90 lines
2.5 KiB
Python
90 lines
2.5 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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frozen_backbone=True,
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with_sobel=False,
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backbone=dict(
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type='ResNet',
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depth=50,
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in_channels=3,
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out_indices=[0, 1, 2, 3, 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='MultiClsHead',
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pool_type='specified',
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in_indices=[0, 1, 2, 3, 4],
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with_last_layer_unpool=True,
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backbone='resnet50',
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norm_cfg=dict(type='BN', momentum=0.1, affine=False),
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num_classes=205))
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# dataset settings
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data_source_cfg = dict(
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type='Places205',
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memcached=False,
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mclient_path='/mnt/lustre/share/memcached_client')
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data_train_list = 'data/places205/meta/train_labeled.txt'
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data_train_root = 'data/places205/train'
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data_test_list = 'data/places205/meta/val_labeled.txt'
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data_test_root = 'data/places205/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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dict(
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type='ColorJitter',
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brightness=0.4,
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contrast=0.4,
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saturation=0.4,
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hue=0.),
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dict(type='ToTensor'),
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dict(type='Lighting'),
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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='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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]
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data = dict(
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imgs_per_gpu=256, # total 256
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workers_per_gpu=8,
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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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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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# 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=4,
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eval_param=dict(topk=(1, )))
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]
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# optimizer
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optimizer = dict(
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type='SGD',
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lr=0.01,
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momentum=0.9,
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weight_decay=0.0001,
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paramwise_options=dict(norm_decay_mult=0.),
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nesterov=True)
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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 = 90
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