def find_and_replace_pattern(self, graph: Graph):
transposed_for_IE_flag = 'transposed_for_IE'
for fc_node in graph.get_op_nodes(type='FullyConnected'):
weights_node = fc_node.in_node(1)
fc_node.in_edge(1)['bin'] = 'weights'
if weights_node.has_and_set(transposed_for_IE_flag):
continue
weights_node.value = np.transpose(weights_node.value)
weights_node[transposed_for_IE_flag] = True
log.debug(
"Transposed weights {} for FC node {}; weights.shape = {}"
"".format(weights_node.name, fc_node.name, weights_node.shape))
weights_node.shape = np.array(weights_node.value.shape)
# FIXME remove this line and make transformation run recursively when recursive transformation run feature is
# implemented
for_each_sub_graph_recursively(graph, self.find_and_replace_pattern)
def emit_ir(graph: Graph, argv: argparse.Namespace):
NormalizeTI().find_and_replace_pattern(graph)
for_graph_and_each_sub_graph_recursively(
graph,
RemoveConstOps().find_and_replace_pattern)
for_graph_and_each_sub_graph_recursively(
graph,
CreateConstNodesReplacement().find_and_replace_pattern)
if not graph.graph['cmd_params'].generate_experimental_IR_V10:
for_each_sub_graph_recursively(
graph,
RemoveOutputOps().find_and_replace_pattern)
if not graph.graph['cmd_params'].generate_experimental_IR_V10:
for_graph_and_each_sub_graph_recursively(
graph,
RemoveOutputOps().find_and_replace_pattern)
prepare_emit_ir(
graph=graph,
data_type=graph.graph['cmd_params'].data_type,
output_dir=argv.output_dir,
output_model_name=argv.model_name,
mean_data=graph.graph['mf'] if 'mf' in graph.graph else None,
input_names=graph.graph['input_names']
if 'input_names' in graph.graph else [],
meta_info=get_meta_info(argv))
if not (argv.framework == 'tf'
and argv.tensorflow_custom_operations_config_update):
output_dir = argv.output_dir if argv.output_dir != '.' else os.getcwd()
print('\n[ SUCCESS ] Generated IR version {} model.'.format(
get_ir_version(argv)))
print('[ SUCCESS ] XML file: {}.xml'.format(
os.path.join(output_dir, argv.model_name)))
print('[ SUCCESS ] BIN file: {}.bin'.format(
os.path.join(output_dir, argv.model_name)))
return 0
def find_and_replace_pattern(self, graph: Graph):
for_each_sub_graph_recursively(
graph, self.reshapes_with_two_inputs_to_reshape_with_dim)