MMDetection 开始

本文将引导快速使用 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：

现有模型进行推断

Faster RCNN

以 R-50-FPN 为例，

Backbone	Style	Lr schd	Mem (GB)	Inf time (fps)	box AP	Config	Download
R-50-FPN	pytorch	1x	4.0	21.4	37.4	config	model \| log

下载 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/gpu
lr=0.01 for 4 GPUs * 2 imgs/gpu
lr=0.02 for 8 GPUs and 2 img/gpu (batch size = 8*2 = 16), DEFAULT
lr=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_epochs
runtime 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 loss

pip 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

环境准备​

基础环境​

开发环境​

现有模型进行推断​

Faster RCNN​

现有模型进行测试​

准备数据集​

测试现有模型​

标准数据集训练模型​

准备数据集​

准备配置文件​

训练模型​

自定义数据集训练模型​

自定义数据集​

准备配置文件​

训练模型​

查看训练 loss​

测试模型​

环境准备

基础环境

开发环境

现有模型进行推断

Faster RCNN

现有模型进行测试

准备数据集

测试现有模型

标准数据集训练模型

准备数据集

准备配置文件

训练模型

自定义数据集训练模型

自定义数据集

准备配置文件

训练模型

查看训练 loss

测试模型