131 lines
3.5 KiB
Python
131 lines
3.5 KiB
Python
_base_ = [
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'../../_base_/datasets/imagenet_bs64_swin_224.py',
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'../../_base_/schedules/imagenet_bs1024_adamw_swin.py',
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'../../_base_/default_runtime.py'
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]
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# CAE fine-tuning setting
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# dataset
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data_preprocessor = dict(
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num_classes=1000,
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# RGB format normalization parameters
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mean=[127.5, 127.5, 127.5],
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std=[127.5, 127.5, 127.5],
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# convert image from BGR to RGB
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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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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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train_dataloader = dict(dataset=dict(pipeline=train_pipeline), batch_size=128)
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val_dataloader = dict(dataset=dict(pipeline=test_pipeline), batch_size=128)
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# model settings
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model = dict(
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type='ImageClassifier',
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backbone=dict(
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type='BEiTViT',
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arch='base',
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img_size=224,
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patch_size=16,
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final_norm=False, # do not use final norm
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drop_path_rate=0.1,
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layer_scale_init_value=0.1,
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out_type='avg_featmap',
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use_abs_pos_emb=True,
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use_rel_pos_bias=True,
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use_shared_rel_pos_bias=False,
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init_cfg=dict(type='Pretrained', checkpoint='')),
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neck=None,
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head=dict(
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type='LinearClsHead',
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num_classes=1000,
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in_channels=768,
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loss=dict(
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type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
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init_cfg=dict(type='TruncNormal', layer='Linear', std=2e-5)),
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train_cfg=dict(augments=[
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dict(type='Mixup', alpha=0.8),
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dict(type='CutMix', alpha=1.0)
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]))
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# optimizer wrapper
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optim_wrapper = dict(
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optimizer=dict(
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type='AdamW', lr=8e-3, betas=(0.9, 0.999), weight_decay=0.05),
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constructor='LearningRateDecayOptimWrapperConstructor',
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paramwise_cfg=dict(
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layer_decay_rate=0.65,
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custom_keys={
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'.ln': dict(decay_mult=0.0),
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'.bias': dict(decay_mult=0.0),
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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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# learning rate scheduler
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param_scheduler = [
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dict(
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type='LinearLR',
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start_factor=1e-4,
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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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by_epoch=True,
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begin=5,
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end=100,
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eta_min=1e-6,
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convert_to_iter_based=True)
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]
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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=3))
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train_cfg = dict(by_epoch=True, max_epochs=100)
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randomness = dict(seed=0)
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