75 lines
2.0 KiB
Python
75 lines
2.0 KiB
Python
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_base_ = [
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'../../_base_/datasets/imagenet_bs32_pil_resize.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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train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='ToPIL', to_rgb=True),
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dict(type='MAERandomResizedCrop', size=224, interpolation=3),
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dict(type='torchvision/RandomHorizontalFlip', p=0.5),
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dict(type='ToNumpy', to_bgr=True),
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dict(type='PackInputs'),
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]
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# dataset settings
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train_dataloader = dict(
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batch_size=2048, drop_last=True, dataset=dict(pipeline=train_pipeline))
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val_dataloader = dict(drop_last=False)
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test_dataloader = dict(drop_last=False)
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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='VisionTransformer',
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arch='base',
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img_size=224,
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patch_size=16,
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frozen_stages=12,
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out_type='cls_token',
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final_norm=True,
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init_cfg=dict(type='Pretrained', prefix='backbone.')),
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neck=dict(type='ClsBatchNormNeck', input_features=768),
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head=dict(
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type='VisionTransformerClsHead',
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num_classes=1000,
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in_channels=768,
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loss=dict(type='CrossEntropyLoss'),
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init_cfg=[dict(type='TruncNormal', layer='Linear', std=0.01)]))
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# optimizer
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optim_wrapper = dict(
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_delete_=True,
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type='AmpOptimWrapper',
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optimizer=dict(type='LARS', lr=6.4, weight_decay=0.0, momentum=0.9))
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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=10,
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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=80,
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by_epoch=True,
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begin=10,
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end=90,
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eta_min=0.0,
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convert_to_iter_based=True)
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]
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# runtime settings
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train_cfg = dict(by_epoch=True, max_epochs=90)
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default_hooks = dict(
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checkpoint=dict(type='CheckpointHook', interval=1, max_keep_ckpts=1),
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logger=dict(type='LoggerHook', interval=10))
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randomness = dict(seed=0, diff_rank_seed=True)
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