diff --git a/ACL_PyTorch/built-in/audio/LSTM/ReadMe.md b/ACL_PyTorch/built-in/audio/LSTM/ReadMe.md
index 2897da6e701e578373ccf76eac49d2485a1d31af..9391b24d9f6c962c8a1a6549f705076c428e8116 100644
--- a/ACL_PyTorch/built-in/audio/LSTM/ReadMe.md
+++ b/ACL_PyTorch/built-in/audio/LSTM/ReadMe.md
@@ -316,7 +316,7 @@ LSTM是一种特殊的RNN模型,与普通RNN相比,LSTM可以更好地解决
4. 性能验证。
- 可使用ais_infer推理工具的纯推理模式验证不同batch_size的om模型的性能,参考命令如下:
+ 可使用ais_bench推理工具的纯推理模式验证不同batch_size的om模型的性能,参考命令如下:
```
python3 -m ais_bench --model=${om_model_path} --loop=20 --batchsize=${batch_size}
diff --git a/ACL_PyTorch/built-in/cv/CascadeRCNN-DCN/README.md b/ACL_PyTorch/built-in/cv/CascadeRCNN-DCN/README.md
index 15c4efa6de48534856d4735cb253015f9f77bc3b..525547a64ee91d0ebd60c92b1468a854f457cc3e 100644
--- a/ACL_PyTorch/built-in/cv/CascadeRCNN-DCN/README.md
+++ b/ACL_PyTorch/built-in/cv/CascadeRCNN-DCN/README.md
@@ -302,7 +302,7 @@
4. 性能验证。
- 可使用ais_infer推理工具的纯推理模式验证不同batch_size的om模型的性能,参考命令如下:
+ 可使用ais_bench推理工具的纯推理模式验证不同batch_size的om模型的性能,参考命令如下:
```
python -m ais_bench --model ./cascade.om --loop 100 --batchsize 1
diff --git a/ACL_PyTorch/built-in/cv/SE_ResNet50_Pytorch_Infer/README.md b/ACL_PyTorch/built-in/cv/SE_ResNet50_Pytorch_Infer/README.md
index 522873d50a8deb5f6673502ea95de84fa708d878..f8a2c5626c306d3ad7491a932af345b1708cc865 100644
--- a/ACL_PyTorch/built-in/cv/SE_ResNet50_Pytorch_Infer/README.md
+++ b/ACL_PyTorch/built-in/cv/SE_ResNet50_Pytorch_Infer/README.md
@@ -199,8 +199,6 @@
推理后的输出默认在当前目录result下。
- >**说明:**
- >执行ais-infer工具请选择与运行环境架构相同的命令。参数详情请参见。
3. 精度验证。
@@ -214,7 +212,7 @@
执行后模型精度结果保存在./accuracy_result.json文件中
4. 性能验证。
- 可使用ais_infer推理工具的纯推理模式验证不同batch_size的om模型的性能,参考命令如下:
+ 可使用ais_bench推理工具的纯推理模式验证不同batch_size的om模型的性能,参考命令如下:
```
python3 -m ais_bench --model=${om_model_path} --loop=20 --batchsize=${batch_size}
diff --git a/ACL_PyTorch/built-in/cv/SFA3D_for_Pytorch/README.md b/ACL_PyTorch/built-in/cv/SFA3D_for_Pytorch/README.md
index 87252375cee0933473590c6c0f1552cab424fa01..0bcae36ddaf430b876717489bd3fad6cfc4d47d7 100644
--- a/ACL_PyTorch/built-in/cv/SFA3D_for_Pytorch/README.md
+++ b/ACL_PyTorch/built-in/cv/SFA3D_for_Pytorch/README.md
@@ -235,11 +235,11 @@ SFA3D(Super Fast and Accurate 3D Object Detection based on 3D LiDAR Point Clou
2. 执行推理。
```
- mkdir ais_infer_result
+ mkdir ais_bench_result
python3 -m ais_bench --model SFA3D_bs${n}.om
--input ${input_data_save_path}
--batchsize=${n}
- --output ais_infer_result
+ --output ais_bench_result
```
参数说明:\
@@ -248,7 +248,7 @@ SFA3D(Super Fast and Accurate 3D Object Detection based on 3D LiDAR Point Clou
--input:模型输入,支持bin文件和目录,此例为数据文件夹路径。\
--output:推理结果输出路径。
- 推理后样本的输出在当前目录的ais_infer_result文件夹下,默认会建立日期+时间的子文件夹保存输出结果。
