def moc_emit_ir(ngraph_function: Model, argv: argparse.Namespace):
output_dir = argv.output_dir if argv.output_dir != '.' else os.getcwd()
# Apply preprocessing (mean/scale/reverse_channels/convert_layout/etc)
apply_preprocessing(ov_function=ngraph_function, argv=argv)
# Apply transformations
from openvino.tools.mo.back.offline_transformations import apply_user_transformations, apply_moc_transformations
apply_user_transformations(ngraph_function, parse_transform(argv.transform))
apply_moc_transformations(ngraph_function)
if argv.compress_fp16:
from openvino.tools.mo.back.offline_transformations import compress_model
compress_model(ngraph_function)
orig_model_name = os.path.normpath(os.path.join(output_dir, argv.model_name))
from openvino.offline_transformations_pybind import serialize # pylint: disable=import-error,no-name-in-module
serialize(ngraph_function, (orig_model_name + ".xml").encode('utf-8'), (orig_model_name + ".bin").encode('utf-8'))
del argv.feManager
# add meta information to IR
append_ir_info(file=orig_model_name,
meta_info=get_meta_info(argv),
mean_data=None,
input_names=None)
print('[ SUCCESS ] Generated IR version {} model.'.format(get_ir_version(argv)))
print('[ SUCCESS ] XML file: {}.xml'.format(orig_model_name))
print('[ SUCCESS ] BIN file: {}.bin'.format(orig_model_name))
return 0
def moc_emit_ir(ngraph_function: Model, argv: argparse.Namespace):
output_dir = argv.output_dir if argv.output_dir != '.' else os.getcwd()
# Apply preprocessing (mean/scale/reverse_channels/convert_layout/etc)
apply_preprocessing(ov_function=ngraph_function, argv=argv)
# Apply transformations
from openvino.tools.mo.back.offline_transformations import apply_user_transformations, apply_moc_transformations, \
apply_moc_legacy_transformations
apply_moc_transformations(ngraph_function)
from openvino.offline_transformations import compress_quantize_weights_transformation
compress_quantize_weights_transformation(ngraph_function)
if argv.framework == "onnx":
# set OldApi map in IR to be executed via OV API 1.x and for parity with legacy MO
params_with_custom_types = [] if argv.placeholder_data_types is None \
else list(argv.placeholder_data_types.keys())
apply_moc_legacy_transformations(ngraph_function,
params_with_custom_types)
apply_user_transformations(ngraph_function,
parse_transform(argv.transform))
if argv.compress_fp16:
from openvino.tools.mo.back.offline_transformations import compress_model
compress_model(ngraph_function)
orig_model_name = os.path.normpath(
os.path.join(output_dir, argv.model_name))
from openvino.runtime import serialize # pylint: disable=import-error,no-name-in-module
from openvino.offline_transformations import generate_mapping_file # pylint: disable=import-error,no-name-in-module
serialize(ngraph_function, (orig_model_name + ".xml").encode('utf-8'),
(orig_model_name + ".bin").encode('utf-8'))
del argv.feManager
path_to_mapping = orig_model_name + ".mapping"
extract_names = argv.framework in ['tf', 'mxnet', 'kaldi']
generate_mapping_file(ngraph_function, path_to_mapping.encode('utf-8'),
extract_names)
# add meta information to IR
append_ir_info(file=orig_model_name,
meta_info=get_meta_info(argv),
mean_data=None,
input_names=None,
legacy_path=False)
print('[ SUCCESS ] Generated IR version {} model.'.format(
get_ir_version(argv)))
print('[ SUCCESS ] XML file: {}.xml'.format(orig_model_name))
print('[ SUCCESS ] BIN file: {}.bin'.format(orig_model_name))
return 0
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 'feManager' in argv:
del argv.feManager
mean_data = deepcopy(graph.graph['mf']) if 'mf' in graph.graph else None
input_names = deepcopy(
graph.graph['input_names']) if 'input_names' in graph.graph else []
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=mean_data,
input_names=input_names,
meta_info=get_meta_info(argv),
use_temporary_path=True)
# This graph cleanup is required to avoid double memory consumption
graph.clear()
if not (argv.framework == 'tf'
and argv.tensorflow_custom_operations_config_update):
output_dir = argv.output_dir if argv.output_dir != '.' else os.getcwd()
orig_model_name = os.path.normpath(
os.path.join(output_dir, argv.model_name))
return_code = "not executed"
try:
if not argv.legacy_ir_generation:
from openvino.tools.mo.back.offline_transformations import apply_offline_transformations
apply_offline_transformations(orig_model_name, argv)
if "compress_fp16" in argv and argv.compress_fp16:
# restore data_type cmd parameter
argv.data_type = 'FP16'
return_code = 0
except Exception as e:
return_code = "failed"
log.error(e)
message = str(
dict({
"platform": platform.system(),
"mo_version": get_simplified_mo_version(),
"ie_version": get_simplified_ie_version(env=os.environ),
"python_version": sys.version,
"return_code": return_code
}))
t = tm.Telemetry()
t.send_event('mo', 'offline_transformations_status', message)
if return_code != 0:
raise Error("offline transformations step has failed.")
for suf in [".xml", ".bin", ".mapping"]:
# remove existing files
path_to_file = orig_model_name + "_tmp" + suf
if os.path.exists(path_to_file):
os.remove(path_to_file)
# add meta information to IR
append_ir_info(file=orig_model_name,
meta_info=get_meta_info(argv),
mean_data=mean_data,
input_names=input_names)
print('[ SUCCESS ] Generated IR version {} model.'.format(
get_ir_version(argv)))
print('[ SUCCESS ] XML file: {}.xml'.format(orig_model_name))
print('[ SUCCESS ] BIN file: {}.bin'.format(orig_model_name))
return 0