def moc_emit_ir(ngraph_function: Function, argv: argparse.Namespace):
output_dir = argv.output_dir if argv.output_dir != '.' else os.getcwd()
network = function_to_cnn(ngraph_function)
from mo.back.offline_transformations import apply_user_transformations, apply_moc_transformations
apply_user_transformations(network, parse_transform(argv.transform))
apply_moc_transformations(network)
orig_model_name = os.path.normpath(
os.path.join(output_dir, argv.model_name))
network.serialize(orig_model_name + ".xml", orig_model_name + ".bin")
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 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"
# This try-except is additional reinsurance that the IE
# dependency search does not break the MO pipeline
try:
if not argv.legacy_ir_generation:
path_to_offline_transformations = os.path.join(
os.path.realpath(os.path.dirname(__file__)), 'back',
'offline_transformations.py')
cmd = [
sys.executable, path_to_offline_transformations,
"--input_model", orig_model_name, "--framework",
argv.framework, "--transform", argv.transform
]
if "compress_fp16" in argv and argv.compress_fp16:
cmd += ["--compress_fp16"]
# restore data_type cmd parameter
argv.data_type = 'FP16'
status = subprocess.run(cmd, env=os.environ)
return_code = status.returncode
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
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)
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 []
# Remove temporary ie_is_available key from argv no to have it in IR
ie_is_available = argv.ie_is_available
del argv.ie_is_available
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"
# This try-except is additional reinsurance that the IE
# dependency search does not break the MO pipeline
try:
if not argv.legacy_ir_generation and ie_is_available:
path_to_offline_transformations = os.path.join(
os.path.realpath(os.path.dirname(__file__)), 'back',
'offline_transformations.py')
status = subprocess.run([
sys.executable, path_to_offline_transformations,
"--input_model", orig_model_name, "--framework",
argv.framework, "--transform", argv.transform
],
env=os.environ)
return_code = status.returncode
except Exception as e:
return_code = "failed"
log.error(e, extra={'is_warning': True})
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 IR wasn't produced by offline_transformations step we need to fallback to IR
# produced by prepare_ir. This IR needs to be renamed from XXX_tmp.xml to XXX.xml
suffixes = [".xml", ".bin", ".mapping"]
if return_code != 0:
if len(argv.transform) != 0:
# Remove temporary IR before throwing exception
for suf in suffixes:
path_to_file = orig_model_name + "_tmp" + suf
if os.path.exists(path_to_file):
os.remove(path_to_file)
raise Error("Failed to apply transformations: {}".format(
argv.transform))
log.error("Using fallback to produce IR.",
extra={'is_warning': True})
for suf in suffixes:
# remove existing files
path_to_file = orig_model_name + suf
if os.path.exists(path_to_file):
os.remove(path_to_file)
# rename tmp IR to original name
os.rename(orig_model_name + "_tmp" + suf,
orig_model_name + suf)
else:
for suf in suffixes:
# 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
