chengchunlei
115 lines
4.0 KiB
Markdown
115 lines
4.0 KiB
Markdown
EN|[CH](README.md)
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## 1.Install requirements：
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```
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pip3.7.5 install python-opencv
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cd backend_C++/dnmetis_backend
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pip3.7.5 setup.py install
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```
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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。
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## 2.Download model(.om)
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Here is an example of how to run npu inference of efficientnet-b8:\
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1.download efficientnet-b8 model(.om): \
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URL：[baidu pan](https://pan.baidu.com/s/1N-kpQoDe3NRxvjFKjAT9AA) \
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Extracted code：tvg0
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Original tensorflow model of efficientnet-b8(.pb):\
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URL：[baidu pan](https://pan.baidu.com/s/1CajdSlNTh6k35RoyOn-3Ug)\
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Extracted code：slqm
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If you want to acknowledge how to generate om from pb，pls download efficientnet-b8.pb and execute ATC cmd：\
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.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
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2.Imagenet-val dataset and labels in val_map.txt:
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Here is an example of 10 pictures of Imagenet-val dataset：
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## 3.Start execute the inference:
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bash run_inference.sh
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Log:
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```
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[INFO] start backend_predict is -1518493925
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[INFO] start Execute is -1518490258
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[INFO] model execute success
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[INFO] end Execute is -1518350716
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[INFO] npu compute cost 139.476000 ms
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[INFO] 1.output data success
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[INFO] 2.output data success
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[INFO] execute sample success
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[INFO] Pure device execute time is 0.000000 ms
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[INFO] end backend_predict is -1518346882
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img_orig: ILSVRC2012_val_00000010.JPEG label: 332 predictions: 332
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Predict total jpeg: 10 Accuracy: 0.8
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```
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As you seen, "139.47 ms" is the npu inference time，"0.8" is the top1 Accuracy of 10 pictures。
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## 4.Top1 Accuracy of entire Imagenet2012-val Datasets(5w pictures):
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## 5.modify main.py for your own model:
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Only need to concern about the dataset，pre-process，post-process：
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### pre-process:
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```
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def resize_with_aspectratio(img, out_height, out_width, scale=87.5, inter_pol=cv2.INTER_LINEAR):
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height, width = img.shape[:2]
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new_height = int(100. * out_height / scale)
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new_width = int(100. * out_width / scale)
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if height > width:
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w = new_width
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h = int(new_height * height / width)
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else:
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h = new_height
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w = int(new_width * width / height)
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img = cv2.resize(img, (w, h), interpolation=inter_pol)
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return img
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def center_crop(img, out_height, out_width):
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height, width = img.shape[:2]
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left = int((width - out_width) / 2)
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right = int((width + out_width) / 2)
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top = int((height - out_height) / 2)
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bottom = int((height + out_height) / 2)
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img = img[top:bottom, left:right]
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return img
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def pre_process_noisy(img, dims=None, precision="fp32"):
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img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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output_height, output_width, _ = dims
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cv2_interpol = cv2.INTER_CUBIC
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img = resize_with_aspectratio(img, output_height, output_width, inter_pol=cv2_interpol)
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img = center_crop(img, output_height, output_width)
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MEAN_RGB = [0.485 * 255, 0.456 * 255, 0.406 * 255]
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STDDEV_RGB = [0.229 * 255, 0.224 * 255, 0.225 * 255]
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if precision=="fp32":
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img = np.asarray(img, dtype='float32')
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if precision=="fp16":
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img = np.asarray(img, dtype='float16')
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means = np.array([0.485 * 255, 0.456 * 255, 0.406 * 255], dtype=np.float32)
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img -= means
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stddev = np.array([0.229 * 255, 0.224 * 255, 0.225 * 255], dtype=np.float32)
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img /= stddev
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return img
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```
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### inference and post-process:
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```
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predictions = backend.predict(args.feed[i])
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#print(args.feed[i].shape)
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print('img_orig:',args.image_list[i],'label:',args.label_list[i],'predictions:',np.argmax(predictions),'\n')
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```
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