def call(self, inputs, training=None):
inputs = _preprocess_conv3d_input(inputs, self.data_format)
if self.data_format == 'channels_last':
dilation = (1, ) + self.dilation_rate + (1, )
else:
dilation = self.dilation_rate + (1, ) + (1, )
inputs = tf.split(inputs[0],
self.input_dim // self.group_size,
axis=1 if self._data_format == 'NCDHW' else 4)
outputs = tf.concat([
tf.nn.conv3d(inp,
self.depthwise_kernel,
strides=self._strides,
padding=self._padding,
dilations=dilation,
data_format=self._data_format) for inp in inputs
],
axis=1 if format == 'NCDHW' else 4)
if self.bias is not None:
outputs = K.bias_add(outputs,
self.bias,
data_format=self.data_format)
if self.activation is not None:
return self.activation(outputs)
return outputs
def call(self, inputs, training=None):
inputs = _preprocess_conv3d_input(inputs, self.data_format)
if self.data_format == 'channels_last':
dilation = (1,) + tuple(self.dilation_rate) + (1,)
else:
dilation = tuple(self.dilation_rate) + (1,) + (1,)
if self._data_format == 'NCDHW':
outputs = tf.concat(
[tf.nn.conv3d(inputs[0][:, i:i+self.input_dim//self.groups, :, :, :], self.depthwise_kernel[:, :, :, i:i+self.input_dim//self.groups, :],
strides=self._strides,
padding=self._padding,
dilations=dilation,
data_format=self._data_format) for i in range(0, self.input_dim, self.input_dim//self.groups)], axis=1)
else:
outputs = tf.concat(
[tf.nn.conv3d(inputs[0][:, :, :, :, i:i+self.input_dim//self.groups], self.depthwise_kernel[:, :, :, i:i+self.input_dim//self.groups, :],
strides=self._strides,
padding=self._padding,
dilations=dilation,
data_format=self._data_format) for i in range(0, self.input_dim, self.input_dim//self.groups)], axis=-1)
if self.bias is not None:
outputs = K.bias_add(
outputs,
self.bias,
data_format=self.data_format)
if self.activation is not None:
return self.activation(outputs)
return outputs
def call(self, inputs, training=None):
inputs = _preprocess_conv3d_input(inputs, self.data_format)
if self.data_format == 'channels_last':
dilation = (1, ) + self.dilation_rate + (1, )
else:
dilation = self.dilation_rate + (1, ) + (1, )
inputs = tf.split(inputs[0], self.group_num, axis=self.channel_axis)
outputs = tf.concat([
tf.nn.conv3d(inp,
self.kernel[i, :, :, :, :, :],
strides=self._strides,
padding=self._padding,
dilations=dilation,
data_format=self._data_format)
for i, inp in enumerate(inputs)
],
axis=self.channel_axis)
if self.bias is not None:
outputs = K.bias_add(outputs,
self.bias,
data_format=self.data_format)
if self.activation is not None:
return self.activation(outputs)
return outputs
def deconv3d(x,
kernel,
output_shape,
strides=(1, 1, 1),
border_mode='valid',
dim_ordering='default',
image_shape=None,
filter_shape=None):
'''3D deconvolution (i.e. transposed convolution).
# Arguments
x: input tensor.
kernel: kernel tensor.
output_shape: 1D int tensor for the output shape.
strides: strides tuple.
border_mode: string, "same" or "valid".
dim_ordering: "tf" or "th".
Whether to use Theano or TensorFlow dimension ordering
for inputs/kernels/ouputs.
# Returns
A tensor, result of transposed 3D convolution.
# Raises
ValueError: if `dim_ordering` is neither `tf` or `th`.
