Deprecated alias for backward compatibiltiy.

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

Post-processing for panoptic segmentation, by merging semantic segmentation label and class agnostic instance segmentation label. Arg

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.

Similar to pariwise_iou but compute the IoA (intersection over boxes2 area). Args: boxes1,boxes2 (Boxes): two `Boxes`. Contains N &

converting number to string :param float params_num: number :returns str: number >>> params_to_string(1e9) '1000.0 M' >>> params

Parse information from a line in a requirements text file

Parse a PASCAL VOC xml file.

Paste a single mask in an image. This is a per-box implementation of :func:`paste_masks_in_image`. This function has larger quantization

Rearrange the tensor layout from the network output, i.e.: list[Tensor]: #lvl tensors of shape (N, A x K, Hi, Wi) to per-image prediction

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

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

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

Decode all the predicted box regression deltas to proposals. Find the top proposals by applying NMS and removing boxes that are too s

Args: instances (Instances): the output of the model img_id (str): the image id in COCO Returns: list[dict]: the res

Normalize, pad and batch the input images.

Normalize, pad and batch the input images.

Process the pair of inputs and outputs. If they contain batches, the pairs can be consumed one-by-one using `zip`: .. code-b

Losses for fine / coarse segmentation: cross-entropy for segmentation unnormalized scores given ground truth labels at annota

Compute losses for U/V coordinates: smooth L1 loss between estimated coordinates and the ground truth. Args: pro

Fake losses for fine / coarse segmentation. These are used when no suitable ground truth data was found in a batch. The loss has a va

Fake losses for U/V coordinates. These are used when no suitable ground truth data was found in a batch. The loss has a value 0

Computes coarse segmentation loss as cross-entropy for raw unnormalized scores given ground truth labels. Args: