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..d7ddc8a4b3722af9fe0e43770f8448fd43f46669 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 --pth_path=real_pre_train_model_path
+
+# finetuning 1p 
+bash test/train_finetune_1p.sh --data_path=real_data_path --pth_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..32cb29fefc5587590915ef112ffcb8cf2e04b2bc 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('--pth_path', default='', type=str, metavar='PATH',
+                    help='path to pretrained checkpoint (default: none)')
 parser.add_argument('--world-size', default=-1, type=int,
                     help='number of nodes for distributed training')
 parser.add_argument('--rank', default=-1, type=int,
@@ -82,7 +84,7 @@ parser.add_argument('--multiprocessing-distributed', action='store_true',
                          'N processes per node, which has N GPUs. This is the '
                          'fastest way to use PyTorch for either single node or '
                          'multi node data parallel training')
-parser.add_argument('--num-classes', default=1000, type=int,
+parser.add_argument('--num_classes', default=1000, type=int,
                     help='The number of classes.')
 ## for ascend 910
 parser.add_argument('--device', default='npu', type=str, help='npu or gpu')
@@ -190,9 +192,14 @@ def main_worker(gpu, ngpus_per_node, args):
         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"]
-        model.load_state_dict({k.replace('module.',''):v for k, v in pretrained_dict.items()})
+        if args.pth_path:
+            print("load pth you give")
+            pretrained_dict = torch.load(args.pth_path, map_location="cpu")["state_dict"]
+        else:
+            pretrained_dict = torch.load("./model_best.pth.tar", map_location="cpu")["state_dict"]
+        
         if "fc.weight" in pretrained_dict:
+            print("pop fc layer weight")
             pretrained_dict.pop('fc.weight')
             pretrained_dict.pop('fc.bias')
         model.load_state_dict(pretrained_dict, strict=False)
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..d34f47b5e08af2face35096ec34a07adacfbcd14 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"
@@ -8,11 +8,10 @@ Network="WideResNet50_2_ID1627_for_PyTorch"
 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
 data_path=""
-
+# checkpoint文件路径,以实际路径为准
+pth_path=""
 # 训练epoch
 train_epochs=200
 # 学习率
@@ -28,6 +27,8 @@ do
         workers=`echo ${para#*=}`
     elif [[ $para == --data_path* ]];then
         data_path=`echo ${para#*=}`
+    elif [[ $para == --pth_path* ]];then
+        pth_path=`echo ${para#*=}`
     fi
 done
 
@@ -37,6 +38,11 @@ if [[ $data_path == "" ]];then
     exit 1
 fi
 
+# 校验是否传入 pth_path , 验证脚本需要传入此参数
+if [[ $pth_path == "" ]];then
+    echo "[Error] para \"pth_path\" must be confing"
+    exit 1
+fi
 
 ###############指定训练脚本执行路径###############
 # cd到与test文件夹同层级目录下执行脚本，提高兼容性；test_path_dir为包含test文件夹的路径
@@ -73,7 +79,7 @@ fi
 python3.7 ./main.py \
     ${data_path} \
     --evaluate \
-    --resume ${resume} \
+    --resume ${pth_path} \
     --addr=$(hostname -I |awk '{print $1}') \
     --seed=49 \
     --workers=${workers} \
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..1821a33a110e974026c47fa537ce66ef5bdbace3 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"
@@ -10,7 +10,8 @@ batch_size=256
 export RANK_SIZE=1
 # 数据集路径,保持为空,不需要修改
 data_path=""
-
+# checkpoint文件路径,以实际路径为准
+pth_path=""
 # 训练epoch
 train_epochs=200
 # 指定训练所使用的npu device卡id
@@ -25,6 +26,8 @@ do
         device_id=`echo ${para#*=}`
     elif [[ $para == --data_path* ]];then
         data_path=`echo ${para#*=}`
+    elif [[ $para == --pth_path* ]];then
+        pth_path=`echo ${para#*=}`
     fi
 done
 
@@ -33,6 +36,13 @@ if [[ $data_path == "" ]];then
     echo "[Error] para \"data_path\" must be confing"
     exit 1
 fi
+
+# 校验是否传入 pth_path , 验证脚本需要传入此参数
+if [[ $pth_path == "" ]];then
+    echo "[Error] para \"pth_path\" must be confing"
+    exit 1
+fi
+
 # 校验是否指定了device_id,分动态分配device_id与手动指定device_id,此处不需要修改
 if [ $ASCEND_DEVICE_ID ];then
     echo "device id is ${ASCEND_DEVICE_ID}"
@@ -95,6 +105,7 @@ python3.7 ./main.py \
     --loss-scale=32 \
     --amp \
     --pretrained \
+    --pth_path=${pth_path} \
     --num_classes=1200 \
     --batch-size=${batch_size} > ${test_path_dir}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 &