132 lines
6.3 KiB
Markdown
132 lines
6.3 KiB
Markdown
# CTCLoss {#openvino_docs_ops_sequence_CTCLoss_4}
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**Versioned name**: *CTCLoss-4*
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**Category**: *Sequence processing*
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**Short description**: *CTCLoss* computes the CTC (Connectionist Temporal Classification) Loss.
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**Detailed description**:
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*CTCLoss* operation is presented in [Connectionist Temporal Classification - Labeling Unsegmented Sequence Data with Recurrent Neural Networks: Graves et al., 2016](http://www.cs.toronto.edu/~graves/icml_2006.pdf)
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*CTCLoss* estimates likelihood that a target `labels[i,:]` can occur (or is real) for given input sequence of logits `logits[i,:,:]`.
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Briefly, *CTCLoss* operation finds all sequences aligned with a target `labels[i,:]`, computes log-probabilities of the aligned sequences using `logits[i,:,:]`
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and computes a negative sum of these log-probabilies.
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Input sequences of logits `logits` can have different lengths. The length of each sequence `logits[i,:,:]` equals `logit_length[i]`.
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A length of target sequence `labels[i,:]` equals `label_length[i]`. The length of the target sequence must not be greater than the length of corresponding input sequence `logits[i,:,:]`.
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Otherwise, the operation behaviour is undefined.
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*CTCLoss* calculation scheme:
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1. Compute probability of `j`-th character at time step `t` for `i`-th input sequence from `logits` using softmax formula:
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\f[
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p_{i,t,j} = \frac{\exp(logits[i,t,j])}{\sum^{K}_{k=0}{\exp(logits[i,t,k])}}
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\f]
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2. For a given `i`-th target from `labels[i,:]` find all aligned paths.
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A path `S = (c1,c2,...,cT)` is aligned with a target `G=(g1,g2,...,gT)` if both chains are equal after decoding.
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The decoding extracts substring of length `label_length[i]` from a target `G`, merges repeated characters in `G` in case *preprocess_collapse_repeated* equal to true and
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finds unique elements in the order of character occurrence in case *unique* equal to true.
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The decoding merges repeated characters in `S` in case *ctc_merge_repeated* equal to true and removes blank characters represented by `blank_index`.
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By default, `blank_index` is equal to `C-1`, where `C` is a number of classes including the blank.
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For example, in case default *ctc_merge_repeated*, *preprocess_collapse_repeated*, *unique* and `blank_index` a target sequence `G=(0,3,2,2,2,2,2,4,3)` of a length `label_length[i]=4` is processed
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to `(0,3,2,2)` and a path `S=(0,0,4,3,2,2,4,2,4)` of a length `logit_length[i]=9` is also processed to `(0,3,2,2)`, where `C=5`.
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There exist other paths that are also aligned with `G`, for instance, `0,4,3,3,2,4,2,2,2`. Paths checked for alignment with a target `label[:,i]` must be of length `logit_length[i] = L_i`.
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Compute probabilities of these aligned paths (alignments) as follows:
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\f[
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p(S) = \prod_{t=1}^{L_i} p_{i,t,ct}
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\f]
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3. Finally, compute negative log of summed up probabilities of all found alignments:
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\f[
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CTCLoss = - \ln \sum_{S} p(S)
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\f]
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**Note 1**: This calculation scheme does not provide steps for optimal implementation and primarily serves for better explanation.
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**Note 2**: This is recommended to compute a log-probability \f$ \ln p(S)\f$ for an aligned path as a sum of log-softmax of input logits. It helps to avoid underflow and overflow during calculation.
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Having log-probabilities for aligned paths, log of summed up probabilities for these paths can be computed as follows:
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\f[
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\ln(a + b) = \ln(a) + \ln(1 + \exp(\ln(b) - \ln(a)))
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\f]
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**Attributes**
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* *preprocess_collapse_repeated*
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* **Description**: *preprocess_collapse_repeated* is a flag for a preprocessing step before loss calculation, wherein repeated labels in `labels[i,:]` passed to the loss are merged into single labels.
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* **Range of values**: true or false
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* **Type**: `boolean`
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* **Default value**: false
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* **Required**: *no*
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* *ctc_merge_repeated*
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* **Description**: *ctc_merge_repeated* is a flag for merging repeated characters in a potential alignment during the CTC loss calculation.
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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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* *unique*
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* **Description**: *unique* is a flag to find unique elements for a target `labels[i,:]` before matching with potential alignments. Unique elements in the processed `labels[i,:]` are sorted in the order of their occurrence in original `labels[i,:]`. For example, the processed sequence for `labels[i,:]=(0,1,1,0,1,3,3,2,2,3)` of length `label_length[i]=10` will be `(0,1,3,2)` in case *unique* equal to true.
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* **Range of values**: true or false
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* **Type**: `boolean`
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* **Default value**: false
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* **Required**: *no*
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**Inputs**
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* **1**: `logits` - Input tensor with a batch of sequences of logits. Type of elements is *T_F*. Shape of the tensor is `[N, T, C]`, where `N` is the batch size, `T` is the maximum sequence length and `C` is the number of classes including the blank. **Required.**
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* **2**: `logit_length` - 1D input tensor of type *T1* and of a shape `[N]`. The tensor must consist of non-negative values not greater than `T`. Lengths of input sequences of logits `logits[i,:,:]`. **Required.**
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* **3**: `labels` - 2D tensor with shape `[N, T]` of type *T2*. A length of a target sequence `labels[i,:]` is equal to `label_length[i]` and must contain of integers from a range `[0; C-1]` except `blank_index`. **Required.**
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* **4**: `label_length` - 1D tensor of type *T1* and of a shape `[N]`. The tensor must consist of non-negative values not greater than `T` and `label_length[i] <= logit_length[i]` for all possible `i`. **Required.**
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* **5**: `blank_index` - Scalar of type *T2*. Set the class index to use for the blank label. Default value is `C-1`. **Optional.**
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**Output**
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* **1**: Output tensor with shape `[N]`, negative sum of log-probabilities of alignments. Type of elements is *T_F*.
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**Types**
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* *T_F*: any supported floating-point type.
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* *T1*, *T2*: `int32` or `int64`.
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**Example**
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```xml
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<layer ... type="CTCLoss" ...>
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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">
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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="3">
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<dim>8</dim>
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</port>
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<port id="4"> <!-- blank_index value is: 120 -->
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</input>
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<output>
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<port id="0">
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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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```
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