def moc_emit_ir(ngraph_function: Model, argv: argparse.Namespace):
output_dir = argv.output_dir if argv.output_dir != '.' else os.getcwd()
# Apply preprocessing (mean/scale/reverse_channels/convert_layout/etc)
apply_preprocessing(ov_function=ngraph_function, argv=argv)
# Apply transformations
from openvino.tools.mo.back.offline_transformations import apply_user_transformations, apply_moc_transformations
apply_user_transformations(ngraph_function, parse_transform(argv.transform))
apply_moc_transformations(ngraph_function)
if argv.compress_fp16:
from openvino.tools.mo.back.offline_transformations import compress_model
compress_model(ngraph_function)
orig_model_name = os.path.normpath(os.path.join(output_dir, argv.model_name))
from openvino.offline_transformations_pybind import serialize # pylint: disable=import-error,no-name-in-module
serialize(ngraph_function, (orig_model_name + ".xml").encode('utf-8'), (orig_model_name + ".bin").encode('utf-8'))
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 apply_offline_transformations(input_model: str,
framework: str,
transforms: list,
compress_fp16=False):
# This variable is only needed by GenerateMappingFile transformation
# to produce correct mapping
extract_names = framework in ['tf', 'mxnet', 'kaldi']
from openvino.offline_transformations_pybind import generate_mapping_file, serialize # pylint: disable=import-error,no-name-in-module
from openvino.frontend import FrontEndManager, FrontEnd # pylint: disable=no-name-in-module,import-error
fem = FrontEndManager()
# We have to separate fe object lifetime from fem to
# avoid segfault during object destruction. So fe must
# be destructed before fem object explicitly.
def read_model(path_to_xml):
fe = fem.load_by_framework(framework="ir")
function = fe.convert(fe.load(path_to_xml))
return function
func = read_model(input_model + "_tmp.xml")
apply_user_transformations(func, transforms)
apply_moc_transformations(func)
if compress_fp16:
compress_model(func)
serialize(func,
str(input_model + ".xml").encode('utf-8'),
(input_model + ".bin").encode('utf-8'))
path_to_mapping = input_model + ".mapping"
generate_mapping_file(func, path_to_mapping.encode('utf-8'), extract_names)
def apply_offline_transformations(input_model: str, argv: argparse.Namespace):
# This variable is only needed by GenerateMappingFile transformation
# to produce correct mapping
extract_names = argv.framework in ['tf', 'mxnet', 'kaldi']
from openvino.offline_transformations_pybind import generate_mapping_file, serialize # pylint: disable=import-error,no-name-in-module
from openvino.frontend import FrontEndManager, FrontEnd # pylint: disable=no-name-in-module,import-error
from openvino.tools.mo.back.preprocessing import apply_preprocessing # pylint: disable=no-name-in-module,import-error
fem = FrontEndManager()
# We have to separate fe object lifetime from fem to
# avoid segfault during object destruction. So fe must
# be destructed before fem object explicitly.
def read_model(path_to_xml):
fe = fem.load_by_framework(framework="ir")
function = fe.convert(fe.load(path_to_xml))
return function
func = read_model(input_model + "_tmp.xml")
# TODO: use ngraph preprocessing (Mean/Scale/ReverseInputChannels) for legacy frontends
reverse_input_channels = False
if 'reverse_input_channels' in argv:
reverse_input_channels = argv.reverse_input_channels
argv.reverse_input_channels = False
mean_scale_values = {}
if 'mean_scale_values' in argv:
mean_scale_values = argv.mean_scale_values
argv.mean_scale_values = {}
scale = None
if 'scale' in argv:
scale = argv.scale
argv.scale = None
# Apply preprocessing for layouts only
apply_preprocessing(ov_function=func, argv=argv)
if 'reverse_input_channels' in argv:
argv.reverse_input_channels = reverse_input_channels
if 'mean_scale_values' in argv:
argv.mean_scale_values = mean_scale_values
if 'scale' in argv:
argv.scale = scale
apply_user_transformations(func, parse_transform(argv.transform))
apply_moc_transformations(func)
if "compress_fp16" in argv and argv.compress_fp16:
compress_model(func)
serialize(func,
str(input_model + ".xml").encode('utf-8'),
(input_model + ".bin").encode('utf-8'))
path_to_mapping = input_model + ".mapping"
generate_mapping_file(func, path_to_mapping.encode('utf-8'), extract_names)
def test_serialize_pass():
core = Core()
xml_path = "serialized_function.xml"
bin_path = "serialized_function.bin"
func = get_test_function()
serialize(func, xml_path, bin_path)
assert func is not None
res_func = core.read_model(model=xml_path, weights=bin_path)
assert func.get_parameters() == res_func.get_parameters()
assert func.get_ordered_ops() == res_func.get_ordered_ops()
os.remove(xml_path)
os.remove(bin_path)
def test_Version_ir_v11():
core = Core()
xml_path = "./serialized_function.xml"
bin_path = "./serialized_function.bin"
shape = [100, 100, 2]
parameter_a = ov.opset8.parameter(shape, dtype=np.float32, name="A")
parameter_b = ov.opset8.parameter(shape, dtype=np.float32, name="B")
model = ov.opset8.floor(
ov.opset8.minimum(ov.opset8.abs(parameter_a), parameter_b))
func = Model(model, [parameter_a, parameter_b], "Model")
serialize(func, xml_path, bin_path, "IR_V11")
res_func = core.read_model(model=xml_path, weights=bin_path)
assert func.get_parameters() == res_func.get_parameters()
assert func.get_ordered_ops() == res_func.get_ordered_ops()
os.remove(xml_path)
os.remove(bin_path)
def dump_exec_graph(compiled_model, model_path, weight_path=None):
if not weight_path:
weight_path = model_path[:model_path.find(".xml")] + ".bin"
serialize(compiled_model.get_runtime_model(), model_path, weight_path)
def dump_exec_graph(exe_network, model_path, weight_path=None):
if not weight_path:
weight_path = model_path[:model_path.find(".xml")] + ".bin"
serialize(exe_network.get_runtime_model(), model_path, weight_path)