Versioned name: ROIAlign-3
Category: Object detection
Short description: ROIAlign is a pooling layer used over feature maps of non-uniform input sizes and outputs a feature map of a fixed size.
Detailed description: Reference.
ROIAlign performs the following for each Region of Interest (ROI) for each input feature map:
- Multiply box coordinates with spatial_scale to produce box coordinates relative to the input feature map size.
- Divide the box into bins according to the sampling_ratio attribute.
- Apply bilinear interpolation with 4 points in each bin and apply maximum or average pooling based on mode attribute to produce output feature map element.
Attributes
- pooled_h
- Description: pooled_h is the height of the ROI output feature map.
- Range of values: a positive integer
- Type:
int
- Default value: None
- Required: yes
- pooled_w
- Description: pooled_w is the width of the ROI output feature map.
- Range of values: a positive integer
- Type:
int
- Default value: None
- Required: yes
- sampling_ratio
- Description: sampling_ratio is the number of bins over height and width to use to calculate each output feature map element. If the value is equal to 0 then use adaptive number of elements over height and width:
ceil(roi_height / pooled_h)
and ceil(roi_width / pooled_w)
respectively.
- Range of values: a non-negative integer
- Type:
int
- Default value: None
- Required: yes
- spatial_scale
- Description: spatial_scale is a multiplicative spatial scale factor to translate ROI coordinates from their input spatial scale to the scale used when pooling.
- Range of values: a positive floating-point number
- Type:
float
- Default value: None
- Required: yes
- mode
- Description: mode specifies a method to perform pooling to produce output feature map elements.
- Range of values:
- max - maximum pooling
- avg - average pooling
- Type: string
- Default value: None
- Required: yes
Inputs:
- 1: 4D input tensor of shape
[N, C, H, W]
with feature maps of type T. Required.
- 2: 2D input tensor of shape
[NUM_ROIS, 4]
describing box consisting of 4 element tuples: [x_1, y_1, x_2, y_2]
in relative coordinates of type T. The box height and width are calculated the following way: roi_width = max(spatial_scale * (x_2 - x_1), 1.0)
, roi_height = max(spatial_scale * (y_2 - y_1), 1.0)
, so the malformed boxes are expressed as a box of size 1 x 1
. Required.
- 3: 1D input tensor of shape
[NUM_ROIS]
with batch indices of type IND_T. Required.
Outputs:
- 1: 4D output tensor of shape
[NUM_ROIS, C, pooled_h, pooled_w]
with feature maps of type T.
Types
- T: any supported floating point type.
- IND_T: any supported integer type.
Example
<layer ... type="ROIAlign" ... >
<data pooled_h="6" pooled_w="6" spatial_scale="16.0" sampling_ratio="2" mode="avg"/>
<input>
<port id="0">
<dim>7</dim>
<dim>256</dim>
<dim>200</dim>
<dim>200</dim>
</port>
<port id="1">
<dim>1000</dim>
<dim>4</dim>
</port>
<port id="2">
<dim>1000</dim>
</port>
</input>
<output>
<port id="3" precision="FP32">
<dim>1000</dim>
<dim>256</dim>
<dim>6</dim>
<dim>6</dim>
</port>
</output>
</layer>