'''
if dim_ordering == 'default':
dim_ordering = image_dim_ordering()
if dim_ordering not in {'th', 'tf'}:
raise ValueError('Unknown dim_ordering ' + str(dim_ordering))
x = _preprocess_conv3d_input(x, dim_ordering)
output_shape = _preprocess_deconv_output_shape(x, output_shape,
dim_ordering)
kernel = _preprocess_conv3d_kernel(kernel, dim_ordering)
kernel = tf.transpose(kernel, (0, 1, 2, 4, 3))
padding = _preprocess_border_mode(border_mode)
strides = (1, ) + strides + (1, )
x = tf.nn.conv3d_transpose(x,
kernel,
output_shape,
strides,
padding=padding)
return _postprocess_conv3d_output(x, dim_ordering)
def call(self, inputs, training=None):
inputs = _preprocess_conv3d_input(inputs, self.data_format)
if self.data_format == 'channels_last':
b, d, h, w, c = inputs[0].shape
channels_l = [
tf.expand_dims(inputs[0][:, :, :, :, i], -1)
for i in range(0, c)
]
dilation = (1, ) + self.dilation_rate + (1, )
else:
b, c, d, h, w = inputs[0].shape
channels_l = [
tf.expand_dims(inputs[0][:, i, :, :, :], 1)
for i in range(0, c)
]
dilation = self.dilation_rate + (1, ) + (1, )
outputs = tf.convert_to_tensor([
tf.nn.conv3d(inp_c,
self.depthwise_kernel,
strides=self._strides,
padding=self._padding,
dilations=dilation,
data_format=self._data_format) for inp_c in channels_l
])
# Add original channels dim at the end of the tensor
outputs = tf.transpose(outputs, (1, 2, 3, 4, 5, 0))
# Convert to tensor [batch, depth, height, width, new_channels/depth_multiplier * original_channels]
shape = outputs.get_shape().as_list()
outputs = tf.reshape(
outputs, [-1, shape[1], shape[2], shape[3], shape[4] * shape[5]])
print(outputs.shape)
if self.bias:
outputs = K.bias_add(outputs,
self.bias,
data_format=self.data_format)
if self.activation is not None:
return self.activation(outputs)
return outputs
def deconv3d(x,
kernel,
output_shape,
strides=(1, 1, 1),
padding='valid',
data_format='default',
image_shape=None,
filter_shape=None):
'''3D deconvolution (i.e. transposed convolution).
# Arguments
x: input tensor.
kernel: kernel tensor.
output_shape: 1D int tensor for the output shape.
strides: strides tuple.
padding: string, "same" or "valid".
data_format: "tf" or "th".
Whether to use Theano or TensorFlow dimension ordering
for inputs/kernels/ouputs.
# Returns
A tensor, result of transposed 3D convolution.
# Raises
ValueError: if `data_format` is neither `tf` or `th`.
'''
if data_format == 'default':
data_format = image_data_format()
if data_format not in {'channels_first', 'channels_last'}:
raise ValueError('Unknown data_format ' + str(data_format))
x = _preprocess_conv3d_input(x, data_format)
output_shape = _preprocess_deconv_output_shape(x, output_shape,
data_format)
kernel = tf.transpose(kernel, (0, 1, 2, 4, 3))
padding = _preprocess_padding(padding)
strides = (1, ) + strides + (1, )
x = tf.nn.conv3d_transpose(x,
kernel,
output_shape,
strides,
padding=padding)
return _postprocess_conv3d_output(x, data_format)
def call(self, inputs, training=None):
inputs = _preprocess_conv3d_input(inputs, self.data_format)
if self.data_format == 'channels_last':
dilation = (1,) + self.dilation_rate + (1,)
else:
dilation = self.dilation_rate + (1,) + (1,)
outputs = tf.nn.conv3d(inputs[0], self.depthwise_kernel, strides=self._strides, padding= self._padding, dilations = dilation, data_format=self._data_format)
if self.bias is not None:
outputs = K.bias_add(
outputs,
self.bias,
data_format=self.data_format)
if self.activation is not None:
return self.activation(outputs)
return outputs