def compute_output_shape(self, input_shape):
input_shape = tf.TensorShape(input_shape).as_list()
output_shape = list(input_shape)
if self.data_format == 'channels_first':
c_axis, h_axis, w_axis = 1, 2, 3
else:
c_axis, h_axis, w_axis = 3, 1, 2
kernel_h, kernel_w = self.kernel_size
stride_h, stride_w = self.strides
if self.output_padding is None:
out_pad_h = out_pad_w = None
else:
out_pad_h, out_pad_w = self.output_padding
output_shape[c_axis] = self.filters
output_shape[h_axis] = conv_utils.deconv_output_length(
output_shape[h_axis],
kernel_h,
padding=self.padding,
output_padding=out_pad_h,
stride=stride_h,
dilation=self.dilation_rate[0])
output_shape[w_axis] = conv_utils.deconv_output_length(
output_shape[w_axis],
kernel_w,
padding=self.padding,
output_padding=out_pad_w,
stride=stride_w,
dilation=self.dilation_rate[1])
return tf.TensorShape(output_shape)
def call(self, inputs):
inputs_shape = tf.shape(inputs)
batch_size = inputs_shape[0]
if self.data_format == "channels_first":
t_axis = 2
else:
t_axis = 1
length = inputs_shape[t_axis]
if self.output_padding is None:
output_padding = None
else:
output_padding = self.output_padding[0]
# Infer the dynamic output shape:
out_length = conv_utils.deconv_output_length(
length,
self.kernel_size[0],
padding=self.padding,
output_padding=output_padding,
stride=self.strides[0],
dilation=self.dilation_rate[0],
)
if self.data_format == "channels_first":
output_shape = (batch_size, self.filters, out_length)
else:
output_shape = (batch_size, out_length, self.filters)
data_format = conv_utils.convert_data_format(self.data_format, ndim=3)
output_shape_tensor = tf.stack(output_shape)
outputs = tf.nn.conv1d_transpose(
inputs,
self.kernel,
output_shape_tensor,
strides=self.strides,
padding=self.padding.upper(),
data_format=data_format,
dilations=self.dilation_rate,
)
if not tf.executing_eagerly():
# Infer the static output shape:
out_shape = self.compute_output_shape(inputs.shape)
outputs.set_shape(out_shape)
if self.use_bias:
outputs = tf.nn.bias_add(outputs,
self.bias,
data_format=data_format)
if self.activation is not None:
return self.activation(outputs)
return outputs
def compute_output_shape(self, input_shape):
input_shape = tf.TensorShape(input_shape).as_list()
output_shape = list(input_shape)
if self.data_format == "channels_first":
c_axis, d_axis, h_axis, w_axis = 1, 2, 3, 4
else:
c_axis, d_axis, h_axis, w_axis = 4, 1, 2, 3
kernel_d, kernel_h, kernel_w = self.kernel_size
stride_d, stride_h, stride_w = self.strides
if self.output_padding is None:
out_pad_d = out_pad_h = out_pad_w = None
else:
out_pad_d, out_pad_h, out_pad_w = self.output_padding
output_shape[c_axis] = self.filters
output_shape[d_axis] = conv_utils.deconv_output_length(
output_shape[d_axis],
kernel_d,
padding=self.padding,
output_padding=out_pad_d,
stride=stride_d,
)
output_shape[h_axis] = conv_utils.deconv_output_length(
output_shape[h_axis],
kernel_h,
padding=self.padding,
output_padding=out_pad_h,
stride=stride_h,
)
output_shape[w_axis] = conv_utils.deconv_output_length(
output_shape[w_axis],
kernel_w,
padding=self.padding,
output_padding=out_pad_w,
stride=stride_w,
)
return tf.TensorShape(output_shape)
def compute_output_shape(self, input_shape):
input_shape = tf.TensorShape(input_shape).as_list()
output_shape = list(input_shape)
if self.data_format == 'channels_first':
c_axis, t_axis = 1, 2
else:
c_axis, t_axis = 2, 1
