oneflow.nn.functional.ctc_greedy_decoder¶

oneflow.nn.functional.ctc_greedy_decoder(log_probs: Tensor, input_lengths: Tensor, merge_repeated: bool = True) → Tensor¶

Performs greedy decoding on the logits given in input (best path).

Parameters

log_probs (oneflow.Tensor) – A Tensor of shape [input_length, batch_size, num_labels]. The logarithmized probabilities of the outputs (e.g. obtained with flow.nn.logsoftmax()).
input_lengths (oneflow.Tensor) – A Tensor of shape [batch_size]. It represent the lengths of the inputs. And the lengths are specified for each sequence to achieve masking under the assumption that sequences are padded to equal lengths.
merge_repeated (bool, optional) – If merge_repeated is True, merge repeated classes in output. This means that if consecutive logits’ maximum indices are the same, only the first of these is emitted. Defaults to True.

Returns

A Tensor of shape [batch_size, input_length], The decoded outputs. neg_sum_logits(oneflow.Tensor): A float matrix (batch_size x 1) containing, for the sequence found, the negative of the sum of the greatest logit at each timeframe.

Return type

decoded(oneflow.Tensor)

For example:

>>> import oneflow as flow
>>> import numpy as np
>>> log_probs = flow.tensor(
...     [
...         [[-1.54, -1.20, -1.95, -1.65, -1.81], [-1.84, -1.74, -1.58, -1.55, -1.12]],
...         [[-1.68, -1.48, -1.89, -1.30, -2.07], [-1.13, -1.45, -1.24, -1.61, -1.66]],
...         [[-1.56, -1.40, -2.83, -1.67, -1.48], [-1.20, -2.01, -2.05, -1.95, -1.24]],
...         [[-2.09, -1.76, -1.36, -1.67, -1.45], [-1.85, -1.48, -1.34, -2.16, -1.55]],
...     ]
... )
>>> input_lengths = flow.tensor([4, 4])
>>> decoded, neg_sum_logits = flow.nn.functional.ctc_greedy_decoder(log_probs, input_lengths)
>>> decoded
tensor([[1, 3, 1, 2],
        [0, 2, 0, 0]], dtype=oneflow.int64)
>>> neg_sum_logits
tensor([[5.2600],
        [4.7900]], dtype=oneflow.float32)