# MaxRoiPool#

## MaxRoiPool - 1#

**Version**

**name**: MaxRoiPool (GitHub)**domain**:**main****since_version**:**1****function**: False**support_level**: SupportType.COMMON**shape inference**: True

This version of the operator has been available
**since version 1**.

**Summary**

ROI max pool consumes an input tensor X and region of interests (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]).

**Attributes**

**pooled_shape**(required): ROI pool output shape (height, width).**spatial_scale**: Multiplicative spatial scale factor to translate ROI coordinates from their input scale to the scale used when pooling. Default value is`1.0`

.

**Inputs**

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

**Type Constraints**

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

**Examples**