Upload dnmetis test tool for NPU inference

2020-10-23 16:46:51 +08:00
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+## 1.Install dnmetis_backend
+
+As README.md:
+backend_C++/dnmetis_backend/README.md
+
+## 2.Download dataset and model(.om)
+
+1.download  Imagenet-val dataset URL: http://www.image-net.org/download-imageurls
+
+2.download  efficientnet-b8 model(.om) URL: obs://hwwheel23/efficientnet-b8.om 
+
+3.process the original Imagenet-val dataset as list:
+
+![输入图片说明](https://images.gitee.com/uploads/images/2020/0918/234302_a572d632_5418572.jpeg "无标题.jpg")
+
+
+
+## 3.Start execute the inference:
+
+sh run_efficientnet-b8.sh
+
+or 
+
+python3.7 main.py --model=/data/hwwheel123/model/efficientnet-b8.om --image_size='672,672,3' --inputs='images:0' --outputs='Softmax:0' --precision=fp16
+
+
+## 4.ATC offline model generate (optional):
+
+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
+
+## 5.Imagenet2012-val Top1 Accuracy:
+
+![输入图片说明](https://images.gitee.com/uploads/images/2020/0919/010210_5cf496fc_5418572.png "屏幕截图.png")