更换文档检测模型
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paddle_detection/configs/semi_det/rtdetr_ssod/README.md
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paddle_detection/configs/semi_det/rtdetr_ssod/README.md
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简体中文 | [English](README_en.md)
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# RTDETR-SSOD(基于RTDETR的配套半监督目标检测方法)
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# 复现模型指标注意事项: 模型中指标是采用在监督数据训练饱和后加载监督数据所训练的模型进行半监督训练
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- 例如 使用 baseline/rtdetr_r50vd_6x_coco_sup005.yml使用5%coco数据训练全监督模型，得到rtdetr_r50vd_6x_coco_sup005.pdparams,在rt_detr_ssod005_coco_no_warmup.yml中设置
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- pretrain_student_weights: rtdetr_r50vd_6x_coco_sup005.pdparams
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- pretrain_teacher_weights: rtdetr_r50vd_6x_coco_sup005.pdparams
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- 1.使用coco数据集5%和10%有标记数据和voc数据集VOC2007trainval 所训练的权重已给出请参考 semi_det/baseline/README.md.
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- 2.rt_detr_ssod_voc_no_warmup.yml rt_detr_ssod005_coco_no_warmup.yml rt_detr_ssod010_coco_no_warmup.yml 是使用训练好的全监督权中直接开启半监督训练（推荐）
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## RTDETR-SSOD模型库
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| 模型 | 监督数据比例 | Sup Baseline | Sup Epochs (Iters) | Sup mAP<sup>val<br>0.5:0.95 | Semi mAP<sup>val<br>0.5:0.95 | Semi Epochs (Iters) | 模型下载 | 配置文件 |
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| :------------: | :---------: | :---------------------: | :---------------------: |:---------------------------: |:----------------------------: | :------------------: |:--------: |:----------: |
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| RTDETR-SSOD | 5% | [sup_config](../baseline/rtdetr_r50vd_6x_coco_sup005.yml) | - | 39.0 | **42.3** | - | [download](https://bj.bcebos.com/v1/paddledet/rt_detr_ssod005_coco_no_warmup.pdparams) | [config](./rt_detr_ssod005_coco_no_warmup.yml) |
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| RTDETR-SSOD | 10% | [sup_config](../baseline/rtdetr_r50vd_6x_coco_sup010.yml) | -| 42.3 | **44.8** | - | [download](https://bj.bcebos.com/v1/paddledet/data/semidet/rtdetr_ssod/rt_detr_ssod010_coco/rt_detr_ssod010_coco_no_warmup.pdparams) | [config](./rt_detr_ssod010_coco_with_warmup.yml) |
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| RTDETR-SSOD(VOC)| VOC | [sup_config](../baseline/rtdetr_r50vd_6x_coco_voc2007.yml) | - | 62.7 | **65.8(LSJ)** | - | [download](https://bj.bcebos.com/v1/paddledet/data/semidet/rtdetr_ssod/rt_detr_ssod_voc/rt_detr_ssod_voc_no_warmup.pdparams) | [config](./rt_detr_ssod_voc_with_warmup.yml) |
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**注意:**
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- 以上模型训练默认使用8 GPUs，监督数据总batch_size默认为16，无监督数据总batch_size默认也为16，默认初始学习率为0.01。如果改动了总batch_size，请按线性比例相应地调整学习率；
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- **监督数据比例**是指使用的有标签COCO数据集占 COCO train2017 全量训练集的百分比，使用的无标签COCO数据集一般也是相同比例，但具体图片和有标签数据的图片不重合；
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- `Semi Epochs (Iters)`表示**半监督训练**的模型的 Epochs (Iters)，如果使用**自定义数据集**，需自行根据Iters换算到对应的Epochs调整，最好保证总Iters 和COCO数据集的设置较为接近；
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- `Sup mAP`是**只使用有监督数据训练**的模型的精度，请参照**基础检测器的配置文件** 和 [baseline](../baseline)；
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- `Semi mAP`是**半监督训练**的模型的精度，模型下载和配置文件的链接均为**半监督模型**；
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- `LSJ`表示 **large-scale jittering**，表示使用更大范围的多尺度训练，可进一步提升精度，但训练速度也会变慢；
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- 半监督检测的配置讲解，请参照[文档](../README.md/#半监督检测配置)；
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- `Dense Teacher`原文使用`R50-va-caffe`预训练，PaddleDetection中默认使用`R50-vb`预训练，如果使用`R50-vd`结合[SSLD](../../../docs/feature_models/SSLD_PRETRAINED_MODEL.md)的预训练模型，可进一步显著提升检测精度，同时backbone部分配置也需要做出相应更改，如：
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```python
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pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet50_vd_ssld_v2_pretrained.pdparams
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ResNet:
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depth: 50
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variant: d
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norm_type: bn
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freeze_at: 0
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return_idx: [1, 2, 3]
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num_stages: 4
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lr_mult_list: [0.05, 0.05, 0.1, 0.15]
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```
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## 使用说明
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仅训练时必须使用半监督检测的配置文件去训练，评估、预测、部署也可以按基础检测器的配置文件去执行。
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### 训练
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```bash
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# 单卡训练 (不推荐，需按线性比例相应地调整学习率)
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CUDA_VISIBLE_DEVICES=0 python tools/train.py -c configs/semi_det/rtdetr_ssod/rt_detr_ssod010_coco_no_warmup.yml --eval
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# 多卡训练
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python -m paddle.distributed.launch --log_dir=denseteacher_fcos_semi010/ --gpus 0,1,2,3,4,5,6,7 tools/train.py -c configs/semi_det/rtdetr_ssod/rt_detr_ssod010_coco_no_warmup.yml --eval
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```
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### 评估
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```bash
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CUDA_VISIBLE_DEVICES=0 python tools/eval.py -c configs/semi_det/rtdetr_ssod/rt_detr_ssod010_coco_no_warmup.yml -o weights=output/rt_detr_ssod/model_final/model_final.pdparams
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```
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### 预测
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```bash
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CUDA_VISIBLE_DEVICES=0 python tools/infer.py -c configs/semi_det/rtdetr_ssod/rt_detr_ssod010_coco_no_warmup.yml -o weights=output/rt_detr_ssod/model_final/model_final.pdparams --infer_img=demo/000000014439.jpg
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```
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### 部署
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部署可以使用半监督检测配置文件，也可以使用基础检测器的配置文件去部署和使用。
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```bash
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# 导出模型
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CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c configs/semi_det/rtdetr_ssod/rt_detr_ssod010_coco_no_warmup.yml -o weights=https://paddledet.bj.bcebos.com/models/rt_detr_ssod010_coco_no_warmup.pdparams
