound = 0 + ma_type.shapeRange.sizeRanges[0].upperBound = -1 + ma_type.shapeRange.sizeRanges.add() + ma_type.shapeRange.sizeRanges[1].lowerBound = output_sizes[i] + ma_type.shapeRange.sizeRanges[1].upperBound = output_sizes[i] + del ma_type.shape[:] + + nms = nms_spec.nonMaximumSuppression + nms.confidenceInputFeatureName = out0.name # 1x507x80 + nms.coordinatesInputFeatureName = out1.name # 1x507x4 + nms.confidenceOutputFeatureName = "confidence" + nms.coordinatesOutputFeatureName = "coordinates" + nms.iouThresholdInputFeatureName = "iouThreshold" + nms.confidenceThresholdInputFeatureName = "confidenceThreshold" + nms.iouThreshold = 0.45 + nms.confidenceThreshold = 0.25 + nms.pickTop.perClass = True + nms.stringClassLabels.vector.extend(names.values()) + nms_model = ct.models.MLModel(nms_spec) + + # 4. Pipeline models together + pipeline = ct.models.pipeline.Pipeline( + input_features=[ + ("image", ct.models.datatypes.Array(3, ny, nx)), + ("iouThreshold", ct.models.datatypes.Double()), + ("confidenceThreshold", ct.models.datatypes.Double()), + ], + output_features=["confidence", "coordinates"], + ) + pipeline.add_model(model) + pipeline.add_model(nms_model) + + # Correct datatypes + pipeline.spec.description.input[0].ParseFromString(model._spec.description.input[0].SerializeToString()) + pipeline.spec.description.output[0].ParseFromString(nms_model._spec.description.output[0].SerializeToString()) + pipeline.spec.description.output[1].ParseFromString(nms_model._spec.description.output[1].SerializeToString()) + + # Update metadata + pipeline.spec.specificationVersion = 5 + pipeline.spec.description.metadata.userDefined.update( + {"IoU threshold": str(nms.iouThreshold), "Confidence threshold": str(nms.confidenceThreshold)} + ) + + # Save the model + model = ct.models.MLModel(pipeline.spec, weights_dir=weights_dir) + model.input_description["image"] = "Input image" + model.input_description["iouThreshold"] = f"(optional) IoU threshold override (default: {nms.iouThreshold})" + model.input_description["confidenceThreshold"] = ( + f"(optional) Confidence threshold override (default: {nms.confidenceThreshold})" + ) + model.output_description["confidence"] = 'Boxes × Class confidence (see user-defined metadata "classes")' + model.output_description["coordinates"] = "Boxes × [x, y, width, height] (relative to image size)" + LOGGER.info(f"{prefix} pipeline success") + return model + + def add_callback(self, event: str, callback): + """Appends the given callback.""" + self.callbacks[event].append(callback) + + def run_callbacks(self, event: str): + """Execute all callbacks for a given event.""" + for callback in self.callbacks.get(event, []): + callback(self) + + +class IOSDetectModel(torch.nn.Module): + """Wrap an Ultralytics YOLO model for Apple iOS CoreML export.""" + + def __init__(self, model, im): + """Initialize the IOSDetectModel class with a YOLO model and example image.""" + super().__init__() + _, _, h, w = im.shape # batch, channel, height, width + self.model = model + self.nc = len(model.names) # number of classes + if w == h: + self.normalize = 1.0 / w # scalar + else: + self.normalize = torch.tensor([1.0 / w, 1.0 / h, 1.0 / w, 1.0 / h]) # broadcast (slower, smaller) + + def forward(self, x): + """Normalize predictions of object detection model with input size-dependent factors.""" + xywh, cls = self.model(x)[0].transpose(0, 1).split((4, self.nc), 1) + return cls, xywh * self.normalize # confidence (3780, 80), coordinates (3780, 4) diff --git a/examples/Ultralytics Module/main.py b/examples/Ultralytics Module/main.py new file mode 100644 index 0000000..c58b9ce --- /dev/null +++ b/examples/Ultralytics Module/main.py @@ -0,0 +1,130 @@ +# Ultralytics YOLO 🚀, AGPL-3.0 license + +import argparse + +import cv2.dnn +import numpy as np + +from ultralytics.utils import ASSETS, yaml_load +from ultralytics.utils.checks import check_yaml + +CLASSES = yaml_load(check_yaml("coco8.yaml"))["names"] +colors = np.random.uniform(0, 255, size=(len(CLASSES), 3)) + + +def draw_bounding_box(img, class_id, confidence, x, y, x_plus_w, y_plus_h): + """ + Draws bounding boxes on the input image based on the provided arguments. + + Args: + img (numpy.ndarray): The input image to draw the bounding box on. + class_id (int): Class ID of the detected object. + confidence (float): Confidence score of the detected object. + x (int): X-coordinate of the top-left corner of the bounding box. + y (int): Y-coordinate of the top-left corner of the bounding box. + x_plus_w (int): X-coordinate of the bottom-right corner of the bounding box. + y_plus_h (int): Y-coordinate of the bottom-right corner of the bounding box. + """ + label = f"{CLASSES[class_id]} ({confidence:.2f})" + color = colors[class_id] + cv2.rectangle(img, (x, y), (x_plus_w, y_plus_h), color, 2) + cv2.putText(img, label, (x - 10, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2) + + +def main(onnx_model, input_image): + """ + Main function to load ONNX model, perform inference, draw bounding boxes, and display the output image. + + Args: + onnx_model (str): Path to the ONNX model. + input_image (str): Path to the input image. + + Returns: + list: List of dictionaries containing detection information such as class_id, class_name, confidence, etc. + """ + # Load the ONNX model + model: cv2.dnn.Net = cv2.dnn.readNetFromONNX(onnx_model) + + # Read the input image + original_image: np.ndarray = cv2.imread(input_image) + [height, width, _] = original_image.shape + + # Prepare a square image for inference + length = max((height, width)) + image = np.zeros((length, length, 3), np.uint8) + image[0:height, 0:width] = original_image + + # Calculate scale factor + scale = length / 640 + + # Preprocess the image and prepare blob for model + blob = cv2.dnn.blobFromImage(image, scalefactor=1 / 255, size=(640, 640), swapRB=True) + model.setInput(blob) + + # Perform inference + outputs = model.forward() + + # Prepare output array + outputs = np.array([cv2.transpose(outputs[0])]) + rows = outputs.shape[1] + + boxes = [] + scores = [] + class_ids = [] + + # Iterate through output to collect bounding boxes, confidence scores, and class IDs + for i in range(rows): + classes_scores = outputs[0][i][4:] + (minScore, maxScore, minClassLoc, (x, maxClassIndex)) = cv2.minMaxLoc(classes_scores) + if maxScore >= 0.25: + box = [ + outputs[0][i][0] - (0.5 * outputs[0][i][2]), + outputs[0][i][1] - (0.5 * outputs[0][i][3]), + outputs[0][i][2], + outputs[0][i][3], + ] + boxes.append(box) + scores.append(maxScore) + class_ids.append(maxClassIndex) + + # Apply NMS (Non-maximum suppression) + result_boxes = cv2.dnn.NMSBoxes(boxes, scores, 0.25, 0.45, 0.5) + + detections = [] + + # Iterate through NMS results to draw bounding boxes and labels + for i in range(len(result_boxes)): + index = result_boxes[i] + box = boxes[index] + detection = { + "class_id": class_ids[index], + "class_name": CLASSES[class_ids[index]], + "confidence": scores[index], + "box": box, + "scale": scale, + } + detections.append(detection) + draw_bounding_box( + original_image, + class_ids[index], + scores[index], + round(box[0] * scale), + round(box[1] * scale), + round((box[0] + box[2]) * scale), + round((box[1] + box[3]) * scale), + ) + + # Display the image with bounding boxes + cv2.imshow("image", original_image) + cv2.waitKey(0) + cv2.destroyAllWindows() + + return detections + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--model", default="yolov8n.onnx", help="Input your ONNX model.") + parser.add_argument("--img", default=str(ASSETS / "bus.jpg"), help="Path to input image.") + args = parser.parse_args() + main(args.model, args.img) diff --git a/examples/Ultralytics Module/model.py b/examples/Ultralytics Module/model.py new file mode 100644 index 0000000..fcafc9f --- /dev/null +++ b/examples/Ultralytics Module/model.py @@ -0,0 +1,1129 @@ +# Ultralytics YOLO 🚀, AGPL-3.0 license + +import inspect +from pathlib import Path +from typing import List, Union + +import numpy as np +import torch +from PIL import Image + +from ultralytics.cfg import TASK2DATA, get_cfg, get_save_dir +from ultralytics.engine.results import Results +from ultralytics.hub import HUB_WEB_ROOT, HUBTrainingSession +from ultralytics.nn.tasks import attempt_load_one_weight, guess_model_task, nn, yaml_model_load +from ultralytics.utils import ( + ARGV, + ASSETS, + DEFAULT_CFG_DICT, + LOGGER, + RANK, + SETTINGS, + callbacks, + checks, + emojis, + yaml_load, +) + + +class Model(nn.Module): + """ + A base class for implementing YOLO models, unifying APIs across different model types. + + This class provides a common interface for various operations related to YOLO models, such as training, + validation, prediction, exporting, and benchmarking. It handles different types of models, including those + loaded from local files, Ultralytics HUB, or Triton Server. + + Attributes: + callbacks (Dict): A dictionary of callback functions for various events during model operations. + predictor (BasePredictor): The predictor object used for making predictions. + model (nn.Module): The underlying PyTorch model. + trainer (BaseTrainer): The trainer object used for training the model. + ckpt (Dict): The checkpoint data if the model is loaded from a *.pt file. + cfg (str): The configuration of the model if loaded from a *.yaml file. + ckpt_path (str): The path to the checkpoint file. + overrides (Dict): A dictionary of overrides for model configuration. + metrics (Dict): The latest training/validation metrics. + session (HUBTrainingSession): The Ultralytics HUB session, if applicable. + task (str): The type of task the model is intended for. + model_name (str): The name of the model. + + Methods: + __call__: Alias for the predict method, enabling the model instance to be callable. + _new: Initializes a new model based on a configuration file. + _load: Loads a model from a checkpoint file. + _check_is_pytorch_model: Ensures that the model is a PyTorch model. + reset_weights: Resets the model's weights to their initial state. + load: Loads model weights from a specified file. + save: Saves the current state of the model to a file. + info: Logs or returns information about the model. + fuse: Fuses Conv2d and BatchNorm2d layers for optimized inference. + predict: Performs object detection predictions. + track: Performs object tracking. + val: Validates the model on a dataset. + benchmark: Benchmarks the model on various export formats. + export: Exports the model to different formats. + train: Trains the model on a dataset. + tune: Performs hyperparameter tuning. + _apply: Applies a function to the model's tensors. + add_callback: Adds a callback function for an event. + clear_callback: Clears all callbacks for an event. + reset_callbacks: Resets all callbacks to their default functions. + + Examples: + >>> from ultralytics import YOLO + >>> model = YOLO("yolov8n.pt") + >>> results = model.predict("image.jpg") + >>> model.train(data="coco128.yaml", epochs=3) + >>> metrics = model.val() + >>> model.export(format="onnx") + """ + + def __init__( + self, + model: Union[str, Path] = "yolov8n.pt", + task: str = None, + verbose: bool = False, + ) -> None: + """ + Initializes a new instance of the YOLO model class. + + This constructor sets up the model based on the provided model path or name. It handles various types of + model sources, including local files, Ultralytics HUB models, and Triton Server models. The method + initializes several important attributes of the model and prepares it for operations like training, + prediction, or export. + + Args: + model (Union[str, Path]): Path or name of the model to load or create. Can be a local file path, a + model name from Ultralytics HUB, or a Triton Server model. + task (str | None): The task type associated with the YOLO model, specifying its application domain. + verbose (bool): If True, enables verbose output during the model's initialization and subsequent + operations. + + Raises: + FileNotFoundError: If the specified model file does not exist or is inaccessible. + ValueError: If the model file or configuration is invalid or unsupported. + ImportError: If required dependencies for specific model types (like HUB SDK) are not installed. + + Examples: + >>> model = Model("yolov8n.pt") + >>> model = Model("path/to/model.yaml", task="detect") + >>> model = Model("hub_model", verbose=True) + """ + super().__init__() + self.callbacks = callbacks.get_default_callbacks() + self.predictor = None # reuse predictor + self.model = None # model object + self.trainer = None # trainer object + self.ckpt = None # if loaded from *.pt + self.cfg = None # if loaded from *.yaml + self.ckpt_path = None + self.overrides = {} # overrides for trainer object + self.metrics = None # validation/training metrics + self.session = None # HUB session + self.task = task # task type + model = str(model).strip() + + # Check if Ultralytics HUB model from https://hub.ultralytics.com + if self.is_hub_model(model): + # Fetch model from HUB + checks.check_requirements("hub-sdk>=0.0.12") + session = HUBTrainingSession.create_session(model) + model = session.model_file + if session.train_args: # training sent from HUB + self.session = session + + # Check if Triton Server model + elif self.is_triton_model(model): + self.model_name = self.model = model + return + + # Load or create new YOLO model + if Path(model).suffix in {".yaml", ".yml"}: + self._new(model, task=task, verbose=verbose) + else: + self._load(model, task=task) + + def __call__( + self, + source: Union[str, Path, int, Image.Image, list, tuple, np.ndarray, torch.Tensor] = None, + stream: bool = False, + **kwargs, + ) -> list: + """ + Alias for the predict method, enabling the model instance to be callable for predictions. + + This method simplifies the process of making predictions by allowing the model instance to be called + directly with the required arguments. + + Args: + source (str | Path | int | PIL.Image | np.ndarray | torch.Tensor | List | Tuple): The source of + the image(s) to make predictions on. Can be a file path, URL, PIL image, numpy array, PyTorch + tensor, or a list/tuple of these. + stream (bool): If True, treat the input source as a continuous stream for predictions. + **kwargs (Any): Additional keyword arguments to configure the prediction process. + + Returns: + (List[ultralytics.engine.results.Results]): A list of prediction results, each encapsulated in a + Results object. + + Examples: + >>> model = YOLO("yolov8n.pt") + >>> results = model("https://ultralytics.com/images/bus.jpg") + >>> for r in results: + ... print(f"Detected {len(r)} objects in image") + """ + return self.predict(source, stream, **kwargs) + + @staticmethod + def is_triton_model(model: str) -> bool: + """ + Checks if the given model string is a Triton Server URL. + + This static method determines whether the provided model string represents a valid Triton Server URL by + parsing its components using urllib.parse.urlsplit(). + + Args: + model (str): The model string to be checked. + + Returns: + (bool): True if the model string is a valid Triton Server URL, False otherwise. + + Examples: + >>> Model.is_triton_model("http://localhost:8000/v2/models/yolov8n") + True + >>> Model.is_triton_model("yolov8n.pt") + False + """ + from urllib.parse import urlsplit + + url = urlsplit(model) + return url.netloc and url.path and url.scheme in {"http", "grpc"} + + @staticmethod + def is_hub_model(model: str) -> bool: + """ + Check if the provided model is an Ultralytics HUB model. + + This static method determines whether the given model string represents a valid Ultralytics HUB model + identifier. + + Args: + model (str): The model string to check. + + Returns: + (bool): True if the model is a valid Ultralytics HUB model, False otherwise. + + Examples: + >>> Model.is_hub_model("https://hub.ultralytics.com/models/MODEL") + True + >>> Model.is_hub_model("yolov8n.pt") + False + """ + return model.startswith(f"{HUB_WEB_ROOT}/models/") + + def _new(self, cfg: str, task=None, model=None, verbose=False) -> None: + """ + Initializes a new model and infers the task type from the model definitions. + + This method creates a new model instance based on the provided configuration file. It loads the model + configuration, infers the task type if not specified, and initializes the model using the appropriate + class from the task map. + + Args: + cfg (str): Path to the model configuration file in YAML format. + task (str | None): The specific task for the model. If None, it will be inferred from the config. + model (torch.nn.Module | None): A custom model instance. If provided, it will be used instead of creating + a new one. + verbose (bool): If True, displays model information during loading. + + Raises: + ValueError: If the configuration file is invalid or the task cannot be inferred. + ImportError: If the required dependencies for the specified task are not installed. + + Examples: + >>> model = Model() + >>> model._new("yolov8n.yaml", task="detect", verbose=True) + """ + cfg_dict = yaml_model_load(cfg) + self.cfg = cfg + self.task = task or guess_model_task(cfg_dict) + self.model = (model or self._smart_load("model"))(cfg_dict, verbose=verbose and RANK == -1) # build model + self.overrides["model"] = self.cfg + self.overrides["task"] = self.task + + # Below added to allow export from YAMLs + self.model.args = {**DEFAULT_CFG_DICT, **self.overrides} # combine default and model args (prefer model args) + self.model.task = self.task + self.model_name = cfg + + def _load(self, weights: str, task=None) -> None: + """ + Loads a model from a checkpoint file or initializes it from a weights file. + + This method handles loading models from either .pt checkpoint files or other weight file formats. It sets + up the model, task, and related attributes based on the loaded weights. + + Args: + weights (str): Path to the model weights file to be loaded. + task (str | None): The task associated with the model. If None, it will be inferred from the model. + + Raises: + FileNotFoundError: If the specified weights file does not exist or is inaccessible. + ValueError: If the weights file format is unsupported or invalid. + + Examples: + >>> model = Model() + >>> model._load("yolov8n.pt") + >>> model._load("path/to/weights.pth", task="detect") + """ + if weights.lower().startswith(("https://", "http://", "rtsp://", "rtmp://", "tcp://")): + weights = checks.check_file(weights, download_dir=SETTINGS["weights_dir"]) # download and return local file + weights = checks.check_model_file_from_stem(weights) # add suffix, i.e. yolov8n -> yolov8n.pt + + if Path(weights).suffix == ".pt": + self.model, self.ckpt = attempt_load_one_weight(weights) + self.task = self.model.args["task"] + self.overrides = self.model.args = self._reset_ckpt_args(self.model.args) + self.ckpt_path = self.model.pt_path + else: + weights = checks.check_file(weights) # runs in all cases, not redundant with above call + self.model, self.ckpt = weights, None + self.task = task or guess_model_task(weights) + self.ckpt_path = weights + self.overrides["model"] = weights + self.overrides["task"] = self.task + self.model_name = weights + + def _check_is_pytorch_model(self) -> None: + """ + Checks if the model is a PyTorch model and raises a TypeError if it's not. + + This method verifies that the model is either a PyTorch module or a .pt file. It's used to ensure that + certain operations that require a PyTorch model are only performed on compatible model types. + + Raises: + TypeError: If the model is not a PyTorch module or a .pt file. The error message provides detailed + information about supported model formats and operations. + + Examples: + >>> model = Model("yolov8n.pt") + >>> model._check_is_pytorch_model() # No error raised + >>> model = Model("yolov8n.onnx") + >>> model._check_is_pytorch_model() # Raises TypeError + """ + pt_str = isinstance(self.model, (str, Path)) and Path(self.model).suffix == ".pt" + pt_module = isinstance(self.model, nn.Module) + if not (pt_module or pt_str): + raise TypeError( + f"model='{self.model}' should be a *.pt PyTorch model to run this method, but is a different format. " + f"PyTorch models can train, val, predict and export, i.e. 