dev container changes + poc code

UBCAgroBot · Oct 30, 2024 · d83f37b · d83f37b
1 parent 1ba87b5
commit d83f37b
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Showing 14 changed files with 662 additions and 622 deletions.
diff --git a/.devcontainer/ROS/devcontainer.json b/.devcontainer/ROS/devcontainer.json
@@ -15,4 +15,46 @@
         "-it"
     ],
     "postCreateCommand": "bash -c 'echo source /opt/ros/humble/setup.bash >> ~/.bashrc && source /opt/ros/humble/setup.bash'"
-}
+}
+
+// {
+//     "name": "ROS 2 Development Container",
+//     "privileged": true,
+//     "remoteUser": "YOUR_USERNAME",
+//     "build": {
+//         "dockerfile": "Dockerfile",
+//         "args": {
+//             "USERNAME": "YOUR_USERNAME"
+//         }
+//     },
+//     "workspaceFolder": "/home/ws",
+//     "workspaceMount": "source=${localWorkspaceFolder},target=/home/ws,type=bind",
+//     "customizations": {
+//         "vscode": {
+//             "extensions":[
+//                 "ms-vscode.cpptools",
+//                 "ms-vscode.cpptools-themes",
+//                 "twxs.cmake",
+//                 "donjayamanne.python-extension-pack",
+//                 "eamodio.gitlens",
+//                 "ms-iot.vscode-ros"
+//             ]
+//         }
+//     },
+//     "containerEnv": {
+//         "DISPLAY": "unix:0",
+//         "ROS_LOCALHOST_ONLY": "1",
+//         "ROS_DOMAIN_ID": "42"
+//     },
+//     "runArgs": [
+//         "--net=host",
+//         "--pid=host",
+//         "--ipc=host",
+//         "-e", "DISPLAY=${env:DISPLAY}"
+//     ],
+//     "mounts": [
+//        "source=/tmp/.X11-unix,target=/tmp/.X11-unix,type=bind,consistency=cached",
+//        "source=/dev/dri,target=/dev/dri,type=bind,consistency=cached"
+//     ],
+//     "postCreateCommand": "sudo rosdep update && sudo rosdep install --from-paths src --ignore-src -y && sudo chown -R $(whoami) /home/ws/"
+// }
diff --git a/cpp_wip/cuda_stream_manager.cpp b/cpp_wip/cuda_stream_manager.cpp
@@ -1,41 +1,41 @@
-// cuda_stream_manager.hpp
-#pragma once
-#include <cuda_runtime.h>
-#include <memory>
-
-class CudaStreamManager {
-public:
-    CudaStreamManager() {
-        // Create a single CUDA stream
-        cudaStreamCreate(&stream_);
-
-        // Create CUDA events
-        cudaEventCreate(&preprocess_done_);
-        cudaEventCreate(&inference_done_);
-    }
-
-    ~CudaStreamManager() {
-        // Destroy CUDA stream and events
-        cudaStreamDestroy(stream_);
-        cudaEventDestroy(preprocess_done_);
-        cudaEventDestroy(inference_done_);
-    }
-
-    cudaStream_t getStream() const {
-        return stream_;
-    }
-
-    cudaEvent_t& getPreprocessEvent() {
-        return preprocess_done_;
-    }
-
-    cudaEvent_t& getInferenceEvent() {
-        return inference_done_;
-    }
-
-private:
-    cudaStream_t stream_;
-    cudaEvent_t preprocess_done_, inference_done_;
-};
-
-using CudaStreamManagerPtr = std::shared_ptr<CudaStreamManager>;
+// cuda_stream_manager.hpp
+#pragma once
+#include <cuda_runtime.h>
+#include <memory>
+
+class CudaStreamManager {
+public:
+    CudaStreamManager() {
+        // Create a single CUDA stream
+        cudaStreamCreate(&stream_);
+
+        // Create CUDA events
+        cudaEventCreate(&preprocess_done_);
+        cudaEventCreate(&inference_done_);
+    }
+
+    ~CudaStreamManager() {
+        // Destroy CUDA stream and events
+        cudaStreamDestroy(stream_);
+        cudaEventDestroy(preprocess_done_);
+        cudaEventDestroy(inference_done_);
+    }
+
+    cudaStream_t getStream() const {
+        return stream_;
+    }
+
+    cudaEvent_t& getPreprocessEvent() {
+        return preprocess_done_;
+    }
+
+    cudaEvent_t& getInferenceEvent() {
+        return inference_done_;
+    }
+
+private:
+    cudaStream_t stream_;
+    cudaEvent_t preprocess_done_, inference_done_;
+};
+
+using CudaStreamManagerPtr = std::shared_ptr<CudaStreamManager>;
diff --git a/cpp_wip/preprocessing_node.cpp b/cpp_wip/preprocessing_node.cpp
@@ -1,23 +1,23 @@
-// preprocessing_node.cpp
-#include <rclcpp/rclcpp.hpp>
-#include "cuda_stream_manager.hpp"
-
-class PreprocessingNode : public rclcpp::Node {
-public:
-    PreprocessingNode(const CudaStreamManagerPtr& cuda_manager)
-        : Node("preprocessing_node"), cuda_manager_(cuda_manager) {}
-
-    void preprocess() {
