_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)

_base_ = ['./mask2former_swin-b-p4-w12-384_lsj_8x2_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)
