Spaces:
Runtime error
Runtime error
File size: 1,946 Bytes
412c852 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 |
_base_ = 'knet-s3_r50-d8_upernet_8xb2-adamw-80k_ade20k-512x512.py'
checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220308-f41b89d3.pth' # noqa
# model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
num_stages = 3
conv_kernel_size = 1
model = dict(
type='EncoderDecoder',
pretrained=checkpoint_file,
backbone=dict(
_delete_=True,
type='SwinTransformer',
embed_dims=96,
depths=[2, 2, 6, 2],
num_heads=[3, 6, 12, 24],
window_size=7,
mlp_ratio=4,
qkv_bias=True,
qk_scale=None,
drop_rate=0.,
attn_drop_rate=0.,
drop_path_rate=0.3,
use_abs_pos_embed=False,
patch_norm=True,
out_indices=(0, 1, 2, 3)),
decode_head=dict(
kernel_generate_head=dict(in_channels=[96, 192, 384, 768])),
auxiliary_head=dict(in_channels=384))
optim_wrapper = dict(
_delete_=True,
type='OptimWrapper',
# modify learning rate following the official implementation of Swin Transformer # noqa
optimizer=dict(
type='AdamW', lr=0.00006, betas=(0.9, 0.999), weight_decay=0.0005),
paramwise_cfg=dict(
custom_keys={
'absolute_pos_embed': dict(decay_mult=0.),
'relative_position_bias_table': dict(decay_mult=0.),
'norm': dict(decay_mult=0.)
}),
clip_grad=dict(max_norm=1, norm_type=2))
# learning policy
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0,
end=1000),
dict(
type='MultiStepLR',
begin=1000,
end=80000,
milestones=[60000, 72000],
by_epoch=False,
)
]
# 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
|