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# 导出权重预测
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CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/rt_detr_ssod010_coco_no_warmup --image_file=demo/000000014439_640x640.jpg --device=GPU
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# 部署测速
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CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/rt_detr_ssod010_coco_no_warmup --image_file=demo/000000014439_640x640.jpg --device=GPU --run_benchmark=True # --run_mode=trt_fp16
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# 导出ONNX
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paddle2onnx --model_dir output_inference/drt_detr_ssod010_coco_no_warmup/ --model_filename model.pdmodel --params_filename model.pdiparams --opset_version 12 --save_file rt_detr_ssod010_coco_no_warmup.onnx
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```
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# RTDETR-SSOD 下游任务
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我们验证了RTDETR-SSOD模型强大的泛化能力，在低光、工业、交通等不同场景下游任务检测效果稳定提升!
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voc数据集采用[voc](https://github.com/thsant/wgisd)，是一个广泛使用的计算机视觉数据集，用于目标检测、图像分割和场景理解等任务。该数据集包含20个类别的图像,处理后的COCO格式，包含图片标注训练集5011张，图片无标注训练集11540张，测试集2510张，20个类别；
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低光数据集使用[ExDark](https://github.com/cs-chan/Exclusively-Dark-Image-Dataset/tree/master/Dataset)，该数据集是一个专门在低光照环境下拍摄出针对低光目标检测的数据集，包括从极低光环境到暮光环境等10种不同光照条件下的图片，处理后的COCO格式，包含图片训练集5891张，测试集1472张，12个类别;
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工业数据集使用[PKU-Market-PCB](https://robotics.pkusz.edu.cn/resources/dataset/)，该数据集用于印刷电路板（PCB）的瑕疵检测，提供了6种常见的PCB缺陷;
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商超数据集[SKU110k](https://github.com/eg4000/SKU110K_CVPR19)是商品超市场景下的密集目标检测数据集，包含11,762张图片和超过170个实例。其中包括8,233张用于训练的图像、588张用于验证的图像和2,941张用于测试的图像;
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自动驾驶数据集使用[sslad](https://soda-2d.github.io/index.html);
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交通数据集使用[visdrone](http://aiskyeye.com/home/);
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## 下游数据集实验结果：
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| 数据集 | 业务方向 | 划分 | labeled数据量 | 全监督mAP | 半监督mAP |
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|----------|-----------|---------------------|-----------------|------------------|--------------|
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| voc | 通用 | voc07, 12；1:2 | 5000 | 63.1 | 65.8（+2.7） |
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| visdrone | 无人机交通 | 1:9 | 647 | 19.4 | 20.6 (+1.2) |
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| pcb | 工业缺陷 | 1:9 | 55 | 22.9 | 26.8 (+3.9) |
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| sku110k | 商品 | 1:9 | 821 | 38.9 | 52.4 (+13.5) |
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| sslad | 自动驾驶 | 1:32 | 4967 | 42.1 | 43.3 (+1.2) |
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| exdark | 低光照 | 1:9 | 589 | 39.6 | 44.1 (+4.5) |
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_BASE_: [
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'../../runtime.yml',
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'../../rtdetr/_base_/rtdetr_r50vd.yml',
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'../../rtdetr/_base_/rtdetr_reader.yml',
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]
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eval_interval: 4000
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save_interval: 4000
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weights: output/rt_detr_ssod/model_final
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find_unused_parameters: True
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save_dir: output
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log_iter: 1
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ssod_method: Semi_RTDETR
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### global config
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use_simple_ema: True
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ema_decay: 0.9996
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use_gpu: true
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### reader config
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worker_num: 4
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SemiTrainReader:
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sample_transforms:
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- Decode: {}
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- RandomDistort: {prob: 0.8}
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- RandomExpand: {fill_value: [0., 0., 0.]}
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- RandomCrop: {prob: 0.8}
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- RandomFlip: {}
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weak_aug:
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- RandomFlip: {prob: 0.0}
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strong_aug:
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- StrongAugImage: {transforms: [
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RandomColorJitter: {prob: 0.8, brightness: 0.4, contrast: 0.4, saturation: 0.4, hue: 0.1},
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RandomErasingCrop: {},
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RandomGaussianBlur: {prob: 0.5, sigma: [0.1, 2.0]},
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RandomGrayscale: {prob: 0.2},
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]}
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sup_batch_transforms:
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- BatchRandomResizeForSSOD: {target_size: [480, 512, 544, 576, 608, 640, 640, 640, 672, 704, 736, 768, 800], random_size: True, random_interp: True, keep_ratio: False}
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- NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
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- NormalizeBox: {}
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- BboxXYXY2XYWH: {}
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- Permute: {}
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unsup_batch_transforms:
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- BatchRandomResizeForSSOD: {target_size: [480, 512, 544, 576, 608, 640, 640, 640, 672, 704, 736, 768, 800], random_size: True, random_interp: True, keep_ratio: False}
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- NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
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- NormalizeBox: {}
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- BboxXYXY2XYWH: {}
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- Permute: {}
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sup_batch_size: 2
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unsup_batch_size: 2
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shuffle: true