-        // Perform GPU preprocessing here using cuda_manager_->getStream()
-
-        // Signal that preprocessing is done
-        cudaEventRecord(cuda_manager_->getPreprocessEvent(), cuda_manager_->getStream());
-    }
-
-private:
-    CudaStreamManagerPtr cuda_manager_;
-};
-
-// Register as a composable node
-#include "rclcpp_components/register_node_macro.hpp"
-RCLCPP_COMPONENTS_REGISTER_NODE(PreprocessingNode)
+// preprocessing_node.cpp
+#include <rclcpp/rclcpp.hpp>
+#include "cuda_stream_manager.hpp"
+
+class PreprocessingNode : public rclcpp::Node {
+public:
+    PreprocessingNode(const CudaStreamManagerPtr& cuda_manager)
+        : Node("preprocessing_node"), cuda_manager_(cuda_manager) {}
+
+    void preprocess() {
+        // Perform GPU preprocessing here using cuda_manager_->getStream()
+
+        // Signal that preprocessing is done
+        cudaEventRecord(cuda_manager_->getPreprocessEvent(), cuda_manager_->getStream());
+    }
+
+private:
+    CudaStreamManagerPtr cuda_manager_;
+};
+
+// Register as a composable node
+#include "rclcpp_components/register_node_macro.hpp"
+RCLCPP_COMPONENTS_REGISTER_NODE(PreprocessingNode)
diff --git a/python_wip/cuda_download_1.py b/python_wip/cuda_download_1.py
@@ -1,42 +1,42 @@
-import pyzed.sl as sl
-
-# Create a ZED Camera object
-zed = sl.Camera()
-
-# Create InitParameters object and set configuration parameters
-init_params = sl.InitParameters()
-init_params.camera_resolution = sl.RESOLUTION.HD720  # Set resolution
-init_params.depth_mode = sl.DEPTH_MODE.ULTRA  # Set depth mode
-
-# Open the camera
-status = zed.open(init_params)
-if status != sl.ERROR_CODE.SUCCESS:
-    print(f"Camera failed to open: {status}")
-    exit(1)
-
-# Create a Mat object for the image (GPU memory type)
-image_gpu = sl.Mat(zed.get_camera_information().camera_resolution.width,
-                   zed.get_camera_information().camera_resolution.height,
-                   sl.MAT_TYPE.U8_C4, sl.MEM.GPU)
-
-# Capture an image frame
-runtime_params = sl.RuntimeParameters()
-
-if zed.grab(runtime_params) == sl.ERROR_CODE.SUCCESS:
-    # Retrieve image directly into GPU memory
-    zed.retrieve_image(image_gpu, sl.VIEW.LEFT, sl.MEM.GPU)
-
-    # Now `image_gpu` holds the image in GPU memory
-    print("Image captured and stored in CUDA memory")
-
-# Close the camera
-zed.close()
-
-# Create a CPU Mat to store the image
-image_cpu = sl.Mat()
-
-# Copy image from GPU to CPU
-image_gpu.copy_to(image_cpu)
-
-# Save the image (this is in CPU memory now)
-image_cpu.write("image_from_cuda.png")
+import pyzed.sl as sl
+
+# Create a ZED Camera object
+zed = sl.Camera()
+
+# Create InitParameters object and set configuration parameters
+init_params = sl.InitParameters()
+init_params.camera_resolution = sl.RESOLUTION.HD720  # Set resolution
+init_params.depth_mode = sl.DEPTH_MODE.ULTRA  # Set depth mode
+
+# Open the camera
+status = zed.open(init_params)
+if status != sl.ERROR_CODE.SUCCESS:
+    print(f"Camera failed to open: {status}")
+    exit(1)
+
+# Create a Mat object for the image (GPU memory type)
+image_gpu = sl.Mat(zed.get_camera_information().camera_resolution.width,
+                   zed.get_camera_information().camera_resolution.height,
+                   sl.MAT_TYPE.U8_C4, sl.MEM.GPU)
+
+# Capture an image frame
+runtime_params = sl.RuntimeParameters()
+
+if zed.grab(runtime_params) == sl.ERROR_CODE.SUCCESS:
+    # Retrieve image directly into GPU memory
+    zed.retrieve_image(image_gpu, sl.VIEW.LEFT, sl.MEM.GPU)
+
+    # Now `image_gpu` holds the image in GPU memory
+    print("Image captured and stored in CUDA memory")
+
+# Close the camera
+zed.close()
+
+# Create a CPU Mat to store the image
+image_cpu = sl.Mat()
+
+# Copy image from GPU to CPU
+image_gpu.copy_to(image_cpu)
+
+# Save the image (this is in CPU memory now)
+image_cpu.write("image_from_cuda.png")
diff --git a/python_wip/cuda_stream_inference.py b/python_wip/cuda_stream_inference.py
@@ -1,51 +1,51 @@
-# inference_node.py
-import rclpy
-from rclpy.node import Node
-from cuda_manager import CudaStreamManager
-import pycuda.driver as cuda
-import numpy as np
-
-class InferenceNode(Node):
-    def __init__(self, cuda_manager):
-        super().__init__('inference_node')
-        self.cuda_manager = cuda_manager
-
-    def infer(self):
-        self.get_logger().info("Waiting for preprocessing to complete...")
