- updated pipeline for tensorflow

- added traiuning and inferece fro classificateion class for pytorch
- added parsing to ONNX

configs/config_ConvNet.py

@@ -21,25 +21,24 @@ def load_config():
     config = ConfigFlags().return_flags()
 
     config.net = 'ConvNet'
-    config.training_mode = True
+    config.training_mode = False
     config.data_set = 'MNIST'
     config.image_size = [28, 28, 1]
-
     config.lr = 0.001
     config.lr_decay = 0.1
-    config.ref_steps = 10
-    config.ref_patience = 1
+    config.ref_steps = 3
+    config.ref_patience = 3
     config.batch_size = 32
-    config.num_epochs = 1000
+    config.num_epochs = 3
     config.loss = 'softmax'
-    config.optimizer = 'adam'
+    config.optimizer = 'sgd'
     config.gradcam_record = True
     config.gradcam_layers = 6
     config.gradcam_layers_max = 6
     config.mi_record = False
     config.gpu_load = 0.8
     config.num_classes = 10
-    config.class_labels = [i for i in range(9)]
+    config.class_labels = [str(i) for i in range(10)]
     config.num_filters = 16
     config.upconv = 'upconv'
     config.nonlin = 'relu'
@@ -53,10 +52,11 @@ def load_config():
     config.normalize = True
     config.zero_center = True
     config.dropout = 0.4
-    config.chpnt2load = ''
+    # config.chpnt2load = r'E:\SCCH_PROJECTS\DL_implementaitons\00_templates\training_engine_git\experiments\ckpnt_logs\MNIST\1565681155.1648371\1565681155.1648371_split_0'
+    config.chpnt2load = r'experiments/ckpnt_logs/MNIST/1565680688.729741/1565680688.729741_split_0'
     config.multi_task = False
     config.cross_val = 1
-    config.framework = 'tensorflow'
+    config.framework = 'pytorch'
     config.experiment_path = None
 
 

configs/config_ONNXtest.py

@@ -0,0 +1,86 @@
+#!/usr/bin/env python
+# -----------------------------------------------------------------------------
+# Copyright (C) Software Competence Center Hagenberg GmbH (SCCH)
+# All rights reserved.
+# -----------------------------------------------------------------------------
+# This document contains proprietary information belonging to SCCH.
+# Passing on and copying of this document, use and communication of its
+# contents is not permitted without prior written authorization.
+# -----------------------------------------------------------------------------
+# Created on : 5/9/2019 3:49 PM $ 
+# by : Shepeleva $ 
+# SVN  $
+#
+
+# --- imports -----------------------------------------------------------------
+
+#!/usr/bin/env python
+# -----------------------------------------------------------------------------
+# Copyright (C) Software Competence Center Hagenberg GmbH (SCCH)
+# All rights reserved.
+# -----------------------------------------------------------------------------
+# This document contains proprietary information belonging to SCCH.
+# Passing on and copying of this document, use and communication of its
+# contents is not permitted without prior written authorization.
+# -----------------------------------------------------------------------------
+# Created on : 3/21/2019 8:30 AM $
+# by : Shepeleva $
+# SVN  $
+#
+
+# --- imports -----------------------------------------------------------------
+
+
+from configs.config import ConfigFlags
+
+
+def load_config():
+    config = ConfigFlags().return_flags()
+
+    config.net = r'E:\o156.onnx'
+
+    config.autotune = False
+    config.training_mode = True
+
+    config.data_set = 'CIFAR10'
+    config.image_size = [32, 32, 3]
+    config.data_folder = None
+    config.data_file = None
+
+
+    config.lr = 0.001
+    config.lr_decay = 0.1
+    config.ref_steps = 10
+    config.ref_patience = 10
+    config.batch_size = 128
+    config.num_epochs = 2
+    config.loss = 'softmax'
+    config.optimizer = 'sgd'
+    config.gradcam_record = True
+    config.gradcam_layers = 6
+    config.gradcam_layers_max = 6
+    config.mi_record = False
+    config.gpu_load = 0.8
+    config.num_classes = 10
+    config.class_labels = [i for i in range(9)]
+    config.num_filters = 64
+    config.upconv = 'upconv'
+    config.nonlin = 'relu'
+    config.task_type = 'classification'
+    config.accuracy = 'percent'
+    config.augmentation = {'flip_hor': False,
+                           'flip_vert': False}
+    config.data_split = 0.7
+    config.long_summary = True
+    config.trainable_layers = 'all'
+    config.normalize = True
+    config.zero_center = True
+    config.dropout = 0.4
+    config.chpnt2load = ''
+    config.multi_task = False
+    config.cross_val = 1
+    config.framework = 'tensorflow'
+    config.experiment_path = None
+
+
+    return config

configs/config_UNet.py

@@ -0,0 +1,66 @@
+#!/usr/bin/env python
+# -----------------------------------------------------------------------------
+# Copyright (C) Software Competence Center Hagenberg GmbH (SCCH)
+# All rights reserved.
+# -----------------------------------------------------------------------------
+# This document contains proprietary information belonging to SCCH.
+# Passing on and copying of this document, use and communication of its
+# contents is not permitted without prior written authorization.
+# -----------------------------------------------------------------------------
+# Created on : 01/08/2018 16:29 $
+# by : shepeleva $
+# SVN : $
+#
+
+# --- imports -----------------------------------------------------------------
+
+from configs.config import ConfigFlags
+
+
+def load_config():
+    config = ConfigFlags().return_flags()
+
+    config.autotune = False
+
+    config.net = 'UNet'
+    config.training_mode = True
+    config.data_set = 'rubble_sample'
+    config.data_dir = r'F:\Datasets\Rubblemaster_segmentation'
+    config.image_size = [1024, 1024, 1]
+
+    config.lr = 0.001
+    config.lr_decay = 0.1
+    config.ref_steps = 3
+    config.ref_patience = 3
+    config.batch_size = 2
+    config.num_epochs = 100
+    config.loss = 'dice_jaccard'
+    config.optimizer = 'sgd'
+    config.gradcam_record = True
+    config.gradcam_layers = 6
+    config.gradcam_layers_max = 6
+    config.mi_record = False
+    config.gpu_load = 0.8
+    config.num_classes = 1
+    config.class_labels = [i for i in range(1)]
+    config.num_filters = 8
+    config.upconv = 'upconv'
+    config.nonlin = 'relu'
+    config.task_type = 'segmentation'
+    config.accuracy = 'IoU'
+    config.augmentation = {'flip_hor': False,
+                           'flip_vert': False}
+    config.data_split = 0.7
+    config.long_summary = True
+    config.trainable_layers = 'all'
+    config.normalize = True
+    config.zero_center = True
+    config.dropout = 0.4
+    config.chpnt2load = ''
+    config.multi_task = False
+    config.cross_val = 1
+    config.framework = 'pytorch'
+    config.experiment_path = None
+
+
+    return config

configs/config_VGG19.py

@@ -0,0 +1,70 @@
+#!/usr/bin/env python
+# -----------------------------------------------------------------------------
+# Copyright (C) Software Competence Center Hagenberg GmbH (SCCH)
+# All rights reserved.
+# -----------------------------------------------------------------------------
+# This document contains proprietary information belonging to SCCH.
+# Passing on and copying of this document, use and communication of its
+# contents is not permitted without prior written authorization.
+# -----------------------------------------------------------------------------
+# Created on : 3/21/2019 8:30 AM $ 
+# by : Shepeleva $ 
+# SVN  $
+#
+
+# --- imports -----------------------------------------------------------------
+
+
+from configs.config import ConfigFlags
+
+
+def load_config():
+    config = ConfigFlags().return_flags()
+
+    config.net = 'VGG19'
+
+    config.autotune = False
+    config.training_mode = True
+
+    config.data_set = 'CIFAR10'
+    config.image_size = [32, 32, 3]
+    config.data_folder = None
+    config.data_file = None
+
+
+    config.lr = 0.001
+    config.lr_decay = 0.1
+    config.ref_steps = 10
+    config.ref_patience = 10
+    config.batch_size = 128
+    config.num_epochs = 2
+    config.loss = 'softmax'
+    config.optimizer = 'sgd'
+    config.gradcam_record = True
+    config.gradcam_layers = 6
+    config.gradcam_layers_max = 6
+    config.mi_record = False
+    config.gpu_load = 0.8
+    config.num_classes = 10
+    config.class_labels = [i for i in range(9)]
+    config.num_filters = 64
+    config.upconv = 'upconv'
+    config.nonlin = 'relu'
+    config.task_type = 'classification'
+    config.accuracy = 'percent'
+    config.augmentation = {'flip_hor': False,
+                           'flip_vert': False}
+    config.data_split = 0.7
+    config.long_summary = True
+    config.trainable_layers = 'all'
+    config.normalize = True
+    config.zero_center = True
+    config.dropout = 0.4
+    config.chpnt2load = ''
+    config.multi_task = False
+    config.cross_val = 1
+    config.framework = 'tensorflow'
+    config.experiment_path = None
+
+
+    return config

main.py

@@ -22,7 +22,6 @@
 
 EXPERIMENT_ID = 'ConvNet'
 
-
 def run(experiment_id):
     config = importlib.import_module('configs.config_' + experiment_id)
     args = config.load_config()
@@ -32,7 +31,7 @@ def run(experiment_id):
     else:
         inference = InferenceRunner(experiment_id=experiment_id)
         inference.start_inference()
-        raise ValueError('Inference not implemented yet')
+        # raise ValueError('Inference not implemented yet')
 
 
 if __name__ == '__main__':