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

Gather indices and features. Args: ind (Tensor): Indices for the batch of features. feature (Tensor): Batch of featur

Gather indices and features.

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__

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

Return the base directory for evolution configurations

Return the base directory for HRNet configurations

Return the base directory for quick schedules configurations

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

Args: input (dict): one dataset dict with "image" field being a CHW tensor Returns: dict: one output dict

Initialize confidence estimation layers based on configuration options Args: cfg (CfgNode): configuration options

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

Log the accuracy metrics to EventStorage.

Perform recursive lookup for the given type to find registered converter. If a converter was found for some base class, it ge

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

Momentum update of the target network.

Momentum update of the key encoder.