44 lines
1.3 KiB
Python
44 lines
1.3 KiB
Python
_base_ = [
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'../_base_/models/upernet_convnext.py', '../_base_/datasets/ade20k.py',
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'../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
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]
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crop_size = (512, 512)
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data_preprocessor = dict(size=crop_size)
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model = dict(
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data_preprocessor=data_preprocessor,
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decode_head=dict(in_channels=[128, 256, 512, 1024], num_classes=150),
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auxiliary_head=dict(in_channels=512, num_classes=150),
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test_cfg=dict(mode='slide', crop_size=crop_size, stride=(341, 341)),
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)
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optim_wrapper = dict(
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_delete_=True,
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type='AmpOptimWrapper',
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optimizer=dict(
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type='AdamW', lr=0.0001, betas=(0.9, 0.999), weight_decay=0.05),
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paramwise_cfg={
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'decay_rate': 0.9,
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'decay_type': 'stage_wise',
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'num_layers': 12
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},
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constructor='LearningRateDecayOptimizerConstructor',
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loss_scale='dynamic')
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param_scheduler = [
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dict(
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type='LinearLR', start_factor=1e-6, by_epoch=False, begin=0, end=1500),
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dict(
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type='PolyLR',
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power=1.0,
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begin=1500,
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end=160000,
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eta_min=0.0,
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by_epoch=False,
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)
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]
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# By default, models are trained on 8 GPUs with 2 images per GPU
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train_dataloader = dict(batch_size=2)
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val_dataloader = dict(batch_size=1)
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test_dataloader = val_dataloader
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