ops_ps_roi_align {torchvisionlib}

Performs Position-Sensitive Region of Interest (RoI) Align operator

Description

The (RoI) Align operator is mentioned in Light-Head R-CNN.

Usage

ops_ps_roi_align(
  input,
  boxes,
  output_size,
  spatial_scale = 1,
  sampling_ratio = -1
)

nn_ps_roi_align(output_size, spatial_scale = 1, sampling_ratio = -1)

Arguments

input

(Tensor[N, C, H, W]): The input tensor, i.e. a batch with N elements. Each element contains C feature maps of dimensions ⁠H x W⁠.

boxes

(Tensor[K, 5] or List[Tensor[L, 4]]): the box coordinates in (x1, y1, x2, y2) format where the regions will be taken from. The coordinate must satisfy ⁠0 <= x1 < x2⁠ and ⁠0 <= y1 < y2⁠. If a single Tensor is passed, then the first column should contain the index of the corresponding element in the batch, i.e. a number in ⁠[1, N]⁠. If a list of Tensors is passed, then each Tensor will correspond to the boxes for an element i in the batch.

output_size

(int or Tuple[int, int]): the size of the output (in bins or pixels) after the pooling is performed, as (height, width).

spatial_scale

(float): a scaling factor that maps the box coordinates to the input coordinates. For example, if your boxes are defined on the scale of a 224x224 image and your input is a 112x112 feature map (resulting from a 0.5x scaling of the original image), you'll want to set this to 0.5. Default: 1.0

sampling_ratio

(int): number of sampling points in the interpolation grid used to compute the output value of each pooled output bin. If > 0, then exactly ⁠sampling_ratio x sampling_ratio⁠ sampling points per bin are used. If <= 0, then an adaptive number of grid points are used (computed as ceil(roi_width / output_width), and likewise for height). Default: -1

Value

Tensor[K, C / (output_size[1] * output_size[2]), output_size[1], output_size[2]]: The pooled RoIs

Functions

• nn_ps_roi_align(): The torch::nn_module() wrapper for ops_ps_roi_align().

Examples

if (torchvisionlib_is_installed()) {
library(torch)
library(torchvisionlib)
input <- torch_randn(1, 3, 28, 28)
boxes <- list(torch_tensor(matrix(c(1,1,5,5), ncol = 4)))
roi <- nn_ps_roi_align(output_size = c(1, 1))
roi(input, boxes)
}