Transform outputs for a single batch item into labeled boxes. Args: cls_scores (list[Tensor]): Box scores for a single scale leve

Transform outputs for a single batch item into bbox predictions. Args: cls_scores (list[Tensor]): Box scores for a single scale l

Transform outputs for a single batch item into bbox predictions. Args: tl_heat (Tensor): Top-left corner heatmap for current leve

Find the predefined detector config directory.

Load a configuration as a python module.

Get guided anchors and loc masks for a single level. Args: square (tensor): Squares of a single level. shape_pred (te

Get points of a single scale level.

Get repeat factor for each images in the dataset. Args: dataset (:obj:`CustomDataset`): The dataset repeat_thr (float

Initialize corner embedding layers. Only include corner embedding branch with two parts: prefix `tl_` for top-left and `br_` for bott

Initialize corner keypoint layers. Including corner heatmap branch and corner offset branch. Each branch has two parts: prefix `tl_`

Initialize layers of the head.

Initialize layers of the head.

Initialize layers for CornerHead. Including two parts: corner keypoint layers and corner embedding layers

Initialize predictor layers of the head.

Private function to load bounding box annotations. Args: results (dict): Result dict from :obj:`mmdet.CustomDataset`. Re

Private function to load label annotations. Args: results (dict): Result dict from :obj:`mmdet.CustomDataset`. Returns:

Private function to load mask annotations. Args: results (dict): Result dict from :obj:`mmdet.CustomDataset`. Returns:

Private function to load semantic segmentation annotations. Args: results (dict): Result dict from :obj:`dataset`. Retur

Run forward function and calculate loss for mask head in training.

Run forward function and calculate loss for mask head in training.

Run forward function and calculate loss for point head in training.

Pad images according to ``self.size``.

Pad masks according to ``results['pad_shape']``.

Pad semantic segmentation map according to ``results['pad_shape']``.

Parse bbox and mask annotation. Args: ann_info (list[dict]): Annotation info of an image. with_mask (bool): Whether t

Private function to convert masks represented with polygon to bitmaps. Args: mask_ann (list | dict): Polygon mask annotat

Compute the area of a component of a polygon. Using the shoelace formula: https://stackoverflow.com/questions/24467972/calculate-area

Convert proposal results to COCO json style.

Get another random index from the same group as the given index.

Ramdom jitter positive proposals for training.

Randomly sample an img_scale according to ``ratio_range`` and ``multiscale_mode``. If ``ratio_range`` is specified, a ratio will be s

Resize bounding boxes with ``results['scale_factor']``.

Resize images with ``results['scale']``.

Resize masks with ``results['scale']``

Resize semantic segmentation map with ``results['scale']``.

Sample negative samples.

Sample negative samples. Score-HLR sampler is done in the following steps: 1. Take the maximum positive score prediction of each nega

Sample positive samples.

Randomly sample some positive samples.

Convert instance segmentation results to COCO json style.

Set flag according to image aspect ratio. Images with aspect ratio greater than 1 will be set as group 1, otherwise group 0.

Around padding the original image without cropping. The padding mode and value are from ``test_pad_mode``. Args: results

Random crop and around padding the original image. Args: results (dict): Image infomations in the augment pipeline. Retu

Adjusts the compatibility of widths and groups. Args: widths (list[int]): Width of each stage. bottleneck_ratio (floa

Associative Embedding Loss in one image. Associative Embedding Loss including two parts: pull loss and push loss. Pull loss makes embedding v

Allreduce gradients. Args: params (list[torch.Parameters]): List of parameters of a model coalesce (bool, optional): Whether allr

Assign only one gt index to each prior box. Gts with large gt_priority are more likely to be assigned. Args: is_bbox_in_

Asynchronized test for box head without augmentation.

Asynchronized test for mask head without augmentation.

Test function with test time augmentation.

Test det bboxes with test time augmentation.

Test for mask head with test time augmentation.

Calculate average precision (for single or multiple scales). Args: recalls (ndarray): shape (num_scales, num_dets) or (num_dets, )

Calculate balanced L1 loss. Please see the `Libra R-CNN <https://arxiv.org/pdf/1904.02701.pdf>`_ Args: pred (torch.Tensor): The pred

Compute deltas of proposals w.r.t. gt. We usually compute the deltas of x, y, w, h of proposals w.r.t ground truth bboxes to get regression t

Decode bounding box based on distances. Args: points (Tensor): Shape (n, 2), [x, y]. bbox (Tensor): Shape (n, 4), "xyxy" format

Flip bboxes horizontally. Args: bboxes (numpy.ndarray): Bounding boxes, shape (..., 4*k) img_shape (tuple[int]): Imag

Calculate overlap between two set of bboxes. If ``is_aligned`` is ``False``, then calculate the ious between each bbox of bboxes1 and bboxes2

Encode ground truth boxes to tblr coordinate. It first convert the gt coordinate to tblr format, (top, bottom, left, right), relative to pri

Compute the area of an array of bboxes. Args: bboxes (Tensor): The coordinates ox bboxes. Shape: (m, 4) Returns: Tensor: Are

BIoULoss. This is an implementation of paper `Improving Object Localization with Fitness NMS and Bounded IoU Loss. <https://arxiv.org/abs

Build RoI operator to extract feature from each level feature map. Args: layer_cfg (dict): Dictionary to construct and config RoI

Build shared head.

Compute point specific representation regions. See Grid R-CNN Plus (https://arxiv.org/abs/1906.05688) for details.

Calculate positive recall with score threshold. Args: cls_scores (list[Tensor]): Classification scores at all fpn levels.

Compute centerness targets. Args: pos_bbox_targets (Tensor): BBox targets of positive bboxes in shape (num_pos, 4

Get bboxes according to center points. Only used in :class:`MaxIoUAssigner`.

r"""`Implementation of paper `Enhancing Geometric Factors into Model Learning and Inference for Object Detection and Instance Segmentation <ht

Convert keys in checkpoints. There can be some breaking changes during the development of mmdetection, and this tool is used for upgrading ch