ConstantLR

class torch.optim.lr_scheduler.ConstantLR(optimizer, factor=0.3333333333333333, total_iters=5, last_epoch=-1) [source]

Multiply the learning rate of each parameter group by a small constant factor.

The multiplication is done until the number of epoch reaches a pre-defined milestone: total_iters. Notice that such multiplication of the small constant factor can happen simultaneously with other changes to the learning rate from outside this scheduler. When last_epoch=-1, sets initial lr as lr.

Parameters

• optimizer (Optimizer) – Wrapped optimizer.
• factor (float) – The number we multiply learning rate until the milestone. Default: 1./3.
• total_iters (int) – The number of steps that the scheduler multiplies the learning rate by the factor. Default: 5.
• last_epoch (int) – The index of the last epoch. Default: -1.

Example

>>> # Assuming optimizer uses lr = 0.05 for all groups
>>> # lr = 0.025   if epoch == 0
>>> # lr = 0.025   if epoch == 1
>>> # lr = 0.025   if epoch == 2
>>> # lr = 0.025   if epoch == 3
>>> # ...
>>> # lr = 0.05    if epoch >= 40
>>> scheduler = ConstantLR(optimizer, factor=0.5, total_iters=40)
>>> for epoch in range(100):
>>>     train(...)
>>>     validate(...)
>>>     scheduler.step()

get_last_lr() [source]

Return last computed learning rate by current scheduler.

Return type

list[float]

get_lr() [source]

Compute the learning rate of each parameter group.

Return type

list[float]

load_state_dict(state_dict) [source]

Load the scheduler’s state.

Parameters

state_dict (dict) – scheduler state. Should be an object returned from a call to state_dict().

state_dict() [source]

Return the state of the scheduler as a dict.

It contains an entry for every variable in self.__dict__ which is not the optimizer.

Return type

dict[str, Any]

step(epoch=None) [source]

Perform a step.