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PaddleDetection
未验证 提交 fb38470f 编写于 作者: W wangxinxin08 提交者： GitHub
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[cherry-pick] add ppyolov2 (#2628) 

* add ppyolov2

* fix bugs and modify docs

* modify code and doc according to review

* fix bugs while resolving conflicts
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configs/ppyolo/README.md
浏览文件 @ fb38470f
......@@ -38,17 +38,20 @@ PP-YOLO improved performance and speed of YOLOv3 with following methods:
| Model | GPU number | images/GPU | backbone | input shape | Box AP<sup>val</sup> | Box AP<sup>test</sup> | V100 FP32(FPS) | V100 TensorRT FP16(FPS) | download | config |
|:------------------------:|:-------:|:-------------:|:----------:| :-------:| :------------------: | :-------------------: | :------------: | :---------------------: | :------: | :------: |
| PP-YOLO | 8 | 24 | ResNet50vd | 608 | 44.8 | 45.2 | 72.9 | 155.6 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml) |
| PP-YOLO | 8 | 24 | ResNet50vd | 512 | 43.9 | 44.4 | 89.9 | 188.4 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml) |
| PP-YOLO | 8 | 24 | ResNet50vd | 416 | 42.1 | 42.5 | 109.1 | 215.4 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml) |
| PP-YOLO | 8 | 24 | ResNet50vd | 320 | 38.9 | 39.3 | 132.2 | 242.2 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml) |
| PP-YOLO_2x | 8 | 24 | ResNet50vd | 608 | 45.3 | 45.9 | 72.9 | 155.6 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_2x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_r50vd_dcn_2x_coco.yml) |
| PP-YOLO_2x | 8 | 24 | ResNet50vd | 512 | 44.4 | 45.0 | 89.9 | 188.4 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_2x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_r50vd_dcn_2x_coco.yml) |
| PP-YOLO_2x | 8 | 24 | ResNet50vd | 416 | 42.7 | 43.2 | 109.1 | 215.4 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_2x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_r50vd_dcn_2x_coco.yml) |
| PP-YOLO_2x | 8 | 24 | ResNet50vd | 320 | 39.5 | 40.1 | 132.2 | 242.2 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_2x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_r50vd_dcn_2x_coco.yml) |
| PP-YOLO_ResNet18vd | 4 | 32 | ResNet18vd | 512 | 29.2 | 29.5 | 357.1 | 657.9 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r18vd_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_r18vd_coco.yml) |
| PP-YOLO_ResNet18vd | 4 | 32 | ResNet18vd | 416 | 28.6 | 28.9 | 409.8 | 719.4 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r18vd_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_r18vd_coco.yml) |
| PP-YOLO_ResNet18vd | 4 | 32 | ResNet18vd | 320 | 26.2 | 26.4 | 480.7 | 763.4 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r18vd_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_r18vd_coco.yml) |
| PP-YOLO | 8 | 24 | ResNet50vd | 608 | 44.8 | 45.2 | 72.9 | 155.6 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml) |
| PP-YOLO | 8 | 24 | ResNet50vd | 512 | 43.9 | 44.4 | 89.9 | 188.4 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml) |
| PP-YOLO | 8 | 24 | ResNet50vd | 416 | 42.1 | 42.5 | 109.1 | 215.4 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml) |
| PP-YOLO | 8 | 24 | ResNet50vd | 320 | 38.9 | 39.3 | 132.2 | 242.2 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml) |
| PP-YOLO_2x | 8 | 24 | ResNet50vd | 608 | 45.3 | 45.9 | 72.9 | 155.6 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_2x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_2x_coco.yml) |
| PP-YOLO_2x | 8 | 24 | ResNet50vd | 512 | 44.4 | 45.0 | 89.9 | 188.4 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_2x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_2x_coco.yml) |
| PP-YOLO_2x | 8 | 24 | ResNet50vd | 416 | 42.7 | 43.2 | 109.1 | 215.4 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_2x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_2x_coco.yml) |
| PP-YOLO_2x | 8 | 24 | ResNet50vd | 320 | 39.5 | 40.1 | 132.2 | 242.2 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_2x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_2x_coco.yml) |
| PP-YOLO | 4 | 32 | ResNet18vd | 512 | 29.2 | 29.5 | 357.1 | 657.9 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r18vd_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r18vd_coco.yml) |
| PP-YOLO | 4 | 32 | ResNet18vd | 416 | 28.6 | 28.9 | 409.8 | 719.4 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r18vd_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r18vd_coco.yml) |
| PP-YOLO | 4 | 32 | ResNet18vd | 320 | 26.2 | 26.4 | 480.7 | 763.4 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r18vd_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r18vd_coco.yml) |
| PP-YOLOv2 | 8 | 12 | ResNet50vd | 640 | 49.1 | 49.5 | - | - | [model](https://paddledet.bj.bcebos.com/models/ppyolov2_r50vd_dcn_365e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml) |
| PP-YOLOv2 | 8 | 12 | ResNet101vd | 640 | 49.7 | 50.1 | - | - | [model](https://paddledet.bj.bcebos.com/models/ppyolov2_r101vd_dcn_365e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolov2_r101vd_dcn_365e_coco.yml) |
**Notes:**
......@@ -62,8 +65,8 @@ PP-YOLO improved performance and speed of YOLOv3 with following methods:
| Model | GPU number | images/GPU | Model Size | input shape | Box AP<sup>val</sup> | Box AP50<sup>val</sup> | Kirin 990 1xCore(FPS) | download | config |
|:----------------------------:|:-------:|:-------------:|:----------:| :-------:| :------------------: | :--------------------: | :--------------------: | :------: | :------: |
| PP-YOLO_MobileNetV3_large | 4 | 32 | 28MB | 320 | 23.2 | 42.6 | 14.1 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_mbv3_large_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_mbv3_large_coco.yml) |
| PP-YOLO_MobileNetV3_small | 4 | 32 | 16MB | 320 | 17.2 | 33.8 | 21.5 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_mbv3_small_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_mbv3_small_coco.yml) |
| PP-YOLO_MobileNetV3_large | 4 | 32 | 28MB | 320 | 23.2 | 42.6 | 14.1 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_mbv3_large_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_mbv3_large_coco.yml) |
| PP-YOLO_MobileNetV3_small | 4 | 32 | 16MB | 320 | 17.2 | 33.8 | 21.5 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_mbv3_small_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_mbv3_small_coco.yml) |
**Notes:**
......@@ -91,9 +94,9 @@ PP-YOLO trained on Pascal VOC dataset as follows:
| Model | GPU number | images/GPU | backbone | input shape | Box AP50<sup>val</sup> | download | config |
|:------------------:|:----------:|:----------:|:----------:| :----------:| :--------------------: | :------: | :-----: |
| PP-YOLO | 8 | 12 | ResNet50vd | 608 | 84.9 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_voc.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_r50vd_dcn_voc.yml) |
| PP-YOLO | 8 | 12 | ResNet50vd | 416 | 84.3 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_voc.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_r50vd_dcn_voc.yml) |
| PP-YOLO | 8 | 12 | ResNet50vd | 320 | 82.2 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_voc.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_r50vd_dcn_voc.yml) |
| PP-YOLO | 8 | 12 | ResNet50vd | 608 | 84.9 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_voc.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_voc.yml) |
| PP-YOLO | 8 | 12 | ResNet50vd | 416 | 84.3 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_voc.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_voc.yml) |
| PP-YOLO | 8 | 12 | ResNet50vd | 320 | 82.2 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_voc.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_voc.yml) |
## Getting Start
......@@ -184,4 +187,23 @@ Optimizing method and ablation experiments of PP-YOLO compared with YOLOv3.
- Performance and inference spedd are measure with input shape as 608
- All models are trained on COCO train2017 datast and evaluated on val2017 & test-dev2017 dataset，`Box AP` is evaluation results as `mAP(IoU=0.5:0.95)`.
- Inference speed is tested on single Tesla V100 with batch size as 1 following test method and environment configuration in benchmark above.
- [YOLOv3-DarkNet53](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/yolov3/yolov3_darknet53_270e_coco.yml) with mAP as 39.0 is optimized YOLOv3 model in PaddleDetection，see [Model Zoo](https://github.com/PaddlePaddle/PaddleDetection/blob/master/docs/MODEL_ZOO.md) for details.
