203 lines
6.7 KiB
Markdown
203 lines
6.7 KiB
Markdown
简体中文 | [English](README_en.md)
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## 模型
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| Model | Epoch | backbone | input shape | $AP^{val}$ | $AP^{val}_{50}$| Params(M) | FLOPs(G) | T4 TensorRT FP16(FPS) | Pretrained Model | config |
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|:--------------:|:-----:|:----------:| :-------:|:--------------------------:|:---------------------------:|:---------:|:--------:| :---------------------: |:------------------------------------------------------------------------------------:|:-------------------------------------------:|
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| RT-DETR-R18 | 6x | ResNet-18 | 640 | 46.5 | 63.8 | 20 | 60 | 217 | [download](https://bj.bcebos.com/v1/paddledet/models/rtdetr_r18vd_dec3_6x_coco.pdparams) | [config](./configs/rtdetr/rtdetr_r18vd_6x_coco.yml)
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| RT-DETR-R34 | 6x | ResNet-34 | 640 | 48.9 | 66.8 | 31 | 92 | 161 | [download](https://bj.bcebos.com/v1/paddledet/models/rtdetr_r34vd_dec4_6x_coco.pdparams) | [config](./configs/rtdetr/rtdetr_r34vd_6x_coco.yml)
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| RT-DETR-R50-m | 6x | ResNet-50 | 640 | 51.3 | 69.6 | 36 | 100 | 145 | [download](https://bj.bcebos.com/v1/paddledet/models/rtdetr_r50vd_m_6x_coco.pdparams) | [config](./configs/rtdetr/rtdetr_r50vd_m_6x_coco.yml)
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| RT-DETR-R50 | 6x | ResNet-50 | 640 | 53.1 | 71.3 | 42 | 136 | 108 | [download](https://bj.bcebos.com/v1/paddledet/models/rtdetr_r50vd_6x_coco.pdparams) | [config](./configs/rtdetr/rtdetr_r50vd_6x_coco.yml)
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| RT-DETR-R101 | 6x | ResNet-101 | 640 | 54.3 | 72.7 | 76 | 259 | 74 | [download](https://bj.bcebos.com/v1/paddledet/models/rtdetr_r101vd_6x_coco.pdparams) | [config](./configs/rtdetr/rtdetr_r101vd_6x_coco.yml)
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| RT-DETR-L | 6x | HGNetv2 | 640 | 53.0 | 71.6 | 32 | 110 | 114 | [download](https://bj.bcebos.com/v1/paddledet/models/rtdetr_hgnetv2_l_6x_coco.pdparams) | [config](./configs/rtdetr/rtdetr_hgnetv2_l_6x_coco.yml)
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| RT-DETR-X | 6x | HGNetv2 | 640 | 54.8 | 73.1 | 67 | 234 | 74 | [download](https://bj.bcebos.com/v1/paddledet/models/rtdetr_hgnetv2_x_6x_coco.pdparams) | [config](./configs/rtdetr/rtdetr_hgnetv2_x_6x_coco.yml)
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**注意事项:**
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- RT-DETR 使用4个GPU训练。
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- RT-DETR 在COCO train2017上训练，并在val2017上评估。
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## 快速开始
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<details open>
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<summary>依赖包</summary>
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<!-- - PaddlePaddle == 2.4.2 -->
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```bash
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pip install -r requirements.txt
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```
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</details>
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<details>
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<summary>准备数据</summary>
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- 修改[配置文件`dataset_dir`](configs/datasets/coco_detection.yml)
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</details>
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<details>
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<summary>训练&评估</summary>
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- 单卡GPU上训练:
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```shell
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# training on single-GPU
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export CUDA_VISIBLE_DEVICES=0
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python tools/train.py -c configs/rtdetr/rtdetr_r50vd_6x_coco.yml --eval
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```
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- 多卡GPU上训练:
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```shell
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# training on multi-GPU
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export CUDA_VISIBLE_DEVICES=0,1,2,3
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python -m paddle.distributed.launch --gpus 0,1,2,3 tools/train.py -c configs/rtdetr/rtdetr_r50vd_6x_coco.yml --fleet --eval
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```
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- 评估:
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```shell
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python tools/eval.py -c configs/rtdetr/rtdetr_r50vd_6x_coco.yml \
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-o weights=https://bj.bcebos.com/v1/paddledet/models/rtdetr_r50vd_6x_coco.pdparams
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```
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- 测试:
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```shell
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python tools/infer.py -c configs/rtdetr/rtdetr_r50vd_6x_coco.yml \
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-o weights=https://bj.bcebos.com/v1/paddledet/models/rtdetr_r50vd_6x_coco.pdparams \
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--infer_img=./demo/000000570688.jpg
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```
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详情请参考[快速开始文档](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/docs/tutorials/GETTING_STARTED.md).
