From 6b76b6fcbc557c8244eb7da1c24f28734288ea16 Mon Sep 17 00:00:00 2001 From: wangxinxin08 <69842442+wangxinxin08@users.noreply.github.com> Date: Mon, 1 Feb 2021 19:31:48 +0800 Subject: [PATCH] [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 --- dygraph/configs/ppyolo/README.md | 34 +++++++- dygraph/configs/ppyolo/README_cn.md | 30 +++++++ .../configs/ppyolo/_base_/optimizer_1x.yml | 1 - .../configs/ppyolo/_base_/optimizer_2x.yml | 1 - .../ppyolo/_base_/ppyolo_mbv3_large.yml | 58 +++++++++++++ .../ppyolo/_base_/ppyolo_mbv3_small.yml | 58 +++++++++++++ .../configs/ppyolo/_base_/ppyolo_r18vd.yml | 60 ++++++++++++++ .../ppyolo/_base_/ppyolo_r50vd_dcn.yml | 4 +- .../configs/ppyolo/ppyolo_mbv3_large_coco.yml | 82 +++++++++++++++++++ .../configs/ppyolo/ppyolo_mbv3_small_coco.yml | 82 +++++++++++++++++++ dygraph/configs/ppyolo/ppyolo_r18vd_coco.yml | 82 +++++++++++++++++++ .../ppyolo/ppyolo_r50vd_dcn_1x_coco.yml | 1 + .../ppyolo/ppyolo_r50vd_dcn_1x_minicoco.yml | 11 ++- .../ppyolo/ppyolo_r50vd_dcn_2x_coco.yml | 1 + .../configs/ppyolo/ppyolo_r50vd_dcn_voc.yml | 44 ++++++++++ dygraph/configs/yolov3/README.md | 1 + .../yolov3/_base_/yolov3_darknet53.yml | 1 - .../yolov3/_base_/yolov3_r50vd_dcn.yml | 47 +++++++++++ .../yolov3/yolov3_darknet53_270e_coco.yml | 4 +- .../yolov3/yolov3_mobilenet_v1_270e_coco.yml | 4 +- .../yolov3/yolov3_mobilenet_v1_270e_voc.yml | 4 +- .../yolov3_mobilenet_v3_large_270e_coco.yml | 4 +- .../yolov3_mobilenet_v3_large_270e_voc.yml | 4 +- .../yolov3/yolov3_r50vd_dcn_270e_coco.yml | 10 +++ dygraph/deploy/python/infer.py | 7 ++ .../ppdet/data/transform/batch_operator.py | 3 +- .../ppdet/data/transform/batch_operators.py | 3 +- dygraph/ppdet/engine/trainer.py | 6 +- dygraph/ppdet/metrics/coco_utils.py | 13 ++- dygraph/ppdet/metrics/metrics.py | 8 +- dygraph/ppdet/modeling/heads/yolo_head.py | 7 +- dygraph/ppdet/modeling/losses/yolo_loss.py | 2 +- dygraph/ppdet/modeling/necks/yolo_fpn.py | 68 +++++++++------ dygraph/ppdet/modeling/shape_spec.py | 5 +- dygraph/ppdet/modeling/utils/bbox_util.py | 3 +- dygraph/ppdet/optimizer.py | 16 ++-- dygraph/ppdet/py_op/post_process.py | 13 ++- dygraph/ppdet/utils/checkpoint.py | 2 +- dygraph/tools/eval.py | 13 ++- 39 files changed, 728 insertions(+), 69 deletions(-) create mode 100644 dygraph/configs/ppyolo/_base_/ppyolo_mbv3_large.yml create mode 100644 dygraph/configs/ppyolo/_base_/ppyolo_mbv3_small.yml create mode 100644 dygraph/configs/ppyolo/_base_/ppyolo_r18vd.yml create mode 100644 dygraph/configs/ppyolo/ppyolo_mbv3_large_coco.yml create mode 100644 dygraph/configs/ppyolo/ppyolo_mbv3_small_coco.yml create mode 100644 dygraph/configs/ppyolo/ppyolo_r18vd_coco.yml create mode 100644 dygraph/configs/ppyolo/ppyolo_r50vd_dcn_voc.yml create mode 100644 dygraph/configs/yolov3/_base_/yolov3_r50vd_dcn.yml create mode 100644 dygraph/configs/yolov3/yolov3_r50vd_dcn_270e_coco.yml diff --git a/dygraph/configs/ppyolo/README.md b/dygraph/configs/ppyolo/README.md index 17b1e0da3..2d8a1be7d 100644 --- a/dygraph/configs/ppyolo/README.md +++ b/dygraph/configs/ppyolo/README.md @@ -37,9 +37,18 @@ PP-YOLO improved performance and speed of YOLOv3 with following methods: ### PP-YOLO | Model | GPU number | images/GPU | backbone | input shape | Box APval | Box APtest | 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 | 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 | 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:** @@ -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. - 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 APval | Box AP50val | 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 APval is evaluation results of `mAP(IoU=0.5:0.95)`, Box APval 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 AP50val | 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 ### 1. Training diff --git a/dygraph/configs/ppyolo/README_cn.md b/dygraph/configs/ppyolo/README_cn.md index c1bd09d13..604c57f32 100644 --- a/dygraph/configs/ppyolo/README_cn.md +++ b/dygraph/configs/ppyolo/README_cn.md @@ -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 APval | Box APtest | 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 | 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 | 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:** @@ -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)。 - 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 APval | Box AP50val | 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 APval为`mAP(IoU=0.5:0.95)`评估结果, Box AP50val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 AP50val | 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 ### 1. 训练 diff --git a/dygraph/configs/ppyolo/_base_/optimizer_1x.yml b/dygraph/configs/ppyolo/_base_/optimizer_1x.yml index fe51b296c..8e6301e32 100644 --- a/dygraph/configs/ppyolo/_base_/optimizer_1x.yml +++ b/dygraph/configs/ppyolo/_base_/optimizer_1x.yml @@ -13,7 +13,6 @@ LearningRate: steps: 4000 OptimizerBuilder: - clip_grad_by_norm: 35. optimizer: momentum: 0.9 type: Momentum diff --git a/dygraph/configs/ppyolo/_base_/optimizer_2x.yml b/dygraph/configs/ppyolo/_base_/optimizer_2x.yml index c601a1860..92ddbf2a7 100644 --- a/dygraph/configs/ppyolo/_base_/optimizer_2x.yml +++ b/dygraph/configs/ppyolo/_base_/optimizer_2x.yml @@ -13,7 +13,6 @@ LearningRate: steps: 4000 OptimizerBuilder: - clip_grad_by_norm: 35. optimizer: momentum: 0.9 type: Momentum diff --git a/dygraph/configs/ppyolo/_base_/ppyolo_mbv3_large.yml b/dygraph/configs/ppyolo/_base_/ppyolo_mbv3_large.yml new file mode 100644 index 000000000..7b96fa9d7 --- /dev/null +++ b/dygraph/configs/ppyolo/_base_/ppyolo_mbv3_large.yml @@ -0,0 +1,58 @@ +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 diff --git a/dygraph/configs/ppyolo/_base_/ppyolo_mbv3_small.yml b/dygraph/configs/ppyolo/_base_/ppyolo_mbv3_small.yml new file mode 100644 index 000000000..edbf6a45c --- /dev/null +++ b/dygraph/configs/ppyolo/_base_/ppyolo_mbv3_small.yml @@ -0,0 +1,58 @@ +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 diff --git a/dygraph/configs/ppyolo/_base_/ppyolo_r18vd.yml b/dygraph/configs/ppyolo/_base_/ppyolo_r18vd.yml new file mode 100644 index 000000000..6a7bf0962 --- /dev/null +++ b/dygraph/configs/ppyolo/_base_/ppyolo_r18vd.yml @@ -0,0 +1,60 @@ +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 diff --git a/dygraph/configs/ppyolo/_base_/ppyolo_r50vd_dcn.yml b/dygraph/configs/ppyolo/_base_/ppyolo_r50vd_dcn.yml index 18111ad02..186251ca3 100644 --- a/dygraph/configs/ppyolo/_base_/ppyolo_r50vd_dcn.yml +++ b/dygraph/configs/ppyolo/_base_/ppyolo_r50vd_dcn.yml @@ -1,6 +1,5 @@ architecture: YOLOv3 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 norm_type: sync_bn use_ema: true @@ -55,7 +54,7 @@ IouAwareLoss: BBoxPostProcess: decode: name: YOLOBox - conf_thresh: 0.005 + conf_thresh: 0.01 downsample_ratio: 32 clip_bbox: true scale_x_y: 1.05 @@ -66,3 +65,4 @@ BBoxPostProcess: post_threshold: 0.01 nms_top_k: -1 normalized: false + background_label: -1 diff --git a/dygraph/configs/ppyolo/ppyolo_mbv3_large_coco.yml b/dygraph/configs/ppyolo/ppyolo_mbv3_large_coco.yml new file mode 100644 index 000000000..b6f00b741 --- /dev/null +++ b/dygraph/configs/ppyolo/ppyolo_mbv3_large_coco.yml @@ -0,0 +1,82 @@ +_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 diff --git a/dygraph/configs/ppyolo/ppyolo_mbv3_small_coco.yml b/dygraph/configs/ppyolo/ppyolo_mbv3_small_coco.yml new file mode 100644 index 000000000..1cdfe6dea --- /dev/null +++ b/dygraph/configs/ppyolo/ppyolo_mbv3_small_coco.yml @@ -0,0 +1,82 @@ +_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 diff --git a/dygraph/configs/ppyolo/ppyolo_r18vd_coco.yml b/dygraph/configs/ppyolo/ppyolo_r18vd_coco.yml new file mode 100644 index 000000000..e89dbb842 --- /dev/null +++ b/dygraph/configs/ppyolo/ppyolo_r18vd_coco.yml @@ -0,0 +1,82 @@ +_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 diff --git a/dygraph/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml b/dygraph/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml index 4b1e2a797..918f3401e 100644 --- a/dygraph/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml +++ b/dygraph/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml @@ -7,3 +7,4 @@ _BASE_: [ ] snapshot_epoch: 16 +weights: output/ppyolo_r50vd_dcn_1x_coco/model_final diff --git a/dygraph/configs/ppyolo/ppyolo_r50vd_dcn_1x_minicoco.yml b/dygraph/configs/ppyolo/ppyolo_r50vd_dcn_1x_minicoco.yml index 18945a9bd..87b976b99 100644 --- a/dygraph/configs/ppyolo/ppyolo_r50vd_dcn_1x_minicoco.yml +++ b/dygraph/configs/ppyolo/ppyolo_r50vd_dcn_1x_minicoco.yml @@ -7,7 +7,8 @@ _BASE_: [ ] snapshot_epoch: 8 -use_ema: false +use_ema: true +weights: output/ppyolo_r50vd_dcn_1x_minicoco/model_final TrainReader: batch_size: 12 @@ -33,3 +34,11 @@ LearningRate: - !LinearWarmup start_factor: 0. steps: 4000 + +OptimizerBuilder: + optimizer: + momentum: 0.9 + type: Momentum + regularizer: + factor: 0.0005 + type: L2 diff --git a/dygraph/configs/ppyolo/ppyolo_r50vd_dcn_2x_coco.yml b/dygraph/configs/ppyolo/ppyolo_r50vd_dcn_2x_coco.yml index 87646baf7..ac6531fe7 100644 --- a/dygraph/configs/ppyolo/ppyolo_r50vd_dcn_2x_coco.yml +++ b/dygraph/configs/ppyolo/ppyolo_r50vd_dcn_2x_coco.yml @@ -7,3 +7,4 @@ _BASE_: [ ] snapshot_epoch: 16 +weights: output/ppyolo_r50vd_dcn_2x_coco/model_final diff --git a/dygraph/configs/ppyolo/ppyolo_r50vd_dcn_voc.yml b/dygraph/configs/ppyolo/ppyolo_r50vd_dcn_voc.yml new file mode 100644 index 000000000..4b2bcc492 --- /dev/null +++ b/dygraph/configs/ppyolo/ppyolo_r50vd_dcn_voc.yml @@ -0,0 +1,44 @@ +_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 diff --git a/dygraph/configs/yolov3/README.md b/dygraph/configs/yolov3/README.md index 116fc0901..8de4ea8ad 100644 --- a/dygraph/configs/yolov3/README.md +++ b/dygraph/configs/yolov3/README.md @@ -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 | 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) | +| 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 | 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) | diff --git a/dygraph/configs/yolov3/_base_/yolov3_darknet53.yml b/dygraph/configs/yolov3/_base_/yolov3_darknet53.yml index 796c24501..0f91cf030 100644 --- a/dygraph/configs/yolov3/_base_/yolov3_darknet53.yml +++ b/dygraph/configs/yolov3/_base_/yolov3_darknet53.yml @@ -1,6 +1,5 @@ architecture: YOLOv3 pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/DarkNet53_pretrained.tar -use_fine_grained_loss: false load_static_weights: True norm_type: sync_bn diff --git a/dygraph/configs/yolov3/_base_/yolov3_r50vd_dcn.yml b/dygraph/configs/yolov3/_base_/yolov3_r50vd_dcn.yml new file mode 100644 index 000000000..f122cc365 --- /dev/null +++ b/dygraph/configs/yolov3/_base_/yolov3_r50vd_dcn.yml @@ -0,0 +1,47 @@ +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 diff --git a/dygraph/configs/yolov3/yolov3_darknet53_270e_coco.yml b/dygraph/configs/yolov3/yolov3_darknet53_270e_coco.yml index 15bc38e6c..4fbd401d3 100644 --- a/dygraph/configs/yolov3/yolov3_darknet53_270e_coco.yml +++ b/dygraph/configs/yolov3/yolov3_darknet53_270e_coco.yml @@ -5,4 +5,6 @@ _BASE_: [ '_base_/yolov3_darknet53.yml', '_base_/yolov3_reader.yml', ] -weights: output/yolov3_darknet53_coco/model_final + +snapshot_epoch: 5 +weights: output/yolov3_darknet53_270e_coco/model_final diff --git a/dygraph/configs/yolov3/yolov3_mobilenet_v1_270e_coco.yml b/dygraph/configs/yolov3/yolov3_mobilenet_v1_270e_coco.yml index 4c3c30427..b9dd33bdb 100644 --- a/dygraph/configs/yolov3/yolov3_mobilenet_v1_270e_coco.yml +++ b/dygraph/configs/yolov3/yolov3_mobilenet_v1_270e_coco.yml @@ -5,4 +5,6 @@ _BASE_: [ '_base_/yolov3_mobilenet_v1.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 diff --git a/dygraph/configs/yolov3/yolov3_mobilenet_v1_270e_voc.yml b/dygraph/configs/yolov3/yolov3_mobilenet_v1_270e_voc.yml index 5d4fb1929..df44e9262 100644 --- a/dygraph/configs/yolov3/yolov3_mobilenet_v1_270e_voc.yml +++ b/dygraph/configs/yolov3/yolov3_mobilenet_v1_270e_voc.yml @@ -5,7 +5,9 @@ _BASE_: [ '_base_/yolov3_mobilenet_v1.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: inputs_def: diff --git a/dygraph/configs/yolov3/yolov3_mobilenet_v3_large_270e_coco.yml b/dygraph/configs/yolov3/yolov3_mobilenet_v3_large_270e_coco.yml index dd06756ac..d1b8af566 100644 --- a/dygraph/configs/yolov3/yolov3_mobilenet_v3_large_270e_coco.yml +++ b/dygraph/configs/yolov3/yolov3_mobilenet_v3_large_270e_coco.yml @@ -5,4 +5,6 @@ _BASE_: [ '_base_/yolov3_mobilenet_v3_large.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 diff --git a/dygraph/configs/yolov3/yolov3_mobilenet_v3_large_270e_voc.yml b/dygraph/configs/yolov3/yolov3_mobilenet_v3_large_270e_voc.yml index 26cd965d3..4b459415a 100644 --- a/dygraph/configs/yolov3/yolov3_mobilenet_v3_large_270e_voc.yml +++ b/dygraph/configs/yolov3/yolov3_mobilenet_v3_large_270e_voc.yml @@ -5,7 +5,9 @@ _BASE_: [ '_base_/yolov3_mobilenet_v3_large.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: inputs_def: diff --git a/dygraph/configs/yolov3/yolov3_r50vd_dcn_270e_coco.yml b/dygraph/configs/yolov3/yolov3_r50vd_dcn_270e_coco.yml new file mode 100644 index 000000000..a07cbdde1 --- /dev/null +++ b/dygraph/configs/yolov3/yolov3_r50vd_dcn_270e_coco.yml @@ -0,0 +1,10 @@ +_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 diff --git a/dygraph/deploy/python/infer.py b/dygraph/deploy/python/infer.py index e0aae03e5..41ddadf1b 100644 --- a/dygraph/deploy/python/infer.py +++ b/dygraph/deploy/python/infer.py @@ -135,6 +135,13 @@ class Detector(object): output_names = self.predictor.get_output_names() boxes_tensor = self.predictor.get_output_handle(output_names[0]) 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: masks_tensor = self.predictor.get_output_handle(output_names[2]) np_masks = masks_tensor.copy_to_cpu() diff --git a/dygraph/ppdet/data/transform/batch_operator.py b/dygraph/ppdet/data/transform/batch_operator.py index 8712d8151..aabd0cf5d 100644 --- a/dygraph/ppdet/data/transform/batch_operator.py +++ b/dygraph/ppdet/data/transform/batch_operator.py @@ -290,7 +290,8 @@ class Gt2YoloTargetOp(BaseOperator): iou = jaccard_overlap( [0., 0., gw, gh], [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 target[idx, 0, gj, gi] = gx * grid_w - gi target[idx, 1, gj, gi] = gy * grid_h - gj diff --git a/dygraph/ppdet/data/transform/batch_operators.py b/dygraph/ppdet/data/transform/batch_operators.py index 143f2afa9..345c33898 100644 --- a/dygraph/ppdet/data/transform/batch_operators.py +++ b/dygraph/ppdet/data/transform/batch_operators.py @@ -319,7 +319,8 @@ class Gt2YoloTarget(BaseOperator): iou = jaccard_overlap( [0., 0., gw, gh], [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 target[idx, 0, gj, gi] = gx * grid_w - gi target[idx, 1, gj, gi] = gy * grid_h - gj diff --git a/dygraph/ppdet/engine/trainer.py b/dygraph/ppdet/engine/trainer.py index 33aaaaba2..d18492a4e 100644 --- a/dygraph/ppdet/engine/trainer.py +++ b/dygraph/ppdet/engine/trainer.py @@ -114,7 +114,11 @@ class Trainer(object): self._metrics = [] return 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': self._metrics = [ VOCMetric( diff --git a/dygraph/ppdet/metrics/coco_utils.py b/dygraph/ppdet/metrics/coco_utils.py index 5ace10df0..40929d0ad 100644 --- a/dygraph/ppdet/metrics/coco_utils.py +++ b/dygraph/ppdet/metrics/coco_utils.py @@ -24,7 +24,7 @@ from ppdet.utils.logger import setup_logger 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. The output format is dictionary containing bbox or mask result. @@ -41,9 +41,14 @@ def get_infer_results(outs, catid): infer_res = {} if 'bbox' in outs: - infer_res['bbox'] = get_det_res(outs['bbox'], outs['score'], - outs['label'], outs['bbox_num'], im_id, - catid) + infer_res['bbox'] = get_det_res( + outs['bbox'], + outs['score'], + outs['label'], + outs['bbox_num'], + im_id, + catid, + bias=bias) if 'mask' in outs: # mask post process diff --git a/dygraph/ppdet/metrics/metrics.py b/dygraph/ppdet/metrics/metrics.py index 6647b8382..304495f65 100644 --- a/dygraph/ppdet/metrics/metrics.py +++ b/dygraph/ppdet/metrics/metrics.py @@ -49,12 +49,13 @@ class Metric(paddle.metric.Metric): class COCOMetric(Metric): - def __init__(self, anno_file): + def __init__(self, anno_file, **kwargs): assert os.path.isfile(anno_file), \ "anno_file {} not a file".format(anno_file) self.anno_file = anno_file self.clsid2catid, self.catid2name = get_categories('COCO', anno_file) - + # TODO: bias should be unified + self.bias = kwargs.get('bias', 0) self.reset() def reset(self): @@ -72,7 +73,8 @@ class COCOMetric(Metric): outs['im_id'] = im_id.numpy() if isinstance(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[ 'bbox'] if 'bbox' in infer_results else [] self.results['mask'] += infer_results[ diff --git a/dygraph/ppdet/modeling/heads/yolo_head.py b/dygraph/ppdet/modeling/heads/yolo_head.py index ab32ce1e6..d6453a3a4 100644 --- a/dygraph/ppdet/modeling/heads/yolo_head.py +++ b/dygraph/ppdet/modeling/heads/yolo_head.py @@ -39,15 +39,16 @@ class YOLOv3Head(nn.Layer): self.yolo_outputs = [] for i in range(len(self.anchors)): + if self.iou_aware: - num_filters = self.num_outputs * (self.num_classes + 6) + num_filters = len(self.anchors[i]) * (self.num_classes + 6) 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) yolo_output = self.add_sublayer( name, nn.Conv2D( - in_channels=1024 // (2**i), + in_channels=128 * (2**self.num_outputs) // (2**i), out_channels=num_filters, kernel_size=1, stride=1, diff --git a/dygraph/ppdet/modeling/losses/yolo_loss.py b/dygraph/ppdet/modeling/losses/yolo_loss.py index ad679a079..149139989 100644 --- a/dygraph/ppdet/modeling/losses/yolo_loss.py +++ b/dygraph/ppdet/modeling/losses/yolo_loss.py @@ -188,4 +188,4 @@ class YOLOv3Loss(nn.Layer): loss += v yolo_losses['loss'] = loss - return yolo_losses \ No newline at end of file + return yolo_losses diff --git a/dygraph/ppdet/modeling/necks/yolo_fpn.py b/dygraph/ppdet/modeling/necks/yolo_fpn.py index 4ef6935b3..f89b32053 100644 --- a/dygraph/ppdet/modeling/necks/yolo_fpn.py +++ b/dygraph/ppdet/modeling/necks/yolo_fpn.py @@ -249,6 +249,7 @@ class PPYOLOFPN(nn.Layer): 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) if self.coord_conv: ConvLayer = CoordConv else: @@ -269,32 +270,53 @@ class PPYOLOFPN(nn.Layer): if i > 0: ch_in += 512 // (2**i) channel = 64 * (2**self.num_blocks) // (2**i) - base_cfg = [ - # name of layer, Layer, args - ['conv0', ConvLayer, [ch_in, channel, 1]], - ['conv1', ConvBNLayer, [channel, channel * 2, 3]], - ['conv2', ConvLayer, [channel * 2, channel, 1]], - ['conv3', ConvBNLayer, [channel, channel * 2, 3]], - ['route', ConvLayer, [channel * 2, channel, 1]], - ['tip', ConvLayer, [channel, channel * 2, 3]] - ] - for conf in base_cfg: - filter_size = conf[-1][-1] - conf.append(dict(padding=filter_size // 2, norm_type=norm_type)) - if i == 0: - if self.spp: - pool_size = [5, 9, 13] + base_cfg = [] + c_in, c_out = ch_in, channel + for j in range(self.conv_block_num): + base_cfg += [ + [ + 'conv{}'.format(2 * j), ConvLayer, [c_in, c_out, 1], + dict( + 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) + ], + ] + c_in, c_out = c_out * 2, c_out + + 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 self.spp: + spp_cfg = [[ + 'spp', SPP, [channel * 4, channel, 1], dict( + pool_size=[5, 9, 13], norm_type=norm_type) + ]] + else: + spp_cfg = [] + cfg = base_cfg[0:3] + spp_cfg + base_cfg[ + 3:4] + dropblock_cfg + base_cfg[4:6] + else: + 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, - [channel * (len(pool_size) + 1), channel, 1], dict( - pool_size=pool_size, norm_type=norm_type) + 'spp', SPP, [c_in * 4, c_in, 1], dict( + pool_size=[5, 9, 13], norm_type=norm_type) ]] else: spp_cfg = [] - cfg = base_cfg[0:3] + spp_cfg + base_cfg[ - 3:4] + dropblock_cfg + base_cfg[4:6] - else: - cfg = base_cfg[0:2] + dropblock_cfg + base_cfg[2:6] + cfg = spp_cfg + dropblock_cfg + base_cfg name = 'yolo_block.{}'.format(i) yolo_block = self.add_sublayer(name, PPYOLODetBlock(cfg, name)) self.yolo_blocks.append(yolo_block) @@ -305,7 +327,7 @@ class PPYOLOFPN(nn.Layer): name, ConvBNLayer( ch_in=channel, - ch_out=channel // 2, + ch_out=256 // (2**i), filter_size=1, stride=1, padding=0, diff --git a/dygraph/ppdet/modeling/shape_spec.py b/dygraph/ppdet/modeling/shape_spec.py index 78e4a3b00..a4d4a2fea 100644 --- a/dygraph/ppdet/modeling/shape_spec.py +++ b/dygraph/ppdet/modeling/shape_spec.py @@ -28,5 +28,6 @@ class ShapeSpec( stride: """ - def __new__(cls, *, channels=None, height=None, width=None, stride=None): - return super().__new__(cls, channels, height, width, stride) + def __new__(cls, channels=None, height=None, width=None, stride=None): + return super(ShapeSpec, cls).