Revise PriorBoxClustered Spec

docs/ops/detection/PriorBoxClustered_1.md

@@ -6,105 +6,99 @@
 
 **Short description**: *PriorBoxClustered* operation generates prior boxes of specified sizes normalized to the input image size.
 
+**Detailed description**
+
+Let
+\f[
+W \equiv image\_width, \quad H \equiv image\_height.
+\f]
+
+Then calculations of *PriorBoxClustered* can be written as
+    \f[
+    center_x=(w+offset)*step
+    \f]
+    \f[
+    center_y=(h+offset)*step
+    \f]
+    \f[
+    w \subset \left( 0, W \right )
+    \f]
+    \f[
+    h \subset \left( 0, H \right )
+    \f]
+For each \f$s = \overline{0, W - 1}\f$ calculates the prior boxes coordinates:
+    \f[
+    xmin = \frac{center_x - \frac{width_s}{2}}{W}
+    \f]
+    \f[
+    ymin = \frac{center_y - \frac{height_s}{2}}{H}
+    \f]
+    \f[
+    xmax = \frac{center_x - \frac{width_s}{2}}{W}
+    \f]
+    \f[
+    ymax = \frac{center_y - \frac{height_s}{2}}{H}
+    \f]
+If *clip* is defined, the coordinates of prior boxes are recalculated with the formula:
+\f$coordinate = \min(\max(coordinate,0), 1)\f$
+
 **Attributes**
 
 * *width (height)*
 
   * **Description**: *width (height)* specifies desired boxes widths (heights) in pixels.
-  * **Range of values**: floating point positive numbers
-  * **Type**: float[]
+  * **Range of values**: floating-point positive numbers
+  * **Type**: `float[]`
   * **Default value**: 1.0
   * **Required**: *no*
 
 * *clip*
 
-  * **Description**: *clip* is a flag that denotes if each value in the output tensor should be clipped within [0,1].
+  * **Description**: *clip* is a flag that denotes if each value in the output tensor should be clipped within `[0,1]`.
   * **Range of values**:
     * false or 0 - clipping is not performed
-    * true or 1  - each value in the output tensor is within [0,1]
-  * **Type**: boolean
+    * true or 1  - each value in the output tensor is within `[0,1]`
+  * **Type**: `boolean`
   * **Default value**: true
   * **Required**: *no*
 
 * *step (step_w, step_h)*
 
   * **Description**: *step (step_w, step_h)* is a distance between box centers. For example, *step* equal 85 means that the distance between neighborhood prior boxes centers is 85. If both *step_h* and *step_w* are 0 then they are updated with value of *step*. If after that they are still 0 then they are calculated as input image width(height) divided with first input width(height). 
-  * **Range of values**: floating point positive number
-  * **Type**: float
+  * **Range of values**: floating-point positive number
+  * **Type**: `float`
   * **Default value**: 0.0
   * **Required**: *no*
 
 * *offset*
 
   * **Description**: *offset* is a shift of box respectively to top left corner. For example, *offset* equal 85 means that the shift of neighborhood prior boxes centers is 85.
-  * **Range of values**: floating point positive number
-  * **Type**: float
-  * **Default value**: None
+  * **Range of values**: floating-point positive number
+  * **Type**: `float`
   * **Required**: *yes*
 
 * *variance*
 
-  * **Description**: *variance* denotes a variance of adjusting bounding boxes.
-  * **Range of values**: floating point positive numbers
-  * **Type**: float[]
+  * **Description**: *variance* denotes a variance of adjusting bounding boxes. The attribute could be 0, 1 or 4 elements.
+  * **Range of values**: floating-point positive numbers
+  * **Type**: `float[]`
   * **Default value**: []
   * **Required**: *no*
 
-* *img_h (img_w)*
-
-  * **Description**: *img_h (img_w)* specifies height (width) of input image. These attributes are taken from the second input `image_size` height(width) unless provided explicitly as the value for this attributes.
-  * **Range of values**: floating point positive number
-  * **Type**: float
-  * **Default value**: 0
-  * **Required**: *no*
-
 **Inputs**:
 
-*   **1**: `output_size` - 1D tensor with two integer elements `[height, width]`. Specifies the spatial size of generated grid with boxes. Required.
+*   **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 with two integer elements `[image_height, image_width]` that specifies shape of the image for which boxes are generated. Optional.
+*   **2**: `image_size` - 1D tensor of type *T_INT* with two elements `[image_height, image_width]` that specifies shape of the image for which boxes are generated. Optional.
 
 **Outputs**:
 
-*   **1**: 2D tensor of shape `[2, 4 * height * width * priors_per_point]` with box coordinates. The `priors_per_point` is the number of boxes generated per each grid element. The number depends on layer attribute values.
+*   **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 layer attribute values.
 
-**Detailed description**
+**Types**
 
-*PriorBoxClustered* computes coordinates of prior boxes by following:
-1.  Calculates the *center_x* and *center_y* of prior box:
-    \f[
-    W \equiv Width \quad Of \quad Image
-    \f]
-    \f[
-    H \equiv Height \quad Of \quad Image
-    \f]
-    \f[
-    center_x=(w+offset)*step
-    \f]
-    \f[
-    center_y=(h+offset)*step
-    \f]
-    \f[
-    w \subset \left( 0, W \right )
-    \f]
-    \f[
-    h \subset \left( 0, H \right )
-    \f]
-2.  For each \f$s \subset \left( 0, W \right )\f$ calculates the prior boxes coordinates:
-    \f[
-    xmin = \frac{center_x - \frac{width_s}{2}}{W}
-    \f]
-    \f[
-    ymin = \frac{center_y - \frac{height_s}{2}}{H}
-    \f]
-    \f[
-    xmax = \frac{center_x - \frac{width_s}{2}}{W}
-    \f]
-    \f[
-    ymax = \frac{center_y - \frac{height_s}{2}}{H}
-    \f]
-If *clip* is defined, the coordinates of prior boxes are recalculated with the formula:
-\f$coordinate = \min(\max(coordinate,0), 1)\f$
+* *T_INT*: any supported integer type.
+* *T_OUT*: supported floating-point type.
 
 **Example**