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- _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)
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