127 lines
3.6 KiB
Python
127 lines
3.6 KiB
Python
# mmcls:: means we use the default settings from MMClassification
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_base_ = [
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'mmcls::_base_/datasets/imagenet_bs64_swin_224.py',
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'mmcls::_base_/schedules/imagenet_bs1024_adamw_swin.py',
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'mmcls::_base_/default_runtime.py'
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]
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# Fine-tuning 30 epoch is for models which have intermediate fine-tuning
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# on ImageNet-21k after self-supervised pretrain.
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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='BEiT',
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arch='base',
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img_size=224,
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patch_size=16,
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drop_path_rate=0.1,
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avg_token=True,
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output_cls_token=False,
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use_abs_pos_emb=False,
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use_rel_pos_bias=True,
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use_shared_rel_pos_bias=False),
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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=0.02)]),
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)
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file_client_args = dict(backend='disk')
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train_pipeline = [
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dict(type='LoadImageFromFile', file_client_args=file_client_args),
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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(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(type='PackClsInputs')
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]
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test_pipeline = [
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dict(type='LoadImageFromFile', file_client_args=file_client_args),
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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='PackClsInputs')
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]
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train_dataloader = dict(batch_size=128, dataset=dict(pipeline=train_pipeline))
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val_dataloader = dict(batch_size=128, dataset=dict(pipeline=test_pipeline))
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test_dataloader = val_dataloader
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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',
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lr=5e-5,
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weight_decay=0.05,
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eps=1e-8,
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betas=(0.9, 0.999),
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model_type='vit', # layer-wise lr decay type
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layer_decay_rate=0.75), # layer-wise lr decay factor
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constructor='mmselfsup.LearningRateDecayOptimWrapperConstructor',
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paramwise_cfg=dict(
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_delete_=True,
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custom_keys={
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# the following configurations are designed for BEiTs
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'.ln': dict(decay_mult=0.0),
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'.bias': dict(decay_mult=0.0),
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'q_bias': dict(decay_mult=0.0),
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'v_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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'.gamma': 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=20,
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convert_to_iter_based=True),
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dict(
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type='CosineAnnealingLR',
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by_epoch=True,
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begin=20,
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end=30,
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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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# runtime settings
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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=2))
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train_cfg = dict(by_epoch=True, max_epochs=30)
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randomness = dict(seed=0)
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