37 lines
1.1 KiB
Python
37 lines
1.1 KiB
Python
_base_ = [
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'../_base_/models/segmenter_vit-b16_mask.py',
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'../_base_/datasets/ade20k.py', '../_base_/default_runtime.py',
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'../_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 = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segmenter/vit_small_p16_384_20220308-410f6037.pth' # noqa
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backbone_norm_cfg = dict(type='LN', eps=1e-6, requires_grad=True)
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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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img_size=(512, 512),
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embed_dims=384,
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num_heads=6,
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),
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decode_head=dict(
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type='SegmenterMaskTransformerHead',
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in_channels=384,
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channels=384,
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num_classes=150,
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num_layers=2,
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num_heads=6,
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embed_dims=384,
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dropout_ratio=0.0,
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loss_decode=dict(
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type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)))
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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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