152 lines
3.8 KiB
Python
152 lines
3.8 KiB
Python
_base_ = [
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'../_base_/datasets/imagenet_bs512_mocov3.py',
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'../_base_/default_runtime.py',
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]
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# dataset settings
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# the difference between ResNet50 and ViT pipeline is the `scale` in
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# `RandomResizedCrop`, `scale=(0.08, 1.)` in ViT pipeline
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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=(0.08, 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=0.4,
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contrast=0.4,
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saturation=0.2,
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hue=0.1)
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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=1.),
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dict(type='Solarize', thr=128, prob=0.),
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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=224,
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crop_ratio_range=(0.08, 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=0.4,
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contrast=0.4,
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saturation=0.2,
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hue=0.1)
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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=0.1),
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dict(type='Solarize', thr=128, prob=0.2),
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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=[1, 1],
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transforms=[view_pipeline1, view_pipeline2]),
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dict(type='PackInputs')
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]
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train_dataloader = dict(batch_size=256, dataset=dict(pipeline=train_pipeline))
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# model settings
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temperature = 0.2
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model = dict(
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type='MoCoV3',
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base_momentum=0.01,
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backbone=dict(
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type='MoCoV3ViT',
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arch='mocov3-small', # embed_dim = 384
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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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neck=dict(
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type='NonLinearNeck',
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in_channels=384,
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hid_channels=4096,
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out_channels=256,
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num_layers=3,
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with_bias=False,
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with_last_bn=True,
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with_last_bn_affine=False,
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with_last_bias=False,
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with_avg_pool=False),
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head=dict(
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type='MoCoV3Head',
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predictor=dict(
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type='NonLinearNeck',
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in_channels=256,
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hid_channels=4096,
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out_channels=256,
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num_layers=2,
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with_bias=False,
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with_last_bn=True,
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with_last_bn_affine=False,
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with_last_bias=False,
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with_avg_pool=False),
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loss=dict(type='CrossEntropyLoss', loss_weight=2 * temperature),
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temperature=temperature))
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# optimizer
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optim_wrapper = dict(
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type='AmpOptimWrapper',
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loss_scale='dynamic',
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optimizer=dict(type='AdamW', lr=2.4e-3, weight_decay=0.1))
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find_unused_parameters = True
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# learning rate scheduler
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param_scheduler = [
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dict(
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type='LinearLR',
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start_factor=1e-4,
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by_epoch=True,
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begin=0,
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end=40,
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convert_to_iter_based=True),
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dict(
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type='CosineAnnealingLR',
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T_max=260,
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by_epoch=True,
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begin=40,
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end=300,
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convert_to_iter_based=True)
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]
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# runtime settings
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train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=300)
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# only keeps the latest 3 checkpoints
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default_hooks = dict(checkpoint=dict(max_keep_ckpts=3))
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# NOTE: `auto_scale_lr` is for automatically scaling LR
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# based on the actual training batch size.
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auto_scale_lr = dict(base_batch_size=4096)
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