torch.nn.functional.ctc_loss

torch.nn.functional.ctc_loss(log_probs, targets, input_lengths, target_lengths, blank=0, reduction='mean', zero_infinity=False)

The Connectionist Temporal Classification loss.

See CTCLoss for details.

Note

In some circumstances when given tensors on a CUDA device and using CuDNN, this operator may select a nondeterministic algorithm to increase performance. If this is undesirable, you can try to make the operation deterministic (potentially at a performance cost) by setting torch.backends.cudnn.deterministic = True. See Reproducibility for more information.

Note

This operation may produce nondeterministic gradients when given tensors on a CUDA device. See Reproducibility for more information.

Parameters

• log_probs – $(T, N, C)$ where C = number of characters in alphabet including blank, T = input length, and N = batch size. The logarithmized probabilities of the outputs (e.g. obtained with torch.nn.functional.log_softmax()).

• targets – $(N, S)$ or (sum(target_lengths)). Targets cannot be blank. In the second form, the targets are assumed to be concatenated.

• input_lengths – $(N)$. Lengths of the inputs (must each be $\leq T$)

• target_lengths – $(N)$. Lengths of the targets

• blank (int, optional) – Blank label. Default $0$.

• reduction (string, optional) – Specifies the reduction to apply to the output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean': the output losses will be divided by the target lengths and then the mean over the batch is taken, 'sum': the output will be summed. Default: 'mean'

• zero_infinity (bool, optional) – Whether to zero infinite losses and the associated gradients. Default: False Infinite losses mainly occur when the inputs are too short to be aligned to the targets.

Example:

>>> log_probs = torch.randn(50, 16, 20).log_softmax(2).detach().requires_grad_()
>>> targets = torch.randint(1, 20, (16, 30), dtype=torch.long)
>>> input_lengths = torch.full((16,), 50, dtype=torch.long)
>>> target_lengths = torch.randint(10,30,(16,), dtype=torch.long)
>>> loss = F.ctc_loss(log_probs, targets, input_lengths, target_lengths)
>>> loss.backward()