Visualizes predictions on frames of the input video. Args: video (cv2.VideoCapture): a :class:`VideoCapture` object, who

Save the graph as SVG format. Args: output_file (str): a SVG file inputs: optional inputs given to the model

Save the model as caffe2's protobuf format. Args: output_dir (str): the output directory to save protobuf files.

Set the random seed for the RNG in torch, numpy and python. Args: seed (int): if None, will use a strong random seed.

Create configs and perform basic setups.

Return `num_samples` (or fewer, if not enough found) random samples from `labels` which is a mixture of positives & negatives. It will tr

Helper function to synchronize (barrier) among all processes when using distributed training

Convert InstancesList to List[Instances]. The input `instances_list` can also be a List[Instances], in this case this method is a non

Tversky loss function. probas: [P, C] Variable, class probabilities at each prediction (between 0 and 1) labels: [P] Tensor, grou

Upgrade a config from its current version to a newer version. Args: cfg (CfgNode): to_version (int): defaults to the latest

Creates an Activated Batch Normalization module Parameters ---------- num_features : int Number of feature channe

NOTE: this interface is experimental. Args: num_keypoints (int): number of keypoints to predict loss_weight

Args: box2box_transform (Box2BoxTransform/Box2BoxTransformRotated): box2box transform instance for proposal-to-de

NOTE: this interface is experimental. Args: vis_period (int): visualization period

Deformable convolution from :paper:`deformconv`. Arguments are similar to :class:`Conv2D`. Extra arguments: Args:

Args: cfg (CfgNode): num_gpus (int): if 0, will run on CPU

Assert that each stride is 2x times its preceding stride, i.e. "contiguous in log2".

ONNX exported network doesn't have concept of device, assign necessary device option for each op in order to make it runable on GPU runtime.

Given two lists of boxes of N ground truth boxes and M anchor boxes, compute the assignment between the two, following the assignment rules i

Returns: bool: whether the arguments contain CfgNode and should be considered forwarded to from_config.

Configurations for some libraries.

Convert different format of boxes to an NxB array, where B = 4 or 5 is the box dimension.

Convert different format of masks or polygons to a tuple of masks and polygons. Returns: list[GenericMask]:

Creates gradient clipping closure to clip by value or by norm, according to the provided config.

Args: boxes (list[Tensor]): per-image predicted boxes, each of shape Ri x 4 image_sizes (list[tuple]): list of image

Args: masks: N, 1, H, W boxes: N, 4 img_h, img_w (int): skip_empty (bool): only paste masks within the region tha