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PriorBox-8 specification (#8000)

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* New version of PriorBox operation to add new 'min_max_aspect_ratios_order' attribute

* Apply suggestions from code review

Co-authored-by: Tomasz Dołbniak <tomasz.dolbniak@intel.com>

* Apply suggestions from code review

Co-authored-by: Tatiana Savina <tatiana.savina@intel.com>

Co-authored-by: Tomasz Dołbniak <tomasz.dolbniak@intel.com>
Co-authored-by: Tatiana Savina <tatiana.savina@intel.com>
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1
docs/doxygen/ie_docs.xml
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@ -222,6 +222,7 @@ limitations under the License.
<tab type="user" title="Power-1" url="@ref openvino_docs_ops_arithmetic_Power_1"/>
<tab type="user" title="PriorBoxClustered-1" url="@ref openvino_docs_ops_detection_PriorBoxClustered_1"/>
<tab type="user" title="PriorBox-1" url="@ref openvino_docs_ops_detection_PriorBox_1"/>
<tab type="user" title="PriorBox-8" url="@ref openvino_docs_ops_detection_PriorBox_8"/>
<tab type="user" title="Proposal-1" url="@ref openvino_docs_ops_detection_Proposal_1"/>
<tab type="user" title="Proposal-4" url="@ref openvino_docs_ops_detection_Proposal_4"/>
<tab type="user" title="RandomUniform-8" url="@ref openvino_docs_ops_generation_RandomUniform_8"/>

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docs/ops/detection/PriorBox_8.md Normal file
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@ -0,0 +1,184 @@
## PriorBox<a name="PriorBox"></a> {#openvino_docs_ops_detection_PriorBox_8}
**Versioned name**: *PriorBox-8*
**Category**: *Object detection*
**Short description**: *PriorBox* operation generates prior boxes of specified sizes and aspect ratios across all dimensions.
**Detailed description**:
*PriorBox* computes coordinates of prior boxes by the following rules:
1. First, it calculates *center_x* and *center_y* of a prior box:
\f[
W \equiv Width \quad Of \quad Image \\
H \equiv Height \quad Of \quad Image
\f]
* If step equals 0:
\f[
center_x=(w+0.5) \\
center_y=(h+0.5)
\f]
* else:
\f[
center_x=(w+offset)*step \\
center_y=(h+offset)*step \\
w \subset \left( 0, W \right ) \\
h \subset \left( 0, H \right )
\f]
2. Then, it calculates coordinates of prior boxes for each \f$ s \subset \left( 0, min\_sizes \right ) \f$:
\f[
xmin = \frac{\frac{center_x - s}{2}}{W}
\f]
\f[
ymin = \frac{\frac{center_y - s}{2}}{H}
\f]
\f[
xmax = \frac{\frac{center_x + s}{2}}{W}
\f]
\f[
ymin = \frac{\frac{center_y + s}{2}}{H}
\f]
3. If *clip* attribute is set to true, each output value is clipped between \f$ \left< 0, 1 \right> \f$.
**Attributes**:
* *min_size (max_size)*
* **Description**: *min_size (max_size)* is the minimum (maximum) box size in pixels.
* **Range of values**: positive floating-point numbers
* **Type**: `float[]`
* **Default value**: []
* **Required**: *no*
* *aspect_ratio*
* **Description**: *aspect_ratio* is a variance of aspect ratios. Duplicate values are ignored.
* **Range of values**: a set of positive integer numbers
* **Type**: `float[]`
* **Default value**: []
* **Required**: *no*
* *flip*
* **Description**: *flip* is a flag that denotes that each *aspect_ratio* is duplicated and flipped. For example, *flip* equals 1 and *aspect_ratio* equals `[4.0,2.0]`, meaning that the aspect_ratio is equal to `[4.0,2.0,0.25,0.5]`.
* **Range of values**:
* false or 0 - each *aspect_ratio* is flipped
* true or 1 - each *aspect_ratio* is not flipped
* **Type**: `boolean`
* **Default value**: false
* **Required**: *no*
* *clip*
* **Description**: *clip* is a flag that denotes if each value in the output tensor should be clipped to the `[0,1]` interval.
* **Range of values**:
* false or 0 - clipping is not performed
* true or 1 - each value in the output tensor is clipped to the `[0,1]` interval.
* **Type**: `boolean`
* **Default value**: false
* **Required**: *no*
* *step*
* **Description**: *step* is a distance between box centers.
* **Range of values**: floating-point non-negative number
* **Type**: `float`
* **Default value**: 0
* **Required**: *no*
* *offset*
* **Description**: *offset* is a shift of a box to the top left corner respectively.
* **Range of values**: floating-point non-negative number
* **Type**: `float`
* **Required**: *yes*
* *variance*
* **Description**: *variance* denotes a variance of adjusting bounding boxes. The attribute could contain 0, 1, or 4 elements.
* **Range of values**: floating-point positive numbers
* **Type**: `float[]`
* **Default value**: []
* **Required**: *no*
* *scale_all_sizes*
* **Description**: *scale_all_sizes* is a flag that denotes type of inference. For example, *scale_all_sizes* equals 0 means that *max_size* attribute is ignored.
* **Range of values**:
* false - *max_size* is ignored
* true - *max_size* is used
* **Type**: `boolean`
* **Default value**: true
* **Required**: *no*
* *fixed_ratio*
* **Description**: *fixed_ratio* is an aspect ratio of a box.
* **Range of values**: a list of positive floating-point numbers
* **Type**: `float[]`
* **Default value**: []
* **Required**: *no*
* *fixed_size*
* **Description**: *fixed_size* is an initial box size in pixels.
* **Range of values**: a list of positive floating-point numbers
* **Type**: `float[]`
* **Default value**: []
* **Required**: *no*
* *density*
* **Description**: *density* is the square root of the number of boxes of each type.
* **Range of values**: a list of positive floating-point numbers
* **Type**: `float[]`
* **Default value**: []
* **Required**: *no*
* *min_max_aspect_ratios_order*
* **Description**: *min_max_aspect_ratios_order* is a flag that denotes the order of output prior box. If set true, the output prior box is in [min, max, aspect_ratios] order, which is consistent with Caffe. Note that the order affects the weights order of the preceding convolution layer and does not affect the final detection results.
* **Range of values**:
* false - the output prior box is in [min, aspect_ratios, max] order
* true - the output prior box is in [min, max, aspect_ratios] order
* **Type**: `boolean`
* **Default value**: true
* **Required**: *no*
**Inputs**:
* **1**: `output_size` - 1D tensor of type *T_INT* with two elements `[height, width]`. Specifies the spatial size of generated grid with boxes. **Required.**
* **2**: `image_size` - 1D tensor of type *T_INT* with two elements `[image_height, image_width]`. Specifies shape of the image for which boxes are generated. **Required.**
**Outputs**:
* **1**: 2D tensor of shape `[2, 4 * height * width * priors_per_point]` and type *T_OUT* with box coordinates. The `priors_per_point` is the number of boxes generated per each grid element. The number depends on operation attribute values.
**Types**
* *T_INT*: any supported integer type.
* *T_OUT*: supported floating-point type.
**Example**
```xml
<layer type="PriorBox" ...>
<data aspect_ratio="2.0" clip="false" density="" fixed_ratio="" fixed_size="" flip="true" max_size="38.46" min_size="16.0" offset="0.5" step="16.0" variance="0.1,0.1,0.2,0.2"/>
<input>
<port id="0">
<dim>2</dim> <!-- values: [24, 42] -->
</port>
<port id="1">
<dim>2</dim> <!-- values: [384, 672] -->
</port>
</input>
<output>
<port id="2">
<dim>2</dim>
<dim>16128</dim>
</port>
</output>
</layer>
```

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docs/ops/opset8.md
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@ -114,7 +114,7 @@ declared in `namespace opset8`.
* [Power](arithmetic/Power_1.md)
* [PReLU](activation/PReLU_1.md)
* [PriorBoxClustered](detection/PriorBoxClustered_1.md)
* [PriorBox](detection/PriorBox_1.md)
* [PriorBox](detection/PriorBox_8.md)
* [Proposal](detection/Proposal_4.md)
* [PSROIPooling](detection/PSROIPooling_1.md)
* [RandomUniform](generation/RandomUniform_8.md)