105 lines
4.3 KiB
Markdown
105 lines
4.3 KiB
Markdown
# CTCGreedyDecoderSeqLen {#openvino_docs_ops_sequence_CTCGreedyDecoderSeqLen_6}
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@sphinxdirective
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.. meta::
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:description: Learn about CTCGreedyDecoderSeqLen-6 - a sequence processing
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operation, which can be performed on two required input tensors.
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**Versioned name**: *CTCGreedyDecoderSeqLen-6*
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**Category**: *Sequence processing*
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**Short description**: *CTCGreedyDecoderSeqLen* performs greedy decoding of the logits provided as the first input. The sequence lengths are provided as the second input.
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**Detailed description**:
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This operation is similar to the `TensorFlow CTCGreedyDecoder <https://www.tensorflow.org/api_docs/python/tf/nn/ctc_greedy_decoder>`__.
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The operation *CTCGreedyDecoderSeqLen* implements best path decoding.
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Decoding is done in two steps:
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1. Concatenate the most probable classes per time-step which yields the best path.
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2. Remove duplicate consecutive elements if the attribute *merge_repeated* is true and then remove all blank elements.
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Sequences in the batch can have different length. The lengths of sequences are coded in the second input integer tensor ``sequence_length``.
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The main difference between :doc:`CTCGreedyDecoder <openvino_docs_ops_sequence_CTCGreedyDecoder_1>` and CTCGreedyDecoderSeqLen is in the second input. CTCGreedyDecoder uses 2D input floating-point tensor with sequence masks for each sequence in the batch while CTCGreedyDecoderSeqLen uses 1D integer tensor with sequence lengths.
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**Attributes**
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* *merge_repeated*
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* **Description**: *merge_repeated* is a flag for merging repeated labels during the CTC calculation. If the value is false the sequence ``ABB*B*B`` (where '*' is the blank class) will look like ``ABBBB``. But if the value is true, the sequence will be ``ABBB``.
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* **Range of values**: true or false
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* **Type**: ``boolean``
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* **Default value**: true
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* **Required**: *no*
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* *classes_index_type*
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* **Description**: the type of output tensor with classes indices
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* **Range of values**: "i64" or "i32"
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* **Type**: string
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* **Default value**: "i32"
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* **Required**: *no*
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* *sequence_length_type*
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* **Description**: the type of output tensor with sequence length
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* **Range of values**: "i64" or "i32"
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* **Type**: string
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* **Default value**: "i32"
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* **Required**: *no*
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**Inputs**
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* **1**: ``data`` - input tensor of type *T_F* of shape ``[N, T, C]`` with a batch of sequences. Where ``T`` is the maximum sequence length, ``N`` is the batch size and ``C`` is the number of classes. **Required.**
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* **2**: ``sequence_length`` - input tensor of type *T_I* of shape ``[N]`` with sequence lengths. The values of sequence length must be less or equal to ``T``. **Required.**
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* **3**: ``blank_index`` - scalar or 1D tensor with 1 element of type *T_I*. Specifies the class index to use for the blank class. Regardless of the value of ``merge_repeated`` attribute, if the output index for a given batch and time step corresponds to the ``blank_index``, no new element is emitted. Default value is `C-1`. **Optional.**
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**Output**
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* **1**: Output tensor of type *T_IND1* shape ``[N, T]`` and containing the decoded classes. All elements that do not code sequence classes are filled with -1.
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* **2**: Output tensor of type *T_IND2* shape ``[N]`` and containing length of decoded class sequence for each batch.
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**Types**
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* *T_F*: any supported floating-point type.
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* *T_I*: ``int32`` or ``int64``.
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* *T_IND1*: ``int32`` or ``int64`` and depends on ``classes_index_type`` attribute.
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* *T_IND2*: ``int32`` or ``int64`` and depends on ``sequence_length_type`` attribute.
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**Example**
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.. code-block:: xml
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:force:
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<layer ... type="CTCGreedyDecoderSeqLen" version="opset6">
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<data merge_repeated="true" classes_index_type="i64" sequence_length_type="i64"/>
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<input>
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<port id="0">
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<dim>8</dim>
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<dim>20</dim>
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<dim>128</dim>
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</port>
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<port id="1">
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<dim>8</dim>
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</port>
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<port id="2"/> < !-- blank_index = 120 -->
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</input>
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<output>
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<port id="0" precision="I64">
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<dim>8</dim>
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<dim>20</dim>
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</port>
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<port id="1" precision="I64">
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<dim>8</dim>
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</port>
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</output>
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</layer>
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@endsphinxdirective
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