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PaddleDetection
未验证 提交 6b76b6fc 编写于 作者: W wangxinxin08 提交者： GitHub
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[Dygraph]yolo series (#2148) 

* add ppyolo r18vd mbv3, yolov3 r50vd

* modify TestReader of ppyolo mbv3 r18vd

* add clip to avoid nan, modify ema to apply ema on bn mean and bn var

* fix code resulting in nan

* add yolov3 r50vd dcn configs

* finish yolo_series and fix some problems

* hide --bias flag and modify docs

* modify --bias and fix deploy/python/infer
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dygraph/configs/ppyolo/README.md
浏览文件 @ 6b76b6fc
...@@ -37,9 +37,18 @@ PP-YOLO improved performance and speed of YOLOv3 with following methods: ...@@ -37,9 +37,18 @@ PP-YOLO improved performance and speed of YOLOv3 with following methods:
### PP-YOLO ### PP-YOLO
| 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 | | 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://paddlemodels.bj.bcebos.com/object_detection/dygraph/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 | 608 | 44.8 | 45.2 | 72.9 | 155.6 | [model](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/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://paddlemodels.bj.bcebos.com/object_detection/dygraph/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://paddlemodels.bj.bcebos.com/object_detection/dygraph/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://paddlemodels.bj.bcebos.com/object_detection/dygraph/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://paddlemodels.bj.bcebos.com/object_detection/dygraph/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 | 608 | 45.3 | 45.9 | 72.9 | 155.6 | [model](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/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://paddlemodels.bj.bcebos.com/object_detection/dygraph/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://paddlemodels.bj.bcebos.com/object_detection/dygraph/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://paddlemodels.bj.bcebos.com/object_detection/dygraph/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 | 29.3 | 29.5 | 357.1 | 657.9 | [model](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/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://paddlemodels.bj.bcebos.com/object_detection/dygraph/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://paddlemodels.bj.bcebos.com/object_detection/dygraph/ppyolo_r18vd_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_r18vd_coco.yml) |
**Notes:** **Notes:**
...@@ -49,6 +58,29 @@ PP-YOLO improved performance and speed of YOLOv3 with following methods: ...@@ -49,6 +58,29 @@ PP-YOLO improved performance and speed of YOLOv3 with following methods:
- PP-YOLO FP32 inference speed testing uses inference model exported by `tools/export_model.py` 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-YOLO FP32 inference speed testing uses inference model exported by `tools/export_model.py` 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.
- TensorRT FP16 inference speed testing exclude the time cost of bounding-box decoding(`yolo_box`) part comparing with FP32 testing above, which means that data reading, bounding-box decoding and post-processing(NMS) is excluded(test method same as [YOLOv4(AlexyAB)](https://github.com/AlexeyAB/darknet) too) - TensorRT FP16 inference speed testing exclude the time cost of bounding-box decoding(`yolo_box`) part comparing with FP32 testing above, which means that data reading, bounding-box decoding and post-processing(NMS) is excluded(test method same as [YOLOv4(AlexyAB)](https://github.com/AlexeyAB/darknet) too)
### PP-YOLO for mobile
| 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://paddlemodels.bj.bcebos.com/object_detection/dygraph/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://paddlemodels.bj.bcebos.com/object_detection/dygraph/ppyolo_mbv3_small_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_mbv3_small_coco.yml) |
**Notes:**
- PP-YOLO_MobileNetV3 is trained on COCO train2017 datast and evaluated on val2017 dataset，Box AP<sup>val</sup> is evaluation results of `mAP(IoU=0.5:0.95)`, Box AP<sup>val</sup> is evaluation results of `mAP(IoU=0.5)`.
- PP-YOLO_MobileNetV3 used 4 GPUs for training and mini-batch size as 32 on each GPU, if GPU number and mini-batch size is changed, learning rate and iteration times should be adjusted according [FAQ](../../../docs/FAQ.md).
- PP-YOLO_MobileNetV3 inference speed is tested on Kirin 990 with 1 thread.
### PP-YOLO on Pascal VOC
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/dygraph/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://paddlemodels.bj.bcebos.com/object_detection/dygraph/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://paddlemodels.bj.bcebos.com/object_detection/dygraph/ppyolo_r50vd_dcn_voc.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_r50vd_dcn_voc.yml) |
## Getting Start ## Getting Start
### 1. Training ### 1. Training
......
dygraph/configs/ppyolo/README_cn.md
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...@@ -39,7 +39,16 @@ PP-YOLO从如下方面优化和提升YOLOv3模型的精度和速度： ...@@ -39,7 +39,16 @@ 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 | | 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://paddlemodels.bj.bcebos.com/object_detection/dygraph/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 | 608 | 44.8 | 45.2 | 72.9 | 155.6 | [model](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/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://paddlemodels.bj.bcebos.com/object_detection/dygraph/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://paddlemodels.bj.bcebos.com/object_detection/dygraph/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://paddlemodels.bj.bcebos.com/object_detection/dygraph/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://paddlemodels.bj.bcebos.com/object_detection/dygraph/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 | 608 | 45.3 | 45.9 | 72.9 | 155.6 | [model](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/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://paddlemodels.bj.bcebos.com/object_detection/dygraph/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://paddlemodels.bj.bcebos.com/object_detection/dygraph/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://paddlemodels.bj.bcebos.com/object_detection/dygraph/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 | 29.3 | 29.5 | 357.1 | 657.9 | [model](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/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://paddlemodels.bj.bcebos.com/object_detection/dygraph/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://paddlemodels.bj.bcebos.com/object_detection/dygraph/ppyolo_r18vd_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_r18vd_coco.yml) |
**注意:** **注意:**
...@@ -50,6 +59,27 @@ PP-YOLO从如下方面优化和提升YOLOv3模型的精度和速度： ...@@ -50,6 +59,27 @@ PP-YOLO从如下方面优化和提升YOLOv3模型的精度和速度：
- TensorRT FP16的速度测试相比于FP32去除了`yolo_box`(bbox解码)部分耗时，即不包含数据预处理，bbox解码和NMS(与[YOLOv4(AlexyAB)](https://github.com/AlexeyAB/darknet)测试方法一致)。 - TensorRT FP16的速度测试相比于FP32去除了`yolo_box`(bbox解码)部分耗时，即不包含数据预处理，bbox解码和NMS(与[YOLOv4(AlexyAB)](https://github.com/AlexeyAB/darknet)测试方法一致)。
- PP-YOLO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 7.5.1，TensorRT推理速度测试使用TensorRT 5.1.2.2。 - PP-YOLO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 7.5.1，TensorRT推理速度测试使用TensorRT 5.1.2.2。
### PP-YOLO 轻量级模型
| 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://paddlemodels.bj.bcebos.com/object_detection/dygraph/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://paddlemodels.bj.bcebos.com/object_detection/dygraph/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 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](../../../docs/FAQ.md)调整学习率和迭代次数。
- PP-YOLO_MobileNetV3 模型推理速度测试环境配置为麒麟990芯片单线程。
### Pascal VOC数据集上的PP-YOLO
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://paddlemodels.bj.bcebos.com/object_detection/dygraph/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://paddlemodels.bj.bcebos.com/object_detection/dygraph/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://paddlemodels.bj.bcebos.com/object_detection/dygraph/ppyolo_r50vd_dcn_voc.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_r50vd_dcn_voc.yml) |
## 使用说明 ## 使用说明
### 1. 训练 ### 1. 训练
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...@@ -13,7 +13,6 @@ LearningRate: ...@@ -13,7 +13,6 @@ LearningRate:
steps: 4000 steps: 4000
OptimizerBuilder: OptimizerBuilder:
clip_grad_by_norm: 35.