- [YOLOv3-DarkNet53](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_darknet53_270e_coco.yml) with mAP as 39.0 is optimized YOLOv3 model in PaddleDetection，see [Model Zoo](https://github.com/PaddlePaddle/PaddleDetection/blob/master/docs/MODEL_ZOO.md) for details.
## Citation
```
@misc{long2020ppyolo,
title={PP-YOLO: An Effective and Efficient Implementation of Object Detector},
author={Xiang Long and Kaipeng Deng and Guanzhong Wang and Yang Zhang and Qingqing Dang and Yuan Gao and Hui Shen and Jianguo Ren and Shumin Han and Errui Ding and Shilei Wen},
year={2020},
eprint={2007.12099},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
@misc{ppdet2019,
title={PaddleDetection, Object detection and instance segmentation toolkit based on PaddlePaddle.},
author={PaddlePaddle Authors},
howpublished = {\url{https://github.com/PaddlePaddle/PaddleDetection}},
year={2019}
}
```
configs/ppyolo/README_cn.md
浏览文件 @ fb38470f
......@@ -38,17 +38,19 @@ PP-YOLO从如下方面优化和提升YOLOv3模型的精度和速度：
| 模型 | GPU个数 | 每GPU图片个数 | 骨干网络 | 输入尺寸 | Box AP<sup>val</sup> | Box AP<sup>test</sup> | V100 FP32(FPS) | V100 TensorRT FP16(FPS) | 模型下载 | 配置文件 |
|:------------------------:|:-------:|:-------------:|:----------:| :-------:| :------------------: | :-------------------: | :------------: | :---------------------: | :------: | :------: |
| PP-YOLO | 8 | 24 | ResNet50vd | 608 | 44.8 | 45.2 | 72.9 | 155.6 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml) |
| PP-YOLO | 8 | 24 | ResNet50vd | 512 | 43.9 | 44.4 | 89.9 | 188.4 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml) |
| PP-YOLO | 8 | 24 | ResNet50vd | 416 | 42.1 | 42.5 | 109.1 | 215.4 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml) |
| PP-YOLO | 8 | 24 | ResNet50vd | 320 | 38.9 | 39.3 | 132.2 | 242.2 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml) |
| PP-YOLO_2x | 8 | 24 | ResNet50vd | 608 | 45.3 | 45.9 | 72.9 | 155.6 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_2x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_r50vd_dcn_2x_coco.yml) |
| PP-YOLO_2x | 8 | 24 | ResNet50vd | 512 | 44.4 | 45.0 | 89.9 | 188.4 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_2x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_r50vd_dcn_2x_coco.yml) |
| PP-YOLO_2x | 8 | 24 | ResNet50vd | 416 | 42.7 | 43.2 | 109.1 | 215.4 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_2x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_r50vd_dcn_2x_coco.yml) |
| PP-YOLO_2x | 8 | 24 | ResNet50vd | 320 | 39.5 | 40.1 | 132.2 | 242.2 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_2x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_r50vd_dcn_2x_coco.yml) |
| PP-YOLO_ResNet18vd | 4 | 32 | ResNet18vd | 512 | 29.2 | 29.5 | 357.1 | 657.9 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r18vd_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_r18vd_coco.yml) |
| PP-YOLO_ResNet18vd | 4 | 32 | ResNet18vd | 416 | 28.6 | 28.9 | 409.8 | 719.4 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r18vd_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_r18vd_coco.yml) |
| PP-YOLO_ResNet18vd | 4 | 32 | ResNet18vd | 320 | 26.2 | 26.4 | 480.7 | 763.4 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r18vd_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_r18vd_coco.yml) |
| PP-YOLO | 8 | 24 | ResNet50vd | 608 | 44.8 | 45.2 | 72.9 | 155.6 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml) |
| PP-YOLO | 8 | 24 | ResNet50vd | 512 | 43.9 | 44.4 | 89.9 | 188.4 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml) |
| PP-YOLO | 8 | 24 | ResNet50vd | 416 | 42.1 | 42.5 | 109.1 | 215.4 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml) |
| PP-YOLO | 8 | 24 | ResNet50vd | 320 | 38.9 | 39.3 | 132.2 | 242.2 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml) |
| PP-YOLO_2x | 8 | 24 | ResNet50vd | 608 | 45.3 | 45.9 | 72.9 | 155.6 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_2x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_2x_coco.yml) |
| PP-YOLO_2x | 8 | 24 | ResNet50vd | 512 | 44.4 | 45.0 | 89.9 | 188.4 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_2x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_2x_coco.yml) |
| PP-YOLO_2x | 8 | 24 | ResNet50vd | 416 | 42.7 | 43.2 | 109.1 | 215.4 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_2x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_2x_coco.yml) |
| PP-YOLO_2x | 8 | 24 | ResNet50vd | 320 | 39.5 | 40.1 | 132.2 | 242.2 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_2x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_2x_coco.yml) |
| PP-YOLO | 4 | 32 | ResNet18vd | 512 | 29.2 | 29.5 | 357.1 | 657.9 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r18vd_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r18vd_coco.yml) |
| PP-YOLO | 4 | 32 | ResNet18vd | 416 | 28.6 | 28.9 | 409.8 | 719.4 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r18vd_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r18vd_coco.yml) |
| PP-YOLO | 4 | 32 | ResNet18vd | 320 | 26.2 | 26.4 | 480.7 | 763.4 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r18vd_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r18vd_coco.yml) |
| PP-YOLOv2 | 8 | 12 | ResNet50vd | 640 | 49.1 | 49.5 | - | - | [model](https://paddledet.bj.bcebos.com/models/ppyolov2_r50vd_dcn_365e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml) |
| PP-YOLOv2 | 8 | 12 | ResNet101vd | 640 | 49.7 | 50.1 | - | - | [model](https://paddledet.bj.bcebos.com/models/ppyolov2_r101vd_dcn_365e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolov2_r101vd_dcn_365e_coco.yml) |
**注意:**
......@@ -63,8 +65,8 @@ PP-YOLO从如下方面优化和提升YOLOv3模型的精度和速度：
| 模型 | GPU个数 | 每GPU图片个数 | 模型体积 | 输入尺寸 | Box AP<sup>val</sup> | Box AP50<sup>val</sup> | Kirin 990 1xCore (FPS) | 模型下载 | 配置文件 |
|:----------------------------:|:-------:|:-------------:|:----------:| :-------:| :------------------: | :--------------------: | :--------------------: | :------: | :------: |
| PP-YOLO_MobileNetV3_large | 4 | 32 | 28MB | 320 | 23.2 | 42.6 | 14.1 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyolo_mbv3_large_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_mbv3_large_coco.yml) |
| PP-YOLO_MobileNetV3_small | 4 | 32 | 16MB | 320 | 17.2 | 33.8 | 21.5 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyolo_mbv3_small_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_mbv3_small_coco.yml) |
| PP-YOLO_MobileNetV3_large | 4 | 32 | 28MB | 320 | 23.2 | 42.6 | 14.1 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyolo_mbv3_large_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_mbv3_large_coco.yml) |
| PP-YOLO_MobileNetV3_small | 4 | 32 | 16MB | 320 | 17.2 | 33.8 | 21.5 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyolo_mbv3_small_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_mbv3_small_coco.yml) |
- PP-YOLO_MobileNetV3 模型使用COCO数据集中train2017作为训练集，使用val2017作为测试集，Box AP<sup>val</sup>为`mAP(IoU=0.5:0.95)`评估结果, Box AP50<sup>val</sup>为`mAP(IoU=0.5)`评估结果。
