77 lines
2.3 KiB
Python
77 lines
2.3 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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with_sobel=True,
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backbone=dict(
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type='ResNet',
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depth=50,
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in_channels=2,
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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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frozen_stages=4),
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head=dict(
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type='ClsHead', with_avg_pool=True, in_channels=2048,
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num_classes=1000))
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# dataset settings
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data_source_cfg = dict(
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type='ImageNet',
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memcached=True,
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mclient_path='/mnt/lustre/share/memcached_client')
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data_train_list = 'data/imagenet/meta/train_labeled.txt'
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data_train_root = 'data/imagenet/train'
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data_test_list = 'data/imagenet/meta/val_labeled.txt'
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data_test_root = 'data/imagenet/val'
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dataset_type = 'ClassificationDataset'
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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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]
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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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]
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# prefetch
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prefetch = False
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if not prefetch:
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train_pipeline.extend([dict(type='ToTensor'), dict(type='Normalize', **img_norm_cfg)])
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test_pipeline.extend([dict(type='ToTensor'), dict(type='Normalize', **img_norm_cfg)])
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data = dict(
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imgs_per_gpu=32, # total 32*8=256, 8GPU linear cls
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workers_per_gpu=5,
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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, root=data_train_root,
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**data_source_cfg),
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pipeline=train_pipeline,
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prefetch=prefetch),
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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, root=data_test_root, **data_source_cfg),
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pipeline=test_pipeline,
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prefetch=prefetch))
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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=1,
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imgs_per_gpu=128,
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workers_per_gpu=4,
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prefetch=prefetch,
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img_norm_cfg=img_norm_cfg,
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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=30., momentum=0.9, weight_decay=0.)
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# learning policy
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lr_config = dict(policy='step', step=[60, 80])
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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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