* Doc Migration from Gitlab (#1289) * doc migration * fix * Update FakeQuantize_1.md * Update performance_benchmarks.md * Updates graphs for FPGA * Update performance_benchmarks.md * Change DL Workbench structure (#1) * Changed DL Workbench structure * Fixed tags * fixes * Update ie_docs.xml * Update performance_benchmarks_faq.md * Fixes in DL Workbench layout * Fixes for CVS-31290 * [DL Workbench] Minor correction * Fix for CVS-30955 * Added nGraph deprecation notice as requested by Zoe * fix broken links in api doxy layouts * CVS-31131 fixes * Additional fixes * Fixed POT TOC * Update PAC_Configure.md PAC DCP 1.2.1 install guide. * Update inference_engine_intro.md * fix broken link * Update opset.md * fix * added opset4 to layout * added new opsets to layout, set labels for them * Update VisionAcceleratorFPGA_Configure.md Updated from 2020.3 to 2020.4 Co-authored-by: domi2000 <domi2000@users.noreply.github.com>
2.7 KiB
CTCGreedyDecoder
Versioned name: CTCGreedyDecoder-1
Category: Sequence processing
Short description: CTCGreedyDecoder performs greedy decoding on the logits given in input (best path).
Detailed description:
This operation is similar Reference
Given an input sequence \f$X\f$ of length \f$T\f$, CTCGreedyDecoder assumes the probability of a length \f$T\f$ character sequence \f$C\f$ is given by \f[ p(C|X) = \prod_{t=1}^{T} p(c_{t}|X) \f]
Sequences in the batch can have different length. The lengths of sequences are coded as values 1 and 0 in the second input tensor sequence_mask. Value sequence_mask[j, i] specifies whether there is a sequence symbol at index i in the sequence i in the batch of sequences. If there is no symbol at j-th position sequence_mask[j, i] = 0, and sequence_mask[j, i] = 1 otherwise. Starting from j = 0, sequence_mass[j, i] are equal to 1 up to the particular index j = last_sequence_symbol, which is defined independently for each sequence i. For j > last_sequence_symbol, values in sequence_mask[j, i] are all zeros.
Attributes
-
merge_repeated
- Description: merge_repeated is a flag for merging repeated labels during the CTC calculation.
- Range of values: True or False
- Type:
boolean - Default value: True
- Required: no
Inputs
-
1:
data- Input tensor with a batch of sequences. Type of elements is any supported floating point type. Shape of the tensor is[T, N, C], whereTis the maximum sequence length,Nis the batch size andCis the number of classes. Required. -
2:
sequence_mask- 2D input floating point tensor with sequence masks for each sequence in the batch. Populated with values 0 and 1. Shape of this input is[T, N]. Required.
Output
- 1: Output tensor with shape
[N, T, 1, 1]and integer elements containing final sequence class indices. A final sequence can be shorter that the sizeTof the tensor, all elements that do not code sequence classes are filled with -1. Type of elements is floating point, but all values are integers.
Example
<layer ... type="CTCGreedyDecoder" ...>
<input>
<port id="0">
<dim>20</dim>
<dim>8</dim>
<dim>128</dim>
</port>
<port id="1">
<dim>20</dim>
<dim>8</dim>
</port>
</input>
<output>
<port id="0">
<dim>8</dim>
<dim>20</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>