62 lines
2.1 KiB
Python
62 lines
2.1 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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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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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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img_norm_cfg = dict(
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mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True)
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crop_size = (640, 640)
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train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='LoadAnnotations', reduce_zero_label=True),
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dict(type='Resize', img_scale=(2048, 640), ratio_range=(0.5, 2.0)),
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dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
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dict(type='RandomFlip', prob=0.5),
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dict(type='PhotoMetricDistortion'),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
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dict(type='DefaultFormatBundle'),
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dict(type='Collect', keys=['img', 'gt_semantic_seg'])
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]
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test_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(
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type='MultiScaleFlipAug',
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img_scale=(2048, 640),
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# img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
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flip=False,
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transforms=[
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dict(type='Resize', keep_ratio=True),
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dict(type='RandomFlip'),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='ImageToTensor', keys=['img']),
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dict(type='Collect', keys=['img'])
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])
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]
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data = dict(
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# num_gpus: 8 -> batch_size: 8
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samples_per_gpu=1,
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train=dict(pipeline=train_pipeline),
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val=dict(pipeline=test_pipeline),
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test=dict(pipeline=test_pipeline))
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