+ 推理后样本的输出在当前目录的ais_bench_result文件夹下,默认会建立日期+时间的子文件夹保存输出结果。
3. 精度验证。
@@ -269,7 +269,7 @@ SFA3D(Super Fast and Accurate 3D Object Detection based on 3D LiDAR Point Clou
参数说明:\
--dataset_dir:KITTI数据集路径,默认路径为 "./SFA3D/dataset/kitti/" 目录。\
- --result_path:推理结果路径,默认为ais_infer_result目录下文件,例如 "./ais_infer_result/dumpdata_outputs/ "。
+ --result_path:推理结果路径,默认为ais_bench_result目录下文件,例如 "./ais_bench_result/dumpdata_outputs/ "。
2. 开源模型loss统计。
diff --git a/ACL_PyTorch/contrib/audio/FastPitch/README.md b/ACL_PyTorch/contrib/audio/FastPitch/README.md
index a49f4e196ae5173ee52d8d6e3b9e68366fa80d29..06c14f22a8859003b3e86fa5b0fff908cdbd48b4 100644
--- a/ACL_PyTorch/contrib/audio/FastPitch/README.md
+++ b/ACL_PyTorch/contrib/audio/FastPitch/README.md
@@ -228,7 +228,7 @@ Fastpitch模型由双向 Transformer 主干(也称为 Transformer 编码器)
5. 性能验证。
- 可使用ais_infer推理工具的纯推理模式验证不同batch_size的om模型的性能,参考命令如下:
+ 可使用ais_bench推理工具的纯推理模式验证不同batch_size的om模型的性能,参考命令如下:
```
python3 -m ais_bench --model=${om_model_path} --loop=20 --batchsize=${batch_size}
diff --git a/ACL_PyTorch/contrib/cv/classfication/Swin-Transformer_tiny/README.md b/ACL_PyTorch/contrib/cv/classfication/Swin-Transformer_tiny/README.md
index 73af06e21f37a04a0af2ec6ac454c5a9dc507932..a013f951df93759c224190f80e55c521cb521568 100644
--- a/ACL_PyTorch/contrib/cv/classfication/Swin-Transformer_tiny/README.md
+++ b/ACL_PyTorch/contrib/cv/classfication/Swin-Transformer_tiny/README.md
@@ -230,7 +230,7 @@ Swin-Transformer是针对于图片处理设计的基于Transformer架构的神
```
python3 swin_postprocess.py --input_dir=outputs/bs16/ --label_path=val_label.txt --save_path=./result_bs16.json
```
- 注:--input_dir指定的路径不是固定,具体路径为ais-infer工具推理命令中'--output/--output_dirname'指定目录下的生成推理结果所在路径
+ 注:--input_dir指定的路径不是固定,具体路径为ais_bench工具推理命令中'--output/--output_dirname'指定目录下的生成推理结果所在路径
--input_dir:生成推理结果所在路径
diff --git a/ACL_PyTorch/contrib/cv/detection/CTPN/README.md b/ACL_PyTorch/contrib/cv/detection/CTPN/README.md
index d1a5c025fd4e57491a3bac21b5e90be65c1228d5..5ad1b4413bf8889d9bf7e26da2cf19f70f475b3e 100644
--- a/ACL_PyTorch/contrib/cv/detection/CTPN/README.md
+++ b/ACL_PyTorch/contrib/cv/detection/CTPN/README.md
@@ -247,10 +247,10 @@ CTPN是一种文字检测算法,它结合了CNN与LSTM深度网络,能有效
请访问[ais_bench推理工具](https://gitee.com/ascend/tools/tree/master/ais-bench_workload/tool/ais_bench)代码仓,根据readme文档进行工具安装。
- 2. 执行推理(${ais_infer_path}请根据实际的推理工具路径填写)。
+ 2. 执行推理(${ais_bench_path}请根据实际的推理工具路径填写)。
```
- python3 task_process.py --interpreter="python3 ${ais_infer_path}/ais_infer.py" --om_path=./ctpn_bs1.om --src_dir=./pre_bin/images_bin --res_dir=./result --batch_size=1 --device=0
