# ****************************************************************************** # Copyright 2017-2019 Intel Corporation # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ****************************************************************************** import onnx onnx_protobuf = onnx.load('/path/to/model/cntk_ResNet20_CIFAR10/model.onnx') # Convert a serialized ONNX model to an ngraph model from ngraph_onnx.onnx_importer.importer import import_onnx_model ng_model = import_onnx_model(onnx_protobuf)[0] # Using an ngraph runtime (CPU backend), create a callable computation import ngraph as ng runtime = ng.runtime(backend_name='CPU') resnet = runtime.computation(ng_model['output'], *ng_model['inputs']) # Load or create an image import numpy as np picture = np.ones([1, 3, 32, 32]) # Run ResNet inference on picture resnet(picture)