diff --git a/PyTorch/built-in/cv/classification/CRNN_for_PyTorch/README.md b/PyTorch/built-in/cv/classification/CRNN_for_PyTorch/README.md
index a1ebd3cc45edc3535a118a534a620ad4cfd9feba..bfc2be29646e37d2873b4188956e703a2e24b73e 100644
--- a/PyTorch/built-in/cv/classification/CRNN_for_PyTorch/README.md
+++ b/PyTorch/built-in/cv/classification/CRNN_for_PyTorch/README.md
@@ -128,6 +128,13 @@ CRNN (Convolutional Recurrent Neural Network) 于2015年由华中科技大学的
    ```
    
    训练完成后，权重文件保存在当前路径下，并输出模型训练精度和性能信息。
+   
+   - 在线推理
+     启动在线推理。
+     首先在./LMDB_config.yaml 中，将TRAIN.RESUME.IS_RESUME的值改为True，并为FILE指定一个pth权重
+     ```
+     bash ./test/train_eval_1p.sh --data_path=/data/crnn_data #在线推理,data_path根据真实数据集路径修改
+     ```
 
 # 训练结果展示
 
diff --git a/PyTorch/built-in/cv/classification/CRNN_for_PyTorch/main_8p.py b/PyTorch/built-in/cv/classification/CRNN_for_PyTorch/main_8p.py
index 62d8e7df640b42675face970e40d310974b8bfee..4026a74300e3aa30d6062711ff0fe207a4b16f41 100644
--- a/PyTorch/built-in/cv/classification/CRNN_for_PyTorch/main_8p.py
+++ b/PyTorch/built-in/cv/classification/CRNN_for_PyTorch/main_8p.py
@@ -59,6 +59,8 @@ def parse_arg():
     parser.add_argument('--start_step', default=0, type=int, help='start_step')
     parser.add_argument('--stop_step', default=1000, type=int,help='stop_step')
     parser.add_argument('--ND', type=ast.literal_eval, default=False, help="enable nd compile")
+    parser.add_argument('--evaluate', dest='evaluate', help="this is evaluate")
+    parser.add_argument('--batch_size', default=2560, type=int, help='size of each batch')
     args = parser.parse_args()
     with open(args.cfg, 'r') as f:
         config = yaml.safe_load(f)
@@ -68,6 +70,10 @@ def parse_arg():
 
 
 def main():
+    # DynamicRNN算子加入黑名单
+    option = {}
+    option['NPU_FUZZY_COMPILE_BLACKLIST'] = "DynamicRNN"
+    torch.npu.set_option(option)
     # load config
     config, args = parse_arg()
     if args.ND:
@@ -149,7 +155,7 @@ def main():
         checkpoint = torch.load(model_state_file, map_location=device)
         if 'state_dict' in checkpoint.keys():
             last_epoch = checkpoint['epoch']
-            model.load_state_dict(checkpoint['state_dict'])
+            model.load_state_dict({k.replace('module.',''):v for k,v in checkpoint['state_dict'].items()})
             best_acc = checkpoint['best_acc']
             optimizer.load_state_dict(checkpoint['optimizer'])
             if config.TRAIN.AMP:
@@ -193,6 +199,11 @@ def main():
     converter = utils.strLabelConverter(config.DATASET.ALPHABETS)
     if config.DISTRIBUTED.RANK % npus_per_node == 0:
         checkpoint_dir, log_dir = utils.create_output_folder(config)
+
+    if args.evaluate:
+        acc = validate(config, val_loader, val_dataset, converter, model, criterion, device, 1)
+        return
+
     for epoch in range(last_epoch, config.TRAIN.END_EPOCH):
         train(config, train_loader, train_dataset, converter, model, criterion, optimizer, device, epoch, npus_per_node,
               npu)
@@ -294,6 +305,8 @@ def validate(config, val_loader, dataset, converter, model, criterion, device, e
     model.eval()
     n_correct = 0
     n_total = 0
+    batch_num = len(val_loader)
+    start = time.time()
     with torch.no_grad():
         for i, (inp, idx) in enumerate(val_loader):
             labels = idx
@@ -317,6 +330,8 @@ def validate(config, val_loader, dataset, converter, model, criterion, device, e
                     n_correct += 1
             if (i + 1) % config.PRINT_FREQ == 0:
                 print('Epoch: [{0}][{1}/{2}]'.format(epoch, i, len(val_loader)))
+    cost = time.time() - start
+    print("Time:", cost / batch_num)
     raw_preds = converter.decode(preds.data, preds_size.data, raw=True)[:config.TEST.NUM_TEST_DISP]
     for raw_pred, pred, gt in zip(raw_preds, sim_preds, labels):
         print('%-20s => %-20s, gt: %-20s' % (raw_pred, pred, gt))
diff --git a/PyTorch/built-in/cv/classification/CRNN_for_PyTorch/test/train_eval_1p.sh b/PyTorch/built-in/cv/classification/CRNN_for_PyTorch/test/train_eval_1p.sh
new file mode 100644
index 0000000000000000000000000000000000000000..bea387cc8af9453cf049c728bffa6c2cac8c2bef
--- /dev/null
+++ b/PyTorch/built-in/cv/classification/CRNN_for_PyTorch/test/train_eval_1p.sh
@@ -0,0 +1,131 @@
+#!/bin/bash
+
+
+#集合通信参数,不需要修改
+export RANK_SIZE=1
+
+# 数据集路径,保持为空,不需要修改
+data_path=""
+
+#网络名称,同目录名称,需要模型审视修改
+Network="CRNN_for_PyTorch"
+
+#训练batch_size,,需要模型审视修改
+batch_size=2560
+
+# 指定训练所使用的npu device卡id
+device_id=0
+# 参数校验，data_path为必传参数，其他参数的增删由模型自身决定；此处新增参数需在上面有定义并赋值
+for para in $*
+do
+    if [[ $para == --data_path* ]];then
+        data_path=`echo ${para#*=}`
+    elif [[ $para == --epochs* ]];then
+        epochs=`echo ${para#*=}`
+    elif [[ $para == --device_id* ]];then
+        device_id=`echo ${para#*=}`
+    elif [[ $para == --batch_size* ]];then
+        batch_size=`echo ${para#*=}`
+    fi
+done
+
+#校验是否传入data_path,不需要修改
+if [[ $data_path == "" ]];then
+    echo "[Error] para \"data_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}"
+elif [ ${device_id} ];then
+    export ASCEND_DEVICE_ID=${device_id}
+    echo "device id is ${ASCEND_DEVICE_ID}"
+else
+    "[Error] device id must be config"
+    exit 1
+fi
+###############指定训练脚本执行路径###############
+# cd到与test文件夹同层级目录下执行脚本，提高兼容性；test_path_dir为包含test文件夹的路径
+cur_path=`pwd`
+cur_path_last_dirname=${cur_path##*/}
+if [ x"${cur_path_last_dirname}" == x"test" ];then
+    test_path_dir=${cur_path}
+    cd ..
+    cur_path=`pwd`
+else
+    test_path_dir=${cur_path}/test
+fi
+
+#创建DeviceID输出目录，不需要修改
+if [ -d ${test_path_dir}/output/${ASCEND_DEVICE_ID} ];then
+    rm -rf ${test_path_dir}/output/$ASCEND_DEVICE_ID
+    mkdir -p ${test_path_dir}/output/$ASCEND_DEVICE_ID
+else
+    mkdir -p ${test_path_dir}/output/$ASCEND_DEVICE_ID
+fi
+
+#################启动训练脚本#################
+#训练开始时间，不需要修改
+start_time=$(date +%s)
+# 非平台场景时source 环境变量
+check_etp_flag=`env | grep etp_running_flag`
+etp_flag=`echo ${check_etp_flag#*=}`
+if [ x"${etp_flag}" != x"true" ];then
+    source ${test_path_dir}/env_npu.sh
+fi
+
+# 必要参数替换配置文件
+cur_path=`pwd`
+sed -i "0,/BATCH_SIZE_PER_GPU.*$/s//BATCH_SIZE_PER_GPU\: ${batch_size}/g" ${cur_path}/LMDB_config.yaml
+sed -i "s/END_EPOCH.*$/END_EPOCH\: ${epochs}/g" ${cur_path}/LMDB_config.yaml
+sed -i "s|TRAIN_ROOT.*$|TRAIN_ROOT\: ${data_path}/MJ_LMDB|g" ${cur_path}/LMDB_config.yaml
+sed -i "s|TEST_ROOT.*$|TEST_ROOT\: ${data_path}/IIIT5K_lmdb|g" ${cur_path}/LMDB_config.yaml
+sed -i "s/DEVICE_ID.*$/DEVICE_ID\: ${device_id}/g" ${cur_path}/LMDB_config.yaml
+
+python3 main_8p.py \
+    --cfg LMDB_config.yaml \
+    --evaluate True \
+    --npu $ASCEND_DEVICE_ID > ${test_path_dir}/output/${ASCEND_DEVICE_ID}/test_${ASCEND_DEVICE_ID}.log 2>&1 &
+wait
+
+##################获取训练数据################
+#训练结束时间，不需要修改
+end_time=$(date +%s)
+e2e_time=$(( $end_time - $start_time ))
+
+#结果打印，不需要修改
+echo "------------------ Final result ------------------"
+#输出性能FPS，需要模型审视修改
+FPS=`grep -a 'Time:' ${test_path_dir}/output/${ASCEND_DEVICE_ID}/test_${ASCEND_DEVICE_ID}.log | tail -n 1 | awk '{print$2}' | awk '{sum+=$1} END {print ('$batch_size')/(sum/NR)}'`
+FPS=${FPS#* }  # 去除前面的空格字符
+#打印，不需要修改
+echo "Final Performance images/sec : $FPS"
+
+#输出训练精度,需要模型审视修改
+eval_accuracy=`grep -a 'best acc is:' ${test_path_dir}/output/${ASCEND_DEVICE_ID}/test_${ASCEND_DEVICE_ID}.log|awk 'END{print}'|awk -F " " '{print $NF}'`
+#打印，不需要修改
+echo "Final Train Accuracy : ${eval_accuracy}"
+echo "E2E Training Duration sec : $e2e_time"
+
+#稳定性精度看护结果汇总
+#训练用例信息，不需要修改
+BatchSize=${batch_size}
+DeviceType=`uname -m`
+CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'acc'
+
+#从test_$ASCEND_DEVICE_ID.log提取Loss到test_${CaseName}_loss.txt中，需要模型审视修改
+grep -a 'Loss' ${test_path_dir}/output/$ASCEND_DEVICE_ID/test_$ASCEND_DEVICE_ID.log|awk -F "Loss " '{print $NF}' | awk -F " " '{print $1}' >> ${test_path_dir}/output/$ASCEND_DEVICE_ID/test_${CaseName}_loss.txt
+#最后一个迭代loss值，不需要修改
+ActualLoss=`awk 'END {print}' ${test_path_dir}/output/$ASCEND_DEVICE_ID/test_${CaseName}_loss.txt`
+
+#关键信息打印到${CaseName}.log中，不需要修改
+echo "Network = ${Network}" > ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "RankSize = ${RANK_SIZE}" >> ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "BatchSize = ${BatchSize}" >> ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "DeviceType = ${DeviceType}" >> ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "CaseName = ${CaseName}" >> ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "ActualFPS = ${ActualFPS}" >> ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "TrainingTime = ${TrainingTime}" >> ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "EvalAccuracy = ${eval_accuracy}" >> ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "ActualLoss = ${ActualLoss}" >> ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "E2ETrainingTime = ${e2e_time}" >> ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log