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drop_last: true
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collate_batch: false
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use_shared_memory: false
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EvalReader:
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sample_transforms:
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- Decode: {}
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- Resize: {target_size: [640, 640], keep_ratio: False}
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- NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
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- Permute: {}
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batch_size: 2
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shuffle: false
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drop_last: false
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TestReader:
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sample_transforms:
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- Decode: {}
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- Resize: { target_size: [640, 640], keep_ratio: False }
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- NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
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- Permute: {}
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batch_size: 1
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shuffle: false
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drop_last: false
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pretrain_student_weights: https://bj.bcebos.com/v1/paddledet/data/semidet/rtdetr_ssod/baseline/rtdetr_r50vd_6x_coco_sup005.pdparams
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pretrain_teacher_weights: https://bj.bcebos.com/v1/paddledet/data/semidet/rtdetr_ssod/baseline/rtdetr_r50vd_6x_coco_sup005.pdparams
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hidden_dim: 256
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use_focal_loss: True
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eval_size: [640, 640]
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architecture: DETR
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DETR:
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backbone: ResNet
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neck: HybridEncoder
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transformer: RTDETRTransformer
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detr_head: DINOHead
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post_process: DETRPostProcess
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post_process_semi: DETRBBoxSemiPostProcess
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ResNet:
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depth: 50
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variant: d
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norm_type: bn
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freeze_at: 0
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return_idx: [1, 2, 3]
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lr_mult_list: [0.1, 0.1, 0.1, 0.1]
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num_stages: 4
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freeze_stem_only: True
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HybridEncoder:
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hidden_dim: 256
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use_encoder_idx: [2]
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num_encoder_layers: 1
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encoder_layer:
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name: TransformerLayer
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d_model: 256
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nhead: 8
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dim_feedforward: 1024
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dropout: 0.
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activation: 'gelu'
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expansion: 1.0
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PPDETRTransformer:
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num_queries: 300
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position_embed_type: sine
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feat_strides: [8, 16, 32]
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num_levels: 3
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nhead: 8
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num_decoder_layers: 6
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dim_feedforward: 1024
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dropout: 0.0
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activation: relu
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num_denoising: 100
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label_noise_ratio: 0.5
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box_noise_scale: 1.0
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learnt_init_query: False
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DINOHead:
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loss:
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name: DINOLoss
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loss_coeff: {class: 1, bbox: 5, giou: 2}
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aux_loss: True
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use_vfl: True
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matcher:
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name: HungarianMatcher
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matcher_coeff: {class: 2, bbox: 5, giou: 2}
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DETRPostProcess:
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num_top_queries: 300
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SSOD: DETR_SSOD
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DETR_SSOD:
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teacher: DETR
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student: DETR
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train_cfg:
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sup_weight: 1.0
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unsup_weight: 1.0
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ema_start_iters: -1
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pseudo_label_initial_score_thr: 0.7
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min_pseduo_box_size: 0
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concat_sup_data: True
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test_cfg:
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inference_on: teacher
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metric: COCO
