grounding_dino_swin-t_finetune_8xb4_50e_brain_tumor.py

_base_ = '../grounding_dino_swin-t_pretrain_obj365.py'

# https://universe.roboflow.com/roboflow-100/brain-tumor-m2pbp/dataset/2
data_root = 'data/brain_tumor_v2/'
class_name = ('label0', 'label1', 'label2')
label_name = '_annotations.coco.json'

palette = [(220, 20, 60), (255, 0, 0), (0, 0, 142)]

metainfo = dict(classes=class_name, palette=palette)

train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(type='RandomFlip', prob=0.5),
    dict(
        type='RandomChoice',
        transforms=[
            [
                dict(
                    type='RandomChoiceResize',
                    scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333),
                            (608, 1333), (640, 1333), (672, 1333), (704, 1333),
                            (736, 1333), (768, 1333), (800, 1333)],
                    keep_ratio=True)
            ],
            [
                dict(
                    type='RandomChoiceResize',
                    # The radio of all image in train dataset < 7
                    # follow the original implement
                    scales=[(400, 4200), (500, 4200), (600, 4200)],
                    keep_ratio=True),
                dict(
                    type='RandomCrop',
                    crop_type='absolute_range',
                    crop_size=(384, 600),
                    allow_negative_crop=True),
                dict(
                    type='RandomChoiceResize',
                    scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333),
                            (608, 1333), (640, 1333), (672, 1333), (704, 1333),
                            (736, 1333), (768, 1333), (800, 1333)],
                    keep_ratio=True)
            ]
        ]),
    dict(
        type='PackDetInputs',
        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
                   'scale_factor', 'flip', 'flip_direction', 'text',
                   'custom_entities'))
]

train_dataloader = dict(
    sampler=dict(_delete_=True, type='DefaultSampler', shuffle=True),
    batch_sampler=dict(type='AspectRatioBatchSampler'),
    dataset=dict(
        _delete_=True,
        type='RepeatDataset',
        times=10,
        dataset=dict(
            type='CocoDataset',
            data_root=data_root,
            metainfo=metainfo,
            filter_cfg=dict(filter_empty_gt=False, min_size=32),
            pipeline=train_pipeline,
            return_classes=True,
            data_prefix=dict(img='train/'),
            ann_file='train/' + label_name)))

val_dataloader = dict(
    dataset=dict(
        metainfo=metainfo,
        data_root=data_root,
        return_classes=True,
        ann_file='valid/' + label_name,
        data_prefix=dict(img='valid/')))
test_dataloader = val_dataloader

val_evaluator = dict(
    type='CocoMetric',
    ann_file=data_root + 'valid/' + label_name,
    metric='bbox',
    format_only=False)
test_evaluator = val_evaluator

optim_wrapper = dict(
    _delete_=True,
    type='OptimWrapper',
    optimizer=dict(type='AdamW', lr=0.0001, weight_decay=0.0001),
    clip_grad=dict(max_norm=0.1, norm_type=2),
    paramwise_cfg=dict(custom_keys={
        'absolute_pos_embed': dict(decay_mult=0.),
        'backbone': dict(lr_mult=0.1)
    }))

# learning policy
max_epochs = 5
param_scheduler = [
    dict(
        type='MultiStepLR',
        begin=0,
        end=max_epochs,
        by_epoch=True,
        milestones=[4],
        gamma=0.1)
]
train_cfg = dict(max_epochs=max_epochs, val_interval=1)

default_hooks = dict(checkpoint=dict(max_keep_ckpts=1, save_best='auto'))

load_from = 'https://download.openmmlab.com/mmdetection/v3.0/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det_20231204_095047-b448804b.pth'  # noqa