MultiStepLR

class torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones, gamma=0.1, last_epoch=-1)

Decays the learning rate of each parameter group by gamma once the number of epoch reaches one of the milestones.

Notice that such decay 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.

• milestones (list) – List of epoch indices. Must be increasing.

• gamma (float) – Multiplicative factor of learning rate decay. Default: 0.1.

• last_epoch (int) – The index of last epoch. Default: -1.

Example

>>> # Assuming optimizer uses lr = 0.05 for all groups
>>> # lr = 0.05     if epoch < 30
>>> # lr = 0.005    if 30 <= epoch < 80
>>> # lr = 0.0005   if epoch >= 80
>>> scheduler = MultiStepLR(optimizer, milestones=[30, 80], gamma=0.1)
>>> for epoch in range(100):
>>>     train(...)
>>>     validate(...)
>>>     scheduler.step()

get_last_lr()

Get the most recent learning rates computed by this scheduler.

Returns:

A list of learning rates with entries for each of the optimizer’s param_groups, with the same types as their group["lr"]s.

Return type:

list[float | Tensor]

Note

The returned Tensors are copies, and never alias the optimizer’s group["lr"]s.

get_lr()

Compute the next learning rate for each of the optimizer’s param_groups.

If the current epoch is in milestones, decays the group["lr"]s in the optimizer’s param_groups by gamma.

Returns:

A list of learning rates for each of the optimizer’s param_groups with the same types as their current group["lr"]s.

Return type:

list[float | Tensor]

Note

If you’re trying to inspect the most recent learning rate, use get_last_lr() instead.

Note

The returned Tensors are copies, and never alias the optimizer’s group["lr"]s.

Note

If the current epoch appears in milestones n times, we scale by gamma to the power of n

load_state_dict(state_dict)

Load the scheduler’s state.

Parameters:

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

state_dict()

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)

Step the scheduler.

Parameters:

epoch (int, optional) –

Deprecated since version 1.4: If provided, sets last_epoch to epoch and uses _get_closed_form_lr() if it is available. This is not universally supported. Use step() without arguments instead.

Note

Call this method after calling the optimizer’s step().