从零开始的目标检测和关键点检测（二）：训练一个Glue的RTMDet模型-Toy模板网

这篇具有很好参考价值的文章主要介绍了从零开始的目标检测和关键点检测（二）：训练一个Glue的RTMDet模型。希望对大家有所帮助。如果存在错误或未考虑完全的地方，请大家不吝赐教，您也可以点击"举报违法"按钮提交疑问。

从零开始的目标检测和关键点检测（一）：用labelme标注数据集

从零开始的目标检测和关键点检测（三）：训练一个Glue的RTMPose模型

在[1]用labelme标注自己的数据集中已经标注好数据集(关键点和检测框)，通过labelme2coco脚本将所有的labelme json文件集成为两个coco格式的json文件，即train_coco.json和val_coco.json。训练一个RTMDet模型，需要重写config文件。

一、config文件解读

1、数据集类型即coco格式的数据集，metainfo是指框的类别，因为这里只有一个glue的类，因此NUM_CLASSES为1，注意metainfo类别名后的逗号，

# 数据集类型及路径
dataset_type = 'CocoDataset'
data_root = 'data/glue_134_Keypoint/'
metainfo = {'classes': ('glue',)}
NUM_CLASSES = len(metainfo['classes'])

2、加载backnbone预训练权重和RTMDet-tiny预训练权重

# RTMDet-tiny
load_from = 'https://download.openmmlab.com/mmdetection/v3.0/rtmdet/rtmdet_tiny_8xb32-300e_coco/rtmdet_tiny_8xb32-300e_coco_20220902_112414-78e30dcc.pth'
backbone_pretrain = 'https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-tiny_imagenet_600e.pth'
deepen_factor = 0.167
widen_factor = 0.375
in_channels = [96, 192, 384]
neck_out_channels = 96
num_csp_blocks = 1
exp_on_reg = False

3、训练参数设置，如epoch、batchsize…

MAX_EPOCHS = 200
TRAIN_BATCH_SIZE = 8
VAL_BATCH_SIZE = 4
stage2_num_epochs = 20
base_lr = 0.004
VAL_INTERVAL = 5  # 每隔多少轮评估保存一次模型权重

4、default_runtime，即默认设置，在config文件夹的default_runtime.py可看到。不同的MM-框架的默认设置不一样(如default_scope = 'mmdet')，可以包含这个.py也可以直接复制过来。

default_scope = 'mmdet'
default_hooks = dict(
    timer=dict(type='IterTimerHook'),
    logger=dict(type='LoggerHook', interval=1),
    param_scheduler=dict(type='ParamSchedulerHook'),
    checkpoint=dict(type='CheckpointHook', interval=10, max_keep_ckpts=2, save_best='coco/bbox_mAP'),
    # auto coco/bbox_mAP_50 coco/bbox_mAP_75 coco/bbox_mAP_s
    sampler_seed=dict(type='DistSamplerSeedHook'),
    visualization=dict(type='DetVisualizationHook'))
env_cfg = dict(
    cudnn_benchmark=False,
    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
    dist_cfg=dict(backend='nccl'))
vis_backends = [dict(type='LocalVisBackend')]
visualizer = dict(
    type='DetLocalVisualizer',
    vis_backends=[dict(type='LocalVisBackend')],
    name='visualizer')
log_processor = dict(type='LogProcessor', window_size=50, by_epoch=True)
log_level = 'INFO'
load_from = None
resume = False

5、训练超参数配置

train_cfg = dict(
    type='EpochBasedTrainLoop',
    max_epochs=MAX_EPOCHS,
    val_interval=VAL_INTERVAL,
    dynamic_intervals=[(MAX_EPOCHS - stage2_num_epochs, 1)])

val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')

# 学习率
param_scheduler = [
    dict(
        type='LinearLR', start_factor=1e-05, by_epoch=False, begin=0,
        end=1000),
    dict(
        type='CosineAnnealingLR',
        eta_min=0.0002,
        begin=150,
        end=300,
        T_max=150,
        by_epoch=True,
        convert_to_iter_based=True)
]

# 优化器
optim_wrapper = dict(
    type='OptimWrapper',
    optimizer=dict(type='AdamW', lr=base_lr, weight_decay=0.05),
    paramwise_cfg=dict(
        norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True))
auto_scale_lr = dict(enable=False, base_batch_size=16)

6、数据处理pipeline，做数据预处理(数据增强)

