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.. _op_ai_onnx_MaxRoiPool-1:

MaxRoiPool - version 1
======================

This page documents version **1** of operator **MaxRoiPool**. See :doc:`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).