'model.train(data=...)', but exported " + f"formats like ONNX, TensorRT etc. only support 'predict' and 'val' modes, " + f"i.e. 'yolo predict model=yolov8n.onnx'.\nTo run CUDA or MPS inference please pass the device " + f"argument directly in your inference command, i.e. 'model.predict(source=..., device=0)'" + ) + + def reset_weights(self) -> "Model": + """ + Resets the model's weights to their initial state. + + This method iterates through all modules in the model and resets their parameters if they have a + 'reset_parameters' method. It also ensures that all parameters have 'requires_grad' set to True, + enabling them to be updated during training. + + Returns: + (Model): The instance of the class with reset weights. + + Raises: + AssertionError: If the model is not a PyTorch model. + + Examples: + >>> model = Model("yolov8n.pt") + >>> model.reset_weights() + """ + self._check_is_pytorch_model() + for m in self.model.modules(): + if hasattr(m, "reset_parameters"): + m.reset_parameters() + for p in self.model.parameters(): + p.requires_grad = True + return self + + def load(self, weights: Union[str, Path] = "yolov8n.pt") -> "Model": + """ + Loads parameters from the specified weights file into the model. + + This method supports loading weights from a file or directly from a weights object. It matches parameters by + name and shape and transfers them to the model. + + Args: + weights (Union[str, Path]): Path to the weights file or a weights object. + + Returns: + (Model): The instance of the class with loaded weights. + + Raises: + AssertionError: If the model is not a PyTorch model. + + Examples: + >>> model = Model() + >>> model.load("yolov8n.pt") + >>> model.load(Path("path/to/weights.pt")) + """ + self._check_is_pytorch_model() + if isinstance(weights, (str, Path)): + self.overrides["pretrained"] = weights # remember the weights for DDP training + weights, self.ckpt = attempt_load_one_weight(weights) + self.model.load(weights) + return self + + def save(self, filename: Union[str, Path] = "saved_model.pt", use_dill=True) -> None: + """ + Saves the current model state to a file. + + This method exports the model's checkpoint (ckpt) to the specified filename. It includes metadata such as + the date, Ultralytics version, license information, and a link to the documentation. + + Args: + filename (Union[str, Path]): The name of the file to save the model to. + use_dill (bool): Whether to try using dill for serialization if available. + + Raises: + AssertionError: If the model is not a PyTorch model. + + Examples: + >>> model = Model("yolov8n.pt") + >>> model.save("my_model.pt") + """ + self._check_is_pytorch_model() + from copy import deepcopy + from datetime import datetime + + from ultralytics import __version__ + + updates = { + "model": deepcopy(self.model).half() if isinstance(self.model, nn.Module) else self.model, + "date": datetime.now().isoformat(), + "version": __version__, + "license": "AGPL-3.0 License (https://ultralytics.com/license)", + "docs": "https://docs.ultralytics.com", + } + torch.save({**self.ckpt, **updates}, filename, use_dill=use_dill) + + def info(self, detailed: bool = False, verbose: bool = True): + """ + Logs or returns model information. + + This method provides an overview or detailed information about the model, depending on the arguments + passed. It can control the verbosity of the output and return the information as a list. + + Args: + detailed (bool): If True, shows detailed information about the model layers and parameters. + verbose (bool): If True, prints the information. If False, returns the information as a list. + + Returns: + (List[str]): A list of strings containing various types of information about the model, including + model summary, layer details, and parameter counts. Empty if verbose is True. + + Raises: + TypeError: If the model is not a PyTorch model. + + Examples: + >>> model = Model("yolov8n.pt") + >>> model.info() # Prints model summary + >>> info_list = model.info(detailed=True, verbose=False) # Returns detailed info as a list + """ + self._check_is_pytorch_model() + return self.model.info(detailed=detailed, verbose=verbose) + + def fuse(self): + """ + Fuses Conv2d and BatchNorm2d layers in the model for optimized inference. + + This method iterates through the model's modules and fuses consecutive Conv2d and BatchNorm2d layers + into a single layer. This fusion can significantly improve inference speed by reducing the number of + operations and memory accesses required during forward passes. + + The fusion process typically involves folding the BatchNorm2d parameters (mean, variance, weight, and + bias) into the preceding Conv2d layer's weights and biases. This results in a single Conv2d layer that + performs both convolution and normalization in one step. + + Raises: + TypeError: If the model is not a PyTorch nn.Module. + + Examples: + >>> model = Model("yolov8n.pt") + >>> model.fuse() + >>> # Model is now fused and ready for optimized inference + """ + self._check_is_pytorch_model() + self.model.fuse() + + def embed( + self, + source: Union[str, Path, int, list, tuple, np.ndarray, torch.Tensor] = None, + stream: bool = False, + **kwargs, + ) -> list: + """ + Generates image embeddings based on the provided source. + + This method is a wrapper around the 'predict()' method, focusing on generating embeddings from an image + source. It allows customization of the embedding process through various keyword arguments. + + Args: + source (str | Path | int | List | Tuple | np.ndarray | torch.Tensor): The source of the image for + generating embeddings. Can be a file path, URL, PIL image, numpy array, etc. + stream (bool): If True, predictions are streamed. + **kwargs (Any): Additional keyword arguments for configuring the embedding process. + + Returns: + (List[torch.Tensor]): A list containing the image embeddings. + + Raises: + AssertionError: If the model is not a PyTorch model. + + Examples: + >>> model = YOLO("yolov8n.pt") + >>> image = "https://ultralytics.com/images/bus.jpg" + >>> embeddings = model.embed(image) + >>> print(embeddings[0].shape) + """ + if not kwargs.get("embed"): + kwargs["embed"] = [len(self.model.model) - 2] # embed second-to-last layer if no indices passed + return self.predict(source, stream, **kwargs) + + def predict( + self, + source: Union[str, Path, int, Image.Image, list, tuple, np.ndarray, torch.Tensor] = None, + stream: bool = False, + predictor=None, + **kwargs, + ) -> List[Results]: + """ + Performs predictions on the given image source using the YOLO model. + + This method facilitates the prediction process, allowing various configurations through keyword arguments. + It supports predictions with custom predictors or the default predictor method. The method handles different + types of image sources and can operate in a streaming mode. + + Args: + source (str | Path | int | PIL.Image | np.ndarray | torch.Tensor | List | Tuple): The source + of the image(s) to make predictions on. Accepts various types including file paths, URLs, PIL + images, numpy arrays, and torch tensors. + stream (bool): If True, treats the input source as a continuous stream for predictions. + predictor (BasePredictor | None): An instance of a custom predictor class for making predictions. + If None, the method uses a default predictor. + **kwargs (Any): Additional keyword arguments for configuring the prediction process. + + Returns: + (List[ultralytics.engine.results.Results]): A list of prediction results, each encapsulated in a + Results object. + + Examples: + >>> model = YOLO("yolov8n.pt") + >>> results = model.predict(source="path/to/image.jpg", conf=0.25) + >>> for r in results: + ... print(r.boxes.data) # print detection bounding boxes + + Notes: + - If 'source' is not provided, it defaults to the ASSETS constant with a warning. + - The method sets up a new predictor if not already present and updates its arguments with each call. + - For SAM-type models, 'prompts' can be passed as a keyword argument. + """ + if source is None: + source = ASSETS + LOGGER.warning(f"WARNING ⚠️ 'source' is missing. Using 'source={source}'.") + + is_cli = (ARGV[0].endswith("yolo") or ARGV[0].endswith("ultralytics")) and any( + x in ARGV for x in ("predict", "track", "mode=predict", "mode=track") + ) + + custom = {"conf": 0.25, "batch": 1, "save": is_cli, "mode": "predict"} # method defaults + args = {**self.overrides, **custom, **kwargs} # highest priority args on the right + prompts = args.pop("prompts", None) # for SAM-type models + + if not self.predictor: + self.predictor = predictor or self._smart_load("predictor")(overrides=args, _callbacks=self.callbacks) + self.predictor.setup_model(model=self.model, verbose=is_cli) + else: # only update args if predictor is already setup + self.predictor.args = get_cfg(self.predictor.args, args) + if "project" in args or "name" in args: + self.predictor.save_dir = get_save_dir(self.predictor.args) + if prompts and hasattr(self.predictor, "set_prompts"): # for SAM-type models + self.predictor.set_prompts(prompts) + return self.predictor.predict_cli(source=source) if is_cli else self.predictor(source=source, stream=stream) + + def track( + self, + source: Union[str, Path, int, list, tuple, np.ndarray, torch.Tensor] = None, + stream: bool = False, + persist: bool = False, + **kwargs, + ) -> List[Results]: + """ + Conducts object tracking on the specified input source using the registered trackers. + + This method performs object tracking using the model's predictors and optionally registered trackers. It handles + various input sources such as file paths or video streams, and supports customization through keyword arguments. + The method registers trackers if not already present and can persist them between calls. + + Args: + source (Union[str, Path, int, List, Tuple, np.ndarray, torch.Tensor], optional): Input source for object + tracking. Can be a file path, URL, or video stream. + stream (bool): If True, treats the input source as a continuous video stream. Defaults to False. + persist (bool): If True, persists trackers between different calls to this method. Defaults to False. + **kwargs (Any): Additional keyword arguments for configuring the tracking process. + + Returns: + (List[ultralytics.engine.results.Results]): A list of tracking results, each a Results object. + + Raises: + AttributeError: If the predictor does not have registered trackers. + + Examples: + >>> model = YOLO("yolov8n.pt") + >>> results = model.track(source="path/to/video.mp4", show=True) + >>> for r in results: + ... print(r.boxes.id) # print tracking IDs + + Notes: + - This method sets a default confidence threshold of 0.1 for ByteTrack-based tracking. + - The tracking mode is explicitly set in the keyword arguments. + - Batch size is set to 1 for tracking in videos. + """ + if not hasattr(self.predictor, "trackers"): + from ultralytics.trackers import register_tracker + + register_tracker(self, persist) + kwargs["conf"] = kwargs.get("conf") or 0.1 # ByteTrack-based method needs low confidence predictions as input + kwargs["batch"] = kwargs.get("batch") or 1 # batch-size 1 for tracking in videos + kwargs["mode"] = "track" + return self.predict(source=source, stream=stream, **kwargs) + + def val( + self, + validator=None, + **kwargs, + ): + """ + Validates the model using a specified dataset and validation configuration. + + This method facilitates the model validation process, allowing for customization through various settings. It + supports validation with a custom validator or the default validation approach. The method combines default + configurations, method-specific defaults, and user-provided arguments to configure the validation process. + + Args: + validator (ultralytics.engine.validator.BaseValidator | None): An instance of a custom validator class for + validating the model. + **kwargs (Any): Arbitrary keyword arguments for customizing the validation process. + + Returns: + (ultralytics.utils.metrics.DetMetrics): Validation metrics obtained from the validation process. + + Raises: + AssertionError: If the model is not a PyTorch model. + + Examples: + >>> model = YOLO("yolov8n.pt") + >>> results = model.val(data="coco128.yaml", imgsz=640) + >>> print(results.box.map) # Print mAP50-95 + """ + custom = {"rect": True} # method defaults + args = {**self.overrides, **custom, **kwargs, "mode": "val"} # highest priority args on the right + + validator = (validator or self._smart_load("validator"))(args=args, _callbacks=self.callbacks) + validator(model=self.model) + self.metrics = validator.metrics + return validator.metrics + + def benchmark( + self, + **kwargs, + ): + """ + Benchmarks the model across various export formats to evaluate performance. + + This method assesses the model's performance in different export formats, such as ONNX, TorchScript, etc. + It uses the 'benchmark' function from the ultralytics.utils.benchmarks module. The benchmarking is + configured using a combination of default configuration values, model-specific arguments, method-specific + defaults, and any additional user-provided keyword arguments. + + Args: + **kwargs (Any): Arbitrary keyword arguments to customize the benchmarking process. These are combined with + default configurations, model-specific arguments, and method defaults. Common options include: + - data (str): Path to the dataset for benchmarking. + - imgsz (int | List[int]): Image size for benchmarking. + - half (bool): Whether to use half-precision (FP16) mode. + - int8 (bool): Whether to use int8 precision mode. + - device (str): Device to run the benchmark on (e.g., 'cpu', 'cuda'). + - verbose (bool): Whether to print detailed benchmark information. + + Returns: + (Dict): A dictionary containing the results of the benchmarking process, including metrics for + different export formats. + + Raises: + AssertionError: If the model is not a PyTorch model. + + Examples: + >>> model = YOLO("yolov8n.pt") + >>> results = model.benchmark(data="coco8.yaml", imgsz=640, half=True) + >>> print(results) + """ + self._check_is_pytorch_model() + from ultralytics.utils.benchmarks import benchmark + + custom = {"verbose": False} # method defaults + args = {**DEFAULT_CFG_DICT, **self.model.args, **custom, **kwargs, "mode": "benchmark"} + return benchmark( + model=self, + data=kwargs.get("data"), # if no 'data' argument passed set data=None for default datasets + imgsz=args["imgsz"], + half=args["half"], + int8=args["int8"], + device=args["device"], + verbose=kwargs.get("verbose"), + ) + + def export( + self, + **kwargs, + ) -> str: + """ + Exports the model to a different format suitable for deployment. + + This method facilitates the export of the model to various formats (e.g., ONNX, TorchScript) for deployment + purposes. It uses the 'Exporter' class for the export process, combining model-specific overrides, method + defaults, and any additional arguments provided. + + Args: + **kwargs (Dict): Arbitrary keyword arguments to customize the export process. These are combined with + the model's overrides and method defaults. Common arguments include: + format (str): Export format (e.g., 'onnx', 'engine', 'coreml'). + half (bool): Export model in half-precision. + int8 (bool): Export model in int8 precision. + device (str): Device to run the export on. + workspace (int): Maximum memory workspace size for TensorRT engines. + nms (bool): Add Non-Maximum Suppression (NMS) module to model. + simplify (bool): Simplify ONNX model. + + Returns: + (str): The path to the exported model file. + + Raises: + AssertionError: If the model is not a PyTorch model. + ValueError: If an unsupported export