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 import GenerateMappingFile, Serialize # pylint: disable=import-error,no-name-in-module
from openvino.inference_engine import IENetwork # pylint: disable=import-error,no-name-in-module
from ngraph.frontend import FrontEndManager, FrontEnd # pylint: disable=no-name-in-module,import-error
from ngraph.impl import Function # 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_network(path_to_xml):
fe = fem.load_by_framework(framework="ir")
f = fe.convert(fe.load(path_to_xml))
return IENetwork(Function.to_capsule(f))
net = read_network(input_model + "_tmp.xml")
apply_user_transformations(net, transforms)
apply_moc_transformations(net)
if compress_fp16:
compress_model(net)
Serialize(net, str(input_model + ".xml").encode('utf-8'), (input_model + ".bin").encode('utf-8'))
path_to_mapping = input_model + ".mapping"
GenerateMappingFile(net, path_to_mapping.encode('utf-8'), extract_names)
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('mo')
telemetry.force_shutdown(1.0)
return 1
if __name__ == "__main__":
from mo.utils.cli_parser import get_all_cli_parser
fe_manager = FrontEndManager()
sys.exit(main(get_all_cli_parser(fe_manager), fe_manager, None))
return make_model(graph, producer_name="ngraph ONNX Importer")
def run_function(function, *inputs, expected):
runtime = get_runtime()
computation = runtime.computation(function)
actual = computation(*inputs)
assert len(actual) == len(expected)
for i in range(len(actual)):
np.testing.assert_allclose(expected[i],
actual[i],
rtol=1e-3,
atol=1e-6)
fem = FrontEndManager()
onnx_model_filename = "model.onnx"
ONNX_FRONTEND_NAME = "onnx_experimental"
def setup_module():
onnx.save_model(create_onnx_model(), onnx_model_filename)
def teardown_module():
os.remove(onnx_model_filename)
def skip_if_onnx_frontend_is_disabled():
front_ends = fem.get_available_front_ends()
if ONNX_FRONTEND_NAME not in front_ends:
# Copyright (C) 2021 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
import sys
from mo.utils.cli_parser import get_onnx_cli_parser
if __name__ == "__main__":
from mo.main import main
from ngraph.frontend import FrontEndManager # pylint: disable=no-name-in-module,import-error
sys.exit(main(get_onnx_cli_parser(), FrontEndManager(), 'onnx'))