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num_classes: 80
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# partial labeled COCO, use `SemiCOCODataSet` rather than `COCODataSet`
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TrainDataset:
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!SemiCOCODataSet
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image_dir: train2017
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anno_path: semi_annotations/instances_train2017.1@5.json
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dataset_dir: dataset/coco
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data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
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# partial unlabeled COCO, use `SemiCOCODataSet` rather than `COCODataSet`
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UnsupTrainDataset:
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!SemiCOCODataSet
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image_dir: train2017
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anno_path: semi_annotations/instances_train2017.1@5-unlabeled.json
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dataset_dir: dataset/coco
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data_fields: ['image']
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supervised: False
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EvalDataset:
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!COCODataSet
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image_dir: val2017
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anno_path: annotations/instances_val2017.json
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dataset_dir: dataset/coco
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allow_empty: true
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TestDataset:
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!ImageFolder
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anno_path: annotations/instances_val2017.json # also support txt (like VOC's label_list.txt)
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dataset_dir: dataset/coco # if set, anno_path will be 'dataset_dir/anno_path'
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epoch: 400 #epoch: 60
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LearningRate:
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base_lr: 0.0002
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schedulers:
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- !PiecewiseDecay
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gamma: 1.0
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milestones: [400]
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use_warmup: false
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- !LinearWarmup
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start_factor: 0.001
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steps: 2000
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OptimizerBuilder:
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clip_grad_by_norm: 0.1
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regularizer: false
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optimizer:
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type: AdamW
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weight_decay: 0.0001
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@@ -0,0 +1,215 @@
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_BASE_: [
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'../../runtime.yml',
|
||||
'../../rtdetr/_base_/rtdetr_r50vd.yml',
|
||||
'../../rtdetr/_base_/rtdetr_reader.yml',
|
||||
]
|
||||
|
||||
#for debug
|
||||
eval_interval: 4000
|
||||
save_interval: 4000
|
||||
weights: output/rt_detr_ssod/model_final
|
||||
find_unused_parameters: True
|
||||
save_dir: output
|
||||
log_iter: 50
|
||||
ssod_method: Semi_RTDETR
|
||||
### global config
|
||||
use_simple_ema: True
|
||||
ema_decay: 0.9996
|
||||
use_gpu: true
|
||||
|
||||
### reader config
|
||||
worker_num: 4
|
||||
|
||||
SemiTrainReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- RandomDistort: {prob: 0.8}
|
||||
- RandomExpand: {fill_value: [0., 0., 0.]}
|
||||
- RandomCrop: {prob: 0.8}
|
||||
- RandomFlip: {}
|
||||
weak_aug:
|
||||
- RandomFlip: {prob: 0.0}
|
||||
strong_aug:
|
||||
- StrongAugImage: {transforms: [
|
||||
RandomColorJitter: {prob: 0.8, brightness: 0.4, contrast: 0.4, saturation: 0.4, hue: 0.1},
|
||||
RandomErasingCrop: {},
|
||||
RandomGaussianBlur: {prob: 0.5, sigma: [0.1, 2.0]},
|
||||
RandomGrayscale: {prob: 0.2},
|
||||
]}
|
||||
sup_batch_transforms:
|
||||
- BatchRandomResizeForSSOD: {target_size: [480, 512, 544, 576, 608, 640, 640, 640, 672, 704, 736, 768, 800], random_size: True, random_interp: True, keep_ratio: False}
|
||||
- NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
|
||||
- NormalizeBox: {}
|
||||
- BboxXYXY2XYWH: {}
|
||||
- Permute: {}
|
||||
unsup_batch_transforms:
|
||||
- BatchRandomResizeForSSOD: {target_size: [480, 512, 544, 576, 608, 640, 640, 640, 672, 704, 736, 768, 800], random_size: True, random_interp: True, keep_ratio: False}
|
||||
- NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
|
||||
- NormalizeBox: {}
|
||||
- BboxXYXY2XYWH: {}
|
||||
- Permute: {}
|
||||
sup_batch_size: 2
|
||||
unsup_batch_size: 2
|
||||
shuffle: true
|
||||
drop_last: true
|
||||
collate_batch: false
|
||||
use_shared_memory: false
|
||||
|
||||
EvalReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: [640, 640], keep_ratio: False}
|
||||
- NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
|
||||
- Permute: {}
|
||||
batch_size: 2
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
|
||||
|
||||
TestReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: { target_size: [640, 640], keep_ratio: False }
|
||||
- NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
|
||||
- Permute: {}
|
||||
batch_size: 1
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
|
||||
|
||||
pretrain_student_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet50_vd_ssld_v2_pretrained.pdparams
|
||||
pretrain_teacher_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet50_vd_ssld_v2_pretrained.pdparams
|
||||
|
||||
hidden_dim: 256
|
||||
use_focal_loss: True
|
||||
eval_size: [640, 640]
|
||||
|
||||
architecture: DETR
|
||||
DETR:
|
||||
backbone: ResNet
|
||||
neck: HybridEncoder
|
||||
transformer: RTDETRTransformer
|
||||
detr_head: DINOHead
|
||||
post_process: DETRPostProcess
|
||||
post_process_semi: DETRBBoxSemiPostProcess
|
||||
ResNet:
|
||||
# index 0 stands for res2
|
||||
depth: 50
|
||||
variant: d
|
||||
norm_type: bn
|
||||
freeze_at: 0
|
||||
return_idx: [1, 2, 3]
|
||||
lr_mult_list: [0.1, 0.1, 0.1, 0.1]
|
||||
num_stages: 4
|
||||
freeze_stem_only: True
|
||||
|
||||
HybridEncoder:
|
||||
hidden_dim: 256
|
||||
use_encoder_idx: [2]
|
||||
num_encoder_layers: 1
|
||||
encoder_layer:
|
||||
name: TransformerLayer
|
||||
d_model: 256
|
||||
nhead: 8
|
||||
dim_feedforward: 1024
|
||||
dropout: 0.
