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- _base_ = [
- '../_base_/models/upernet_convnext.py', '../_base_/datasets/ade20k.py',
- '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
- ]
- crop_size = (512, 512)
- checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-small_3rdparty_32xb128-noema_in1k_20220301-303e75e3.pth' # noqa
- model = dict(
- backbone=dict(
- type='mmcls.ConvNeXt',
- arch='small',
- out_indices=[0, 1, 2, 3],
- drop_path_rate=0.3,
- layer_scale_init_value=1.0,
- gap_before_final_norm=False,
- init_cfg=dict(
- type='Pretrained', checkpoint=checkpoint_file,
- prefix='backbone.')),
- decode_head=dict(
- in_channels=[96, 192, 384, 768],
- num_classes=150,
- ),
- auxiliary_head=dict(in_channels=384, num_classes=150),
- test_cfg=dict(mode='slide', crop_size=crop_size, stride=(341, 341)),
- )
-
- optimizer = dict(
- constructor='LearningRateDecayOptimizerConstructor',
- _delete_=True,
- type='AdamW',
- lr=0.0001,
- betas=(0.9, 0.999),
- weight_decay=0.05,
- paramwise_cfg={
- 'decay_rate': 0.9,
- 'decay_type': 'stage_wise',
- 'num_layers': 12
- })
-
- 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)
- # fp16 settings
- optimizer_config = dict(type='Fp16OptimizerHook', loss_scale='dynamic')
- # fp16 placeholder
- fp16 = dict()
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