Args: anchors (list[Boxes]): A list of #feature level Boxes. The Boxes contains anchors of this image on the spec

Args: anchors (list[list[Boxes]]): a list of N=#image elements. Each is a list of #feature level Boxes. The Boxes

Returns metadata associated with COCO DensePose data Args: base_path: Optional[os.PathLike] Base path used to load metadata from

Get the model from the specified file (relative to the base configuration directory)

Return a list of configs to include in package for model zoo. Copy over these configs inside detectron2/model_zoo.

Creates a function that converts outputs of the caffe2 model to detectron2's standard format. The function uses information i

Returns the color map for visualizing the segmentation mask. Args: num_cls: Number of classes Returns: The color map

Returns the color map for visualizing the segmentation mask. Inputs: =num_cls= Number of classes. Returns: The co

Returns the color map for visualizing the segmentation mask. Args: num_cls: Number of classes Returns: The color map

Take the output blobs from init_net by running it. Outputs: params: dict from blob name to numpy array device_options: dict f

Convert box-normalized [0, 1] x [0, 1] point cooordinates to image-level coordinates. Args: boxes_coords (Tensor): A tensor of shape

Get all the quick schedules configuration files (relative to the base configuration directory)

For ONNX exported model, GroupNorm will be represented as ATen op, this can be a drop in replacement from ATen to GroupNorm

Guess the version of a partial config where the VERSION field is not specified. Returns the version, or the latest if cannot make a guess.

Extract predicted keypoint locations from heatmaps. Args: maps (Tensor): (#ROIs, #keypoints, POOL_H, POOL_W). The predicted heatmap

Change S, I, U and V to take into account a Horizontal flip.

Returns: dict[name -> HistoryBuffer]: the HistoryBuffer for all scalars

Idenfity the reshape sub-graph in a protobuf. The reshape sub-graph is defined as matching the following pattern: (input_blob) -> Op_1 -

Run inference on the given inputs. Args: batched_inputs (list[dict]): same as in :meth:`forward` detected_in

A context where the model is temporarily changed to eval mode, and restored to previous mode afterwards. Args: model: a torch Mo

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

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

Args: box_size (height, width): Size of the reference box. boundary_threshold (int): Boxes that extend beyond the ref

Post process each predicted keypoint heatmap in `pred_keypoint_logits` into (x, y, score) and add it to the `pred_instances` as a `pred_k

Arguments: pred_keypoint_logits (Tensor): A tensor of shape (N, K, S, S) where N is the total number of instances in the batc

Args: anchors (list[RotatedBoxes]): anchors for each feature map. gt_instances: the ground-truth instances for each i

Args: anchors (list[Boxes]): anchors for each feature map. gt_instances: the ground-truth instances for each image.

Returns: dict[name -> number]: the scalars that's added in the current iteration.

Neural network layers that makes predictions from regional input features.

List all registered data. Returns: list[str]

Loads a JSON file with annotations in COCO instances format. Replaces `detectron2.data.data.coco.load_coco_json` to handle metadata in a

Args: dir (str): a directory used to save Caffe2Model with :meth:`save_protobuf`. The files "mode

Load Pascal VOC detection annotations to Detectron2 format. Args: dirname: Contain "Annotations", "ImageSets", "JPEGImages"

Log no more than once per n seconds. Args: lvl (int): the logging level msg (str): n (int): name (str): name

Args: predictions: return values of :meth:`forward()`. proposals (list[Instances]): proposals that match the features

Args: For `gt_classes` and `gt_anchors_deltas` parameters, see :meth:`RetinaNet.get_ground_truth`. Th

Create the model and start the evaluation process.

Returns a list of dicts that represents a single COCO data point for object detection. The single instance given by `mask` is represented by

Makes a donut shaped binary mask.

Create a resnet stage by creating many blocks for TridentNet.

Compute pairwise intersection over union (IOU) of two sets of matched boxes. The box order must be (xmin, ymin, xmax, ymax). Similar to b

If gradient clipping is enabled through config options, wraps the existing optimizer instance of some type OptimizerType to become an instanc

nanmean compatible with generators.

Create dataset dicts for panoptic segmentation, by merging two dicts using "file_name" field to match their entries. Args: detec

Yields: A context within which all the events added to this storage will be prefixed by the name scope.

Find boxes that are non-empty. A box is considered empty, if either of its side is no larger than threshold. Returns:

Args: boxes (ndarray): an Nx5 numpy array of (x_center, y_center, width, height, angle_degrees) format

Args: masks (tensor): A tensor of shape (B, M, M) representing B masks. padding (int): Number of cells to pad on all sides.

Parse a PASCAL VOC xml file.

Loss function definition. Args: preds: [[parsing result1, parsing result2],[edge result]] target: [parsing l