|
||||
activation: 'gelu'
|
||||
expansion: 1.0
|
||||
|
||||
|
||||
RTDETRTransformer:
|
||||
num_queries: 300
|
||||
position_embed_type: sine
|
||||
feat_strides: [8, 16, 32]
|
||||
num_levels: 3
|
||||
nhead: 8
|
||||
num_decoder_layers: 6
|
||||
dim_feedforward: 1024
|
||||
dropout: 0.0
|
||||
activation: relu
|
||||
num_denoising: 100
|
||||
label_noise_ratio: 0.5
|
||||
box_noise_scale: 1.0
|
||||
learnt_init_query: False
|
||||
|
||||
DINOHead:
|
||||
loss:
|
||||
name: DINOLoss
|
||||
loss_coeff: {class: 1, bbox: 5, giou: 2}
|
||||
aux_loss: True
|
||||
use_vfl: True
|
||||
matcher:
|
||||
name: HungarianMatcher
|
||||
matcher_coeff: {class: 2, bbox: 5, giou: 2}
|
||||
use_uni_match: True
|
||||
DETRPostProcess:
|
||||
num_top_queries: 300
|
||||
|
||||
|
||||
|
||||
SSOD: DETR_SSOD
|
||||
DETR_SSOD:
|
||||
teacher: DETR
|
||||
student: DETR
|
||||
train_cfg:
|
||||
sup_weight: 1.0
|
||||
unsup_weight: 1.0
|
||||
ema_start_iters: 10000
|
||||
pseudo_label_initial_score_thr: 0.7
|
||||
min_pseduo_box_size: 0
|
||||
concat_sup_data: True
|
||||
test_cfg:
|
||||
inference_on: teacher
|
||||
|
||||
|
||||
|
||||
metric: COCO
|
||||
num_classes: 80
|
||||
|
||||
# partial labeled COCO, use `SemiCOCODataSet` rather than `COCODataSet`
|
||||
TrainDataset:
|
||||
!SemiCOCODataSet
|
||||
image_dir: train2017
|
||||
anno_path: semi_annotations/instances_train2017.1@5.json
|
||||
dataset_dir: dataset/coco
|
||||
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
|
||||
# partial unlabeled COCO, use `SemiCOCODataSet` rather than `COCODataSet`
|
||||
UnsupTrainDataset:
|
||||
!SemiCOCODataSet
|
||||
image_dir: train2017
|
||||
anno_path: semi_annotations/instances_train2017.1@5-unlabeled.json
|
||||
dataset_dir: dataset/coco
|
||||
data_fields: ['image']
|
||||
supervised: False
|
||||
|
||||
EvalDataset:
|
||||
!COCODataSet
|
||||
image_dir: val2017
|
||||
anno_path: annotations/instances_val2017.json
|
||||
dataset_dir: dataset/coco
|
||||
allow_empty: true
|
||||
|
||||
TestDataset:
|
||||
!ImageFolder
|
||||
anno_path: annotations/instances_val2017.json # also support txt (like VOC's label_list.txt)
|
||||
dataset_dir: dataset/coco # if set, anno_path will be 'dataset_dir/anno_path'
|
||||
|
||||
epoch: 500 #epoch: 60
|
||||
|
||||
LearningRate:
|
||||
base_lr: 0.0002
|
||||
schedulers:
|
||||
- !PiecewiseDecay
|
||||
gamma: 1.0
|
||||
milestones: [500]
|
||||
use_warmup: false
|
||||
- !LinearWarmup
|
||||
start_factor: 0.001
|
||||
steps: 2000
|
||||
|
||||
OptimizerBuilder:
|
||||
clip_grad_by_norm: 0.1
|
||||
regularizer: false
|
||||
optimizer:
|
||||
type: AdamW
|
||||
weight_decay: 0.0001
|
||||
@@ -0,0 +1,212 @@
|
||||
_BASE_: [
|
||||
'../../runtime.yml',
|
||||
'../../rtdetr/_base_/rtdetr_r50vd.yml',
|
||||
'../../rtdetr/_base_/rtdetr_reader.yml',
|
||||
]
|
||||
eval_interval: 4000
|
||||
save_interval: 4000
|
||||
weights: output/rt_detr_ssod/model_final
|
||||
find_unused_parameters: True
|
||||
save_dir: output
|
||||
log_iter: 1
|
||||
ssod_method: Semi_RTDETR
|
||||
### global config
|
||||
use_simple_ema: True
|
||||
ema_decay: 0.9996
|
||||
use_gpu: true
|
||||
|
||||
### reader config
|
||||
worker_num: 4
|
||||
|
||||
SemiTrainReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- RandomDistort: {prob: 0.8}
|
||||
- RandomExpand: {fill_value: [0., 0., 0.]}
|
||||
- RandomCrop: {prob: 0.8}
|
||||
- RandomFlip: {}
|
||||
weak_aug:
|
||||
- RandomFlip: {prob: 0.0}
|
||||
strong_aug:
|
||||
- StrongAugImage: {transforms: [
|
||||
RandomColorJitter: {prob: 0.8, brightness: 0.4, contrast: 0.4, saturation: 0.4, hue: 0.1},
|
||||
RandomErasingCrop: {},
|
||||
RandomGaussianBlur: {prob: 0.5, sigma: [0.1, 2.0]},
|
||||
RandomGrayscale: {prob: 0.2},
|
||||
]}
|
||||
sup_batch_transforms:
|
||||
- BatchRandomResizeForSSOD: {target_size: [480, 512, 544, 576, 608, 640, 640, 640, 672, 704, 736, 768, 800], random_size: True, random_interp: True, keep_ratio: False}
|
||||
- NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
|
||||
- NormalizeBox: {}
|
||||
- BboxXYXY2XYWH: {}
|
||||
- Permute: {}
|
||||
unsup_batch_transforms:
|
||||
