def main(cli_parser: argparse.ArgumentParser, framework: str):
telemetry = tm.Telemetry(app_name='Model Optimizer',
app_version=get_simplified_mo_version())
telemetry.start_session()
telemetry.send_event('mo', 'version', get_simplified_mo_version())
try:
# Initialize logger with 'ERROR' as default level to be able to form nice messages
# before arg parser deliver log_level requested by user
init_logger('ERROR', False)
argv = cli_parser.parse_args()
if framework:
argv.framework = framework
ov_update_message = None
if not hasattr(argv, 'silent') or not argv.silent:
ov_update_message = get_ov_update_message()
ret_code = driver(argv)
if ov_update_message:
print(ov_update_message)
telemetry.send_event('mo', 'conversion_result', 'success')
telemetry.end_session()
telemetry.force_shutdown(1.0)
return ret_code
except (FileNotFoundError, NotADirectoryError) as e:
log.error('File {} was not found'.format(
str(e).split('No such file or directory:')[1]))
log.debug(traceback.format_exc())
except Error as err:
analysis_results = AnalysisResults()
if analysis_results.get_messages() is not None:
for el in analysis_results.get_messages():
log.error(el, extra={'analysis_info': True})
log.error(err)
log.debug(traceback.format_exc())
except FrameworkError as err:
log.error(err, extra={'framework_error': True})
log.debug(traceback.format_exc())
except Exception as err:
log.error("-------------------------------------------------")
log.error("----------------- INTERNAL ERROR ----------------")
log.error("Unexpected exception happened.")
log.error(
"Please contact Model Optimizer developers and forward the following information:"
)
log.error(str(err))
log.error(traceback.format_exc())
log.error("---------------- END OF BUG REPORT --------------")
log.error("-------------------------------------------------")
telemetry.send_event('mo', 'conversion_result', 'fail')
telemetry.end_session()
telemetry.force_shutdown(1.0)
return 1
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)
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()
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 find_ie_version(silent=True):
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, orig_model_name], env=os.environ, timeout=10)
return_code = status.returncode
if return_code != 0 and not argv.silent:
print("[ WARNING ] offline_transformations return code {}".format(return_code))
except Exception as e:
pass
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)
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 prepare_ir(argv: argparse.Namespace):
is_tf, is_caffe, is_mxnet, is_kaldi, is_onnx = deduce_framework_by_namespace(
argv)
if not any([is_tf, is_caffe, is_mxnet, is_kaldi, is_onnx]):
raise Error(
'Framework {} is not a valid target. Please use --framework with one from the list: caffe, tf, '
'mxnet, kaldi, onnx. ' + refer_to_faq_msg(15), argv.framework)
if is_tf and not argv.input_model and not argv.saved_model_dir and not argv.input_meta_graph:
raise Error(
'Path to input model or saved model dir is required: use --input_model, --saved_model_dir or '
'--input_meta_graph')
elif is_mxnet and not argv.input_model and not argv.input_symbol and not argv.pretrained_model_name:
raise Error(
'Path to input model or input symbol or pretrained_model_name is required: use --input_model or '
'--input_symbol or --pretrained_model_name')
elif is_caffe and not argv.input_model and not argv.input_proto:
raise Error(
'Path to input model or input proto is required: use --input_model or --input_proto'
)
elif (is_kaldi or is_onnx) and not argv.input_model:
raise Error('Path to input model is required: use --input_model.')
log.debug(str(argv))
log.debug("Model Optimizer started")
t = tm.Telemetry()
t.start_session()
model_name = "<UNKNOWN_NAME>"
if argv.model_name:
model_name = argv.model_name
elif argv.input_model:
model_name = get_model_name(argv.input_model)
elif is_tf and argv.saved_model_dir:
model_name = "saved_model"
elif is_tf and argv.input_meta_graph:
model_name = get_model_name(argv.input_meta_graph)
elif is_mxnet and argv.input_symbol:
model_name = get_model_name(argv.input_symbol)
argv.model_name = model_name
log.debug('Output model name would be {}{{.xml, .bin}}'.format(
argv.model_name))
# if --input_proto is not provided, try to retrieve another one
# by suffix substitution from model file name
if is_caffe and not argv.input_proto:
argv.input_proto = replace_ext(argv.input_model, '.caffemodel',
'.prototxt')
if not argv.input_proto:
raise Error(
"Cannot find prototxt file: for Caffe please specify --input_proto - a "
+
"protobuf file that stores topology and --input_model that stores "
+ "pretrained weights. " + refer_to_faq_msg(20))
log.info('Deduced name for prototxt: {}'.format(argv.input_proto))
if not argv.silent:
print_argv(argv, is_caffe, is_tf, is_mxnet, is_kaldi, is_onnx,
argv.model_name)
# This try-except is additional reinsurance that the IE
# dependency search does not break the MO pipeline
try:
if not find_ie_version(silent=argv.silent) and not argv.silent:
print(
"[ WARNING ] Could not find the Inference Engine Python API. At this moment, the Inference Engine dependency is not required, but will be required in future releases."
)
print(
"[ WARNING ] Consider building the Inference Engine Python API from sources or try to install OpenVINO (TM) Toolkit using \"install_prerequisites.{}\""
.format("bat" if sys.platform == "windows" else "sh"))
# If the IE was not found, it will not print the MO version, so we have to print it manually
print("{}: \t{}".format("Model Optimizer version", get_version()))
except Exception as e:
pass
ret_code = check_requirements(framework=argv.framework)
if ret_code:
raise Error(
'check_requirements exit with return code {}'.format(ret_code))
if is_tf and argv.tensorflow_use_custom_operations_config is not None:
argv.transformations_config = argv.tensorflow_use_custom_operations_config
if is_caffe and argv.mean_file and argv.mean_values:
raise Error(
'Both --mean_file and mean_values are specified. Specify either mean file or mean values. '
+ refer_to_faq_msg(17))
elif is_caffe and argv.mean_file and argv.mean_file_offsets:
values = get_tuple_values(argv.mean_file_offsets,
t=int,
num_exp_values=2)
mean_file_offsets = np.array([int(x) for x in values[0].split(',')])
if not all([offset >= 0 for offset in mean_file_offsets]):
raise Error(
"Negative value specified for --mean_file_offsets option. "
"Please specify positive integer values in format '(x,y)'. " +
refer_to_faq_msg(18))
argv.mean_file_offsets = mean_file_offsets
if argv.scale and argv.scale_values:
raise Error(
'Both --scale and --scale_values are defined. Specify either scale factor or scale values per input '
+ 'channels. ' + refer_to_faq_msg(19))
if argv.scale and argv.scale < 1.0:
log.error(
"The scale value is less than 1.0. This is most probably an issue because the scale value specifies "
"floating point value which all input values will be *divided*.",
extra={'is_warning': True})
if argv.input_model and (is_tf and argv.saved_model_dir):
raise Error('Both --input_model and --saved_model_dir are defined. '
'Specify either input model or saved model directory.')
if is_tf:
if argv.saved_model_tags is not None:
if ' ' in argv.saved_model_tags:
raise Error(
'Incorrect saved model tag was provided. Specify --saved_model_tags with no spaces in it'
)
argv.saved_model_tags = argv.saved_model_tags.split(',')
argv.output = argv.output.split(',') if argv.output else None
argv.placeholder_shapes, argv.placeholder_data_types = get_placeholder_shapes(
argv.input, argv.input_shape, argv.batch)
mean_values = parse_tuple_pairs(argv.mean_values)
scale_values = parse_tuple_pairs(argv.scale_values)
mean_scale = get_mean_scale_dictionary(mean_values, scale_values,
argv.input)
argv.mean_scale_values = mean_scale
if not os.path.exists(argv.output_dir):
try:
os.makedirs(argv.output_dir)
except PermissionError as e:
raise Error(
"Failed to create directory {}. Permission denied! " +
refer_to_faq_msg(22), argv.output_dir) from e
else:
if not os.access(argv.output_dir, os.W_OK):
raise Error(
"Output directory {} is not writable for current user. " +
refer_to_faq_msg(22), argv.output_dir)
log.debug("Placeholder shapes : {}".format(argv.placeholder_shapes))
if hasattr(argv,
'extensions') and argv.extensions and argv.extensions != '':
extensions = argv.extensions.split(',')
else:
extensions = None
argv.freeze_placeholder_with_value, argv.input = get_freeze_placeholder_values(
argv.input, argv.freeze_placeholder_with_value)
if is_tf:
t.send_event('mo', 'framework', 'tf')
from mo.front.tf.register_custom_ops import get_front_classes
import_extensions.load_dirs(argv.framework, extensions,
get_front_classes)
elif is_caffe:
t.send_event('mo', 'framework', 'caffe')
from mo.front.caffe.register_custom_ops import get_front_classes
import_extensions.load_dirs(argv.framework, extensions,
get_front_classes)
elif is_mxnet:
t.send_event('mo', 'framework', 'mxnet')
from mo.front.mxnet.register_custom_ops import get_front_classes
import_extensions.load_dirs(argv.framework, extensions,
get_front_classes)
elif is_kaldi:
t.send_event('mo', 'framework', 'kaldi')
from mo.front.kaldi.register_custom_ops import get_front_classes
import_extensions.load_dirs(argv.framework, extensions,
get_front_classes)
elif is_onnx:
t.send_event('mo', 'framework', 'onnx')
from mo.front.onnx.register_custom_ops import get_front_classes
import_extensions.load_dirs(argv.framework, extensions,
get_front_classes)
graph = unified_pipeline(argv)
return graph
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 []
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 find_ie_version(silent=True):
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], env=os.environ, timeout=10)
return_code = status.returncode
if return_code != 0 and not argv.silent:
log.error("offline_transformations return code {}".format(return_code), extra={'is_warning': True})
except Exception as e:
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:
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
def send_telemetry(mo_version: str, message: str, event_type: str):
t = tm.Telemetry(app_name='Model Optimizer', app_version=mo_version)
t.start_session()
t.send_event(execution_type, event_type, message)
t.end_session()
t.force_shutdown(1.0)