mindspore.ops.ctc_greedy_decoder
- mindspore.ops.ctc_greedy_decoder(inputs, sequence_length, merge_repeated=True)[source]
Performs greedy decoding on the logits given in inputs.
Note
On Ascend, 'merge_repeated' can not be set to false.
- Parameters
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.
merge_repeated (bool) – If
true
, merge repeated classes in output. Default:True
.
- Returns
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.
- Raises
TypeError – If merge_repeated is not a bool.
ValueError – If length of shape of inputs is not equal to 3.
ValueError – If length of shape of sequence_length is not equal to 1.
ValueError – If value in the sequence_length is larger than max_time.
- 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.ctc_greedy_decoder(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]]