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_base_ = [ |
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'../_base_/models/san_vit-b16.py', |
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'../_base_/datasets/pascal_voc12_aug.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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|
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metainfo = dict( |
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classes=('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', |
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'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', |
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'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor'), |
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palette=[[128, 0, 0], [0, 128, 0], [128, 128, 0], [0, 0, 128], |
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[128, 0, 128], [0, 128, 128], [128, 128, 128], [64, 0, 0], |
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[192, 0, 0], [64, 128, 0], [192, 128, 0], [64, 0, 128], |
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[192, 0, 128], [64, 128, 128], [192, 128, 128], [0, 64, 0], |
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[128, 64, 0], [0, 192, 0], [128, 192, 0], [0, 64, 128]]) |
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test_pipeline = [ |
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dict(type='LoadImageFromFile'), |
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dict(type='ResizeShortestEdge', scale=crop_size, max_size=2560), |
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dict(type='LoadAnnotations'), |
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dict(type='PackSegInputs') |
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] |
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|
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train_dataloader = dict(batch_size=2) |
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val_dataloader = dict( |
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batch_size=1, dataset=dict(metainfo=metainfo, pipeline=test_pipeline)) |
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test_dataloader = val_dataloader |
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|
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data_preprocessor = dict( |
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mean=[122.7709, 116.7460, 104.0937], |
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std=[68.5005, 66.6322, 70.3232], |
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size_divisor=640, |
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test_cfg=dict(size_divisor=32)) |
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model = dict( |
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data_preprocessor=data_preprocessor, |
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pretrained='pretrain/vit_base_patch16_224.pth', |
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text_encoder=dict(dataset_name='voc'), |
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decode_head=dict(num_classes=20)) |
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|
|
|
|
|
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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=0.00006, betas=(0.9, 0.999), weight_decay=0.01), |
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paramwise_cfg=dict( |
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custom_keys={ |
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'pos_embed': dict(decay_mult=0.), |
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'cls_token': dict(decay_mult=0.), |
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'norm': dict(decay_mult=0.) |
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})) |
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|
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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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|
