Versioned name: ROIPooling-1
Category: Object detection
Short description: ROIPooling is a pooling layer used over feature maps of non-uniform input sizes and outputs a feature map of a fixed size.
Detailed description: deepsense.io reference
Attributes
- pooled_h
- Description: pooled_h is the height of the ROI output feature map. For example, pooled_h equal to 6 means that the height of the output of ROIPooling is 6.
- Range of values: a non-negative integer
- Type:
int
- Default value: None
- Required: yes
- pooled_w
- Description: pooled_w is the width of the ROI output feature map. For example, pooled_w equal to 6 means that the width of the output of ROIPooling is 6.
- Range of values: a non-negative integer
- Type:
int
- Default value: None
- Required: yes
- spatial_scale
- Description: spatial_scale is the ratio of the input feature map over the input image size.
- Range of values: a positive floating-point number
- Type:
float
- Default value: None
- Required: yes
- method
- Description: method specifies a method to perform pooling. If the method is bilinear, the input box coordinates are normalized to the
[0, 1]
interval.
- Range of values: max or bilinear
- Type: string
- Default value: max
- Required: no
Inputs:
- 1: 4D input tensor of shape
[1, C, H, W]
with feature maps. Required.
- 2: 2D input tensor of shape
[NUM_ROIS, 5]
describing box consisting of 5 element tuples: [batch_id, x_1, y_1, x_2, y_2]
. Required.
Outputs:
- 1: 4D output tensor of shape
[NUM_ROIS, C, pooled_h, pooled_w]
with feature maps. Required.
Example
<layer ... type="ROIPooling" ... >
<data pooled_h="6" pooled_w="6" spatial_scale="0.062500"/>
<input> ... </input>
<output> ... </output>
</layer>