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- _base_ = [
- '../_base_/models/segmenter_vit-b16_mask.py',
- '../_base_/datasets/ade20k_640x640.py', '../_base_/default_runtime.py',
- '../_base_/schedules/schedule_160k.py'
- ]
- checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segmenter/vit_large_p16_384_20220308-d4efb41d.pth' # noqa
-
- model = dict(
- pretrained=checkpoint,
- backbone=dict(
- type='VisionTransformer',
- img_size=(640, 640),
- embed_dims=1024,
- num_layers=24,
- num_heads=16),
- decode_head=dict(
- type='SegmenterMaskTransformerHead',
- in_channels=1024,
- channels=1024,
- num_heads=16,
- embed_dims=1024),
- test_cfg=dict(mode='slide', crop_size=(640, 640), stride=(608, 608)))
-
- optimizer = dict(lr=0.001, weight_decay=0.0)
-
- img_norm_cfg = dict(
- mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True)
- crop_size = (640, 640)
- train_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(type='LoadAnnotations', reduce_zero_label=True),
- dict(type='Resize', img_scale=(2560, 640), ratio_range=(0.5, 2.0)),
- dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
- dict(type='RandomFlip', prob=0.5),
- dict(type='PhotoMetricDistortion'),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
- dict(type='DefaultFormatBundle'),
- dict(type='Collect', keys=['img', 'gt_semantic_seg'])
- ]
- test_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(
- type='MultiScaleFlipAug',
- img_scale=(2560, 640),
- # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
- flip=False,
- transforms=[
- dict(type='Resize', keep_ratio=True),
- dict(type='RandomFlip'),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='ImageToTensor', keys=['img']),
- dict(type='Collect', keys=['img'])
- ])
- ]
- data = dict(
- # num_gpus: 8 -> batch_size: 8
- samples_per_gpu=1,
- train=dict(pipeline=train_pipeline),
- val=dict(pipeline=test_pipeline),
- test=dict(pipeline=test_pipeline))
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