refine ppyoloe docs, test=document_fix (#5641)

* refine ppyoloe docs, test=document_fix

* fix some problem of docs, test=document_fix

configs/ppyoloe/README.md

@@ -35,13 +35,13 @@ PP-YOLOE is composed of following methods:
 
 - PP-YOLOE is trained on COCO train2017 dataset and evaluated on val2017 & test-dev2017 dataset，Box AP<sup>test</sup> is evaluation results of `mAP(IoU=0.5:0.95)`.
 - PP-YOLOE used 8 GPUs for mixed precision training, if GPU number and mini-batch size is changed, learning rate and iteration times should be adjusted according [FAQ](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/docs/tutorials/FAQ).
-- PP-YOLOE inference speed is tesed on single Tesla V100 with batch size as 1, CUDA 10.2, CUDNN 7.6.5, TensorRT 6.0.1.8 in TensorRT mode.
-- PP-YOLOE inference speed testing uses inference model exported by `tools/export_model.py` with `-o exclude_nms=True` and benchmarked by running `depoly/python/infer.py` with `--run_benchmark`. All testing results do not contains the time cost of data reading and post-processing(NMS), which is same as [YOLOv4(AlexyAB)](https://github.com/AlexeyAB/darknet) in testing method.
+- PP-YOLOE inference speed is tesed on single Tesla V100 with batch size as 1, **CUDA 10.2**, **CUDNN 7.6.5**, **TensorRT 6.0.1.8** in TensorRT mode.
+- Refer to [Speed testing](#Speed-testing) to reproduce the speed testing results of PP-YOLOE.
 - If you set `--run_benchmark=True`，you should install these dependencies at first, `pip install pynvml psutil GPUtil`.
 
 ## Getting Start
 
-### 1. Training
+### Training
 
 Training PP-YOLOE with mixed precision on 8 GPUs with following command
 
@@ -51,7 +51,7 @@ python -m paddle.distributed.launch --gpus 0,1,2,3,4,5,6,7 tools/train.py -c con
 
 ** Notes: ** use `--amp` to train with default config to avoid out of memeory.
 
-### 2. Evaluation
+### Evaluation
 
 Evaluating PP-YOLOE on COCO val2017 dataset in single GPU with following commands:
 
@@ -61,7 +61,7 @@ CUDA_VISIBLE_DEVICES=0 python tools/eval.py -c configs/ppyoloe/ppyoloe_crn_l_300
 
 For evaluation on COCO test-dev2017 dataset, please download COCO test-dev2017 dataset from [COCO dataset download](https://cocodataset.org/#download) and decompress to COCO dataset directory and configure `EvalDataset` like `configs/ppyolo/ppyolo_test.yml`.
 
-### 3. Inference
+### Inference
 
 Inference images in single GPU with following commands, use `--infer_img` to inference a single image and `--infer_dir` to inference all images in the directory.
 
@@ -73,56 +73,96 @@ CUDA_VISIBLE_DEVICES=0 python tools/infer.py -c configs/ppyoloe/ppyoloe_crn_l_30
 CUDA_VISIBLE_DEVICES=0 python tools/infer.py -c configs/ppyoloe/ppyoloe_crn_l_300e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams --infer_dir=demo
 ```
 
-### 4. Deployment
+### Exporting models
 
-- 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)
-<!-- - [Paddle-Lite](https://github.com/PaddlePaddle/Paddle-Lite) -->
+For deployment on GPU or speed testing, model should be first exported to inference model using `tools/export_model.py`.
 
-For deployment on GPU or benchmarked, model should be first exported to inference model using `tools/export_model.py`.
-
-Exporting PP-YOLOE for Paddle Inference **without TensorRT**, use following command.
+**Exporting PP-YOLOE for Paddle Inference without TensorRT**, use following command
 
 ```bash
 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.
+**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 -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 Paddle Inference.
+If you want to export PP-YOLOE model to **ONNX format**, use following command refer to [PaddleDetection Model Export as ONNX Format Tutorial](../../deploy/EXPORT_ONNX_MODEL_en.md).
 
 ```bash
-# inference single image
-CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyoloe_crn_l_300e_coco --image_file=demo/000000014439_640x640.jpg --device=gpu
+# export inference model
+python tools/export_model.py -c configs/ppyoloe/ppyoloe_crn_l_300e_coco.yml --output_dir=output_inference -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams
 
-# inference all images in the directory
-CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyoloe_crn_l_300e_coco --image_dir=demo/ --device=gpu
+# install paddle2onnx
+pip install paddle2onnx
+
+# convert to onnx
+paddle2onnx --model_dir output_inference/ppyoloe_crn_l_300e_coco --model_filename model.pdmodel --params_filename model.pdiparams --opset_version 11 --save_file ppyoloe_crn_l_300e_coco.onnx
 
-# benchmark
-CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyoloe_crn_l_300e_coco --image_file=demo/000000014439_640x640.jpg --device=gpu --run_benchmark=True
 ```
 
-If you want to export PP-YOLOE model to **ONNX format**, use following command refer to [PaddleDetection Model Export as ONNX Format Tutorial](../../deploy/EXPORT_ONNX_MODEL_en.md).
+**Notes:** ONNX model only supports batch_size=1 now
+
+### Speed testing
+
+For fair comparison, the speed in [Model Zoo](#Model-Zoo) do not contains the time cost of data reading and post-processing(NMS), which is same as [YOLOv4(AlexyAB)](https://github.com/AlexeyAB/darknet) in testing method. Thus, you should export model with extra `-o exclude_nms=True` setting.
+
+**Using Paddle Inference without TensorRT** to test speed, run following command
 
 ```bash
 # export inference model
-python tools/export_model.py configs/ppyoloe/ppyoloe_crn_l_300e_coco.yml --output_dir=output_inference -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 exclude_nms=True
 
-# install paddle2onnx
-pip install paddle2onnx
+# speed testing with run_benchmark=True
+CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyoloe_crn_l_300e_coco --image_file=demo/000000014439_640x640.jpg --run_mode=paddle --device=gpu --run_benchmark=True
+```
 
-# convert to onnx
-paddle2onnx --model_dir output_inference/ppyoloe_crn_l_300e_coco --model_filename model.pdmodel --params_filename model.pdiparams --opset_version 11 --save_file ppyoloe_crn_l_300e_coco.onnx
+**Using Paddle Inference with TensorRT** to test speed, run following command
+
+```bash
+# export inference model with 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 exclude_nms=True trt=True
+
+# speed testing with run_benchmark=True,run_mode=trt_fp32/trt_fp16
+CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyoloe_crn_l_300e_coco --image_file=demo/000000014439_640x640.jpg --run_mode=trt_fp16 --device=gpu --run_benchmark=True
+
+```
+
+### Deployment
+
+PP-YOLOE can be deployed by following approches:
+  - Paddle Inference [Python](../../deploy/python) & [C++](../../deploy/cpp)
+  - [Paddle-TensorRT](../../deploy/TENSOR_RT.md)
+  - [PaddleServing](https://github.com/PaddlePaddle/Serving)
+
+Next, we will introduce how to use Paddle Inference to deploy PP-YOLOE models in TensorRT FP16 mode.
+
+First, refer to [Paddle Inference Docs](https://www.paddlepaddle.org.cn/inference/master/user_guides/download_lib.html#python), download and install packages corresponding to CUDA, CUDNN and TensorRT version.
+
+Then, Exporting PP-YOLOE for Paddle Inference **with TensorRT**, use following command.
+
+```bash
+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
+```
+
+Finally, inference in TensorRT FP16 mode.
+
+```bash
+# inference single image
+CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyoloe_crn_l_300e_coco --image_file=demo/000000014439_640x640.jpg --device=gpu --run_mode=trt_fp16
+
+# inference all images in the directory
+CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyoloe_crn_l_300e_coco --image_dir=demo/ --device=gpu  --run_mode=trt_fp16
 
 ```
 
-### 5. Other Datasets
+**Notes: **
+- TensorRT will perform optimization for the current hardware platform according to the definition of the network, generate an inference engine and serialize it into a file. This inference engine is only applicable to the current hardware hardware platform. If your hardware and software platform has not changed, you can set `use_static=True` in [enable_tensorrt_engine](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.4/deploy/python/infer.py#L660). In this way, the serialized file generated will be saved in the `output_inference` folder, and the saved serialized file will be loaded the next time when TensorRT is executed.
+- PaddleDetection release/2.4 and later versions will support NMS calling TensorRT, which requires PaddlePaddle release/2.3 and later versions.
+
+### Other Datasets
 
