- class mindspore.ops.CTCGreedyDecoder(merge_repeated=True)
Performs greedy decoding on the logits given in inputs.
merge_repeated (bool) – If true, merge repeated classes in output. Default: True.
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.
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 \((batch\_size, max\_decoded\_legth)\). 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:
>>> 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) >>> ctc_greedyDecoder = ops.CTCGreedyDecoder() >>> decoded_indices, decoded_values, decoded_shape, log_probability = ctc_greedyDecoder(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]]