58 lines
1.7 KiB
Python
58 lines
1.7 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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checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-tiny_3rdparty_32xb128-noema_in1k_20220301-795e9634.pth' # noqa
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model = dict(
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data_preprocessor=data_preprocessor,
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backbone=dict(
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type='mmcls.ConvNeXt',
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arch='tiny',
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out_indices=[0, 1, 2, 3],
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drop_path_rate=0.4,
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layer_scale_init_value=1.0,
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gap_before_final_norm=False,
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init_cfg=dict(
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type='Pretrained', checkpoint=checkpoint_file,
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prefix='backbone.')),
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decode_head=dict(
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in_channels=[96, 192, 384, 768],
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num_classes=150,
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),
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auxiliary_head=dict(in_channels=384, 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': 6
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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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