51 lines
1.5 KiB
Python
51 lines
1.5 KiB
Python
_base_ = [
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'../_base_/models/upernet_beit.py', '../_base_/datasets/ade20k_640x640.py',
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'../_base_/default_runtime.py', '../_base_/schedules/schedule_320k.py'
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]
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crop_size = (640, 640)
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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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pretrained='pretrain/beit_large_patch16_224_pt22k_ft22k.pth',
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backbone=dict(
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type='BEiT',
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embed_dims=1024,
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num_layers=24,
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num_heads=16,
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mlp_ratio=4,
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qv_bias=True,
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init_values=1e-6,
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drop_path_rate=0.2,
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out_indices=[7, 11, 15, 23]),
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neck=dict(embed_dim=1024, rescales=[4, 2, 1, 0.5]),
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decode_head=dict(
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in_channels=[1024, 1024, 1024, 1024], num_classes=150, channels=1024),
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auxiliary_head=dict(in_channels=1024, num_classes=150),
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test_cfg=dict(mode='slide', crop_size=(640, 640), stride=(426, 426)))
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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=2e-5, betas=(0.9, 0.999), weight_decay=0.05),
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constructor='LayerDecayOptimizerConstructor',
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paramwise_cfg=dict(num_layers=24, layer_decay_rate=0.95),
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accumulative_counts=2)
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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=3000),
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dict(
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type='PolyLR',
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power=1.0,
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begin=3000,
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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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train_dataloader = dict(batch_size=1)
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val_dataloader = dict(batch_size=1)
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test_dataloader = val_dataloader
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