+ python3 task_process.py --interpreter="python3 ${ais_bench_path}/ais_infer.py" --om_path=./ctpn_bs1.om --src_dir=./pre_bin/images_bin --res_dir=./result --batch_size=1 --device=0
```
- 参数说明:
- --interpreter:推理工具。
diff --git a/ACL_PyTorch/contrib/cv/detection/Detr/README.md b/ACL_PyTorch/contrib/cv/detection/Detr/README.md
index 13be0cd4d41cfe49b5745e36238dd36ebcad1946..0be42d0f9d4e54dac63e0f88bd753686901ed653 100755
--- a/ACL_PyTorch/contrib/cv/detection/Detr/README.md
+++ b/ACL_PyTorch/contrib/cv/detection/Detr/README.md
@@ -232,7 +232,7 @@ DETR是将目标检测视为一个集合预测问题(集合其实和anchors的
- --ais_path:ais_bench推理工具推理文件路径
- --img_path:前处理的图片文件路径
- --mask_path:前处理的mask文件路径
- - --out_put:ais_infer推理数据输出路径
+ - --out_put:ais_bench推理数据输出路径
- --result:推理数据最终汇总路径
- --batch_size:batch大小,可选1或4
diff --git a/ACL_PyTorch/contrib/cv/detection/FCENet/readme.md b/ACL_PyTorch/contrib/cv/detection/FCENet/readme.md
index 217d9ed57e6f96dd67918363e7e4be4053413288..45b2fad134a80ff97a41c4f16d4371f2a565243b 100644
--- a/ACL_PyTorch/contrib/cv/detection/FCENet/readme.md
+++ b/ACL_PyTorch/contrib/cv/detection/FCENet/readme.md
@@ -169,7 +169,7 @@ FCENet,使用傅里叶变换来得到文本的包围框,该方法在弯曲
## 推理验证
1. 对数据集推理
- 该离线模型使用ais_infer作为推理工具,请参考[**安装文档**](https://gitee.com/ascend/tools/tree/master/ais-bench_workload/tool/ais_bench#%E4%B8%80%E9%94%AE%E5%AE%89%E8%A3%85)安装推理后端包aclruntime与推理前端包ais_bench。完成安装后,执行以下命令预处理后的数据进行推理。
+ 安装ais_bench推理工具。请访问[ais_bench推理工具](https://gitee.com/ascend/tools/tree/master/ais-bench_workload/tool/ais_bench)代码仓,根据readme文档进行工具安装。
```bash
python3 -m ais_bench \
--model ./fcenet_bs${batch_size} \
diff --git a/ACL_PyTorch/contrib/cv/detection/NAS_FPN/README.md b/ACL_PyTorch/contrib/cv/detection/NAS_FPN/README.md
index a7753d0fa6d2181a1d07fe0d0fde62c3e29fb578..a13223627e26518127f71107e70478399d6f4c39 100644
--- a/ACL_PyTorch/contrib/cv/detection/NAS_FPN/README.md
+++ b/ACL_PyTorch/contrib/cv/detection/NAS_FPN/README.md
@@ -268,7 +268,7 @@ python3 mmdetection_coco_postprocess.py --bin_data_path=../result/2022_xx_xx-xx_
--img_path:推理数据集。
---is_ais_infer: 是否使用的是ais_infer进行推理。
+--is_ais_infer: 是否使用的是ais_bench进行推理。
评测结果的mAP值需要使用官方的pycocotools工具,首先将后处理输出的txt文件转化为coco数据集评测精度的标准json格式。
diff --git a/ACL_PyTorch/contrib/cv/detection/YOLOV3/README.md b/ACL_PyTorch/contrib/cv/detection/YOLOV3/README.md
index d2de3baca59a06358673ce20bb30375f7b7c6cfc..60d781b4ac783dd239c79dc52f33d5fc5db77395 100755
--- a/ACL_PyTorch/contrib/cv/detection/YOLOV3/README.md
+++ b/ACL_PyTorch/contrib/cv/detection/YOLOV3/README.md
@@ -264,12 +264,12 @@ YOLOv3是一种端到端的one-stage目标检测模型。相比与YOLOv2,YOLOv
b. 执行推理。
- mkdir ais_infer_result
+ mkdir ais_bench_result
source /usr/local/Ascend/ascend-toolkit/set_env.sh
python3 -m ais_bench --model yolov3_bsn.om
--input yolov3_bin
--batchsize=n
