Evaluate the box proposals in predictions. Fill self._results with the metrics for "box_proposals" task.

Evaluate the box proposals in predictions. Fill self._results with the metrics for "box_proposals" task.

Evaluate predictions on the given tasks. Fill self._results with the metrics of the tasks.

Evaluate predictions on the given tasks. Fill self._results with the metrics of the tasks. Args: predictions (li

Evaluate predictions on densepose. Return results with the metrics of the tasks.

Evaluate detection proposal recall metrics. This function is a much faster alternative to the official COCO API recall evaluation code. Howev

Evaluate detection proposal recall metrics. This function is a much faster alternative to the official LVIS API recall evaluation code. Howev

Evaluate the coco results using COCOEval API.

Evaluate the coco results using COCOEval API.

Args: iou_type (str): kpt_oks_sigmas (list[float]): class_names (None or list[str]): if provided, will use it to predict

Extract arrays of I, U and V values at given points as numpy arrays given the data mode stored in self._dpDataMode

Dynamically creates a new type that inherits the type of a given instance and overrides the `step` method to add gradient clipping

Statically infer the status of each blob, the status can be such as device type (CPU/GPU), layout (NCHW/NHWC), data type (float32/int8),

Use `from_config` to obtain explicit arguments. Returns: dict: arguments to be used for cls.__init__

Returns metadata for "separated" version of the panoptic segmentation dataset.

Return the index list of relevant operator to produce target blob from source blob, if there's no dependency, return empty list.

Create a list of dataset indices (with repeats) to use for one epoch. Args: generator (torch.Generator): pseudo random n

Return the base directory for evolution configurations

Return the base directory for quick schedules configurations

Compute (fractional) per-image repeat factors. Args: See __init__. Returns: torch.Tensor: the i-th

Returns: list[Tensor]: #featuremap tensors, each is (#locations x #cell_anchors) x 4

Prepare tensors used in grid sampling. Args: z_est (:obj: `torch.Tensor`): tensor of size (N,C,H,W) with estimated value

Args: input (dict): one dataset dict Returns: dict: one output dict

Encode keypoint locations into a target heatmap for use in SoftmaxWithLoss across space. Maps keypoints from the half-open interval [x1, x2)

Load COCO annotations from a JSON file Args: json_file: str Path to the file to load annotations from Returns:

Log the accuracy metrics to EventStorage.

Match proposals with groundtruth using the matcher at the given stage. Label the proposals as foreground or background based on the m

Paste a list of masks that are of various resolutions (e.g., 28 x 28) into an image. The location, height, and width for pasting each mask is

Post-process the output boxes for TensorMask. The input images are often resized when entering an object detector. As a result, we often

Returns: Tensor: A Tensors of predicted class-specific or class-agnostic boxes for all images in a batch. Element

Prepare ._gts and ._dts for evaluation based on params :return: None