def subprocess_main(framework=None):
"""
Please keep this file compatible with python2 in order to check user python version.
This function checks that Inference Engine Python API available and working as expected
and then in sub-process it executes main_<fw>.py files. Due to some OSs specifics we can't
just add paths to Python modules and libraries into current env. So to make Inference Engine
Python API to be available inside MO we need to use subprocess with new env.
"""
ret_code = check_python_version()
if ret_code:
sys.exit(ret_code)
from mo.utils.find_ie_version import find_ie_version
find_ie_version(silent=True)
mo_root_path = os.path.join(os.path.dirname(__file__), os.pardir)
python_path_key = 'PYTHONPATH'
if python_path_key not in os.environ:
os.environ[python_path_key] = mo_root_path
else:
os.environ[python_path_key] = os.pathsep.join(
[os.environ[python_path_key], mo_root_path])
path_to_main = os.path.join(
os.path.realpath(os.path.dirname(__file__)),
'main_{}.py'.format(framework) if framework else 'main.py')
# python2 compatible code. Do not remove.
args = [sys.executable, path_to_main]
for arg in sys.argv[1:]:
args.append(arg)
status = subprocess.run(args, env=os.environ)
sys.exit(status.returncode)
def setup_env():
ret_code = check_python_version()
if ret_code:
sys.exit(ret_code)
from mo.utils.find_ie_version import find_ie_version
find_ie_version(silent=True)
mo_root_path = os.path.join(os.path.dirname(__file__), os.pardir)
python_path_key = 'PYTHONPATH'
if python_path_key not in os.environ:
os.environ[python_path_key] = mo_root_path
else:
os.environ[python_path_key] = os.pathsep.join(
[os.environ[python_path_key], mo_root_path])
def setup_env():
ret_code = check_python_version()
if ret_code:
sys.exit(ret_code)
from mo.utils.find_ie_version import find_ie_version
ie_found = True
try:
ie_found = find_ie_version(silent=True)
except Exception:
ie_found = False
if not ie_found:
log_ie_not_found()
sys.exit(1)
mo_root_path = os.path.join(os.path.dirname(__file__), os.pardir)
python_path_key = 'PYTHONPATH'
if python_path_key not in os.environ:
os.environ[python_path_key] = mo_root_path
else:
os.environ[python_path_key] = os.pathsep.join([os.environ[python_path_key], mo_root_path])
return 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)
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))
# 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=100)
if status.returncode != 0 and not argv.silent:
print("[ WARNING ] offline_transformations return code {}".
format(status.returncode))
except Exception as e:
# TODO: send error message
pass
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 arguments_post_parsing(argv: argparse.Namespace):
moc_front_end, available_moc_front_ends = get_moc_frontends(argv)
is_tf, is_caffe, is_mxnet, is_kaldi, is_onnx =\
deduce_framework_by_namespace(argv) if not moc_front_end else [False, False, False, False, False]
if not any([is_tf, is_caffe, is_mxnet, is_kaldi, is_onnx]):
frameworks = ['tf', 'caffe', 'mxnet', 'kaldi', 'onnx']
frameworks = list(set(frameworks + available_moc_front_ends))
if argv.framework not in frameworks:
if argv.use_legacy_frontend:
raise Error(
'Framework {} is not a valid target when using the --use_legacy_frontend flag. '
'The following legacy frameworks are available: {}' +
refer_to_faq_msg(15), argv.framework, frameworks)
else:
raise Error(
'Framework {} is not a valid target. Please use --framework with one from the list: {}. '
+ refer_to_faq_msg(15), argv.framework, frameworks)
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")
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
def raise_ie_not_found():
raise Error(
"Could not find the Inference Engine or nGraph Python API.\n"
"Consider building the Inference Engine and nGraph Python APIs from sources or try to install OpenVINO (TM) Toolkit using \"install_prerequisites.{}\""
.format("bat" if sys.platform == "windows" else "sh"))
try:
if not find_ie_version(silent=argv.silent):
raise_ie_not_found()
except Exception as e:
raise_ie_not_found()
if 'data_type' in argv and argv.data_type in ['FP16', 'half']:
argv.data_type = 'FP32'
argv.compress_fp16 = True
else:
argv.compress_fp16 = False
# This is just to check that transform key is valid and transformations are available
check_available_transforms(parse_transform(argv.transform))
if argv.legacy_ir_generation and len(argv.transform) != 0:
raise Error(
"--legacy_ir_generation and --transform keys can not be used at the same time."
)
# For C++ frontends there are no specific Python installation requirements, check only generic ones
if moc_front_end:
ret_code = check_requirements()
else:
ret_code = check_requirements(framework=argv.framework)
if ret_code:
raise Error(
'check_requirements exited 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:
from mo.front.tf.register_custom_ops import get_front_classes
import_extensions.load_dirs(argv.framework, extensions,
get_front_classes)
elif is_caffe:
send_framework_info('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:
send_framework_info('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:
send_framework_info('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:
send_framework_info('onnx')
from mo.front.onnx.register_custom_ops import get_front_classes
import_extensions.load_dirs(argv.framework, extensions,
get_front_classes)
return argv
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
