reppoitsv2_swin_tiny_patch4_window7_mstrain_480_960_giou_gfocal_bifpn_adamw_3x_coco.py

_base_ = [
    '../_base_/models/reppointsv2_swin_bifpn.py',
    '../_base_/datasets/coco_detection.py',
    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
    backbone=dict(
        embed_dim=96,
        depths=[2, 2, 6, 2],
        num_heads=[3, 6, 12, 24],
        window_size=7,
        ape=False,
        drop_path_rate=0.2,
        patch_norm=True,
        use_checkpoint=False
    ),
    neck=dict(in_channels=[192, 384, 768]),
)

# multi-scale training
img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)

train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(type='Resize',
         img_scale=[(480, 1333), (512, 1333), (544, 1333), (576, 1333),
                    (608, 1333), (640, 1333), (672, 1333), (704, 1333),
                    (736, 1333), (768, 1333), (800, 1333), (832, 1333),
                    (864, 1333), (896, 1333), (928, 1333), (960, 1333)],
         multiscale_mode='value',
         keep_ratio=True),
    dict(type='RandomFlip', flip_ratio=0.5),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='Pad', size_divisor=32),
    dict(type='LoadRPDV2Annotations'),
    dict(type='RPDV2FormatBundle'),
    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_sem_map', 'gt_sem_weights']),
]

data = dict(train=dict(pipeline=train_pipeline))

optimizer = dict(_delete_=True, type='AdamW', lr=0.0001, betas=(0.9, 0.999), weight_decay=0.05,
                paramwise_cfg=dict(custom_keys={'absolute_pos_embed': dict(decay_mult=0.),
                                                'relative_position_bias_table': dict(decay_mult=0.),
                                                'norm': dict(decay_mult=0.)}))
lr_config = dict(step=[27, 33])
runner = dict(type='EpochBasedRunnerAmp', max_epochs=36)

fp16 = None
optimizer_config = dict(
    type="DistOptimizerHook",
    update_interval=1,
    grad_clip=None,
    coalesce=True,
    bucket_size_mb=-1,
    use_fp16=True,
)

evaluation = dict(metric=['bbox'])