diff --git "a/Pytorch\350\256\255\347\273\203\347\244\272\344\276\213/WideResNet50_2_ID1627_for_PyTorch/README.md" "b/Pytorch\350\256\255\347\273\203\347\244\272\344\276\213/WideResNet50_2_ID1627_for_PyTorch/README.md"
index e504fa9f28690c0519334a088035f61c7167602b..cd3b369a37557ba6a160e89886c188826f3433ca 100644
--- "a/Pytorch\350\256\255\347\273\203\347\244\272\344\276\213/WideResNet50_2_ID1627_for_PyTorch/README.md"
+++ "b/Pytorch\350\256\255\347\273\203\347\244\272\344\276\213/WideResNet50_2_ID1627_for_PyTorch/README.md"
@@ -30,6 +30,12 @@ bash ./test/train_full_8p.sh --data_path=real_data_path
 
 # training 8p performance
 bash ./test/train_performance_8p.sh --data_path=real_data_path
+
+#test 8p accuracy
+bash test/train_eval_8p.sh --data_path=real_data_path --weight_path=real_pre_train_model_path
+
+# finetuning 1p 
+bash test/train_finetune_1p.sh --data_path=real_data_path --weight_path=real_pre_train_model_path
 ```
 
 Log path:
diff --git "a/Pytorch\350\256\255\347\273\203\347\244\272\344\276\213/WideResNet50_2_ID1627_for_PyTorch/main.py" "b/Pytorch\350\256\255\347\273\203\347\244\272\344\276\213/WideResNet50_2_ID1627_for_PyTorch/main.py"
index a99b07efc19ac24399e8c4b512e67653d42a4315..2cd1f50ed96be48de00bcacf85e36bb81bb7573a 100644
--- "a/Pytorch\350\256\255\347\273\203\347\244\272\344\276\213/WideResNet50_2_ID1627_for_PyTorch/main.py"
+++ "b/Pytorch\350\256\255\347\273\203\347\244\272\344\276\213/WideResNet50_2_ID1627_for_PyTorch/main.py"
@@ -65,6 +65,8 @@ parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true',
                     help='evaluate model on validation set')
 parser.add_argument('--pretrained', dest='pretrained', action='store_true',
                     help='use pre-trained model')
+parser.add_argument('--weight_path', default='', help='pretrained weight path')                   
+parser.add_argument('--num_classes', default=None,type=int, help='num_classes')                   
 parser.add_argument('--world-size', default=-1, type=int,
                     help='number of nodes for distributed training')
 parser.add_argument('--rank', default=-1, type=int,
@@ -189,9 +191,16 @@ def main_worker(gpu, ngpus_per_node, args):
     if args.pretrained:
         print("=> using pre-trained model wide_resnet50_2")
         model = resnet_0_6_0.wide_resnet50_2(num_classes=args.num_classes)
-        print("loading model of yours...")