- --output ais_infer_result
+ --output ais_bench_result
推理后的输出默认在当前目录result下。
@@ -277,11 +277,11 @@ YOLOv3是一种端到端的one-stage目标检测模型。相比与YOLOv2,YOLOv
c. 模型后处理。
解析输出特征图。
- 解析ais_infer输出文件,经过阈值过滤,nms,坐标转换等输出坐标信息和类别信息txt文件。
+ 解析ais_bench输出文件,经过阈值过滤,nms,坐标转换等输出坐标信息和类别信息txt文件。
python3.7 bin_to_predict_yolo_pytorch.py
- --bin_data_path ais_infer_result/${ais_infer输出的结果}/
+ --bin_data_path ais_bench_result/${ais_bench输出的结果}/
--det_results_path detection-results/
--origin_jpg_path val2014/
--coco_class_names coco2014.names
diff --git a/ACL_PyTorch/contrib/cv/detection/YOLOX/readme.md b/ACL_PyTorch/contrib/cv/detection/YOLOX/readme.md
index b2687a61ad14367a3366d460fb5b0e6b0aa6bc07..a5e5a4c74a9bbfad1bb69606968379ed336f96cf 100644
--- a/ACL_PyTorch/contrib/cv/detection/YOLOX/readme.md
+++ b/ACL_PyTorch/contrib/cv/detection/YOLOX/readme.md
@@ -224,7 +224,7 @@ YOLOX是基于往年对YOLO系列众多改进而产生的目标检测模型,
- 参数说明:
- --dataroot:数据集路径
- - --dump_dir:`ais-infer`推理结果文件目录
+ - --dump_dir:`ais_bench`推理结果文件目录
# 模型推理性能&精度
diff --git a/ACL_PyTorch/contrib/cv/detection/ch_ppocr_server_v2.0_det/README.md b/ACL_PyTorch/contrib/cv/detection/ch_ppocr_server_v2.0_det/README.md
index f7f0e10c52fead661de9631f8163e89b289007f4..45c5001201958ab739a442e14414b09f20c16fab 100644
--- a/ACL_PyTorch/contrib/cv/detection/ch_ppocr_server_v2.0_det/README.md
+++ b/ACL_PyTorch/contrib/cv/detection/ch_ppocr_server_v2.0_det/README.md
@@ -217,13 +217,13 @@ ch_PP-OCRv2_det是基于PP-OCRv2的中文文本检测模型,PP-OCRv2在PP-OCR
b. 执行推理。
```
python3 ch_server_det_ais_infer.py \
- --ais_infer=${path_to_ais-infer}/ais_infer.py \
+ --ais_infer=${path_to_ais_bench}/ais_infer.py \
--model=./ch_ppocr_server_det_bs${batchsize}.om \
--inputs=./pre_data \
--batchsize=${batchsize}
```
- `${path_to_ais-infer}`为ais_infer.py脚本的存放路径。`${batchsize}`表示不同batch的om模型。
+ `${path_to_ais_bench}`为ais_infer.py脚本的存放路径。`${batchsize}`表示不同batch的om模型。
c. 精度验证。
diff --git a/ACL_PyTorch/contrib/cv/detection/en_PP-OCRv3_det/README.md b/ACL_PyTorch/contrib/cv/detection/en_PP-OCRv3_det/README.md
index 65e21a4d7821048189f18b9ce57d5d4e4ebc9fa9..a5ddcbf3ed27592946aa9bfbbf10b4f3e4679b15 100644
--- a/ACL_PyTorch/contrib/cv/detection/en_PP-OCRv3_det/README.md
+++ b/ACL_PyTorch/contrib/cv/detection/en_PP-OCRv3_det/README.md
@@ -230,7 +230,7 @@ en_PP-OCRv3_det是基于[[PP-OCRv3](https://github.com/PaddlePaddle/PaddleOCR/bl
```
python en_PP-OCRv3_det_ais_infer.py \
- --ais_infer=${path_to_ais-infer}/ais_infer.py \
+ --ais_infer=${path_to_ais_bench}/ais_infer.py \
--model=./en_PP-OCRv3_det_bs${batchsize}.om \
--inputs=./pre_data \
--batchsize=${batchsize}
@@ -242,7 +242,7 @@ en_PP-OCRv3_det是基于[[PP-OCRv3](https://github.com/PaddlePaddle/PaddleOCR/bl
