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Merged
merged 4 commits into from
Jun 18, 2020
Merged

hyperpose compatible #1089

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ad8b2b9
hyperpose compatible:
Gyx-One Jun 18, 2020
e7b399a
yapf fixed
Gyx-One Jun 18, 2020
e406221
docstring fixed
Gyx-One Jun 18, 2020
cdb9b0c
Merge branch 'master' into new_data_format_fixed
Gyx-One Jun 18, 2020
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6 changes: 3 additions & 3 deletions tensorlayer/layers/normalization.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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)

Expand All @@ -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)

Expand Down Expand Up @@ -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
)
Expand Down
18 changes: 12 additions & 6 deletions tensorlayer/layers/pooling.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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

Expand Down Expand Up @@ -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

Expand Down Expand Up @@ -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")

Expand Down Expand Up @@ -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")

Expand Down
2 changes: 1 addition & 1 deletion tensorlayer/models/vgg.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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


Expand Down