重命名 dnmetis/README.md 为 dnmetis/README_EN.md

2020-10-26 10:52:02 +08:00
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-## 1.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](https://pan.baidu.com/s/1N-kpQoDe3NRxvjFKjAT9AA) \
-Extracted code：tvg0  
-
-Original tensorflow model of efficientnet-b8(.pb):\
-URL：[baidu pan](https://pan.baidu.com/s/1CajdSlNTh6k35RoyOn-3Ug)\
-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:
-
-Here is an example of 10 pictures of Imagenet-val dataset：/
-![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:
-
-bash run_inference.sh
-
-Log:
-```
-[INFO]  start backend_predict is -1518493925
-[INFO]  start Execute is -1518490258
-[INFO]  model execute success
-[INFO]  end Execute is -1518350716
-[INFO]  npu compute cost 139.476000 ms
-[INFO]  1.output data success
-[INFO]  2.output data success
-[INFO]  execute sample success
-[INFO]  Pure device execute time is 0.000000 ms
-[INFO]  end backend_predict is -1518346882
-img_orig: ILSVRC2012_val_00000010.JPEG label: 332 predictions: 332
-
-Predict total jpeg: 10  Accuracy:  0.8
-```
-As you seen, "139.47 ms" is the npu inference time，"0.8" is the top1 Accuracy of 10 pictures。
-
-## 4.Top1 Accuracy of entire Imagenet2012-val Datasets(5w pictures):
-
-![输入图片说明](https://images.gitee.com/uploads/images/2020/0919/010210_5cf496fc_5418572.png "屏幕截图.png")
-
-
-## 5.modify main.py for your own model:
-
-Only need to concern about the dataset，pre-process，post-process：
-
-### pre-process:
-```
-def resize_with_aspectratio(img, out_height, out_width, scale=87.5, inter_pol=cv2.INTER_LINEAR):
-    height, width = img.shape[:2]
-    new_height = int(100. * out_height / scale)
-    new_width = int(100. * out_width / scale)
-    if height > width:
-        w = new_width
-        h = int(new_height * height / width)
-    else:
-        h = new_height
-        w = int(new_width * width / height)
-    img = cv2.resize(img, (w, h), interpolation=inter_pol)
-    return img
-
-def center_crop(img, out_height, out_width):
-    height, width = img.shape[:2]
-    left = int((width - out_width) / 2)
-    right = int((width + out_width) / 2)
-    top = int((height - out_height) / 2)
-    bottom = int((height + out_height) / 2)
-    img = img[top:bottom, left:right]
-    return img
-def pre_process_noisy(img, dims=None, precision="fp32"):
-    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-    output_height, output_width, _ = dims
-    cv2_interpol = cv2.INTER_CUBIC
-    img = resize_with_aspectratio(img, output_height, output_width, inter_pol=cv2_interpol)
-    img = center_crop(img, output_height, output_width)
-    MEAN_RGB = [0.485 * 255, 0.456 * 255, 0.406 * 255]
-    STDDEV_RGB = [0.229 * 255, 0.224 * 255, 0.225 * 255]
-
-    if precision=="fp32":
-        img = np.asarray(img, dtype='float32')
-    if precision=="fp16":
-        img = np.asarray(img, dtype='float16')
-
-    means = np.array([0.485 * 255, 0.456 * 255, 0.406 * 255], dtype=np.float32)
-    img -= means
-    stddev = np.array([0.229 * 255, 0.224 * 255, 0.225 * 255], dtype=np.float32)
-    img /= stddev
-    return img
-```
-
-### inference and post-process:
-```
-        predictions = backend.predict(args.feed[i])
-        #print(args.feed[i].shape)
-        print('img_orig:',args.image_list[i],'label:',args.label_list[i],'predictions:',np.argmax(predictions),'\n')
-```
-
-