- PP-YOLO_MobileNetV3 模型训练过程中使用4GPU，每GPU batch size为32进行训练，如训练GPU数和batch size不使用上述配置，须参考[FAQ](https://github.com/PaddlePaddle/PaddleDetection/blob/master/docs/FAQ.md)调整学习率和迭代次数。
......@@ -76,9 +78,9 @@ PP-YOLO在Pascal VOC数据集上训练模型如下:
| 模型 | GPU个数 | 每GPU图片个数 | 骨干网络 | 输入尺寸 | Box AP50<sup>val</sup> | 模型下载 | 配置文件 |
|:------------------:|:-------:|:-------------:|:----------:| :----------:| :--------------------: | :------: | :-----: |
| PP-YOLO | 8 | 12 | ResNet50vd | 608 | 84.9 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_voc.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_r50vd_dcn_voc.yml) |
| PP-YOLO | 8 | 12 | ResNet50vd | 416 | 84.3 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_voc.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_r50vd_dcn_voc.yml) |
| PP-YOLO | 8 | 12 | ResNet50vd | 320 | 82.2 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_voc.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_r50vd_dcn_voc.yml) |
| PP-YOLO | 8 | 12 | ResNet50vd | 608 | 84.9 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_voc.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_voc.yml) |
| PP-YOLO | 8 | 12 | ResNet50vd | 416 | 84.3 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_voc.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_voc.yml) |
| PP-YOLO | 8 | 12 | ResNet50vd | 320 | 82.2 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_voc.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_voc.yml) |
## 使用说明
......@@ -169,4 +171,23 @@ PP-YOLO模型相对于YOLOv3模型优化项消融实验数据如下表所示。
- 精度与推理速度数据均为使用输入图像尺寸为608的测试结果
- Box AP为在COCO train2017数据集训练，val2017和test-dev2017数据集上评估`mAP(IoU=0.5:0.95)`数据
- 推理速度为单卡V100上，batch size=1, 使用上述benchmark测试方法的测试结果，测试环境配置为CUDA 10.2，CUDNN 7.5.1
- [YOLOv3-DarkNet53](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/yolov3/yolov3_darknet53_270e_coco.yml)精度38.9为PaddleDetection优化后的YOLOv3模型，可参见[模型库](https://github.com/PaddlePaddle/PaddleDetection/blob/master/docs/MODEL_ZOO.md)
- [YOLOv3-DarkNet53](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_darknet53_270e_coco.yml)精度38.9为PaddleDetection优化后的YOLOv3模型，可参见[模型库](https://github.com/PaddlePaddle/PaddleDetection/blob/master/docs/MODEL_ZOO.md)
## 引用
```
@misc{long2020ppyolo,
title={PP-YOLO: An Effective and Efficient Implementation of Object Detector},
author={Xiang Long and Kaipeng Deng and Guanzhong Wang and Yang Zhang and Qingqing Dang and Yuan Gao and Hui Shen and Jianguo Ren and Shumin Han and Errui Ding and Shilei Wen},
year={2020},
eprint={2007.12099},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
@misc{ppdet2019,
title={PaddleDetection, Object detection and instance segmentation toolkit based on PaddlePaddle.},
author={PaddlePaddle Authors},
howpublished = {\url{https://github.com/PaddlePaddle/PaddleDetection}},
year={2019}
}
```
configs/ppyolo/_base_/optimizer_365e.yml 0 → 100644
浏览文件 @ fb38470f
epoch: 365
LearningRate:
base_lr: 0.005
schedulers:
- !PiecewiseDecay
gamma: 0.1
milestones:
- 243
- !LinearWarmup
start_factor: 0.
steps: 4000
OptimizerBuilder:
clip_grad_by_norm: 35.
optimizer:
momentum: 0.9
type: Momentum
regularizer:
factor: 0.0005
type: L2
configs/ppyolo/_base_/ppyolov2_r50vd_dcn.yml 0 → 100644
浏览文件 @ fb38470f
architecture: YOLOv3
pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet50_vd_ssld_pretrained.pdparams
norm_type: sync_bn
use_ema: true
ema_decay: 0.9998
YOLOv3:
backbone: ResNet
neck: PPYOLOPAN
yolo_head: YOLOv3Head
post_process: BBoxPostProcess
ResNet:
depth: 50
variant: d
return_idx: [1, 2, 3]
dcn_v2_stages: [3]
freeze_at: -1
freeze_norm: false
norm_decay: 0.
PPYOLOPAN:
drop_block: true
block_size: 3
keep_prob: 0.9
spp: true
YOLOv3Head:
anchors: [[10, 13], [16, 30], [33, 23],
[30, 61], [62, 45], [59, 119],
[116, 90], [156, 198], [373, 326]]
anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
loss: YOLOv3Loss
iou_aware: true
iou_aware_factor: 0.5
YOLOv3Loss:
ignore_thresh: 0.7
downsample: [32, 16, 8]
label_smooth: false
scale_x_y: 1.05
iou_loss: IouLoss
iou_aware_loss: IouAwareLoss
IouLoss:
loss_weight: 2.5
loss_square: true
IouAwareLoss:
loss_weight: 1.0
BBoxPostProcess:
decode:
name: YOLOBox
conf_thresh: 0.01
downsample_ratio: 32
clip_bbox: true
scale_x_y: 1.05
nms:
name: MatrixNMS
keep_top_k: 100
score_threshold: 0.01
post_threshold: 0.01
nms_top_k: -1
background_label: -1
configs/ppyolo/_base_/ppyolov2_reader.yml 0 → 100644
浏览文件 @ fb38470f
worker_num: 8
TrainReader:
inputs_def:
num_max_boxes: 100
sample_transforms:
- Decode: {}
- Mixup: {alpha: 1.5, beta: 1.5}
- RandomDistort: {}
- RandomExpand: {fill_value: [123.675, 116.28, 103.53]}
- RandomCrop: {}
- RandomFlip: {}
batch_transforms:
- BatchRandomResize: {target_size: [320, 352, 384, 416, 448, 480, 512, 544, 576, 608, 640, 672, 704, 736, 768], random_size: True, random_interp: True, keep_ratio: False}
- NormalizeBox: {}
- PadBox: {num_max_boxes: 100}
- BboxXYXY2XYWH: {}
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
- Permute: {}
- Gt2YoloTarget: {anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]], anchors: [[10, 13], [16, 30], [33, 23], [30, 61], [62, 45], [59, 119], [116, 90], [156, 198], [373, 326]], downsample_ratios: [32, 16, 8]}
batch_size: 12
shuffle: true
drop_last: true
mixup_epoch: 25000
use_shared_memory: true
EvalReader:
sample_transforms:
- Decode: {}
- Resize: {target_size: [640, 640], keep_ratio: False, interp: 2}
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
- Permute: {}
batch_size: 8
drop_empty: false
TestReader:
inputs_def:
image_shape: [3, 640, 640]
sample_transforms:
- Decode: {}
- Resize: {target_size: [640, 640], keep_ratio: False, interp: 2}
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
- Permute: {}
batch_size: 1
configs/ppyolo/ppyolov2_r101vd_dcn_365e_coco.yml 0 → 100644
浏览文件 @ fb38470f
_BASE_: [
'../datasets/coco_detection.yml',
'../runtime.yml',
'./_base_/ppyolov2_r50vd_dcn.yml',
'./_base_/optimizer_365e.yml',
'./_base_/ppyolov2_reader.yml',
]
snapshot_epoch: 8
weights: output/ppyolov2_r101vd_dcn_365e_coco/model_final
pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet101_vd_ssld_pretrained.pdparams
ResNet:
depth: 101
variant: d
return_idx: [1, 2, 3]
dcn_v2_stages: [3]
freeze_at: -1
freeze_norm: false
norm_decay: 0.
configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml 0 → 100644
浏览文件 @ fb38470f
_BASE_: [
'../datasets/coco_detection.yml',
'../runtime.yml',
'./_base_/ppyolov2_r50vd_dcn.yml',
'./_base_/optimizer_365e.yml',
'./_base_/ppyolov2_reader.yml',
]
snapshot_epoch: 8
weights: output/ppyolov2_r50vd_dcn_365e_coco/model_final
ppdet/modeling/backbones/darknet.py
浏览文件 @ fb38470f
......@@ -18,7 +18,7 @@ import paddle.nn.functional as F
from paddle import ParamAttr
from paddle.regularizer import L2Decay
from ppdet.core.workspace import register, serializable
from ppdet.modeling.ops import batch_norm
from ppdet.modeling.ops import batch_norm, mish
from ..shape_spec import ShapeSpec
__all__ = ['DarkNet', 'ConvBNLayer']
......@@ -77,6 +77,8 @@ class ConvBNLayer(nn.Layer):
out = self.batch_norm(out)
if self.act == 'leaky':
out = F.leaky_relu(out, 0.1)
elif self.act == 'mish':
out = mish(out)
return out
......