- BatchRandomResizeForSSOD: {target_size: [480, 512, 544, 576, 608, 640, 640, 640, 672, 704, 736, 768, 800], random_size: True, random_interp: True, keep_ratio: False}
|
||||
- NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
|
||||
- NormalizeBox: {}
|
||||
- BboxXYXY2XYWH: {}
|
||||
- Permute: {}
|
||||
sup_batch_size: 2
|
||||
unsup_batch_size: 2
|
||||
shuffle: true
|
||||
drop_last: true
|
||||
collate_batch: false
|
||||
use_shared_memory: false
|
||||
|
||||
EvalReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: [640, 640], keep_ratio: False}
|
||||
- NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
|
||||
- Permute: {}
|
||||
batch_size: 2
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
|
||||
|
||||
TestReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: { target_size: [640, 640], keep_ratio: False }
|
||||
- NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
|
||||
- Permute: {}
|
||||
batch_size: 1
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
|
||||
|
||||
pretrain_student_weights: https://bj.bcebos.com/v1/paddledet/data/semidet/rtdetr_ssod/baseline/rtdetr_r50vd_6x_coco_sup010.pdparams
|
||||
pretrain_teacher_weights: https://bj.bcebos.com/v1/paddledet/data/semidet/rtdetr_ssod/baseline/rtdetr_r50vd_6x_coco_sup010.pdparams
|
||||
|
||||
hidden_dim: 256
|
||||
use_focal_loss: True
|
||||
eval_size: [640, 640]
|
||||
|
||||
architecture: DETR
|
||||
DETR:
|
||||
backbone: ResNet
|
||||
neck: HybridEncoder
|
||||
transformer: RTDETRTransformer
|
||||
detr_head: DINOHead
|
||||
post_process: DETRPostProcess
|
||||
post_process_semi: DETRBBoxSemiPostProcess
|
||||
ResNet:
|
||||
depth: 50
|
||||
variant: d
|
||||
norm_type: bn
|
||||
freeze_at: 0
|
||||
return_idx: [1, 2, 3]
|
||||
lr_mult_list: [0.1, 0.1, 0.1, 0.1]
|
||||
num_stages: 4
|
||||
freeze_stem_only: True
|
||||
|
||||
HybridEncoder:
|
||||
hidden_dim: 256
|
||||
use_encoder_idx: [2]
|
||||
num_encoder_layers: 1
|
||||
encoder_layer:
|
||||
name: TransformerLayer
|
||||
d_model: 256
|
||||
nhead: 8
|
||||
dim_feedforward: 1024
|
||||
dropout: 0.
|
||||
activation: 'gelu'
|
||||
expansion: 1.0
|
||||
|
||||
|
||||
PPDETRTransformer:
|
||||
num_queries: 300
|
||||
position_embed_type: sine
|
||||
feat_strides: [8, 16, 32]
|
||||
num_levels: 3
|
||||
nhead: 8
|
||||
num_decoder_layers: 6
|
||||
dim_feedforward: 1024
|
||||
dropout: 0.0
|
||||
activation: relu
|
||||
num_denoising: 100
|
||||
label_noise_ratio: 0.5
|
||||
box_noise_scale: 1.0
|
||||
learnt_init_query: False
|
||||
|
||||
DINOHead:
|
||||
loss:
|
||||
name: DINOLoss
|
||||
loss_coeff: {class: 1, bbox: 5, giou: 2}
|
||||
aux_loss: True
|
||||
use_vfl: True
|
||||
matcher:
|
||||
name: HungarianMatcher
|
||||
matcher_coeff: {class: 2, bbox: 5, giou: 2}
|
||||
|
||||
DETRPostProcess:
|
||||
num_top_queries: 300
|
||||
|
||||
|
||||
|
||||
SSOD: DETR_SSOD
|
||||
DETR_SSOD:
|
||||
teacher: DETR
|
||||
student: DETR
|
||||
train_cfg:
|
||||
sup_weight: 1.0
|
||||
unsup_weight: 1.0
|
||||
ema_start_iters: -1
|
||||
pseudo_label_initial_score_thr: 0.7
|
||||
min_pseduo_box_size: 0
|
||||
concat_sup_data: True
|
||||
test_cfg:
|
||||
inference_on: teacher
|
||||
|
||||
|
||||
|
||||
metric: COCO
|
||||
num_classes: 80
|
||||
|
||||
# partial labeled COCO, use `SemiCOCODataSet` rather than `COCODataSet`
|
||||
TrainDataset:
|
||||
!SemiCOCODataSet
|
||||
image_dir: train2017
|
||||
anno_path: semi_annotations/instances_train2017.1@10.json
|
||||
dataset_dir: dataset/coco
|
||||
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
|
||||
# partial unlabeled COCO, use `SemiCOCODataSet` rather than `COCODataSet`
|
||||
UnsupTrainDataset:
|
||||
!SemiCOCODataSet
|
||||
image_dir: train2017
|
||||
anno_path: semi_annotations/instances_train2017.1@10-unlabeled.json
|
||||
dataset_dir: dataset/coco
|
||||
data_fields: ['image']
|
||||
supervised: False
|
||||
|
||||
EvalDataset:
|
||||
!COCODataSet
|
||||
image_dir: val2017
|
||||
anno_path: annotations/instances_val2017.json
|
||||
dataset_dir: dataset/coco
|
||||
allow_empty: true
|
||||
|
||||
TestDataset:
|
||||
!ImageFolder
|
||||
anno_path: annotations/instances_val2017.json # also support txt (like VOC's label_list.txt)
