mirror of https://github.com/alibaba/EasyCV.git
91 lines
2.4 KiB
Python
91 lines
2.4 KiB
Python
_base_ = 'configs/base.py'
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log_config = dict(
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interval=10,
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hooks=[dict(type='TextLoggerHook'),
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dict(type='TensorboardLoggerHook')])
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# model settings
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model = dict(
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type='Classification',
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backbone=dict(
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type='ResNeXt',
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depth=50,
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groups=32,
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base_width=4,
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out_indices=[4], # 0: conv-1, x: stage-x
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norm_cfg=dict(type='BN')),
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head=dict(
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type='ClsHead',
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with_avg_pool=True,
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in_channels=2048,
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loss_config=dict(
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type='CrossEntropyLossWithLabelSmooth',
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label_smooth=0,
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),
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num_classes=1000))
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data_train_list = 'data/imagenet_raw/meta/train_labeled.txt'
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data_train_root = 'data/imagenet_raw/train/'
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data_test_list = 'data/imagenet_raw/meta/val_labeled.txt'
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data_test_root = 'data/imagenet_raw/val/'
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data_all_list = 'data/imagenet_raw/meta/all_labeled.txt'
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data_root = 'data/imagenet_raw/'
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dataset_type = 'ClsDataset'
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img_norm_cfg = dict(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
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train_pipeline = [
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dict(type='RandomResizedCrop', size=224),
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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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dict(type='Collect', keys=['img', 'gt_labels'])
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]
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test_pipeline = [
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dict(type='Resize', size=256),
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dict(type='CenterCrop', size=224),
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dict(type='ToTensor'),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='Collect', keys=['img', 'gt_labels'])
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]
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data = dict(
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imgs_per_gpu=32, # total 256
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workers_per_gpu=4,
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train=dict(
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type=dataset_type,
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data_source=dict(
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list_file=data_train_list,
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root=data_train_root,
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type='ClsSourceImageList'),
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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(
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list_file=data_test_list,
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root=data_test_root,
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type='ClsSourceImageList'),
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pipeline=test_pipeline))
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eval_config = dict(initial=True, interval=1, gpu_collect=True)
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eval_pipelines = [
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dict(
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mode='test',
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data=data['val'],
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dist_eval=True,
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evaluators=[dict(type='ClsEvaluator', topk=(1, 5))],
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)
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]
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# additional hooks
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custom_hooks = []
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# optimizer
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optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001)
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# learning policy
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lr_config = dict(policy='step', step=[30, 60, 90])
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checkpoint_config = dict(interval=10)
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# runtime settings
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total_epochs = 100
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