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- _base_ = ['./mask2former_swin-b-p4-w12-384_8xb2-lsj-50e_coco-panoptic.py']
- pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22k.pth' # noqa
- model = dict(
- backbone=dict(
- embed_dims=192,
- num_heads=[6, 12, 24, 48],
- init_cfg=dict(type='Pretrained', checkpoint=pretrained)),
- panoptic_head=dict(num_queries=200, in_channels=[192, 384, 768, 1536]))
- train_dataloader = dict(batch_size=1, num_workers=1)
- # learning policy
- max_iters = 737500
- param_scheduler = dict(end=max_iters, milestones=[655556, 710184])
- # Before 735001th iteration, we do evaluation every 5000 iterations.
- # After 735000th iteration, we do evaluation every 737500 iterations,
- # which means that we do evaluation at the end of training.'
- interval = 5000
- dynamic_intervals = [(max_iters // interval * interval + 1, max_iters)]
- train_cfg = dict(
- max_iters=max_iters,
- val_interval=interval,
- dynamic_intervals=dynamic_intervals)
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