|
||||
dataset_dir: dataset/coco # if set, anno_path will be 'dataset_dir/anno_path'
|
||||
|
||||
epoch: 400 #epoch: 60
|
||||
|
||||
LearningRate:
|
||||
base_lr: 0.0002
|
||||
schedulers:
|
||||
- !PiecewiseDecay
|
||||
gamma: 1.0
|
||||
milestones: [400]
|
||||
use_warmup: false
|
||||
- !LinearWarmup
|
||||
start_factor: 0.001
|
||||
steps: 2000
|
||||
|
||||
OptimizerBuilder:
|
||||
clip_grad_by_norm: 0.1
|
||||
regularizer: false
|
||||
optimizer:
|
||||
type: AdamW
|
||||
weight_decay: 0.0001
|
||||
@@ -0,0 +1,215 @@
|
||||
_BASE_: [
|
||||
'../../runtime.yml',
|
||||
'../../rtdetr/_base_/rtdetr_r50vd.yml',
|
||||
'../../rtdetr/_base_/rtdetr_reader.yml',
|
||||
]
|
||||
|
||||
#for debug
|
||||
eval_interval: 4000
|
||||
save_interval: 4000
|
||||
weights: output/rt_detr_ssod/model_final
|
||||
find_unused_parameters: True
|
||||
save_dir: output
|
||||
log_iter: 50
|
||||
ssod_method: Semi_RTDETR
|
||||
### global config
|
||||
use_simple_ema: True
|
||||
ema_decay: 0.9996
|
||||
use_gpu: true
|
||||
|
||||
### reader config
|
||||
worker_num: 4
|
||||
|
||||
SemiTrainReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- RandomDistort: {prob: 0.8}
|
||||
- RandomExpand: {fill_value: [0., 0., 0.]}
|
||||
- RandomCrop: {prob: 0.8}
|
||||
- RandomFlip: {}
|
||||
weak_aug:
|
||||
- RandomFlip: {prob: 0.0}
|
||||
strong_aug:
|
||||
- StrongAugImage: {transforms: [
|
||||
RandomColorJitter: {prob: 0.8, brightness: 0.4, contrast: 0.4, saturation: 0.4, hue: 0.1},
|
||||
RandomErasingCrop: {},
|
||||
RandomGaussianBlur: {prob: 0.5, sigma: [0.1, 2.0]},
|
||||
RandomGrayscale: {prob: 0.2},
|
||||
]}
|
||||
sup_batch_transforms:
|
||||
- BatchRandomResizeForSSOD: {target_size: [480, 512, 544, 576, 608, 640, 640, 640, 672, 704, 736, 768, 800], random_size: True, random_interp: True, keep_ratio: False}
|
||||
- NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
|
||||
- NormalizeBox: {}
|
||||
- BboxXYXY2XYWH: {}
|
||||
- Permute: {}
|
||||
unsup_batch_transforms:
|
||||
- BatchRandomResizeForSSOD: {target_size: [480, 512, 544, 576, 608, 640, 640, 640, 672, 704, 736, 768, 800], random_size: True, random_interp: True, keep_ratio: False}
|
||||
- NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
|
||||
- NormalizeBox: {}
|
||||
- BboxXYXY2XYWH: {}
|
||||
- Permute: {}
|
||||
sup_batch_size: 2
|
||||
unsup_batch_size: 2
|
||||
shuffle: true
|
||||
drop_last: true
|
||||
collate_batch: false
|
||||
use_shared_memory: false
|
||||
|
||||
EvalReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: {target_size: [640, 640], keep_ratio: False}
|
||||
- NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
|
||||
- Permute: {}
|
||||
batch_size: 2
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
|
||||
|
||||
TestReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- Resize: { target_size: [640, 640], keep_ratio: False }
|
||||
- NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
|
||||
- Permute: {}
|
||||
batch_size: 1
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
|
||||
|
||||
pretrain_student_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet50_vd_ssld_v2_pretrained.pdparams
|
||||
pretrain_teacher_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet50_vd_ssld_v2_pretrained.pdparams
|
||||
|
||||
hidden_dim: 256
|
||||
use_focal_loss: True
|
||||
eval_size: [640, 640]
|
||||
|
||||
architecture: DETR
|
||||
DETR:
|
||||
backbone: ResNet
|
||||
neck: HybridEncoder
|
||||
transformer: RTDETRTransformer
|
||||
detr_head: DINOHead
|
||||
post_process: DETRPostProcess
|
||||
post_process_semi: DETRBBoxSemiPostProcess
|
||||
ResNet:
|
||||
# index 0 stands for res2
|
||||
depth: 50
|
||||
variant: d
|
||||
norm_type: bn
|
||||
freeze_at: 0
|
||||
return_idx: [1, 2, 3]
|
||||
lr_mult_list: [0.1, 0.1, 0.1, 0.1]
|
||||
num_stages: 4
|
||||
freeze_stem_only: True
|
||||
|
||||
HybridEncoder:
|
||||
hidden_dim: 256
|
||||
use_encoder_idx: [2]
|
||||
num_encoder_layers: 1
|
||||
encoder_layer:
|
||||
name: TransformerLayer
|
||||
d_model: 256
|
||||
nhead: 8
|
||||
dim_feedforward: 1024
|
||||
dropout: 0.
