def test_single_pass_with_multiple_args(self):
self.assertEqual(
parse_transform("LowLatency[num_iterations=2;dummy_attr=3.14]"),
[("LowLatency", {
"num_iterations": 2,
"dummy_attr": 3.14
})])
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 test_single_pass_with_multiple_args(self):
self.assertEqual(
parse_transform(
"LowLatency2[use_const_initializer=True;dummy_attr=3.14]"),
[("LowLatency2", {
"use_const_initializer": True,
"dummy_attr": 3.14
})])
def test_multiple_passes_with_args(self):
self.assertEqual(
parse_transform(
"LowLatency2[use_const_initializer=True],DummyPass[type=ReLU]"
), [("LowLatency2", {
"use_const_initializer": True
}), ("DummyPass", {
"type": "ReLU"
})])
def test_multiple_passes_with_args(self):
self.assertEqual(
parse_transform(
"LowLatency[num_iterations=2],DummyPass[type=ReLU]"),
[("LowLatency", {
"num_iterations": 2
}), ("DummyPass", {
"type": "ReLU"
})])
def test_multiple_passes_with_args2(self):
self.assertEqual(
parse_transform(
"LowLatency[num_iterations=2,3,4.15],DummyPass1,DummyPass2[types=ReLU,PReLU;values=1,2,3]"
), [("LowLatency", {
"num_iterations": [2, 3, 4.15]
}), ("DummyPass1", {}),
("DummyPass2", {
"types": ["ReLU", "PReLU"],
"values": [1, 2, 3]
})])
def test_multiple_passes_with_args2(self):
self.assertEqual(
parse_transform(
"LowLatency2[use_const_initializer=True,False],DummyPass1,"
"DummyPass2[types=ReLU,PReLU;values=1,2,3]"),
[("LowLatency2", {
"use_const_initializer": [True, False]
}), ("DummyPass1", {}),
("DummyPass2", {
"types": ["ReLU", "PReLU"],
"values": [1, 2, 3]
})])
def test_multiple_passes_no_args(self):
self.assertEqual(parse_transform("DummyPass,LowLatency22"),
[("DummyPass", {}), ("LowLatency22", {})])
def test_single_pass_with_args(self):
self.assertEqual(
parse_transform("LowLatency2[use_const_initializer=True]"),
[("LowLatency2", {
"use_const_initializer": True
})])
def test_single_pass(self):
self.assertEqual(parse_transform("LowLatency2"), [("LowLatency2", {})])
def test_empty(self):
self.assertEqual(parse_transform(""), [])
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
available_transformations[name](net, **args)
ApplyMOCTransformations(net, False)
def apply_offline_transformations(input_model: str, framework: str,
transforms: list):
# This variable is only needed by GenerateMappingFile transformation
# to produce correct mapping
extract_names = framework in ['tf', 'mxnet', 'kaldi']
from openvino.inference_engine import read_network # pylint: disable=import-error,no-name-in-module
from openvino.offline_transformations import GenerateMappingFile # pylint: disable=import-error,no-name-in-module
net = read_network(input_model + "_tmp.xml", input_model + "_tmp.bin")
apply_moc_transformations(net, transforms)
net.serialize(input_model + ".xml", input_model + ".bin")
path_to_mapping = input_model + ".mapping"
GenerateMappingFile(net, path_to_mapping.encode('utf-8'), extract_names)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--input_model")
parser.add_argument("--framework")
parser.add_argument("--transform")
args = parser.parse_args()
apply_offline_transformations(args.input_model, args.framework,
parse_transform(args.transform))
def test_single_pass_with_args(self):
self.assertEqual(parse_transform("LowLatency[num_iterations=2]"),
[("LowLatency", {
"num_iterations": 2
})])
def apply_moc_transformations(net: object):
from openvino.offline_transformations import ApplyMOCTransformations # pylint: disable=import-error,no-name-in-module
ApplyMOCTransformations(net, False)
def apply_offline_transformations(input_model: str, framework: str, transforms: list):
# This variable is only needed by GenerateMappingFile transformation
# to produce correct mapping
extract_names = framework in ['tf', 'mxnet', 'kaldi']
from openvino.inference_engine import read_network # pylint: disable=import-error,no-name-in-module
from openvino.offline_transformations import GenerateMappingFile # pylint: disable=import-error,no-name-in-module
net = read_network(input_model + "_tmp.xml", input_model + "_tmp.bin")
apply_user_transformations(net, transforms)
apply_moc_transformations(net)
net.serialize(input_model + ".xml", input_model + ".bin")
path_to_mapping = input_model + ".mapping"
GenerateMappingFile(net, path_to_mapping.encode('utf-8'), extract_names)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--input_model")
parser.add_argument("--framework")
parser.add_argument("--transform")
args = parser.parse_args()
apply_offline_transformations(args.input_model, args.framework, parse_transform(args.transform))
