Derive the desired score numbers from summarized COCOeval. Args: coco_eval (None or COCOEval): None represents no predic

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

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 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.

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

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 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.

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

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

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

Returns: gt_objectness_logits: list of N tensors. Tensor i is a vector whose length is the total number of anchor

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

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

Randomly modifies given color to produce a slightly different color than the color given. Args: color (tuple[double]): a

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.

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

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

Uniform float random number between low and high.

Args: features (list[Tensor]): #lvl input features to ROIHeads proposals (list[Instances]): #image Instances, with th

Args: Same as in :meth:`__init__()`. Returns: fig (matplotlib.pyplot.figure): top level container for all th

Returns: tuple[str]: tasks that can be evaluated under the given configuration.

Returns: tuple[str]: tasks that can be evaluated under the given configuration.

Returns: tuple[str]: tasks that can be evaluated under the given configuration.

Args: metrics_dict (dict): dict of scalar metrics

Args: cfg (CfgNode): a detectron2 config Returns: CfgNode: an updated config with new options that :func:`export_caffe2_mode

Augment `proposals` with ground-truth boxes from `gt_boxes`. Args: Same as `add_ground_truth_to_proposals`, but with gt_boxes and pr

Called after each iteration.

Called after the last iteration.

Convert pred_mask_logits to estimated foreground probability masks while also extracting only the masks for the predicted classes in pred_ins

Compute the mask prediction loss defined in the Mask R-CNN paper. Args: pred_mask_logits (Tensor): A tensor of shape (B, C, Hmask, W

Apply transformation `deltas` (dx, dy, dw, dh, da) to `boxes`. Args: deltas (Tensor): transformation deltas of shape (N,

Returns all the metadata as a dict. Note that modifications to the returned dict will not reflect on the Metadata object.

Map each box in `box_lists` to a feature map level index and return the assignment vector. Args: box_lists (list[Boxes] | list[R

Called before each iteration.

Called before the first iteration.

Build a mask head defined by `cfg.MODEL.ROI_AMODAL_MASK_HEAD.NAME`.

Create evaluator(s) for a given dataset. This uses the special metadata "evaluator_type" associated with each builtin dataset.

Build a list of default hooks, including timing, evaluation, checkpointing, lr scheduling, precise BN, writing events. Retur

Build a list of default hooks, including timing, evaluation, checkpointing, lr scheduling, precise BN, writing events. Retur

Build a keypoint head from `cfg.MODEL.ROI_KEYPOINT_HEAD.NAME`.

It now calls :func:`detectron2.solver.build_lr_scheduler`. Overwrite it if you'd like a different scheduler.