MMDetection 快速开始，训练自定义数据集

#MMDetection 快速开始，训练自定义数据集| 来源: 网络整理| 查看: 265

本文将快速引导使用 MMDetection ，记录了实践中需注意的一些问题。

环境准备基础环境Nvidia 显卡的主机Ubuntu 18.04系统安装，可见制作 USB 启动盘，及系统安装Nvidia Driver驱动安装，可见 Ubuntu 初始配置 - Nvidia 驱动开发环境

下载并安装 Anaconda ,之后于 Terminal 执行：

# 创建 Python 虚拟环境 conda create -n open-mmlab python=3.7 -y conda activate open-mmlab # 安装 PyTorch with CUDA conda install pytorch==1.6.0 torchvision==0.7.0 cudatoolkit=10.2 -c pytorch -y # 安装 MMCV pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu102/torch1.6.0/index.html # 安装 MMDetection git clone https://github.com/open-mmlab/mmdetection.git cd mmdetection pip install -r requirements/build.txt pip install -v -e .

pytorch==1.7.0 时多卡训练会发生问题，需参考此 Issue。命令参考：

conda install pytorch==1.7.0 torchvision==0.8.1 cudatoolkit=10.2 -c pytorch -y pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu102/torch1.7.0/index.html

更多安装方式，可见官方文档：

MMDetection - InstallationMMCV - Installation现有模型进行推断Faster RCNN

以 R-50-FPN 为例，下载其 model 文件到 mmdetection/checkpoints/。之后，进行推断，

conda activate open-mmlab cd mmdetection/ python demo/image_demo.py \ demo/demo.jpg \ configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py \ checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth

现有模型进行测试准备数据集

下载 COCO 数据集，如下放进 mmdetection/data/coco/ 目录，

mmdetection ├── data │ ├── coco │ │ ├── annotations │ │ ├── train2017 │ │ ├── val2017 │ │ ├── test2017测试现有模型cd mmdetection/ # single-gpu testing python tools/test.py \ configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py \ checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth \ --out results.pkl \ --eval bbox \ --show # multi-gpu testing bash tools/dist_test.sh \ configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py \ checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth \ 2 \ --out results.pkl \ --eval bbox

效果如下，

结果如下，

loading annotations into memory... Done (t=0.33s) creating index... index created! [>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 5000/5000, 15.3 task/s, elapsed: 328s, ETA: 0s writing results to results.pkl Evaluating bbox... Loading and preparing results... DONE (t=0.89s) creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=26.17s). Accumulating evaluation results... DONE (t=4.10s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.374 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.581 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.404 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.212 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.410 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.481 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.517 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.517 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.517 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.326 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.557 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.648 OrderedDict([('bbox_mAP', 0.374), ('bbox_mAP_50', 0.581), ('bbox_mAP_75', 0.404), ('bbox_mAP_s', 0.212), ('bbox_mAP_m', 0.41), ('bbox_mAP_l', 0.481), ('bbox_mAP_copypaste', '0.374 0.581 0.404 0.212 0.410 0.481')])标准数据集训练模型准备数据集

同前一节的 COCO 数据集。

准备配置文件

配置文件为 configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py。

需要依照自己的 GPU 情况，修改 lr 学习速率参数，说明如下：

lr=0.005 for 2 GPUs * 2 imgs/gpulr=0.01 for 4 GPUs * 2 imgs/gpulr=0.02 for 8 GPUs and 2 img/gpu (batch size = 8*2 = 16), DEFAULTlr=0.08 for 16 GPUs * 4 imgs/gpu_base_ = [ '../_base_/models/faster_rcnn_r50_fpn.py', '../_base_/datasets/coco_detection.py', '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' ] # optimizer optimizer = dict(type='SGD', lr=0.005, momentum=0.9, weight_decay=0.0001)训练模型cd mmdetection/ # single-gpu training python tools/train.py \ configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py \ --work-dir _train # multi-gpu training bash ./tools/dist_train.sh \ configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py \ 2 \ --work-dir _train

