mirror of
https://github.com/open-mmlab/mmpretrain.git
synced 2025-06-03 14:59:18 +08:00
99 lines
2.9 KiB
Python
99 lines
2.9 KiB
Python
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import os.path as osp
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# run with 8 GPUs
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# model settings
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model = dict(
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type='ImageClassifier',
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backbone=dict(
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type='ResNet',
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depth=50,
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num_stages=4,
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out_indices=(3, ),
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style='pytorch'),
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neck=dict(type='GlobalAveragePooling'),
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head=dict(
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type='LinearHead',
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num_classes=1000,
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in_channels=2048,
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loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
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))
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# dataset settings
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dataset_type = 'ImageNet'
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img_norm_cfg = dict(
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mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
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memcached_root = '/mnt/lustre/share/memcached_client/'
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train_pipeline = [
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dict(
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type='LoadImageFromFile',
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file_client_args=dict(
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backend='memcached',
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server_list_cfg=osp.join(memcached_root, 'server_list.conf'),
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client_cfg=osp.join(memcached_root, 'client.conf'))),
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dict(type='ToPIL'),
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dict(type='RandomResizedCrop', size=224),
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dict(type='RandomHorizontalFlip', p=0.5),
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dict(type='ToNumpy'),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='ImageToTensor', keys=['img']),
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dict(type='ToTensor', keys=['gt_labels']),
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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(
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type='LoadImageFromFile',
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file_client_args=dict(
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backend='memcached',
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server_list_cfg=osp.join(memcached_root, 'server_list.conf'),
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client_cfg=osp.join(memcached_root, 'client.conf'))),
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dict(type='ToPIL'),
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dict(type='Resize', size=256),
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dict(type='CenterCrop', size=224),
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dict(type='ToNumpy'),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='ImageToTensor', keys=['img']),
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dict(type='ToTensor', keys=['gt_labels']),
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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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samples_per_gpu=64,
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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_prefix='data/imagenet/train',
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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_prefix='data/imagenet/val',
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ann_file='data/imagenet/meta/val.txt',
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pipeline=test_pipeline),
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test=dict(
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# replace `data/val` with `data/test` for standard test
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type=dataset_type,
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data_prefix='data/imagenet/val',
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ann_file='data/imagenet/meta/val.txt',
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pipeline=test_pipeline))
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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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optimizer_config = dict(grad_clip=None)
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# learning policy
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lr_config = dict(policy='step', step=[30, 60, 90])
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# checkpoint saving
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checkpoint_config = dict(interval=1)
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# yapf:disable
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log_config = dict(
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interval=100,
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hooks=[
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dict(type='TextLoggerHook'),
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# dict(type='TensorboardLoggerHook')
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])
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# yapf:enable
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# runtime settings
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total_epochs = 100
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dist_params = dict(backend='nccl')
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log_level = 'INFO'
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work_dir = './work_dirs/imagenet_resnet50_batch256'
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load_from = None
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resume_from = None
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workflow = [('train', 1)]
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