- --inputs:输入数据集路径。
- --batchsize:om模型的batchsize。
- `${path_to_ais-infer}`为ais_infer.py脚本的存放路径。`${batchsize}`表示不同batch的om模型。。
+ `${path_to_ais_bench}`为ais_infer.py脚本的存放路径。`${batchsize}`表示不同batch的om模型。。
推理完成后结果保存在`en_PP-OCRv3_det/results_bs${batchsize}`目录下。
diff --git a/ACL_PyTorch/contrib/cv/detection/yolor/README.md b/ACL_PyTorch/contrib/cv/detection/yolor/README.md
index 067fd1ec194fccab1abc13889f04fa108d1cbc1e..90a08c6c8ea8b3193c16ac876be200995a794b4b 100644
--- a/ACL_PyTorch/contrib/cv/detection/yolor/README.md
+++ b/ACL_PyTorch/contrib/cv/detection/yolor/README.md
@@ -245,7 +245,7 @@ b. 执行推理。
c. 精度验证。
-调用yolor_postprocess.py,可以获得Accuracy数据。修改yolor_postprocess.py第109行output_path为ais_infer推理的output路径。
+调用yolor_postprocess.py,可以获得Accuracy数据。修改yolor_postprocess.py第109行output_path为ais_bench推理的output路径。
```
python3 yolor_postprocess.py --data ./coco.yaml --img 1344 --batch 1 --conf 0.001 --iou 0.65 --npu 0 --name yolor_p6_val --names ./yolor/data/coco.names
diff --git a/ACL_PyTorch/contrib/cv/detection/yolor/yolor_postprocess.py b/ACL_PyTorch/contrib/cv/detection/yolor/yolor_postprocess.py
index 92e495ad9140990d5498fb5322732e3e10544e17..f86d7a522ad130c30b6f198c84f45c9f333dc830 100644
--- a/ACL_PyTorch/contrib/cv/detection/yolor/yolor_postprocess.py
+++ b/ACL_PyTorch/contrib/cv/detection/yolor/yolor_postprocess.py
@@ -106,7 +106,7 @@ def test(data,
loss = torch.zeros(3, device=device)
jdict, stats, ap, ap_class, wandb_images = [], [], [], [], []
# output_path = './result/dumpOutput_device0'
- output_path = './tools/ais-bench_workload/tool/ais_infer/2022_09_20-09_55_08'
+ output_path = './tools/ais-bench_workload/tool/ais_bench/2022_09_20-09_55_08'
pbar = tqdm(dataloader)
# ort_session = ort.InferenceSession('yolor_bs1simp.onnx')
diff --git a/ACL_PyTorch/contrib/cv/gan/GAN/README.md b/ACL_PyTorch/contrib/cv/gan/GAN/README.md
index 898049472b38589f7e143dbb21bf00db6dfd0951..6d74ae2b1d5921436ebf8890ea7a574d2e07a6a7 100644
--- a/ACL_PyTorch/contrib/cv/gan/GAN/README.md
+++ b/ACL_PyTorch/contrib/cv/gan/GAN/README.md
@@ -235,7 +235,7 @@
d. 性能验证。
- ais_infer纯推理验证不同batch_size的om模型的性能,参考命令如下:
+ ais_bench纯推理验证不同batch_size的om模型的性能,参考命令如下:
```
python3.7 -m ais_bench --model=${om_model_path} --loop=100 --batchsize=${batch_size}
diff --git a/ACL_PyTorch/contrib/cv/image_registration/superpoint/README.md b/ACL_PyTorch/contrib/cv/image_registration/superpoint/README.md
index 6e73a534c83f07f0aa80aa0d8979ff7998355652..02535955deab3cf7f6821d8b04c08efc32bc4b9a 100644
--- a/ACL_PyTorch/contrib/cv/image_registration/superpoint/README.md
+++ b/ACL_PyTorch/contrib/cv/image_registration/superpoint/README.md
@@ -230,8 +230,7 @@
