ROIAlign#
Versioned name: ROIAlign-9
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 based on aligned_mode attribute.
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
Required: yes
pooled_w
Description: pooled_w is the width of the ROI output feature map.
Range of values: a positive integer
Type:
int
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)
andceil(roi_width / pooled_w)
respectively.Range of values: a non-negative integer
Type:
int
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
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
Required: yes
aligned_mode
Description: aligned_mode specifies how to transform the coordinate in original tensor to the resized tensor.
Range of values: name of the transformation mode in string format (here spatial_scale is resized_shape[x] / original_shape[x], resized_shape[x] is the shape of resized tensor in axis x, original_shape[x] is the shape of original tensor in axis x and x_original is a coordinate in axis x, for any axis x from the input axes):
asymmetric - the coordinate in the resized tensor axis x is calculated according to the formula x_original * spatial_scale
half_pixel_for_nn - the coordinate in the resized tensor axis x is x_original * spatial_scale - 0.5
half_pixel - the coordinate in the resized tensor axis x is calculated as ((x_original + 0.5) * spatial_scale) - 0.5
Type: string
Default value: asymmetric
Required: no
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:If aligned_mode equals asymmetric:
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 size1 x 1
.else:
roi_width = spatial_scale * (x_2 - x_1)
,roi_height = spatial_scale * (y_2 - y_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" aligned_mode="half_pixel"/>
<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>