def deconvolution_infer(node: Node):
dims = int64_array(node.in_node(0).shape)
dilations = int64_array(node.dilations)
strides = int64_array(node.strides)
input_n = dims[0]
kernel_shape = int64_array(node.kernel)
kdims = np.where(dilations != 0, (kernel_shape - 1) * dilations + 1,
kernel_shape)
oc = node.output
if node.has_valid('auto_pad') and node.auto_pad in [
'valid', 'same_upper', 'same_lower'
]:
auto_pad = node.auto_pad
if auto_pad == 'valid':
od_temp = (dims[2::] - 1) * strides + kdims
else:
od_temp = dims[2::] * strides
else:
od_temp = strides * (dims[2::] -
1) + kdims - node.pads_begin - node.pads_end
out_shape = [input_n, oc]
for d in od_temp:
out_shape.append(np.int64(d))
node['output_shape'] = int64_array(out_shape)
for n in node.out_nodes():
node.out_node(n).shape = node['output_shape']
def const_shape_infer(node: Node):
i = len(node.in_nodes())
for num in node.out_nodes():
node.out_node(num).shape = int64_array(node.ports[i])
i += 1