mirror of
https://github.com/open-mmlab/mmclassification.git
synced 2025-06-03 21:53:55 +08:00
08dc8c75d3
* remove basehead * add moco series * add byol simclr simsiam * add ut * update configs * add simsiam hook * add and refactor beit * update ut * add cae * update extract_feat * refactor cae * add mae * refactor data preprocessor * update heads * add maskfeat * add milan * add simmim * add mixmim * fix lint * fix ut * fix lint * add eva * add densecl * add barlowtwins * add swav * fix lint * update readtherdocs rst * update docs * update * Decrease UT memory usage * Fix docstring * update DALLEEncoder * Update model docs * refactor dalle encoder * update docstring * fix ut * fix config error * add val_cfg and test_cfg * refactor clip generator * fix lint * pass check * fix ut * add lars * update type of BEiT in configs * Use MMEngine style momentum in EMA. * apply mmpretrain solarize --------- Co-authored-by: mzr1996 <mzr1996@163.com>
160 lines
4.0 KiB
Python
160 lines
4.0 KiB
Python
_base_ = [
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'../_base_/schedules/imagenet_lars_coslr_200e.py',
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'../_base_/default_runtime.py',
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]
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# dataset settings
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dataset_type = 'ImageNet'
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data_root = 'data/imagenet/'
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data_preprocessor = dict(
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type='SelfSupDataPreprocessor',
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mean=[123.675, 116.28, 103.53],
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std=[58.395, 57.12, 57.375],
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to_rgb=True)
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num_crops = [2, 6]
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color_distort_strength = 1.0
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view_pipeline1 = [
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dict(
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type='RandomResizedCrop',
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scale=224,
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crop_ratio_range=(0.14, 1.),
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backend='pillow'),
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dict(
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type='RandomApply',
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transforms=[
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dict(
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type='ColorJitter',
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brightness=0.8 * color_distort_strength,
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contrast=0.8 * color_distort_strength,
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saturation=0.8 * color_distort_strength,
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hue=0.2 * color_distort_strength)
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],
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prob=0.8),
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dict(
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type='RandomGrayscale',
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prob=0.2,
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keep_channels=True,
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channel_weights=(0.114, 0.587, 0.2989)),
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dict(
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type='GaussianBlur',
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magnitude_range=(0.1, 2.0),
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magnitude_std='inf',
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prob=0.5),
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dict(type='RandomFlip', prob=0.5),
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]
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view_pipeline2 = [
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dict(
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type='RandomResizedCrop',
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scale=96,
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crop_ratio_range=(0.05, 0.14),
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backend='pillow'),
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dict(
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type='RandomApply',
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transforms=[
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dict(
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type='ColorJitter',
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brightness=0.8 * color_distort_strength,
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contrast=0.8 * color_distort_strength,
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saturation=0.8 * color_distort_strength,
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hue=0.2 * color_distort_strength)
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],
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prob=0.8),
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dict(
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type='RandomGrayscale',
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prob=0.2,
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keep_channels=True,
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channel_weights=(0.114, 0.587, 0.2989)),
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dict(
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type='GaussianBlur',
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magnitude_range=(0.1, 2.0),
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magnitude_std='inf',
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prob=0.5),
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dict(type='RandomFlip', prob=0.5),
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]
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train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(
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type='MultiView',
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num_views=num_crops,
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transforms=[view_pipeline1, view_pipeline2]),
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dict(type='PackInputs')
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]
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batch_size = 32
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train_dataloader = dict(
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batch_size=batch_size,
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num_workers=8,
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drop_last=True,
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persistent_workers=True,
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sampler=dict(type='DefaultSampler', shuffle=True),
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collate_fn=dict(type='default_collate'),
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dataset=dict(
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type=dataset_type,
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data_root=data_root,
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ann_file='meta/train.txt',
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data_prefix=dict(img_path='train/'),
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pipeline=train_pipeline))
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# model settings
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model = dict(
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type='SwAV',
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data_preprocessor=dict(
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mean=(123.675, 116.28, 103.53),
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std=(58.395, 57.12, 57.375),
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to_rgb=True),
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backbone=dict(
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type='ResNet',
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depth=50,
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norm_cfg=dict(type='SyncBN'),
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zero_init_residual=True),
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neck=dict(
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type='SwAVNeck',
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in_channels=2048,
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hid_channels=2048,
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out_channels=128,
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with_avg_pool=True),
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head=dict(
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type='SwAVHead',
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loss=dict(
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type='SwAVLoss',
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feat_dim=128, # equal to neck['out_channels']
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epsilon=0.05,
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temperature=0.1,
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num_crops=num_crops,
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)))
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# optimizer
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optim_wrapper = dict(type='OptimWrapper', optimizer=dict(type='LARS', lr=0.6))
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find_unused_parameters = True
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# learning policy
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param_scheduler = [
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dict(
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type='CosineAnnealingLR',
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T_max=200,
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eta_min=6e-4,
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by_epoch=True,
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begin=0,
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end=200,
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convert_to_iter_based=True)
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]
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# runtime settings
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default_hooks = dict(
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# only keeps the latest 3 checkpoints
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checkpoint=dict(type='CheckpointHook', interval=10, max_keep_ckpts=3))
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# additional hooks
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custom_hooks = [
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dict(
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type='SwAVHook',
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priority='VERY_HIGH',
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batch_size=batch_size,
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epoch_queue_starts=15,
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crops_for_assign=[0, 1],
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feat_dim=128,
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queue_length=3840,
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frozen_layers_cfg=dict(prototypes=5005))
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]
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