Merge branch 'master' into Release

Author: Laicheng0830

tensorlayer/layers/normalization.py

@@ -92,7 +92,7 @@ def _bias_scale(x, b, data_format):
     if data_format == 'NHWC':
         return x * b
     elif data_format == 'NCHW':
-        return x * _to_channel_first_bias(b)
+        return x * b
     else:
         raise ValueError('invalid data_format: %s' % data_format)
 
@@ -102,7 +102,7 @@ def _bias_add(x, b, data_format):
     if data_format == 'NHWC':
         return tf.add(x, b)
     elif data_format == 'NCHW':
-        return tf.add(x, _to_channel_first_bias(b))
+        return tf.add(x, b)
     else:
         raise ValueError('invalid data_format: %s' % data_format)
 
@@ -291,9 +291,9 @@ def forward(self, inputs):
         if self.axes is None:
             self.axes = [i for i in range(len(inputs.shape)) if i != self.channel_axis]
 
+        mean, var = tf.nn.moments(inputs, self.axes, keepdims=False)
         if self.is_train:
             # update moving_mean and moving_var
-            mean, var = tf.nn.moments(inputs, self.axes, keepdims=False)
             self.moving_mean = moving_averages.assign_moving_average(
                 self.moving_mean, mean, self.decay, zero_debias=False
             )

tensorlayer/layers/pooling.py

@@ -324,17 +324,19 @@ def __repr__(self):
         return s.format(classname=self.__class__.__name__, **self.__dict__)
 
     def build(self, inputs_shape=None):
-        self._strides = [1, self.strides[0], self.strides[1], 1]
         if self.data_format == 'channels_last':
+            self._strides = [1, self.strides[0], self.strides[1], 1]
             self.data_format = 'NHWC'
         elif self.data_format == 'channels_first':
             self.data_format = 'NCHW'
+            self._strides = [1, 1, self.strides[0], self.strides[1]]
         else:
             raise Exception("unsupported data format")
 
     def forward(self, inputs):
         outputs = tf.nn.max_pool(
-            input=inputs, ksize=self.filter_size, strides=self._strides, padding=self.padding, name=self.name
+            input=inputs, ksize=self.filter_size, strides=self._strides, padding=self.padding, name=self.name,
+            data_format=self.data_format
         )
         return outputs
 
@@ -397,17 +399,19 @@ def __repr__(self):
         return s.format(classname=self.__class__.__name__, **self.__dict__)
 
     def build(self, inputs_shape=None):
-        self._strides = [1, self.strides[0], self.strides[1], 1]
         if self.data_format == 'channels_last':
             self.data_format = 'NHWC'
+            self._strides = [1, self.strides[0], self.strides[1], 1]
         elif self.data_format == 'channels_first':
             self.data_format = 'NCHW'
+            self._strides = [1, 1, self.strides[0], self.strides[1]]
         else:
             raise Exception("unsupported data format")
 
     def forward(self, inputs):
         outputs = tf.nn.avg_pool(
-            input=inputs, ksize=self.filter_size, strides=self._strides, padding=self.padding, name=self.name
+            input=inputs, ksize=self.filter_size, strides=self._strides, padding=self.padding, name=self.name,
+            data_format=self.data_format
         )
         return outputs
 
@@ -473,11 +477,12 @@ def __repr__(self):
         return s.format(classname=self.__class__.__name__, **self.__dict__)
 
     def build(self, inputs_shape=None):
-        self._strides = [1, self.strides[0], self.strides[1], self.strides[2], 1]
         if self.data_format == 'channels_last':
             self.data_format = 'NDHWC'
+            self._strides = [1, self.strides[0], self.strides[1], self.strides[2], 1]
         elif self.data_format == 'channels_first':
             self.data_format = 'NCDHW'
+            self._strides = [1, 1, self.strides[0], self.strides[1], self.strides[2]]
         else:
             raise Exception("unsupported data format")
 
@@ -554,11 +559,12 @@ def __repr__(self):
         return s.format(classname=self.__class__.__name__, **self.__dict__)
 
     def build(self, inputs_shape=None):
-        self._strides = [1, self.strides[0], self.strides[1], self.strides[2], 1]
         if self.data_format == 'channels_last':
             self.data_format = 'NDHWC'
+            self._strides = [1, self.strides[0], self.strides[1], self.strides[2], 1]
         elif self.data_format == 'channels_first':
             self.data_format = 'NCDHW'
+            self._strides = [1, 1, self.strides[0], self.strides[1], self.strides[2]]
         else:
             raise Exception("unsupported data format")
 

tensorlayer/models/vgg.py

@@ -105,7 +105,7 @@ def forward(self, inputs):
 
         inputs = inputs * 255 - np.array([123.68, 116.779, 103.939], dtype=np.float32).reshape([1, 1, 1, 3])
 
-        out = self.layers(inputs)
+        out = self.layers.forward(inputs)
         return out