134 lines
3.3 KiB
Python
134 lines
3.3 KiB
Python
_base_ = [
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'../../_base_/models/mixmim/mixmim_base.py',
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'../../_base_/datasets/imagenet_bs64_swin_224.py',
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'../../_base_/default_runtime.py'
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]
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# dataset settings
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dataset_type = 'ImageNet'
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data_root = 'data/imagenet/'
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data_preprocessor = dict(
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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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)
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bgr_mean = data_preprocessor['mean'][::-1]
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bgr_std = data_preprocessor['std'][::-1]
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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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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='RandAugment',
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policies='timm_increasing',
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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='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='PackInputs'),
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]
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train_dataloader = dict(
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batch_size=128,
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num_workers=16,
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dataset=dict(
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type=dataset_type,
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data_root=data_root,
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ann_file='meta/train.txt',
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data_prefix='train',
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pipeline=train_pipeline),
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sampler=dict(type='DefaultSampler', shuffle=True),
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persistent_workers=True,
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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='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='PackInputs'),
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]
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val_dataloader = dict(
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batch_size=64,
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num_workers=8,
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pin_memory=True,
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collate_fn=dict(type='default_collate'),
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dataset=dict(
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type=dataset_type,
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data_root=data_root,
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ann_file='meta/val.txt',
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data_prefix='val',
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pipeline=test_pipeline),
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sampler=dict(type='DefaultSampler', shuffle=False),
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persistent_workers=True,
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)
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test_dataloader = val_dataloader
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model = dict(
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backbone=dict(
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init_cfg=dict(type='Pretrained', checkpoint='', prefix='backbone.')))
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# optimizer
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optim_wrapper = dict(
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type='OptimWrapper',
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optimizer=dict(
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type='AdamW',
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lr=5e-4 * (8 * 128 / 256),
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betas=(0.9, 0.999),
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weight_decay=0.05),
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constructor='LearningRateDecayOptimWrapperConstructor',
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paramwise_cfg=dict(
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layer_decay_rate=0.7,
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custom_keys={
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'.ln': dict(decay_mult=0.0), # do not decay on ln and bias
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'.bias': dict(decay_mult=0.0)
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}))
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param_scheduler = [
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dict(
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type='LinearLR',
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start_factor=1e-6,
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by_epoch=True,
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begin=0,
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end=5,
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convert_to_iter_based=True),
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dict(
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type='CosineAnnealingLR',
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T_max=95,
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eta_min=1e-6,
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by_epoch=True,
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begin=5,
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end=100,
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convert_to_iter_based=True)
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]
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train_cfg = dict(by_epoch=True, max_epochs=100, val_interval=10)
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val_cfg = dict()
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test_cfg = dict()
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default_hooks = dict(
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# save checkpoint per epoch.
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checkpoint=dict(type='CheckpointHook', interval=1, max_keep_ckpts=1))
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