ppdet/modeling/losses/iou_aware_loss.py
浏览文件 @ fb38470f
......@@ -42,7 +42,7 @@ class IouAwareLoss(IouLoss):
iou = bbox_iou(
pbox, gbox, giou=self.giou, diou=self.diou, ciou=self.ciou)
iou.stop_gradient = True
ioup = F.sigmoid(ioup)
loss_iou_aware = (-iou * paddle.log(ioup)).sum(-2, keepdim=True)
loss_iou_aware = F.binary_cross_entropy_with_logits(
ioup, iou, reduction='none')
loss_iou_aware = loss_iou_aware * self.loss_weight
return loss_iou_aware
ppdet/modeling/necks/yolo_fpn.py
浏览文件 @ fb38470f
......@@ -25,6 +25,32 @@ from ..shape_spec import ShapeSpec
__all__ = ['YOLOv3FPN', 'PPYOLOFPN']
def add_coord(x):
b = x.shape[0]
if self.data_format == 'NCHW':
h = x.shape[2]
w = x.shape[3]
else:
h = x.shape[1]
w = x.shape[2]
gx = paddle.arange(w, dtype='float32') / (w - 1.) * 2.0 - 1.
if self.data_format == 'NCHW':
gx = gx.reshape([1, 1, 1, w]).expand([b, 1, h, w])
else:
gx = gx.reshape([1, 1, w, 1]).expand([b, h, w, 1])
gx.stop_gradient = True
gy = paddle.arange(h, dtype='float32') / (h - 1.) * 2.0 - 1.
if self.data_format == 'NCHW':
gy = gy.reshape([1, 1, h, 1]).expand([b, 1, h, w])
else:
gy = gy.reshape([1, h, 1, 1]).expand([b, h, w, 1])
gy.stop_gradient = True
return gx, gy
class YoloDetBlock(nn.Layer):
def __init__(self, ch_in, channel, norm_type, name, data_format='NCHW'):
"""
......@@ -87,6 +113,7 @@ class SPP(nn.Layer):
pool_size,
norm_type,
name,
act='leaky',
data_format='NCHW'):
"""
SPP layer, which consist of four pooling layer follwed by conv layer
......@@ -101,6 +128,7 @@ class SPP(nn.Layer):
"""
super(SPP, self).__init__()
self.pool = []
self.data_format = data_format
for size in pool_size:
pool = self.add_sublayer(
'{}.pool1'.format(name),
......@@ -118,13 +146,18 @@ class SPP(nn.Layer):
padding=k // 2,
norm_type=norm_type,
name=name,
act=act,
data_format=data_format)
def forward(self, x):
outs = [x]
for pool in self.pool:
outs.append(pool(x))
y = paddle.concat(outs, axis=1)
if self.data_format == "NCHW":
y = paddle.concat(outs, axis=1)
else:
y = paddle.concat(outs, axis=-1)
y = self.conv(y)
return y
......@@ -204,28 +237,7 @@ class CoordConv(nn.Layer):
self.data_format = data_format
def forward(self, x):
b = x.shape[0]
if self.data_format == 'NCHW':
h = x.shape[2]
w = x.shape[3]
else:
h = x.shape[1]
w = x.shape[2]
gx = paddle.arange(w, dtype='float32') / (w - 1.) * 2.0 - 1.
if self.data_format == 'NCHW':
gx = gx.reshape([1, 1, 1, w]).expand([b, 1, h, w])
else:
gx = gx.reshape([1, 1, w, 1]).expand([b, h, w, 1])
gx.stop_gradient = True
gy = paddle.arange(h, dtype='float32') / (h - 1.) * 2.0 - 1.
if self.data_format == 'NCHW':
gy = gy.reshape([1, 1, h, 1]).expand([b, 1, h, w])
else:
gy = gy.reshape([1, h, 1, 1]).expand([b, h, w, 1])
gy.stop_gradient = True
gx, gy = add_coord(x)
if self.data_format == 'NCHW':
y = paddle.concat([x, gx, gy], axis=1)
else:
......@@ -273,7 +285,6 @@ class PPYOLOTinyDetBlock(nn.Layer):
data_format='NCHW'):
"""
PPYOLO Tiny DetBlock layer
Args:
ch_in (list): input channel number
ch_out (list): output channel number
......@@ -333,6 +344,73 @@ class PPYOLOTinyDetBlock(nn.Layer):
return route, tip
class PPYOLODetBlockCSP(nn.Layer):
def __init__(self,
cfg,
ch_in,
ch_out,
act,
norm_type,
name,
data_format='NCHW'):
"""
PPYOLODetBlockCSP layer
Args:
cfg (list): layer configs for this block
ch_in (int): input channel
ch_out (int): output channel
act (str): default mish
name (str): block name
data_format (str): data format, NCHW or NHWC
"""
super(PPYOLODetBlockCSP, self).__init__()
self.data_format = data_format
self.conv1 = ConvBNLayer(
ch_in,
ch_out,
1,
padding=0,
act=act,
norm_type=norm_type,
name=name + '.left',
data_format=data_format)
self.conv2 = ConvBNLayer(
ch_in,
ch_out,
1,
padding=0,
act=act,
norm_type=norm_type,
name=name + '.right',
data_format=data_format)
self.conv3 = ConvBNLayer(
ch_out * 2,
ch_out * 2,
1,
padding=0,
act=act,
norm_type=norm_type,
name=name,
data_format=data_format)
self.conv_module = nn.Sequential()
for idx, (layer_name, layer, args, kwargs) in enumerate(cfg):
kwargs.update(name=name + layer_name, data_format=data_format)
self.conv_module.add_sublayer(layer_name, layer(*args, **kwargs))
def forward(self, inputs):
conv_left = self.conv1(inputs)
conv_right = self.conv2(inputs)
conv_left = self.conv_module(conv_left)
if self.data_format == 'NCHW':
conv = paddle.concat([conv_left, conv_right], axis=1)
else:
conv = paddle.concat([conv_left, conv_right], axis=-1)
conv = self.conv3(conv)
return conv, conv
@register
@serializable
class YOLOv3FPN(nn.Layer):
......@@ -430,7 +508,12 @@ class PPYOLOFPN(nn.Layer):
in_channels=[512, 1024, 2048],
norm_type='bn',
data_format='NCHW',
**kwargs):
coord_conv=False,
conv_block_num=3,
drop_block=False,
block_size=3,
keep_prob=0.9,
spp=False):
"""
PPYOLOFPN layer
......@@ -438,7 +521,12 @@ class PPYOLOFPN(nn.Layer):
in_channels (list): input channels for fpn
norm_type (str): batch norm type, default bn
data_format (str): data format, NCHW or NHWC
kwargs: extra key-value pairs, such as parameter of DropBlock and spp
coord_conv (bool): whether use CoordConv or not
conv_block_num (int): conv block num of each pan block
drop_block (bool): whether use DropBlock or not
block_size (int): block size of DropBlock
keep_prob (float): keep probability of DropBlock
spp (bool): whether use spp or not
"""
super(PPYOLOFPN, self).__init__()
......@@ -446,14 +534,12 @@ class PPYOLOFPN(nn.Layer):
self.in_channels = in_channels
self.num_blocks = len(in_channels)
# parse kwargs
self.coord_conv = kwargs.get('coord_conv', False)
self.drop_block = kwargs.get('drop_block', False)
if self.drop_block:
self.block_size = kwargs.get('block_size', 3)
self.keep_prob = kwargs.get('keep_prob', 0.9)
self.spp = kwargs.get('spp', False)
self.conv_block_num = kwargs.get('conv_block_num', 2)
self.coord_conv = coord_conv
self.drop_block = drop_block
self.block_size = block_size
self.keep_prob = keep_prob
self.spp = spp
self.conv_block_num = conv_block_num
self.data_format = data_format
if self.coord_conv:
ConvLayer = CoordConv
......@@ -583,14 +669,12 @@ class PPYOLOTinyFPN(nn.Layer):
**kwargs):
"""
PPYOLO Tiny FPN layer
Args:
in_channels (list): input channels for fpn
detection_block_channels (list): channels in fpn
norm_type (str): batch norm type, default bn
data_format (str): data format, NCHW or NHWC
kwargs: extra key-value pairs, such as parameter of DropBlock and spp
"""
super(PPYOLOTinyFPN, self).__init__()
assert len(in_channels) > 0, "in_channels length should > 0"
......@@ -681,3 +765,197 @@ class PPYOLOTinyFPN(nn.Layer):
@property
def out_shape(self):
return [ShapeSpec(channels=c) for c in self._out_channels]
@register
@serializable
class PPYOLOPAN(nn.Layer):
__shared__ = ['norm_type', 'data_format']
def __init__(self,
in_channels=[512, 1024, 2048],
norm_type='bn',
data_format='NCHW',
act='mish',
conv_block_num=3,
drop_block=False,
block_size=3,
keep_prob=0.9,
spp=False):
"""
PPYOLOPAN layer with SPP, DropBlock and CSP connection.