__new__(cls, channels, height, width, + stride) diff --git a/dygraph/ppdet/modeling/utils/bbox_util.py b/dygraph/ppdet/modeling/utils/bbox_util.py index 440b162f8..6ea3682b4 100644 --- a/dygraph/ppdet/modeling/utils/bbox_util.py +++ b/dygraph/ppdet/modeling/utils/bbox_util.py @@ -106,8 +106,7 @@ def bbox_iou(box1, box2, giou=False, diou=False, ciou=False, eps=1e-9): x2 = paddle.minimum(px2, gx2) y2 = paddle.minimum(py2, gy2) - overlap = (x2 - x1) * (y2 - y1) - overlap = overlap.clip(0) + overlap = ((x2 - x1).clip(0)) * ((y2 - y1).clip(0)) area1 = (px2 - px1) * (py2 - py1) area1 = area1.clip(0) diff --git a/dygraph/ppdet/optimizer.py b/dygraph/ppdet/optimizer.py index e2e6123b3..c476e2edb 100644 --- a/dygraph/ppdet/optimizer.py +++ b/dygraph/ppdet/optimizer.py @@ -243,19 +243,15 @@ class ModelEMA(object): self._decay = decay model_dict = model.state_dict() 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.stop_gradient = True - self.state_dict[k] = v - else: - self.state_dict[k] = model_dict[k] + v = decay * v + (1 - decay) * model_dict[k] + v.stop_gradient = True + self.state_dict[k] = v self.step += 1 def apply(self): state_dict = dict() 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.stop_gradient = True - state_dict[k] = v + v = v / (1 - self._decay**self.step) + v.stop_gradient = True + state_dict[k] = v return state_dict diff --git a/dygraph/ppdet/py_op/post_process.py b/dygraph/ppdet/py_op/post_process.py index fcaeb2861..e7c5d9dbf 100755 --- a/dygraph/ppdet/py_op/post_process.py +++ b/dygraph/ppdet/py_op/post_process.py @@ -4,8 +4,13 @@ import numpy as np import cv2 -def get_det_res(bboxes, scores, labels, bbox_nums, image_id, - label_to_cat_id_map): +def get_det_res(bboxes, + scores, + labels, + bbox_nums, + image_id, + label_to_cat_id_map, + bias=0): det_res = [] k = 0 for i in range(len(bbox_nums)): @@ -19,8 +24,8 @@ def get_det_res(bboxes, scores, labels, bbox_nums, image_id, k = k + 1 xmin, ymin, xmax, ymax = box.tolist() category_id = label_to_cat_id_map[label] - w = xmax - xmin - h = ymax - ymin + w = xmax - xmin + bias + h = ymax - ymin + bias bbox = [xmin, ymin, w, h] dt_res = { 'image_id': cur_image_id, diff --git a/dygraph/ppdet/utils/checkpoint.py b/dygraph/ppdet/utils/checkpoint.py index 1f4562233..38a7dc01a 100644 --- a/dygraph/ppdet/utils/checkpoint.py +++ b/dygraph/ppdet/utils/checkpoint.py @@ -163,7 +163,7 @@ def load_pretrain_weight(model, model.backbone.set_dict(param_state_dict) else: ignore_set = set() - for name, weight in model_dict: + for name, weight in model_dict.items(): if name in param_state_dict: if weight.shape != param_state_dict[name].shape: param_state_dict.pop(name, None) diff --git a/dygraph/tools/eval.py b/dygraph/tools/eval.py index 998710bff..690cc5501 100755 --- a/dygraph/tools/eval.py +++ b/dygraph/tools/eval.py @@ -47,7 +47,10 @@ def parse_args(): help="Evaluation directory, default is current directory.") 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( "--slim_config", @@ -55,6 +58,12 @@ def parse_args(): type=str, 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() return args @@ -77,6 +86,8 @@ def main(): FLAGS = parse_args() cfg = load_config(FLAGS.config) + # TODO: bias should be unified + cfg['bias'] = 1 if FLAGS.bias else 0 merge_config(FLAGS.opt) if FLAGS.slim_config: slim_cfg = load_config(FLAGS.slim_config) -- GitLab