python -m ais_bench --model=${om_model_path} --loop=20 --batchsize=${batch_size}
```
| 参数 | 说明
- | -------- |---------------------------|
- | ais_infer_path | ais_infer文件路径 |
+ | -------- |---------------------------|
| om_model_path | 模型文件保存的位置 |
|batchsize | batchsize大小 |
diff --git a/ACL_PyTorch/contrib/cv/pose_estimation/PoseC3D/README.md b/ACL_PyTorch/contrib/cv/pose_estimation/PoseC3D/README.md
index 18e44f080aef4a81d32ea36dd04f46815af8cda1..161048dacc074419f21d6ea37bc0bbc55f9a8f22 100644
--- a/ACL_PyTorch/contrib/cv/pose_estimation/PoseC3D/README.md
+++ b/ACL_PyTorch/contrib/cv/pose_estimation/PoseC3D/README.md
@@ -177,7 +177,7 @@ PoseC3D 是一种基于 3D-CNN 的骨骼行为识别框架,同时具备良好
## 推理验证
1. 对数据集推理
- 该离线模型使用ais_infer作为推理工具,请参考[**安装文档**](https://gitee.com/ascend/tools/tree/master/ais-bench_workload/tool/ais_bench#%E4%B8%80%E9%94%AE%E5%AE%89%E8%A3%85)安装推理后端包aclruntime与推理前端包ais_bench。完成安装后,执行以下命令预处理后的数据进行推理。
+ 安装ais_bench推理工具。请访问[ais_bench推理工具](https://gitee.com/ascend/tools/tree/master/ais-bench_workload/tool/ais_bench)代码仓,根据readme文档进行工具安装。
```bash
python -m ais_bench \
--model ./posec3d_bs${bs}.om \
diff --git a/ACL_PyTorch/contrib/cv/quality_enhancement/ADNet/README.md b/ACL_PyTorch/contrib/cv/quality_enhancement/ADNet/README.md
index 42826f00233b951f224c4364ecad29547cdce476..560de88bcc70a4ac069deb8f86d877705f024d33 100644
--- a/ACL_PyTorch/contrib/cv/quality_enhancement/ADNet/README.md
+++ b/ACL_PyTorch/contrib/cv/quality_enhancement/ADNet/README.md
@@ -257,7 +257,7 @@
d. 性能验证。
- 可使用ais_infer推理工具的纯推理模式验证不同batch_size的om模型的性能,参考命令如下:
+ 可使用ais_bench推理工具的纯推理模式验证不同batch_size的om模型的性能,参考命令如下:
```python
python3 -m ais_bench --model=${om_model_path} --loop=20 --batchsize=${batch_size} --device=${device_id} --outfmt=BIN
diff --git a/ACL_PyTorch/contrib/cv/segmentation/DeeplabV3/README.md b/ACL_PyTorch/contrib/cv/segmentation/DeeplabV3/README.md
index 3a3dfada91240c0294b0352faa8aba44b8ae9e50..f69bfc949d37ca6419b950c371cf6d7692eba417 100644
--- a/ACL_PyTorch/contrib/cv/segmentation/DeeplabV3/README.md
+++ b/ACL_PyTorch/contrib/cv/segmentation/DeeplabV3/README.md
@@ -239,7 +239,7 @@ DeeplabV3是一个经典的图像语义分割网络,在v1和v2版本基础上
```
- 参数说明:
- - --output_path:ais_infer生成推理结果所在路径。
+ - --output_path:ais_bench生成推理结果所在路径。
- --gt_path:标签数据路径。
- --result_path:为生成结果文件
diff --git a/ACL_PyTorch/contrib/cv/segmentation/ErfNet/README.md b/ACL_PyTorch/contrib/cv/segmentation/ErfNet/README.md
index 6f3bc723b32e1f5b878bf7b9873c757072a9b6cc..67ab051a6b2d2dc4cbac69d22ca25687c7fe3615 100644
--- a/ACL_PyTorch/contrib/cv/segmentation/ErfNet/README.md
+++ b/ACL_PyTorch/contrib/cv/segmentation/ErfNet/README.md
@@ -193,7 +193,7 @@ python3 -m ais_bench --model ${user_path}/ErfNet/ErfNet_bs1.om --input=${user_pa