Args:
in_channels (list): input channels for fpn
norm_type (str): batch norm type, default bn
data_format (str): data format, NCHW or NHWC
act (str): activation function, default mish
conv_block_num (int): conv block num of each pan block
drop_block (bool): whether use DropBlock or not
block_size (int): block size of DropBlock
keep_prob (float): keep probability of DropBlock
spp (bool): whether use spp or not
"""
super(PPYOLOPAN, self).__init__()
assert len(in_channels) > 0, "in_channels length should > 0"
self.in_channels = in_channels
self.num_blocks = len(in_channels)
# parse kwargs
self.drop_block = drop_block
self.block_size = block_size
self.keep_prob = keep_prob
self.spp = spp
self.conv_block_num = conv_block_num
self.data_format = data_format
if self.drop_block:
dropblock_cfg = [[
'dropblock', DropBlock, [self.block_size, self.keep_prob],
dict()
]]
else:
dropblock_cfg = []
# fpn
self.fpn_blocks = []
self.fpn_routes = []
fpn_channels = []
for i, ch_in in enumerate(self.in_channels[::-1]):
if i > 0:
ch_in += 512 // (2**(i - 1))
channel = 512 // (2**i)
base_cfg = []
for j in range(self.conv_block_num):
base_cfg += [
# name, layer, args
[
'{}.0'.format(j), ConvBNLayer, [channel, channel, 1],
dict(
padding=0, act=act, norm_type=norm_type)
],
[
'{}.1'.format(j), ConvBNLayer, [channel, channel, 3],
dict(
padding=1, act=act, norm_type=norm_type)
]
]
if i == 0 and self.spp:
base_cfg[3] = [
'spp', SPP, [channel * 4, channel, 1], dict(
pool_size=[5, 9, 13], act=act, norm_type=norm_type)
]
cfg = base_cfg[:4] + dropblock_cfg + base_cfg[4:]
name = 'fpn.{}'.format(i)
fpn_block = self.add_sublayer(
name,
PPYOLODetBlockCSP(cfg, ch_in, channel, act, norm_type, name,
data_format))
self.fpn_blocks.append(fpn_block)
fpn_channels.append(channel * 2)
if i < self.num_blocks - 1:
name = 'fpn_transition.{}'.format(i)
route = self.add_sublayer(
name,
ConvBNLayer(
ch_in=channel * 2,
ch_out=channel,
filter_size=1,
stride=1,
padding=0,
act=act,
norm_type=norm_type,
data_format=data_format,
name=name))
self.fpn_routes.append(route)
# pan
self.pan_blocks = []
self.pan_routes = []
self._out_channels = [512 // (2**(self.num_blocks - 2)), ]
for i in reversed(range(self.num_blocks - 1)):
name = 'pan_transition.{}'.format(i)
route = self.add_sublayer(
name,
ConvBNLayer(
ch_in=fpn_channels[i + 1],
ch_out=fpn_channels[i + 1],
filter_size=3,
stride=2,
padding=1,
act=act,
norm_type=norm_type,
data_format=data_format,
name=name))
self.pan_routes = [route, ] + self.pan_routes
base_cfg = []
ch_in = fpn_channels[i] + fpn_channels[i + 1]
channel = 512 // (2**i)
for j in range(self.conv_block_num):
base_cfg += [
# name, layer, args
[
'{}.0'.format(j), ConvBNLayer, [channel, channel, 1],
dict(
padding=0, act=act, norm_type=norm_type)
],
[
'{}.1'.format(j), ConvBNLayer, [channel, channel, 3],
dict(
padding=1, act=act, norm_type=norm_type)
]
]
cfg = base_cfg[:4] + dropblock_cfg + base_cfg[4:]
name = 'pan.{}'.format(i)
pan_block = self.add_sublayer(
name,
PPYOLODetBlockCSP(cfg, ch_in, channel, act, norm_type, name,
data_format))
self.pan_blocks = [pan_block, ] + self.pan_blocks
self._out_channels.append(channel * 2)
self._out_channels = self._out_channels[::-1]
def forward(self, blocks):
assert len(blocks) == self.num_blocks
blocks = blocks[::-1]
# fpn
fpn_feats = []
for i, block in enumerate(blocks):
if i > 0:
if self.data_format == 'NCHW':
block = paddle.concat([route, block], axis=1)
else:
block = paddle.concat([route, block], axis=-1)
route, tip = self.fpn_blocks[i](block)
fpn_feats.append(tip)
if i < self.num_blocks - 1:
route = self.fpn_routes[i](route)
route = F.interpolate(
route, scale_factor=2., data_format=self.data_format)
pan_feats = [fpn_feats[-1], ]
route = fpn_feats[self.num_blocks - 1]
for i in reversed(range(self.num_blocks - 1)):
block = fpn_feats[i]
route = self.pan_routes[i](route)
if self.data_format == 'NCHW':
block = paddle.concat([route, block], axis=1)
else:
block = paddle.concat([route, block], axis=-1)
route, tip = self.pan_blocks[i](block)
pan_feats.append(tip)
return pan_feats[::-1]
@classmethod
def from_config(cls, cfg, input_shape):
return {'in_channels': [i.channels for i in input_shape], }
@property
def out_shape(self):
return [ShapeSpec(channels=c) for c in self._out_channels]
ppdet/modeling/ops.py
浏览文件 @ fb38470f
......@@ -41,9 +41,14 @@ __all__ = [
'collect_fpn_proposals',
'matrix_nms',
'batch_norm',
'mish',
]
def mish(x):
return x * paddle.tanh(F.softplus(x))
def batch_norm(ch,
norm_type='bn',
norm_decay=0.,
......