-        self.cuda_manager.get_preprocess_event().synchronize()
-        self.get_logger().info("Starting inference on GPU...")
-
-        # Simulate inference on GPU
-        data = np.random.randn(1024, 1024).astype(np.float32)
-        gpu_data = cuda.mem_alloc(data.nbytes)
-        cuda.memcpy_htod_async(gpu_data, data, self.cuda_manager.get_stream())
-
-        # Signal inference completion
-        self.cuda_manager.get_inference_event().record(self.cuda_manager.get_stream())
-        self.get_logger().info("Inference complete.")
-
-# post processing:
-# postprocessing_node.py
-import rclpy
-from rclpy.node import Node
-from cuda_manager import CudaStreamManager
-import pycuda.driver as cuda
-import numpy as np
-
-class PostprocessingNode(Node):
-    def __init__(self, cuda_manager):
-        super().__init__('postprocessing_node')
-        self.cuda_manager = cuda_manager
-
-    def postprocess(self):
-        self.get_logger().info("Waiting for inference to complete...")
-        self.cuda_manager.get_inference_event().synchronize()
-        self.get_logger().info("Starting postprocessing on GPU...")
-
-        # Simulate postprocessing on GPU
-        data = np.random.randn(1024, 1024).astype(np.float32)
-        gpu_data = cuda.mem_alloc(data.nbytes)
-        cuda.memcpy_htod_async(gpu_data, data, self.cuda_manager.get_stream())
-
-        # Assume postprocessing is complete
-        self.get_logger().info("Postprocessing complete.")
+# inference_node.py
+import rclpy
+from rclpy.node import Node
+from cuda_manager import CudaStreamManager
+import pycuda.driver as cuda
+import numpy as np
+
+class InferenceNode(Node):
+    def __init__(self, cuda_manager):
+        super().__init__('inference_node')
+        self.cuda_manager = cuda_manager
+
+    def infer(self):
+        self.get_logger().info("Waiting for preprocessing to complete...")
+        self.cuda_manager.get_preprocess_event().synchronize()
+        self.get_logger().info("Starting inference on GPU...")
+
+        # Simulate inference on GPU
+        data = np.random.randn(1024, 1024).astype(np.float32)
+        gpu_data = cuda.mem_alloc(data.nbytes)
+        cuda.memcpy_htod_async(gpu_data, data, self.cuda_manager.get_stream())
+
+        # Signal inference completion
+        self.cuda_manager.get_inference_event().record(self.cuda_manager.get_stream())
+        self.get_logger().info("Inference complete.")
+
+# post processing:
+# postprocessing_node.py
+import rclpy
+from rclpy.node import Node
+from cuda_manager import CudaStreamManager
+import pycuda.driver as cuda
+import numpy as np
+
+class PostprocessingNode(Node):
+    def __init__(self, cuda_manager):
+        super().__init__('postprocessing_node')
+        self.cuda_manager = cuda_manager
+
+    def postprocess(self):
+        self.get_logger().info("Waiting for inference to complete...")
+        self.cuda_manager.get_inference_event().synchronize()
+        self.get_logger().info("Starting postprocessing on GPU...")
+
+        # Simulate postprocessing on GPU
+        data = np.random.randn(1024, 1024).astype(np.float32)
+        gpu_data = cuda.mem_alloc(data.nbytes)
+        cuda.memcpy_htod_async(gpu_data, data, self.cuda_manager.get_stream())
+
+        # Assume postprocessing is complete
+        self.get_logger().info("Postprocessing complete.")