MMDetection系列 | 1. MMDetection安装流程与测试

史逸春

2023-12-01

如有错误，恳请指出。

这篇博客记录安装mmdetection的过程，以及记录对公共数据集进行推理测试与训练的简单使用。

1. 创建openmmlab环境

conda create --name openmmlab python=3.9 -y
conda activate openmmlab

2. 安装pytorch

pip install torch==1.11.0+cu102 torchvision==0.12.0+cu102 torchaudio==0.11.0 --extra-index-url https://download.pytorch.org/whl/cu102

在官方资料中写者，10.2版本的cuda有更好的兼容性，所以选择安装cuda10.2版本

对于基于 Ampere 的 NVIDIA GPU，例如 GeForce 30 系列和 NVIDIA A100，CUDA 11 是必须的。
对于较旧的 NVIDIA GPU，CUDA 11 向后兼容，但 CUDA 10.2 提供更好的兼容性并且更轻量级。

3. 安装mmcv-full

pip install -U openmim
pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/cu102/torch1.11.0/index.html

# 我使用的安装命令：
pip install mmcv-full==1.5.3 -f https://download.openmmlab.com/mmcv/dist/cu113/torch1.11.0/index.html

这里的cuda101与torch1.11.0需要与安装pytorch时匹配

4. 将mmdetection源码下载

方法1：git clone下载

git clone https://github.com/open-mmlab/mmdetection.git
cd mmdetection
pip install -v -e .

方法2：本地直接下载再丢到服务器上

5. 安装mmdet

pip install mmdet==2.24.0

这里需要注意，本地下载的mmdet需要与pip install安装的mmdet版本一直，比如我本地下载的源码的2.24.0版本的，那么这里pip install就需要指定安装mmdet==2.24.0版本

6. 推理测试

mim download mmdet --config yolov3_mobilenetv2_320_300e_coco --dest .

# 输出：
(openmmlab) [fs@localhost mmdetection-2.24.0]$ mim download mmdet
--config yolov3_mobilenetv2_320_300e_coco --dest . /home/fs/anaconda3/envs/openmmlab/lib/python3.9/site-packages/_distutils_hack/__init__.py:30: UserWarning: Setuptools is replacing distutils.   warnings.warn("Setuptools is replacing distutils.") processing yolov3_mobilenetv2_320_300e_coco... downloading ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 14.5/14.5 MiB 9.9 MB/s eta 0:00:00 Successfully downloaded yolov3_mobilenetv2_320_300e_coco_20210719_215349-d18dff72.pth to /home/fs/LLC/Detection/mmdetection/mmdetection-2.24.0 Successfully dumped yolov3_mobilenetv2_320_300e_coco.py to /home/fs/LLC/Detection/mmdetection/mmdetection-2.24.0

python demo/image_demo.py demo/demo.jpg yolov3_mobilenetv2_320_300e_coco.py yolov3_mobilenetv2_320_300e_coco_20210719_215349-d18dff72.pth --device cpu --out-file result.jpg

# 输出：
(openmmlab) [fs@localhost mmdetection-2.24.0]$ python demo/image_demo.py demo/demo.jpg yolov3_mobilenetv2_320_300e_coco.py yolov3_mobilenetv2_320_300e_coco_20210719_215349-d18dff72.pth --device cpu --out-file result.jpg
load checkpoint from local path: yolov3_mobilenetv2_320_300e_coco_20210719_215349-d18dff72.pth
/home/fs/anaconda3/envs/openmmlab/lib/python3.9/site-packages/mmdet/models/dense_heads/yolo_head.py:269: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at  ../torch/csrc/utils/tensor_new.cpp:210.)
  flatten_bboxes /= flatten_bboxes.new_tensor(

此时，就可以在目录下看见result.jpg图像，至此mmdet安装成功。如果推理过程中出现任何形如 undefined symbol 或者 ImportError 的错误，都是因为没有正确安装mmdet。

需要注意的就是mmcv-full需要与torch相匹配，然后pip install mmdet需要与下载的源码相匹配。

更多测试例子（具体的参数配置可以查看相应的demo代码）：

单个图像执行推理测试

python demo/image_demo.py demo/demo.jpg configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py checkpoints/faster_rcnn/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth --device cpu

单个视频执行推理测试

python demo/video_demo.py demo/demo.mp4 configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py checkpoints/faster_rcnn/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth --device cpu --out result.mp4

对网络摄像头现场演示

python demo/webcam_demo.py configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py checkpoints/faster_rcnn/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth --device cpu

7. 测试模型数据集

export CUDA_VISIBLE_DEVICES='-1'
python tools/test.py configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712.py checkpoints/faster_rcnn/faster_rcnn_r50_fpn_1x_voc0712_20220320_192712-54bef0f3.pth --eval mAP