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</details>
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## 部署
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<details open>
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<summary>1. 导出模型 </summary>
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```shell
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python tools/export_model.py -c configs/rtdetr/rtdetr_r50vd_6x_coco.yml \
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-o weights=https://bj.bcebos.com/v1/paddledet/models/rtdetr_r50vd_6x_coco.pdparams trt=True \
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--output_dir=output_inference
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```
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</details>
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<details>
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<summary>2. 转换模型至ONNX </summary>
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- 安装[Paddle2ONNX](https://github.com/PaddlePaddle/Paddle2ONNX) 和 ONNX
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```shell
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pip install onnx==1.13.0
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pip install paddle2onnx==1.0.5
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```
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- 转换模型:
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```shell
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paddle2onnx --model_dir=./output_inference/rtdetr_r50vd_6x_coco/ \
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--model_filename model.pdmodel \
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--params_filename model.pdiparams \
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--opset_version 16 \
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--save_file rtdetr_r50vd_6x_coco.onnx
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```
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</details>
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<details>
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<summary>3. 转换成TensorRT </summary>
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- 确保TensorRT的版本>=8.5.1
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- TRT推理可以参考[RT-DETR](https://github.com/lyuwenyu/RT-DETR)的部分代码或者其他网络资源
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```shell
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trtexec --onnx=./rtdetr_r50vd_6x_coco.onnx \
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--workspace=4096 \
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--shapes=image:1x3x640x640 \
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--saveEngine=rtdetr_r50vd_6x_coco.trt \
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--avgRuns=100 \
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--fp16
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```
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-
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</details>
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## 其他
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<details>
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<summary>1. 参数量和计算量统计 </summary>
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1. 找到[本地安装paddle的flops源代码](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/hapi/dynamic_flops.py#L28), 并修改为
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```python
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# anaconda3/lib/python3.8/site-packages/paddle/hapi/dynamic_flops.py
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def flops(net, input_size, inputs=None, custom_ops=None, print_detail=False):
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if isinstance(net, nn.Layer):
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# If net is a dy2stat model, net.forward is StaticFunction instance,
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# we set net.forward to original forward function.
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_, net.forward = unwrap_decorators(net.forward)
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# by lyuwenyu
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if inputs is None:
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inputs = paddle.randn(input_size)
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return dynamic_flops(
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net, inputs=inputs, custom_ops=custom_ops, print_detail=print_detail
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)
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elif isinstance(net, paddle.static.Program):
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return static_flops(net, print_detail=print_detail)
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else:
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warnings.warn(
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"Your model must be an instance of paddle.nn.Layer or paddle.static.Program."
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)
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return -1
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```
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2. 使用以下代码片段实现参数量和计算量的统计
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```python
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import paddle
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from ppdet.core.workspace import load_config, merge_config
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from ppdet.core.workspace import create
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cfg_path = './configs/rtdetr/rtdetr_r50vd_6x_coco.yml'
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cfg = load_config(cfg_path)
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model = create(cfg.architecture)
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blob = {
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'image': paddle.randn([1, 3, 640, 640]),
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'im_shape': paddle.to_tensor([[640, 640]]),
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'scale_factor': paddle.to_tensor([[1., 1.]])
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}
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paddle.flops(model, None, blob, custom_ops=None, print_detail=False)
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```
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</details>
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<details open>
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<summary>2. YOLOs端到端速度测速 </summary>
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- 可以参考[RT-DETR](https://github.com/lyuwenyu/RT-DETR) benchmark部分或者其他网络资源
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</details>
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## 引用RT-DETR
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如果需要在你的研究中使用RT-DETR，请通过以下方式引用我们的论文：
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```
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@misc{lv2023detrs,
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title={DETRs Beat YOLOs on Real-time Object Detection},
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author={Wenyu Lv and Shangliang Xu and Yian Zhao and Guanzhong Wang and Jinman Wei and Cheng Cui and Yuning Du and Qingqing Dang and Yi Liu},
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year={2023},
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eprint={2304.08069},
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archivePrefix={arXiv},
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primaryClass={cs.CV}
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}
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```
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