def deconv3d(x,
kernel,
output_shape,
strides=(1, 1, 1),
border_mode='valid',
dim_ordering='default',
image_shape=None,
filter_shape=None):
'''3D deconvolution (transposed convolution).
# Arguments
kernel: kernel tensor.
output_shape: desired dimensions of output.
strides: strides tuple.
border_mode: string, "same" or "valid".
dim_ordering: "tf" or "th".
Whether to use Theano or TensorFlow dimension ordering
in inputs/kernels/ouputs.
'''
flip_filters = False
if dim_ordering == 'default':
dim_ordering = image_dim_ordering()
if dim_ordering not in {'th', 'tf'}:
raise ValueError('Unknown dim_ordering ' + str(dim_ordering))
if dim_ordering == 'tf':
output_shape = (output_shape[0], output_shape[4], output_shape[1],
output_shape[2], output_shape[3])
x = _preprocess_conv3d_input(x, dim_ordering)
kernel = _preprocess_conv3d_kernel(kernel, dim_ordering)
kernel = kernel.dimshuffle((1, 0, 2, 3, 4))
th_border_mode = _preprocess_border_mode(border_mode)
if hasattr(kernel, '_keras_shape'):
kernel_shape = kernel._keras_shape
else:
# Will only work if `kernel` is a shared variable.
kernel_shape = kernel.eval().shape
filter_shape = _preprocess_conv3d_filter_shape(dim_ordering, filter_shape)
filter_shape = tuple(filter_shape[i] for i in (1, 0, 2, 3, 4))
conv_out = T.nnet.abstract_conv.conv3d_grad_wrt_inputs(
x,
kernel,
output_shape,
filter_shape=filter_shape,
border_mode=th_border_mode,
subsample=strides,
filter_flip=not flip_filters)
conv_out = _postprocess_conv3d_output(conv_out, x, border_mode,
kernel_shape, strides, dim_ordering)
return conv_out
def deconv3d(x,
kernel,
output_shape,
strides=(1, 1, 1),
padding='valid',
data_format=None,
filter_shape=None):
'''3D deconvolution (transposed convolution).
# Arguments
kernel: kernel tensor.
output_shape: desired dimensions of output.
strides: strides tuple.
padding: string, "same" or "valid".
data_format: "channels_last" or "channels_first".
Whether to use Theano or TensorFlow dimension ordering
in inputs/kernels/ouputs.
'''
flip_filters = False
if data_format is None:
data_format = image_data_format()
if data_format not in {'channels_first', 'channels_last'}:
raise ValueError('Unknown data_format: ' + str(data_format))
if data_format == 'channels_last':
output_shape = (output_shape[0], output_shape[4], output_shape[1],
output_shape[2], output_shape[3])
x = _preprocess_conv3d_input(x, data_format)
kernel = _preprocess_conv3d_kernel(kernel, data_format)
kernel = kernel.dimshuffle((1, 0, 2, 3, 4))
th_padding = _preprocess_padding(padding)
if hasattr(kernel, '_keras_shape'):
kernel_shape = kernel._keras_shape
else:
# Will only work if `kernel` is a shared variable.
kernel_shape = kernel.eval().shape
filter_shape = _preprocess_conv3d_filter_shape(filter_shape, data_format)
filter_shape = tuple(filter_shape[i] for i in (1, 0, 2, 3, 4))
conv_out = T.nnet.abstract_conv.conv3d_grad_wrt_inputs(
x,
kernel,
output_shape,
filter_shape=filter_shape,
border_mode=th_padding,
subsample=strides,
filter_flip=not flip_filters)
conv_out = _postprocess_conv3d_output(conv_out, x, padding, kernel_shape,
strides, data_format)
return conv_out
