79 lines
2.0 KiB
Python
79 lines
2.0 KiB
Python
_base_ = [
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'../_base_/models/resnest50.py',
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'../_base_/datasets/imagenet_bs64.py',
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'../_base_/default_runtime.py',
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'./_randaug_policies.py',
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]
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# dataset settings
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# lighting params, in order of BGR
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EIGVAL = [55.4625, 4.7940, 1.1475]
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EIGVEC = [
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[-0.5836, -0.6948, 0.4203],
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[-0.5808, -0.0045, -0.8140],
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[-0.5675, 0.7192, 0.4009],
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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='RandAugment',
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policies={{_base_.policies}},
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num_policies=2,
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magnitude_level=12),
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dict(type='EfficientNetRandomCrop', scale=224, backend='pillow'),
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dict(type='RandomFlip', prob=0.5, direction='horizontal'),
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dict(type='ColorJitter', brightness=0.4, contrast=0.4, saturation=0.4),
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dict(
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type='Lighting',
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eigval=EIGVAL,
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eigvec=EIGVEC,
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alphastd=0.1,
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to_rgb=False),
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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(type='EfficientNetCenterCrop', crop_size=256, backend='pillow'),
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dict(type='PackInputs'),
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]
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train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
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val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
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test_dataloader = dict(dataset=dict(pipeline=test_pipeline))
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# schedule settings
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optim_wrapper = dict(
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optimizer=dict(type='SGD', lr=0.8, momentum=0.9, weight_decay=1e-4),
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paramwise_cfg=dict(bias_decay_mult=0., norm_decay_mult=0.),
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)
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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=1e-6,
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by_epoch=True,
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begin=0,
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end=5,
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# update by iter
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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=265,
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by_epoch=True,
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begin=5,
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end=270,
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)
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]
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train_cfg = dict(by_epoch=True, max_epochs=270)
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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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# base_batch_size = (32 GPUs) x (64 samples per GPU)
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auto_scale_lr = dict(base_batch_size=2048)
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