-        pretrained_dict = torch.load("./model_best.pth.tar", map_location="cpu")["state_dict"]
+        
+        if os.path.exists(args.weight_path):
+            print("loading model of your give")
+            pretrained_dict = torch.load(args.weight_path, map_location="cpu")["state_dict"]
+        else:
+            print("loading model default ")
+            pretrained_dict = torch.load("./model_best.pth.tar", map_location="cpu")["state_dict"]
+       
         model.load_state_dict({k.replace('module.',''):v for k, v in pretrained_dict.items()})
+        
         if "fc.weight" in pretrained_dict:
             pretrained_dict.pop('fc.weight')
             pretrained_dict.pop('fc.bias')
@@ -359,7 +368,10 @@ def main_worker(gpu, ngpus_per_node, args):
         num_workers=args.workers, pin_memory=False, drop_last=True)
 
     if args.evaluate:
+        print("===eval start===")
         validate(val_loader, model, criterion, args, ngpus_per_node)
+        print("===eval over===")
+        
         return
         
     if args.prof:
@@ -389,6 +401,7 @@ def main_worker(gpu, ngpus_per_node, args):
                                                     and args.rank % ngpus_per_node == 0):
 
             ############## npu modify begin #############
+            print("=========save model======")
             if args.amp:
                 save_checkpoint({
                     'epoch': epoch + 1,
diff --git "a/Pytorch\350\256\255\347\273\203\347\244\272\344\276\213/WideResNet50_2_ID1627_for_PyTorch/test/train_eval_8p.sh" "b/Pytorch\350\256\255\347\273\203\347\244\272\344\276\213/WideResNet50_2_ID1627_for_PyTorch/test/train_eval_8p.sh"
index 7f09f2fbfe86ed7dd5925d55228290cfb1d9dc1f..0afbaaf4580816fe0b2911daa1a569838337641d 100644
--- "a/Pytorch\350\256\255\347\273\203\347\244\272\344\276\213/WideResNet50_2_ID1627_for_PyTorch/test/train_eval_8p.sh"
+++ "b/Pytorch\350\256\255\347\273\203\347\244\272\344\276\213/WideResNet50_2_ID1627_for_PyTorch/test/train_eval_8p.sh"
@@ -9,8 +9,8 @@ batch_size=4096
 # 训练使用的npu卡数
 export RANK_SIZE=8
 # checkpoint文件路径,以实际路径为准
-resume=/home/checkpoint.pth.tar
-# 数据集路径,保持为空,不需要修改
+# resume=/home/checkpoint.pth.tar
+# 数据集路径,需要根据实际情况填入
 data_path=""
 
 # 训练epoch
@@ -28,6 +28,8 @@ do
         workers=`echo ${para#*=}`
     elif [[ $para == --data_path* ]];then
         data_path=`echo ${para#*=}`
+    elif [[ $para == --weight_path* ]];then
+        weight_path=`echo ${para#*=}`
     fi
 done
 
@@ -73,7 +75,6 @@ fi
 python3.7 ./main.py \
     ${data_path} \
     --evaluate \
-    --resume ${resume} \
     --addr=$(hostname -I |awk '{print $1}') \
     --seed=49 \
     --workers=${workers} \
@@ -91,6 +92,8 @@ python3.7 ./main.py \
     --warm_up_epochs=5 \
     --loss-scale=32 \
     --amp \
+    --pretrained \
+    --weight_path=${weight_path} \
     --batch-size=${batch_size} > ${test_path_dir}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 &
 
 wait
diff --git "a/Pytorch\350\256\255\347\273\203\347\244\272\344\276\213/WideResNet50_2_ID1627_for_PyTorch/test/train_finetune_1p.sh" "b/Pytorch\350\256\255\347\273\203\347\244\272\344\276\213/WideResNet50_2_ID1627_for_PyTorch/test/train_finetune_1p.sh"
index 30dac86bf9162a5421cb525f0d8363fec42009f0..f87422ddcb6c197f55ce9a20da315d8cfb09eae6 100644
--- "a/Pytorch\350\256\255\347\273\203\347\244\272\344\276\213/WideResNet50_2_ID1627_for_PyTorch/test/train_finetune_1p.sh"
+++ "b/Pytorch\350\256\255\347\273\203\347\244\272\344\276\213/WideResNet50_2_ID1627_for_PyTorch/test/train_finetune_1p.sh"
@@ -25,6 +25,8 @@ do
         device_id=`echo ${para#*=}`
     elif [[ $para == --data_path* ]];then
         data_path=`echo ${para#*=}`
+    elif [[ $para == --weight_path* ]];then
+        weight_path=`echo ${para#*=}`
     fi
 done
 
@@ -94,8 +96,8 @@ python3.7 ./main.py \
     --warm_up_epochs=5 \
     --loss-scale=32 \
     --amp \
-    --pretrained \
     --num_classes=1200 \
+    --resume=${weight_path} \
     --batch-size=${batch_size} > ${test_path_dir}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 &
 
 wait