def depth_to_space(input, scale, data_format=None):
''' Uses phase shift algorithm to convert channels/depth for spatial resolution '''
if data_format is None:
data_format = image_data_format()
data_format = data_format.lower()
input = _preprocess_conv2d_input(input, data_format)
b, k, row, col = input.shape
out_channels = k // (scale**2)
x = T.reshape(input, (b, scale, scale, out_channels, row, col))
x = T.transpose(x, (0, 3, 4, 1, 5, 2))
out = T.reshape(x, (b, out_channels, row * scale, col * scale))
out = _postprocess_conv2d_output(out, input, None, None, None, data_format)
return out
def depth_to_space(input, scale, data_format=None):
''' Uses phase shift algorithm to convert channels/depth for spatial resolution '''
if data_format is None:
data_format = image_data_format()
data_format = data_format.lower()
input = _preprocess_conv2d_input(input, data_format)
b, k, row, col = input.shape
output_shape = (b, k // (scale ** 2), row * scale, col * scale)
out = T.zeros(output_shape)
r = scale
for y, x in itertools.product(range(scale), repeat=2):
out = T.inc_subtensor(out[:, :, y::r, x::r], input[:, r * y + x:: r * r, :, :])
out = _postprocess_conv2d_output(out, input, None, None, None, data_format)
return out