optimizer: optimizer:
momentum: 0.9 momentum: 0.9
type: Momentum type: Momentum
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...@@ -13,7 +13,6 @@ LearningRate: ...@@ -13,7 +13,6 @@ LearningRate:
steps: 4000 steps: 4000
OptimizerBuilder: OptimizerBuilder:
clip_grad_by_norm: 35.
optimizer: optimizer:
momentum: 0.9 momentum: 0.9
type: Momentum type: Momentum
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architecture: YOLOv3
pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_large_x1_0_ssld_pretrained.tar
load_static_weights: true
norm_type: sync_bn
use_ema: true
ema_decay: 0.9998
YOLOv3:
backbone: MobileNetV3
neck: PPYOLOFPN
yolo_head: YOLOv3Head
post_process: BBoxPostProcess
MobileNetV3:
model_name: large
scale: 1.
with_extra_blocks: false
extra_block_filters: []
feature_maps: [13, 16]
PPYOLOFPN:
feat_channels: [160, 368]
coord_conv: true
conv_block_num: 0
spp: true
drop_block: true
YOLOv3Head:
anchors: [[11, 18], [34, 47], [51, 126],
[115, 71], [120, 195], [254, 235]]
anchor_masks: [[3, 4, 5], [0, 1, 2]]
loss: YOLOv3Loss
YOLOv3Loss:
ignore_thresh: 0.5
downsample: [32, 16]
label_smooth: false
scale_x_y: 1.05
iou_loss: IouLoss
IouLoss:
loss_weight: 2.5
loss_square: true
BBoxPostProcess:
decode:
name: YOLOBox
conf_thresh: 0.005
downsample_ratio: 32
clip_bbox: true
scale_x_y: 1.05
nms:
name: MultiClassNMS
keep_top_k: 100
nms_threshold: 0.45
nms_top_k: 1000
score_threshold: 0.005
normalized: false
dygraph/configs/ppyolo/_base_/ppyolo_mbv3_small.yml 0 → 100644
浏览文件 @ 6b76b6fc
architecture: YOLOv3
pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_small_x1_0_ssld_pretrained.tar
load_static_weights: true
norm_type: sync_bn
use_ema: true
ema_decay: 0.9998
YOLOv3:
backbone: MobileNetV3
neck: PPYOLOFPN
yolo_head: YOLOv3Head
post_process: BBoxPostProcess
MobileNetV3:
model_name: small
scale: 1.
with_extra_blocks: false
extra_block_filters: []
feature_maps: [9, 12]
PPYOLOFPN:
feat_channels: [96, 304]
coord_conv: true
conv_block_num: 0
spp: true
drop_block: true
YOLOv3Head:
anchors: [[11, 18], [34, 47], [51, 126],
[115, 71], [120, 195], [254, 235]]
anchor_masks: [[3, 4, 5], [0, 1, 2]]
loss: YOLOv3Loss
YOLOv3Loss:
ignore_thresh: 0.5
downsample: [32, 16]
label_smooth: false
scale_x_y: 1.05
iou_loss: IouLoss
IouLoss:
loss_weight: 2.5
loss_square: true
BBoxPostProcess:
decode:
name: YOLOBox
conf_thresh: 0.005
downsample_ratio: 32
clip_bbox: true
scale_x_y: 1.05
nms:
name: MultiClassNMS
keep_top_k: 100
nms_threshold: 0.45
nms_top_k: 1000
score_threshold: 0.005
normalized: false
dygraph/configs/ppyolo/_base_/ppyolo_r18vd.yml 0 → 100644
浏览文件 @ 6b76b6fc
architecture: YOLOv3
pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet18_vd_pretrained.tar
load_static_weights: true
norm_type: sync_bn
use_ema: true
ema_decay: 0.9998
YOLOv3:
backbone: ResNet
neck: PPYOLOFPN
yolo_head: YOLOv3Head
post_process: BBoxPostProcess
ResNet:
depth: 18
variant: d
return_idx: [2, 3]
freeze_at: -1
freeze_norm: false
norm_decay: 0.
