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Migrate parameter scheduler from MMCV to MMEngine — mmengine 0.10.7 documentation

MMCV 1.x version uses LrUpdaterHook and MomentumUpdaterHook to adjust the learning rate and momentum. However, the design of LrUpdaterHook has been difficult to meet more abundant customization requirements due to the development of the training strategies. Hence, MMEngine proposes parameter schedulers (ParamScheduler).

The interface of the parameter scheduler is consistent with PyTroch’s learning rate scheduler (LRScheduler). In addition, the parameter scheduler provides stronger functions. For details, please refer to Parameter Scheduler User Guide.

MMEngine uses LRScheduler instead of LrUpdaterHook. The field in the config file is changed from the original lr_config to param_scheduler. The learning rate config in MMCV corresponds to the parameter scheduler config in MMEngine as follows:

The learning rate warm-up can be achieved through the combination of schedulers by specifying the effective range begin and end. There are 3 learning rate warm-up methods in MMCV, namely 'constant', 'linear', 'exp'. The corresponding config in MMEngine should be modified as follows:

lr_config = dict(
    warmup='constant',
    warmup_ratio=0.1,
    warmup_iters=500,
    warmup_by_epoch=False
)

param_scheduler = [
    dict(type='ConstantLR',
         factor=0.1,
         begin=0,
         end=500,
         by_epoch=False),
    dict(...) # the main learning rate scheduler
]

lr_config = dict(
    warmup='linear',
    warmup_ratio=0.1,
    warmup_iters=500,
    warmup_by_epoch=False
)

param_scheduler = [
    dict(type='LinearLR',
         start_factor=0.1,
         begin=0,
         end=500,
         by_epoch=False),
    dict(...) # the main learning rate scheduler
]

lr_config = dict(
    warmup='exp',
    warmup_ratio=0.1,
    warmup_iters=500,
    warmup_by_epoch=False
)

param_scheduler = [
    dict(type='ExponentialLR',
         gamma=0.1,
         begin=0,
         end=500,
         by_epoch=False),
    dict(...) # the main learning rate scheduler
]

lr_config = dict(policy='fixed')

param_scheduler = [
    dict(type='ConstantLR', factor=1)
]

lr_config = dict(
    policy='step',
    step=[8, 11],
    gamma=0.1,
    by_epoch=True
)

param_scheduler = [
    dict(type='MultiStepLR',
         milestones=[8, 11],
         gamma=0.1,
         by_epoch=True)
]

lr_config = dict(
    policy='poly',
    power=0.7,
    min_lr=0.001,
    by_epoch=True
)

param_scheduler = [
    dict(type='PolyLR',
         power=0.7,
         eta_min=0.001,
         begin=0,
         end=num_epochs,
         by_epoch=True)
]

lr_config = dict(
    policy='exp',
    power=0.5,
    by_epoch=True
)

param_scheduler = [
    dict(type='ExponentialLR',
         gamma=0.5,
         begin=0,
         end=num_epochs,
         by_epoch=True)
]

lr_config = dict(
    policy='CosineAnnealing',
    min_lr=0.5,
    by_epoch=True
)

param_scheduler = [
    dict(type='CosineAnnealingLR',
         eta_min=0.5,
         T_max=num_epochs,
         begin=0,
         end=num_epochs,
         by_epoch=True)
]

The learning rate strategy combined by multiple phases like FlatCosineAnnealing originally needs to be achieved by rewriting a Hook. But in MMEngine, it can be achieved with combining two parameter scheduler configs:

lr_config = dict(
    policy='FlatCosineAnnealing',
    start_percent=0.5,
    min_lr=0.005,
    by_epoch=True
)

param_scheduler = [
    dict(type='ConstantLR', factor=1, begin=0, end=num_epochs * 0.75)
    dict(type='CosineAnnealingLR',
         eta_min=0.005,
         begin=num_epochs * 0.75,
         end=num_epochs,
         T_max=num_epochs * 0.25,
         by_epoch=True)
]

lr_config = dict(policy='CosineRestart',
                 periods=[5, 10, 15],
                 restart_weights=[1, 0.7, 0.3],
                 min_lr=0.001,
                 by_epoch=True)

param_scheduler = [
    dict(type='CosineRestartLR',
         periods=[5, 10, 15],
         restart_weights=[1, 0.7, 0.3],
         eta_min=0.001,
         by_epoch=True)
]

lr_config = dict(policy='OneCycle',
                 max_lr=0.02,
                 total_steps=90000,
                 pct_start=0.3,
                 anneal_strategy='cos',
                 div_factor=25,
                 final_div_factor=1e4,
                 three_phase=True,
                 by_epoch=False)

param_scheduler = [
    dict(type='OneCycleLR',
         eta_max=0.02,
         total_steps=90000,
         pct_start=0.3,
         anneal_strategy='cos',
         div_factor=25,
         final_div_factor=1e4,
         three_phase=True,
         by_epoch=False)
]

Notice: by_epoch defaults to False in MMCV. It now defaults to True in MMEngine.

lr_config = dict(
    policy='LinearAnnealing',
    min_lr_ratio=0.01,
    by_epoch=True
)

param_scheduler = [
    dict(type='LinearLR',
         start_factor=1,
         end_factor=0.01,
         begin=0,
         end=num_epochs,
         by_epoch=True)
]

MMCV uses momentum_config field and MomentumUpdateHook to adjust momentum. The momentum in MMEngine is also controlled by the parameter scheduler. Users can simply change the LR of the learning rate scheduler to Momentum to use the same strategy to adjust the momentum. The momentum scheduler shares the same param_scheduler field in the config with the learning rate scheduler:

lr_config = dict(...)
momentum_config = dict(
    policy='CosineAnnealing',
    min_momentum=0.1,
    by_epoch=True
)

param_scheduler = [
    # config of learning rate schedulers
    dict(...),
    # config of momentum schedulers
    dict(type='CosineAnnealingMomentum',
         eta_min=0.1,
         T_max=num_epochs,
         begin=0,
         end=num_epochs,
         by_epoch=True)
]

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