71 lines
1.9 KiB
Python
71 lines
1.9 KiB
Python
_base_ = [
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'../_base_/models/mobilenet_v3/mobilenet_v3_small_050_imagenet.py',
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'../_base_/datasets/imagenet_bs128_mbv3.py',
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'../_base_/default_runtime.py',
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]
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# model settings
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model = dict(backbone=dict(norm_cfg=dict(type='BN', eps=1e-5, momentum=0.1)))
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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(
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type='AutoAugment',
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policies='imagenet',
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hparams=dict(pad_val=[round(x) for x in [103.53, 116.28, 123.675]])),
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dict(
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type='RandomErasing',
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erase_prob=0.2,
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mode='rand',
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min_area_ratio=0.02,
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max_area_ratio=1 / 3,
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fill_color=[103.53, 116.28, 123.675],
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fill_std=[57.375, 57.12, 58.395]),
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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=256,
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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(dataset=dict(pipeline=train_pipeline))
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val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
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# If you want standard test, please manually configure the test dataset
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test_dataloader = val_dataloader
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# schedule settings
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optim_wrapper = dict(
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optimizer=dict(
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type='RMSprop',
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lr=0.064,
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alpha=0.9,
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momentum=0.9,
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eps=0.0316,
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weight_decay=1e-5))
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param_scheduler = dict(type='StepLR', by_epoch=True, step_size=2, gamma=0.973)
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train_cfg = dict(by_epoch=True, max_epochs=600, val_interval=10)
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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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# base_batch_size = (8 GPUs) x (128 samples per GPU)
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auto_scale_lr = dict(base_batch_size=1024)
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