Package 'torchvisionlib'

Performs Deformable Convolution v2,

Description

Ddescribed in Deformable ConvNets v2: More Deformable, Better Results if mask is not NULL and performs Deformable Convolution, described in Deformable Convolutional Networks if mask is NULL.

Usage

ops_deform_conv2d(
  input,
  offset,
  weight,
  bias = NULL,
  stride = c(1, 1),
  padding = c(0, 0),
  dilation = c(1, 1),
  mask = NULL
)

Arguments

input	(Tensor[batch_size, in_channels, in_height, in_width]): input tensor
offset	(Tensor[batch_size, 2 * offset_groups * kernel_height * kernel_width, out_height, out_width]): offsets to be applied for each position in the convolution kernel.
weight	(⁠Tensor[out_channels, in_channels // groups, kernel_height, kernel_width]⁠): convolution weights, split into groups of size (in_channels // groups)
bias	(Tensor[out_channels]): optional bias of shape (out_channels,). Default: NULL
stride	(int or Tuple[int, int]): distance between convolution centers. Default: 1
padding	(int or Tuple[int, int]): height/width of padding of zeroes around each image. Default: 0
dilation	(int or Tuple[int, int]): the spacing between kernel elements. Default: 1
mask	(Tensor[batch_size, offset_groups * kernel_height * kernel_width, out_height, out_width]): masks to be applied for each position in the convolution kernel. Default: NULL

Value

Tensor[batch_sz, out_channels, out_h, out_w]: result of convolution

Examples

if (torchvisionlib_is_installed()) {
  library(torch)
  input <- torch_rand(4, 3, 10, 10)
  kh <- kw <- 3
  weight <- torch_rand(5, 3, kh, kw)
  # offset and mask should have the same spatial size as the output
  # of the convolution. In this case, for an input of 10, stride of 1
  # and kernel size of 3, without padding, the output size is 8
  offset <- torch_rand(4, 2 * kh * kw, 8, 8)
  mask <- torch_rand(4, kh * kw, 8, 8)
  out <- ops_deform_conv2d(input, offset, weight, mask = mask)
  print(out$shape)
}

---

Performs non-maximum suppression (NMS) on the boxes

Description

Performs non-maximum suppression (NMS) on the boxes according to their intersection-over-union (IoU).

Usage

ops_nms(boxes, scores, iou_threshold)

Arguments

boxes	Tensor[N,4] boxes to perform NMS on. They are expected to be in ⁠(x1, y1, x2, y2)⁠ format with ⁠0 <= x1 < x2⁠ and ⁠0 <= y1 < y2⁠.
scores	Tensor[N] scores for each one of the boxes.
iou_threshold	float discards all overlapping boxes with IoU > iou_threshold.

Details

NMS iteratively removes lower scoring boxes which have an IoU greater than iou_threshold with another (higher scoring) box.

If multiple boxes have the exact same score and satisfy the IoU criterion with respect to a reference box, the selected box is not guaranteed to be the same between CPU and GPU. This is similar to the behavior of argsort in PyTorch when repeated values are present.

Value

int64 tensor with the indices of the elements that have been kept by NMS, sorted in decreasing order of scores

Examples

if (torchvisionlib_is_installed()) {
  ops_nms(torch::torch_rand(3, 4), torch::torch_rand(3), 0.5)
}

---

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)
}

---

Checks if an installation of torchvisionlib was found.

Description

Checks if an installation of torchvisionlib was found.

Install additional libraries

Usage

torchvisionlib_is_installed()

install_torchvisionlib(url = Sys.getenv("TORCHVISIONLIB_URL", unset = NA))

Arguments

url	Url for the binaries. Can also be the file path to the binaries.

---

Read JPEG's directly into torch tensors

Description

Read JPEG's directly into torch tensors

Usage

vision_read_jpeg(path)

Arguments

path	path to JPEG file

---