static/configs/ppyolo/README.md
浏览文件 @ fb38470f
......@@ -38,21 +38,24 @@ PP-YOLO improved performance and speed of YOLOv3 with following methods:
| Model | GPU number | images/GPU | backbone | input shape | Box AP<sup>val</sup> | Box AP<sup>test</sup> | V100 FP32(FPS) | V100 TensorRT FP16(FPS) | download | config |
|:------------------------:|:----------:|:----------:|:----------:| :----------:| :------------------: | :-------------------: | :------------: | :---------------------: | :------: | :-----: |
| YOLOv4(AlexyAB) | - | - | CSPDarknet | 608 | - | 43.5 | 62 | 105.5 | [model](https://paddlemodels.bj.bcebos.com/object_detection/yolov4_cspdarknet.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/yolov4/yolov4_csdarknet.yml) |
| YOLOv4(AlexyAB) | - | - | CSPDarknet | 512 | - | 43.0 | 83 | 138.4 | [model](https://paddlemodels.bj.bcebos.com/object_detection/yolov4_cspdarknet.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/yolov4/yolov4_csdarknet.yml) |
| YOLOv4(AlexyAB) | - | - | CSPDarknet | 416 | - | 41.2 | 96 | 164.0 | [model](https://paddlemodels.bj.bcebos.com/object_detection/yolov4_cspdarknet.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/yolov4/yolov4_csdarknet.yml) |
| YOLOv4(AlexyAB) | - | - | CSPDarknet | 320 | - | 38.0 | 123 | 199.0 | [model](https://paddlemodels.bj.bcebos.com/object_detection/yolov4_cspdarknet.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/yolov4/yolov4_csdarknet.yml) |
| PP-YOLO | 8 | 24 | ResNet50vd | 608 | 44.8 | 45.2 | 72.9 | 155.6 | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo.yml) |
| PP-YOLO | 8 | 24 | ResNet50vd | 512 | 43.9 | 44.4 | 89.9 | 188.4 | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo.yml) |
| PP-YOLO | 8 | 24 | ResNet50vd | 416 | 42.1 | 42.5 | 109.1 | 215.4 | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo.yml) |
| PP-YOLO | 8 | 24 | ResNet50vd | 320 | 38.9 | 39.3 | 132.2 | 242.2 | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo.yml) |
| PP-YOLO_2x | 8 | 24 | ResNet50vd | 608 | 45.3 | 45.9 | 72.9 | 155.6 | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_2x.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo_2x.yml) |
| PP-YOLO_2x | 8 | 24 | ResNet50vd | 512 | 44.4 | 45.0 | 89.9 | 188.4 | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_2x.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo_2x.yml) |
| PP-YOLO_2x | 8 | 24 | ResNet50vd | 416 | 42.7 | 43.2 | 109.1 | 215.4 | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_2x.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo_2x.yml) |
| PP-YOLO_2x | 8 | 24 | ResNet50vd | 320 | 39.5 | 40.1 | 132.2 | 242.2 | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_2x.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo_2x.yml) |
| PP-YOLO_ResNet18vd | 4 | 32 | ResNet18vd | 512 | 29.3 | 29.5 | 357.1 | 657.9 | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_r18vd.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo_r18vd.yml) |
| PP-YOLO_ResNet18vd | 4 | 32 | ResNet18vd | 416 | 28.6 | 28.9 | 409.8 | 719.4 | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_r18vd.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo_r18vd.yml) |
| PP-YOLO_ResNet18vd | 4 | 32 | ResNet18vd | 320 | 26.2 | 26.4 | 480.7 | 763.4 | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_r18vd.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo_r18vd.yml) |
| YOLOv4(AlexyAB) | - | - | CSPDarknet | 608 | - | 43.5 | 62 | 105.5 | [model](https://paddlemodels.bj.bcebos.com/object_detection/yolov4_cspdarknet.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/static/configs/yolov4/yolov4_csdarknet.yml) |
| YOLOv4(AlexyAB) | - | - | CSPDarknet | 512 | - | 43.0 | 83 | 138.4 | [model](https://paddlemodels.bj.bcebos.com/object_detection/yolov4_cspdarknet.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/static/configs/yolov4/yolov4_csdarknet.yml) |
| YOLOv4(AlexyAB) | - | - | CSPDarknet | 416 | - | 41.2 | 96 | 164.0 | [model](https://paddlemodels.bj.bcebos.com/object_detection/yolov4_cspdarknet.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/static/configs/yolov4/yolov4_csdarknet.yml) |
| YOLOv4(AlexyAB) | - | - | CSPDarknet | 320 | - | 38.0 | 123 | 199.0 | [model](https://paddlemodels.bj.bcebos.com/object_detection/yolov4_cspdarknet.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/static/configs/yolov4/yolov4_csdarknet.yml) |
| PP-YOLO | 8 | 24 | ResNet50vd | 608 | 44.8 | 45.2 | 72.9 | 155.6 | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/static/configs/ppyolo/ppyolo.yml) |
| PP-YOLO | 8 | 24 | ResNet50vd | 512 | 43.9 | 44.4 | 89.9 | 188.4 | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/static/configs/ppyolo/ppyolo.yml) |
| PP-YOLO | 8 | 24 | ResNet50vd | 416 | 42.1 | 42.5 | 109.1 | 215.4 | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/static/configs/ppyolo/ppyolo.yml) |
| PP-YOLO | 8 | 24 | ResNet50vd | 320 | 38.9 | 39.3 | 132.2 | 242.2 | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/static/configs/ppyolo/ppyolo.yml) |
| PP-YOLO_2x | 8 | 24 | ResNet50vd | 608 | 45.3 | 45.9 | 72.9 | 155.6 | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_2x.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/static/configs/ppyolo/ppyolo_2x.yml) |
| PP-YOLO_2x | 8 | 24 | ResNet50vd | 512 | 44.4 | 45.0 | 89.9 | 188.4 | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_2x.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/static/configs/ppyolo/ppyolo_2x.yml) |
| PP-YOLO_2x | 8 | 24 | ResNet50vd | 416 | 42.7 | 43.2 | 109.1 | 215.4 | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_2x.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/static/configs/ppyolo/ppyolo_2x.yml) |
| PP-YOLO_2x | 8 | 24 | ResNet50vd | 320 | 39.5 | 40.1 | 132.2 | 242.2 | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_2x.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/static/configs/ppyolo/ppyolo_2x.yml) |
| PP-YOLO | 4 | 32 | ResNet18vd | 512 | 29.3 | 29.5 | 357.1 | 657.9 | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_r18vd.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/static/configs/ppyolo/ppyolo_r18vd.yml) |
| PP-YOLO | 4 | 32 | ResNet18vd | 416 | 28.6 | 28.9 | 409.8 | 719.4 | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_r18vd.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/static/configs/ppyolo/ppyolo_r18vd.yml) |
| PP-YOLO | 4 | 32 | ResNet18vd | 320 | 26.2 | 26.4 | 480.7 | 763.4 | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_r18vd.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/static/configs/ppyolo/ppyolo_r18vd.yml) |
| PP-YOLOv2 | 8 | 12 | ResNet50vd | 640 | 49.1 | 49.5 | - | - | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolov2_r50vd_dcn.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/static/configs/ppyolo/ppyolov2_r50vd_dcn.yml) |
| PP-YOLOv2 | 8 | 12 | ResNet101vd | 640 | 49.7 | 50.1 | - | - | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolov2_r101vd_dcn.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/static/configs/ppyolo/ppyolov2_r101vd_dcn.yml) |
**Notes:**
......@@ -69,8 +72,8 @@ PP-YOLO improved performance and speed of YOLOv3 with following methods:
| Model | GPU number | images/GPU | Model Size | input shape | Box AP<sup>val</sup> | Box AP50<sup>val</sup> | Kirin 990 1xCore(FPS) | download | inference model download | config |
|:----------------------------:|:----------:|:----------:| :--------: | :----------:| :------------------: | :--------------------: | :-------------------: | :------: | :----------------------: | :-----: |
| PP-YOLO_MobileNetV3_large | 4 | 32 | 18MB | 320 | 23.2 | 42.6 | 15.6 | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_mobilenet_v3_large.pdparams) | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_mobilenet_v3_large.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo_mobilenet_v3_large.yml) |
| PP-YOLO_MobileNetV3_small | 4 | 32 | 11MB | 320 | 17.2 | 33.8 | 28.6 | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_mobilenet_v3_small.pdparams) | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_mobilenet_v3_small.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo_mobilenet_v3_small.yml) |
| PP-YOLO_MobileNetV3_large | 4 | 32 | 18MB | 320 | 23.2 | 42.6 | 15.6 | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_mobilenet_v3_large.pdparams) | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_mobilenet_v3_large.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/static/configs/ppyolo/ppyolo_mobilenet_v3_large.yml) |
| PP-YOLO_MobileNetV3_small | 4 | 32 | 11MB | 320 | 17.2 | 33.8 | 28.6 | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_mobilenet_v3_small.pdparams) | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_mobilenet_v3_small.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/static/configs/ppyolo/ppyolo_mobilenet_v3_small.yml) |
**Notes:**
......@@ -82,7 +85,7 @@ PP-YOLO improved performance and speed of YOLOv3 with following methods:
| Model | GPU number | images/GPU | Prune Ratio | Teacher Model | Model Size | input shape | Box AP<sup>val</sup> | Kirin 990 1xCore(FPS) | download | inference model download | config |
|:----------------------------:|:----------:|:----------:| :---------: | :-----------------------: | :--------: | :----------:| :------------------: | :-------------------: | :------: | :----------------------: | :-----: |
| PP-YOLO_MobileNetV3_small | 4 | 32 | 75% | PP-YOLO_MobileNetV3_large | 4.2MB | 320 | 16.2 | 39.8 | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_mobilenet_v3_small_prune75_distillby_mobilenet_v3_large.pdparams) | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_mobilenet_v3_small_prune75_distillby_mobilenet_v3_large.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo_mobilenet_v3_small.yml) |
| PP-YOLO_MobileNetV3_small | 4 | 32 | 75% | PP-YOLO_MobileNetV3_large | 4.2MB | 320 | 16.2 | 39.8 | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_mobilenet_v3_small_prune75_distillby_mobilenet_v3_large.pdparams) | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_mobilenet_v3_small_prune75_distillby_mobilenet_v3_large.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/static/configs/ppyolo/ppyolo_mobilenet_v3_small.yml) |
- Slim PP-YOLO is trained by slim traing method from [Distill pruned model](../../slim/extentions/distill_pruned_model/README.md)，distill training pruned PP-YOLO_MobileNetV3_small model with PP-YOLO_MobileNetV3_large model as the teacher model
- Pruning detectiom head of PP-YOLO model with ratio as 75%, while the arguments are `--pruned_params="yolo_block.0.2.conv.weights,yolo_block.0.tip.conv.weights,yolo_block.1.2.conv.weights,yolo_block.1.tip.conv.weights" --pruned_ratios="0.75,0.75,0.75,0.75"`
......@@ -108,9 +111,9 @@ PP-YOLO trained on Pascal VOC dataset as follows:
| Model | GPU number | images/GPU | backbone | input shape | Box AP50<sup>val</sup> | download | config |
|:------------------:|:----------:|:----------:|:----------:| :----------:| :--------------------: | :------: | :-----: |
| PP-YOLO | 8 | 12 | ResNet50vd | 608 | 84.9 | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_voc.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo_voc.yml) |
| PP-YOLO | 8 | 12 | ResNet50vd | 416 | 84.3 | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_voc.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo_voc.yml) |
| PP-YOLO | 8 | 12 | ResNet50vd | 320 | 82.2 | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_voc.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo_voc.yml) |
| PP-YOLO | 8 | 12 | ResNet50vd | 608 | 84.9 | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_voc.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/static/configs/ppyolo/ppyolo_voc.yml) |
| PP-YOLO | 8 | 12 | ResNet50vd | 416 | 84.3 | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_voc.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/static/configs/ppyolo/ppyolo_voc.yml) |
| PP-YOLO | 8 | 12 | ResNet50vd | 320 | 82.2 | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_voc.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/static/configs/ppyolo/ppyolo_voc.yml) |
## Getting Start
......
