MaxRoiPool - version 1#

This page documents version 1 of operator MaxRoiPool. See MaxRoiPool for the latest version (since version 22).

  • Domain: ai.onnx

  • Since version: 1

ROI max pool consumes an input tensor X and regions of interest (RoIs) to apply max pooling across each RoI, to produce output 4-D tensor of shape (num_rois, channels, pooled_shape[0], pooled_shape[1]).

Inputs

  • X (T): Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data.

  • rois (T): RoIs (Regions of Interest) to pool over. Should be a 2-D tensor of shape (num_rois, 5) given as [[batch_id, x1, y1, x2, y2], …].

Outputs

  • Y (T): RoI pooled output 4-D tensor of shape (num_rois, channels, pooled_shape[0], pooled_shape[1]).

Attributes

  • pooled_shape (int[]): ROI pool output shape (height, width).

  • spatial_scale (float): Multiplicative spatial scale factor to translate ROI coordinates from their input scale to the scale used when pooling.

Type Constraints

  • T: Constrain input and output types to float tensors. Allowed types: tensor(double), tensor(float), tensor(float16).