def infer(node: Node):
"""
Deconvolution has an input argument that explicitly determines output shape, so in contrast
to the forward Conv2d we shouldn't infer output shape. We just use this output shape as
an input shape and pass it to our utilities that computes numeric values for padding.
They also deliver output shape that is interpreted here as input shape for convolution.
We need to check that the real input shape and shape inferred by those utility functions match.
"""
output_shape = shape_array(node.in_node(2).value)
output_shape[0] = node.in_port(0).data.get_shape()[0]
kernel_shape = node.in_port(1).data.get_shape()
node['kernel_shape'] = kernel_shape
if output_shape is None or kernel_shape is None or node.spatial_dims is None or node.stride is None:
return
if not node.has_valid('kernel_spatial_idx'):
node['kernel_spatial_idx'] = np.delete(
[x for x in range(len(kernel_shape))],
(node.input_feature_channel, node.output_feature_channel))
if not node.has_valid('dilation'):
node['dilation'] = np.full([len(output_shape)], 1, dtype=np.int64)
if node.has_valid('get_group'):
node['group'] = node.get_group(node)
spatial_dims = node.spatial_dims
output_spatial = shape_array(output_shape[spatial_dims])
stride_spatial = shape_array(node.stride[spatial_dims])
node['kernel_spatial'] = shape_array(
kernel_shape[node.kernel_spatial_idx])
node.pad_spatial_shape, input_spatial_for_check = tf_window_op_pad_infer(
output_spatial, node.kernel_spatial, stride_spatial, node.auto_pad)
assert compatible_shapes(input_spatial_for_check,
node.in_node(0).shape[spatial_dims])
pad = np.zeros((len(output_shape), 2), dtype=np.int64)
pad[spatial_dims] = node.pad_spatial_shape
node.pad = pad
node.output = output_shape[node.channel_dims][0]
node.output_shape = output_shape
node.out_port(0).data.set_shape(output_shape)
mark_input_bins(node, ['weights'], 1)
assign_dims_to_weights(node.in_node(1), node.kernel_spatial_idx,
node.input_feature_channel,
node.output_feature_channel, len(kernel_shape))
# OK, now we are sure this is a supported Deconvolution layer
node.type = 'Deconvolution'
node.op = 'Deconv2D'
# Add permute_attrs
PermuteAttrs.create_permute_attrs(
node,
attrs=[
('pad', 'input:0'),
('stride', 'input:0'),
('output_shape', 'input:0'),
('batch_dims', 'input:0'),
('channel_dims', 'input:0'),
('spatial_dims', 'input:0'),
('kernel_shape', 'input:1'),
('kernel_spatial_idx', 'input:1'),
('input_feature_channel', 'input:1'),
('output_feature_channel', 'input:1'),
])
# is needed to permute Deconv weights from the original TF [H, W, C_OUT, C_IN] into IE [C_IN, C_OUT, H, W]
# but for other nodes in weights subgraph permutations must turned off
# by marking with MarkSubGraphsWithCorrectLayout even if graph layout is NCHW.
PermuteAttrs.set_permutation(
node.in_node(1), node, node.soft_get('get_weights_permute', None))
PermuteInputs().set_input_permutation(node.in_node(1), node, 'input:1',
'transpose')
PermuteInputs().set_input_permutation(node.in_node(2), node, 'input:0',
'shape')
node['force_precision_in_ports'] = {2: 'int64'}
