mindspore.ops.CTCGreedyDecoder

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class mindspore.ops.CTCGreedyDecoder(merge_repeated=True)[source]

Performs greedy decoding on the logits given in inputs.

Refer to mindspore.ops.ctc_greedy_decoder() for more details.

Note

On Ascend, ‘merge_repeated’ can not be set to false.

Parameters

merge_repeated (bool, optional) – If True , merge repeated classes in output. Default: True .

Inputs:

  • inputs (Tensor) - The input Tensor must be a 3-D tensor whose shape is \((max\_time, batch\_size, num\_classes)\). num_classes must be num_labels + 1 classes, num_labels indicates the number of actual labels. Blank labels are reserved. Default blank label is num_classes - 1. Data type must be float32 or float64.

  • sequence_length (Tensor) - A tensor containing sequence lengths with the shape of \((batch\_size, )\). The type must be int32. Each value in the tensor must be equal to or less than max_time.

Outputs:

  • decoded_indices (Tensor) - A tensor with shape of \((total\_decoded\_outputs, 2)\). Data type is int64.

  • decoded_values (Tensor) - A tensor with shape of \((total\_decoded\_outputs, )\), it stores the decoded classes. Data type is int64.

  • decoded_shape (Tensor) - A tensor with shape of \((batch\_size, max\_decoded\_length)\). Data type is int64.

  • log_probability (Tensor) - A tensor with shape of \((batch\_size, 1)\), containing sequence log-probability, has the same type as inputs.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> inputs = Tensor(np.array([[[0.6, 0.4, 0.2], [0.8, 0.6, 0.3]],
...                           [[0.0, 0.6, 0.0], [0.5, 0.4, 0.5]]]), mindspore.float32)
>>> sequence_length = Tensor(np.array([2, 2]), mindspore.int32)
>>> decoded_indices, decoded_values, decoded_shape, log_probability = ops.CTCGreedyDecoder()(inputs,
...                                                                                          sequence_length)
>>> print(decoded_indices)
[[0 0]
 [0 1]
 [1 0]]
>>> print(decoded_values)
[0 1 0]
>>> print(decoded_shape)
[2 2]
>>> print(log_probability)
[[-1.2]
 [-1.3]]