format is specified. + RuntimeError: If the export process fails due to errors. + + Examples: + >>> model = YOLO("yolov8n.pt") + >>> model.export(format="onnx", dynamic=True, simplify=True) + 'path/to/exported/model.onnx' + """ + self._check_is_pytorch_model() + from .exporter import Exporter + + custom = { + "imgsz": self.model.args["imgsz"], + "batch": 1, + "data": None, + "device": None, # reset to avoid multi-GPU errors + "verbose": False, + } # method defaults + args = {**self.overrides, **custom, **kwargs, "mode": "export"} # highest priority args on the right + return Exporter(overrides=args, _callbacks=self.callbacks)(model=self.model) + + def train( + self, + trainer=None, + **kwargs, + ): + """ + Trains the model using the specified dataset and training configuration. + + This method facilitates model training with a range of customizable settings. It supports training with a + custom trainer or the default training approach. The method handles scenarios such as resuming training + from a checkpoint, integrating with Ultralytics HUB, and updating model and configuration after training. + + When using Ultralytics HUB, if the session has a loaded model, the method prioritizes HUB training + arguments and warns if local arguments are provided. It checks for pip updates and combines default + configurations, method-specific defaults, and user-provided arguments to configure the training process. + + Args: + trainer (BaseTrainer | None): Custom trainer instance for model training. If None, uses default. + **kwargs (Any): Arbitrary keyword arguments for training configuration. Common options include: + data (str): Path to dataset configuration file. + epochs (int): Number of training epochs. + batch_size (int): Batch size for training. + imgsz (int): Input image size. + device (str): Device to run training on (e.g., 'cuda', 'cpu'). + workers (int): Number of worker threads for data loading. + optimizer (str): Optimizer to use for training. + lr0 (float): Initial learning rate. + patience (int): Epochs to wait for no observable improvement for early stopping of training. + + Returns: + (Dict | None): Training metrics if available and training is successful; otherwise, None. + + Raises: + AssertionError: If the model is not a PyTorch model. + PermissionError: If there is a permission issue with the HUB session. + ModuleNotFoundError: If the HUB SDK is not installed. + + Examples: + >>> model = YOLO("yolov8n.pt") + >>> results = model.train(data="coco128.yaml", epochs=3) + """ + self._check_is_pytorch_model() + if hasattr(self.session, "model") and self.session.model.id: # Ultralytics HUB session with loaded model + if any(kwargs): + LOGGER.warning("WARNING ⚠️ using HUB training arguments, ignoring local training arguments.") + kwargs = self.session.train_args # overwrite kwargs + + checks.check_pip_update_available() + + overrides = yaml_load(checks.check_yaml(kwargs["cfg"])) if kwargs.get("cfg") else self.overrides + custom = { + # NOTE: handle the case when 'cfg' includes 'data'. + "data": overrides.get("data") or DEFAULT_CFG_DICT["data"] or TASK2DATA[self.task], + "model": self.overrides["model"], + "task": self.task, + } # method defaults + args = {**overrides, **custom, **kwargs, "mode": "train"} # highest priority args on the right + if args.get("resume"): + args["resume"] = self.ckpt_path + + self.trainer = (trainer or self._smart_load("trainer"))(overrides=args, _callbacks=self.callbacks) + if not args.get("resume"): # manually set model only if not resuming + self.trainer.model = self.trainer.get_model(weights=self.model if self.ckpt else None, cfg=self.model.yaml) + self.model = self.trainer.model + + self.trainer.hub_session = self.session # attach optional HUB session + self.trainer.train() + # Update model and cfg after training + if RANK in {-1, 0}: + ckpt = self.trainer.best if self.trainer.best.exists() else self.trainer.last + self.model, _ = attempt_load_one_weight(ckpt) + self.overrides = self.model.args + self.metrics = getattr(self.trainer.validator, "metrics", None) # TODO: no metrics returned by DDP + return self.metrics + + def tune( + self, + use_ray=False, + iterations=10, + *args, + **kwargs, + ): + """ + Conducts hyperparameter tuning for the model, with an option to use Ray Tune. + + This method supports two modes of hyperparameter tuning: using Ray Tune or a custom tuning method. + When Ray Tune is enabled, it leverages the 'run_ray_tune' function from the ultralytics.utils.tuner module. + Otherwise, it uses the internal 'Tuner' class for tuning. The method combines default, overridden, and + custom arguments to configure the tuning process. + + Args: + use_ray (bool): If True, uses Ray Tune for hyperparameter tuning. Defaults to False. + iterations (int): The number of tuning iterations to perform. Defaults to 10. + *args (List): Variable length argument list for additional arguments. + **kwargs (Dict): Arbitrary keyword arguments. These are combined with the model's overrides and defaults. + + Returns: + (Dict): A dictionary containing the results of the hyperparameter search. + + Raises: + AssertionError: If the model is not a PyTorch model. + + Examples: + >>> model = YOLO("yolov8n.pt") + >>> results = model.tune(use_ray=True, iterations=20) + >>> print(results) + """ + self._check_is_pytorch_model() + if use_ray: + from ultralytics.utils.tuner import run_ray_tune + + return run_ray_tune(self, max_samples=iterations, *args, **kwargs) + else: + from .tuner import Tuner + + custom = {} # method defaults + args = {**self.overrides, **custom, **kwargs, "mode": "train"} # highest priority args on the right + return Tuner(args=args, _callbacks=self.callbacks)(model=self, iterations=iterations) + + def _apply(self, fn) -> "Model": + """ + Applies a function to model tensors that are not parameters or registered buffers. + + This method extends the functionality of the parent class's _apply method by additionally resetting the + predictor and updating the device in the model's overrides. It's typically used for operations like + moving the model to a different device or changing its precision. + + Args: + fn (Callable): A function to be applied to the model's tensors. This is typically a method like + to(), cpu(), cuda(), half(), or float(). + + Returns: + (Model): The model instance with the function applied and updated attributes. + + Raises: + AssertionError: If the model is not a PyTorch model. + + Examples: + >>> model = Model("yolov8n.pt") + >>> model = model._apply(lambda t: t.cuda()) # Move model to GPU + """ + self._check_is_pytorch_model() + self = super()._apply(fn) # noqa + self.predictor = None # reset predictor as device may have changed + self.overrides["device"] = self.device # was str(self.device) i.e. device(type='cuda', index=0) -> 'cuda:0' + return self + + @property + def names(self) -> list: + """ + Retrieves the class names associated with the loaded model. + + This property returns the class names if they are defined in the model. It checks the class names for validity + using the 'check_class_names' function from the ultralytics.nn.autobackend module. If the predictor is not + initialized, it sets it up before retrieving the names. + + Returns: + (Dict[int, str]): A dict of class names associated with the model. + + Raises: + AttributeError: If the model or predictor does not have a 'names' attribute. + + Examples: + >>> model = YOLO("yolov8n.pt") + >>> print(model.names) + {0: 'person', 1: 'bicycle', 2: 'car', ...} + """ + from ultralytics.nn.autobackend import check_class_names + + if hasattr(self.model, "names"): + return check_class_names(self.model.names) + if not self.predictor: # export formats will not have predictor defined until predict() is called + self.predictor = self._smart_load("predictor")(overrides=self.overrides, _callbacks=self.callbacks) + self.predictor.setup_model(model=self.model, verbose=False) + return self.predictor.model.names + + @property + def device(self) -> torch.device: + """ + Retrieves the device on which the model's parameters are allocated. + + This property determines the device (CPU or GPU) where the model's parameters are currently stored. It is + applicable only to models that are instances of nn.Module. + + Returns: + (torch.device): The device (CPU/GPU) of the model. + + Raises: + AttributeError: If the model is not a PyTorch nn.Module instance. + + Examples: + >>> model = YOLO("yolov8n.pt") + >>> print(model.device) + device(type='cuda', index=0) # if CUDA is available + >>> model = model.to("cpu") + >>> print(model.device) + device(type='cpu') + """ + return next(self.model.parameters()).device if isinstance(self.model, nn.Module) else None + + @property + def transforms(self): + """ + Retrieves the transformations applied to the input data of the loaded model. + + This property returns the transformations if they are defined in the model. The transforms + typically include preprocessing steps like resizing, normalization, and data augmentation + that are applied to input data before it is fed into the model. + + Returns: + (object | None): The transform object of the model if available, otherwise None. + + Examples: + >>> model = YOLO("yolov8n.pt") + >>> transforms = model.transforms + >>> if transforms: + ... print(f"Model transforms: {transforms}") + ... else: + ... print("No transforms defined for this model.") + """ + return self.model.transforms if hasattr(self.model, "transforms") else None + + def add_callback(self, event: str, func) -> None: + """ + Adds a callback function for a specified event. + + This method allows registering custom callback functions that are triggered on specific events during + model operations such as training or inference. Callbacks provide a way to extend and customize the + behavior of the model at various stages of its lifecycle. + + Args: + event (str): The name of the event to attach the callback to. Must be a valid event name recognized + by the Ultralytics framework. + func (Callable): The callback function to be registered. This function will be called when the + specified event occurs. + + Raises: + ValueError: If the event name is not recognized or is invalid. + + Examples: + >>> def on_train_start(trainer): + ... print("Training is starting!") + >>> model = YOLO("yolov8n.pt") + >>> model.add_callback("on_train_start", on_train_start) + >>> model.train(data="coco128.yaml", epochs=1) + """ + self.callbacks[event].append(func) + + def clear_callback(self, event: str) -> None: + """ + Clears all callback functions registered for a specified event. + + This method removes all custom and default callback functions associated with the given event. + It resets the callback list for the specified event to an empty list, effectively removing all + registered callbacks for that event. + + Args: + event (str): The name of the event for which to clear the callbacks. This should be a valid event name + recognized by the Ultralytics callback system. + + Examples: + >>> model = YOLO("yolov8n.pt") + >>> model.add_callback("on_train_start", lambda: print("Training started")) + >>> model.clear_callback("on_train_start") + >>> # All callbacks for 'on_train_start' are now removed + + Notes: + - This method affects both custom callbacks added by the user and default callbacks + provided by the Ultralytics framework. + - After calling this method, no callbacks will be executed for the specified event + until new ones are added. + - Use with caution as it removes all callbacks, including essential ones that might + be required for proper functioning of certain operations. + """ + self.callbacks[event] = [] + + def reset_callbacks(self) -> None: + """ + Resets all callbacks to their default functions. + + This method reinstates the default callback functions for all events, removing any custom callbacks that were + previously added. It iterates through all default callback events and replaces the current callbacks with the + default ones. + + The default callbacks are defined in the 'callbacks.default_callbacks' dictionary, which contains predefined + functions for various events in the model's lifecycle, such as on_train_start, on_epoch_end, etc. + + This method is useful when you want to revert to the original set of callbacks after making custom + modifications, ensuring consistent behavior across different runs or experiments. + + Examples: + >>> model = YOLO("yolov8n.pt") + >>> model.add_callback("on_train_start", custom_function) + >>> model.reset_callbacks() + # All callbacks are now reset to their default functions + """ + for event in callbacks.default_callbacks.keys(): + self.callbacks[event] = [callbacks.default_callbacks[event][0]] + + @staticmethod + def _reset_ckpt_args(args: dict) -> dict: + """ + Resets specific arguments when loading a PyTorch model checkpoint. + + This static method filters the input arguments dictionary to retain only a specific set of keys that are + considered important for model loading. It's used to ensure that only relevant arguments are preserved + when loading a model from a checkpoint, discarding any unnecessary or potentially conflicting settings. + + Args: + args (dict): A dictionary containing various model arguments and settings. + + Returns: + (dict): A new dictionary containing only the specified include keys from the input arguments. + + Examples: + >>> original_args = {"imgsz": 640, "data": "coco.yaml", "task": "detect", "batch": 16, "epochs": 100} + >>> reset_args = Model._reset_ckpt_args(original_args) + >>> print(reset_args) + {'imgsz': 640, 'data': 'coco.yaml', 'task': 'detect'} + """ + include = {"imgsz", "data", "task", "single_cls"} # only remember these arguments when loading a PyTorch model + return {k: v for k, v in args.items() if k in include} + + # def __getattr__(self, attr): + # """Raises error if object has no requested attribute.""" + # name = self.__class__.__name__ + # raise AttributeError(f"'{name}' object has no attribute '{attr}'. See valid attributes below.\n{self.__doc__}") + + def _smart_load(self, key: str): + """ + Loads the appropriate module based on the model task. + + This method dynamically selects and returns the correct module (model, trainer, validator, or predictor) + based on the current task of the model and the provided key. It uses the task_map attribute to determine + the correct module to load. + + Args: + key (str): The type of module to load. Must be one of 'model', 'trainer', 'validator', or 'predictor'. + + Returns: + (object): The loaded module corresponding to the specified key and current task. + + Raises: + NotImplementedError: If the specified key is not supported for the current task. + + Examples: + >>> model = Model(task="detect") + >>> predictor = model._smart_load("predictor") + >>> trainer = model._smart_load("trainer") + + Notes: + - This method is typically used internally by other methods of the Model class. + - The task_map attribute should be properly initialized with the correct mappings for each task. + """ + try: + return self.task_map[self.task][key] + except Exception as e: + name = self.__class__.__name__ + mode = inspect.stack()[1][3] # get the function name. + raise NotImplementedError( + emojis(f"WARNING ⚠️ '{name}' model does not support '{mode}' mode for '{self.task}' task yet.") + ) from e + + @property + def task_map(self) -> dict: + """ + Provides a mapping from model tasks to corresponding classes for different modes. + + This property method returns a dictionary that maps each supported task (e.g., detect, segment, classify) + to a nested dictionary. The nested dictionary contains mappings for different operational modes + (model, trainer, validator, predictor) to their respective class implementations. + + The mapping allows for dynamic loading of appropriate classes based on the model's task and the + desired operational mode. This facilitates a flexible and extensible architecture for handling + various tasks and modes within the Ultralytics framework. + + Returns: + (Dict[str, Dict[str, Any]]): A dictionary where keys are task names (str) and values are + nested dictionaries. Each nested dictionary has keys 'model', 'trainer', 'validator', and + 'predictor', mapping to their respective class implementations. + + Examples: + >>> model = Model() + >>> task_map = model.task_map + >>> detect_class_map = task_map["detect"] + >>> segment_class_map = task_map["segment"] + + Note: + The actual implementation of this method may vary depending on the specific tasks and + classes supported by the Ultralytics framework. The docstring provides a general + description of the expected behavior and structure. + """ + raise NotImplementedError("Please provide task map for your model!") diff --git a/examples/Ultralytics Module/predictor.py b/examples/Ultralytics Module/predictor.py new file mode 100644 index 0000000..8ace18f --- /dev/null +++ b/examples/Ultralytics Module/predictor.py @@ -0,0 +1,403 @@ +# Ultralytics YOLO 🚀, AGPL-3.0 license +""" +Run prediction on images, videos, directories, globs, YouTube, webcam, streams, etc. + +Usage - sources: + $ yolo mode=predict model=yolov8n.pt source=0 # webcam + img.jpg # image + vid.mp4 # video + screen # screenshot + path/ # directory + list.txt # list of images + list.streams # list of streams + 'path/*.jpg' # glob + 'https://youtu.be/LNwODJXcvt4' # YouTube + 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP, TCP stream + +Usage - formats: + $ yolo mode=predict model=yolov8n.pt # PyTorch + yolov8n.torchscript # TorchScript + yolov8n.onnx # ONNX Runtime or OpenCV DNN with dnn=True + yolov8n_openvino_model # OpenVINO + yolov8n.engine # TensorRT + yolov8n.mlpackage # CoreML (macOS-only) + yolov8n_saved_model # TensorFlow SavedModel + yolov8n.pb # TensorFlow GraphDef + yolov8n.tflite # TensorFlow Lite + yolov8n_edgetpu.tflite # TensorFlow Edge TPU + yolov8n_paddle_model # PaddlePaddle + yolov8n_ncnn_model # NCNN +""" + +import platform +import re +import threading +from pathlib import Path + +import cv2 +import numpy as np +import torch + +from ultralytics.cfg import get_cfg, get_save_dir +from ultralytics.data import load_inference_source +from ultralytics.data.augment import LetterBox, classify_transforms +from ultralytics.nn.autobackend import AutoBackend +from ultralytics.utils import DEFAULT_CFG, LOGGER, MACOS, WINDOWS, callbacks, colorstr, ops +from ultralytics.utils.checks import check_imgsz, check_imshow +from ultralytics.utils.files import increment_path +from ultralytics.utils.torch_utils import select_device, smart_inference_mode + +STREAM_WARNING = """ +WARNING ⚠️ inference results will accumulate in RAM unless `stream=True` is passed, causing potential out-of-memory +errors for large sources or long-running streams and videos. See https://docs.ultralytics.com/modes/predict/ for help. + +Example: + results = model(source=..., stream=True) # generator of Results objects + for r in results: + boxes = r.boxes # Boxes object for bbox outputs + masks = r.masks # Masks object for segment masks outputs + probs = r.probs # Class probabilities for classification outputs +""" + + +class BasePredictor: + """ + BasePredictor. + + A base class for creating predictors. + + Attributes: + args (SimpleNamespace): Configuration for the predictor. + save_dir (Path): Directory to save results. + done_warmup (bool): Whether the predictor has finished setup. + model (nn.Module): Model used for prediction. + data (dict): Data configuration. + device (torch.device): Device used for prediction. + dataset (Dataset): Dataset used for prediction. + vid_writer (dict): Dictionary of {save_path: video_writer, ...