Operators

torchvision.ops implements operators that are specific for Computer Vision.

Note

All operators have native support for TorchScript.

batched_nms(boxes, scores, idxs, iou_threshold)	Performs non-maximum suppression in a batched fashion.
box_area(boxes)	Computes the area of a set of bounding boxes, which are specified by their (x1, y1, x2, y2) coordinates.
box_convert(boxes, in_fmt, out_fmt)	Converts boxes from given in_fmt to out_fmt.
box_iou(boxes1, boxes2)	Return intersection-over-union (Jaccard index) between two sets of boxes.
clip_boxes_to_image(boxes, size)	Clip boxes so that they lie inside an image of size size.
deform_conv2d(input, offset, weight[, bias, …])	Performs Deformable Convolution v2, described in Deformable ConvNets v2: More Deformable, Better Results if mask is not None and Performs Deformable Convolution, described in Deformable Convolutional Networks if mask is None.
generalized_box_iou(boxes1, boxes2)	Return generalized intersection-over-union (Jaccard index) between two sets of boxes.
generalized_box_iou_loss(boxes1, boxes2[, …])	Original implementation from https://github.com/facebookresearch/fvcore/blob/bfff2ef/fvcore/nn/giou_loss.py
masks_to_boxes(masks)	Compute the bounding boxes around the provided masks.
nms(boxes, scores, iou_threshold)	Performs non-maximum suppression (NMS) on the boxes according to their intersection-over-union (IoU).
ps_roi_align(input, boxes, output_size[, …])	Performs Position-Sensitive Region of Interest (RoI) Align operator mentioned in Light-Head R-CNN.
ps_roi_pool(input, boxes, output_size[, …])	Performs Position-Sensitive Region of Interest (RoI) Pool operator described in R-FCN
remove_small_boxes(boxes, min_size)	Remove boxes which contains at least one side smaller than min_size.
roi_align(input, boxes, output_size[, …])	Performs Region of Interest (RoI) Align operator with average pooling, as described in Mask R-CNN.
roi_pool(input, boxes, output_size[, …])	Performs Region of Interest (RoI) Pool operator described in Fast R-CNN
sigmoid_focal_loss(inputs, targets[, alpha, …])	Original implementation from https://github.com/facebookresearch/fvcore/blob/master/fvcore/nn/focal_loss.py .
stochastic_depth(input, p, mode[, training])	Implements the Stochastic Depth from “Deep Networks with Stochastic Depth” used for randomly dropping residual branches of residual architectures.

RoIAlign(output_size, spatial_scale, …)	See roi_align().
PSRoIAlign(output_size, spatial_scale, …)	See ps_roi_align().
RoIPool(output_size, spatial_scale)	See roi_pool().
PSRoIPool(output_size, spatial_scale)	See ps_roi_pool().
DeformConv2d(in_channels, out_channels, …)	See deform_conv2d().
MultiScaleRoIAlign(featmap_names, …)	Multi-scale RoIAlign pooling, which is useful for detection with or without FPN.
FeaturePyramidNetwork(in_channels_list, …)	Module that adds a FPN from on top of a set of feature maps.
StochasticDepth(p, mode)	See stochastic_depth().
FrozenBatchNorm2d(num_features, eps)	BatchNorm2d where the batch statistics and the affine parameters are fixed
SqueezeExcitation(input_channels, …)	This block implements the Squeeze-and-Excitation block from https://arxiv.org/abs/1709.01507 (see Fig.