288 lines
10 KiB
Python
288 lines
10 KiB
Python
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rand_increasing_policies = [
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dict(type='AutoContrast'),
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dict(type='Equalize'),
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dict(type='Invert'),
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dict(type='Rotate', magnitude_key='angle', magnitude_range=(0, 30)),
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dict(type='Posterize', magnitude_key='bits', magnitude_range=(4, 0)),
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dict(type='Solarize', magnitude_key='thr', magnitude_range=(256, 0)),
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dict(
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type='SolarizeAdd',
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magnitude_key='magnitude',
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magnitude_range=(0, 110)),
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dict(
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type='ColorTransform',
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magnitude_key='magnitude',
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magnitude_range=(0, 0.9)),
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dict(type='Contrast', magnitude_key='magnitude', magnitude_range=(0, 0.9)),
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dict(
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type='Brightness', magnitude_key='magnitude',
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magnitude_range=(0, 0.9)),
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dict(
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type='Sharpness', magnitude_key='magnitude', magnitude_range=(0, 0.9)),
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dict(
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type='Shear',
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magnitude_key='magnitude',
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magnitude_range=(0, 0.3),
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direction='horizontal'),
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dict(
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type='Shear',
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magnitude_key='magnitude',
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magnitude_range=(0, 0.3),
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direction='vertical'),
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dict(
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type='Translate',
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magnitude_key='magnitude',
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magnitude_range=(0, 0.45),
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direction='horizontal'),
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dict(
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type='Translate',
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magnitude_key='magnitude',
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magnitude_range=(0, 0.45),
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direction='vertical')
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]
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dataset_type = 'ImageNet'
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img_norm_cfg = dict(
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mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
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train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(
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type='RandomResizedCrop',
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size=224,
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backend='pillow',
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interpolation='bicubic'),
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dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
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dict(
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type='RandAugment',
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policies=rand_increasing_policies,
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num_policies=2,
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total_level=10,
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magnitude_level=9,
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magnitude_std=0.5,
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hparams=dict(pad_val=[104, 116, 124], interpolation='bicubic')),
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dict(
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type='RandomErasing',
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erase_prob=0.25,
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mode='rand',
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min_area_ratio=0.02,
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max_area_ratio=0.3333333333333333,
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fill_color=[103.53, 116.28, 123.675],
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fill_std=[57.375, 57.12, 58.395]),
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dict(
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type='Normalize',
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mean=[123.675, 116.28, 103.53],
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std=[58.395, 57.12, 57.375],
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to_rgb=True),
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dict(type='ImageToTensor', keys=['img']),
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dict(type='ToTensor', keys=['gt_label']),
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dict(type='Collect', keys=['img', 'gt_label'])
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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='Resize',
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size=(256, -1),
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backend='pillow',
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interpolation='bicubic'),
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dict(type='CenterCrop', crop_size=224),
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dict(
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type='Normalize',
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mean=[123.675, 116.28, 103.53],
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std=[58.395, 57.12, 57.375],
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to_rgb=True),
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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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data = dict(
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samples_per_gpu=32,
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workers_per_gpu=4,
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train=dict(
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type='ImageNet',
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data_prefix='/data/vdb/ziyuan.tw/yimian/gzn/datasets/',
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pipeline=[
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dict(type='LoadImageFromFile'),
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dict(
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type='RandomResizedCrop',
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size=224,
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backend='pillow',
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interpolation='bicubic'),
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dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
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dict(
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type='RandAugment',
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policies=[
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dict(type='AutoContrast'),
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dict(type='Equalize'),
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dict(type='Invert'),
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dict(
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type='Rotate',
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magnitude_key='angle',
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magnitude_range=(0, 30)),
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dict(
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type='Posterize',
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magnitude_key='bits',
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magnitude_range=(4, 0)),
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dict(
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type='Solarize',
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magnitude_key='thr',
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magnitude_range=(256, 0)),
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dict(
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type='SolarizeAdd',
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magnitude_key='magnitude',
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magnitude_range=(0, 110)),
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dict(
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type='ColorTransform',
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magnitude_key='magnitude',
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magnitude_range=(0, 0.9)),
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dict(
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type='Contrast',
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magnitude_key='magnitude',
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magnitude_range=(0, 0.9)),
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dict(
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type='Brightness',
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magnitude_key='magnitude',
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magnitude_range=(0, 0.9)),
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dict(
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type='Sharpness',
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magnitude_key='magnitude',
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magnitude_range=(0, 0.9)),
