55 lines
1.6 KiB
Python
55 lines
1.6 KiB
Python
_base_ = [
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'../_base_/models/upernet_mae.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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pretrained='./pretrain/mae_pretrain_vit_base_mmcls.pth',
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backbone=dict(
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type='MAE',
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img_size=(512, 512),
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patch_size=16,
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embed_dims=768,
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num_layers=12,
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num_heads=12,
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mlp_ratio=4,
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init_values=1.0,
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drop_path_rate=0.1,
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out_indices=[3, 5, 7, 11]),
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neck=dict(embed_dim=768, rescales=[4, 2, 1, 0.5]),
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decode_head=dict(
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in_channels=[768, 768, 768, 768], num_classes=150, channels=768),
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auxiliary_head=dict(in_channels=768, num_classes=150),
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test_cfg=dict(mode='slide', crop_size=(512, 512), stride=(341, 341)))
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optim_wrapper = dict(
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_delete_=True,
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type='OptimWrapper',
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optimizer=dict(
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type='AdamW', lr=1e-4, betas=(0.9, 0.999), weight_decay=0.05),
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paramwise_cfg=dict(num_layers=12, layer_decay_rate=0.65),
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constructor='LayerDecayOptimizerConstructor')
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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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eta_min=0.0,
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power=1.0,
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begin=1500,
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end=160000,
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by_epoch=False,
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)
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]
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# mixed precision
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fp16 = dict(loss_scale='dynamic')
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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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