chengchunlei1.Install requirements：
pip3.7.5 install python-opencv
cd backend_C++/dnmetis_backend
pip3.7.5 setup.py install
Details of dnmetis_backend installation can be found in backend_C++/dnmetis_backend/README.md. Notice that, you just need to install requirements once for a brand new Ai1-Inference environment。
2.Download model(.om)
Here is an example of how to run npu inference of efficientnet-b8:
1.download efficientnet-b8 model(.om):
URL：baidu pan
Extracted code：tvg0
Original tensorflow model of efficientnet-b8(.pb):
URL：baidu pan
Extracted code：slqm
If you want to acknowledge how to generate om from pb，pls download efficientnet-b8.pb and execute ATC cmd：
.atc --model=MODELDIR/efficientnet−b8.pb −−framework=3 −−inputshape=′images:1,672,672,3′−−output=MODELDIR/efficientnet-b8 --mode=0 --out_nodes='Softmax:0' --soc_version=Ascend310 --input_fp16_nodes=images --output_type=FP16
2.Imagenet-val dataset and labels in val_map.txt:
3.Start execute the inference:
bash run_inference.sh
4.Top1 Accuracy of entire Imagenet2012-val Datasets(5w pictures):
5.modify main.py for your own model:
1.download efficientnet-b8 model(.pb) URL: obs://hwwheel23/efficientnet-b8.pb
2.atc --model=$MODEL_DIR/efficientnet-b8.pb --framework=3 --input_shape='images:1,672,672,3' --output=$MODEL_DIR/efficientnet-b8 --mode=0 --out_nodes='Softmax:0' --soc_version=Ascend310 --input_fp16_nodes=images --output_type=FP16