MaxRoiPool

 Graphs in : cluster, ONNX model architecture.

X (heterogeneous) – T : object, 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 : object, 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], …].

 Parameters : cluster,

pooled_shape : array, ROI pool output shape (height, width).
Default value “empty”.
spatial_scale : float, multiplicative spatial scale factor to translate ROI coordinates from their input scale to the scale used when pooling.
Default value “1”.
 training? : boolean, whether the layer is in training mode (can store data for backward).
Default value “True”.
 lda coeff : float, defines the coefficient by which the loss derivative will be multiplied before being sent to the previous layer (since during the backward run we go backwards).
Default value “1”.

 name (optional) : string, name of the node.