Small Cleanup

convert_weights/convert_dims.py

@@ -37,7 +37,7 @@ def get_layers(model):
 		try:
 			layer = model.layers[i]
 			if layer.trainable:
-				bad = ["pooling", "flatten", "dropout", "activation"]
+				bad = ["pooling", "flatten", "dropout", "activation", "concatenate"]
 				if not any(word in layer.name for word in bad):
 					result.append(layer)
 		except:
@@ -77,6 +77,7 @@ def get_layers(model):
 	# 	weights[0] = shuffle_rows(weights[0])
 	# 	th_layer.set_weights(weights)
 	# 	print('converted ', th_layer.name)
+	# elif th_layer.__class__.__name__ not in bad_classes:
 	else:
 		th_layer.set_weights(weights_list[index])
 		print('Set: ', th_layer.name)

convert_weights/convert_kernels.py

@@ -24,7 +24,7 @@ def get_layers(model):
 		try:
 			layer = model.layers[i]
 			if layer.trainable:
-				bad = ["pooling", "flatten", "dropout", "activation"]
+				bad = ["pooling", "flatten", "dropout", "activation", "concatenate"]
 				if not any(word in layer.name for word in bad):
 					result.append(layer)
 		except:
@@ -37,7 +37,7 @@ def get_layers(model):
 
 K.set_image_dim_ordering('tf')
 
-th_model = inception_v4.create_model(weights_path="../weights/inception-v4_weights_tf_dim_ordering_tf_kernels.h5")
+th_model = inception_v4.create_model(weights="../weights/inception-v4_weights_tf_dim_ordering_tf_kernels.h5")
 
 convert_all_kernels_in_model(th_model)
 print("Converted all Kernels")

convert_weights/convert_weights_to_keras.py

@@ -24,6 +24,8 @@ def natural_keys(myobject):
 
 def setWeights(layers, weights):
 	for index, layer in enumerate(layers):
+		if "dense" in layer.name:
+			continue
 		layer.set_weights(weights[index])
 		print(layer.name + " weights have been set!")
 	print("Finished Setting Weights!")
@@ -37,7 +39,7 @@ def get_layers(model):
 		try:
 			layer = model.layers[i]
 			if layer.trainable:
-				bad = ["pooling", "flatten", "dropout", "activation"]
+				bad = ["pooling", "flatten", "dropout", "activation", "concatenate"]
 				if not any(word in layer.name for word in bad):
 					result.append(layer)
 		except:
@@ -63,7 +65,7 @@ def get_layers(model):
 	setWeights(layers, weights)
 
 	# Save model weights in h5 format
-	model.save_weights("../weights/inception-v4_weights_tf_dim_ordering_tf_kernels.h5")
+	model.save_weights("../weights/inception-v4_weights_tf_dim_ordering_tf_kernels_notop.h5")
 	print("Finished saving weights in h5 format")
 
 

convert_weights/get_inception_weights.py

@@ -27,7 +27,6 @@ def get_weights():
 					value = sess.run(key)
 					if y == 1:
 						current_bn = []
-						current_bn.append(np.ones(value.shape[-1]))
 						current_bn.append(value)
 					elif y == 2:
 						current_bn.append(value)

convert_weights/helper_net/inception_v4.py

@@ -49,7 +49,7 @@ def block_inception_a(inputs, scope=None, reuse=None):
       with tf.variable_scope('Branch_3'):
         branch_3 = slim.avg_pool2d(inputs, [3, 3], scope='AvgPool_0a_3x3')
         branch_3 = slim.conv2d(branch_3, 96, [1, 1], scope='Conv2d_0b_1x1')
-      return tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
+      return tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
 
 
 def block_reduction_a(inputs, scope=None, reuse=None):
@@ -69,7 +69,7 @@ def block_reduction_a(inputs, scope=None, reuse=None):
       with tf.variable_scope('Branch_2'):
         branch_2 = slim.max_pool2d(inputs, [3, 3], stride=2, padding='VALID',
                                    scope='MaxPool_1a_3x3')
-      return tf.concat(3, [branch_0, branch_1, branch_2])
+      return tf.concat(axis=3, values=[branch_0, branch_1, branch_2])
 
 
 def block_inception_b(inputs, scope=None, reuse=None):
@@ -93,7 +93,7 @@ def block_inception_b(inputs, scope=None, reuse=None):
       with tf.variable_scope('Branch_3'):
         branch_3 = slim.avg_pool2d(inputs, [3, 3], scope='AvgPool_0a_3x3')
         branch_3 = slim.conv2d(branch_3, 128, [1, 1], scope='Conv2d_0b_1x1')
-      return tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
+      return tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
 
