PolynomialLR

class tensorplay.optim.lr_scheduler.PolynomialLR(optimizer: Optimizer, total_iters: int = 5, power: float = 1.0, last_epoch: int = -1)[source]

Decays the learning rate of each parameter group using a polynomial function in the given total_iters.

When last_epoch=-1, sets initial lr as lr.

Parameters:

• optimizer (Optimizer) – Wrapped optimizer.

• total_iters (int) – The number of steps that the scheduler decays the learning rate. Default: 5.

• power (float) – The power of the polynomial. Default: 1.0.

Example

>>> # xdoctest: +SKIP("undefined vars")
>>> # Assuming optimizer uses lr = 0.05 for all groups
>>> # lr = 0.0490   if epoch == 0
>>> # lr = 0.0481   if epoch == 1
>>> # lr = 0.0472   if epoch == 2
>>> # ...
>>> # lr = 0.0      if epoch >= 50
>>> scheduler = PolynomialLR(optimizer, total_iters=50, power=0.9)
>>> for epoch in range(100):
>>>     train(...)
>>>     validate(...)
>>>     scheduler.step()

get_last_lr() → list[float | TensorBase]

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() → list[float | TensorBase][source]

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

Scales the group["lr"]s in the optimizer’s param_groups such that the learning rates follow

\[\texttt{base\_lr} \cdot \left(1 - \frac{\texttt{last\_epoch}} {\texttt{total\_iters}} \right)^\texttt{power}\]

Returns the current learning rates unchanged after total_iters is reached.

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.

load_state_dict(state_dict: dict[str, Any]) → None

Load the scheduler’s state.

Parameters:

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

state_dict() → dict[str, Any]

Return the state of the scheduler as a dict.

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

step(epoch: int | None = None) → 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().