PPYOLOFPN:
feat_channels: [512, 512]
drop_block: true
block_size: 3
keep_prob: 0.9
conv_block_num: 0
YOLOv3Head:
anchor_masks: [[3, 4, 5], [0, 1, 2]]
anchors: [[10, 14], [23, 27], [37, 58],
[81, 82], [135, 169], [344, 319]]
loss: YOLOv3Loss
YOLOv3Loss:
ignore_thresh: 0.7
downsample: [32, 16]
label_smooth: false
scale_x_y: 1.05
iou_loss: IouLoss
IouLoss:
loss_weight: 2.5
loss_square: true
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
normalized: false
background_label: -1
dygraph/configs/ppyolo/_base_/ppyolo_r50vd_dcn.yml
浏览文件 @ 6b76b6fc
architecture: YOLOv3 architecture: YOLOv3
pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_ssld_pretrained.tar pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_ssld_pretrained.tar
weights: output/ppyolo_r50vd_dcn/model_final
load_static_weights: true load_static_weights: true
norm_type: sync_bn norm_type: sync_bn
use_ema: true use_ema: true
...@@ -55,7 +54,7 @@ IouAwareLoss: ...@@ -55,7 +54,7 @@ IouAwareLoss:
BBoxPostProcess: BBoxPostProcess:
decode: decode:
name: YOLOBox name: YOLOBox
conf_thresh: 0.005 conf_thresh: 0.01
downsample_ratio: 32 downsample_ratio: 32
clip_bbox: true clip_bbox: true
scale_x_y: 1.05 scale_x_y: 1.05
...@@ -66,3 +65,4 @@ BBoxPostProcess: ...@@ -66,3 +65,4 @@ BBoxPostProcess:
post_threshold: 0.01 post_threshold: 0.01
nms_top_k: -1 nms_top_k: -1
normalized: false normalized: false
background_label: -1
dygraph/configs/ppyolo/ppyolo_mbv3_large_coco.yml 0 → 100644
浏览文件 @ 6b76b6fc
_BASE_: [
'../datasets/coco_detection.yml',
'../runtime.yml',
'./_base_/ppyolo_mbv3_large.yml',
'./_base_/optimizer_1x.yml',
'./_base_/ppyolo_reader.yml',
]
snapshot_epoch: 10
weights: output/ppyolo_mbv3_large_coco/model_final
TrainReader:
inputs_def:
num_max_boxes: 90
sample_transforms:
- DecodeOp: {}
- MixupOp: {alpha: 1.5, beta: 1.5}
- RandomDistortOp: {}
- RandomExpandOp: {fill_value: [123.675, 116.28, 103.53]}
- RandomCropOp: {}
- RandomFlipOp: {}
batch_transforms:
- BatchRandomResizeOp:
target_size: [224, 256, 288, 320, 352, 384, 416, 448, 480, 512]
random_size: True
random_interp: True
keep_ratio: False
- NormalizeBoxOp: {}
- PadBoxOp: {num_max_boxes: 90}
- BboxXYXY2XYWHOp: {}
- NormalizeImageOp: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
- PermuteOp: {}
- Gt2YoloTargetOp:
anchor_masks: [[3, 4, 5], [0, 1, 2]]
anchors: [[11, 18], [34, 47], [51, 126], [115, 71], [120, 195], [254, 235]]
downsample_ratios: [32, 16]
iou_thresh: 0.25
num_classes: 80
batch_size: 32
mixup_epoch: 200
shuffle: true
EvalReader:
sample_transforms:
- DecodeOp: {}
- ResizeOp: {target_size: [320, 320], keep_ratio: False, interp: 2}
- NormalizeImageOp: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
- PermuteOp: {}
batch_size: 8
drop_empty: false
TestReader:
inputs_def:
image_shape: [3, 320, 320]
sample_transforms:
- DecodeOp: {}
- ResizeOp: {target_size: [320, 320], keep_ratio: False, interp: 2}
- NormalizeImageOp: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
- PermuteOp: {}
batch_size: 1
epoch: 270
LearningRate:
base_lr: 0.005
schedulers:
- !PiecewiseDecay
gamma: 0.1
milestones:
- 162
- 216
- !LinearWarmup
start_factor: 0.
steps: 4000
OptimizerBuilder:
optimizer:
momentum: 0.9
type: Momentum
regularizer:
factor: 0.0005
type: L2
dygraph/configs/ppyolo/ppyolo_mbv3_small_coco.yml 0 → 100644
浏览文件 @ 6b76b6fc
_BASE_: [
'../datasets/coco_detection.yml',
'../runtime.yml',
'./_base_/ppyolo_mbv3_small.yml',
'./_base_/optimizer_1x.yml',
'./_base_/ppyolo_reader.yml',
]
snapshot_epoch: 10
weights: output/ppyolo_mbv3_small_coco/model_final
TrainReader:
inputs_def:
num_max_boxes: 90
sample_transforms:
- DecodeOp: {}
- MixupOp: {alpha: 1.5, beta: 1.5}
- RandomDistortOp: {}
- RandomExpandOp: {fill_value: [123.675, 116.28, 103.53]}
- RandomCropOp: {}
- RandomFlipOp: {}
batch_transforms:
- BatchRandomResizeOp:
target_size: [224, 256, 288, 320, 352, 384, 416, 448, 480, 512]
random_size: True
random_interp: True
keep_ratio: False
- NormalizeBoxOp: {}
- PadBoxOp: {num_max_boxes: 90}
- BboxXYXY2XYWHOp: {}
- NormalizeImageOp: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
- PermuteOp: {}
- Gt2YoloTargetOp:
anchor_masks: [[3, 4, 5], [0, 1, 2]]
anchors: [[11, 18], [34, 47], [51, 126], [115, 71], [120, 195], [254, 235]]
downsample_ratios: [32, 16]
iou_thresh: 0.25
num_classes: 80
batch_size: 32
mixup_epoch: 200
shuffle: true
EvalReader:
sample_transforms:
- DecodeOp: {}
- ResizeOp: {target_size: [320, 320], keep_ratio: False, interp: 2}
- NormalizeImageOp: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
- PermuteOp: {}
batch_size: 8
drop_empty: false
TestReader:
inputs_def:
image_shape: [3, 320, 320]
sample_transforms:
- DecodeOp: {}
- ResizeOp: {target_size: [320, 320], keep_ratio: False, interp: 2}
- NormalizeImageOp: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
- PermuteOp: {}
batch_size: 1
epoch: 270
LearningRate:
base_lr: 0.005
schedulers:
- !PiecewiseDecay
gamma: 0.1
milestones:
- 162
- 216
- !LinearWarmup
start_factor: 0.