if self.output_padding is None:
output_padding = None
else:
output_padding = self.output_padding[0]
output_shape[c_axis] = self.filters
output_shape[t_axis] = conv_utils.deconv_output_length(
output_shape[t_axis],
self.kernel_size[0],
padding=self.padding,
output_padding=output_padding,
stride=self.strides[0],
dilation=self.dilation_rate[0])
return tf.TensorShape(output_shape)
def test_deconv_output_length(self):
self.assertEqual(
4, conv_utils.deconv_output_length(4, 2, 'same', stride=1))
self.assertEqual(
8, conv_utils.deconv_output_length(4, 2, 'same', stride=2))
self.assertEqual(
5, conv_utils.deconv_output_length(4, 2, 'valid', stride=1))
self.assertEqual(
8, conv_utils.deconv_output_length(4, 2, 'valid', stride=2))
self.assertEqual(
3, conv_utils.deconv_output_length(4, 2, 'full', stride=1))
self.assertEqual(
6, conv_utils.deconv_output_length(4, 2, 'full', stride=2))
self.assertEqual(
5,
conv_utils.deconv_output_length(4,
2,
'same',
output_padding=2,
stride=1))
self.assertEqual(
7,
conv_utils.deconv_output_length(4,
2,
'same',
output_padding=1,
stride=2))
self.assertEqual(
7,
conv_utils.deconv_output_length(4,
2,
'valid',
output_padding=2,
stride=1))
self.assertEqual(
9,
conv_utils.deconv_output_length(4,
2,
'valid',
output_padding=1,
stride=2))
self.assertEqual(
5,
conv_utils.deconv_output_length(4,
2,
'full',
output_padding=2,
stride=1))
self.assertEqual(
7,
conv_utils.deconv_output_length(4,
2,
'full',
output_padding=1,
stride=2))
self.assertEqual(
5,
conv_utils.deconv_output_length(4,
2,
'same',
output_padding=1,
stride=1,
dilation=2))
self.assertEqual(
12,
conv_utils.deconv_output_length(4,
2,
'valid',
output_padding=2,
stride=2,
dilation=3))
self.assertEqual(
6,
conv_utils.deconv_output_length(4,
2,
'full',
output_padding=2,
stride=2,
dilation=3))
def test_deconv_output_length(self):
self.assertEqual(
4, conv_utils.deconv_output_length(4, 2, "same", stride=1))
self.assertEqual(
8, conv_utils.deconv_output_length(4, 2, "same", stride=2))
self.assertEqual(
5, conv_utils.deconv_output_length(4, 2, "valid", stride=1))
self.assertEqual(
8, conv_utils.deconv_output_length(4, 2, "valid", stride=2))
self.assertEqual(
3, conv_utils.deconv_output_length(4, 2, "full", stride=1))
self.assertEqual(
6, conv_utils.deconv_output_length(4, 2, "full", stride=2))
self.assertEqual(
5,
conv_utils.deconv_output_length(4,
2,
"same",
output_padding=2,
stride=1),
)
self.assertEqual(
7,
conv_utils.deconv_output_length(4,
2,
"same",
output_padding=1,
stride=2),
)
self.assertEqual(
7,
conv_utils.deconv_output_length(4,
2,
"valid",
output_padding=2,
stride=1),
)
self.assertEqual(
9,
conv_utils.deconv_output_length(4,
2,
"valid",
output_padding=1,
stride=2),
)
self.assertEqual(
5,
conv_utils.deconv_output_length(4,
2,
"full",
output_padding=2,
stride=1),
)
self.assertEqual(
7,
conv_utils.deconv_output_length(4,
2,
"full",
output_padding=1,
stride=2),
)
self.assertEqual(
5,
conv_utils.deconv_output_length(4,
2,
"same",
output_padding=1,
stride=1,
dilation=2),
)
self.assertEqual(
12,
conv_utils.deconv_output_length(4,
2,
"valid",
output_padding=2,
stride=2,
dilation=3),
)
self.assertEqual(
6,
conv_utils.deconv_output_length(4,
2,
"full",
output_padding=2,
stride=2,
dilation=3),
)
def call(self, inputs):
inputs_shape = tf.shape(inputs)
batch_size = inputs_shape[0]
if self.data_format == 'channels_first':
h_axis, w_axis = 2, 3
else:
h_axis, w_axis = 1, 2
# Use the constant height and weight when possible.
# TODO(scottzhu): Extract this into a utility function that can be applied
# to all convolutional layers, which currently lost the static shape
# information due to tf.shape().
height, width = None, None
if inputs.shape.rank is not None:
dims = inputs.shape.as_list()
height = dims[h_axis]
width = dims[w_axis]
height = height if height is not None else inputs_shape[h_axis]
width = width if width is not None else inputs_shape[w_axis]
kernel_h, kernel_w = self.kernel_size
stride_h, stride_w = self.strides
if self.output_padding is None:
out_pad_h = out_pad_w = None
else:
out_pad_h, out_pad_w = self.output_padding
# Infer the dynamic output shape:
out_height = conv_utils.deconv_output_length(
height,
kernel_h,
padding=self.padding,
output_padding=out_pad_h,
stride=stride_h,
dilation=self.dilation_rate[0])
out_width = conv_utils.deconv_output_length(
width,
kernel_w,
padding=self.padding,
output_padding=out_pad_w,
stride=stride_w,
dilation=self.dilation_rate[1])
if self.data_format == 'channels_first':
output_shape = (batch_size, self.filters, out_height, out_width)
else:
output_shape = (batch_size, out_height, out_width, self.filters)
output_shape_tensor = tf.stack(output_shape)
outputs = backend.conv2d_transpose(inputs,
self.kernel,
output_shape_tensor,
strides=self.strides,
padding=self.padding,
data_format=self.data_format,
dilation_rate=self.dilation_rate)
if not tf.executing_eagerly():
# Infer the static output shape:
out_shape = self.compute_output_shape(inputs.shape)
outputs.set_shape(out_shape)
if self.use_bias:
outputs = tf.nn.bias_add(
outputs,
self.bias,
data_format=conv_utils.convert_data_format(self.data_format,
ndim=4))
if self.activation is not None:
return self.activation(outputs)
return outputs
def call(self, inputs):
inputs_shape = tf.shape(inputs)
batch_size = inputs_shape[0]
if self.data_format == 'channels_first':
d_axis, h_axis, w_axis = 2, 3, 4
else:
d_axis, h_axis, w_axis = 1, 2, 3
depth = inputs_shape[d_axis]
height = inputs_shape[h_axis]
width = inputs_shape[w_axis]
kernel_d, kernel_h, kernel_w = self.kernel_size
stride_d, stride_h, stride_w = self.strides
if self.output_padding is None:
out_pad_d = out_pad_h = out_pad_w = None
else:
out_pad_d, out_pad_h, out_pad_w = self.output_padding
# Infer the dynamic output shape:
out_depth = conv_utils.deconv_output_length(depth,
kernel_d,
padding=self.padding,
output_padding=out_pad_d,
stride=stride_d)
out_height = conv_utils.deconv_output_length(height,
kernel_h,
padding=self.padding,
output_padding=out_pad_h,
stride=stride_h)
out_width = conv_utils.deconv_output_length(width,
kernel_w,
padding=self.padding,
output_padding=out_pad_w,
stride=stride_w)
if self.data_format == 'channels_first':
output_shape = (batch_size, self.filters, out_depth, out_height,
out_width)
strides = (1, 1, stride_d, stride_h, stride_w)
else:
output_shape = (batch_size, out_depth, out_height, out_width,
self.filters)
strides = (1, stride_d, stride_h, stride_w, 1)
output_shape_tensor = tf.stack(output_shape)
outputs = tf.nn.conv3d_transpose(
inputs,
self.kernel,
output_shape_tensor,
strides,
data_format=conv_utils.convert_data_format(self.data_format,
ndim=5),
padding=self.padding.upper())
if not tf.executing_eagerly():
# Infer the static output shape:
out_shape = self.compute_output_shape(inputs.shape)
outputs.set_shape(out_shape)
if self.use_bias:
outputs = tf.nn.bias_add(
outputs,
self.bias,
data_format=conv_utils.convert_data_format(self.data_format,
ndim=4))
if self.activation is not None:
return self.activation(outputs)
return outputs
