roi_perspective_transform

paddle.fluid.layers.detection. roi_perspective_transform ( input, rois, transformed_height, transformed_width, spatial_scale=1.0, name=None ) [source]

The rois of this op should be a LoDTensor.

ROI perspective transform op applies perspective transform to map each roi into an rectangular region. Perspective transform is a type of transformation in linear algebra.

Parameters

  • input (Variable) – 4-D Tensor, input of ROIPerspectiveTransformOp. The format of input tensor is NCHW. Where N is batch size, C is the number of input channels, H is the height of the feature, and W is the width of the feature. The data type is float32.

  • rois (Variable) – 2-D LoDTensor, ROIs (Regions of Interest) to be transformed. It should be a 2-D LoDTensor of shape (num_rois, 8). Given as [[x1, y1, x2, y2, x3, y3, x4, y4], …], (x1, y1) is the top left coordinates, and (x2, y2) is the top right coordinates, and (x3, y3) is the bottom right coordinates, and (x4, y4) is the bottom left coordinates. The data type is the same as input

  • transformed_height (int) – The height of transformed output.

  • transformed_width (int) – The width of transformed output.

  • spatial_scale (float) – Spatial scale factor to scale ROI coords. Default: 1.0

  • name (str, optional) – The default value is None. Normally there is no need for user to set this property. For more information, please refer to Name

Returns

A tuple with three Variables. (out, mask, transform_matrix)

out: The output of ROIPerspectiveTransformOp which is a 4-D tensor with shape (num_rois, channels, transformed_h, transformed_w). The data type is the same as input

mask: The mask of ROIPerspectiveTransformOp which is a 4-D tensor with shape (num_rois, 1, transformed_h, transformed_w). The data type is int32

transform_matrix: The transform matrix of ROIPerspectiveTransformOp which is a 2-D tensor with shape (num_rois, 9). The data type is the same as input

Return Type:

tuple

Examples

import paddle.fluid as fluid

x = fluid.data(name='x', shape=[100, 256, 28, 28], dtype='float32')
rois = fluid.data(name='rois', shape=[None, 8], lod_level=1, dtype='float32')
out, mask, transform_matrix = fluid.layers.roi_perspective_transform(x, rois, 7, 7, 1.0)