# DataLoader
backend_args = None
train_pipeline = [
    dict(type='LoadImageFromFile', backend_args=None),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(
        type='CachedMosaic',
        img_scale=(640, 640),
        pad_val=114.0,
        max_cached_images=20,
        random_pop=False),
    dict(
        type='RandomResize',
        scale=(1280, 1280),
        ratio_range=(0.5, 2.0),
        keep_ratio=True),
    dict(type='RandomCrop', crop_size=(640, 640)),
    dict(type='YOLOXHSVRandomAug'),
    dict(type='RandomFlip', prob=0.5),
    dict(type='Pad', size=(640, 640), pad_val=dict(img=(114, 114, 114))),
    dict(
        type='CachedMixUp',
        img_scale=(640, 640),
        ratio_range=(1.0, 1.0),
        max_cached_images=10,
        random_pop=False,
        pad_val=(114, 114, 114),
        prob=0.5),
    dict(type='PackDetInputs')
]
test_pipeline = [
    dict(type='LoadImageFromFile', backend_args=None),
    dict(type='Resize', scale=(640, 640), keep_ratio=True),
    dict(type='Pad', size=(640, 640), pad_val=dict(img=(114, 114, 114))),
    dict(
        type='PackDetInputs',
        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
                   'scale_factor'))
]

7、加载数据和标注并用对应pipeliane做预处理

train_dataloader = dict(
    batch_size=TRAIN_BATCH_SIZE,
    num_workers=4,
    persistent_workers=True,
    sampler=dict(type='DefaultSampler', shuffle=True),
    batch_sampler=None,
    dataset=dict(
        type='CocoDataset',
        data_root=data_root,
        metainfo=metainfo,
        ann_file='train_coco.json',
        data_prefix=dict(img='images/'),
        filter_cfg=dict(filter_empty_gt=True, min_size=32),
        pipeline=train_pipeline,
        backend_args=None),
    pin_memory=True)
val_dataloader = dict(
    batch_size=VAL_BATCH_SIZE,
    num_workers=2,
    persistent_workers=True,
    drop_last=False,
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=dict(
        type='CocoDataset',
        data_root=data_root,
        metainfo=metainfo,
        ann_file='val_coco.json',
        data_prefix=dict(img='images/'),
        test_mode=True,
        pipeline=test_pipeline,
        backend_args=None))
test_dataloader = val_dataloader

8、定义模型结构backbone + neck + head

# 模型结构
model = dict(
    type='RTMDet',
    data_preprocessor=dict(
        type='DetDataPreprocessor',
        mean=[103.53, 116.28, 123.675],
        std=[57.375, 57.12, 58.395],
        bgr_to_rgb=False,
        batch_augments=None),
    backbone=dict(
        type='CSPNeXt',
        arch='P5',
        expand_ratio=0.5,
        deepen_factor=deepen_factor,
        widen_factor=widen_factor,
        channel_attention=True,
        norm_cfg=dict(type='SyncBN'),
        act_cfg=dict(type='SiLU', inplace=True),
        init_cfg=dict(
            type='Pretrained',
            prefix='backbone.',
            checkpoint=backbone_pretrain

        )),
    neck=dict(
        type='CSPNeXtPAFPN',
        in_channels=in_channels,
        out_channels=neck_out_channels,
        num_csp_blocks=num_csp_blocks,
        expand_ratio=0.5,
        norm_cfg=dict(type='SyncBN'),
        act_cfg=dict(type='SiLU', inplace=True)),
    bbox_head=dict(
        type='RTMDetSepBNHead',
        num_classes=NUM_CLASSES,
        in_channels=neck_out_channels,
        stacked_convs=2,
        feat_channels=neck_out_channels,
        anchor_generator=dict(
            type='MlvlPointGenerator', offset=0, strides=[8, 16, 32]),
        bbox_coder=dict(type='DistancePointBBoxCoder'),
        loss_cls=dict(
            type='QualityFocalLoss',
            use_sigmoid=True,
            beta=2.0,
            loss_weight=1.0),
        loss_bbox=dict(type='GIoULoss', loss_weight=2.0),
        with_objectness=False,
        exp_on_reg=exp_on_reg,
        share_conv=True,
        pred_kernel_size=1,
        norm_cfg=dict(type='SyncBN'),
        act_cfg=dict(type='SiLU', inplace=True)),
    train_cfg=dict(
        assigner=dict(type='DynamicSoftLabelAssigner', topk=13),
        allowed_border=-1,
        pos_weight=-1,
        debug=False),
    test_cfg=dict(
        nms_pre=30000,
        min_bbox_size=0,
        score_thr=0.001,
        nms=dict(type='nms', iou_threshold=0.65),
        max_per_img=300))

二、开始训练

1、开始训练

python tools/train.py data/glue_134_Keypoint/rtmdet_tiny_glue.py

训练结果

 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.719
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.483
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.766
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.766
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.766

2、测试一下训练结果

python demo/image_demo.py data/glue_134_Keypoint/test_image/test.png data/glue_134_Keypoint/rtmdet_tiny_glue.py --weights work_dirs/rtmdet_tiny_glue/best_coco_bbox_mAP_epoch_180.pth --device cpu

从零开始的目标检测和关键点检测（二）：训练一个Glue的RTMDet模型,目标检测,python,目标检测