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_base_ = 'knet-s3_swin-t_upernet_8xb2-adamw-80k_ade20k-512x512.py'
checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_large_patch4_window7_224_22k_20220308-d5bdebaf.pth' # noqa
# model settings
model = dict(
pretrained=checkpoint_file,
backbone=dict(
embed_dims=192,
depths=[2, 2, 18, 2],
num_heads=[6, 12, 24, 48],
window_size=7,
use_abs_pos_embed=False,
drop_path_rate=0.3,
patch_norm=True),
decode_head=dict(
kernel_generate_head=dict(in_channels=[192, 384, 768, 1536])),
auxiliary_head=dict(in_channels=768))
# In K-Net implementation we use batch size 2 per GPU as default
train_dataloader = dict(batch_size=2, num_workers=2)
val_dataloader = dict(batch_size=1, num_workers=4)
test_dataloader = val_dataloader
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