python ErfNet_postprocess.py ${user_path}/output/2022_07_15-14_16_46/sumary.json ${user_path}/ErfNet/gt_label/
```
-“${user_path}/output/2022_07_15-14_16_46/sumary.json”:ais_infer推理结果汇总数据保存路径。
+“${user_path}/output/2022_07_15-14_16_46/sumary.json”:ais_bench推理结果汇总数据保存路径。
${user_path}/ErfNet/gt_label/:合并后的验证集路径。
diff --git a/ACL_PyTorch/contrib/cv/segmentation/OCRNet/README.md b/ACL_PyTorch/contrib/cv/segmentation/OCRNet/README.md
index 0c5159d55a47376ef33a84cfe08eca076f75e14f..b9b844cb681da712234965126676bae10225def1 100644
--- a/ACL_PyTorch/contrib/cv/segmentation/OCRNet/README.md
+++ b/ACL_PyTorch/contrib/cv/segmentation/OCRNet/README.md
@@ -205,7 +205,8 @@
## 推理验证
-1. 该离线模型使用ais_infer作为推理工具,请参考[**安装文档**](https://gitee.com/ascend/tools/tree/master/ais-bench_workload/tool/ais_bench#%E4%B8%80%E9%94%AE%E5%AE%89%E8%A3%85)安装推理后端包aclruntime与推理前端包ais_bench。完成安装后,执行以下命令预处理后的数据进行推理。
+1. 安装ais_bench推理工具。
+ 请访问[ais_bench推理工具](https://gitee.com/ascend/tools/tree/master/ais-bench_workload/tool/ais_bench)代码仓,根据readme文档进行工具安装。
```bash
python3 -m ais_bench
--model ./ocrnet_optimize_bs${batch_size}.om \
diff --git a/ACL_PyTorch/contrib/cv/segmentation/Transformer-SSL/README.md b/ACL_PyTorch/contrib/cv/segmentation/Transformer-SSL/README.md
index 4c93845567a05097927ae86654a9ac88bee21add..5183f006f90365dc95fde74b0e3faa87e6e9cced 100755
--- a/ACL_PyTorch/contrib/cv/segmentation/Transformer-SSL/README.md
+++ b/ACL_PyTorch/contrib/cv/segmentation/Transformer-SSL/README.md
@@ -239,7 +239,7 @@ Transformer-SSL使用不同的IOU阈值,训练多个级联的检测器。它
- 参数说明:
- --ann_file_path:为标签信息文件。
- - --bin_file_path:为ais_infer推理结果存放路径。
+ - --bin_file_path:为ais_bench推理结果存放路径。
# 模型推理性能&精度
该模型只支持bs1推理
diff --git a/ACL_PyTorch/contrib/cv/segmentation/Ultra-Fast-Lane-Detection/README.md b/ACL_PyTorch/contrib/cv/segmentation/Ultra-Fast-Lane-Detection/README.md
index cdf0126c64cac5b9b7c1ea83e828c2dbe69df167..b03a2413d2e4c5cd1e970f1b040b38e7a2a6751c 100644
--- a/ACL_PyTorch/contrib/cv/segmentation/Ultra-Fast-Lane-Detection/README.md
+++ b/ACL_PyTorch/contrib/cv/segmentation/Ultra-Fast-Lane-Detection/README.md
@@ -208,7 +208,7 @@
2. 离线推理
- 使用ais_infer工具将预处理后的数据传入模型并执行推理:
+ 使用ais_bench工具将预处理后的数据传入模型并执行推理:
```shell
# 设置环境变量
source /usr/local/Ascend/ascend-toolkit/set_env.sh
diff --git a/ACL_PyTorch/contrib/cv/segmentation/YOLACT/README.md b/ACL_PyTorch/contrib/cv/segmentation/YOLACT/README.md
index bce6ca4a2e513a758abfff95e12445f9fbeed569..ab2b6c2d2490a77197a2d91397eb068dc78c79b6 100644
--- a/ACL_PyTorch/contrib/cv/segmentation/YOLACT/README.md
+++ b/ACL_PyTorch/contrib/cv/segmentation/YOLACT/README.md
@@ -214,7 +214,7 @@ YOLACT是2019年发表在ICCV上面的一个实时实例分割的模型,它主