steps: 4000
OptimizerBuilder:
optimizer:
momentum: 0.9
type: Momentum
regularizer:
factor: 0.0005
type: L2
dygraph/configs/ppyolo/ppyolo_r18vd_coco.yml 0 → 100644
浏览文件 @ 6b76b6fc
_BASE_: [
'../datasets/coco_detection.yml',
'../runtime.yml',
'./_base_/ppyolo_r18vd.yml',
'./_base_/optimizer_1x.yml',
'./_base_/ppyolo_reader.yml',
]
snapshot_epoch: 10
weights: output/ppyolo_r18vd_coco/model_final
TrainReader:
sample_transforms:
- DecodeOp: {}
- MixupOp: {alpha: 1.5, beta: 1.5}
- RandomDistortOp: {}
- RandomExpandOp: {fill_value: [123.675, 116.28, 103.53]}
- RandomCropOp: {}
- RandomFlipOp: {}
batch_transforms:
- BatchRandomResizeOp:
target_size: [320, 352, 384, 416, 448, 480, 512, 544, 576, 608]
random_size: True
random_interp: True
keep_ratio: False
- NormalizeBoxOp: {}
- PadBoxOp: {num_max_boxes: 50}
- BboxXYXY2XYWHOp: {}
- NormalizeImageOp:
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
is_scale: True
- PermuteOp: {}
- Gt2YoloTargetOp:
anchor_masks: [[3, 4, 5], [0, 1, 2]]
anchors: [[10, 14], [23, 27], [37, 58], [81, 82], [135, 169], [344, 319]]
downsample_ratios: [32, 16]
batch_size: 32
mixup_epoch: 500
shuffle: true
EvalReader:
sample_transforms:
- DecodeOp: {}
- ResizeOp: {target_size: [512, 512], keep_ratio: False, interp: 2}
- NormalizeImageOp: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
- PermuteOp: {}
batch_size: 8
drop_empty: false
TestReader:
inputs_def:
image_shape: [3, 512, 512]
sample_transforms:
- DecodeOp: {}
- ResizeOp: {target_size: [512, 512], keep_ratio: False, interp: 2}
- NormalizeImageOp: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
- PermuteOp: {}
batch_size: 1
epoch: 270
LearningRate:
base_lr: 0.004
schedulers:
- !PiecewiseDecay
gamma: 0.1
milestones:
- 162
- 216
- !LinearWarmup
start_factor: 0.
steps: 4000
OptimizerBuilder:
optimizer:
momentum: 0.9
type: Momentum
regularizer:
factor: 0.0005
type: L2
dygraph/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml
浏览文件 @ 6b76b6fc
...@@ -7,3 +7,4 @@ _BASE_: [ ...@@ -7,3 +7,4 @@ _BASE_: [
] ]
snapshot_epoch: 16 snapshot_epoch: 16
weights: output/ppyolo_r50vd_dcn_1x_coco/model_final
dygraph/configs/ppyolo/ppyolo_r50vd_dcn_1x_minicoco.yml
浏览文件 @ 6b76b6fc
...@@ -7,7 +7,8 @@ _BASE_: [ ...@@ -7,7 +7,8 @@ _BASE_: [
] ]
snapshot_epoch: 8 snapshot_epoch: 8
use_ema: false use_ema: true
weights: output/ppyolo_r50vd_dcn_1x_minicoco/model_final
TrainReader: TrainReader:
batch_size: 12 batch_size: 12
...@@ -33,3 +34,11 @@ LearningRate: ...@@ -33,3 +34,11 @@ LearningRate:
- !LinearWarmup - !LinearWarmup
start_factor: 0. start_factor: 0.
steps: 4000 steps: 4000
OptimizerBuilder:
optimizer:
momentum: 0.9
type: Momentum
regularizer:
factor: 0.0005
type: L2
dygraph/configs/ppyolo/ppyolo_r50vd_dcn_2x_coco.yml
浏览文件 @ 6b76b6fc
...@@ -7,3 +7,4 @@ _BASE_: [ ...@@ -7,3 +7,4 @@ _BASE_: [
] ]
snapshot_epoch: 16 snapshot_epoch: 16
weights: output/ppyolo_r50vd_dcn_2x_coco/model_final
dygraph/configs/ppyolo/ppyolo_r50vd_dcn_voc.yml 0 → 100644
浏览文件 @ 6b76b6fc
_BASE_: [
'../datasets/voc.yml',
'../runtime.yml',
'./_base_/ppyolo_r50vd_dcn.yml',
'./_base_/optimizer_1x.yml',
'./_base_/ppyolo_reader.yml',
]
snapshot_epoch: 83
weights: output/ppyolo_r50vd_dcn_voc/model_final
TrainReader:
batch_transforms:
- BatchRandomResizeOp: {target_size: [320, 352, 384, 416, 448, 480, 512, 544, 576, 608], random_size: True, random_interp: True, keep_ratio: False}
- NormalizeBoxOp: {}
- PadBoxOp: {num_max_boxes: 50}
- BboxXYXY2XYWHOp: {}
- NormalizeImageOp: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
- PermuteOp: {}
- Gt2YoloTargetOp: {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], num_classes: 20}
mixup_epoch: 350
batch_size: 12
epoch: 583
LearningRate:
base_lr: 0.00333
schedulers:
- !PiecewiseDecay
gamma: 0.1
milestones:
- 466
- 516
- !LinearWarmup
start_factor: 0.
steps: 4000
OptimizerBuilder:
optimizer:
momentum: 0.9
type: Momentum
regularizer:
factor: 0.0005
type: L2
dygraph/configs/yolov3/README.md
浏览文件 @ 6b76b6fc
...@@ -12,6 +12,7 @@ ...@@ -12,6 +12,7 @@
| DarkNet53 | 608 | 8 | 270e | ---- | 39.0 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/yolov3_darknet53_270e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/yolov3/yolov3_darknet53_270e_coco.yml) | | DarkNet53 | 608 | 8 | 270e | ---- | 39.0 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/yolov3_darknet53_270e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/yolov3/yolov3_darknet53_270e_coco.yml) |
| DarkNet53 | 416 | 8 | 270e | ---- | 37.5 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/yolov3_darknet53_270e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/yolov3/yolov3_darknet53_270e_coco.yml) | | DarkNet53 | 416 | 8 | 270e | ---- | 37.5 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/yolov3_darknet53_270e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/yolov3/yolov3_darknet53_270e_coco.yml) |
| DarkNet53 | 320 | 8 | 270e | ---- | 34.6 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/yolov3_darknet53_270e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/yolov3/yolov3_darknet53_270e_coco.yml) | | DarkNet53 | 320 | 8 | 270e | ---- | 34.6 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/yolov3_darknet53_270e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/yolov3/yolov3_darknet53_270e_coco.yml) |
| ResNet50_vd | 608 | 8 | 270e | ---- | 39.1 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/yolov3_r50vd_dcn_270e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/yolov3/yolov3_r50vd_dcn_270e_coco.yml) |
| MobileNet-V1 | 608 | 8 | 270e | ---- | 28.8 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/yolov3_mobilenet_v1_270e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/yolov3/yolov3_mobilenet_v1_270e_coco.yml) | | MobileNet-V1 | 608 | 8 | 270e | ---- | 28.8 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/yolov3_mobilenet_v1_270e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/yolov3/yolov3_mobilenet_v1_270e_coco.yml) |
| MobileNet-V1 | 416 | 8 | 270e | ---- | 28.7 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/yolov3_mobilenet_v1_270e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/yolov3/yolov3_mobilenet_v1_270e_coco.yml) | | MobileNet-V1 | 416 | 8 | 270e | ---- | 28.7 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/yolov3_mobilenet_v1_270e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/yolov3/yolov3_mobilenet_v1_270e_coco.yml) |
| MobileNet-V1 | 320 | 8 | 270e | ---- | 26.5 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/yolov3_mobilenet_v1_270e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/yolov3/yolov3_mobilenet_v1_270e_coco.yml) | | MobileNet-V1 | 320 | 8 | 270e | ---- | 26.5 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/yolov3_mobilenet_v1_270e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/yolov3/yolov3_mobilenet_v1_270e_coco.yml) |
......
