add dnmetis/README.md.

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+中文|[英文](README_EN.md)
+
+## 1.安装依赖：
+```
+pip3.7.5 install python-opencv
+cd backend_C++/dnmetis_backend
+pip3.7.5 setup.py install
+```
+安装dnmetis_backend的细节可以在backend_C++/dnmetis_backend/README.md看到，对于一个全新的Ai1推理环境，只需要安装一次依赖，不需要重复安装。
+
+## 2.下载om模型(.om)
+
+如下示例展示如果在NPU上运行efficientnet-b8模型:\
+1.下载efficientnet-b8 model(.om): \
+链接：[百度网盘](https://pan.baidu.com/s/1N-kpQoDe3NRxvjFKjAT9AA) \
+提取码：tvg0  
+下载的om模型放到model文件夹.
+
+原生的TensorFlow模型efficientnet-b8(.pb):\
+链接：[百度网盘](https://pan.baidu.com/s/1CajdSlNTh6k35RoyOn-3Ug)\
+提取码：slqm 
+
+如果想了解如果从pb模型如何转换成om模型，请下载efficientnet-b8.pb模型，使用ATC模型转换工具，或者执行转换命令：\
+.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数据集和标签:
+
+这里的示例仅仅展示了从Imagenet-val数据集挑选的10张图片：/
+![输入图片说明](https://images.gitee.com/uploads/images/2020/0918/234302_a572d632_5418572.jpeg "无标题.jpg")
+
+
+
+## 3.执行推理:
+建议提交的PR代码统一使用run_inference.sh作为入口：
+bash run_inference.sh
+
+执行日志:
+```
+[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
+```
+如上所示, "139.476 ms"是NPU的推理时间，"0.8" 是10张图片的top1精度。
+
+## 4.完整的5w张Imagenet2012-val数据集精度:
+
+![输入图片说明](https://images.gitee.com/uploads/images/2020/0919/010210_5cf496fc_5418572.png "屏幕截图.png")
+
+
+## 5.main.py修改点:
+
+如果需要使用你自己的模型来推理和计算精度，请修改main.py\
+只需要关心数据集、预处理和后处理代码部分：
+
+### 预处理:
+```
+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
+```
+
+### 推理和后处理:
+```
+        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')
+```