Functions¶
Spatial Pooling¶
psroi_pooling_2d¶
-
chainercv.functions.
psroi_pooling_2d
(x, rois, roi_indices, out_c, out_h, out_w, spatial_scale, group_size)[source]¶ Position Sensitive Region of Interest (ROI) pooling function.
This function computes position sensitive average of input spatial patch with the given region of interests. Each ROI is splitted into \((group\_size, group\_size)\) regions, and position sensitive values in each region is computed.
Parameters: - x (Variable) – Input variable. The shape is expected to be 4 dimentional: (n: batch, c: channel, h, height, w: width).
- rois (array) – Input roi. The shape is expected to
be \((R, 4)\), and each datum is set as below:
(y_min, x_min, y_max, x_max). The dtype is
numpy.float32
. - roi_indices (array) – Input roi indices. The shape is expected to
be \((R, )\). The dtype is
numpy.int32
. - out_c (int) – Channels of output image after pooled.
- out_h (int) – Height of output image after pooled.
- out_w (int) – Width of output image after pooled.
- spatial_scale (float) – Scale of the roi is resized.
- group_size (int) – Position sensitive group size.
Returns: Output variable.
Return type: Variable
See the original paper proposing PSROIPooling: R-FCN.