_base_ = [
    '../_base_/models/upernet_swin.py', '../_base_/datasets/ade20k.py',
    '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
]
model = dict(
    backbone=dict(
        embed_dim=96,
        depths=[2, 2, 18, 2],
        num_heads=[3, 6, 12, 24],
        window_size=7,
        ape=False,
        drop_path_rate=0.3,
        patch_norm=True,
        use_checkpoint=False
    ),
    decode_head=dict(
        in_channels=[96, 192, 384, 768],
        num_classes=150
    ),
    auxiliary_head=dict(
        in_channels=384,
        num_classes=150
    ))

# AdamW optimizer, no weight decay for position embedding & layer norm in backbone
optimizer = dict(_delete_=True, type='AdamW', lr=0.00006, betas=(0.9, 0.999), weight_decay=0.01,
                 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.)}))

lr_config = dict(_delete_=True, policy='poly',
                 warmup='linear',
                 warmup_iters=1500,
                 warmup_ratio=1e-6,
                 power=1.0, min_lr=0.0, by_epoch=False)

# By default, models are trained on 8 GPUs with 2 images per GPU
data=dict(samples_per_gpu=2)

f377eeb3d3bb48d3bc6b8a7a8ad6a0ef27de69a5
<mask token>

_base_ = ['../_base_/models/upernet_swin.py',
    '../_base_/datasets/ade20k.py', '../_base_/default_runtime.py',
    '../_base_/schedules/schedule_160k.py']
model = dict(backbone=dict(embed_dim=96, depths=[2, 2, 18, 2], num_heads=[3,
    6, 12, 24], window_size=7, ape=False, drop_path_rate=0.3, patch_norm=
    True, use_checkpoint=False), decode_head=dict(in_channels=[96, 192, 384,
    768], num_classes=150), auxiliary_head=dict(in_channels=384,
    num_classes=150))
optimizer = dict(_delete_=True, type='AdamW', lr=6e-05, betas=(0.9, 0.999),
    weight_decay=0.01, paramwise_cfg=dict(custom_keys={'absolute_pos_embed':
    dict(decay_mult=0.0), 'relative_position_bias_table': dict(decay_mult=
    0.0), 'norm': dict(decay_mult=0.0)}))
lr_config = dict(_delete_=True, policy='poly', warmup='linear',
    warmup_iters=1500, warmup_ratio=1e-06, power=1.0, min_lr=0.0, by_epoch=
    False)
data = dict(samples_per_gpu=2)

_base_ = [
    '../_base_/models/upernet_swin.py', '../_base_/datasets/ade20k.py',
    '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
]
model = dict(
    backbone=dict(
        embed_dim=96,
        depths=[2, 2, 18, 2],
        num_heads=[3, 6, 12, 24],
        window_size=7,
        ape=False,
        drop_path_rate=0.3,
        patch_norm=True,
        use_checkpoint=False
    ),
    decode_head=dict(
        in_channels=[96, 192, 384, 768],
        num_classes=150
    ),
    auxiliary_head=dict(
        in_channels=384,
        num_classes=150
    ))

# AdamW optimizer, no weight decay for position embedding & layer norm in backbone
optimizer = dict(_delete_=True, type='AdamW', lr=0.00006, betas=(0.9, 0.999), weight_decay=0.01,
                 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.)}))

lr_config = dict(_delete_=True, policy='poly',
                 warmup='linear',
                 warmup_iters=1500,
                 warmup_ratio=1e-6,
                 power=1.0, min_lr=0.0, by_epoch=False)

# By default, models are trained on 8 GPUs with 2 images per GPU
data=dict(samples_per_gpu=2)
