maskformer_swin-l-p4-w12_64xb1-ms-300e_coco.py

_base_ = './maskformer_r50_ms-16xb1-75e_coco.py'

pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22k.pth'  # noqa
depths = [2, 2, 18, 2]
model = dict(
    backbone=dict(
        _delete_=True,
        type='SwinTransformer',
        pretrain_img_size=384,
        embed_dims=192,
        patch_size=4,
        window_size=12,
        mlp_ratio=4,
        depths=depths,
        num_heads=[6, 12, 24, 48],
        qkv_bias=True,
        qk_scale=None,
        drop_rate=0.,
        attn_drop_rate=0.,
        drop_path_rate=0.3,
        patch_norm=True,
        out_indices=(0, 1, 2, 3),
        with_cp=False,
        convert_weights=True,
        init_cfg=dict(type='Pretrained', checkpoint=pretrained)),
    panoptic_head=dict(
        in_channels=[192, 384, 768, 1536],  # pass to pixel_decoder inside
        pixel_decoder=dict(
            _delete_=True,
            type='PixelDecoder',
            norm_cfg=dict(type='GN', num_groups=32),
            act_cfg=dict(type='ReLU')),
        enforce_decoder_input_project=True))

# optimizer

# weight_decay = 0.01
# norm_weight_decay = 0.0
# embed_weight_decay = 0.0
embed_multi = dict(lr_mult=1.0, decay_mult=0.0)
norm_multi = dict(lr_mult=1.0, decay_mult=0.0)
custom_keys = {
    'norm': norm_multi,
    'absolute_pos_embed': embed_multi,
    'relative_position_bias_table': embed_multi,
    'query_embed': embed_multi
}

optim_wrapper = dict(
    optimizer=dict(lr=6e-5, weight_decay=0.01),
    paramwise_cfg=dict(custom_keys=custom_keys, norm_decay_mult=0.0))

max_epochs = 300

# learning rate
param_scheduler = [
    dict(
        type='LinearLR', start_factor=1e-6, by_epoch=False, begin=0, end=1500),
    dict(
        type='MultiStepLR',
        begin=0,
        end=max_epochs,
        by_epoch=True,
        milestones=[250],
        gamma=0.1)
]

train_cfg = dict(max_epochs=max_epochs)

# NOTE: `auto_scale_lr` is for automatically scaling LR,
# USER SHOULD NOT CHANGE ITS VALUES.
# base_batch_size = (64 GPUs) x (1 samples per GPU)
auto_scale_lr = dict(base_batch_size=64)