def call(self, inputs, **kwargs):
source, target = inputs
source_shape = K.shape(source)
target_shape = K.shape(target)
if K.image_data_format() == 'channels_last':
source_height, source_width = source_shape[1:3]
target_height, target_width = target_shape[1:3]
else:
source_height, source_width = source_shape[2:4]
target_height, target_width = target_shape[2:4]
return K.resize_images(source, target_height / source_height,
target_width / source_width,
K.image_data_format())
def call(self, inputs):
# print("in call")
kernel_shape = K.get_value(self.kernel).shape
input_shape = K.shape(inputs)
output_shape = self.compute_output_shape(input_shape)
output_shape = [None]+list(output_shape[1:])
print("output_shape:{}".format(output_shape))
print("kernel_shape:{}".format(kernel_shape))
outputs = K.deconv3d(inputs, self.kernel, output_shape,
strides=self.strides,
padding=self.padding,
data_format=self.data_format,
filter_shape=kernel_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 int_shape(x):
return KC.int_shape(x) if KC.backend() == 'tensorflow' else KC.shape(x)
def __int_shape(self, x):
return KC.int_shape(x) if self.backend == 'tensorflow' else KC.shape(x)