# 输出：
load checkpoint from local path: checkpoints/faster_rcnn/faster_rcnn_r50_fpn_1x_voc0712_20220320_192712-54bef0f3.pth
[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 4952/4952, 0.9 task/s, elapsed: 5806s, ETA:     0s
---------------iou_thr: 0.5---------------

+-------------+------+-------+--------+-------+
| class       | gts  | dets  | recall | ap    |
+-------------+------+-------+--------+-------+
| aeroplane   | 285  | 806   | 0.944  | 0.857 |
| bicycle     | 337  | 1050  | 0.938  | 0.872 |
| bird        | 459  | 1054  | 0.891  | 0.797 |
| boat        | 263  | 1315  | 0.875  | 0.709 |
| bottle      | 469  | 1421  | 0.802  | 0.700 |
| bus         | 213  | 694   | 0.939  | 0.859 |
| car         | 1201 | 3284  | 0.957  | 0.878 |
| cat         | 358  | 881   | 0.969  | 0.892 |
| chair       | 756  | 4236  | 0.868  | 0.649 |
| cow         | 244  | 883   | 0.963  | 0.852 |
| diningtable | 206  | 2077  | 0.942  | 0.743 |
| dog         | 489  | 1294  | 0.982  | 0.878 |
| horse       | 348  | 1034  | 0.945  | 0.860 |
| motorbike   | 325  | 990   | 0.932  | 0.849 |
| person      | 4528 | 11747 | 0.940  | 0.862 |
| pottedplant | 480  | 1984  | 0.788  | 0.554 |
| sheep       | 242  | 716   | 0.938  | 0.822 |
| sofa        | 239  | 1344  | 0.950  | 0.783 |
| train       | 282  | 994   | 0.936  | 0.848 |
| tvmonitor   | 308  | 975   | 0.909  | 0.808 |
+-------------+------+-------+--------+-------+
| mAP         |      |       |        | 0.804 |
+-------------+------+-------+--------+-------+
OrderedDict([('mAP', 0.8035303354263306), ('AP50', 0.804)])

但是，可以现在只验证了VOC2007的数据集部分，没有验证VOC2012的数据集部分，要想两个部分都进行验证，需要重新配置数据集的脚本文件。不过，在VOC2012原本的数据集中本身的Main目录下就没有test.txt，这里就以val.txt来替换，更改如下：

voc0712_val.py
…

data = dict(
    samples_per_gpu=2,
    workers_per_gpu=2,
    train=dict(
        type='RepeatDataset',
        times=3,
        dataset=dict(
            type=dataset_type,
            ann_file=[
                data_root + 'VOC2007/ImageSets/Main/trainval.txt',
                data_root + 'VOC2012/ImageSets/Main/trainval.txt'
            ],
            img_prefix=[data_root + 'VOC2007/', data_root + 'VOC2012/'],
            pipeline=train_pipeline)),
    val=dict(
        type=dataset_type,
        ann_file=[
            data_root + 'VOC2007/ImageSets/Main/test.txt',
            data_root + 'VOC2012/ImageSets/Main/test.txt',
        ],
        img_prefix=[data_root + 'VOC2007/', data_root + 'VOC2012/'],
        pipeline=test_pipeline),
    test=dict(
        type=dataset_type,
        ann_file=[
            data_root + 'VOC2007/ImageSets/Main/test.txt',
            data_root + 'VOC2012/ImageSets/Main/val.txt',
        ],
        img_prefix=[data_root + 'VOC2007/', data_root + 'VOC2012/'],
        pipeline=test_pipeline))
evaluation = dict(interval=1, metric='mAP')

config配置文件：

_base_ = [
    '../_base_/models/faster_rcnn_r50_fpn.py',
    '../_base_/datasets/voc0712_val.py',        # 只更改了这个voc数据集的配置文件
    '../_base_/default_runtime.py'
]
model = dict(roi_head=dict(bbox_head=dict(num_classes=20)))
# optimizer
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
# learning policy
# actual epoch = 3 * 3 = 9
lr_config = dict(policy='step', step=[3])
# runtime settings
runner = dict(
    type='EpochBasedRunner', max_epochs=4)  # actual epoch = 4 * 3 = 12

输出：

load checkpoint from local path: checkpoints/faster_rcnn/faster_rcnn_r50_fpn_1x_voc0712_20220320_192712-54bef0f3.pth
[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 10775/10775, 38.5 task/s, elapsed: 280s, ETA:     0s
Evaluateing data/VOCdevkit/VOC2007/ImageSets/Main/test.txt with 4952 images now

