Args: z (:obj: `torch.Tensor`): tensor of size (N,C,H,W) with data to be resampled bbox_xywh_src (:obj: `torch.Tensor

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.

Args: metrics_dict (dict): dict of scalar metrics

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

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

Add config for TensorMask.

Add config for tridentnet.

Called after each iteration.

Called after the last iteration.

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.

Converts a binary mask to COCO polygon representation Args: binary_mask: a 2D binary numpy array where '1's represent the object t

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

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

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

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

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

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. By default it contains writers that write metrics to the screen, a json file, and a tenso

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

Delete all the stored histograms for visualization. This should be called after histograms are written to tensorboard.

Delete all the stored images for visualization. This should be called after images are written to tensorboard.

Clone the RotatedBoxes. Returns: RotatedBoxes