vit-adapter-s-3x.py

# Copyright (c) Shanghai AI Lab. All rights reserved.
_base_ = [
    '../_base_/models/mask_rcnn_r50_fpn.py',
    '../_base_/datasets/coco_instance.py',
    '../_base_/schedules/schedule_3x.py',
    '../_base_/default_runtime.py'
]
# pretrained = 'https://dl.fbaipublicfiles.com/deit/deit_small_patch16_224-cd65a155.pth'
pretrained = 'pretrained/deit_small_patch16_224-cd65a155.pth'
model = dict(
    backbone=dict(
        _delete_=True,
        type='ViTAdapter',
        patch_size=16,
        embed_dim=384,
        depth=12,
        num_heads=6,
        mlp_ratio=4,
        drop_path_rate=0.2,
        layer_scale=False,
        conv_inplane=64,
        n_points=4,
        deform_num_heads=6,
        cffn_ratio=0.25,
        deform_ratio=1.0,
        interaction_indexes=[[0, 2], [3, 5], [6, 8], [9, 11]],
        window_attn=[False] * 12,
        window_size=[None] * 12,
        pretrained=pretrained),
    neck=dict(
        type='FPN',
        in_channels=[384, 384, 384, 384],
        out_channels=256,
        num_outs=5))
# optimizer
img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
# augmentation strategy originates from DETR / Sparse RCNN
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
    dict(type='RandomFlip', flip_ratio=0.5),
    dict(type='AutoAugment',
         policies=[
             [
                 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)],
                      multiscale_mode='value',
                      keep_ratio=True)
             ],
             [
                 dict(type='Resize',
                      img_scale=[(400, 1333), (500, 1333), (600, 1333)],
                      multiscale_mode='value',
                      keep_ratio=True),
                 dict(type='RandomCrop',
                      crop_type='absolute_range',
                      crop_size=(384, 600),
                      allow_negative_crop=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)],
                      multiscale_mode='value',
                      override=True,
                      keep_ratio=True)
             ]
         ]),
    dict(type='RandomCrop',
         crop_type='absolute_range',
         crop_size=(1024, 1024),
         allow_negative_crop=True),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='Pad', size_divisor=32),
    dict(type='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
]
data = dict(samples_per_gpu=2,
            workers_per_gpu=1,
            train=dict(pipeline=train_pipeline))
optimizer = dict(
    _delete_=True, type='AdamW', lr=0.0001, weight_decay=0.05,
    paramwise_cfg=dict(
    custom_keys={
        'level_embed': dict(decay_mult=0.),
        'pos_embed': dict(decay_mult=0.),
        'norm': dict(decay_mult=0.),
        'bias': dict(decay_mult=0.)
    }))
optimizer_config = dict(grad_clip=None)
fp16 = dict(loss_scale=dict(init_scale=512))
checkpoint_config = dict(
    interval=1,
    max_keep_ckpts=3,
    save_last=True,
)

work_dir = 'output/vit-adapter-s'
exp_name = 'vit-adapter-s'