Changed the parameter "device_opts" from the train function to "context"

src/main/resources/templates/caffe2/CNNCreator.ftl

@@ -99,11 +99,11 @@ ${tc.include(tc.architecture.body)}
                 accuracy = brew.accuracy(model, [output, label], "accuracy", top_k=3)
             return accuracy
 
-    def train(self, num_epoch=1000, batch_size=64, device_opts='gpu', eval_metric='accuracy', opt_type='adam', base_learning_rate=0.001, weight_decay=0.001, policy='fixed', stepsize=1, epsilon=1E-8, beta1=0.9, beta2=0.999, gamma=0.999, momentum=0.9) :
-        if device_opts == 'cpu':
+    def train(self, num_epoch=1000, batch_size=64, context='gpu', eval_metric='accuracy', opt_type='adam', base_learning_rate=0.001, weight_decay=0.001, policy='fixed', stepsize=1, epsilon=1E-8, beta1=0.9, beta2=0.999, gamma=0.999, momentum=0.9) :
+        if context == 'cpu':
             device_opts = core.DeviceOption(caffe2_pb2.CPU, 0)
             print("CPU mode selected")
-        elif device_opts == 'gpu':
+        elif context == 'gpu':
             device_opts = core.DeviceOption(caffe2_pb2.CUDA, 0)
             print("GPU mode selected")
 

src/test/resources/target_code/CNNCreator_Alexnet.py

@@ -192,11 +192,11 @@ class CNNCreator_Alexnet:
                 accuracy = brew.accuracy(model, [output, label], "accuracy", top_k=3)
             return accuracy
 
-    def train(self, num_epoch=1000, batch_size=64, device_opts='gpu', eval_metric='accuracy', opt_type='adam', base_learning_rate=0.001, weight_decay=0.001, policy='fixed', stepsize=1, epsilon=1E-8, beta1=0.9, beta2=0.999, gamma=0.999, momentum=0.9) :
-        if device_opts == 'cpu':
+    def train(self, num_epoch=1000, batch_size=64, context='gpu', eval_metric='accuracy', opt_type='adam', base_learning_rate=0.001, weight_decay=0.001, policy='fixed', stepsize=1, epsilon=1E-8, beta1=0.9, beta2=0.999, gamma=0.999, momentum=0.9) :
+        if context == 'cpu':
             device_opts = core.DeviceOption(caffe2_pb2.CPU, 0)
             print("CPU mode selected")
-        elif device_opts == 'gpu':
+        elif context == 'gpu':
             device_opts = core.DeviceOption(caffe2_pb2.CUDA, 0)
             print("GPU mode selected")
 

src/test/resources/target_code/CNNCreator_CifarClassifierNetwork.py

@@ -278,11 +278,11 @@ class CNNCreator_CifarClassifierNetwork:
                 accuracy = brew.accuracy(model, [output, label], "accuracy", top_k=3)
             return accuracy
 
-    def train(self, num_epoch=1000, batch_size=64, device_opts='gpu', eval_metric='accuracy', opt_type='adam', base_learning_rate=0.001, weight_decay=0.001, policy='fixed', stepsize=1, epsilon=1E-8, beta1=0.9, beta2=0.999, gamma=0.999, momentum=0.9) :
-        if device_opts == 'cpu':
+    def train(self, num_epoch=1000, batch_size=64, context='gpu', eval_metric='accuracy', opt_type='adam', base_learning_rate=0.001, weight_decay=0.001, policy='fixed', stepsize=1, epsilon=1E-8, beta1=0.9, beta2=0.999, gamma=0.999, momentum=0.9) :
+        if context == 'cpu':
             device_opts = core.DeviceOption(caffe2_pb2.CPU, 0)
             print("CPU mode selected")
-        elif device_opts == 'gpu':
+        elif context == 'gpu':
             device_opts = core.DeviceOption(caffe2_pb2.CUDA, 0)
             print("GPU mode selected")
 

src/test/resources/target_code/CNNCreator_VGG16.py

@@ -167,11 +167,11 @@ class CNNCreator_VGG16:
                 accuracy = brew.accuracy(model, [output, label], "accuracy", top_k=3)
             return accuracy
 
-    def train(self, num_epoch=1000, batch_size=64, device_opts='gpu', eval_metric='accuracy', opt_type='adam', base_learning_rate=0.001, weight_decay=0.001, policy='fixed', stepsize=1, epsilon=1E-8, beta1=0.9, beta2=0.999, gamma=0.999, momentum=0.9) :
-        if device_opts == 'cpu':
+    def train(self, num_epoch=1000, batch_size=64, context='gpu', eval_metric='accuracy', opt_type='adam', base_learning_rate=0.001, weight_decay=0.001, policy='fixed', stepsize=1, epsilon=1E-8, beta1=0.9, beta2=0.999, gamma=0.999, momentum=0.9) :
+        if context == 'cpu':
             device_opts = core.DeviceOption(caffe2_pb2.CPU, 0)
             print("CPU mode selected")
-        elif device_opts == 'gpu':
+        elif context == 'gpu':
             device_opts = core.DeviceOption(caffe2_pb2.CUDA, 0)
             print("GPU mode selected")
 

src/test/resources/target_code/CNNTrainer_emptyConfig.py

+from caffe2.python import workspace, core, model_helper, brew, optimizer
+from caffe2.python.predictor import mobile_exporter
+from caffe2.proto import caffe2_pb2
+
+import numpy as np
+import cv2
 import logging
-import mxnet as mx
 import CNNCreator_emptyConfig
 
 if __name__ == "__main__":
@@ -11,3 +16,28 @@ if __name__ == "__main__":
     emptyConfig = CNNCreator_emptyConfig.CNNCreator_emptyConfig()
     emptyConfig.train(
     )
+
+    print '\n********************************************'
+    print("Loading Deploy model")
+
+    context = 'gpu'
+    if context == 'cpu':
+        device_opts = core.DeviceOption(caffe2_pb2.CPU, 0)
+        print("CPU mode selected")
+    elif context == 'gpu':
+        device_opts = core.DeviceOption(caffe2_pb2.CUDA, 0)
+        print("GPU mode selected")
+
+    LeNet.load_net(LeNet.INIT_NET, LeNet.PREDICT_NET, device_opts=device_opts)
+
+    img = cv2.imread("3.jpg")                                   # Load test image
+    img = cv2.resize(img, (28,28))                              # Resize to 28x28
+    img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY )                # Covert to grayscale
+    img = img.reshape((1,1,28,28)).astype('float32')            # Reshape to (1,1,28,28)
+    workspace.FeedBlob("data", img, device_option=device_opts)  # FeedBlob
+    workspace.RunNet('deploy_net', num_iter=1)                  # Forward
+
+    print("\nInput: {}".format(img.shape))
+    pred = workspace.FetchBlob("predictions")
+    print("Output: {}".format(pred))
+    print("Output class: {}".format(np.argmax(pred)))

src/test/resources/target_code/CNNTrainer_fullConfig.py

+from caffe2.python import workspace, core, model_helper, brew, optimizer
+from caffe2.python.predictor import mobile_exporter
+from caffe2.proto import caffe2_pb2
+
+import numpy as np
+import cv2
 import logging
-import mxnet as mx
 import CNNCreator_fullConfig
 
 if __name__ == "__main__":
@@ -10,25 +15,40 @@ if __name__ == "__main__":
 
     fullConfig = CNNCreator_fullConfig.CNNCreator_fullConfig()
     fullConfig.train(
-        batch_size=100,
         num_epoch=5,
-        load_checkpoint=True,
+        batch_size=100,
         context='gpu',
-        normalize=True,
         eval_metric='mse',
-        optimizer='rmsprop',
-        optimizer_params={
-            'weight_decay': 0.01,
-            'centered': True,
-            'gamma2': 0.9,
-            'gamma1': 0.9,
-            'clip_weights': 10.0,
-            'learning_rate_decay': 0.9,
-            'epsilon': 1.0E-6,
-            'rescale_grad': 1.1,
-            'clip_gradient': 10.0,
-            'learning_rate_minimum': 1.0E-5,
-            'learning_rate_policy': 'step',
-            'learning_rate': 0.001,
-            'step_size': 1000}
+        opt_type='rmsprop',
+        base_learning_rate=0.001,
+        weight_decay=0.01,
+        policy='step',
+        stepsize=1000,
+        epsilon=1.0E-6,
+        gamma=0.9,
     )
+
+    print '\n********************************************'
+    print("Loading Deploy model")
+
+    context='gpu'
+    if context == 'cpu':
+        device_opts = core.DeviceOption(caffe2_pb2.CPU, 0)
+        print("CPU mode selected")
+    elif context == 'gpu':
+        device_opts = core.DeviceOption(caffe2_pb2.CUDA, 0)
+        print("GPU mode selected")
+
+    LeNet.load_net(LeNet.INIT_NET, LeNet.PREDICT_NET, device_opts=device_opts)
+
+    img = cv2.imread("3.jpg")                                   # Load test image
+    img = cv2.resize(img, (28,28))                              # Resize to 28x28
+    img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY )                # Covert to grayscale
+    img = img.reshape((1,1,28,28)).astype('float32')            # Reshape to (1,1,28,28)
+    workspace.FeedBlob("data", img, device_option=device_opts)  # FeedBlob
+    workspace.RunNet('deploy_net', num_iter=1)                  # Forward
+
+    print("\nInput: {}".format(img.shape))
+    pred = workspace.FetchBlob("predictions")
+    print("Output: {}".format(pred))
+    print("Output class: {}".format(np.argmax(pred)))

src/test/resources/target_code/CNNTrainer_simpleConfig.py

+from caffe2.python import workspace, core, model_helper, brew, optimizer
+from caffe2.python.predictor import mobile_exporter
+from caffe2.proto import caffe2_pb2
+
+import numpy as np
+import cv2
 import logging
-import mxnet as mx
 import CNNCreator_simpleConfig
 
 if __name__ == "__main__":
@@ -10,9 +15,33 @@ if __name__ == "__main__":
 
     simpleConfig = CNNCreator_simpleConfig.CNNCreator_simpleConfig()
     simpleConfig.train(
-        batch_size=100,
         num_epoch=50,
-        optimizer='adam',
-        optimizer_params={
-            'learning_rate': 0.001}
+        batch_size=100,
+        opt_type='adam',
+        base_learning_rate=0.001,
     )
+
+    print '\n********************************************'
+    print("Loading Deploy model")
+
+    context = 'gpu'
+    if context == 'cpu':
+        device_opts = core.DeviceOption(caffe2_pb2.CPU, 0)
+        print("CPU mode selected")
+    elif context == 'gpu':
+        device_opts = core.DeviceOption(caffe2_pb2.CUDA, 0)
+        print("GPU mode selected")
+
+    LeNet.load_net(LeNet.INIT_NET, LeNet.PREDICT_NET, device_opts=device_opts)
+
+    img = cv2.imread("3.jpg")                                   # Load test image
+    img = cv2.resize(img, (28,28))                              # Resize to 28x28
+    img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY )                # Covert to grayscale
+    img = img.reshape((1,1,28,28)).astype('float32')            # Reshape to (1,1,28,28)
+    workspace.FeedBlob("data", img, device_option=device_opts)  # FeedBlob
+    workspace.RunNet('deploy_net', num_iter=1)                  # Forward
+
+    print("\nInput: {}".format(img.shape))
+    pred = workspace.FetchBlob("predictions")
+    print("Output: {}".format(pred))
+    print("Output class: {}".format(np.argmax(pred)))