Apply func recursively to all DictConfig in cfg.

Accumulate instances data for one image Args: instances_one_image (Instances): instances data to accumulate

Accumulate instances data for one image Args: instances_one_image (Instances): instances data to accumulate

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

Implement operator-level activations counting using jit. This is a wrapper of fvcore.nn.activation_count, that supports standard detection mo

Add config for additional category-related dataset options - category whitelisting - category mapping

Add config for densepose head.

Add configuration options for Continuous Surface Embeddings (CSE)

Augment `proposals` with `gt`. Args: Same as `add_ground_truth_to_proposals`, but with gt and proposals per image. Retu

Add config for HRNet backbone.

Add config for Panoptic-DeepLab.

Add config for point supervision.

Add config for TensorMask.

Add config for tridentnet.

Adjusts the compatibility of widths, bottlenecks, and groups.

Called after each iteration.

Create an :class:`Instances` object used by the models, from instance annotations in the dataset dict. Args: annos (list[dict]):

Use ``T.AugmentationList(augmentations)(inputs)`` instead.

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

Apply transformation `deltas` (dx1, dy1, dx2, dy2) 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.

A function to assemble caffe2 model's outputs (i.e. Dict[str, Tensor]) to detectron2's format (i.e. list of Instances instance). This onl

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.

Benchmark the dataloader where each worker outputs nothing. This eliminates the IPC overhead compared to the regular dataloader.

Benchmark the speed of taking raw samples from the dataset.

Benchmark the speed of taking raw samples from the dataset and map them in a single process.

Benchmark the dataloader by tuning num_workers to [0, 1, self.num_workers].

Args: proposal_boxes/gt_boxes are tensors with the same shape (R, 4 or 5). pred_deltas has shape (R, 4 or 5), or (R,

Build dataset that provides data to bootstrap on Args: dataset_name (str): Name of the dataset, needs to have associated metadata

Build DensePose data filter which selects data for training Args: cfg (CfgNode): configuration options Return: Callable

Build DensePose head based on configurations options Args: cfg (CfgNode): configuration options input_channels (int): input

Build DensePose loss based on configurations options Args: cfg (CfgNode): configuration options Return: An instance of D

Create an instance of DensePose predictor based on configuration options. Args: cfg (CfgNode): configuration options input_c

Similar to `build_detection_train_loader`. But this function uses the given `dataset_name` argument (instead of the names in cfg), and us

Returns: DatasetEvaluator or None It is not implemented by default.

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

Constructs data loader based on inference results of a model.

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

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.

Returns: torch.optim.Optimizer: It now calls :func:`detectron2.solver.build_optimizer`. Overwrite it if you'd li

Args: cfg: a detectron2 CfgNode Returns: backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`.

Build an RPN head defined by `cfg.MODEL.RPN.HEAD_NAME`.

Returns: iterable It now calls :func:`detectron2.data.build_detection_test_loader`. Overwrite it if you'd like a

Build a list of writers to be used using :func:`default_writers()`. If you'd like a different list of writers, you can overwrite it i

For each location of the prediction `sem_seg_logits` we estimate uncerainty as the difference between top first and top second predicted

Concatenates a list of Keypoints into a single Keypoints Arguments: keypoints_list (list[Keypoints]) Returns:

Concatenates a list of BitMasks into a single BitMasks Arguments: bitmasks_list (list[BitMasks]) Returns: