Merge branch 'update_cnncreator' into 'master'

Update cnncreator

See merge request !24

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

@@ -3,6 +3,7 @@ from caffe2.python.predictor import mobile_exporter
 from caffe2.proto import caffe2_pb2
 import numpy as np
 import math
+import datetime
 import logging
 import os
 import sys
@@ -28,6 +29,15 @@ class ${tc.fileNameWithoutEnding}:
 
         return iterations_int
 
+    def get_epoch_as_iter(self, num_epoch, batch_size, dataset_size):   #To print metric durint training process
+        #Force floating point calculation
+        batch_size_float = float(batch_size)
+        dataset_size_float = float(dataset_size)
+
+        epoch_float = math.ceil(dataset_size_float/batch_size_float)
+        epoch_int = int(epoch_float)
+
+        return epoch_int
 
     def add_input(self, model, batch_size, db, db_type, device_opts):
         with core.DeviceScope(device_opts):
@@ -125,7 +135,8 @@ ${tc.include(tc.architecture.body)}
     	data, label, train_dataset_size = self.add_input(train_model, batch_size=batch_size, db=os.path.join(self._data_dir_, 'train_lmdb'), db_type='lmdb', device_opts=device_opts)
     	${tc.join(tc.architectureOutputs, ",", "","")} = self.create_model(train_model, data, device_opts=device_opts, is_test=False)
     	self.add_training_operators(train_model, ${tc.join(tc.architectureOutputs, ",", "","")}, label, device_opts, loss, opt_type, base_learning_rate, policy, stepsize, epsilon, beta1, beta2, gamma, momentum)
-    	self.add_accuracy(train_model, ${tc.join(tc.architectureOutputs, ",", "","")}, label, device_opts, eval_metric)
+    	if not loss == 'euclidean':
+    		self.add_accuracy(train_model, ${tc.join(tc.architectureOutputs, ",", "","")}, label, device_opts, eval_metric)
     	with core.DeviceScope(device_opts):
     		brew.add_weight_decay(train_model, weight_decay)
 
@@ -135,38 +146,62 @@ ${tc.include(tc.architecture.body)}
 
     	# Main Training Loop
     	iterations = self.get_total_num_iter(num_epoch, batch_size, train_dataset_size)
-        print("** Starting Training for " + str(num_epoch) + " epochs = " + str(iterations) + " iterations **")
+        epoch_as_iter = self.get_epoch_as_iter(num_epoch, batch_size, train_dataset_size)
+        print("\n*** Starting Training for " + str(num_epoch) + " epochs = " + str(iterations) + " iterations ***")
+        start_date = datetime.datetime.now()
     	for i in range(iterations):
     		workspace.RunNet(train_model.net)
-    		if i % 50 == 0:
-    			print 'Iter ' + str(i) + ': ' + 'Loss ' + str(workspace.FetchBlob("loss")) + ' - ' + 'Accuracy ' + str(workspace.FetchBlob('accuracy'))
-    	print("Training done")
-
-    	print("== Running Test model ==")
+    		if i % 50 == 0 or i % epoch_as_iter == 0:
+    			if not loss == 'euclidean':
+    				print 'Iter ' + str(i) + ': ' + 'Loss ' + str(workspace.FetchBlob("loss")) + ' - ' + 'Accuracy ' + str(workspace.FetchBlob('accuracy'))
+    			else:
+    				print 'Iter ' + str(i) + ': ' + 'Loss ' + str(workspace.FetchBlob("loss"))
+
+    			current_time = datetime.datetime.now()
+    			elapsed_time = current_time - start_date
+    			print 'Progress: ' + str(i) + '/' + str(iterations) + ', ' +'Current time spent: ' + str(elapsed_time)
+        current_time = datetime.datetime.now()
+        elapsed_time = current_time - start_date
+        print 'Progress: ' + str(iterations) + '/' + str(iterations) + ' Training done' + ', ' + 'Total time spent: ' + str(elapsed_time)
+
+    	print("\n*** Running Test model ***")
     	# == Testing model. ==
     	test_model= model_helper.ModelHelper(name="test_net", arg_scope=arg_scope, init_params=False)
     	data, label, test_dataset_size = self.add_input(test_model, batch_size=batch_size, db=os.path.join(self._data_dir_, 'test_lmdb'), db_type='lmdb', device_opts=device_opts)
     	${tc.join(tc.architectureOutputs, ",", "","")} = self.create_model(test_model, data, device_opts=device_opts, is_test=True)
-    	self.add_accuracy(test_model, predictions, label, device_opts, eval_metric)
+    	if not loss == 'euclidean':
+    		self.add_accuracy(test_model, ${tc.join(tc.architectureOutputs, ",", "","")}, label, device_opts, eval_metric)
     	workspace.RunNetOnce(test_model.param_init_net)
     	workspace.CreateNet(test_model.net, overwrite=True)
 
     	# Main Testing Loop
     	test_accuracy = np.zeros(test_dataset_size/batch_size)
+        start_date = datetime.datetime.now()
     	for i in range(test_dataset_size/batch_size):
     		# Run a forward pass of the net on the current batch
     		workspace.RunNet(test_model.net)
     		# Collect the batch accuracy from the workspace
-    		test_accuracy[i] = workspace.FetchBlob('accuracy')
-
-    	print('Test_accuracy: {:.4f}'.format(test_accuracy.mean()))
+    		if not loss == 'euclidean':
+    			test_accuracy[i] = workspace.FetchBlob('accuracy')
+    			print 'Iter ' + str(i) + ': ' + 'Accuracy ' + str(workspace.FetchBlob("accuracy"))
+    		else:
+    			test_accuracy[i] = workspace.FetchBlob("loss")
+    			print 'Iter ' + str(i) + ': ' + 'Loss ' + str(workspace.FetchBlob("loss"))
+
+    		current_time = datetime.datetime.now()
+    		elapsed_time = current_time - start_date
+    		print 'Progress: ' + str(i) + '/' + str(test_dataset_size/batch_size) + ', ' +'Current time spent: ' + str(elapsed_time)
+        current_time = datetime.datetime.now()
+        elapsed_time = current_time - start_date
+        print 'Progress: ' + str(test_dataset_size/batch_size) + '/' + str(test_dataset_size/batch_size) + ' Testing done' + ', ' + 'Total time spent: ' + str(elapsed_time)
+    	print('Test accuracy mean: {:.9f}'.format(test_accuracy.mean()))
 
     	# == Deployment model. ==
     	# We simply need the main AddModel part.
     	deploy_model = model_helper.ModelHelper(name="deploy_net", arg_scope=arg_scope, init_params=False)
     	self.create_model(deploy_model, "data", device_opts, is_test=True)
 
-    	print("Saving deploy model")
+    	print("\n*** Saving deploy model ***")
     	self.save_net(self._init_net_, self._predict_net_, deploy_model)
 
     def save_net(self, init_net_path, predict_net_path, model):
@@ -189,7 +224,7 @@ ${tc.include(tc.architecture.body)}
     	with open(init_net_path, 'wb') as f:
     		f.write(init_net.SerializeToString())
 
-    	print("Save the model to init_net.pbtxt and predict_net.pbtxt")
+    	print("Save the model to init_net.pbtxt and predict_net.pbtxt as additional information")
 
     	with open(init_net_path.replace('.pb','.pbtxt'), 'w') as f:
     		f.write(str(init_net))
@@ -198,7 +233,6 @@ ${tc.include(tc.architecture.body)}
     	print("== Saved init_net and predict_net ==")
 
     def load_net(self, init_net_path, predict_net_path, device_opts):
-        <#--#TODO: Verify that paths ends in '.pb' and not in '.pbtxt'. The extension '.pbtxt' is not supported at the moment.-->
         if not os.path.isfile(init_net_path):
             logging.error("Network loading failure. File '" + os.path.abspath(init_net_path) + "' does not exist.")
             sys.exit(1)
@@ -217,4 +251,4 @@ ${tc.include(tc.architecture.body)}
     		net_def.ParseFromString(f.read())
     		net_def.device_option.CopyFrom(device_opts)
     		workspace.CreateNet(net_def.SerializeToString(), overwrite=True)
-    	print("== Loaded init_net and predict_net ==")
+    	print("*** Loaded init_net and predict_net ***")

src/main/resources/templates/caffe2/elements/Output.ftl

@@ -4,7 +4,6 @@
 <#elseif element.logisticRegressionOutput>
     		${element.name} = model.net.Sigmoid(${input}, '${element.name}')
 <#elseif element.linearRegressionOutput>
-    		<#--Don't add L2 loss here but within the function "add_training_operators" from CNNCreator.ftl-->
     		${element.name} = ${input}
 </#if>
 

src/main/resources/templates/caffe2/elements/Pooling.ftl

@@ -23,7 +23,7 @@
 	<#assign kernelParameter = "kernel_h=${kernelHeight}, kernel_w=${kernelWidth}">
 </#if>
 <#if element.poolType == "max">
-    		${element.name} = brew.max_pool(model, ${input}, '${element.name}', ${kernelParameter}, ${strideParameter})
+    		${element.name} = brew.max_pool(model, ${input}, '${element.name}', ${kernelParameter}, ${strideParameter}${padParameter})
 <#elseif element.poolType == "avg">
     		${element.name} = brew.average_pool(model, ${input}, '${element.name}', ${kernelParameter}, ${strideParameter}${padParameter})
 </#if>

src/test/resources/target_code/CNNCreator_LeNet.py

@@ -3,6 +3,7 @@ from caffe2.python.predictor import mobile_exporter
 from caffe2.proto import caffe2_pb2
 import numpy as np
 import math
+import datetime
 import logging
 import os
 import sys
@@ -28,6 +29,15 @@ class CNNCreator_LeNet:
 
         return iterations_int
 
+    def get_epoch_as_iter(self, num_epoch, batch_size, dataset_size):   #To print metric durint training process
+        #Force floating point calculation
+        batch_size_float = float(batch_size)
+        dataset_size_float = float(dataset_size)
+
+        epoch_float = math.ceil(dataset_size_float/batch_size_float)
+        epoch_int = int(epoch_float)
+
+        return epoch_int
 
     def add_input(self, model, batch_size, db, db_type, device_opts):
         with core.DeviceScope(device_opts):
@@ -142,7 +152,8 @@ class CNNCreator_LeNet:
     	data, label, train_dataset_size = self.add_input(train_model, batch_size=batch_size, db=os.path.join(self._data_dir_, 'train_lmdb'), db_type='lmdb', device_opts=device_opts)
     	predictions = self.create_model(train_model, data, device_opts=device_opts, is_test=False)
     	self.add_training_operators(train_model, predictions, label, device_opts, loss, opt_type, base_learning_rate, policy, stepsize, epsilon, beta1, beta2, gamma, momentum)
-    	self.add_accuracy(train_model, predictions, label, device_opts, eval_metric)
+    	if not loss == 'euclidean':
+    		self.add_accuracy(train_model, predictions, label, device_opts, eval_metric)
     	with core.DeviceScope(device_opts):
     		brew.add_weight_decay(train_model, weight_decay)
 
@@ -152,38 +163,62 @@ class CNNCreator_LeNet:
 
     	# Main Training Loop
     	iterations = self.get_total_num_iter(num_epoch, batch_size, train_dataset_size)
-        print("** Starting Training for " + str(num_epoch) + " epochs = " + str(iterations) + " iterations **")
+        epoch_as_iter = self.get_epoch_as_iter(num_epoch, batch_size, train_dataset_size)
+        print("\n*** Starting Training for " + str(num_epoch) + " epochs = " + str(iterations) + " iterations ***")
+        start_date = datetime.datetime.now()
     	for i in range(iterations):
     		workspace.RunNet(train_model.net)
-    		if i % 50 == 0:
-    			print 'Iter ' + str(i) + ': ' + 'Loss ' + str(workspace.FetchBlob("loss")) + ' - ' + 'Accuracy ' + str(workspace.FetchBlob('accuracy'))
-    	print("Training done")
-
-    	print("== Running Test model ==")
+    		if i % 50 == 0 or i % epoch_as_iter == 0:
+    			if not loss == 'euclidean':
+    				print 'Iter ' + str(i) + ': ' + 'Loss ' + str(workspace.FetchBlob("loss")) + ' - ' + 'Accuracy ' + str(workspace.FetchBlob('accuracy'))
+    			else:
+    				print 'Iter ' + str(i) + ': ' + 'Loss ' + str(workspace.FetchBlob("loss"))
+
+    			current_time = datetime.datetime.now()
+    			elapsed_time = current_time - start_date
+    			print 'Progress: ' + str(i) + '/' + str(iterations) + ', ' +'Current time spent: ' + str(elapsed_time)
+        current_time = datetime.datetime.now()
+        elapsed_time = current_time - start_date
+        print 'Progress: ' + str(iterations) + '/' + str(iterations) + ' Training done' + ', ' + 'Total time spent: ' + str(elapsed_time)
+
+    	print("\n*** Running Test model ***")
     	# == Testing model. ==
     	test_model= model_helper.ModelHelper(name="test_net", arg_scope=arg_scope, init_params=False)
     	data, label, test_dataset_size = self.add_input(test_model, batch_size=batch_size, db=os.path.join(self._data_dir_, 'test_lmdb'), db_type='lmdb', device_opts=device_opts)
     	predictions = self.create_model(test_model, data, device_opts=device_opts, is_test=True)
-    	self.add_accuracy(test_model, predictions, label, device_opts, eval_metric)
+    	if not loss == 'euclidean':
+    		self.add_accuracy(test_model, predictions, label, device_opts, eval_metric)
     	workspace.RunNetOnce(test_model.param_init_net)
     	workspace.CreateNet(test_model.net, overwrite=True)
 
     	# Main Testing Loop
     	test_accuracy = np.zeros(test_dataset_size/batch_size)
+        start_date = datetime.datetime.now()
     	for i in range(test_dataset_size/batch_size):
     		# Run a forward pass of the net on the current batch
     		workspace.RunNet(test_model.net)
     		# Collect the batch accuracy from the workspace
-    		test_accuracy[i] = workspace.FetchBlob('accuracy')
-
-    	print('Test_accuracy: {:.4f}'.format(test_accuracy.mean()))
+    		if not loss == 'euclidean':
+    			test_accuracy[i] = workspace.FetchBlob('accuracy')
+    			print 'Iter ' + str(i) + ': ' + 'Accuracy ' + str(workspace.FetchBlob("accuracy"))
+    		else:
+    			test_accuracy[i] = workspace.FetchBlob("loss")
+    			print 'Iter ' + str(i) + ': ' + 'Loss ' + str(workspace.FetchBlob("loss"))
+
+    		current_time = datetime.datetime.now()
+    		elapsed_time = current_time - start_date
+    		print 'Progress: ' + str(i) + '/' + str(test_dataset_size/batch_size) + ', ' +'Current time spent: ' + str(elapsed_time)
+        current_time = datetime.datetime.now()
+        elapsed_time = current_time - start_date
+        print 'Progress: ' + str(test_dataset_size/batch_size) + '/' + str(test_dataset_size/batch_size) + ' Testing done' + ', ' + 'Total time spent: ' + str(elapsed_time)
+    	print('Test accuracy mean: {:.9f}'.format(test_accuracy.mean()))
 
     	# == Deployment model. ==
     	# We simply need the main AddModel part.
     	deploy_model = model_helper.ModelHelper(name="deploy_net", arg_scope=arg_scope, init_params=False)
     	self.create_model(deploy_model, "data", device_opts, is_test=True)
 
-    	print("Saving deploy model")
+    	print("\n*** Saving deploy model ***")
     	self.save_net(self._init_net_, self._predict_net_, deploy_model)
 
     def save_net(self, init_net_path, predict_net_path, model):
@@ -206,7 +241,7 @@ class CNNCreator_LeNet:
     	with open(init_net_path, 'wb') as f:
     		f.write(init_net.SerializeToString())
 
-    	print("Save the model to init_net.pbtxt and predict_net.pbtxt")
+    	print("Save the model to init_net.pbtxt and predict_net.pbtxt as additional information")
 
     	with open(init_net_path.replace('.pb','.pbtxt'), 'w') as f:
     		f.write(str(init_net))
@@ -233,4 +268,4 @@ class CNNCreator_LeNet:
     		net_def.ParseFromString(f.read())
     		net_def.device_option.CopyFrom(device_opts)
     		workspace.CreateNet(net_def.SerializeToString(), overwrite=True)
-    	print("== Loaded init_net and predict_net ==")
+    	print("*** Loaded init_net and predict_net ***")

src/test/resources/target_code/CNNCreator_VGG16.py

@@ -3,6 +3,7 @@ from caffe2.python.predictor import mobile_exporter
 from caffe2.proto import caffe2_pb2
 import numpy as np
 import math
+import datetime
 import logging
 import os
 import sys
@@ -28,6 +29,15 @@ class CNNCreator_VGG16:
 
         return iterations_int
 
+    def get_epoch_as_iter(self, num_epoch, batch_size, dataset_size):   #To print metric durint training process
+        #Force floating point calculation
+        batch_size_float = float(batch_size)
+        dataset_size_float = float(dataset_size)
+
+        epoch_float = math.ceil(dataset_size_float/batch_size_float)
+        epoch_int = int(epoch_float)
+
+        return epoch_int
 
     def add_input(self, model, batch_size, db, db_type, device_opts):
         with core.DeviceScope(device_opts):
@@ -70,7 +80,7 @@ class CNNCreator_VGG16:
     		conv2_ = brew.conv(model, relu1_, 'conv2_', dim_in=64, dim_out=64, kernel=3, stride=1, pad=1)
     		# conv2_, output shape: {[64,224,224]}
     		relu2_ = brew.relu(model, conv2_, conv2_)
-    		pool2_ = brew.max_pool(model, relu2_, 'pool2_', kernel=2, stride=2)
+    		pool2_ = brew.max_pool(model, relu2_, 'pool2_', kernel=2, stride=2, pad=1)
     		# pool2_, output shape: {[64,112,112]}
     		conv3_ = brew.conv(model, pool2_, 'conv3_', dim_in=64, dim_out=128, kernel=3, stride=1, pad=1)
     		# conv3_, output shape: {[128,112,112]}
@@ -78,7 +88,7 @@ class CNNCreator_VGG16:
     		conv4_ = brew.conv(model, relu3_, 'conv4_', dim_in=128, dim_out=128, kernel=3, stride=1, pad=1)
     		# conv4_, output shape: {[128,112,112]}
     		relu4_ = brew.relu(model, conv4_, conv4_)
-    		pool4_ = brew.max_pool(model, relu4_, 'pool4_', kernel=2, stride=2)
+    		pool4_ = brew.max_pool(model, relu4_, 'pool4_', kernel=2, stride=2, pad=1)
     		# pool4_, output shape: {[128,56,56]}
     		conv5_ = brew.conv(model, pool4_, 'conv5_', dim_in=128, dim_out=256, kernel=3, stride=1, pad=1)
     		# conv5_, output shape: {[256,56,56]}
@@ -89,7 +99,7 @@ class CNNCreator_VGG16:
     		conv7_ = brew.conv(model, relu6_, 'conv7_', dim_in=256, dim_out=256, kernel=3, stride=1, pad=1)
     		# conv7_, output shape: {[256,56,56]}
     		relu7_ = brew.relu(model, conv7_, conv7_)
-    		pool7_ = brew.max_pool(model, relu7_, 'pool7_', kernel=2, stride=2)
+    		pool7_ = brew.max_pool(model, relu7_, 'pool7_', kernel=2, stride=2, pad=1)
     		# pool7_, output shape: {[256,28,28]}
     		conv8_ = brew.conv(model, pool7_, 'conv8_', dim_in=256, dim_out=512, kernel=3, stride=1, pad=1)
     		# conv8_, output shape: {[512,28,28]}
@@ -100,7 +110,7 @@ class CNNCreator_VGG16:
     		conv10_ = brew.conv(model, relu9_, 'conv10_', dim_in=512, dim_out=512, kernel=3, stride=1, pad=1)
     		# conv10_, output shape: {[512,28,28]}
     		relu10_ = brew.relu(model, conv10_, conv10_)
-    		pool10_ = brew.max_pool(model, relu10_, 'pool10_', kernel=2, stride=2)
+    		pool10_ = brew.max_pool(model, relu10_, 'pool10_', kernel=2, stride=2, pad=1)
     		# pool10_, output shape: {[512,14,14]}
     		conv11_ = brew.conv(model, pool10_, 'conv11_', dim_in=512, dim_out=512, kernel=3, stride=1, pad=1)
     		# conv11_, output shape: {[512,14,14]}
@@ -111,7 +121,7 @@ class CNNCreator_VGG16:
     		conv13_ = brew.conv(model, relu12_, 'conv13_', dim_in=512, dim_out=512, kernel=3, stride=1, pad=1)
     		# conv13_, output shape: {[512,14,14]}
     		relu13_ = brew.relu(model, conv13_, conv13_)
-    		pool13_ = brew.max_pool(model, relu13_, 'pool13_', kernel=2, stride=2)
+    		pool13_ = brew.max_pool(model, relu13_, 'pool13_', kernel=2, stride=2, pad=1)
     		# pool13_, output shape: {[512,7,7]}
     		fc13_ = brew.fc(model, pool13_, 'fc13_', dim_in=512 * 7 * 7, dim_out=4096)
     		# fc13_, output shape: {[4096,1,1]}
@@ -188,7 +198,8 @@ class CNNCreator_VGG16:
     	data, label, train_dataset_size = self.add_input(train_model, batch_size=batch_size, db=os.path.join(self._data_dir_, 'train_lmdb'), db_type='lmdb', device_opts=device_opts)
     	predictions = self.create_model(train_model, data, device_opts=device_opts, is_test=False)
     	self.add_training_operators(train_model, predictions, label, device_opts, loss, opt_type, base_learning_rate, policy, stepsize, epsilon, beta1, beta2, gamma, momentum)
-    	self.add_accuracy(train_model, predictions, label, device_opts, eval_metric)
+    	if not loss == 'euclidean':
+    		self.add_accuracy(train_model, predictions, label, device_opts, eval_metric)
     	with core.DeviceScope(device_opts):
     		brew.add_weight_decay(train_model, weight_decay)
 
@@ -198,38 +209,62 @@ class CNNCreator_VGG16:
 
     	# Main Training Loop
     	iterations = self.get_total_num_iter(num_epoch, batch_size, train_dataset_size)
-        print("** Starting Training for " + str(num_epoch) + " epochs = " + str(iterations) + " iterations **")
+        epoch_as_iter = self.get_epoch_as_iter(num_epoch, batch_size, train_dataset_size)
+        print("\n*** Starting Training for " + str(num_epoch) + " epochs = " + str(iterations) + " iterations ***")
+        start_date = datetime.datetime.now()
     	for i in range(iterations):
     		workspace.RunNet(train_model.net)
-    		if i % 50 == 0:
-    			print 'Iter ' + str(i) + ': ' + 'Loss ' + str(workspace.FetchBlob("loss")) + ' - ' + 'Accuracy ' + str(workspace.FetchBlob('accuracy'))
-    	print("Training done")
-
-    	print("== Running Test model ==")
+    		if i % 50 == 0 or i % epoch_as_iter == 0:
+    			if not loss == 'euclidean':
+    				print 'Iter ' + str(i) + ': ' + 'Loss ' + str(workspace.FetchBlob("loss")) + ' - ' + 'Accuracy ' + str(workspace.FetchBlob('accuracy'))
+    			else:
+    				print 'Iter ' + str(i) + ': ' + 'Loss ' + str(workspace.FetchBlob("loss"))
+
+    			current_time = datetime.datetime.now()
+    			elapsed_time = current_time - start_date
+    			print 'Progress: ' + str(i) + '/' + str(iterations) + ', ' +'Current time spent: ' + str(elapsed_time)
+        current_time = datetime.datetime.now()
+        elapsed_time = current_time - start_date
+        print 'Progress: ' + str(iterations) + '/' + str(iterations) + ' Training done' + ', ' + 'Total time spent: ' + str(elapsed_time)
+
+    	print("\n*** Running Test model ***")
     	# == Testing model. ==
     	test_model= model_helper.ModelHelper(name="test_net", arg_scope=arg_scope, init_params=False)
     	data, label, test_dataset_size = self.add_input(test_model, batch_size=batch_size, db=os.path.join(self._data_dir_, 'test_lmdb'), db_type='lmdb', device_opts=device_opts)
     	predictions = self.create_model(test_model, data, device_opts=device_opts, is_test=True)
-    	self.add_accuracy(test_model, predictions, label, device_opts, eval_metric)
+    	if not loss == 'euclidean':
+    		self.add_accuracy(test_model, predictions, label, device_opts, eval_metric)
     	workspace.RunNetOnce(test_model.param_init_net)
     	workspace.CreateNet(test_model.net, overwrite=True)
 
     	# Main Testing Loop
     	test_accuracy = np.zeros(test_dataset_size/batch_size)
+        start_date = datetime.datetime.now()
     	for i in range(test_dataset_size/batch_size):
     		# Run a forward pass of the net on the current batch
     		workspace.RunNet(test_model.net)
     		# Collect the batch accuracy from the workspace
-    		test_accuracy[i] = workspace.FetchBlob('accuracy')
-
-    	print('Test_accuracy: {:.4f}'.format(test_accuracy.mean()))
+    		if not loss == 'euclidean':
+    			test_accuracy[i] = workspace.FetchBlob('accuracy')
+    			print 'Iter ' + str(i) + ': ' + 'Accuracy ' + str(workspace.FetchBlob("accuracy"))
+    		else:
+    			test_accuracy[i] = workspace.FetchBlob("loss")
+    			print 'Iter ' + str(i) + ': ' + 'Loss ' + str(workspace.FetchBlob("loss"))
+
+    		current_time = datetime.datetime.now()
+    		elapsed_time = current_time - start_date
+    		print 'Progress: ' + str(i) + '/' + str(test_dataset_size/batch_size) + ', ' +'Current time spent: ' + str(elapsed_time)
+        current_time = datetime.datetime.now()
+        elapsed_time = current_time - start_date
+        print 'Progress: ' + str(test_dataset_size/batch_size) + '/' + str(test_dataset_size/batch_size) + ' Testing done' + ', ' + 'Total time spent: ' + str(elapsed_time)
+    	print('Test accuracy mean: {:.9f}'.format(test_accuracy.mean()))
 
     	# == Deployment model. ==
     	# We simply need the main AddModel part.
     	deploy_model = model_helper.ModelHelper(name="deploy_net", arg_scope=arg_scope, init_params=False)
     	self.create_model(deploy_model, "data", device_opts, is_test=True)
 
-    	print("Saving deploy model")
+    	print("\n*** Saving deploy model ***")
     	self.save_net(self._init_net_, self._predict_net_, deploy_model)
 
     def save_net(self, init_net_path, predict_net_path, model):
@@ -252,7 +287,7 @@ class CNNCreator_VGG16:
     	with open(init_net_path, 'wb') as f:
     		f.write(init_net.SerializeToString())
 
-    	print("Save the model to init_net.pbtxt and predict_net.pbtxt")
+    	print("Save the model to init_net.pbtxt and predict_net.pbtxt as additional information")
 
     	with open(init_net_path.replace('.pb','.pbtxt'), 'w') as f:
     		f.write(str(init_net))
@@ -279,4 +314,4 @@ class CNNCreator_VGG16:
     		net_def.ParseFromString(f.read())
     		net_def.device_option.CopyFrom(device_opts)
     		workspace.CreateNet(net_def.SerializeToString(), overwrite=True)
-    	print("== Loaded init_net and predict_net ==")
\ No newline at end of file
+    	print("*** Loaded init_net and predict_net ***")
\ No newline at end of file