请访问[ais_bench推理工具](https://gitee.com/ascend/tools/tree/master/ais-bench_workload/tool/ais_bench)代码仓,根据readme文档进行工具安装。
- 2. 执行推理(${ais_infer_path}请根据实际的推理工具路径填写)。
+ 2. 执行推理。
```
mkdir result
diff --git a/ACL_PyTorch/contrib/cv/super_resolution/RCAN/README.md b/ACL_PyTorch/contrib/cv/super_resolution/RCAN/README.md
index 0b39c810923301141405f62fc470dc9194b12abf..d7393073fcdc324df1e4c03df89a161de95e3b13 100644
--- a/ACL_PyTorch/contrib/cv/super_resolution/RCAN/README.md
+++ b/ACL_PyTorch/contrib/cv/super_resolution/RCAN/README.md
@@ -169,7 +169,7 @@ RCAN设计了一个残差中的残差(RIR)结构来构造深层网络,每
## 推理验证
1. 对数据集推理
- 该离线模型使用ais_infer作为推理工具,请参考[**安装文档**](https://gitee.com/ascend/tools/tree/master/ais-bench_workload/tool/ais_bench#%E4%B8%80%E9%94%AE%E5%AE%89%E8%A3%85)安装推理后端包aclruntime与推理前端包ais_bench。完成安装后,执行以下命令预处理后的数据进行推理。
+ 安装ais_bench推理工具。请访问[ais_bench推理工具](https://gitee.com/ascend/tools/tree/master/ais-bench_workload/tool/ais_bench)代码仓,根据readme文档进行工具安装。
```bash
python -m ais_bench \
--model rcan_bs${bs}.om \
diff --git a/ACL_PyTorch/contrib/cv/video_understanding/SlowFast/README.md b/ACL_PyTorch/contrib/cv/video_understanding/SlowFast/README.md
index 79380e8f133444636d6ed372684cdb6f2e0d2c56..b4b0f48e21d60c2f2cf399e406142b6521757e34 100644
--- a/ACL_PyTorch/contrib/cv/video_understanding/SlowFast/README.md
+++ b/ACL_PyTorch/contrib/cv/video_understanding/SlowFast/README.md
@@ -193,7 +193,7 @@ SlowFast 是用于视频理解的双流框架的卷积神经网络,该网络
## 推理验证
1. 对数据集推理
- 该离线模型使用ais_infer作为推理工具,请参考[**安装文档**](https://gitee.com/ascend/tools/tree/master/ais-bench_workload/tool/ais_bench#%E4%B8%80%E9%94%AE%E5%AE%89%E8%A3%85)安装推理后端包aclruntime与推理前端包ais_bench。完成安装后,执行以下命令预处理后的数据进行推理。
+ 安装ais_bench推理工具。请访问[ais_bench推理工具](https://gitee.com/ascend/tools/tree/master/ais-bench_workload/tool/ais_bench)代码仓,根据readme文档进行工具安装。
```bash
python -m ais_bench \
--model slowfast_bs${bs}.om \
diff --git a/ACL_PyTorch/contrib/cv/video_understanding/X3D/README.md b/ACL_PyTorch/contrib/cv/video_understanding/X3D/README.md
index 93799cc9a58ef452354fdbe62ecedbbf8134afe5..fd03b750ac6fecccd92b2b8d5b2ffea1760382e1 100644
--- a/ACL_PyTorch/contrib/cv/video_understanding/X3D/README.md
+++ b/ACL_PyTorch/contrib/cv/video_understanding/X3D/README.md
@@ -180,7 +180,7 @@ X3D,这是一个高效的视频网络系列,可沿多个网络轴在空间
## 推理验证
1. 对数据集推理
- 该离线模型使用ais_infer作为推理工具,请参考[**安装文档**](https://gitee.com/ascend/tools/tree/master/ais-bench_workload/tool/ais_bench#%E4%B8%80%E9%94%AE%E5%AE%89%E8%A3%85)安装推理后端包aclruntime与推理前端包ais_bench。完成安装后,执行以下命令预处理后的数据进行推理。
+ 安装ais_bench推理工具。请访问[ais_bench推理工具](https://gitee.com/ascend/tools/tree/master/ais-bench_workload/tool/ais_bench)代码仓,根据readme文档进行工具安装。
```bash
python -m ais_bench \
--model x3d_s_bs${bs}.om \