58 lines
1.6 KiB
Python
58 lines
1.6 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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# dataset settings
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preprocess_cfg = dict(
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# BGR format normalization parameters
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mean=[103.53, 116.28, 123.675],
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std=[57.375, 57.12, 58.395],
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to_rgb=False, # The checkpoints from PyCls requires BGR format inputs.
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)
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# lighting params, in order of BGR, from repo. pycls
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EIGVAL = [0.2175, 0.0188, 0.0045]
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EIGVEC = [
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[-0.5836, -0.6948, 0.4203],
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[-0.5808, -0.0045, -0.814],
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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(type='RandomResizedCrop', scale=224),
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dict(type='RandomFlip', 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=False),
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dict(type='PackClsInputs'),
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]
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train_dataloader = dict(batch_size=128, dataset=dict(pipeline=train_pipeline))
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val_dataloader = dict(batch_size=128)
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test_dataloader = dict(batch_size=128)
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# schedule settings
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# sgd with nesterov, base ls is 0.8 for batch_size 1024,
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optim_wrapper = dict(optimizer=dict(lr=0.8, nesterov=True))
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# runtime settings
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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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