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Revise PriorBox Spec (#6364)

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* Revise PriorBox spec

* Tex cosmetics

* use backticks for attr types, add types section

* Add missing default values for optional attributes

* Remove redundant sentences in attr descriptions, add clip to detailed description
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Bartosz Lesniewski 2021-07-01 12:11:36 +02:00 committed by GitHub
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docs/ops/detection/PriorBox_1.md
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@ -6,143 +6,27 @@
**Short description**: *PriorBox* operation generates prior boxes of specified sizes and aspect ratios across all dimensions.
**Attributes**:
* *min_size (max_size)*
* **Description**: *min_size (max_size)* is the minimum (maximum) box size (in pixels). For example, *min_size (max_size)* equal 15 means that the minimum (maximum) box size is 15.
* **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. For example, *aspect_ratio* equal "2.0,3.0" means that for the first box aspect_ratio is equal to 2.0 and for the second box is 3.0.
* **Range of values**: 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 to "4.0,2.0" mean that 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 [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 [0,1] interval.
* **Type**: boolean
* **Default value**: false
* **Required**: *no*
* *step*
* **Description**: *step* is a distance between box centers. For example, *step* equal 85 means that the distance between neighborhood prior boxes centers is 85.
* **Range of values**: floating point non-negative number
* **Type**: float
* **Default value**: 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 non-negative number
* **Type**: float
* **Default value**: None
* **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 the PriorBox layer is inferred in MXNet-like manner. In particular, *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. For example, *fixed_ratio* equal to 2.000000 means that the aspect ratio for the first box aspect ratio is 2.
* **Range of values**: a list of positive floating-point numbers
* **Type**: `float[]`
* **Default value**: None
* **Required**: *no*
* *fixed_size*
* **Description**: *fixed_size* is an initial box size (in pixels). For example, *fixed_size* equal to 15 means that the initial box size is 15.
* **Range of values**: a list of positive floating-point numbers
* **Type**: `float[]`
* **Default value**: None
* **Required**: *no*
* *density*
* **Description**: *density* is the square root of the number of boxes of each type. For example, *density* equal to 2 means that the first box generates four boxes of the same size and with the same shifted centers.
* **Range of values**: a list of positive floating-point numbers
* **Type**: `float[]`
* **Default value**: None
* **Required**: *no*
**Inputs**:
* **1**: `output_size` - 1D tensor with two integer 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. Required.
**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.
**Detailed description**:
*PriorBox* computes coordinates of prior boxes by following:
1. First calculates *center_x* and *center_y* of prior box:
\f[
W \equiv Width \quad Of \quad Image
\f]
\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)
\f]
\f[
center_x=(w+0.5) \\
center_y=(h+0.5)
\f]
* else:
\f[
center_x=(w+offset)*step
\f]
\f[
center_y=(h+offset)*step
\f]
\f[
w \subset \left( 0, W \right )
\f]
\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, for each \f$ s \subset \left( 0, min_sizes \right ) \f$ calculates coordinates of prior boxes:
2. Then, for each \f$ s \subset \left( 0, min\_sizes \right ) \f$ calculates coordinates of prior boxes:
\f[
xmin = \frac{\frac{center_x - s}{2}}{W}
\f]
@ -155,6 +39,118 @@
\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**: 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 to `[4.0,2.0]` mean that 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 `[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 `[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 box respectively to top left corner.
* **Range of values**: floating point non-negative number
* **Type**: `float`
* **Default value**: None
* **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*
**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]` that 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**