Merge branch 'PaddlePaddle:develop' into develop

configs/keypoint/tiny_pose/README.md

@@ -37,16 +37,16 @@ PP-TinyPose是PaddleDetecion针对移动端设备优化的实时关键点检测
 ### 关键点检测模型
 | 模型        | 输入尺寸 | AP (COCO Val) | 单人推理耗时 (FP32) | 单人推理耗时（FP16) |             配置文件             |                           模型权重                           |                         预测部署模型                         |                  Paddle-Lite部署模型（FP32)                  |                  Paddle-Lite部署模型（FP16)                  |
 | :---------- | :------: | :-----------: | :-----------------: | :-----------------: | :------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: |
-| PP-TinyPose |  128*96  |     58.1      |       4.57ms        |       3.27ms        | [Config](./tinypose_128x96.yml)  | [Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.pdparams) | [预测部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.tar) | [Lite部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.tar) | [Lite部署模型(FP16)](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96_fp16.tar) |
-| PP-TinyPose | 256*192  |     68.8      |       14.07ms       |       8.33ms        | [Config](./tinypose_256x192.yml) | [Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192.pdparams) | [预测部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192.tar) | [Lite部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192.tar) | [Lite部署模型(FP16)](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192_fp16.tar) |
+| PP-TinyPose |  128*96  |     58.1      |       4.57ms        |       3.27ms        | [Config](./tinypose_128x96.yml)  | [Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.pdparams) | [预测部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.tar) | [Lite部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96_lite.tar) | [Lite部署模型(FP16)](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96_fp16_lite.tar) |
+| PP-TinyPose | 256*192  |     68.8      |       14.07ms       |       8.33ms        | [Config](./tinypose_256x192.yml) | [Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192.pdparams) | [预测部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192.tar) | [Lite部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192_lite.tar) | [Lite部署模型(FP16)](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192_fp16_lite.tar) |
 
 
 
 ### 行人检测模型
 | 模型                 | 输入尺寸 | mAP (COCO Val) | 平均推理耗时 (FP32) | 平均推理耗时 (FP16) |                           配置文件                           |                           模型权重                           |                         预测部署模型                         |                  Paddle-Lite部署模型（FP32)                  |                  Paddle-Lite部署模型（FP16)                  |
 | :------------------- | :------: | :------------: | :-----------------: | :-----------------: | :----------------------------------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: |
-| PicoDet-S-Pedestrian | 192*192  |      29.0      |       4.30ms        |       2.37ms        | [Config](../../picodet/application/pedestrian_detection/picodet_s_192_pedestrian.yml) | [Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian.pdparams) | [预测部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian.tar) | [Lite部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian.tar) | [Lite部署模型(FP16)](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian_fp16.tar) |
-| PicoDet-S-Pedestrian | 320*320  |      38.5      |       10.26ms       |       6.30ms        | [Config](../../picodet/application/pedestrian_detection/picodet_s_320_pedestrian.yml) | [Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian.pdparams) | [预测部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian.tar) | [Lite部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian.tar) | [Lite部署模型(FP16)](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian_fp16.tar) |
+| PicoDet-S-Pedestrian | 192*192  |      29.0      |       4.30ms        |       2.37ms        | [Config](../../picodet/application/pedestrian_detection/picodet_s_192_pedestrian.yml) | [Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian.pdparams) | [预测部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian.tar) | [Lite部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian_lite.tar) | [Lite部署模型(FP16)](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian_fp16_lite.tar) |
+| PicoDet-S-Pedestrian | 320*320  |      38.5      |       10.26ms       |       6.30ms        | [Config](../../picodet/application/pedestrian_detection/picodet_s_320_pedestrian.yml) | [Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian.pdparams) | [预测部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian.tar) | [Lite部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian_lite.tar) | [Lite部署模型(FP16)](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian_fp16_lite.tar) |
 
 
 

configs/keypoint/tiny_pose/README_en.md

@@ -37,14 +37,14 @@ If you want to deploy it on the mobile devives, you also need:
 ### Keypoint Detection Model
 | Model  | Input Size | AP (COCO Val) | Inference Time for Single Person (FP32)| Inference Time for Single Person（FP16) | Config | Model Weights | Deployment Model | Paddle-Lite Model（FP32) | Paddle-Lite Model（FP16)|
 | :------------------------ | :-------:  | :------: | :------: |:---: | :---: | :---: | :---: | :---: | :---: |
-| PP-TinyPose | 128*96 | 58.1 | 4.57ms | 3.27ms | [Config](./tinypose_128x96.yml) |[Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.pdparams) | [Deployment Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.tar) | [Lite Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.tar) | [Lite Model(FP16)](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96_fp16.tar) |
-| PP-TinyPose | 256*192 | 68.8 | 14.07ms | 8.33ms | [Config](./tinypose_256x192.yml) | [Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192.pdparams) | [Deployment Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192.tar) | [Lite Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192.tar) | [Lite Model(FP16)](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192_fp16.tar) |
+| PP-TinyPose | 128*96 | 58.1 | 4.57ms | 3.27ms | [Config](./tinypose_128x96.yml) |[Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.pdparams) | [Deployment Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.tar) | [Lite Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96_lite.tar) | [Lite Model(FP16)](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96_fp16_lite.tar) |
+| PP-TinyPose | 256*192 | 68.8 | 14.07ms | 8.33ms | [Config](./tinypose_256x192.yml) | [Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192.pdparams) | [Deployment Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192.tar) | [Lite Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192_lite.tar) | [Lite Model(FP16)](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192_fp16_lite.tar) |
 
