Specify operation CTCLoss-4 (openvinotoolkit#1189)

* Specify operation CTCLoss-4

Signed-off-by: Roman Kazantsev <[email protected]>

* Correct documentation for CTCLoss after #1 review

Signed-off-by: Roman Kazantsev <[email protected]>

* Correct documentation for CTCLoss after #2 review

Signed-off-by: Roman Kazantsev <[email protected]>

* Correct documentation for CTCLoss after #3 review

* Correct documentation for CTCLoss after #4 review

Signed-off-by: Roman Kazantsev <[email protected]>

* Correct layout for logits and add more description for unique attribute

Signed-off-by: Roman Kazantsev <[email protected]>

* Correct types for length and indices tensors

Signed-off-by: Roman Kazantsev <[email protected]>

* Correct formulas and punctuation

Signed-off-by: Roman Kazantsev <[email protected]>

docs/ops/opset4.md

@@ -25,6 +25,7 @@ declared in `namespace opset4`.
 * [Broadcast](movement/Broadcast_3.md)
 * [Bucketize](condition/Bucketize_3.md)
 * [CTCGreedyDecoder](sequence/CTCGreedyDecoder_1.md)
+* [CTCLoss](sequence/CTCLoss_4.md)
 * [Ceiling](arithmetic/Ceiling_1.md)
 * [Clamp](activation/Clamp_1.md)
 * [Concat](movement/Concat_1.md)

docs/ops/sequence/CTCLoss_4.md

@@ -0,0 +1,125 @@
+## CTCLoss <a name="CTCLoss"></a>
+
+**Versioned name**: *CTCLoss-4*
+
+**Category**: Sequence processing
+
+**Short description**: *CTCLoss* computes the CTC (Connectionist Temporal Classification) Loss.
+
+**Detailed description**:
+
+*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)
+
+*CTCLoss* estimates likelyhood that a target `labels[i,:]` can occur (or is real) for given input sequence of logits `logits[i,:,:]`.
+Briefly, *CTCLoss* operation finds all sequences aligned with a target `labels[i,:]`, computes log-probabilities of the aligned sequences using `logits[i,:,:]`
+and computes a negative sum of these log-probabilies.
+
+Input sequences of logits `logits` can have different lengths. The length of each sequence `logits[i,:,:]` equals `logit_length[i]`.
+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,:,:]`.
+Otherwise, the operation behaviour is undefined.
+
+*CTCLoss* calculation scheme:
+
+1. Compute probability of `j`-th character at time step `t` for `i`-th input sequence from `logits` using softmax formula:
+\f[
+p_{i,t,j} = \frac{\exp(logits[i,t,j])}{\sum^{K}_{k=0}{\exp(logits[i,t,k])}}
+\f]
+
+2. For a given `i`-th target from `labels[i,:]` find all aligned paths.
+A path `S = (c1,c2,...,cT)` is aligned with a target `G=(g1,g2,...,gT)` if both chains are equal after decoding.
+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
+finds unique elements in the order of character occurence in case *unique* equal to True.
+The decoding merges repeated characters in `S` in case *ctc_merge_repeated* equal to True and removes blank characters represented by `blank_index`.
+By default, `blank_index` is equal to `C-1`, where `C` is a number of classes including the blank.
+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 
+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`.
+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`.
+Compute probabilities of these aligned paths (alignments) as follows:
+\f[
+p(S) = \prod_{t=1}^{L_i} p_{i,t,ct}
+\f]
+
+3. Finally, compute negative sum of log-probabilities of all found alignments:
+\f[
+CTCLoss = \minus \sum_{S} \ln p(S)
+\f]
+
+**Note**:  This calculation scheme does not provide steps for optimal implementation and primarily serves for better explanation.
+
+**Attributes**
+
+* *preprocess_collapse_repeated*
+
+  * **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.
+  * **Range of values**: True or False
+  * **Type**: `boolean`
+  * **Default value**: False
+  * **Required**: *no*
+
+* *ctc_merge_repeated*
+
+  * **Description**: *ctc_merge_repeated* is a flag for merging repeated characters in a potential alignment during the CTC loss calculation.
+  * **Range of values**: True or False
+  * **Type**: `boolean`
+  * **Default value**: True
+  * **Required**: *no*
+
+* *unique*
+
+  * **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 occurence 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.
+  * **Range of values**: True or False
+  * **Type**: `boolean`
+  * **Default value**: False
+  * **Required**: *no*
+
+**Inputs**
+
+* **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.
+
+* **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.
+
+* **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.
+
+* **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.
+
+* **5**: `blank_index` - Scalar of type *T2*. Set the class index to use for the blank label. Default value is `C-1`. Optional.
+
+**Output**
+
+* **1**: Output tensor with shape `[N]`, negative sum of log-probabilities of alignments. Type of elements is *T_F*.
+
+**Types**
+
+* *T_F*: any supported floating point type.
+
+* *T1*, *T2*: `int32` or `int64`.
+
+**Example**
+
+```xml
+<layer ... type="CTCLoss" ...>
+    <input>
+        <port id="0">
+            <dim>8</dim>
+            <dim>20</dim>
+            <dim>128</dim>
+        </port>
+        <port id="1">
+            <dim>8</dim>
+        </port>
+        <port id="2">
+            <dim>8</dim>
+            <dim>20</dim>
+        </port>
+        <port id="3">
+            <dim>8</dim>
+        </port>
+        <port id="4">  <!-- blank_index value is: 120 -->
+    </input>
+    <output>
+        <port id="0">
+            <dim>8</dim>
+        </port>
+    </output>
+</layer>
+```