自定义数据集训练模型自定义数据集

这里从 Pascal VOC 数据集拿出 cat 作为自定义数据集来演示，

conda activate open-mmlab # Dataset Management Framework (Datumaro) pip install 'git+https://github.com/openvinotoolkit/datumaro' # pip install tensorflow datum convert --input-format voc --input-path ~/datasets/VOC2012 \ --output-format coco --output-dir ~/datasets/coco_voc2012_cat \ --filter '/item[annotation/label="cat"]'

数据集需要是 COCO 格式，以上直接用 datum 从 VOC 拿出 cat 并转为了 COCO 格式。

准备配置文件

添加 configs/voc_cat/faster_rcnn_r50_fpn_1x_voc_cat.py 配置文件，内容如下：

# The new config inherits a base config to highlight the necessary modification _base_ = [ '../_base_/models/faster_rcnn_r50_fpn.py', '../_base_/datasets/coco_detection.py', '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' ] # We also need to change the num_classes in head to match the dataset's annotation model = dict( roi_head=dict( bbox_head=dict(num_classes=1))) # Modify dataset related settings dataset_type = 'COCODataset' classes = ('cat',) data_root = '/home/john/datasets/' data = dict( train=dict( img_prefix=data_root + 'VOC2012/JPEGImages/', classes=classes, ann_file=data_root + 'coco_voc2012_cat/annotations/instances_train.json'), val=dict( img_prefix=data_root + 'VOC2012/JPEGImages/', classes=classes, ann_file=data_root + 'coco_voc2012_cat/annotations/instances_val.json'), test=dict( img_prefix=data_root + 'VOC2012/JPEGImages/', classes=classes, ann_file=data_root + 'coco_voc2012_cat/annotations/instances_val.json')) evaluation = dict(interval=100) # Modify schedule related settings optimizer = dict(type='SGD', lr=0.005, momentum=0.9, weight_decay=0.0001) total_epochs = 10000 # Modify runtime related settings checkpoint_config = dict(interval=10) # We can use the pre-trained model to obtain higher performance # load_from = 'checkpoints/*.pth'model 配置 num_classes=1 为类别数量dataset 配置为准备的自定义数据集schedule 配置训练的 lr 及迭代轮次 total_epochsruntime 可配置 checkpoint 间隔多少存一个。默认 1 epoch 1 个，空间不够用

配置可对照 __base__ 的内容覆盖修改，更多说明见官方文档。

训练模型# single-gpu training python tools/train.py \ configs/voc_cat/faster_rcnn_r50_fpn_1x_voc_cat.py \ --work-dir _train_voc_cat # multi-gpu training bash ./tools/dist_train.sh \ configs/voc_cat/faster_rcnn_r50_fpn_1x_voc_cat.py \ 2 \ --work-dir _train_voc_cat

断点恢复时，

bash ./tools/dist_train.sh \ configs/voc_cat/faster_rcnn_r50_fpn_1x_voc_cat.py \ 2 \ --work-dir _train_voc_cat \ --resume-from _train_voc_cat/epoch_100.pth

如发生 ModuleNotFoundError: No module named 'pycocotools' 错误，这样修正：

pip uninstall pycocotools mmpycocotools pip install mmpycocotools查看训练 losspip install seaborn python tools/analyze_logs.py plot_curve \ _train_voc_cat/*.log.json \ --keys loss_cls loss_bbox \ --legend loss_cls loss_bbox

可用 keys 见 log.json 记录。

测试模型# single-gpu testing python tools/test.py \ configs/voc_cat/faster_rcnn_r50_fpn_1x_voc_cat.py \ _train_voc_cat/latest.pth \ --out results.pkl \ --eval bbox \ --show # multi-gpu testing bash tools/dist_test.sh \ configs/voc_cat/faster_rcnn_r50_fpn_1x_voc_cat.py \ _train_voc_cat/latest.pth \ 2 \ --out results.pkl \ --eval bbox GoCoding 个人实践的经验分享，可关注公众号！

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