dygraph/configs/yolov3/_base_/yolov3_darknet53.yml
浏览文件 @ 6b76b6fc
architecture: YOLOv3 architecture: YOLOv3
pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/DarkNet53_pretrained.tar pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/DarkNet53_pretrained.tar
use_fine_grained_loss: false
load_static_weights: True load_static_weights: True
norm_type: sync_bn norm_type: sync_bn
......
dygraph/configs/yolov3/_base_/yolov3_r50vd_dcn.yml 0 → 100644
浏览文件 @ 6b76b6fc
architecture: YOLOv3
pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_ssld_pretrained.tar
load_static_weights: True
norm_type: sync_bn
YOLOv3:
backbone: ResNet
neck: YOLOv3FPN
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.
# YOLOv3FPN:
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
YOLOv3Loss:
ignore_thresh: 0.7
downsample: [32, 16, 8]
label_smooth: false
BBoxPostProcess:
decode:
name: YOLOBox
conf_thresh: 0.005
downsample_ratio: 32
clip_bbox: true
nms:
name: MultiClassNMS
keep_top_k: 100
score_threshold: 0.01
nms_threshold: 0.45
nms_top_k: 1000
normalized: false
dygraph/configs/yolov3/yolov3_darknet53_270e_coco.yml
浏览文件 @ 6b76b6fc
...@@ -5,4 +5,6 @@ _BASE_: [ ...@@ -5,4 +5,6 @@ _BASE_: [
'_base_/yolov3_darknet53.yml', '_base_/yolov3_darknet53.yml',
'_base_/yolov3_reader.yml', '_base_/yolov3_reader.yml',
] ]
weights: output/yolov3_darknet53_coco/model_final
snapshot_epoch: 5
weights: output/yolov3_darknet53_270e_coco/model_final
dygraph/configs/yolov3/yolov3_mobilenet_v1_270e_coco.yml
浏览文件 @ 6b76b6fc
...@@ -5,4 +5,6 @@ _BASE_: [ ...@@ -5,4 +5,6 @@ _BASE_: [
'_base_/yolov3_mobilenet_v1.yml', '_base_/yolov3_mobilenet_v1.yml',
'_base_/yolov3_reader.yml', '_base_/yolov3_reader.yml',
] ]
weights: output/yolov3_mobilenet_v1_coco/model_final
snapshot_epoch: 5
weights: output/yolov3_mobilenet_v1_270e_coco/model_final
dygraph/configs/yolov3/yolov3_mobilenet_v1_270e_voc.yml
浏览文件 @ 6b76b6fc
...@@ -5,7 +5,9 @@ _BASE_: [ ...@@ -5,7 +5,9 @@ _BASE_: [
'_base_/yolov3_mobilenet_v1.yml', '_base_/yolov3_mobilenet_v1.yml',
'_base_/yolov3_reader.yml', '_base_/yolov3_reader.yml',
] ]
weights: output/yolov3_mobilenet_v1_voc/model_final
snapshot_epoch: 5
weights: output/yolov3_mobilenet_v1_270e_voc/model_final
TrainReader: TrainReader:
inputs_def: inputs_def:
......
dygraph/configs/yolov3/yolov3_mobilenet_v3_large_270e_coco.yml
浏览文件 @ 6b76b6fc
...@@ -5,4 +5,6 @@ _BASE_: [ ...@@ -5,4 +5,6 @@ _BASE_: [
'_base_/yolov3_mobilenet_v3_large.yml', '_base_/yolov3_mobilenet_v3_large.yml',
'_base_/yolov3_reader.yml', '_base_/yolov3_reader.yml',
] ]
weights: output/yolov3_mobilenet_v3_large_coco/model_final
snapshot_epoch: 5
weights: output/yolov3_mobilenet_v3_large_270e_coco/model_final
dygraph/configs/yolov3/yolov3_mobilenet_v3_large_270e_voc.yml
浏览文件 @ 6b76b6fc
...@@ -5,7 +5,9 @@ _BASE_: [ ...@@ -5,7 +5,9 @@ _BASE_: [
'_base_/yolov3_mobilenet_v3_large.yml', '_base_/yolov3_mobilenet_v3_large.yml',
'_base_/yolov3_reader.yml', '_base_/yolov3_reader.yml',
] ]
weights: output/yolov3_mobilenet_v3_large_voc/model_final
snapshot_epoch: 5
weights: output/yolov3_mobilenet_v3_large_270e_voc/model_final
TrainReader: TrainReader:
inputs_def: inputs_def:
......
