78 lines
2.5 KiB
Python
78 lines
2.5 KiB
Python
_base_ = [
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'../_base_/models/regnet/regnetx_400mf.py',
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'../_base_/datasets/imagenet_bs32.py',
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'../_base_/schedules/imagenet_bs1024_coslr.py',
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'../_base_/default_runtime.py'
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]
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# Precise BN hook will update the bn stats, so this hook should be executed
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# before CheckpointHook, which has priority of 'NORMAL'. So set the
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# priority of PreciseBNHook to 'ABOVE_NORMAL' here.
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custom_hooks = [
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dict(
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type='PreciseBNHook',
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num_samples=8192,
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interval=1,
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priority='ABOVE_NORMAL')
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]
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# sgd with nesterov, base ls is 0.8 for batch_size 1024,
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# 0.4 for batch_size 512 and 0.2 for batch_size 256 when training ImageNet1k
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optimizer = dict(lr=0.8, nesterov=True)
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# dataset settings
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dataset_type = 'ImageNet'
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# normalization params, in order of BGR
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NORM_MEAN = [103.53, 116.28, 123.675]
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NORM_STD = [57.375, 57.12, 58.395]
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# lighting params, in order of RGB, from repo. pycls
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EIGVAL = [0.2175, 0.0188, 0.0045]
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EIGVEC = [[-0.5675, 0.7192, 0.4009], [-0.5808, -0.0045, -0.814],
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[-0.5836, -0.6948, 0.4203]]
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train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='RandomResizedCrop', size=224),
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dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
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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=25.5, # because the value range of images is [0,255]
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to_rgb=True
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), # BGR image from cv2 in LoadImageFromFile, convert to RGB here
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dict(type='Normalize', mean=NORM_MEAN, std=NORM_STD,
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to_rgb=True), # RGB2BGR
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dict(type='ImageToTensor', keys=['img']),
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dict(type='ToTensor', keys=['gt_label']),
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dict(type='Collect', keys=['img', 'gt_label'])
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]
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test_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='Resize', size=(256, -1)),
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dict(type='CenterCrop', crop_size=224),
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dict(type='Normalize', mean=NORM_MEAN, std=NORM_STD, to_rgb=False),
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dict(type='ImageToTensor', keys=['img']),
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dict(type='Collect', keys=['img'])
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]
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data = dict(
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samples_per_gpu=128,
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workers_per_gpu=8,
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train=dict(
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type=dataset_type,
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data_prefix='data/imagenet/train',
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pipeline=train_pipeline),
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val=dict(
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type=dataset_type,
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data_prefix='data/imagenet/val',
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ann_file='data/imagenet/meta/val.txt',
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pipeline=test_pipeline),
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test=dict(
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# replace `data/val` with `data/test` for standard test
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type=dataset_type,
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data_prefix='data/imagenet/val',
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ann_file='data/imagenet/meta/val.txt',
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pipeline=test_pipeline))
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