Accumulate per image evaluation results and store the result in self.eval :param p: input params for evaluation :return: None

Accumulate per image evaluation results and store the result in self.eval :param p: input params for evaluation :return: None

Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`. https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e

Similar to `build_detection_train_loader`, but uses a batch size of 1. This interface is experimental. Args: dataset (list or to

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

Create a list of default :class:`Augmentation` from config. Now it includes resizing and flipping. Returns: list[Augmentation]

Create a list of default :class:`Augmentation` from config. Now it includes resizing and flipping. Returns: list[Augmentation]

Build a instance embedding branch from `cfg.MODEL.INS_EMBED_HEAD.NAME`.

We estimate uncerainty as L1 distance between 0.0 and the logit prediction in 'logits' for the foreground class in `classes`. Args:

We estimate uncerainty as L1 distance between 0.0 and the logit prediction in 'logits' for the foreground class in `classes`. Args:

We estimate uncerainty as L1 distance between 0.0 and the logit prediction in 'logits' for the foreground class in `classes`. Args:

Clip (in place) the boxes by limiting x coordinates to the range [0, width] and y coordinates to the range [0, height]. Args:

Run per image evaluation on given images and store results (a list of dict) in self.evalVids :return: None

Run per image evaluation on given images and store results (a list of dict) in self.evalVids :return: None

Filter out images with none annotations or only crowd annotations (i.e., images without non-crowd annotations). A common training-time pr

Filter out images with none annotations or only crowd annotations (i.e., images without non-crowd annotations). A common training-time pr

x_list contains a list of tensors to nest together and run

x_list contains a list of tensors to nest together and run

Args: image_size (tuple): height, width Returns: crop_size (tuple): height, width in absolute pixels

Returns the positional encoding used to encode point prompts, applied to a dense set of points the shape of the image encoding.

Dump an "Instances" object to a COCO-format json that's used for evaluation. Args: instances (Instances): video_id (int): th