71 lines
1.8 KiB
Python
71 lines
1.8 KiB
Python
_base_ = ['vit-base-p16_ft-8xb128-coslr-100e_in1k.py']
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# model
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model = dict(backbone=dict(use_window=True, init_values=0.1, qkv_bias=False))
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# optimizer
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optimizer = dict(lr=8e-3)
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# learning policy
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lr_config = dict(warmup_iters=5)
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# dataset
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custom_imports = dict(imports='mmcls.datasets', allow_failed_imports=False)
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preprocess_cfg = dict(
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pixel_mean=[123.675, 116.28, 103.53],
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pixel_std=[58.395, 57.12, 57.375],
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to_rgb=True,
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)
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bgr_mean = preprocess_cfg['pixel_mean'][::-1]
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bgr_std = preprocess_cfg['pixel_std'][::-1]
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# train pipeline
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train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(
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type='mmcls.RandomResizedCrop',
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scale=224,
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backend='pillow',
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interpolation='bicubic'),
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dict(type='RandomFlip', prob=0.5, direction='horizontal'),
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dict(
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type='mmcls.RandAugment',
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policies={{_base_.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(
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pad_val=[round(x) for x in bgr_mean], interpolation='bicubic')),
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dict(
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type='mmcls.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=1 / 3,
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fill_color=bgr_mean,
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fill_std=bgr_std),
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dict(type='PackSelfSupInputs', algorithm_keys=['gt_label']),
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]
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# test pipeline
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test_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(
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type='mmcls.ResizeEdge',
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scale=256,
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edge='short',
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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(type='PackSelfSupInputs', algorithm_keys=['gt_label']),
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]
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data = dict(
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train=dict(pipeline=train_pipeline),
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val=dict(pipeline=test_pipeline),
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samples_per_gpu=128)
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find_unused_parameters = True
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