} writer for saving video output. + """ + + def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None): + """ + Initializes the BasePredictor class. + + Args: + cfg (str, optional): Path to a configuration file. Defaults to DEFAULT_CFG. + overrides (dict, optional): Configuration overrides. Defaults to None. + """ + self.args = get_cfg(cfg, overrides) + self.save_dir = get_save_dir(self.args) + if self.args.conf is None: + self.args.conf = 0.25 # default conf=0.25 + self.done_warmup = False + if self.args.show: + self.args.show = check_imshow(warn=True) + + # Usable if setup is done + self.model = None + self.data = self.args.data # data_dict + self.imgsz = None + self.device = None + self.dataset = None + self.vid_writer = {} # dict of {save_path: video_writer, ...} + self.plotted_img = None + self.source_type = None + self.seen = 0 + self.windows = [] + self.batch = None + self.results = None + self.transforms = None + self.callbacks = _callbacks or callbacks.get_default_callbacks() + self.txt_path = None + self._lock = threading.Lock() # for automatic thread-safe inference + callbacks.add_integration_callbacks(self) + + def preprocess(self, im): + """ + Prepares input image before inference. + + Args: + im (torch.Tensor | List(np.ndarray)): BCHW for tensor, [(HWC) x B] for list. + """ + not_tensor = not isinstance(im, torch.Tensor) + if not_tensor: + im = np.stack(self.pre_transform(im)) + im = im[..., ::-1].transpose((0, 3, 1, 2)) # BGR to RGB, BHWC to BCHW, (n, 3, h, w) + im = np.ascontiguousarray(im) # contiguous + im = torch.from_numpy(im) + + im = im.to(self.device) + im = im.half() if self.model.fp16 else im.float() # uint8 to fp16/32 + if not_tensor: + im /= 255 # 0 - 255 to 0.0 - 1.0 + return im + + def inference(self, im, *args, **kwargs): + """Runs inference on a given image using the specified model and arguments.""" + visualize = ( + increment_path(self.save_dir / Path(self.batch[0][0]).stem, mkdir=True) + if self.args.visualize and (not self.source_type.tensor) + else False + ) + return self.model(im, augment=self.args.augment, visualize=visualize, embed=self.args.embed, *args, **kwargs) + + def pre_transform(self, im): + """ + Pre-transform input image before inference. + + Args: + im (List(np.ndarray)): (N, 3, h, w) for tensor, [(h, w, 3) x N] for list. + + Returns: + (list): A list of transformed images. + """ + same_shapes = len({x.shape for x in im}) == 1 + letterbox = LetterBox(self.imgsz, auto=same_shapes and self.model.pt, stride=self.model.stride) + return [letterbox(image=x) for x in im] + + def postprocess(self, preds, img, orig_imgs): + """Post-processes predictions for an image and returns them.""" + return preds + + def __call__(self, source=None, model=None, stream=False, *args, **kwargs): + """Performs inference on an image or stream.""" + self.stream = stream + if stream: + return self.stream_inference(source, model, *args, **kwargs) + else: + return list(self.stream_inference(source, model, *args, **kwargs)) # merge list of Result into one + + def predict_cli(self, source=None, model=None): + """ + Method used for Command Line Interface (CLI) prediction. + + This function is designed to run predictions using the CLI. It sets up the source and model, then processes + the inputs in a streaming manner. This method ensures that no outputs accumulate in memory by consuming the + generator without storing results. + + Note: + Do not modify this function or remove the generator. The generator ensures that no outputs are + accumulated in memory, which is critical for preventing memory issues during long-running predictions. + """ + gen = self.stream_inference(source, model) + for _ in gen: # sourcery skip: remove-empty-nested-block, noqa + pass + + def setup_source(self, source): + """Sets up source and inference mode.""" + self.imgsz = check_imgsz(self.args.imgsz, stride=self.model.stride, min_dim=2) # check image size + self.transforms = ( + getattr( + self.model.model, + "transforms", + classify_transforms(self.imgsz[0], crop_fraction=self.args.crop_fraction), + ) + if self.args.task == "classify" + else None + ) + self.dataset = load_inference_source( + source=source, + batch=self.args.batch, + vid_stride=self.args.vid_stride, + buffer=self.args.stream_buffer, + ) + self.source_type = self.dataset.source_type + if not getattr(self, "stream", True) and ( + self.source_type.stream + or self.source_type.screenshot + or len(self.dataset) > 1000 # many images + or any(getattr(self.dataset, "video_flag", [False])) + ): # videos + LOGGER.warning(STREAM_WARNING) + self.vid_writer = {} + + @smart_inference_mode() + def stream_inference(self, source=None, model=None, *args, **kwargs): + """Streams real-time inference on camera feed and saves results to file.""" + if self.args.verbose: + LOGGER.info("") + + # Setup model + if not self.model: + self.setup_model(model) + + with self._lock: # for thread-safe inference + # Setup source every time predict is called + self.setup_source(source if source is not None else self.args.source) + + # Check if save_dir/ label file exists + if self.args.save or self.args.save_txt: + (self.save_dir / "labels" if self.args.save_txt else self.save_dir).mkdir(parents=True, exist_ok=True) + + # Warmup model + if not self.done_warmup: + self.model.warmup(imgsz=(1 if self.model.pt or self.model.triton else self.dataset.bs, 3, *self.imgsz)) + self.done_warmup = True + + self.seen, self.windows, self.batch = 0, [], None + profilers = ( + ops.Profile(device=self.device), + ops.Profile(device=self.device), + ops.Profile(device=self.device), + ) + self.run_callbacks("on_predict_start") + for self.batch in self.dataset: + self.run_callbacks("on_predict_batch_start") + paths, im0s, s = self.batch + + # Preprocess + with profilers[0]: + im = self.preprocess(im0s) + + # Inference + with profilers[1]: + preds = self.inference(im, *args, **kwargs) + if self.args.embed: + yield from [preds] if isinstance(preds, torch.Tensor) else preds # yield embedding tensors + continue + + # Postprocess + with profilers[2]: + self.results = self.postprocess(preds, im, im0s) + self.run_callbacks("on_predict_postprocess_end") + + # Visualize, save, write results + n = len(im0s) + for i in range(n): + self.seen += 1 + self.results[i].speed = { + "preprocess": profilers[0].dt * 1e3 / n, + "inference": profilers[1].dt * 1e3 / n, + "postprocess": profilers[2].dt * 1e3 / n, + } + if self.args.verbose or self.args.save or self.args.save_txt or self.args.show: + s[i] += self.write_results(i, Path(paths[i]), im, s) + + # Print batch results + if self.args.verbose: + LOGGER.info("\n".join(s)) + + self.run_callbacks("on_predict_batch_end") + yield from self.results + + # Release assets + for v in self.vid_writer.values(): + if isinstance(v, cv2.VideoWriter): + v.release() + + # Print final results + if self.args.verbose and self.seen: + t = tuple(x.t / self.seen * 1e3 for x in profilers) # speeds per image + LOGGER.info( + f"Speed: %.1fms preprocess, %.1fms inference, %.1fms postprocess per image at shape " + f"{(min(self.args.batch, self.seen), 3, *im.shape[2:])}" % t + ) + if self.args.save or self.args.save_txt or self.args.save_crop: + nl = len(list(self.save_dir.glob("labels/*.txt"))) # number of labels + s = f"\n{nl} label{'s' * (nl > 1)} saved to {self.save_dir / 'labels'}" if self.args.save_txt else "" + LOGGER.info(f"Results saved to {colorstr('bold', self.save_dir)}{s}") + self.run_callbacks("on_predict_end") + + def setup_model(self, model, verbose=True): + """Initialize YOLO model with given parameters and set it to evaluation mode.""" + self.model = AutoBackend( + weights=model or self.args.model, + device=select_device(self.args.device, verbose=verbose), + dnn=self.args.dnn, + data=self.args.data, + fp16=self.args.half, + batch=self.args.batch, + fuse=True, + verbose=verbose, + ) + + self.device = self.model.device # update device + self.args.half = self.model.fp16 # update half + self.model.eval() + + def write_results(self, i, p, im, s): + """Write inference results to a file or directory.""" + string = "" # print string + if len(im.shape) == 3: + im = im[None] # expand for batch dim + if self.source_type.stream or self.source_type.from_img or self.source_type.tensor: # batch_size >= 1 + string += f"{i}: " + frame = self.dataset.count + else: + match = re.search(r"frame (\d+)/", s[i]) + frame = int(match[1]) if match else None # 0 if frame undetermined + + self.txt_path = self.save_dir / "labels" / (p.stem + ("" if self.dataset.mode == "image" else f"_{frame}")) + string += "{:g}x{:g} ".format(*im.shape[2:]) + result = self.results[i] + result.save_dir = self.save_dir.__str__() # used in other locations + string += f"{result.verbose()}{result.speed['inference']:.1f}ms" + + # Add predictions to image + if self.args.save or self.args.show: + self.plotted_img = result.plot( + line_width=self.args.line_width, + boxes=self.args.show_boxes, + conf=self.args.show_conf, + labels=self.args.show_labels, + im_gpu=None if self.args.retina_masks else im[i], + ) + + # Save results + if self.args.save_txt: + result.save_txt(f"{self.txt_path}.txt", save_conf=self.args.save_conf) + if self.args.save_crop: + result.save_crop(save_dir=self.save_dir / "crops", file_name=self.txt_path.stem) + if self.args.show: + self.show(str(p)) + if self.args.save: + self.save_predicted_images(str(self.save_dir / p.name), frame) + + return string + + def save_predicted_images(self, save_path="", frame=0): + """Save video predictions as mp4 at specified path.""" + im = self.plotted_img + + # Save videos and streams + if self.dataset.mode in {"stream", "video"}: + fps = self.dataset.fps if self.dataset.mode == "video" else 30 + frames_path = f'{save_path.split(".", 1)[0]}_frames/' + if save_path not in self.vid_writer: # new video + if self.args.save_frames: + Path(frames_path).mkdir(parents=True, exist_ok=True) + suffix, fourcc = (".mp4", "avc1") if MACOS else (".avi", "WMV2") if WINDOWS else (".avi", "MJPG") + self.vid_writer[save_path] = cv2.VideoWriter( + filename=str(Path(save_path).with_suffix(suffix)), + fourcc=cv2.VideoWriter_fourcc(*fourcc), + fps=fps, # integer required, floats produce error in MP4 codec + frameSize=(im.shape[1], im.shape[0]), # (width, height) + ) + + # Save video + self.vid_writer[save_path].write(im) + if self.args.save_frames: + cv2.imwrite(f"{frames_path}{frame}.jpg", im) + + # Save images + else: + cv2.imwrite(save_path, im) + + def show(self, p=""): + """Display an image in a window using the OpenCV imshow function.""" + im = self.plotted_img + if platform.system() == "Linux" and p not in self.windows: + self.windows.append(p) + cv2.namedWindow(p, cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO) # allow window resize (Linux) + cv2.resizeWindow(p, im.shape[1], im.shape[0]) # (width, height) + cv2.imshow(p, im) + cv2.waitKey(300 if self.dataset.mode == "image" else 1) # 1 millisecond + + def run_callbacks(self, event: str): + """Runs all registered callbacks for a specific event.""" + for callback in self.callbacks.get(event, []): + callback(self) + + def add_callback(self, event: str, func): + """Add callback.""" + self.callbacks[event].append(func) diff --git a/examples/Ultralytics Module/results.py b/examples/Ultralytics Module/results.py new file mode 100644 index 0000000..57cc4b0 --- /dev/null +++ b/examples/Ultralytics Module/results.py @@ -0,0 +1,1741 @@ +# Ultralytics YOLO 🚀, AGPL-3.0 license +""" +Ultralytics Results, Boxes and Masks classes for handling inference results. + +Usage: See https://docs.ultralytics.com/modes/predict/ +""" + +from copy import deepcopy +from functools import lru_cache +from pathlib import Path + +import numpy as np +import torch + +from ultralytics.data.augment import LetterBox +from ultralytics.utils import LOGGER, SimpleClass, ops +from ultralytics.utils.checks import check_requirements +from ultralytics.utils.plotting import Annotator, colors, save_one_box +from ultralytics.utils.torch_utils import smart_inference_mode + + +class BaseTensor(SimpleClass): + """ + Base tensor class with additional methods for easy manipulation and device handling. + + Attributes: + data (torch.Tensor | np.ndarray): Prediction data such as bounding boxes, masks, or keypoints. + orig_shape (Tuple[int, int]): Original shape of the image, typically in the format (height, width). + + Methods: + cpu: Return a copy of the tensor stored in CPU memory. + numpy: Returns a copy of the tensor as a numpy array. + cuda: Moves the tensor to GPU memory, returning a new instance if necessary. + to: Return a copy of the tensor with the specified device and dtype. + + Examples: + >>> import torch + >>> data = torch.tensor([[1, 2, 3], [4, 5, 6]]) + >>> orig_shape = (720, 1280) + >>> base_tensor = BaseTensor(data, orig_shape) + >>> cpu_tensor = base_tensor.cpu() + >>> numpy_array = base_tensor.numpy() + >>> gpu_tensor = base_tensor.cuda() + """ + + def __init__(self, data, orig_shape) -> None: + """ + Initialize BaseTensor with prediction data and the original shape of the image. + + Args: + data (torch.Tensor | np.ndarray): Prediction data such as bounding boxes, masks, or keypoints. + orig_shape (Tuple[int, int]): Original shape of the image in (height, width) format. + + Examples: + >>> import torch + >>> data = torch.tensor([[1, 2, 3], [4, 5, 6]]) + >>> orig_shape = (720, 1280) + >>> base_tensor = BaseTensor(data, orig_shape) + """ + assert isinstance(data, (torch.Tensor, np.ndarray)), "data must be torch.Tensor or np.ndarray" + self.data = data + self.orig_shape = orig_shape + + @property + def shape(self): + """ + Returns the shape of the underlying data tensor. + + Returns: + (Tuple[int, ...]): The shape of the data tensor. + + Examples: + >>> data = torch.rand(100, 4) + >>> base_tensor = BaseTensor(data, orig_shape=(720, 1280)) + >>> print(base_tensor.shape) + (100, 4) + """ + return self.data.shape + + def cpu(self): + """ + Returns a copy of the tensor stored in CPU memory. + + Returns: + (BaseTensor): A new BaseTensor object with the data tensor moved to CPU memory. + + Examples: + >>> data = torch.tensor([[1, 2, 3], [4, 5, 6]]).cuda() + >>> base_tensor = BaseTensor(data, orig_shape=(720, 1280)) + >>> cpu_tensor = base_tensor.cpu() + >>> isinstance(cpu_tensor, BaseTensor) + True + >>> cpu_tensor.data.device + device(type='cpu') + """ + return self if isinstance(self.data, np.ndarray) else self.__class__(self.data.cpu(), self.orig_shape) + + def numpy(self): + """ + Returns a copy of the tensor as a numpy array. + + Returns: + (np.ndarray): A numpy array containing the same data as the original tensor. + + Examples: + >>> data = torch.tensor([[1, 2, 3], [4, 5, 6]]) + >>> orig_shape = (720, 1280) + >>> base_tensor = BaseTensor(data, orig_shape) + >>> numpy_array = base_tensor.numpy() + >>> print(type(numpy_array)) + + """ + return self if isinstance(self.data, np.ndarray) else self.__class__(self.data.numpy(), self.orig_shape) + + def cuda(self): + """ + Moves the tensor to GPU memory. + + Returns: + (BaseTensor): A new BaseTensor instance with the data moved to GPU memory if it's not already a + numpy array, otherwise returns self. + + Examples: + >>> import torch + >>> from ultralytics.engine.results import BaseTensor + >>> data = torch.tensor([[1, 2, 3], [4, 5, 6]]) + >>> base_tensor = BaseTensor(data, orig_shape=(720, 1280)) + >>> gpu_tensor = base_tensor.cuda() + >>> print(gpu_tensor.data.device) + cuda:0 + """ + return self.__class__(torch.as_tensor(self.data).cuda(), self.orig_shape) + + def to(self, *args, **kwargs): + """ + Return a copy of the tensor with the specified device and dtype. + + Args: + *args (Any): Variable length argument list to be passed to torch.Tensor.to(). + **kwargs (Any): Arbitrary keyword arguments to be passed to torch.Tensor.to(). + + Returns: + (BaseTensor): A new BaseTensor instance with the data moved to the specified device and/or dtype. + + Examples: + >>> base_tensor = BaseTensor(torch.randn(3, 4), orig_shape=(480, 640)) + >>> cuda_tensor = base_tensor.to("cuda") + >>> float16_tensor = base_tensor.to(dtype=torch.float16) + """ + return self.__class__(torch.as_tensor(self.data).to(*args, **kwargs), self.orig_shape) + + def __len__(self): # override len(results) + """ + Returns the length of the underlying data tensor. + + Returns: + (int): The number of elements in the first dimension of the data tensor. + + Examples: + >>> data = torch.tensor([[1, 2, 3], [4, 5, 6]]) + >>> base_tensor = BaseTensor(data, orig_shape=(720, 1280)) + >>> len(base_tensor) + 2 + """ + return len(self.data) + + def __getitem__(self, idx): + """ + Returns a new BaseTensor instance containing the specified indexed elements of the data tensor. + + Args: + idx (int | List[int] | torch.Tensor): Index or indices to select from the data tensor. + + Returns: + (BaseTensor): A new BaseTensor instance containing the indexed data. + + Examples: + >>> data = torch.tensor([[1, 2, 3], [4, 5, 6]]) + >>> base_tensor = BaseTensor(data, orig_shape=(720, 1280)) + >>> result = base_tensor[0] # Select the first row + >>> print(result.data) + tensor([1, 2, 3]) + """ + return self.__class__(self.data[idx], self.orig_shape) + + +class Results(SimpleClass): + """ + A class for storing and manipulating inference results. + + This class encapsulates the functionality for handling detection, segmentation, pose estimation, + and classification results from YOLO models. + + Attributes: + orig_img (numpy.ndarray): Original image as a numpy array. + orig_shape (Tuple[int, int]): Original image shape in (height, width) format. + boxes (Boxes | None): Object containing detection bounding boxes. + masks (Masks | None): Object containing detection masks. + probs (Probs | None): Object containing class probabilities for classification tasks. + keypoints (Keypoints | None): Object containing detected keypoints for each object. + obb (OBB | None): Object containing oriented bounding boxes. + speed (Dict[str, float | None]): Dictionary of preprocess, inference, and postprocess speeds. + names (Dict[int, str]): Dictionary mapping class IDs to class names. + path (str): Path to the image file. + _keys (Tuple[str, ...]): Tuple of attribute names for internal use. + + Methods: + update: Updates object attributes with new detection results. + cpu: Returns a copy of the Results object with all tensors on CPU memory. + numpy: Returns a copy of the Results object with all tensors as numpy arrays. + cuda: Returns a copy of the Results object with all tensors on GPU memory. + to: Returns a copy of the Results object with tensors on a specified device and dtype. + new: Returns a new Results object with the same image, path, and names. + plot: Plots detection results on an input image, returning an annotated image. + show: Shows annotated results on screen. + save: Saves annotated results to file. + verbose: Returns a log string for each task, detailing detections and classifications. + save_txt: Saves detection results to a text file. + save_crop: Saves cropped detection images. + tojson: Converts detection results to JSON format. + + Examples: + >>> results = model("path/to/image.jpg") + >>> for result in results: + ... print(result.boxes) # Print detection boxes + ... result.show() # Display the annotated image + ... result.save(filename="result.jpg") # Save annotated image + """ + + def __init__( + self, orig_img, path, names, boxes=None, masks=None, probs=None, keypoints=None, obb=None, speed=None + ) -> None: + """ + Initialize the Results class for storing and manipulating inference results. + + Args: + orig_img (numpy.ndarray): The original image as a numpy array. + path (str): The path to the image file. + names (Dict): A dictionary of class names. + boxes (torch.Tensor | None): A 2D tensor of bounding box coordinates for each detection. + masks (torch.Tensor | None): A 3D tensor of detection masks, where each mask is a binary image. + probs (torch.Tensor | None): A 1D tensor of probabilities of each class for classification task. + keypoints (torch.Tensor | None): A 2D tensor of keypoint coordinates for each detection. + obb (torch.Tensor | None): A 2D tensor of oriented bounding box coordinates for each detection. + speed (Dict | None): A dictionary containing preprocess, inference, and postprocess speeds (ms/image). + + Examples: + >>> results = model("path/to/image.jpg") + >>> result = results[0] # Get the first result + >>> boxes = result.boxes # Get the boxes for the first result + >>> masks = result.masks # Get the masks for the first result + + Notes: + For the default pose model, keypoint indices for human body pose estimation are: + 0: Nose, 1: Left Eye, 2: Right Eye, 3: Left Ear, 4: Right Ear + 5: Left Shoulder, 6: Right Shoulder, 7: Left Elbow, 8: Right Elbow + 9: Left Wrist, 10: Right Wrist, 11: Left Hip, 12: Right Hip + 13: Left Knee, 14: Right Knee, 15: Left Ankle, 16: Right Ankle + """ + self.orig_img = orig_img + self.orig_shape = orig_img.shape[:2] + self.boxes = Boxes(boxes, self.orig_shape) if boxes is not None else None # native size boxes + self.masks = Masks(masks, self.orig_shape) if masks is not None else None # native size or imgsz masks + self.probs = Probs(probs) if probs is not None else None + self.keypoints = Keypoints(keypoints, self.orig_shape) if keypoints is not None else None + self.obb = OBB(obb, self.orig_shape) if obb is not None else None + self.speed = speed if speed is not None else {"preprocess": None, "inference": None, "postprocess": None} + self.names = names + self.path = path + self.save_dir = None + self._keys = "boxes", "masks", "probs", "keypoints", "obb" + + def __getitem__(self, idx): + """ + Return a Results object for a specific index of inference results. + + Args: + idx (int | slice): Index or slice to retrieve from the Results object. + + Returns: + (Results): A new Results object containing the specified subset of inference results. + + Examples: + >>> results = model("path/to/image.jpg") # Perform inference + >>> single_result = results[0] # Get the first result + >>> subset_results = results[1:4] # Get a slice of results + """ + return self._apply("__getitem__", idx) + + def __len__(self): + """ + Return the number of detections in the Results object. + + Returns: + (int): The number of detections, determined by the length of the first non-empty attribute + (boxes, masks, probs, keypoints, or obb). + + Examples: + >>> results = Results(orig_img, path, names, boxes=torch.rand(5, 4)) + >>> len(results) + 5 + """ + for k in self._keys: + v = getattr(self, k) + if v is not None: + return len(v) + + def update(self, boxes=None, masks=None, probs=None, obb=None): + """ + Updates the Results object with new detection data. + + This method allows updating the boxes, masks, probabilities, and oriented bounding boxes (OBB) of the + Results object. It ensures that boxes are clipped to the original image shape. + + Args: + boxes (torch.Tensor | None): A tensor of shape (N, 6) containing bounding box coordinates and + confidence scores. The format is (x1, y1, x2, y2, conf, class). + masks (torch.Tensor | None): A tensor of shape (N, H, W) containing segmentation masks. + probs (torch.Tensor | None): A tensor of shape (num_classes,) containing class probabilities. + obb (torch.Tensor | None): A tensor of shape (N, 5) containing oriented bounding box coordinates. + + Examples: + >>> results = model("image.jpg") + >>> new_boxes = torch.tensor([[100, 100, 200, 200, 0.9, 0]]) + >>> results[0].update(boxes=new_boxes) + """ + if boxes is not None: + self.boxes = Boxes(ops.clip_boxes(boxes, self.orig_shape), self.orig_shape) + if masks is not None: + self.masks = Masks(masks, self.orig_shape) + if probs is not None: + self.probs = probs + if obb is not None: + self.obb = OBB(obb, self.orig_shape) + + def _apply(self, fn, *args, **kwargs): + """ + Applies a function to all non-empty attributes and returns a new Results object with modified attributes. + + This method is internally called by methods like .to(), .cuda(), .cpu(), etc. + + Args: + fn (str): The name of the function to apply. + *args (Any): Variable length argument list to pass to the function. + **kwargs (Any): Arbitrary keyword arguments to pass to the function. + + Returns: + (Results): A new Results object with attributes modified by the applied function. + + Examples: + >>> results = model("path/to/image.jpg") + >>> for result in results: + ... result_cuda = result.cuda() + ... result_cpu = result.cpu() + """ + r = self.new() + for k in self._keys: + v = getattr(self, k) + if v is not None: + setattr(r, k, getattr(v, fn)(*args, **kwargs)) + return r + + def cpu(self): + """ + Returns a copy of the Results object with all its tensors moved to CPU memory. + + This method creates a new Results object with all tensor attributes (boxes, masks, probs, keypoints, obb) + transferred to CPU memory. It's useful for moving data from GPU to CPU for further processing or saving. + + Returns: + (Results): A new Results object with all tensor attributes on CPU memory. + + Examples: + >>> results = model("path/to/image.jpg") # Perform inference + >>> cpu_result = results[0].cpu() # Move the first result to CPU + >>> print(cpu_result.boxes.device) # Output: cpu + """ + return self._apply("cpu") + + def numpy(self): + """ + Converts all tensors in the Results object to numpy arrays. + + Returns: + (Results): A new Results object with all tensors converted to numpy arrays. + + Examples: + >>> results = model("path/to/image.jpg") + >>> numpy_result = results[0].numpy() + >>> type(numpy_result.boxes.data) + + + Notes: + This method creates a new Results object, leaving the original unchanged. It's useful for + interoperability with numpy-based libraries or when CPU-based operations are required. + """ + return self._apply("numpy") + + def cuda(self): + """ + Moves all tensors in the Results object to GPU memory. + + Returns: + (Results): A new Results object with all tensors moved to CUDA device. + + Examples: + >>> results = model("path/to/image.jpg") + >>> cuda_results = results[0].cuda() # Move first result to GPU + >>> for result in results: + ... result_cuda = result.cuda() # Move each result to GPU + """ + return self._apply("cuda") + + def to(self, *args, **kwargs): + """ + Moves all tensors in the Results object to the specified device and dtype. + + Args: + *args (Any): Variable length argument list to be passed to torch.Tensor.to(). + **kwargs (Any): Arbitrary keyword arguments to be passed to torch.Tensor.to(). + + Returns: + (Results): A new Results object with all tensors moved to the specified device and dtype. + + Examples: + >>> results = model("path/to/image.jpg") + >>> result_cuda = results[0].to("cuda") # Move first result to GPU + >>> result_cpu = results[0].to("cpu") # Move first result to CPU + >>> result_half = results[0].to(dtype=torch.float16) # Convert first result to half precision + """ + return self._apply("to", *args, **kwargs) + + def new(self): + """ + Creates a new Results object with the same image, path, names, and speed attributes. + + Returns: + (Results): A new Results object with copied attributes from the original instance. + + Examples: + >>> results = model("path/to/image.jpg") + >>> new_result = results[0].new() + """ + return Results(orig_img=self.orig_img, path=self.path, names=self.names, speed=self.speed) + + def plot( + self, + conf=True, + line_width=None, + font_size=None, + font="Arial.ttf", + pil=False, + img=None, + im_gpu=None, + kpt_radius=5, + kpt_line=True, + labels=True, + boxes=True, + masks=True, + probs=True, + show=False, + save=False, + filename=None, + color_mode="class", + ): + """ + Plots detection results on an input RGB image. + + Args: + conf (bool): Whether to plot detection confidence scores. + line_width (float | None): Line width of bounding boxes. If None, scaled to image size. + font_size (float | None): Font size for text. If None, scaled to image size. + font (str): Font to use for text. + pil (bool): Whether to return the image as a PIL Image. + img (np.ndarray | None): Image to plot on. If None, uses original image. + im_gpu (torch.Tensor | None): Normalized image on GPU for faster mask plotting. + kpt_radius (int): Radius of drawn keypoints. + kpt_line (bool): Whether to draw lines connecting keypoints. + labels (bool): Whether to plot labels of bounding boxes. + boxes (bool): Whether to plot bounding boxes. + masks (bool): Whether to plot masks. + probs (bool): Whether to plot classification probabilities. + show (bool): Whether to display the annotated image. + save (bool): Whether to save the annotated image. + filename (str | None): Filename to save image if save is True. + color_mode (bool): Specify the color mode, e.g., 'instance' or 'class'. Default to 'class'. + + Returns: + (np.ndarray): Annotated image as a numpy array. + + Examples: + >>> results = model("image.jpg") + >>> for result in results: + ... im = result.plot() + ... im.show() + """ + assert color_mode in {"instance", "class"}, f"Expected color_mode='instance' or 'class', not {color_mode}." + if img is None and isinstance(self.orig_img, torch.Tensor): + img = (self.orig_img[0].detach().permute(1, 2, 0).contiguous() * 255).to(torch.uint8).cpu().numpy() + + names = self.names + is_obb = self.obb is not None + pred_boxes, show_boxes = self.obb if is_obb else self.boxes, boxes + pred_masks, show_masks = self.masks, masks + pred_probs, show_probs = self.probs, probs + annotator = Annotator( + deepcopy(self.orig_img if img is None else img), + line_width, + font_size, + font, + pil or (pred_probs is not None and show_probs), # Classify tasks default to pil=True + example=names, + ) + + # Plot Segment results + if pred_masks and show_masks: + if im_gpu is None: + img = LetterBox(pred_masks.shape[1:])(image=annotator.result()) + im_gpu = ( + torch.as_tensor(img, dtype=torch.float16, device=pred_masks.data.device) + .permute(2, 0, 1) + .flip(0) + .contiguous() + / 255 + ) + idx = ( + pred_boxes.id + if pred_boxes.id is not None and color_mode == "instance" + else pred_boxes.cls + if pred_boxes and color_mode == "class" + else reversed(range(len(pred_masks))) + ) + annotator.masks(pred_masks.data, colors=[colors(x, True) for x in idx], im_gpu=im_gpu) + + # Plot Detect results + if pred_boxes is not None and show_boxes: + for i, d in enumerate(reversed(pred_boxes)): + c, conf, id = int(d.cls), float(d.conf) if conf else None, None if d.id is None else int(d.id.item()) + name = ("" if id is None else f"id:{id} ") + names[c] + label = (f"{name} {conf:.2f}" if conf else name) if labels else None + box = d.xyxyxyxy.reshape(-1, 4, 2).squeeze() if is_obb else d.xyxy.squeeze() + annotator.box_label( + box, + label, + color=colors( + c + if color_mode == "class" + else id + if id is not None + else i + if color_mode == "instance" + else None, + True, + ), + rotated=is_obb, + ) + + # Plot Classify results + if pred_probs is not None and show_probs: + text = ",\n".join(f"{names[j] if names else j} {pred_probs.data[j]:.2f}" for j in pred_probs.top5) + x = round(self.orig_shape[0] * 0.03) + annotator.text([x, x], text, txt_color=(255, 255, 255)) # TODO: allow setting colors + + # Plot Pose results + if self.keypoints is not None: + for i, k in enumerate(reversed(self.keypoints.data)): + annotator.kpts( + k, + self.orig_shape, + radius=kpt_radius, + kpt_line=kpt_line, + kpt_color=colors(i, True) if color_mode == "instance" else None, + ) + + # Show results + if show: + annotator.show(self.path) + + # Save results + if save: + annotator.save(filename) + + return annotator.result() + + def show(self, *args, **kwargs): + """ + Display the image with annotated inference results. + + This method plots the detection results on the original image and displays it. It's a convenient way to + visualize the model's predictions directly. + + Args: + *args (Any): Variable length argument list to be passed to the `plot()` method. + **kwargs (Any): Arbitrary keyword arguments to be passed to the `plot()` method. + + Examples: + >>> results = model("path/to/image.jpg") + >>> results[0].show() # Display the first result + >>> for result in results: + ... result.show() # Display all results + """ + self.plot(show=True, *args, **kwargs) + + def save(self, filename=None, *args, **kwargs): + """ + Saves annotated inference results image to file. + + This method plots the detection results on the original image and saves the annotated image to a file. It + utilizes the `plot` method to generate the annotated image and then saves it to the specified filename. + + Args: + filename (str | Path | None): The filename to save the annotated image. If None, a default filename + is generated based on the original image path. + *args (Any): Variable length argument list to be passed to the `plot` method. + **kwargs (Any): Arbitrary keyword arguments to be passed to the `plot` method. + + Examples: + >>> results = model("path/to/image.jpg") + >>> for result in results: + ... result.save("annotated_image.jpg") + >>> # Or with custom plot arguments + >>> for result in results: + ... result.save("annotated_image.jpg", conf=False, line_width=2) + """ + if not filename: + filename = f"results_{Path(self.path).name}" + self.plot(save=True, filename=filename, *args, **kwargs) + return filename + + def verbose(self): + """ + Returns a log string for each task in the results, detailing detection and classification outcomes. + + This method generates a human-readable string summarizing the detection and classification results. It includes + the number of detections for each class and the top probabilities for classification tasks. + + Returns: + (str): A formatted string containing a summary of the results. For detection tasks, it includes the + number of detections per class. For classification tasks, it includes the top 5 class probabilities. + + Examples: + >>> results = model("path/to/image.jpg") + >>> for result in results: + ... print(result.verbose()) + 2 persons, 1 car, 3 traffic lights, + dog 0.92, cat 0.78, horse 0.64, + + Notes: + - If there are no detections, the method returns "(no detections), " for detection tasks. + - For classification tasks, it returns the top 5 class probabilities and their corresponding class names. + - The returned string is comma-separated and ends with a comma and a space. + """ + log_string = "" + probs = self.probs + boxes = self.boxes + if len(self) == 0: + return log_string if probs is not None else f"{log_string}(no detections), " + if probs is not None: + log_string += f"{', '.join(f'{self.names[j]} {probs.data[j]:.2f}' for j in probs.top5)}, " + if boxes: + for c in boxes.cls.unique(): + n = (boxes.cls == c).sum() # detections per class + log_string += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, " + return log_string + + def save_txt(self, txt_file, save_conf=False): + """ + Save detection results to a text file. + + Args: + txt_file (str | Path): Path to the output text file. + save_conf (bool): Whether to include confidence scores in the output. + + Returns: + (str): Path to the saved text file. + + Examples: + >>> from ultralytics import YOLO + >>> model = YOLO("yolov8n.pt") + >>> results = model("path/to/image.jpg") + >>> for result in results: + ... result.save_txt("output.txt") + + Notes: + - The file will contain one line per detection or classification with the following structure: + - For detections: `class confidence x_center y_center width height` + - For classifications: `confidence class_name` + - For masks and keypoints, the specific formats will vary accordingly. + - The function will create the output directory if it does not exist. + - If save_conf is False, the confidence scores will be excluded from the output. + - Existing contents of the file will not be overwritten; new results will be appended. + """ + is_obb = self.obb is not None + boxes = self.obb if is_obb else self.boxes + masks = self.masks + probs = self.probs + kpts = self.keypoints + texts = [] + if probs is not None: + # Classify + [texts.append(f"{probs.data[j]:.2f} {self.names[j]}") for j in probs.top5] + elif boxes: + # Detect/segment/pose + for j, d in enumerate(boxes): + c, conf, id = int(d.cls), float(d.conf), None if d.id is None else int(d.id.item()) + line = (c, *(d.xyxyxyxyn.view(-1) if is_obb else d.xywhn.view(-1))) + if masks: + seg = masks[j].xyn[0].copy().reshape(-1) # reversed mask.xyn, (n,2) to (n*2) + line = (c, *seg) + if kpts is not None: + kpt = torch.cat((kpts[j].xyn, kpts[j].conf[..., None]), 2) if kpts[j].has_visible else kpts[j].xyn + line += (*kpt.reshape(-1).tolist(),) + line += (conf,) * save_conf + (() if id is None else (id,)) + texts.append(("%g " * len(line)).rstrip() % line) + + if texts: + Path(txt_file).parent.mkdir(parents=True, exist_ok=True) # make directory + with open(txt_file, "a") as f: + f.writelines(text + "\n" for text in texts) + + def save_crop(self, save_dir, file_name=Path("im.jpg")): + """ + Saves cropped detection images to specified directory. + + This method saves cropped images of detected objects to a specified directory. Each crop is saved in a + subdirectory named after the object's class, with the filename based on the input file_name. + + Args: + save_dir (str | Path): Directory path where cropped images will be saved. + file_name (str | Path): Base filename for the saved cropped images. Default is Path("im.jpg"). + + Notes: + - This method does not support Classify or Oriented Bounding Box (OBB) tasks. + - Crops are saved as 'save_dir/class_name/file_name.jpg'. + - The method will create necessary subdirectories if they don't exist. + - Original image is copied before cropping to avoid modifying the original. + + Examples: + >>> results = model("path/to/image.jpg") + >>> for result in results: + ... result.save_crop(save_dir="path/to/crops", file_name="detection") + """ + if self.probs is not None: + LOGGER.warning("WARNING ⚠️ Classify task do not support `save_crop`.") + return + if self.obb is not None: + LOGGER.warning("WARNING ⚠️ OBB task do not support `save_crop`.") + return + for d in self.boxes: + save_one_box( + d.xyxy, + self.orig_img.copy(), + file=Path(save_dir) / self.names[int(d.cls)] / f"{Path(file_name)}.jpg", + BGR=True, + ) + + def summary(self, normalize=False, decimals=5): + """ + Converts inference results to a summarized dictionary with optional normalization for box coordinates. + + This method creates a list of detection dictionaries, each containing information about a single + detection or classification result. For classification tasks, it returns the top class and its + confidence. For detection tasks, it includes class information, bounding box coordinates, and + optionally mask segments and keypoints. + + Args: + normalize (bool): Whether to normalize bounding box coordinates by image dimensions. Defaults to False. + decimals (int): Number of decimal places to round the output values to. Defaults to 5. + + Returns: + (List[Dict]): A list of dictionaries, each containing summarized information for a single + detection or classification result. The structure of each dictionary varies based on the + task type (classification or detection) and available information (boxes, masks, keypoints). + + Examples: + >>> results = model("image.jpg") + >>> summary = results[0].summary() + >>> print(summary) + """ + # Create list of detection dictionaries + results = [] + if self.probs is not None: + class_id = self.probs.top1 + results.append( + { + "name": self.names[class_id], + "class": class_id, + "confidence": round(self.probs.top1conf.item(), decimals), + } + ) + return results + + is_obb = self.obb is not None + data = self.obb if is_obb else self.boxes + h, w = self.orig_shape if normalize else (1, 1) + for i, row in enumerate(data): # xyxy, track_id if tracking, conf, class_id + class_id, conf = int(row.cls), round(row.conf.item(), decimals) + box = (row.xyxyxyxy if is_obb else row.xyxy).squeeze().reshape(-1, 2).tolist() + xy = {} + for j, b in enumerate(box): + xy[f"x{j + 1}"] = round(b[0] / w, decimals) + xy[f"y{j + 1}"] = round(b[1] / h, decimals) + result = {"name": self.names[class_id], "class": class_id, "confidence": conf, "box": xy} + if data.is_track: + result["track_id"] = int(row.id.item()) # track ID + if self.masks: + result["segments"] = { + "x": (self.masks.xy[i][:, 0] / w).round(decimals).tolist(), + "y": (self.masks.xy[i][:, 1] / h).round(decimals).tolist(), + } + if self.keypoints is not None: + x, y, visible = self.keypoints[i].data[0].cpu().unbind(dim=1) # torch Tensor + result["keypoints"] = { + "x": (x / w).numpy().round(decimals).tolist(), # decimals named argument required + "y": (y / h).numpy().round(decimals).tolist(), + "visible": visible.numpy().round(decimals).tolist(), + } + results.append(result) + + return results + + def to_df(self, normalize=False, decimals=5): + """ + Converts detection results to a Pandas Dataframe. + + This method converts the detection results into Pandas Dataframe format. It includes information + about detected objects such as bounding boxes, class names, confidence scores, and optionally + segmentation masks and keypoints. + + Args: + normalize (bool): Whether to normalize the bounding box coordinates by the image dimensions. + If True, coordinates will be returned as float values between 0 and 1. Defaults to False. + decimals (int): Number of decimal places to round the output values to. Defaults to 5. + + Returns: + (DataFrame): A Pandas Dataframe containing all the information in results in an organized way. + + Examples: + >>> results = model("path/to/image.jpg") + >>> df_result = results[0].to_df() + >>> print(df_result) + """ + import pandas as pd + + return pd.DataFrame(self.summary(normalize=normalize, decimals=decimals)) + + def to_csv(self, normalize=False, decimals=5, *args, **kwargs): + """ + Converts detection results to a CSV format. + + This method serializes the detection results into a CSV format. It includes information + about detected objects such as bounding boxes, class names, confidence scores, and optionally + segmentation masks and keypoints. + + Args: + normalize (bool): Whether to normalize the bounding box coordinates by the image dimensions. + If True, coordinates will be returned as float values between 0 and 1. Defaults to False. + decimals (int): Number of decimal places to round the output values to. Defaults to 5. + *args (Any): Variable length argument list to be passed to pandas.DataFrame.to_csv(). + **kwargs (Any): Arbitrary keyword arguments to be passed to pandas.DataFrame.to_csv(). + + + Returns: + (str): CSV containing all the information in results in an organized way. + + Examples: + >>> results = model("path/to/image.jpg") + >>> csv_result = results[0].to_csv() + >>> print(csv_result) + """ + return self.to_df(normalize=normalize, decimals=decimals).to_csv(*args, **kwargs) + + def to_xml(self, normalize=False, decimals=5, *args, **kwargs): + """ + Converts detection results to XML format. + + This method serializes the detection results into an XML format. It includes information + about detected objects such as bounding boxes, class names, confidence scores, and optionally + segmentation masks and keypoints. + + Args: + normalize (bool): Whether to normalize the bounding box coordinates by the image dimensions. + If True, coordinates will be returned as float values between 0 and 1. Defaults to False. + decimals (int): Number of decimal places to round the output values to. Defaults to 5. + *args (Any): Variable length argument list to be passed to pandas.DataFrame.to_xml(). + **kwargs (Any): Arbitrary keyword arguments to be passed to pandas.DataFrame.to_xml(). + + Returns: + (str): An XML string containing all the information in results in an organized way. + + Examples: + >>> results = model("path/to/image.jpg") + >>> xml_result = results[0].to_xml() + >>> print(xml_result) + """ + check_requirements("lxml") + df = self.to_df(normalize=normalize, decimals=decimals) + return '\n' if df.empty else df.to_xml(*args, **kwargs) + + def tojson(self, normalize=False, decimals=5): + """Deprecated version of to_json().""" + LOGGER.warning("WARNING ⚠️ 'result.tojson()' is deprecated, replace with 'result.to_json()'.") + return self.to_json(normalize, decimals) + + def to_json(self, normalize=False, decimals=5): + """ + Converts detection results to JSON format. + + This method serializes the detection results into a JSON-compatible format. It includes information + about detected objects such as bounding boxes, class names, confidence scores, and optionally + segmentation masks and keypoints. + + Args: + normalize (bool): Whether to normalize the bounding box coordinates by the image dimensions. + If True, coordinates will be returned as float values between 0 and 1. Defaults to False. + decimals (int): Number of decimal places to round the output values to. Defaults to 5. + + Returns: + (str): A JSON string containing the serialized detection results. + + Examples: + >>> results = model("path/to/image.jpg") + >>> json_result = results[0].to_json() + >>> print(json_result) + + Notes: + - For classification tasks, the JSON will contain class probabilities instead of bounding boxes. + - For object detection tasks, the JSON will include bounding box coordinates, class names, and + confidence scores. + - If available, segmentation masks and keypoints will also be included in the JSON output. + - The method uses the `summary` method internally to generate the data structure before + converting it to JSON. + """ + import json + + return json.dumps(self.summary(normalize=normalize, decimals=decimals), indent=2) + + +class Boxes(BaseTensor): + """ + A class for managing and manipulating detection boxes. + + This class provides functionality for handling detection boxes, including their coordinates, confidence scores, + class labels, and optional tracking IDs. It supports various box formats and offers methods for easy manipulation + and conversion between different coordinate systems. + + Attributes: + data (torch.Tensor | numpy.ndarray): The raw tensor containing detection boxes and associated data. + orig_shape (Tuple[int, int]): The original image dimensions (height, width). + is_track (bool): Indicates whether tracking IDs are included in the box data. + xyxy (torch.Tensor | numpy.ndarray): Boxes in [x1, y1, x2, y2] format. + conf (torch.Tensor | numpy.ndarray): Confidence scores for each box. + cls (torch.Tensor | numpy.ndarray): Class labels for each box. + id (torch.Tensor | numpy.ndarray): Tracking IDs for each box (if available). + xywh (torch.Tensor | numpy.ndarray): Boxes in [x, y, width, height] format. + xyxyn (torch.Tensor | numpy.ndarray): Normalized [x1, y1, x2, y2] boxes relative to orig_shape. + xywhn (torch.Tensor | numpy.ndarray): Normalized [x, y, width, height] boxes relative to orig_shape. + + Methods: + cpu(): Returns a copy of the object with all tensors on CPU memory. + numpy(): Returns a copy of the object with all tensors as numpy arrays. + cuda(): Returns a copy of the object with all tensors on GPU memory. + to(*args, **kwargs): Returns a copy of the object with tensors on specified device and dtype. + + Examples: + >>> import torch + >>> boxes_data = torch.tensor([[100, 50, 150, 100, 0.9, 0], [200, 150, 300, 250, 0.8, 1]]) + >>> orig_shape = (480, 640) # height, width + >>> boxes = Boxes(boxes_data, orig_shape) + >>> print(boxes.xyxy) + >>> print(boxes.conf) + >>> print(boxes.cls) + >>> print(boxes.xywhn) + """ + + def __init__(self, boxes, orig_shape) -> None: + """ + Initialize the Boxes class with detection box data and the original image shape. + + This class manages detection boxes, providing easy access and manipulation of box coordinates, + confidence scores, class identifiers, and optional tracking IDs. It supports multiple formats + for box coordinates, including both absolute and normalized forms. + + Args: + boxes (torch.Tensor | np.ndarray): A tensor or numpy array with detection boxes of shape + (num_boxes, 6) or (num_boxes, 7). Columns should contain + [x1, y1, x2, y2, confidence, class, (optional) track_id]. + orig_shape (Tuple[int, int]): The original image shape as (height, width). Used for normalization. + + Attributes: + data (torch.Tensor): The raw tensor containing detection boxes and their associated data. + orig_shape (Tuple[int, int]): The original image size, used for normalization. + is_track (bool): Indicates whether tracking IDs are included in the box data. + + Examples: + >>> import torch + >>> boxes = torch.tensor([[100, 50, 150, 100, 0.9, 0]]) + >>> orig_shape = (480, 640) + >>> detection_boxes = Boxes(boxes, orig_shape) + >>> print(detection_boxes.xyxy) + tensor([[100., 50., 150., 100.]]) + """ + if boxes.ndim == 1: + boxes = boxes[None, :] + n = boxes.shape[-1] + assert n in {6, 7}, f"expected 6 or 7 values but got {n}" # xyxy, track_id, conf, cls + super().__init__(boxes, orig_shape) + self.is_track = n == 7 + self.orig_shape = orig_shape + + @property + def xyxy(self): + """ + Returns bounding boxes in [x1, y1, x2, y2] format. + + Returns: + (torch.Tensor | numpy.ndarray): A tensor or numpy array of shape (n, 4) containing bounding box + coordinates in [x1, y1, x2, y2] format, where n is the number of boxes. + + Examples: + >>> results = model("image.jpg") + >>> boxes = results[0].boxes + >>> xyxy = boxes.xyxy + >>> print(xyxy) + """ + return self.data[:, :4] + + @property + def conf(self): + """ + Returns the confidence scores for each detection box. + + Returns: + (torch.Tensor | numpy.ndarray): A 1D tensor or array containing confidence scores for each detection, + with shape (N,) where N is the number of detections. + + Examples: + >>> boxes = Boxes(torch.tensor([[10, 20, 30, 40, 0.9, 0]]), orig_shape=(100, 100)) + >>> conf_scores = boxes.conf + >>> print(conf_scores) + tensor([0.9000]) + """ + return self.data[:, -2] + + @property + def cls(self): + """ + Returns the class ID tensor representing category predictions for each bounding box. + + Returns: + (torch.Tensor | numpy.ndarray): A tensor or numpy array containing the class IDs for each detection box. + The shape is (N,), where N is the number of boxes. + + Examples: + >>> results = model("image.jpg") + >>> boxes = results[0].boxes + >>> class_ids = boxes.cls + >>> print(class_ids) # tensor([0., 2., 1.]) + """ + return self.data[:, -1] + + @property + def id(self): + """ + Returns the tracking IDs for each detection box if available. + + Returns: + (torch.Tensor | None): A tensor containing tracking IDs for each box if tracking is enabled, + otherwise None. Shape is (N,) where N is the number of boxes. + + Examples: + >>> results = model.track("path/to/video.mp4") + >>> for result in results: + ... boxes = result.boxes + ... if boxes.is_track: + ... track_ids = boxes.id + ... print(f"Tracking IDs: {track_ids}") + ... else: + ... print("Tracking is not enabled for these boxes.") + + Notes: + - This property is only available when tracking is enabled (i.e., when `is_track` is True). + - The tracking IDs are typically used to associate detections across multiple frames in video analysis. + """ + return self.data[:, -3] if self.is_track else None + + @property + @lru_cache(maxsize=2) # maxsize 1 should suffice + def xywh(self): + """ + Convert bounding boxes from [x1, y1, x2, y2] format to [x, y, width, height] format. + + Returns: + (torch.Tensor | numpy.ndarray): Boxes in [x_center, y_center, width, height] format, where x_center, y_center are the coordinates of + the center point of the bounding box, width, height are the dimensions of the bounding box and the + shape of the returned tensor is (N, 4), where N is the number of boxes. + + Examples: + >>> boxes = Boxes(torch.tensor([[100, 50, 150, 100], [200, 150, 300, 250]]), orig_shape=(480, 640)) + >>> xywh = boxes.xywh + >>> print(xywh) + tensor([[100.0000, 50.0000, 50.0000, 50.0000], + [200.0000, 150.0000, 100.0000, 100.0000]]) + """ + return ops.xyxy2xywh(self.xyxy) + + @property + @lru_cache(maxsize=2) + def xyxyn(self): + """ + Returns normalized bounding box coordinates relative to the original image size. + + This property calculates and returns