MaxRoiPool

MaxRoiPool - 22

Version

  • name: MaxRoiPool (GitHub)

  • domain: main

  • since_version: 22

  • function: False

  • support_level: SupportType.COMMON

  • shape inference: True

This version of the operator has been available since version 22.

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 - INTS (required) :

    ROI pool output shape (height, width).

  • spatial_scale - FLOAT (default is '1.0'):

    Multiplicative spatial scale factor to translate ROI coordinates from their input scale to the scale used when pooling.

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(bfloat16), tensor(double), tensor(float), tensor(float16) ):

    Constrain input and output types to float tensors.

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 - INTS (required) :

    ROI pool output shape (height, width).

  • spatial_scale - FLOAT (default is '1.0'):

    Multiplicative spatial scale factor to translate ROI coordinates from their input scale to the scale used when pooling.

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.