Given a blob object, return the Caffe2 operator that creates this blob as constant. Currently support NumPy tensor and Caffe2 Int8Tensor.

Create an extractor for the provided visualizer

Deal with small clusters.

Create a new output class from an existing one by adding new attributes related to confidence estimation: - sigma_1 (tensor) - sigma_

Splits DensePose predictor outputs into chunks, each chunk corresponds to detections on one image. Predictor output chunks are stored in `pre

Draws keypoints of an instance and follows the rules for keypoint connections to draw lines between appropriate keypoints. This follo

Args: box_coord (tuple): a tuple containing x0, y0, x1, y1 coordinates, where x0 and y0 are the coordinates of th

Args: circle_coord (list(int) or tuple(int)): contains the x and y coordinates of the center of the circle.

Draw a rotated box with label on its top-left corner. Args: rotated_box (tuple): a tuple containing (cnt_x, cnt_y, w, h,

Save extra metadata that will be used by inference in the output protobuf.

Convert semantic segmentation to COCO stuff format with segments encoded as RLEs. See http://cocodataset.org/#format-results

Export a caffe2-compatible Detectron2 model to caffe2 format via ONNX. Arg: model: a caffe2-compatible version of detectron2 model,

Export a detectron2 model to caffe2 format. Args: cfg (CfgNode): a detectron2 config, with extra export-related options

Trace and export a model to onnx format. Args: model (nn.Module): inputs (tuple[args]): the model will be called by `model(*

Export the model to a ``torch.jit.TracedModule`` by tracing. The returned object can be saved to a file by ``.save()``. Retu

Run :func:`torch.jit.script` on a model that uses the :class:`Instances` class. Since attributes of :class:`Instances` are "dynamically" adde

Extract data for mask loss from instances that contain matched GT and estimated bounding boxes. Args: proposals_targets: Iterable

Call `fast_rcnn_inference_single_image_rotated` for all images. Args: boxes (list[Tensor]): A list of Tensors of predicted class-spe

Single-image inference. Return rotated bounding-box detection results by thresholding on scores and applying rotated non-maximum suppression

Find the center points from the center heatmap. Args: center_heatmap: A Tensor of shape [1, H, W] of raw center heatmap output.

For each feature map, select the `pre_nms_topk` highest scoring proposals, apply NMS, clip proposals, and remove small boxes. Return the `pos

Expand a hierarchical dict of scalars into a flat dict of scalars. If results[k1][k2][k3] = v, the returned dict will have the entry {"k1

Args: x (list[Tensor]): A list of feature maps of NCHW shape, with scales matching those used to construct this m

See :class:`Res5ROIHeads.forward`.

Forward computation during training. Args: img (Tensor): Input images of shape (N, C, H, W). Typically these shou

Forward computation during training. Args: img (Tensor): Input images of shape (N, C, H, W). Typically these shou

Forward computation during training. Args: img (Tensor): Input images of shape (N, C, H, W). Typically these shou

Forward computation during training. Args: img (Tensor): Input images of shape (N, C, H, W). Typically these shou

Forward computation during training. Args: img (Tensor): Input images of shape (N, C, H, W). Typically these shou

Forward computation during training. Args: img (Tensor): Input of two concatenated images of shape (N, 2, C, H, W).

Forward computation during training. Args: img (Tensor): Input of two concatenated images of shape (N, 2, C, H, W).

Forward computation during training. Args: img (Tensor): Input of two concatenated images of shape (N, 2, C, H, W).

Forward computation during training. Args: img (Tensor): Input images of shape (N, C, H, W). Typically these shou

Use the given boxes in `instances` to produce other (non-box) per-ROI outputs. This is useful for downstream tasks where a box is kn

Look for keys with the following patterns: 1) Starts with "fpn.inner." Example: "fpn.inner.res2.2.sum.lateral.weight"

Freeze the first several stages of the ResNet. Commonly used in fine-tuning. Layers that produce the same feature map spatia

Remove AliasWithName placeholder and rename the input/output of it

In-place fuse extra copy ops between cpu/gpu for the following case: a -CopyAToB-> b -CopyBToA> c1 -NextOp1-> d1

Generate a tensor storing canonical anchor boxes, which are all anchor boxes of different sizes and aspect_ratios centered at (0, 0).

Generate a tensor storing canonical anchor boxes, which are all anchor boxes of different sizes, aspect_ratios, angles centered at (0

Generate regular square grid of points in [0, 1] x [0, 1] coordinate space. Args: R (int): The number of grids to sample, one for ea