79 lines
2.2 KiB
Python
79 lines
2.2 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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# model settings
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temperature = 1.0
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model = dict(
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type='MoCoV3',
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base_momentum=0.004, # 0.01 for 100e and 300e, 0.004 for 800 and 1000e
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backbone=dict(type='ResNet', depth=50, norm_cfg=dict(type='SyncBN')),
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neck=dict(
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type='NonLinearNeck',
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in_channels=2048,
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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=True),
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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=False,
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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='LARS', lr=4.8, weight_decay=1.5e-6, momentum=0.9),
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paramwise_cfg=dict(
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custom_keys={
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'bn': dict(decay_mult=0, lars_exclude=True),
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'bias': dict(decay_mult=0, lars_exclude=True),
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# bn layer in ResNet block downsample module
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'downsample.1': dict(decay_mult=0, lars_exclude=True),
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}),
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)
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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=10,
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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=790,
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by_epoch=True,
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begin=10,
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end=800,
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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=800)
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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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