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 backpropdata_infer(op: Node):
op['new_input_shapes'] = list()
for n in op.in_nodes():
op.new_input_shapes.append(op.in_node(n).shape)
assert len(op.new_input_shapes) == len(op.old_input_shapes)
for i in range(len(op.new_input_shapes)):
assert np.array_equal(op.new_input_shapes[i], op.old_input_shapes[i]), 'Something wrong happened while ' \
'{} shape infer!'.format(op.old_type)
output_shape = op.ports[len(op.in_nodes())]
# op.output_shape = output_shape
op.out_node().shape = int64_array(output_shape)
op.type = op.old_type
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