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114 lines
2.7 KiB
Python
114 lines
2.7 KiB
Python
_base_ = 'mmpretrain::_base_/default_runtime.py'
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# dataset settings
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dataset_type = 'ImageNet'
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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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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='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=224,
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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(
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batch_size=128,
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num_workers=8,
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dataset=dict(
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type=dataset_type,
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data_root='../../data/imagenet',
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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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)
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val_dataloader = dict(
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batch_size=128,
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num_workers=8,
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dataset=dict(
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type=dataset_type,
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data_root='../../data/imagenet',
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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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)
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val_evaluator = dict(type='Accuracy', topk=(1, 5))
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test_dataloader = val_dataloader
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test_evaluator = val_evaluator
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# model setting
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custom_imports = dict(imports='models')
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model = dict(
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type='ImageClassifier',
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backbone=dict(
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type='InternImage',
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stem_channels=64,
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drop_path_rate=0.1,
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stage_blocks=[4, 4, 18, 4],
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groups=[4, 8, 16, 32]),
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neck=dict(type='GlobalAveragePooling'),
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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(type='CrossEntropyLoss', loss_weight=1.0),
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topk=(1, 5)))
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# optimizer
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optim_wrapper = dict(
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optimizer=dict(type='AdamW', lr=1.25e-04, eps=1e-8, betas=(0.9, 0.999)),
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weight_decay=0.05)
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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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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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by_epoch=True,
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begin=20,
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end=300,
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eta_min=1.25e-06)
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]
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# train, val, test setting
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train_cfg = dict(by_epoch=True, max_epochs=300, val_interval=1)
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val_cfg = dict()
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test_cfg = dict()
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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=128 * 8)
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