60 lines
1.7 KiB
Python
60 lines
1.7 KiB
Python
_base_ = '../../base.py'
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# model settings
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model = dict(
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type='Classification',
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pretrained=None,
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backbone=dict(
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type='ResNet',
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depth=50,
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out_indices=[4], # 4: stage-4
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norm_cfg=dict(type='BN')),
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head=dict(
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type='ClsHead', with_avg_pool=True, in_channels=2048, num_classes=10))
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# dataset settings
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data_source_cfg = dict(type='Cifar10', root='data/cifar/')
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dataset_type = 'ClassificationDataset'
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img_norm_cfg = dict(mean=[0.4914, 0.4822, 0.4465], std=[0.2023, 0.1994, 0.201])
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train_pipeline = [
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dict(type='RandomCrop', size=32, padding=4),
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dict(type='RandomHorizontalFlip'),
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dict(type='ToTensor'),
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dict(type='Normalize', **img_norm_cfg),
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]
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test_pipeline = [
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dict(type='ToTensor'),
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dict(type='Normalize', **img_norm_cfg),
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]
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data = dict(
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imgs_per_gpu=128,
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workers_per_gpu=2,
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train=dict(
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type=dataset_type,
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data_source=dict(split='train', **data_source_cfg),
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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_source=dict(split='test', **data_source_cfg),
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pipeline=test_pipeline),
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test=dict(
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type=dataset_type,
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data_source=dict(split='test', **data_source_cfg),
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pipeline=test_pipeline))
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# additional hooks
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custom_hooks = [
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dict(
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type='ValidateHook',
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dataset=data['val'],
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initial=True,
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interval=10,
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imgs_per_gpu=128,
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workers_per_gpu=8,
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eval_param=dict(topk=(1, 5)))
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]
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# optimizer
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optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005)
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# learning policy
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lr_config = dict(policy='step', step=[150, 250])
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checkpoint_config = dict(interval=50)
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# runtime settings
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total_epochs = 350
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