|
||||
activation: 'gelu'
|
||||
expansion: 1.0
|
||||
|
||||
|
||||
RTDETRTransformer:
|
||||
num_queries: 300
|
||||
position_embed_type: sine
|
||||
feat_strides: [8, 16, 32]
|
||||
num_levels: 3
|
||||
nhead: 8
|
||||
num_decoder_layers: 6
|
||||
dim_feedforward: 1024
|
||||
dropout: 0.0
|
||||
activation: relu
|
||||
num_denoising: 100
|
||||
label_noise_ratio: 0.5
|
||||
box_noise_scale: 1.0
|
||||
learnt_init_query: False
|
||||
|
||||
DINOHead:
|
||||
loss:
|
||||
name: DINOLoss
|
||||
loss_coeff: {class: 1, bbox: 5, giou: 2}
|
||||
aux_loss: True
|
||||
use_vfl: True
|
||||
matcher:
|
||||
name: HungarianMatcher
|
||||
matcher_coeff: {class: 2, bbox: 5, giou: 2}
|
||||
use_uni_match: True
|
||||
DETRPostProcess:
|
||||
num_top_queries: 300
|
||||
|
||||
|
||||
|
||||
SSOD: DETR_SSOD
|
||||
DETR_SSOD:
|
||||
teacher: DETR
|
||||
student: DETR
|
||||
train_cfg:
|
||||
sup_weight: 1.0
|
||||
unsup_weight: 1.0
|
||||
ema_start_iters: 10000
|
||||
pseudo_label_initial_score_thr: 0.7
|
||||
min_pseduo_box_size: 0
|
||||
concat_sup_data: True
|
||||
test_cfg:
|
||||
inference_on: teacher
|
||||
|
||||
|
||||
|
||||
metric: COCO
|
||||
num_classes: 80
|
||||
|
||||
# partial labeled COCO, use `SemiCOCODataSet` rather than `COCODataSet`
|
||||
TrainDataset:
|
||||
!SemiCOCODataSet
|
||||
image_dir: train2017
|
||||
anno_path: semi_annotations/instances_train2017.1@10.json
|
||||
dataset_dir: dataset/coco
|
||||
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
|
||||
# partial unlabeled COCO, use `SemiCOCODataSet` rather than `COCODataSet`
|
||||
UnsupTrainDataset:
|
||||
!SemiCOCODataSet
|
||||
image_dir: train2017
|
||||
anno_path: semi_annotations/instances_train2017.1@10-unlabeled.json
|
||||
dataset_dir: dataset/coco
|
||||
data_fields: ['image']
|
||||
supervised: False
|
||||
|
||||
EvalDataset:
|
||||
!COCODataSet
|
||||
image_dir: val2017
|
||||
anno_path: annotations/instances_val2017.json
|
||||
dataset_dir: dataset/coco
|
||||
allow_empty: true
|
||||
|
||||
TestDataset:
|
||||
!ImageFolder
|
||||
anno_path: annotations/instances_val2017.json # also support txt (like VOC's label_list.txt)
|
||||
dataset_dir: dataset/coco # if set, anno_path will be 'dataset_dir/anno_path'
|
||||
|
||||
epoch: 500 #epoch: 60
|
||||
|
||||
LearningRate:
|
||||
base_lr: 0.0002
|
||||
schedulers:
|
||||
- !PiecewiseDecay
|
||||
gamma: 1.0
|
||||
milestones: [500]
|
||||
use_warmup: false
|
||||
- !LinearWarmup
|
||||
start_factor: 0.001
|
||||
steps: 2000
|
||||
|
||||
OptimizerBuilder:
|
||||
clip_grad_by_norm: 0.1
|
||||
regularizer: false
|
||||
optimizer:
|
||||
type: AdamW
|
||||
weight_decay: 0.0001
|
||||
Reference in New Issue
Block a user