 
 def block_reduction_b(inputs, scope=None, reuse=None):
@@ -115,7 +115,7 @@ def block_reduction_b(inputs, scope=None, reuse=None):
       with tf.variable_scope('Branch_2'):
         branch_2 = slim.max_pool2d(inputs, [3, 3], stride=2, padding='VALID',
                                    scope='MaxPool_1a_3x3')
-      return tf.concat(3, [branch_0, branch_1, branch_2])
+      return tf.concat(axis=3, values=[branch_0, branch_1, branch_2])
 
 
 def block_inception_c(inputs, scope=None, reuse=None):
@@ -128,20 +128,20 @@ def block_inception_c(inputs, scope=None, reuse=None):
         branch_0 = slim.conv2d(inputs, 256, [1, 1], scope='Conv2d_0a_1x1')
       with tf.variable_scope('Branch_1'):
         branch_1 = slim.conv2d(inputs, 384, [1, 1], scope='Conv2d_0a_1x1')
-        branch_1 = tf.concat(3, [
+        branch_1 = tf.concat(axis=3, values=[
             slim.conv2d(branch_1, 256, [1, 3], scope='Conv2d_0b_1x3'),
             slim.conv2d(branch_1, 256, [3, 1], scope='Conv2d_0c_3x1')])
       with tf.variable_scope('Branch_2'):
         branch_2 = slim.conv2d(inputs, 384, [1, 1], scope='Conv2d_0a_1x1')
         branch_2 = slim.conv2d(branch_2, 448, [3, 1], scope='Conv2d_0b_3x1')
         branch_2 = slim.conv2d(branch_2, 512, [1, 3], scope='Conv2d_0c_1x3')
-        branch_2 = tf.concat(3, [
+        branch_2 = tf.concat(axis=3, values=[
             slim.conv2d(branch_2, 256, [1, 3], scope='Conv2d_0d_1x3'),
             slim.conv2d(branch_2, 256, [3, 1], scope='Conv2d_0e_3x1')])
       with tf.variable_scope('Branch_3'):
         branch_3 = slim.avg_pool2d(inputs, [3, 3], scope='AvgPool_0a_3x3')
         branch_3 = slim.conv2d(branch_3, 256, [1, 1], scope='Conv2d_0b_1x1')
-      return tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
+      return tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
 
 
 def inception_v4_base(inputs, final_endpoint='Mixed_7d', scope=None):
@@ -192,7 +192,7 @@ def add_and_check_final(name, net):
         with tf.variable_scope('Branch_1'):
           branch_1 = slim.conv2d(net, 96, [3, 3], stride=2, padding='VALID',
                                  scope='Conv2d_0a_3x3')
-        net = tf.concat(3, [branch_0, branch_1])
+        net = tf.concat(axis=3, values=[branch_0, branch_1])
         if add_and_check_final('Mixed_3a', net): return net, end_points
 
       # 73 x 73 x 160
@@ -207,7 +207,7 @@ def add_and_check_final(name, net):
           branch_1 = slim.conv2d(branch_1, 64, [7, 1], scope='Conv2d_0c_7x1')
           branch_1 = slim.conv2d(branch_1, 96, [3, 3], padding='VALID',
                                  scope='Conv2d_1a_3x3')
-        net = tf.concat(3, [branch_0, branch_1])
+        net = tf.concat(axis=3, values=[branch_0, branch_1])
         if add_and_check_final('Mixed_4a', net): return net, end_points
 
       # 71 x 71 x 192
@@ -218,7 +218,7 @@ def add_and_check_final(name, net):
         with tf.variable_scope('Branch_1'):
           branch_1 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID',
                                      scope='MaxPool_1a_3x3')
-        net = tf.concat(3, [branch_0, branch_1])
+        net = tf.concat(axis=3, values=[branch_0, branch_1])
         if add_and_check_final('Mixed_5a', net): return net, end_points
 
       # 35 x 35 x 384
@@ -269,7 +269,7 @@ def inception_v4(inputs, num_classes=1001, is_training=True,
     reuse: whether or not the network and its variables should be reused. To be
       able to reuse 'scope' must be given.
     scope: Optional variable_scope.
-    create_aux_logits: Whether to include the auxilliary logits.
+    create_aux_logits: Whether to include the auxiliary logits.
 
   Returns:
     logits: the logits outputs of the model.

evaluate_image.py

@@ -55,7 +55,7 @@ def get_processed_image(img_path):
 	im = central_crop(im, 0.875)
 	im = cv2.resize(im, (299, 299))
 	im = preprocess_input(im)
-	if K.image_dim_ordering() == "th":
+	if K.image_data_format() == "channels_first":
 		im = np.transpose(im, (2,0,1))
 		im = im.reshape(-1,3,299,299)
 	else:
@@ -64,7 +64,7 @@ def get_processed_image(img_path):
 
 if __name__ == "__main__":
 	# Create model and load pre-trained weights
-	model = inception_v4.create_model(weights='imagenet')
+	model = inception_v4.create_model(weights='imagenet', include_top=True)
 
 	# Open Class labels dictionary. (human readable label given ID)
 	classes = eval(open('validation_utils/class_names.txt', 'r').read())