 Model | AP | AP<sub>50</sub>
 ---|---|---

configs/ppyoloe/README_cn.md

@@ -35,13 +35,13 @@ PP-YOLOE由以下方法组成
 
 - PP-YOLOE模型使用COCO数据集中train2017作为训练集，使用val2017和test-dev2017作为测试集，Box AP<sup>test</sup>为`mAP(IoU=0.5:0.95)`评估结果。
 - PP-YOLOE模型训练过程中使用8 GPUs进行混合精度训练，如果训练GPU数和batch size不使用上述配置，须参考[FAQ](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/docs/tutorials/FAQ)调整学习率和迭代次数。
-- PP-YOLOE模型推理速度测试采用单卡V100，batch size=1进行测试，使用CUDA 10.2, CUDNN 7.6.5，TensorRT推理速度测试使用TensorRT 6.0.1.8。
-- PP-YOLOE推理速度测试使用`tools/export_model.py`并设置`-o exclude_nms=True`脚本导出的模型，并用`deploy/python/infer.py`设置`--run_benchnark`参数得到。测试结果均为不包含数据预处理和模型输出后处理(NMS)的数据(与[YOLOv4(AlexyAB)](https://github.com/AlexeyAB/darknet)测试方法一致)。
+- PP-YOLOE模型推理速度测试采用单卡V100，batch size=1进行测试，使用**CUDA 10.2**, **CUDNN 7.6.5**，TensorRT推理速度测试使用**TensorRT 6.0.1.8**。
+- 参考[速度测试](##速度测试)以复现PP-YOLOE推理速度测试结果。
 - 如果你设置了`--run_benchnark=True`, 你首先需要安装以下依赖`pip install pynvml psutil GPUtil`。
 
 ## 使用教程
 
-### 1. 训练
+### 训练
 
 执行以下指令使用混合精度训练PP-YOLOE
 
@@ -51,7 +51,7 @@ python -m paddle.distributed.launch --gpus 0,1,2,3,4,5,6,7 tools/train.py -c con
 
 ** 注意: ** 使用默认配置训练需要设置`--amp`以避免显存溢出.
 
-### 2. 评估
+### 评估
 
 执行以下命令在单个GPU上评估COCO val2017数据集
 
@@ -61,7 +61,7 @@ CUDA_VISIBLE_DEVICES=0 python tools/eval.py -c configs/ppyoloe/ppyoloe_crn_l_300
 
 在coco test-dev2017上评估，请先从[COCO数据集下载](https://cocodataset.org/#download)下载COCO test-dev2017数据集，然后解压到COCO数据集文件夹并像`configs/ppyolo/ppyolo_test.yml`一样配置`EvalDataset`。
 
-### 3. 推理
+### 推理
 
 使用以下命令在单张GPU上预测图片，使用`--infer_img`推理单张图片以及使用`--infer_dir`推理文件中的所有图片。
 
@@ -74,59 +74,97 @@ CUDA_VISIBLE_DEVICES=0 python tools/infer.py -c configs/ppyoloe/ppyoloe_crn_l_30
 CUDA_VISIBLE_DEVICES=0 python tools/infer.py -c configs/ppyoloe/ppyoloe_crn_l_300e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams --infer_dir=demo
 ```
 
-### 4. 部署
+### 模型导出
 
-- 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)
+PP-YOLOE在GPU上部署或者速度测试需要通过`tools/export_model.py`导出模型。
 
-
-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Paddle Inference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，运行以下的命令导出模型
 
 ```bash
 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Paddle Inference的TensorRT时，需要指定`-o trt=True`进行导出
+当你**使用Paddle Inference且使用TensorRT**时，需要指定`-o trt=True`来导出模型。
 
 ```bash
 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Paddle Inference模型用于推理和benchnark.
+如果你想将PP-YOLOE模型导出为**ONNX格式**，参考
+[PaddleDetection模型导出为ONNX格式教程](../../deploy/EXPORT_ONNX_MODEL.md)，运行以下命令：
 
 ```bash
-# 推理单张图片
-CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyoloe_crn_l_300e_coco --image_file=demo/000000014439_640x640.jpg --device=gpu
 
-# 推理文件夹下的所有图片
-CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyoloe_crn_l_300e_coco --image_dir=demo/ --device=gpu
+# 导出推理模型
+python tools/export_model.py -c configs/ppyoloe/ppyoloe_crn_l_300e_coco.yml --output_dir=output_inference -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams
 
