# chainer.functions.roi_average_align_2d¶

chainer.functions.roi_average_align_2d(x, rois, roi_indices, outsize, spatial_scale, sampling_ratio=None)[source]

Spatial Region of Interest (ROI) average align function.

This function acts similarly to roi_average_pooling_2d(), but it computes average of input spatial patch with bilinear interpolation for each channel with the region of interest.

Parameters
• x (Variable) – Input variable. The shape is expected to be 4 dimensional: (n: batch, c: channel, h, height, w: width).

• rois (Variable) – Input roi variable. The shape is expected to be (n: data size, 4), and each datum is set as below: (y_min, x_min, y_max, x_max).

• roi_indices (Variable) – Input roi variable. The shape is expected to be (n: data size, ).

• outsize ((int, int) or int) – Expected output size after pooled (height, width). outsize=o and outsize=(o, o) are equivalent.

• spatial_scale (float) – Scale of the roi is resized.

• sampling_ratio ((int, int) or int) – Sampling step for the alignment. It must be an integer over $$1$$ or None, and the value is automatically decided when None is passed. Use of different ratio in height and width axis is also supported by passing tuple of int as (sampling_ratio_h, sampling_ratio_w). sampling_ratio=s and sampling_ratio=(s, s) are equivalent.

Returns

Output variable.

Return type

Variable

See the original paper proposing ROIAlign: Mask R-CNN.