dygraph/configs/yolov3/yolov3_r50vd_dcn_270e_coco.yml 0 → 100644
浏览文件 @ 6b76b6fc
_BASE_: [
'../datasets/coco_detection.yml',
'../runtime.yml',
'_base_/optimizer_270e.yml',
'_base_/yolov3_r50vd_dcn.yml',
'_base_/yolov3_reader.yml',
]
snapshot_epoch: 5
weights: output/yolov3_r50vd_dcn_270e_coco/model_final
dygraph/deploy/python/infer.py
浏览文件 @ 6b76b6fc
...@@ -135,6 +135,13 @@ class Detector(object): ...@@ -135,6 +135,13 @@ class Detector(object):
output_names = self.predictor.get_output_names() output_names = self.predictor.get_output_names()
boxes_tensor = self.predictor.get_output_handle(output_names[0]) boxes_tensor = self.predictor.get_output_handle(output_names[0])
np_boxes = boxes_tensor.copy_to_cpu() np_boxes = boxes_tensor.copy_to_cpu()
score_tensor = self.predictor.get_output_handle(output_names[3])
np_score = score_tensor.copy_to_cpu()
label_tensor = self.predictor.get_output_handle(output_names[2])
np_label = label_tensor.copy_to_cpu()
np_boxes = np.concatenate(
[np_label[:, np.newaxis], np_score[:, np.newaxis], np_boxes],
axis=-1)
if self.pred_config.mask_resolution is not None: if self.pred_config.mask_resolution is not None:
masks_tensor = self.predictor.get_output_handle(output_names[2]) masks_tensor = self.predictor.get_output_handle(output_names[2])
np_masks = masks_tensor.copy_to_cpu() np_masks = masks_tensor.copy_to_cpu()
......
dygraph/ppdet/data/transform/batch_operator.py
浏览文件 @ 6b76b6fc
...@@ -290,7 +290,8 @@ class Gt2YoloTargetOp(BaseOperator): ...@@ -290,7 +290,8 @@ class Gt2YoloTargetOp(BaseOperator):
iou = jaccard_overlap( iou = jaccard_overlap(
[0., 0., gw, gh], [0., 0., gw, gh],
[0., 0., an_hw[mask_i, 0], an_hw[mask_i, 1]]) [0., 0., an_hw[mask_i, 0], an_hw[mask_i, 1]])
if iou > self.iou_thresh: if iou > self.iou_thresh and target[idx, 5, gj,
gi] == 0.:
# x, y, w, h, scale # x, y, w, h, scale
target[idx, 0, gj, gi] = gx * grid_w - gi target[idx, 0, gj, gi] = gx * grid_w - gi
target[idx, 1, gj, gi] = gy * grid_h - gj target[idx, 1, gj, gi] = gy * grid_h - gj
......
dygraph/ppdet/data/transform/batch_operators.py
浏览文件 @ 6b76b6fc
...@@ -319,7 +319,8 @@ class Gt2YoloTarget(BaseOperator): ...@@ -319,7 +319,8 @@ class Gt2YoloTarget(BaseOperator):
iou = jaccard_overlap( iou = jaccard_overlap(
[0., 0., gw, gh], [0., 0., gw, gh],
[0., 0., an_hw[mask_i, 0], an_hw[mask_i, 1]]) [0., 0., an_hw[mask_i, 0], an_hw[mask_i, 1]])
if iou > self.iou_thresh: if iou > self.iou_thresh and target[idx, 5, gj,
gi] == 0.:
# x, y, w, h, scale # x, y, w, h, scale
target[idx, 0, gj, gi] = gx * grid_w - gi target[idx, 0, gj, gi] = gx * grid_w - gi
target[idx, 1, gj, gi] = gy * grid_h - gj target[idx, 1, gj, gi] = gy * grid_h - gj
......
dygraph/ppdet/engine/trainer.py
浏览文件 @ 6b76b6fc
...@@ -114,7 +114,11 @@ class Trainer(object): ...@@ -114,7 +114,11 @@ class Trainer(object):
self._metrics = [] self._metrics = []
return return
if self.cfg.metric == 'COCO': if self.cfg.metric == 'COCO':
self._metrics = [COCOMetric(anno_file=self.dataset.get_anno())] # TODO: bias should be unified
self._metrics = [
COCOMetric(
anno_file=self.dataset.get_anno(), bias=self.cfg.bias)
]
elif self.cfg.metric == 'VOC': elif self.cfg.metric == 'VOC':
self._metrics = [ self._metrics = [
VOCMetric( VOCMetric(
......
dygraph/ppdet/metrics/coco_utils.py
浏览文件 @ 6b76b6fc
...@@ -24,7 +24,7 @@ from ppdet.utils.logger import setup_logger ...@@ -24,7 +24,7 @@ from ppdet.utils.logger import setup_logger
logger = setup_logger(__name__) logger = setup_logger(__name__)
def get_infer_results(outs, catid): def get_infer_results(outs, catid, bias=0):
""" """
Get result at the stage of inference. Get result at the stage of inference.
The output format is dictionary containing bbox or mask result. The output format is dictionary containing bbox or mask result.
...@@ -41,9 +41,14 @@ def get_infer_results(outs, catid): ...@@ -41,9 +41,14 @@ def get_infer_results(outs, catid):
infer_res = {} infer_res = {}
if 'bbox' in outs: if 'bbox' in outs:
infer_res['bbox'] = get_det_res(outs['bbox'], outs['score'], infer_res['bbox'] = get_det_res(
outs['label'], outs['bbox_num'], im_id, outs['bbox'],
catid) outs['score'],
outs['label'],
outs['bbox_num'],
im_id,
catid,
bias=bias)
if 'mask' in outs: if 'mask' in outs:
# mask post process # mask post process
......
dygraph/ppdet/metrics/metrics.py
浏览文件 @ 6b76b6fc
...@@ -49,12 +49,13 @@ class Metric(paddle.metric.Metric): ...@@ -49,12 +49,13 @@ class Metric(paddle.metric.Metric):
class COCOMetric(Metric): class COCOMetric(Metric):
def __init__(self, anno_file): def __init__(self, anno_file, **kwargs):
assert os.path.isfile(anno_file), \ assert os.path.isfile(anno_file), \
"anno_file {} not a file".format(anno_file) "anno_file {} not a file".format(anno_file)
self.anno_file = anno_file self.anno_file = anno_file
self.clsid2catid, self.catid2name = get_categories('COCO', anno_file) self.clsid2catid, self.catid2name = get_categories('COCO', anno_file)
# TODO: bias should be unified
self.bias = kwargs.get('bias', 0)
self.reset() self.reset()
def reset(self): def reset(self):
...@@ -72,7 +73,8 @@ class COCOMetric(Metric): ...@@ -72,7 +73,8 @@ class COCOMetric(Metric):
outs['im_id'] = im_id.numpy() if isinstance(im_id, outs['im_id'] = im_id.numpy() if isinstance(im_id,
paddle.Tensor) else im_id paddle.Tensor) else im_id
infer_results = get_infer_results(outs, self.clsid2catid) infer_results = get_infer_results(
outs, self.clsid2catid, bias=self.bias)
self.results['bbox'] += infer_results[ self.results['bbox'] += infer_results[
'bbox'] if 'bbox' in infer_results else [] 'bbox'] if 'bbox' in infer_results else []
self.results['mask'] += infer_results[ self.results['mask'] += infer_results[
......
dygraph/ppdet/modeling/heads/yolo_head.py
浏览文件 @ 6b76b6fc
...@@ -39,15 +39,16 @@ class YOLOv3Head(nn.Layer): ...@@ -39,15 +39,16 @@ class YOLOv3Head(nn.Layer):
self.yolo_outputs = [] self.yolo_outputs = []
for i in range(len(self.anchors)): for i in range(len(self.anchors)):
if self.iou_aware: if self.iou_aware:
num_filters = self.num_outputs * (self.num_classes + 6) num_filters = len(self.anchors[i]) * (self.num_classes + 6)
else: else:
num_filters = self.num_outputs * (self.num_classes + 5) num_filters = len(self.anchors[i]) * (self.num_classes + 5)
name = 'yolo_output.{}'.format(i) name = 'yolo_output.{}'.format(i)
yolo_output = self.add_sublayer( yolo_output = self.add_sublayer(
name, name,
nn.Conv2D( nn.Conv2D(
in_channels=1024 // (2**i), in_channels=128 * (2**self.num_outputs) // (2**i),
out_channels=num_filters, out_channels=num_filters,
kernel_size=1, kernel_size=1,
stride=1, stride=1,
......
dygraph/ppdet/modeling/necks/yolo_fpn.py
浏览文件 @ 6b76b6fc
...@@ -249,6 +249,7 @@ class PPYOLOFPN(nn.Layer): ...@@ -249,6 +249,7 @@ class PPYOLOFPN(nn.Layer):
self.keep_prob = kwargs.get('keep_prob', 0.9) self.keep_prob = kwargs.get('keep_prob', 0.9)
self.spp = kwargs.get('spp', False) self.spp = kwargs.get('spp', False)
self.conv_block_num = kwargs.get('conv_block_num', 2)
if self.coord_conv: if self.coord_conv:
ConvLayer = CoordConv ConvLayer = CoordConv
else: else:
...@@ -269,25 +270,37 @@ class PPYOLOFPN(nn.Layer): ...@@ -269,25 +270,37 @@ class PPYOLOFPN(nn.Layer):
if i > 0: if i > 0:
ch_in += 512 // (2**i) ch_in += 512 // (2**i)
channel = 64 * (2**self.num_blocks) // (2**i) channel = 64 * (2**self.num_blocks) // (2**i)
base_cfg = [ base_cfg = []
# name of layer, Layer, args c_in, c_out = ch_in, channel
['conv0', ConvLayer, [ch_in, channel, 1]], for j in range(self.conv_block_num):
['conv1', ConvBNLayer, [channel, channel * 2, 3]], base_cfg += [
['conv2', ConvLayer, [channel * 2, channel, 1]], [
['conv3', ConvBNLayer, [channel, channel * 2, 3]], 'conv{}'.format(2 * j), ConvLayer, [c_in, c_out, 1],
['route', ConvLayer, [channel * 2, channel, 1]], dict(
['tip', ConvLayer, [channel, channel * 2, 3]] padding=0, norm_type=norm_type)
],
[
'conv{}'.format(2 * j + 1), ConvBNLayer,
[c_out, c_out * 2, 3], dict(
padding=1, norm_type=norm_type)
],
] ]
for conf in base_cfg: c_in, c_out = c_out * 2, c_out
filter_size = conf[-1][-1]
conf.append(dict(padding=filter_size // 2, norm_type=norm_type)) base_cfg += [[
'route', ConvLayer, [c_in, c_out, 1], dict(
padding=0, norm_type=norm_type)
], [
'tip', ConvLayer, [c_out, c_out * 2, 3], dict(
padding=1, norm_type=norm_type)
]]
if self.conv_block_num == 2:
if i == 0: if i == 0:
if self.spp: if self.spp:
pool_size = [5, 9, 13]
spp_cfg = [[ spp_cfg = [[
'spp', SPP, 'spp', SPP, [channel * 4, channel, 1], dict(
[channel * (len(pool_size) + 1), channel, 1], dict( pool_size=[5, 9, 13], norm_type=norm_type)
pool_size=pool_size, norm_type=norm_type)
]] ]]
else: else:
spp_cfg = [] spp_cfg = []
...@@ -295,6 +308,15 @@ class PPYOLOFPN(nn.Layer): ...@@ -295,6 +308,15 @@ class PPYOLOFPN(nn.Layer):
3:4] + dropblock_cfg + base_cfg[4:6] 3:4] + dropblock_cfg + base_cfg[4:6]
else: else:
cfg = base_cfg[0:2] + dropblock_cfg + base_cfg[2:6] cfg = base_cfg[0:2] + dropblock_cfg + base_cfg[2:6]
elif self.conv_block_num == 0:
if self.spp and i == 0:
spp_cfg = [[
'spp', SPP, [c_in * 4, c_in, 1], dict(
pool_size=[5, 9, 13], norm_type=norm_type)
]]
else:
spp_cfg = []
cfg = spp_cfg + dropblock_cfg + base_cfg
name = 'yolo_block.{}'.format(i) name = 'yolo_block.{}'.format(i)
yolo_block = self.add_sublayer(name, PPYOLODetBlock(cfg, name)) yolo_block = self.add_sublayer(name, PPYOLODetBlock(cfg, name))
self.yolo_blocks.append(yolo_block) self.yolo_blocks.append(yolo_block)
...@@ -305,7 +327,7 @@ class PPYOLOFPN(nn.Layer): ...@@ -305,7 +327,7 @@ class PPYOLOFPN(nn.Layer):
name, name,
ConvBNLayer( ConvBNLayer(
ch_in=channel, ch_in=channel,
ch_out=channel // 2, ch_out=256 // (2**i),
filter_size=1, filter_size=1,
stride=1, stride=1,
padding=0, padding=0,
......
