CosineAnnealingWarmRestarts

class torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, T_0, T_mult=1, eta_min=0, last_epoch=- 1, verbose='deprecated')

Set the learning rate of each parameter group using a cosine annealing schedule, where \(\eta_{max}\) is set to the initial lr, \(T_{cur}\) is the number of epochs since the last restart and \(T_{i}\) is the number of epochs between two warm restarts in SGDR:

\[\eta_t = \eta_{min} + \frac{1}{2}(\eta_{max} - \eta_{min})\left(1 + \cos\left(\frac{T_{cur}}{T_{i}}\pi\right)\right) \]

When \(T_{cur}=T_{i}\), set \(\eta_t = \eta_{min}\). When \(T_{cur}=0\) after restart, set \(\eta_t=\eta_{max}\).

It has been proposed in SGDR: Stochastic Gradient Descent with Warm Restarts.

Parameters:

• optimizer (Optimizer) – Wrapped optimizer.

• T_0 (int) – Number of iterations until the first restart.

• T_mult (int, optional) – A factor by which \(T_{i}\) increases after a restart. Default: 1.

• eta_min (float, optional) – Minimum learning rate. Default: 0.

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

• verbose (bool | str) –

  If True, prints a message to stdout for each update. Default: False.

  Deprecated since version 2.2: verbose is deprecated. Please use get_last_lr() to access the learning rate.

get_last_lr()

Return last computed learning rate by current scheduler.

Return type:

List[float]

load_state_dict(state_dict)

Loads the schedulers state.

Parameters:

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

print_lr(is_verbose, group, lr, epoch=None)

Display the current learning rate.

Deprecated since version 2.4: print_lr() is deprecated. Please use get_last_lr() to access the learning rate.

state_dict()

Returns 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=None)

Step could be called after every batch update

Example

>>> scheduler = CosineAnnealingWarmRestarts(optimizer, T_0, T_mult)
>>> iters = len(dataloader)
>>> for epoch in range(20):
>>>     for i, sample in enumerate(dataloader):
>>>         inputs, labels = sample['inputs'], sample['labels']
>>>         optimizer.zero_grad()
>>>         outputs = net(inputs)
>>>         loss = criterion(outputs, labels)
>>>         loss.backward()
>>>         optimizer.step()
>>>         scheduler.step(epoch + i / iters)

This function can be called in an interleaved way.

Example

>>> scheduler = CosineAnnealingWarmRestarts(optimizer, T_0, T_mult)
>>> for epoch in range(20):
>>>     scheduler.step()
>>> scheduler.step(26)
>>> scheduler.step() # scheduler.step(27), instead of scheduler(20)