---------------iou_thr: 0.5---------------

+-------------+------+-------+--------+-------+
| class       | gts  | dets  | recall | ap    |
+-------------+------+-------+--------+-------+
| aeroplane   | 285  | 805   | 0.944  | 0.857 |
| bicycle     | 337  | 1049  | 0.938  | 0.872 |
| bird        | 459  | 1054  | 0.891  | 0.797 |
| boat        | 263  | 1315  | 0.875  | 0.709 |
| bottle      | 469  | 1419  | 0.804  | 0.701 |
| bus         | 213  | 694   | 0.939  | 0.857 |
| car         | 1201 | 3284  | 0.957  | 0.878 |
| cat         | 358  | 882   | 0.969  | 0.892 |
| chair       | 756  | 4232  | 0.868  | 0.648 |
| cow         | 244  | 885   | 0.963  | 0.852 |
| diningtable | 206  | 2078  | 0.942  | 0.743 |
| dog         | 489  | 1293  | 0.982  | 0.878 |
| horse       | 348  | 1033  | 0.945  | 0.860 |
| motorbike   | 325  | 991   | 0.932  | 0.849 |
| person      | 4528 | 11745 | 0.940  | 0.862 |
| pottedplant | 480  | 1983  | 0.788  | 0.554 |
| sheep       | 242  | 715   | 0.938  | 0.822 |
| sofa        | 239  | 1343  | 0.950  | 0.783 |
| train       | 282  | 997   | 0.936  | 0.848 |
| tvmonitor   | 308  | 975   | 0.909  | 0.808 |
+-------------+------+-------+--------+-------+
| mAP         |      |       |        | 0.804 |
+-------------+------+-------+--------+-------+

Evaluateing data/VOCdevkit/VOC2012/ImageSets/Main/val.txt with 5823 images now

---------------iou_thr: 0.5---------------

+-------------+------+-------+--------+-------+
| class       | gts  | dets  | recall | ap    |
+-------------+------+-------+--------+-------+
| aeroplane   | 433  | 1150  | 0.970  | 0.950 |
| bicycle     | 358  | 1087  | 0.958  | 0.929 |
| bird        | 559  | 1099  | 0.987  | 0.975 |
| boat        | 424  | 1750  | 0.972  | 0.905 |
| bottle      | 630  | 1759  | 0.957  | 0.913 |
| bus         | 301  | 901   | 0.990  | 0.968 |
| car         | 1004 | 2780  | 0.976  | 0.953 |
| cat         | 612  | 1246  | 0.993  | 0.985 |
| chair       | 1176 | 5215  | 0.946  | 0.861 |
| cow         | 298  | 999   | 0.987  | 0.957 |
| diningtable | 305  | 2261  | 0.885  | 0.734 |
| dog         | 759  | 1649  | 0.997  | 0.989 |
| horse       | 360  | 963   | 0.981  | 0.965 |
| motorbike   | 356  | 1094  | 0.986  | 0.959 |
| person      | 4372 | 11152 | 0.978  | 0.955 |
| pottedplant | 489  | 1977  | 0.957  | 0.869 |
| sheep       | 413  | 1010  | 0.964  | 0.950 |
| sofa        | 285  | 1353  | 0.951  | 0.812 |
| train       | 315  | 997   | 0.987  | 0.967 |
| tvmonitor   | 392  | 1123  | 0.954  | 0.926 |
+-------------+------+-------+--------+-------+
| mAP         |      |       |        | 0.926 |
+-------------+------+-------+--------+-------+
{'0_mAP': 0.8035119771957397, '0_AP50': 0.804, '1_mAP': 0.9260713458061218, '1_AP50': 0.926}

8. 训练现有数据集

设置epoch：

# runtime settings = 训练重复次数times x 最大批次次数max_epochs
runner = dict(
    type='EpochBasedRunner', max_epochs=10)  # actual epoch = 4 * 3 = 12

设置打印次数：

log_config = dict(
    interval=500,    # 每50个迭代就打印一次训练信息
    hooks=[
        dict(type='TextLoggerHook'),
        # dict(type='TensorboardLoggerHook')
    ])

设置batch_size：

data = dict(
    samples_per_gpu=16,   # 每个gpu分配16张图像
    workers_per_gpu=2,    # 分配两个gpu
    train=dict(
        type='RepeatDataset',
        times=3,    # 重复训练3次
        ...
    )
)

设置完成后，直接就可以进行python tools/train.py ，进行训练。可以自行进行相关的配置，比如--gpu-id等等。

参考资料：

官方的文档手册：https://mmdetection.readthedocs.io/en/latest/

MMDetection系列 | 1. MMDetection安装流程与测试

1. 创建openmmlab环境

2. 安装pytorch

3. 安装mmcv-full

4. 将mmdetection源码下载

5. 安装mmdet

6. 推理测试

7. 测试模型数据集

8. 训练现有数据集

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