static/configs/ppyolo/README_cn.md
浏览文件 @ fb38470f
此差异已折叠。
static/configs/ppyolo/ppyolov2_r101vd_dcn.yml 0 → 100644
浏览文件 @ fb38470f
architecture: YOLOv3
use_gpu: true
max_iters: 450000
log_iter: 100
save_dir: output
snapshot_iter: 10000
metric: COCO
pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_vd_ssld_pretrained.tar
weights: output/ppyolov2_r101vd_dcn/model_final
num_classes: 80
use_fine_grained_loss: true
use_ema: true
ema_decay: 0.9998
YOLOv3:
backbone: ResNet
yolo_head: YOLOv3PANHead
use_fine_grained_loss: true
ResNet:
norm_type: sync_bn
freeze_at: 0
freeze_norm: false
norm_decay: 0.
depth: 101
feature_maps: [3, 4, 5]
variant: d
dcn_v2_stages: [5]
YOLOv3PANHead:
anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
anchors: [[10, 13], [16, 30], [33, 23],
[30, 61], [62, 45], [59, 119],
[116, 90], [156, 198], [373, 326]]
norm_decay: 0.
iou_aware: true
iou_aware_factor: 0.5
scale_x_y: 1.05
spp: true
yolo_loss: YOLOv3Loss
nms: MatrixNMS
drop_block: true
YOLOv3Loss:
ignore_thresh: 0.7
scale_x_y: 1.05
label_smooth: false
use_fine_grained_loss: true
iou_loss: IouLoss
iou_aware_loss: IouAwareLoss
IouLoss:
loss_weight: 2.5
max_height: 768
max_width: 768
IouAwareLoss:
loss_weight: 1.0
max_height: 768
max_width: 768
MatrixNMS:
background_label: -1
keep_top_k: 100
normalized: false
score_threshold: 0.01
post_threshold: 0.01
LearningRate:
base_lr: 0.005
schedulers:
- !PiecewiseDecay
gamma: 0.1
milestones:
- 300000
- !LinearWarmup
start_factor: 0.
steps: 4000
OptimizerBuilder:
clip_grad_by_norm: 35.
optimizer:
momentum: 0.9
type: Momentum
regularizer:
factor: 0.0005
type: L2
_READER_: 'ppyolov2_reader.yml'
static/configs/ppyolo/ppyolov2_r50vd_dcn.yml 0 → 100644
浏览文件 @ fb38470f
architecture: YOLOv3
use_gpu: true
max_iters: 450000
log_iter: 100
save_dir: output
snapshot_iter: 10000
metric: COCO
pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_ssld_pretrained.tar
weights: output/ppyolov2_r50vd_dcn/model_final
num_classes: 80
use_fine_grained_loss: true
use_ema: true
ema_decay: 0.9998
YOLOv3:
backbone: ResNet
yolo_head: YOLOv3PANHead
use_fine_grained_loss: true
ResNet:
norm_type: sync_bn
freeze_at: 0
freeze_norm: false
norm_decay: 0.
depth: 50
feature_maps: [3, 4, 5]
variant: d
dcn_v2_stages: [5]
YOLOv3PANHead:
anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
anchors: [[10, 13], [16, 30], [33, 23],
[30, 61], [62, 45], [59, 119],
[116, 90], [156, 198], [373, 326]]
norm_decay: 0.
iou_aware: true
iou_aware_factor: 0.5
scale_x_y: 1.05
spp: true
yolo_loss: YOLOv3Loss
nms: MatrixNMS
drop_block: true
YOLOv3Loss:
ignore_thresh: 0.7
scale_x_y: 1.05
label_smooth: false
use_fine_grained_loss: true
iou_loss: IouLoss
iou_aware_loss: IouAwareLoss
IouLoss:
loss_weight: 2.5
max_height: 768
max_width: 768
IouAwareLoss:
loss_weight: 1.0
max_height: 768
max_width: 768
MatrixNMS:
background_label: -1
keep_top_k: 100
normalized: false
score_threshold: 0.01
post_threshold: 0.01
LearningRate:
base_lr: 0.005
schedulers:
- !PiecewiseDecay
gamma: 0.1
milestones:
- 300000
- !LinearWarmup
start_factor: 0.
steps: 4000
OptimizerBuilder:
clip_grad_by_norm: 35.
optimizer:
momentum: 0.9
type: Momentum
regularizer:
factor: 0.0005
type: L2
_READER_: 'ppyolov2_reader.yml'
static/configs/ppyolo/ppyolov2_reader.yml 0 → 100644
浏览文件 @ fb38470f
TrainReader:
inputs_def:
fields: ['image', 'gt_bbox', 'gt_class', 'gt_score']
num_max_boxes: 100
dataset:
!COCODataSet
image_dir: train2017
anno_path: annotations/instances_train2017.json
dataset_dir: dataset/coco
with_background: false
sample_transforms:
- !DecodeImage
to_rgb: True
with_mixup: True
- !MixupImage
alpha: 1.5
beta: 1.5
- !ColorDistort {}
- !RandomExpand
ratio: 2.0
fill_value: [123.675, 116.28, 103.53]
- !RandomCrop {}
- !RandomFlipImage
is_normalized: false
- !NormalizeBox {}
- !PadBox
num_max_boxes: 100
- !BboxXYXY2XYWH {}
batch_transforms:
- !RandomShape
sizes: [320, 352, 384, 416, 448, 480, 512, 544, 576, 608, 640, 672, 704, 736, 768]
random_inter: True
- !NormalizeImage
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
is_scale: True
is_channel_first: false
- !Permute
to_bgr: false
channel_first: True
# Gt2YoloTarget is only used when use_fine_grained_loss set as true,
# this operator will be deleted automatically if use_fine_grained_loss
# is set as false
- !Gt2YoloTarget
anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
anchors: [[10, 13], [16, 30], [33, 23],
[30, 61], [62, 45], [59, 119],
[116, 90], [156, 198], [373, 326]]
downsample_ratios: [32, 16, 8]
batch_size: 12
shuffle: true
mixup_epoch: 25000
drop_last: true
worker_num: 8
bufsize: 4
use_process: true
EvalReader:
inputs_def:
fields: ['image', 'im_size', 'im_id']
num_max_boxes: 100
dataset:
!COCODataSet
image_dir: val2017
anno_path: annotations/instances_val2017.json
dataset_dir: dataset/coco
with_background: false
sample_transforms:
- !DecodeImage
to_rgb: True
- !ResizeImage
target_size: 640
interp: 2
- !NormalizeImage
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
is_scale: True
is_channel_first: false
- !PadBox
num_max_boxes: 50
- !Permute
to_bgr: false
channel_first: True
batch_size: 8
drop_empty: false
worker_num: 8
bufsize: 4
TestReader:
inputs_def:
image_shape: [3, 640, 640]
fields: ['image', 'im_size', 'im_id']
dataset:
!ImageFolder
anno_path: annotations/instances_val2017.json
with_background: false
sample_transforms:
- !DecodeImage
to_rgb: True
- !ResizeImage
target_size: 640
interp: 2
- !NormalizeImage
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
is_scale: True
is_channel_first: false
- !Permute
to_bgr: false
channel_first: True
batch_size: 1
static/ppdet/modeling/anchor_heads/yolo_head.py
浏览文件 @ fb38470f
......@@ -192,6 +192,8 @@ class YOLOv3Head(object):
if act == 'leaky':
out = fluid.layers.leaky_relu(x=out, alpha=0.1)
elif act == 'mish':
out = fluid.layers.mish(out)
return out
def _spp_module(self, input, name=""):
......@@ -657,7 +659,6 @@ class YOLOv4Head(YOLOv3Head):
class PPYOLOTinyHead(YOLOv3Head):
"""
Head block for YOLOv3 network
Args:
norm_decay (float): weight decay for normalization layer weights
num_classes (int): number of output classes
