mirror of
https://github.com/open-mmlab/mmclassification.git
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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>
45 lines
1.1 KiB
Python
45 lines
1.1 KiB
Python
_base_ = [
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'../../_base_/datasets/imagenet_bs32_pil_resize.py',
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'../../_base_/default_runtime.py',
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]
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# dataset settings
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train_dataloader = dict(batch_size=128)
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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='MoCoV3ViT',
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arch='base', # embed_dim = 768
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img_size=224,
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patch_size=16,
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stop_grad_conv1=True,
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frozen_stages=12,
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norm_eval=True),
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head=dict(
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type='VisionTransformerClsHead',
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num_classes=1000,
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in_channels=768,
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loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
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init_cfg=dict(type='Normal', std=0.01, layer='Linear'),
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))
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# optimizer
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optim_wrapper = dict(
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type='OptimWrapper',
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optimizer=dict(type='SGD', lr=12, momentum=0.9, weight_decay=0.))
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# learning rate scheduler
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param_scheduler = [
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dict(type='CosineAnnealingLR', T_max=90, by_epoch=True, begin=0, end=90)
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]
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# runtime settings
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train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=90)
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val_cfg = dict()
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test_cfg = dict()
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default_hooks = dict(
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checkpoint=dict(type='CheckpointHook', interval=10, max_keep_ckpts=3))
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