update dnmetis/README.md.

dnmetis/README.md

@@ -22,6 +22,7 @@ If you want to acknowledge how to generate om from pb，pls download efficientne
 
 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")
 
 
@@ -29,7 +30,21 @@ If you want to acknowledge how to generate om from pb，pls download efficientne
 ## 3.Start execute the inference:
 
 bash run_inference.sh
+```
+[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
+```
 
 ## 4.Top1 Accuracy of entire Imagenet2012-val Datasets(5w pictures):
 
@@ -38,8 +53,57 @@ bash run_inference.sh
 
 ## 5.modify main.py for your own model:
 
-1.download  efficientnet-b8 model(.pb) URL: obs://hwwheel23/efficientnet-b8.pb 
+Only need to concern about the dataset，pre-process，post-process：
 
-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
+###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')
+```