......@@ -781,11 +782,9 @@ class PPYOLOTinyHead(YOLOv3Head):
def _get_outputs(self, input, is_train=True):
"""
Get PP-YOLO tiny head output
Args:
input (list): List of Variables, output of backbone stages
is_train (bool): whether in train or test mode
Returns:
outputs (list): Variables of each output layer
"""
......@@ -838,3 +837,232 @@ class PPYOLOTinyHead(YOLOv3Head):
route = self._upsample(route)
return outputs
@register
class YOLOv3PANHead(YOLOv3Head):
"""
Head block for YOLOv3PANHead network
Args:
conv_block_num (int): number of conv block in each detection block
norm_decay (float): weight decay for normalization layer weights
num_classes (int): number of output classes
anchors (list): anchors
anchor_masks (list): anchor masks
nms (object): an instance of `MultiClassNMS`
"""
__inject__ = ['yolo_loss', 'nms']
__shared__ = ['num_classes', 'weight_prefix_name']
def __init__(self,
conv_block_num=3,
norm_decay=0.,
num_classes=80,
anchors=[[10, 13], [16, 30], [33, 23], [30, 61], [62, 45],
[59, 119], [116, 90], [156, 198], [373, 326]],
anchor_masks=[[6, 7, 8], [3, 4, 5], [0, 1, 2]],
drop_block=False,
iou_aware=False,
iou_aware_factor=0.4,
block_size=3,
keep_prob=0.9,
yolo_loss="YOLOv3Loss",
spp=False,
nms=MultiClassNMS(
score_threshold=0.01,
nms_top_k=1000,
keep_top_k=100,
nms_threshold=0.45,
background_label=-1).__dict__,
weight_prefix_name='',
downsample=[32, 16, 8],
scale_x_y=1.0,
clip_bbox=True,
act='mish'):
super(YOLOv3PANHead, self).__init__(
conv_block_num=conv_block_num,
norm_decay=norm_decay,
num_classes=num_classes,
anchors=anchors,
anchor_masks=anchor_masks,
drop_block=drop_block,
iou_aware=iou_aware,
iou_aware_factor=iou_aware_factor,
block_size=block_size,
keep_prob=keep_prob,
yolo_loss=yolo_loss,
spp=spp,
nms=nms,
weight_prefix_name=weight_prefix_name,
downsample=downsample,
scale_x_y=scale_x_y,
clip_bbox=clip_bbox)
self.act = act
def _detection_block(self,
input,
channel,
conv_block_num=2,
is_first=False,
is_test=True,
name=None):
conv_left = self._conv_bn(
input,
channel,
act=self.act,
filter_size=1,
stride=1,
padding=0,
name='{}.left'.format(name))
conv_right = self._conv_bn(
input,
channel,
act=self.act,
filter_size=1,
stride=1,
padding=0,
name='{}.right'.format(name))
for j in range(conv_block_num):
conv_left = self._conv_bn(
conv_left,
channel,
act=self.act,
filter_size=1,
stride=1,
padding=0,
name='{}.left.{}'.format(name, 2 * j))
if self.use_spp and is_first and j == 1:
c = conv_left.shape[1]
conv_left = self._spp_module(conv_left, name="spp")
conv_left = self._conv_bn(
conv_left,
c,
act=self.act,
filter_size=1,
stride=1,
padding=0,
name='{}.left.{}'.format(name, 2 * j + 1))
else:
conv_left = self._conv_bn(
conv_left,
channel,
act=self.act,
filter_size=3,
stride=1,
padding=1,
name='{}.left.{}'.format(name, 2 * j + 1))
if self.drop_block and j == 1:
conv_left = DropBlock(
conv_left,
block_size=self.block_size,
keep_prob=self.keep_prob,
is_test=is_test)
conv = fluid.layers.concat(input=[conv_left, conv_right], axis=1)
conv = self._conv_bn(
conv,
channel * 2,
act=self.act,
filter_size=1,
stride=1,
padding=0,
name=name)
return conv, conv
def _get_outputs(self, input, is_train=True):
"""
Get YOLOv3 head output
Args:
input (list): List of Variables, output of backbone stages
is_train (bool): whether in train or test mode
Returns:
outputs (list): Variables of each output layer
"""
# get last out_layer_num blocks in reverse order
out_layer_num = len(self.anchor_masks)
blocks = input[-1:-out_layer_num - 1:-1]
# fpn
yolo_feats = []
route = None
for i, block in enumerate(blocks):
if i > 0: # perform concat in first 2 detection_block
block = fluid.layers.concat(input=[route, block], axis=1)
route, tip = self._detection_block(
block,
channel=512 // (2**i),
is_first=i == 0,
is_test=(not is_train),
conv_block_num=self.conv_block_num,
name=self.prefix_name + "fpn.{}".format(i))
yolo_feats.append(tip)
if i < len(blocks) - 1:
# do not perform upsample in the last detection_block
route = self._conv_bn(
input=route,
ch_out=512 // (2**i),
filter_size=1,
stride=1,
padding=0,
act=self.act,
name=self.prefix_name + "fpn_transition.{}".format(i))
# upsample
route = self._upsample(route)
# pan
pan_feats = [yolo_feats[-1]]
route = yolo_feats[out_layer_num - 1]
for i in reversed(range(out_layer_num - 1)):
channel = 512 // (2**i)
route = self._conv_bn(
input=route,
ch_out=channel,
filter_size=3,
stride=2,
padding=1,
act=self.act,
name=self.prefix_name + "pan_transition.{}".format(i))
block = yolo_feats[i]
block = fluid.layers.concat(input=[route, block], axis=1)
route, tip = self._detection_block(
block,
channel=channel,
is_first=False,
is_test=(not is_train),
conv_block_num=self.conv_block_num,
name=self.prefix_name + "pan.{}".format(i))
pan_feats.append(tip)
pan_feats = pan_feats[::-1]
outputs = []
for i, block in enumerate(pan_feats):
if self.iou_aware:
num_filters = len(self.anchor_masks[i]) * (self.num_classes + 6)
else:
num_filters = len(self.anchor_masks[i]) * (self.num_classes + 5)
with fluid.name_scope('yolo_output'):
block_out = fluid.layers.conv2d(
input=block,
num_filters=num_filters,
filter_size=1,
stride=1,
padding=0,
act=None,
param_attr=ParamAttr(
name=self.prefix_name +
"yolo_output.{}.conv.weights".format(i)),
bias_attr=ParamAttr(
regularizer=L2Decay(0.),
name=self.prefix_name +
"yolo_output.{}.conv.bias".format(i)))
outputs.append(block_out)
return outputs
static/ppdet/modeling/losses/iou_aware_loss.py
浏览文件 @ fb38470f
......@@ -74,6 +74,7 @@ class IouAwareLoss(IouLoss):
iouk = self._iou(pred, gt, ioup, eps)
iouk.stop_gradient = True
loss_iou_aware = fluid.layers.cross_entropy(ioup, iouk, soft_label=True)
loss_iou_aware = fluid.layers.sigmoid_cross_entropy_with_logits(ioup,
iouk)
loss_iou_aware = loss_iou_aware * self._loss_weight
return loss_iou_aware
static/ppdet/modeling/losses/yolo_loss.py
浏览文件 @ fb38470f
......@@ -238,7 +238,6 @@ class YOLOv3Loss(object):
along channel dimension
"""
ioup = fluid.layers.slice(output, axes=[1], starts=[0], ends=[an_num])
ioup = fluid.layers.sigmoid(ioup)
oriout = fluid.layers.slice(
output,
axes=[1],
......
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