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dict(
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type='Shear',
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magnitude_key='magnitude',
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magnitude_range=(0, 0.3),
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direction='horizontal'),
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dict(
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type='Shear',
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magnitude_key='magnitude',
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magnitude_range=(0, 0.3),
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direction='vertical'),
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dict(
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type='Translate',
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magnitude_key='magnitude',
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magnitude_range=(0, 0.45),
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direction='horizontal'),
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dict(
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type='Translate',
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magnitude_key='magnitude',
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magnitude_range=(0, 0.45),
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direction='vertical')
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],
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num_policies=2,
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total_level=10,
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magnitude_level=9,
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magnitude_std=0.5,
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hparams=dict(pad_val=[104, 116, 124],
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interpolation='bicubic')),
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dict(
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type='RandomErasing',
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erase_prob=0.25,
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mode='rand',
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min_area_ratio=0.02,
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max_area_ratio=0.3333333333333333,
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fill_color=[103.53, 116.28, 123.675],
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fill_std=[57.375, 57.12, 58.395]),
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dict(
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type='Normalize',
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mean=[123.675, 116.28, 103.53],
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std=[58.395, 57.12, 57.375],
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to_rgb=True),
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dict(type='ImageToTensor', keys=['img']),
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dict(type='ToTensor', keys=['gt_label']),
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dict(type='Collect', keys=['img', 'gt_label'])
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],
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ann_file='/data/vdb/ziyuan.tw/yimian/gzn/datasets/virgo_data/dailytags/train_mmcls.txt',
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classes='/data/vdb/ziyuan.tw/yimian/gzn/datasets/virgo_data/dailytags/classname.txt'),
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val=dict(
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type='ImageNet',
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data_prefix='/data/vdb/ziyuan.tw/yimian/gzn/datasets/',
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ann_file='/data/vdb/ziyuan.tw/yimian/gzn/datasets/virgo_data/dailytags/val_mmcls.txt',
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pipeline=[
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dict(type='LoadImageFromFile'),
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dict(
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type='Resize',
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size=(256, -1),
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backend='pillow',
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interpolation='bicubic'),
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dict(type='CenterCrop', crop_size=224),
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dict(
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type='Normalize',
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mean=[123.675, 116.28, 103.53],
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std=[58.395, 57.12, 57.375],
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to_rgb=True),
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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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classes='/data/vdb/ziyuan.tw/yimian/gzn/datasets/virgo_data/dailytags/classname.txt'),
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test=dict(
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type='ImageNet',
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data_prefix='/data/vdb/ziyuan.tw/yimian/gzn/datasets/',
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ann_file='/data/vdb/ziyuan.tw/yimian/gzn/datasets/virgo_data/dailytags/val_mmcls.txt',
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pipeline=[
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dict(type='LoadImageFromFile'),
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dict(
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type='Resize',
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size=(256, -1),
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backend='pillow',
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interpolation='bicubic'),
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dict(type='CenterCrop', crop_size=224),
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dict(
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type='Normalize',
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mean=[123.675, 116.28, 103.53],
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std=[58.395, 57.12, 57.375],
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to_rgb=True),
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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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classes='/data/vdb/ziyuan.tw/yimian/gzn/datasets/virgo_data/dailytags/classname.txt'))
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evaluation = dict(interval=2, metric='accuracy', save_best='auto')
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paramwise_cfg = dict(
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norm_decay_mult=0.0,
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bias_decay_mult=0.0,
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custom_keys={
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'.cls_token': dict(decay_mult=0.0),
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'.pos_embed': dict(decay_mult=0.0)
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})
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optimizer = dict(
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type='AdamW',
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lr=2e-5, #5e-4 * 32 * 1 / 512, 1.25e-4
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weight_decay=0.1,
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eps=1e-8,
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betas=(0.9, 0.999),
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paramwise_cfg=paramwise_cfg)
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optimizer_config = dict(grad_clip=dict(max_norm=5.0))
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# learning policy
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lr_config = dict(
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policy='CosineAnnealing',
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by_epoch=False,
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min_lr_ratio=1e-2,
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warmup='linear',
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warmup_ratio=1e-3,
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warmup_iters=20,
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warmup_by_epoch=True)
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runner = dict(type='EpochBasedRunner', max_epochs=300)
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checkpoint_config = dict(interval=1, max_keep_ckpts=20, create_symlink=True)
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log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')])
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dist_params = dict(backend='nccl')
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log_level = 'INFO'
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load_from = None
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resume_from = None
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workflow = [('train', 1)]
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model = dict(
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type='ImageClassifier',
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backbone=dict(
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type='NextViT',
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arch='small',
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path_dropout=0.2,
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),
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neck=dict(type='GlobalAveragePooling'),
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head=dict(
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type='LinearClsHead',
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num_classes=1296,
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in_channels=1024,
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loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
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),
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)
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custom_hooks = [dict(type='EMAHook', momentum=4e-05, priority='ABOVE_NORMAL')]
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work_dir = './work_dir/'
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gpu_ids = range(0, 32)
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