dygraph/ppdet/modeling/shape_spec.py
浏览文件 @ 6b76b6fc
...@@ -28,5 +28,6 @@ class ShapeSpec( ...@@ -28,5 +28,6 @@ class ShapeSpec(
stride: stride:
""" """
def __new__(cls, *, channels=None, height=None, width=None, stride=None): def __new__(cls, channels=None, height=None, width=None, stride=None):
return super().__new__(cls, channels, height, width, stride) return super(ShapeSpec, cls).__new__(cls, channels, height, width,
stride)
dygraph/ppdet/modeling/utils/bbox_util.py
浏览文件 @ 6b76b6fc
...@@ -106,8 +106,7 @@ def bbox_iou(box1, box2, giou=False, diou=False, ciou=False, eps=1e-9): ...@@ -106,8 +106,7 @@ def bbox_iou(box1, box2, giou=False, diou=False, ciou=False, eps=1e-9):
x2 = paddle.minimum(px2, gx2) x2 = paddle.minimum(px2, gx2)
y2 = paddle.minimum(py2, gy2) y2 = paddle.minimum(py2, gy2)
overlap = (x2 - x1) * (y2 - y1) overlap = ((x2 - x1).clip(0)) * ((y2 - y1).clip(0))
overlap = overlap.clip(0)
area1 = (px2 - px1) * (py2 - py1) area1 = (px2 - px1) * (py2 - py1)
area1 = area1.clip(0) area1 = area1.clip(0)
......
dygraph/ppdet/optimizer.py
浏览文件 @ 6b76b6fc
...@@ -243,18 +243,14 @@ class ModelEMA(object): ...@@ -243,18 +243,14 @@ class ModelEMA(object):
self._decay = decay self._decay = decay
model_dict = model.state_dict() model_dict = model.state_dict()
for k, v in self.state_dict.items(): for k, v in self.state_dict.items():
if '_mean' not in k and '_variance' not in k:
v = decay * v + (1 - decay) * model_dict[k] v = decay * v + (1 - decay) * model_dict[k]
v.stop_gradient = True v.stop_gradient = True
self.state_dict[k] = v self.state_dict[k] = v
else:
self.state_dict[k] = model_dict[k]
self.step += 1 self.step += 1
def apply(self): def apply(self):
state_dict = dict() state_dict = dict()
for k, v in self.state_dict.items(): for k, v in self.state_dict.items():
if '_mean' not in k and '_variance' not in k:
v = v / (1 - self._decay**self.step) v = v / (1 - self._decay**self.step)
v.stop_gradient = True v.stop_gradient = True
state_dict[k] = v state_dict[k] = v
......
dygraph/ppdet/py_op/post_process.py
浏览文件 @ 6b76b6fc
...@@ -4,8 +4,13 @@ import numpy as np ...@@ -4,8 +4,13 @@ import numpy as np
import cv2 import cv2
def get_det_res(bboxes, scores, labels, bbox_nums, image_id, def get_det_res(bboxes,
label_to_cat_id_map): scores,
labels,
bbox_nums,
image_id,
label_to_cat_id_map,
bias=0):
det_res = [] det_res = []
k = 0 k = 0
for i in range(len(bbox_nums)): for i in range(len(bbox_nums)):
...@@ -19,8 +24,8 @@ def get_det_res(bboxes, scores, labels, bbox_nums, image_id, ...@@ -19,8 +24,8 @@ def get_det_res(bboxes, scores, labels, bbox_nums, image_id,
k = k + 1 k = k + 1
xmin, ymin, xmax, ymax = box.tolist() xmin, ymin, xmax, ymax = box.tolist()
category_id = label_to_cat_id_map[label] category_id = label_to_cat_id_map[label]
w = xmax - xmin w = xmax - xmin + bias
h = ymax - ymin h = ymax - ymin + bias
bbox = [xmin, ymin, w, h] bbox = [xmin, ymin, w, h]
dt_res = { dt_res = {
'image_id': cur_image_id, 'image_id': cur_image_id,
......
dygraph/ppdet/utils/checkpoint.py
浏览文件 @ 6b76b6fc
...@@ -163,7 +163,7 @@ def load_pretrain_weight(model, ...@@ -163,7 +163,7 @@ def load_pretrain_weight(model,
model.backbone.set_dict(param_state_dict) model.backbone.set_dict(param_state_dict)
else: else:
ignore_set = set() ignore_set = set()
for name, weight in model_dict: for name, weight in model_dict.items():
if name in param_state_dict: if name in param_state_dict:
if weight.shape != param_state_dict[name].shape: if weight.shape != param_state_dict[name].shape:
param_state_dict.pop(name, None) param_state_dict.pop(name, None)
......
dygraph/tools/eval.py
浏览文件 @ 6b76b6fc
...@@ -47,7 +47,10 @@ def parse_args(): ...@@ -47,7 +47,10 @@ def parse_args():
help="Evaluation directory, default is current directory.") help="Evaluation directory, default is current directory.")
parser.add_argument( parser.add_argument(
'--json_eval', action='store_true', default=False, help='') '--json_eval',
action='store_true',
default=False,
help='Whether to re eval with already exists bbox.json or mask.json')
parser.add_argument( parser.add_argument(
"--slim_config", "--slim_config",
...@@ -55,6 +58,12 @@ def parse_args(): ...@@ -55,6 +58,12 @@ def parse_args():
type=str, type=str,
help="Configuration file of slim method.") help="Configuration file of slim method.")
# TODO: bias should be unified
parser.add_argument(
"--bias",
action="store_true",
help="whether add bias or not while getting w and h")
args = parser.parse_args() args = parser.parse_args()
return args return args
...@@ -77,6 +86,8 @@ def main(): ...@@ -77,6 +86,8 @@ def main():
FLAGS = parse_args() FLAGS = parse_args()
cfg = load_config(FLAGS.config) cfg = load_config(FLAGS.config)
# TODO: bias should be unified
cfg['bias'] = 1 if FLAGS.bias else 0
merge_config(FLAGS.opt) merge_config(FLAGS.opt)
if FLAGS.slim_config: if FLAGS.slim_config:
slim_cfg = load_config(FLAGS.slim_config) slim_cfg = load_config(FLAGS.slim_config)
......
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