the bounding box coordinates in [x1, y1, x2, y2] format, + normalized to the range [0, 1] based on the original image dimensions. + + Returns: + (torch.Tensor | numpy.ndarray): Normalized bounding box coordinates with shape (N, 4), where N is + the number of boxes. Each row contains [x1, y1, x2, y2] values normalized to [0, 1]. + + Examples: + >>> boxes = Boxes(torch.tensor([[100, 50, 300, 400, 0.9, 0]]), orig_shape=(480, 640)) + >>> normalized = boxes.xyxyn + >>> print(normalized) + tensor([[0.1562, 0.1042, 0.4688, 0.8333]]) + """ + xyxy = self.xyxy.clone() if isinstance(self.xyxy, torch.Tensor) else np.copy(self.xyxy) + xyxy[..., [0, 2]] /= self.orig_shape[1] + xyxy[..., [1, 3]] /= self.orig_shape[0] + return xyxy + + @property + @lru_cache(maxsize=2) + def xywhn(self): + """ + Returns normalized bounding boxes in [x, y, width, height] format. + + This property calculates and returns the normalized bounding box coordinates in the format + [x_center, y_center, width, height], where all values are relative to the original image dimensions. + + Returns: + (torch.Tensor | numpy.ndarray): Normalized bounding boxes with shape (N, 4), where N is the + number of boxes. Each row contains [x_center, y_center, width, height] values normalized + to [0, 1] based on the original image dimensions. + + Examples: + >>> boxes = Boxes(torch.tensor([[100, 50, 150, 100, 0.9, 0]]), orig_shape=(480, 640)) + >>> normalized = boxes.xywhn + >>> print(normalized) + tensor([[0.1953, 0.1562, 0.0781, 0.1042]]) + """ + xywh = ops.xyxy2xywh(self.xyxy) + xywh[..., [0, 2]] /= self.orig_shape[1] + xywh[..., [1, 3]] /= self.orig_shape[0] + return xywh + + +class Masks(BaseTensor): + """ + A class for storing and manipulating detection masks. + + This class extends BaseTensor and provides functionality for handling segmentation masks, + including methods for converting between pixel and normalized coordinates. + + Attributes: + data (torch.Tensor | numpy.ndarray): The raw tensor or array containing mask data. + orig_shape (tuple): Original image shape in (height, width) format. + xy (List[numpy.ndarray]): A list of segments in pixel coordinates. + xyn (List[numpy.ndarray]): A list of normalized segments. + + Methods: + cpu(): Returns a copy of the Masks object with the mask tensor on CPU memory. + numpy(): Returns a copy of the Masks object with the mask tensor as a numpy array. + cuda(): Returns a copy of the Masks object with the mask tensor on GPU memory. + to(*args, **kwargs): Returns a copy of the Masks object with the mask tensor on specified device and dtype. + + Examples: + >>> masks_data = torch.rand(1, 160, 160) + >>> orig_shape = (720, 1280) + >>> masks = Masks(masks_data, orig_shape) + >>> pixel_coords = masks.xy + >>> normalized_coords = masks.xyn + """ + + def __init__(self, masks, orig_shape) -> None: + """ + Initialize the Masks class with detection mask data and the original image shape. + + Args: + masks (torch.Tensor | np.ndarray): Detection masks with shape (num_masks, height, width). + orig_shape (tuple): The original image shape as (height, width). Used for normalization. + + Examples: + >>> import torch + >>> from ultralytics.engine.results import Masks + >>> masks = torch.rand(10, 160, 160) # 10 masks of 160x160 resolution + >>> orig_shape = (720, 1280) # Original image shape + >>> mask_obj = Masks(masks, orig_shape) + """ + if masks.ndim == 2: + masks = masks[None, :] + super().__init__(masks, orig_shape) + + @property + @lru_cache(maxsize=1) + def xyn(self): + """ + Returns normalized xy-coordinates of the segmentation masks. + + This property calculates and caches the normalized xy-coordinates of the segmentation masks. The coordinates + are normalized relative to the original image shape. + + Returns: + (List[numpy.ndarray]): A list of numpy arrays, where each array contains the normalized xy-coordinates + of a single segmentation mask. Each array has shape (N, 2), where N is the number of points in the + mask contour. + + Examples: + >>> results = model("image.jpg") + >>> masks = results[0].masks + >>> normalized_coords = masks.xyn + >>> print(normalized_coords[0]) # Normalized coordinates of the first mask + """ + return [ + ops.scale_coords(self.data.shape[1:], x, self.orig_shape, normalize=True) + for x in ops.masks2segments(self.data) + ] + + @property + @lru_cache(maxsize=1) + def xy(self): + """ + Returns the [x, y] pixel coordinates for each segment in the mask tensor. + + This property calculates and returns a list of pixel coordinates for each segmentation mask in the + Masks object. The coordinates are scaled to match the original image dimensions. + + Returns: + (List[numpy.ndarray]): A list of numpy arrays, where each array contains the [x, y] pixel + coordinates for a single segmentation mask. Each array has shape (N, 2), where N is the + number of points in the segment. + + Examples: + >>> results = model("image.jpg") + >>> masks = results[0].masks + >>> xy_coords = masks.xy + >>> print(len(xy_coords)) # Number of masks + >>> print(xy_coords[0].shape) # Shape of first mask's coordinates + """ + return [ + ops.scale_coords(self.data.shape[1:], x, self.orig_shape, normalize=False) + for x in ops.masks2segments(self.data) + ] + + +class Keypoints(BaseTensor): + """ + A class for storing and manipulating detection keypoints. + + This class encapsulates functionality for handling keypoint data, including coordinate manipulation, + normalization, and confidence values. + + Attributes: + data (torch.Tensor): The raw tensor containing keypoint data. + orig_shape (Tuple[int, int]): The original image dimensions (height, width). + has_visible (bool): Indicates whether visibility information is available for keypoints. + xy (torch.Tensor): Keypoint coordinates in [x, y] format. + xyn (torch.Tensor): Normalized keypoint coordinates in [x, y] format, relative to orig_shape. + conf (torch.Tensor): Confidence values for each keypoint, if available. + + Methods: + cpu(): Returns a copy of the keypoints tensor on CPU memory. + numpy(): Returns a copy of the keypoints tensor as a numpy array. + cuda(): Returns a copy of the keypoints tensor on GPU memory. + to(*args, **kwargs): Returns a copy of the keypoints tensor with specified device and dtype. + + Examples: + >>> import torch + >>> from ultralytics.engine.results import Keypoints + >>> keypoints_data = torch.rand(1, 17, 3) # 1 detection, 17 keypoints, (x, y, conf) + >>> orig_shape = (480, 640) # Original image shape (height, width) + >>> keypoints = Keypoints(keypoints_data, orig_shape) + >>> print(keypoints.xy.shape) # Access xy coordinates + >>> print(keypoints.conf) # Access confidence values + >>> keypoints_cpu = keypoints.cpu() # Move keypoints to CPU + """ + + @smart_inference_mode() # avoid keypoints < conf in-place error + def __init__(self, keypoints, orig_shape) -> None: + """ + Initializes the Keypoints object with detection keypoints and original image dimensions. + + This method processes the input keypoints tensor, handling both 2D and 3D formats. For 3D tensors + (x, y, confidence), it masks out low-confidence keypoints by setting their coordinates to zero. + + Args: + keypoints (torch.Tensor): A tensor containing keypoint data. Shape can be either: + - (num_objects, num_keypoints, 2) for x, y coordinates only + - (num_objects, num_keypoints, 3) for x, y coordinates and confidence scores + orig_shape (Tuple[int, int]): The original image dimensions (height, width). + + Examples: + >>> kpts = torch.rand(1, 17, 3) # 1 object, 17 keypoints (COCO format), x,y,conf + >>> orig_shape = (720, 1280) # Original image height, width + >>> keypoints = Keypoints(kpts, orig_shape) + """ + if keypoints.ndim == 2: + keypoints = keypoints[None, :] + if keypoints.shape[2] == 3: # x, y, conf + mask = keypoints[..., 2] < 0.5 # points with conf < 0.5 (not visible) + keypoints[..., :2][mask] = 0 + super().__init__(keypoints, orig_shape) + self.has_visible = self.data.shape[-1] == 3 + + @property + @lru_cache(maxsize=1) + def xy(self): + """ + Returns x, y coordinates of keypoints. + + Returns: + (torch.Tensor): A tensor containing the x, y coordinates of keypoints with shape (N, K, 2), where N is + the number of detections and K is the number of keypoints per detection. + + Examples: + >>> results = model("image.jpg") + >>> keypoints = results[0].keypoints + >>> xy = keypoints.xy + >>> print(xy.shape) # (N, K, 2) + >>> print(xy[0]) # x, y coordinates of keypoints for first detection + + Notes: + - The returned coordinates are in pixel units relative to the original image dimensions. + - If keypoints were initialized with confidence values, only keypoints with confidence >= 0.5 are returned. + - This property uses LRU caching to improve performance on repeated access. + """ + return self.data[..., :2] + + @property + @lru_cache(maxsize=1) + def xyn(self): + """ + Returns normalized coordinates (x, y) of keypoints relative to the original image size. + + Returns: + (torch.Tensor | numpy.ndarray): A tensor or array of shape (N, K, 2) containing normalized keypoint + coordinates, where N is the number of instances, K is the number of keypoints, and the last + dimension contains [x, y] values in the range [0, 1]. + + Examples: + >>> keypoints = Keypoints(torch.rand(1, 17, 2), orig_shape=(480, 640)) + >>> normalized_kpts = keypoints.xyn + >>> print(normalized_kpts.shape) + torch.Size([1, 17, 2]) + """ + xy = self.xy.clone() if isinstance(self.xy, torch.Tensor) else np.copy(self.xy) + xy[..., 0] /= self.orig_shape[1] + xy[..., 1] /= self.orig_shape[0] + return xy + + @property + @lru_cache(maxsize=1) + def conf(self): + """ + Returns confidence values for each keypoint. + + Returns: + (torch.Tensor | None): A tensor containing confidence scores for each keypoint if available, + otherwise None. Shape is (num_detections, num_keypoints) for batched data or (num_keypoints,) + for single detection. + + Examples: + >>> keypoints = Keypoints(torch.rand(1, 17, 3), orig_shape=(640, 640)) # 1 detection, 17 keypoints + >>> conf = keypoints.conf + >>> print(conf.shape) # torch.Size([1, 17]) + """ + return self.data[..., 2] if self.has_visible else None + + +class Probs(BaseTensor): + """ + A class for storing and manipulating classification probabilities. + + This class extends BaseTensor and provides methods for accessing and manipulating + classification probabilities, including top-1 and top-5 predictions. + + Attributes: + data (torch.Tensor | numpy.ndarray): The raw tensor or array containing classification probabilities. + orig_shape (tuple | None): The original image shape as (height, width). Not used in this class. + top1 (int): Index of the class with the highest probability. + top5 (List[int]): Indices of the top 5 classes by probability. + top1conf (torch.Tensor | numpy.ndarray): Confidence score of the top 1 class. + top5conf (torch.Tensor | numpy.ndarray): Confidence scores of the top 5 classes. + + Methods: + cpu(): Returns a copy of the probabilities tensor on CPU memory. + numpy(): Returns a copy of the probabilities tensor as a numpy array. + cuda(): Returns a copy of the probabilities tensor on GPU memory. + to(*args, **kwargs): Returns a copy of the probabilities tensor with specified device and dtype. + + Examples: + >>> probs = torch.tensor([0.1, 0.3, 0.6]) + >>> p = Probs(probs) + >>> print(p.top1) + 2 + >>> print(p.top5) + [2, 1, 0] + >>> print(p.top1conf) + tensor(0.6000) + >>> print(p.top5conf) + tensor([0.6000, 0.3000, 0.1000]) + """ + + def __init__(self, probs, orig_shape=None) -> None: + """ + Initialize the Probs class with classification probabilities. + + This class stores and manages classification probabilities, providing easy access to top predictions and their + confidences. + + Args: + probs (torch.Tensor | np.ndarray): A 1D tensor or array of classification probabilities. + orig_shape (tuple | None): The original image shape as (height, width). Not used in this class but kept for + consistency with other result classes. + + Attributes: + data (torch.Tensor | np.ndarray): The raw tensor or array containing classification probabilities. + top1 (int): Index of the top 1 class. + top5 (List[int]): Indices of the top 5 classes. + top1conf (torch.Tensor | np.ndarray): Confidence of the top 1 class. + top5conf (torch.Tensor | np.ndarray): Confidences of the top 5 classes. + + Examples: + >>> import torch + >>> probs = torch.tensor([0.1, 0.3, 0.2, 0.4]) + >>> p = Probs(probs) + >>> print(p.top1) + 3 + >>> print(p.top1conf) + tensor(0.4000) + >>> print(p.top5) + [3, 1, 2, 0] + """ + super().__init__(probs, orig_shape) + + @property + @lru_cache(maxsize=1) + def top1(self): + """ + Returns the index of the class with the highest probability. + + Returns: + (int): Index of the class with the highest probability. + + Examples: + >>> probs = Probs(torch.tensor([0.1, 0.3, 0.6])) + >>> probs.top1 + 2 + """ + return int(self.data.argmax()) + + @property + @lru_cache(maxsize=1) + def top5(self): + """ + Returns the indices of the top 5 class probabilities. + + Returns: + (List[int]): A list containing the indices of the top 5 class probabilities, sorted in descending order. + + Examples: + >>> probs = Probs(torch.tensor([0.1, 0.2, 0.3, 0.4, 0.5])) + >>> print(probs.top5) + [4, 3, 2, 1, 0] + """ + return (-self.data).argsort(0)[:5].tolist() # this way works with both torch and numpy. + + @property + @lru_cache(maxsize=1) + def top1conf(self): + """ + Returns the confidence score of the highest probability class. + + This property retrieves the confidence score (probability) of the class with the highest predicted probability + from the classification results. + + Returns: + (torch.Tensor | numpy.ndarray): A tensor containing the confidence score of the top 1 class. + + Examples: + >>> results = model("image.jpg") # classify an image + >>> probs = results[0].probs # get classification probabilities + >>> top1_confidence = probs.top1conf # get confidence of top 1 class + >>> print(f"Top 1 class confidence: {top1_confidence.item():.4f}") + """ + return self.data[self.top1] + + @property + @lru_cache(maxsize=1) + def top5conf(self): + """ + Returns confidence scores for the top 5 classification predictions. + + This property retrieves the confidence scores corresponding to the top 5 class probabilities + predicted by the model. It provides a quick way to access the most likely class predictions + along with their associated confidence levels. + + Returns: + (torch.Tensor | numpy.ndarray): A tensor or array containing the confidence scores for the + top 5 predicted classes, sorted in descending order of probability. + + Examples: + >>> results = model("image.jpg") + >>> probs = results[0].probs + >>> top5_conf = probs.top5conf + >>> print(top5_conf) # Prints confidence scores for top 5 classes + """ + return self.data[self.top5] + + +class OBB(BaseTensor): + """ + A class for storing and manipulating Oriented Bounding Boxes (OBB). + + This class provides functionality to handle oriented bounding boxes, including conversion between + different formats, normalization, and access to various properties of the boxes. + + Attributes: + data (torch.Tensor): The raw OBB tensor containing box coordinates and associated data. + orig_shape (tuple): Original image size as (height, width). + is_track (bool): Indicates whether tracking IDs are included in the box data. + xywhr (torch.Tensor | numpy.ndarray): Boxes in [x_center, y_center, width, height, rotation] format. + conf (torch.Tensor | numpy.ndarray): Confidence scores for each box. + cls (torch.Tensor | numpy.ndarray): Class labels for each box. + id (torch.Tensor | numpy.ndarray): Tracking IDs for each box, if available. + xyxyxyxy (torch.Tensor | numpy.ndarray): Boxes in 8-point [x1, y1, x2, y2, x3, y3, x4, y4] format. + xyxyxyxyn (torch.Tensor | numpy.ndarray): Normalized 8-point coordinates relative to orig_shape. + xyxy (torch.Tensor | numpy.ndarray): Axis-aligned bounding boxes in [x1, y1, x2, y2] format. + + Methods: + cpu(): Returns a copy of the OBB object with all tensors on CPU memory. + numpy(): Returns a copy of the OBB object with all tensors as numpy arrays. + cuda(): Returns a copy of the OBB object with all tensors on GPU memory. + to(*args, **kwargs): Returns a copy of the OBB object with tensors on specified device and dtype. + + Examples: + >>> boxes = torch.tensor([[100, 50, 150, 100, 30, 0.9, 0]]) # xywhr, conf, cls + >>> obb = OBB(boxes, orig_shape=(480, 640)) + >>> print(obb.xyxyxyxy) + >>> print(obb.conf) + >>> print(obb.cls) + """ + + def __init__(self, boxes, orig_shape) -> None: + """ + Initialize an OBB (Oriented Bounding Box) instance with oriented bounding box data and original image shape. + + This class stores and manipulates Oriented Bounding Boxes (OBB) for object detection tasks. It provides + various properties and methods to access and transform the OBB data. + + Args: + boxes (torch.Tensor | numpy.ndarray): A tensor or numpy array containing the detection boxes, + with shape (num_boxes, 7) or (num_boxes, 8). The last two columns contain confidence and class values. + If present, the third last column contains track IDs, and the fifth column contains rotation. + orig_shape (Tuple[int, int]): Original image size, in the format (height, width). + + Attributes: + data (torch.Tensor | numpy.ndarray): The raw OBB tensor. + orig_shape (Tuple[int, int]): The original image shape. + is_track (bool): Whether the boxes include tracking IDs. + + Raises: + AssertionError: If the number of values per box is not 7 or 8. + + Examples: + >>> import torch + >>> boxes = torch.rand(3, 7) # 3 boxes with 7 values each + >>> orig_shape = (640, 480) + >>> obb = OBB(boxes, orig_shape) + >>> print(obb.xywhr) # Access the boxes in xywhr format + """ + if boxes.ndim == 1: + boxes = boxes[None, :] + n = boxes.shape[-1] + assert n in {7, 8}, f"expected 7 or 8 values but got {n}" # xywh, rotation, track_id, conf, cls + super().