-# benchmark
-CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyoloe_crn_l_300e_coco --image_file=demo/000000014439_640x640.jpg --device=gpu --run_benchmark=True
+# 安装paddle2onnx
+pip install paddle2onnx
+
+# 转换成onnx格式
+paddle2onnx --model_dir output_inference/ppyoloe_crn_l_300e_coco --model_filename model.pdmodel --params_filename model.pdiparams --opset_version 11 --save_file ppyoloe_crn_l_300e_coco.onnx
 ```
 
-如果你想将PP-YOLOE模型导出为**ONNX格式**，参考
-[PaddleDetection模型导出为ONNX格式教程](../../deploy/EXPORT_ONNX_MODEL.md)
+**注意：**ONNX模型目前只支持batch_size=1
+
+### 速度测试
 
+为了公平起见，在[模型库](#模型库)中的速度测试结果均为不包含数据预处理和模型输出后处理(NMS)的数据(与[YOLOv4(AlexyAB)](https://github.com/AlexeyAB/darknet)测试方法一致)，需要在导出模型时指定`-o exclude_nms=True`.
+
+**使用Paddle Inference但不使用TensorRT**进行测速，执行以下命令：
 
 ```bash
-# 导出推理模型
-python tools/export_model.py configs/ppyoloe/ppyoloe_crn_l_300e_coco.yml --output_dir=output_inference -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams
+# 导出模型
+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 exclude_nms=True
 
-# 安装paddle2onnx
-pip install paddle2onnx
+# 速度测试，使用run_benchmark=True
+CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyoloe_crn_l_300e_coco --image_file=demo/000000014439_640x640.jpg --run_mode=paddle --device=gpu --run_benchmark=True
+```
 
-# 转换成onnx格式
-paddle2onnx --model_dir output_inference/ppyoloe_crn_l_300e_coco --model_filename model.pdmodel --params_filename model.pdiparams --opset_version 11 --save_file ppyoloe_crn_l_300e_coco.onnx
+**使用Paddle Inference且使用TensorRT**进行测速，执行以下命令：
+
+```bash
+# 导出模型，使用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 exclude_nms=True trt=True
+
+# 速度测试，使用run_benchmark=True, run_mode=trt_fp32/trt_fp16
+CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyoloe_crn_l_300e_coco --image_file=demo/000000014439_640x640.jpg --run_mode=trt_fp16 --device=gpu --run_benchmark=True
+
+```
+
+### 部署
+
+PP-YOLOE可以使用以下方式进行部署：
+  - Paddle Inference [Python](../../deploy/python) & [C++](../../deploy/cpp)
+  - [Paddle-TensorRT](../../deploy/TENSOR_RT.md)
+  - [PaddleServing](https://github.com/PaddlePaddle/Serving)
+
+接下来，我们将介绍PP-YOLOE如何使用Paddle Inference在TensorRT FP16模式下部署
+
+首先，参考[Paddle Inference文档](https://www.paddlepaddle.org.cn/inference/master/user_guides/download_lib.html#python)，下载并安装与你的CUDA, CUDNN和TensorRT相应的wheel包。
+
+然后，运行以下命令导出模型
+
+```bash
+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
+```
+
+最后，使用TensorRT FP16进行推理
+
+```bash
+# 推理单张图片
+CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyolo_r50vd_dcn_1x_coco --image_file=demo/000000014439_640x640.jpg --device=gpu --run_mode=trt_fp16
+
+# 推理文件夹下的所有图片
+CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyolo_r50vd_dcn_1x_coco --image_dir=demo/ --device=gpu --run_mode=trt_fp16
 
 ```
 
+**注意：**
+- TensorRT会根据网络的定义，执行针对当前硬件平台的优化，生成推理引擎并序列化为文件。该推理引擎只适用于当前软硬件平台。如果你的软硬件平台没有发生变化，你可以设置[enable_tensorrt_engine](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.4/deploy/python/infer.py#L660)的参数`use_static=True`，这样生成的序列化文件将会保存在`output_inference`文件夹下，下次执行TensorRT时将加载保存的序列化文件。
+- PaddleDetection release/2.4及其之后的版本将支持NMS调用TensorRT，需要依赖PaddlePaddle release/2.3及其之后的版本
 
-### 5. 泛化性验证
+### 泛化性验证
 
 模型 | AP | AP<sub>50</sub>
 ---|---|---