Region of interest pooling (also known as RoI pooling) is to perform max pooling on inputs of nonuniform sizes to obtain fixed-size feature maps (e.g. 7*7).
The operator has three steps:
1. Dividing each region proposal into equal-sized sections with output_size(h, w);
2. Finding the largest value in each section;
3. Copying these max values to the output buffer.
For more information, please refer to https://stackoverflow.com/questions/43430056/what-is-roi-layer-in-fast-rcnn
Args:
input (Tensor): Input feature, 4D-Tensor with the shape of [N,C,H,W],
where N is the batch size, C is the input channel, H is Height, W is weight.
The data type is float32 or float64.
rois (Tensor): ROIs (Regions of Interest) to pool over.
2D-Tensor or 2D-LoDTensor with the shape of [num_rois,4], the lod level is 1.
Given as [[x1, y1, x2, y2], ...], (x1, y1) is the top left coordinates,
and (x2, y2) is the bottom right coordinates.
output_size (int or tuple[int, int]): The pooled output size(h, w), data type is int32. If int, h and w are both equal to output_size.
spatial_scale (float, optional): Multiplicative spatial scale factor to translate ROI coords from their input scale to the scale used when pooling. Default: 1.0
rois_num (Tensor): The number of RoIs in each image. Default: None
name(str, optional): For detailed information, please refer
to :ref:`api_guide_Name`. Usually name is no need to set and
None by default.
Returns:
Tensor: The pooled feature, 4D-Tensor with the shape of [num_rois, C, output_size[0], output_size[1]].