 ### Pedestrian Detection Model
 | Model  | Input Size | mAP (COCO Val) | Average Inference Time (FP32)| Average Inference Time (FP16) | Config | Model Weights | Deployment Model | Paddle-Lite Model（FP32) | Paddle-Lite Model（FP16)|
 | :------------------------ | :-------:  | :------: | :------: | :---: | :---: | :---: | :---: | :---: | :---: |
-| PicoDet-S-Pedestrian | 192*192 | 29.0 | 4.30ms |  2.37ms | [Config](../../picodet/application/pedestrian_detection/picodet_s_192_pedestrian.yml) |[Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian.pdparams) | [Deployment Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian.tar) | [Lite Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian.tar) | [Lite Model(FP16)](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian_fp16.tar) |
-| PicoDet-S-Pedestrian | 320*320 | 38.5 | 10.26ms |  6.30ms | [Config](../../picodet/application/pedestrian_detection/picodet_s_320_pedestrian.yml) | [Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian.pdparams) | [Deployment Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian.tar) | [Lite Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian.tar) | [Lite Model(FP16)](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian_fp16.tar) |
+| PicoDet-S-Pedestrian | 192*192 | 29.0 | 4.30ms |  2.37ms | [Config](../../picodet/application/pedestrian_detection/picodet_s_192_pedestrian.yml) |[Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian.pdparams) | [Deployment Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian.tar) | [Lite Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian_lite.tar) | [Lite Model(FP16)](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian_fp16_lite.tar) |
+| PicoDet-S-Pedestrian | 320*320 | 38.5 | 10.26ms |  6.30ms | [Config](../../picodet/application/pedestrian_detection/picodet_s_320_pedestrian.yml) | [Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian.pdparams) | [Deployment Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian.tar) | [Lite Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian_lite.tar) | [Lite Model(FP16)](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian_fp16_lite.tar) |
 
 
 **Tips**

configs/ppyoloe/README.md

@@ -75,7 +75,7 @@ CUDA_VISIBLE_DEVICES=0 python tools/infer.py -c configs/ppyoloe/ppyoloe_crn_l_30
 
 ### 4. Deployment
 
-- PaddleInference [Python](../../deploy/python) & [C++](../../deploy/cpp)
+- Paddle Inference [Python](../../deploy/python) & [C++](../../deploy/cpp)
 - [Paddle-TensorRT](../../deploy/TENSOR_RT.md)
 - [Paddle2ONNX](https://github.com/PaddlePaddle/Paddle2ONNX)
 - [PaddleServing](https://github.com/PaddlePaddle/Serving)
@@ -86,16 +86,16 @@ For deployment on GPU or benchmarked, model should be first exported to inferenc
 Exporting PP-YOLOE for Paddle Inference **without TensorRT**, use following command.
 
 ```bash
-python tools/export_model.py configs/ppyoloe/ppyoloe_crn_l_300e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams
+python tools/export_model.py -c configs/ppyoloe/ppyoloe_crn_l_300e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams
 ```
 
 Exporting PP-YOLOE for Paddle Inference **with TensorRT** for better performance, use following command with extra `-o trt=True` setting.
 
 ```bash
-python tools/export_model.py configs/ppyoloe/ppyoloe_crn_l_300e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams trt=True
+python tools/export_model.py -c configs/ppyoloe/ppyoloe_crn_l_300e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams trt=True
 ```
 
-`deploy/python/infer.py` is used to load exported paddle inference model above for inference and benchmark through PaddleInference.
+`deploy/python/infer.py` is used to load exported paddle inference model above for inference and benchmark through Paddle Inference.
 
 ```bash
 # inference single image

configs/ppyoloe/README_cn.md

@@ -76,7 +76,7 @@ CUDA_VISIBLE_DEVICES=0 python tools/infer.py -c configs/ppyoloe/ppyoloe_crn_l_30
 
 ### 4. 部署
 
-- PaddleInference [Python](../../deploy/python) & [C++](../../deploy/cpp)
+- Paddle Inference [Python](../../deploy/python) & [C++](../../deploy/cpp)
 - [Paddle-TensorRT](../../deploy/TENSOR_RT.md)
 - [Paddle2ONNX](https://github.com/PaddlePaddle/Paddle2ONNX)
 - [PaddleServing](https://github.com/PaddlePaddle/Serving)
@@ -84,19 +84,19 @@ CUDA_VISIBLE_DEVICES=0 python tools/infer.py -c configs/ppyoloe/ppyoloe_crn_l_30
 
 PP-YOLOE在GPU上部署或者推理benchmark需要通过`tools/export_model.py`导出模型。
 
-当你使用PaddleInferenced但不使用TensorRT时，运行以下的命令进行导出
+当你使用Paddle Inference但不使用TensorRT时，运行以下的命令进行导出
 
 ```bash
-python tools/export_model.py configs/ppyoloe/ppyoloe_crn_l_300e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams
+python tools/export_model.py -c configs/ppyoloe/ppyoloe_crn_l_300e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams
 ```
 
-当你使用PaddleInference的TensorRT时，需要指定`-o trt=True`进行导出
+当你使用Paddle Inference的TensorRT时，需要指定`-o trt=True`进行导出
 
 ```bash
-python tools/export_model.py configs/ppyoloe/ppyoloe_crn_l_300e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams trt=True
+python tools/export_model.py -c configs/ppyoloe/ppyoloe_crn_l_300e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams trt=True
 ```
 
-`deploy/python/infer.py`使用上述导出后的PaddleInference模型用于推理和benchnark.
+`deploy/python/infer.py`使用上述导出后的Paddle Inference模型用于推理和benchnark.
 
 ```bash
 # 推理单张图片