def generate_port_map(node: Node, src_port_map, dir: str):
result_list = []
for record in src_port_map:
# do not update ids for not-connected output which is used in the Loop operation only
if record['external_port_id'] != -1:
if dir == 'out': # increase the output port id by the number of input ports
# update the port id for proper generation of a "ports" section
record['external_port_id'] += len(node.in_ports())
record['internal_layer_id'] = TensorIterator.find_internal_layer_id(node.body, record['internal_layer_id'])
result_list.append(record)
return result_list
def ti_set_output_port_shape(cycle_node, internal_id, port_num,
iterations_count, axis):
int_node_name = TensorIterator.find_internal_layer_id(
cycle_node.body, internal_id)
int_node = Node(cycle_node.body, int_node_name)
assert int_node.op == 'Result'
out_shape = int_node.in_port(0).data.get_shape().copy()
# inside cycle node Unsqueeze was added to have the first dimension for concatenating results along it
assert len(out_shape) >= 1
if axis is not None:
out_shape[axis] = iterations_count
assert port_num in cycle_node.out_ports()
cycle_node.out_port(port_num).data.set_shape(out_shape)
