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

Similiar to func:``AnchorHead._get_bboxes_single``, but additionally processes coeff_preds_list and uses fast NMS instead of tradition

Transform outputs from the last decoder layer into bbox predictions for each image. Args: cls_score (Tensor): Box score l

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

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.

Find the predefined detector config directory.

Find the predefined detector config directory.

Find the predefined detector config directory.

Load a configuration as a python module.

Load a configuration as a python module.

Load a configuration as a python module.

Load a configuration as a python module.

Randomly generates the absolute crop size based on `crop_type` and `image_size`. Args: image_size (tuple): (h, w).

Grab configs necessary to create a detector. These are deep copied to allow for safe modification of parameters without influencing other tes

Grab configs necessary to create a detector. These are deep copied to allow for safe modification of parameters without influencing other tes

Grab configs necessary to create a detector. These are deep copied to allow for safe modification of parameters without influencing other tes

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

Sinusoid position encoding table.

Equalizes the histogram of one image.

Initialize centripetal layers. Including feature adaption deform convs (feat_adaption), deform offset prediction convs (dcn_off), gui

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 a sparse set of proposal boxes and proposal features.

Initialize layers of the head.

Initialize layers of the head.

A helper function to take a config setting and turn it into a network.

Initialize layers of the head.

Initialize layers of the transformer head.

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

Initialize predictor layers of the head.

Load annotation file.

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

Logging data info of bad index.

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 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

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.

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

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

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

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

Rotate the bboxes.

Rotate the image. Args: results (dict): Result dict from loading pipeline. angle (float): Rotation angle in degrees,

Rotate the masks.

Rotate the segmentation map.

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.

Set flag.

Shear the bboxes.

Shear the image. Args: results (dict): Result dict from loading pipeline. magnitude (int | float): The magnitude used