Document feedback

Question document fragment

When a question document fragment contains a formula, it is displayed as a space.

Submission type

issue

It's a little complicated...

I'd like to ask someone.

PR

Just a small problem.

I can fix it online!

Please select the submission type

Problem type

Specifications and Common Mistakes

- Specifications and Common Mistakes:

- Misspellings or punctuation mistakes,incorrect formulas, abnormal display.

- Incorrect links, empty cells, or wrong formats.

- Chinese characters in English context.

- Minor inconsistencies between the UI and descriptions.

- Low writing fluency that does not affect understanding.

- Incorrect version numbers, including software package names and version numbers on the UI.

Usability

- Usability:

- Incorrect or missing key steps.

- Missing main function descriptions, keyword explanation, necessary prerequisites, or precautions.

- Ambiguous descriptions, unclear reference, or contradictory context.

- Unclear logic, such as missing classifications, items, and steps.

Correctness

- Correctness:

- Technical principles, function descriptions, supported platforms, parameter types, or exceptions inconsistent with that of software implementation.

- Incorrect schematic or architecture diagrams.

- Incorrect commands or command parameters.

- Incorrect code.

- Commands inconsistent with the functions.

- Wrong screenshots.

- Sample code running error, or running results inconsistent with the expectation.

Risk Warnings

- Risk Warnings:

- Lack of risk warnings for operations that may damage the system or important data.

Content Compliance

- Content Compliance:

- Contents that may violate applicable laws and regulations or geo-cultural context-sensitive words and expressions.

- Copyright infringement.

Please select the type of question

Problem description

Describe the bug so that we can quickly locate the problem.

Submit

Document feedback

mindspore.ops.CTCGreedyDecoder

class mindspore.ops.CTCGreedyDecoder(merge_repeated=True)

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 $(max\mathrm{\_}time,batch\mathrm{\_}size,num\mathrm{\_}classes)$. 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 $(batch\mathrm{\_}size,)$. 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 $(total\mathrm{\_}decoded\mathrm{\_}outputs,2)$. Data type is int64.

• decoded_values (Tensor) - A tensor with shape of $(total\mathrm{\_}decoded\mathrm{\_}outputs,)$, it stores the decoded classes. Data type is int64.

• decoded_shape (Tensor) - A tensor with shape of $(batch\mathrm{\_}size,max\mathrm{\_}decoded\mathrm{\_}length)$. Data type is int64.

• log_probability (Tensor) - A tensor with shape of $(batch\mathrm{\_}size,1)$, 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]]