__init__(boxes, orig_shape) + self.is_track = n == 8 + self.orig_shape = orig_shape + + @property + def xywhr(self): + """ + Returns boxes in [x_center, y_center, width, height, rotation] format. + + Returns: + (torch.Tensor | numpy.ndarray): A tensor or numpy array containing the oriented bounding boxes with format + [x_center, y_center, width, height, rotation]. The shape is (N, 5) where N is the number of boxes. + + Examples: + >>> results = model("image.jpg") + >>> obb = results[0].obb + >>> xywhr = obb.xywhr + >>> print(xywhr.shape) + torch.Size([3, 5]) + """ + return self.data[:, :5] + + @property + def conf(self): + """ + Returns the confidence scores for Oriented Bounding Boxes (OBBs). + + This property retrieves the confidence values associated with each OBB detection. The confidence score + represents the model's certainty in the detection. + + Returns: + (torch.Tensor | numpy.ndarray): A tensor or numpy array of shape (N,) containing confidence scores + for N detections, where each score is in the range [0, 1]. + + Examples: + >>> results = model("image.jpg") + >>> obb_result = results[0].obb + >>> confidence_scores = obb_result.conf + >>> print(confidence_scores) + """ + return self.data[:, -2] + + @property + def cls(self): + """ + Returns the class values of the oriented bounding boxes. + + Returns: + (torch.Tensor | numpy.ndarray): A tensor or numpy array containing the class values for each oriented + bounding box. The shape is (N,), where N is the number of boxes. + + Examples: + >>> results = model("image.jpg") + >>> result = results[0] + >>> obb = result.obb + >>> class_values = obb.cls + >>> print(class_values) + """ + return self.data[:, -1] + + @property + def id(self): + """ + Returns the tracking IDs of the oriented bounding boxes (if available). + + Returns: + (torch.Tensor | numpy.ndarray | None): A tensor or numpy array containing the tracking IDs for each + oriented bounding box. Returns None if tracking IDs are not available. + + Examples: + >>> results = model("image.jpg", tracker=True) # Run inference with tracking + >>> for result in results: + ... if result.obb is not None: + ... track_ids = result.obb.id + ... if track_ids is not None: + ... print(f"Tracking IDs: {track_ids}") + """ + return self.data[:, -3] if self.is_track else None + + @property + @lru_cache(maxsize=2) + def xyxyxyxy(self): + """ + Converts OBB format to 8-point (xyxyxyxy) coordinate format for rotated bounding boxes. + + Returns: + (torch.Tensor | numpy.ndarray): Rotated bounding boxes in xyxyxyxy format with shape (N, 4, 2), where N is + the number of boxes. Each box is represented by 4 points (x, y), starting from the top-left corner and + moving clockwise. + + Examples: + >>> obb = OBB(torch.tensor([[100, 100, 50, 30, 0.5, 0.9, 0]]), orig_shape=(640, 640)) + >>> xyxyxyxy = obb.xyxyxyxy + >>> print(xyxyxyxy.shape) + torch.Size([1, 4, 2]) + """ + return ops.xywhr2xyxyxyxy(self.xywhr) + + @property + @lru_cache(maxsize=2) + def xyxyxyxyn(self): + """ + Converts rotated bounding boxes to normalized xyxyxyxy format. + + Returns: + (torch.Tensor | numpy.ndarray): Normalized rotated bounding boxes in xyxyxyxy format with shape (N, 4, 2), + where N is the number of boxes. Each box is represented by 4 points (x, y), normalized relative to + the original image dimensions. + + Examples: + >>> obb = OBB(torch.rand(10, 7), orig_shape=(640, 480)) # 10 random OBBs + >>> normalized_boxes = obb.xyxyxyxyn + >>> print(normalized_boxes.shape) + torch.Size([10, 4, 2]) + """ + xyxyxyxyn = self.xyxyxyxy.clone() if isinstance(self.xyxyxyxy, torch.Tensor) else np.copy(self.xyxyxyxy) + xyxyxyxyn[..., 0] /= self.orig_shape[1] + xyxyxyxyn[..., 1] /= self.orig_shape[0] + return xyxyxyxyn + + @property + @lru_cache(maxsize=2) + def xyxy(self): + """ + Converts oriented bounding boxes (OBB) to axis-aligned bounding boxes in xyxy format. + + This property calculates the minimal enclosing rectangle for each oriented bounding box and returns it in + xyxy format (x1, y1, x2, y2). This is useful for operations that require axis-aligned bounding boxes, such + as IoU calculation with non-rotated boxes. + + Returns: + (torch.Tensor | numpy.ndarray): Axis-aligned bounding boxes in xyxy format with shape (N, 4), where N + is the number of boxes. Each row contains [x1, y1, x2, y2] coordinates. + + Examples: + >>> import torch + >>> from ultralytics import YOLO + >>> model = YOLO("yolov8n-obb.pt") + >>> results = model("path/to/image.jpg") + >>> for result in results: + ... obb = result.obb + ... if obb is not None: + ... xyxy_boxes = obb.xyxy + ... print(xyxy_boxes.shape) # (N, 4) + + Notes: + - This method approximates the OBB by its minimal enclosing rectangle. + - The returned format is compatible with standard object detection metrics and visualization tools. + - The property uses caching to improve performance for repeated access. + """ + x = self.xyxyxyxy[..., 0] + y = self.xyxyxyxy[..., 1] + return ( + torch.stack([x.amin(1), y.amin(1), x.amax(1), y.amax(1)], -1) + if isinstance(x, torch.Tensor) + else np.stack([x.min(1), y.min(1), x.max(1), y.max(1)], -1) + ) diff --git a/examples/Ultralytics Module/validator.py b/examples/Ultralytics Module/validator.py new file mode 100644 index 0000000..5e0f098 --- /dev/null +++ b/examples/Ultralytics Module/validator.py @@ -0,0 +1,338 @@ +# Ultralytics YOLO 🚀, AGPL-3.0 license +""" +Check a model's accuracy on a test or val split of a dataset. + +Usage: + $ yolo mode=val model=yolov8n.pt data=coco8.yaml imgsz=640 + +Usage - formats: + $ yolo mode=val model=yolov8n.pt # PyTorch + yolov8n.torchscript # TorchScript + yolov8n.onnx # ONNX Runtime or OpenCV DNN with dnn=True + yolov8n_openvino_model # OpenVINO + yolov8n.engine # TensorRT + yolov8n.mlpackage # CoreML (macOS-only) + yolov8n_saved_model # TensorFlow SavedModel + yolov8n.pb # TensorFlow GraphDef + yolov8n.tflite # TensorFlow Lite + yolov8n_edgetpu.tflite # TensorFlow Edge TPU + yolov8n_paddle_model # PaddlePaddle + yolov8n_ncnn_model # NCNN +""" + +import json +import time +from pathlib import Path + +import numpy as np +import torch + +from ultralytics.cfg import get_cfg, get_save_dir +from ultralytics.data.utils import check_cls_dataset, check_det_dataset +from ultralytics.nn.autobackend import AutoBackend +from ultralytics.utils import LOGGER, TQDM, callbacks, colorstr, emojis +from ultralytics.utils.checks import check_imgsz +from ultralytics.utils.ops import Profile +from ultralytics.utils.torch_utils import de_parallel, select_device, smart_inference_mode + + +class BaseValidator: + """ + BaseValidator. + + A base class for creating validators. + + Attributes: + args (SimpleNamespace): Configuration for the validator. + dataloader (DataLoader): Dataloader to use for validation. + pbar (tqdm): Progress bar to update during validation. + model (nn.Module): Model to validate. + data (dict): Data dictionary. + device (torch.device): Device to use for validation. + batch_i (int): Current batch index. + training (bool): Whether the model is in training mode. + names (dict): Class names. + seen: Records the number of images seen so far during validation. + stats: Placeholder for statistics during validation. + confusion_matrix: Placeholder for a confusion matrix. + nc: Number of classes. + iouv: (torch.Tensor): IoU thresholds from 0.50 to 0.95 in spaces of 0.05. + jdict (dict): Dictionary to store JSON validation results. + speed (dict): Dictionary with keys 'preprocess', 'inference', 'loss', 'postprocess' and their respective + batch processing times in milliseconds. + save_dir (Path): Directory to save results. + plots (dict): Dictionary to store plots for visualization. + callbacks (dict): Dictionary to store various callback functions. + """ + + def __init__(self, dataloader=None, save_dir=None, pbar=None, args=None, _callbacks=None): + """ + Initializes a BaseValidator instance. + + Args: + dataloader (torch.utils.data.DataLoader): Dataloader to be used for validation. + save_dir (Path, optional): Directory to save results. + pbar (tqdm.tqdm): Progress bar for displaying progress. + args (SimpleNamespace): Configuration for the validator. + _callbacks (dict): Dictionary to store various callback functions. + """ + self.args = get_cfg(overrides=args) + self.dataloader = dataloader + self.pbar = pbar + self.stride = None + self.data = None + self.device = None + self.batch_i = None + self.training = True + self.names = None + self.seen = None + self.stats = None + self.confusion_matrix = None + self.nc = None + self.iouv = None + self.jdict = None + self.speed = {"preprocess": 0.0, "inference": 0.0, "loss": 0.0, "postprocess": 0.0} + + self.save_dir = save_dir or get_save_dir(self.args) + (self.save_dir / "labels" if self.args.save_txt else self.save_dir).mkdir(parents=True, exist_ok=True) + if self.args.conf is None: + self.args.conf = 0.001 # default conf=0.001 + self.args.imgsz = check_imgsz(self.args.imgsz, max_dim=1) + + self.plots = {} + self.callbacks = _callbacks or callbacks.get_default_callbacks() + + @smart_inference_mode() + def __call__(self, trainer=None, model=None): + """Executes validation process, running inference on dataloader and computing performance metrics.""" + self.training = trainer is not None + augment = self.args.augment and (not self.training) + if self.training: + self.device = trainer.device + self.data = trainer.data + # force FP16 val during training + self.args.half = self.device.type != "cpu" and trainer.amp + model = trainer.ema.ema or trainer.model + model = model.half() if self.args.half else model.float() + # self.model = model + self.loss = torch.zeros_like(trainer.loss_items, device=trainer.device) + self.args.plots &= trainer.stopper.possible_stop or (trainer.epoch == trainer.epochs - 1) + model.eval() + else: + callbacks.add_integration_callbacks(self) + model = AutoBackend( + weights=model or self.args.model, + device=select_device(self.args.device, self.args.batch), + dnn=self.args.dnn, + data=self.args.data, + fp16=self.args.half, + ) + # self.model = model + self.device = model.device # update device + self.args.half = model.fp16 # update half + stride, pt, jit, engine = model.stride, model.pt, model.jit, model.engine + imgsz = check_imgsz(self.args.imgsz, stride=stride) + if engine: + self.args.batch = model.batch_size + elif not pt and not jit: + self.args.batch = model.metadata.get("batch", 1) # export.py models default to batch-size 1 + LOGGER.info(f"Setting batch={self.args.batch} input of shape ({self.args.batch}, 3, {imgsz}, {imgsz})") + + if str(self.args.data).split(".")[-1] in {"yaml", "yml"}: + self.data = check_det_dataset(self.args.data) + elif self.args.task == "classify": + self.data = check_cls_dataset(self.args.data, split=self.args.split) + else: + raise FileNotFoundError(emojis(f"Dataset '{self.args.data}' for task={self.args.task} not found ❌")) + + if self.device.type in {"cpu", "mps"}: + self.args.workers = 0 # faster CPU val as time dominated by inference, not dataloading + if not pt: + self.args.rect = False + self.stride = model.stride # used in get_dataloader() for padding + self.dataloader = self.dataloader or self.get_dataloader(self.data.get(self.args.split), self.args.batch) + + model.eval() + model.warmup(imgsz=(1 if pt else self.args.batch, 3, imgsz, imgsz)) # warmup + + self.run_callbacks("on_val_start") + dt = ( + Profile(device=self.device), + Profile(device=self.device), + Profile(device=self.device), + Profile(device=self.device), + ) + bar = TQDM(self.dataloader, desc=self.get_desc(), total=len(self.dataloader)) + self.init_metrics(de_parallel(model)) + self.jdict = [] # empty before each val + for batch_i, batch in enumerate(bar): + self.run_callbacks("on_val_batch_start") + self.batch_i = batch_i + # Preprocess + with dt[0]: + batch = self.preprocess(batch) + + # Inference + with dt[1]: + preds = model(batch["img"], augment=augment) + + # Loss + with dt[2]: + if self.training: + self.loss += model.loss(batch, preds)[1] + + # Postprocess + with dt[3]: + preds = self.postprocess(preds) + + self.update_metrics(preds, batch) + if self.args.plots and batch_i < 3: + self.plot_val_samples(batch, batch_i) + self.plot_predictions(batch, preds, batch_i) + + self.run_callbacks("on_val_batch_end") + stats = self.get_stats() + self.check_stats(stats) + self.speed = dict(zip(self.speed.keys(), (x.t / len(self.dataloader.dataset) * 1e3 for x in dt))) + self.finalize_metrics() + self.print_results() + self.run_callbacks("on_val_end") + if self.training: + model.float() + results = {**stats, **trainer.label_loss_items(self.loss.cpu() / len(self.dataloader), prefix="val")} + return {k: round(float(v), 5) for k, v in results.items()} # return results as 5 decimal place floats + else: + LOGGER.info( + "Speed: {:.1f}ms preprocess, {:.1f}ms inference, {:.1f}ms loss, {:.1f}ms postprocess per image".format( + *tuple(self.speed.values()) + ) + ) + if self.args.save_json and self.jdict: + with open(str(self.save_dir / "predictions.json"), "w") as f: + LOGGER.info(f"Saving {f.name}...") + json.dump(self.jdict, f) # flatten and save + stats = self.eval_json(stats) # update stats + if self.args.plots or self.args.save_json: + LOGGER.info(f"Results saved to {colorstr('bold', self.save_dir)}") + return stats + + def match_predictions(self, pred_classes, true_classes, iou, use_scipy=False): + """ + Matches predictions to ground truth objects (pred_classes, true_classes) using IoU. + + Args: + pred_classes (torch.Tensor): Predicted class indices of shape(N,). + true_classes (torch.Tensor): Target class indices of shape(M,). + iou (torch.Tensor): An NxM tensor containing the pairwise IoU values for predictions and ground of truth + use_scipy (bool): Whether to use scipy for matching (more precise). + + Returns: + (torch.Tensor): Correct tensor of shape(N,10) for 10 IoU thresholds. + """ + # Dx10 matrix, where D - detections, 10 - IoU thresholds + correct = np.zeros((pred_classes.shape[0], self.iouv.shape[0])).astype(bool) + # LxD matrix where L - labels (rows), D - detections (columns) + correct_class = true_classes[:, None] == pred_classes + iou = iou * correct_class # zero out the wrong classes + iou = iou.cpu().numpy() + for i, threshold in enumerate(self.iouv.cpu().tolist()): + if use_scipy: + # WARNING: known issue that reduces mAP in https://github.com/ultralytics/ultralytics/pull/4708 + import scipy # scope import to avoid importing for all commands + + cost_matrix = iou * (iou >= threshold) + if cost_matrix.any(): + labels_idx, detections_idx = scipy.optimize.linear_sum_assignment(cost_matrix, maximize=True) + valid = cost_matrix[labels_idx, detections_idx] > 0 + if valid.any(): + correct[detections_idx[valid], i] = True + else: + matches = np.nonzero(iou >= threshold) # IoU > threshold and classes match + matches = np.array(matches).T + if matches.shape[0]: + if matches.shape[0] > 1: + matches = matches[iou[matches[:, 0], matches[:, 1]].argsort()[::-1]] + matches = matches[np.unique(matches[:, 1], return_index=True)[1]] + # matches = matches[matches[:, 2].argsort()[::-1]] + matches = matches[np.unique(matches[:, 0], return_index=True)[1]] + correct[matches[:, 1].astype(int), i] = True + return torch.tensor(correct, dtype=torch.bool, device=pred_classes.device) + + def add_callback(self, event: str, callback): + """Appends the given callback.""" + self.callbacks[event].append(callback) + + def run_callbacks(self, event: str): + """Runs all callbacks associated with a specified event.""" + for callback in self.callbacks.get(event, []): + callback(self) + + def get_dataloader(self, dataset_path, batch_size): + """Get data loader from dataset path and batch size.""" + raise NotImplementedError("get_dataloader function not implemented for this validator") + + def build_dataset(self, img_path): + """Build dataset.""" + raise NotImplementedError("build_dataset function not implemented in validator") + + def preprocess(self, batch): + """Preprocesses an input batch.""" + return batch + + def postprocess(self, preds): + """Preprocesses the predictions.""" + return preds + + def init_metrics(self, model): + """Initialize performance metrics for the YOLO model.""" + pass + + def update_metrics(self, preds, batch): + """Updates metrics based on predictions and batch.""" + pass + + def finalize_metrics(self, *args, **kwargs): + """Finalizes and returns all metrics.""" + pass + + def get_stats(self): + """Returns statistics about the model's performance.""" + return {} + + def check_stats(self, stats): + """Checks statistics.""" + pass + + def print_results(self): + """Prints the results of the model's predictions.""" + pass + + def get_desc(self): + """Get description of the YOLO model.""" + pass + + @property + def metric_keys(self): + """Returns the metric keys used in YOLO training/validation.""" + return [] + + def on_plot(self, name, data=None): + """Registers plots (e.g. to be consumed in callbacks).""" + self.plots[Path(name)] = {"data": data, "timestamp": time.time()} + + # TODO: may need to put these following functions into callback + def plot_val_samples(self, batch, ni): + """Plots validation samples during training.""" + pass + + def plot_predictions(self, batch, preds, ni): + """Plots YOLO model predictions on batch images.""" + pass + + def pred_to_json(self, preds, batch): + """Convert predictions to JSON format.""" + pass + + def eval_json(self, stats): + """Evaluate and return JSON format of prediction statistics.""" + pass diff --git a/examples/onnx2trt.sh b/examples/onnx2trt.sh new file mode 100644 index 0000000..6f6bcf3 --- /dev/null +++ b/examples/onnx2trt.sh @@ -0,0 +1,18 @@ +#!/usr/bin/env bash + +if [ $# -ne 1 ]; then + echo "Usage: $(basename $0) model.onnx" + exit +fi + +ONNX_MODEL=$1 +TRT_MODEL="${ONNX_MODEL%.*}".trt + +if [ ! -f "${ONNX_MODEL}" ]; then + echo Error: onnx model not found + exit 1 +fi + + +/usr/src/tensorrt/bin/trtexec --onnx="${ONNX_MODEL}" --saveEngine="${TRT_MODEL}" +