CTCLoss¶

class CTCLoss(layout='NTC', label_layout='NT', weight=None, **kwargs)¶

Connectionist Temporal Classification Loss.

Parameters:

layout : str, default ‘NTC’

Layout of prediction tensor. ‘N’, ‘T’, ‘C’ stands for batch size, sequence length, and alphabet_size respectively.

label_layout : str, default ‘NT’

Layout of the labels. ‘N’, ‘T’ stands for batch size, and sequence length respectively.

weight : float or None

Global scalar weight for loss.

Inputs:

pred: unnormalized prediction tensor (before softmax). Its shape depends on layout. If layout is ‘TNC’, pred should have shape (sequence_length, batch_size, alphabet_size). Note that in the last dimension, index alphabet_size-1 is reserved for internal use as blank label. So alphabet_size is one plus the actual alphabet size.
label: zero-based label tensor. Its shape depends on label_layout. If label_layout is ‘TN’, label should have shape (label_sequence_length, batch_size).
pred_lengths: optional (default None), used for specifying the length of each entry when different pred entries in the same batch have different lengths. pred_lengths should have shape (batch_size,).
label_lengths: optional (default None), used for specifying the length of each entry when different label entries in the same batch have different lengths. label_lengths should have shape (batch_size,).

Outputs:

**Example: suppose the vocabulary is [a, b, c], and in one batch we**

have three sequences ‘ba’, ‘cbb’, and ‘abac’. We can index the labels as

`{‘a’: 0, ‘b’: 1, ‘c’: 2, blank: 3}`. Then `alphabet_size` should be 4,

where label 3 is reserved for internal use by `CTCLoss`. We then need to

pad each sequence with `-1` to make a rectangular `label` tensor::