33 lines
1.1 KiB
Python
33 lines
1.1 KiB
Python
_base_ = [
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'../_base_/models/segmenter_vit-b16_mask.py',
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'../_base_/datasets/ade20k_640x640.py', '../_base_/default_runtime.py',
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'../_base_/schedules/schedule_160k.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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checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segmenter/vit_large_p16_384_20220308-d4efb41d.pth' # noqa
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model = dict(
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data_preprocessor=data_preprocessor,
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pretrained=checkpoint,
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backbone=dict(
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type='VisionTransformer',
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img_size=(640, 640),
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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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decode_head=dict(
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type='SegmenterMaskTransformerHead',
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in_channels=1024,
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channels=1024,
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num_heads=16,
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embed_dims=1024),
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test_cfg=dict(mode='slide', crop_size=(640, 640), stride=(608, 608)))
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optimizer = dict(lr=0.001, weight_decay=0.0)
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optim_wrapper = dict(type='OptimWrapper', optimizer=optimizer)
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train_dataloader = dict(
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# num_gpus: 8 -> batch_size: 8
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batch_size=1)
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val_dataloader = dict(batch_size=1)
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