def save_restored_graph(graph: Graph, path: str, meta_data, name=None):
"""
Function to apply all necessary transforms from back stage to prepare and save restored graph and metadata.
:param graph: Graph to save
:param path: Path to saved IR
:param meta_data: Namespace with converting parameters restored from IR
:param name: Name for saved IR
:return:
"""
if name is None:
name = graph.name
if 'data_type' not in meta_data:
log.debug(
'Provided `meta_data` does not contain `data_type` parameter. Set `data_type`'
' parameter value to `FP32`.')
# Set data_type to FP32. All restored constants will be saved in provided data type.
data_type = 'FP32'
# We need to specify this attribute to pass graph transformations. This information will not be saved into IR.
# All constants and placeholders will be saved with same types as restored from IR
graph.graph['cmd_params'].data_type = data_type
else:
data_type = data_type_str_to_precision(
graph.graph['cmd_params'].data_type)
assert data_type in ['FP16', 'FP32'], '`data_type` value {} is not supported by MO,' \
' cannot save graph'.format(data_type)
# List items order matters, do not change it.
transformation_list = [
ConvolutionWithGroupsResolver,
StridedSliceMasksNormalizer,
PackBinaryWeights,
BlobNormalizer,
ConvolutionNormalizer,
MarkNodesWithShapeValues,
]
# We need to run some specific passes from MO back stage.
apply_replacements_list(graph, transformation_list)
# Transformations with enabled=False should be run manually.
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)
prepare_emit_ir(graph,
data_type,
path,
name,
meta_info=meta_data,
used_by_ir_reader=True)
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
