123 lines
3.0 KiB
Python
123 lines
3.0 KiB
Python
_base_ = [
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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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data_preprocessor = dict(
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num_classes=1000,
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# RGB format normalization parameters
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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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# 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(type='NumpyToPIL', to_rgb=True),
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dict(
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type='torchvision/TrivialAugmentWide',
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num_magnitude_bins=31,
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interpolation='bicubic',
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fill=None),
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dict(type='PILToNumpy', to_bgr=True),
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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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dataset=dict(pipeline=train_pipeline),
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sampler=dict(type='RepeatAugSampler', shuffle=True),
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)
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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='ConvNeXt',
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arch='tiny',
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drop_path_rate=0.1,
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layer_scale_init_value=0.,
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use_grn=True,
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),
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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=.02, bias=0.),
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),
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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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)
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custom_hooks = [
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dict(
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type='EMAHook',
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momentum=1e-4,
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evaluate_on_origin=True,
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priority='ABOVE_NORMAL')
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]
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# schedule settings
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# optimizer
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optim_wrapper = dict(
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optimizer=dict(
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type='AdamW', lr=3.2e-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=0.7,
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norm_decay_mult=0.0,
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bias_decay_mult=0.0,
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flat_decay_mult=0.0))
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# learning policy
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param_scheduler = [
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# warm up learning rate scheduler
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dict(
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type='LinearLR',
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start_factor=0.0001,
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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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# main learning rate scheduler
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dict(
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type='CosineAnnealingLR',
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T_max=280,
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eta_min=1.0e-5,
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by_epoch=True,
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begin=20,
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end=300)
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]
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train_cfg = dict(by_epoch=True, max_epochs=300)
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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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# only keeps the latest 2 checkpoints
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checkpoint=dict(type='CheckpointHook', interval=1, max_keep_ckpts=2))
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# NOTE: `auto_scale_lr` is for automatically scaling LR,
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# based on the actual training batch size.
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auto_scale_lr = dict(base_batch_size=2048)
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