Add default meta keys. We set default meta keys including `pad_shape`, `scale_factor` and `img_norm_cfg` to avoid the case where no `

Add the fc branch which consists of a sequential of fc layers.

Adjust the brightness of image.

Apply Color transformation to image.

Adjust the image contrast.

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

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

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

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

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

Function to randomly crop images, bounding boxes, masks, semantic segmentation maps. Args: results (dict): Result dict fr

delete plugins for block if exist. Args: plugin_names (list[str]): List of plugins name to delete.

Create random test data for the ``mmdet.models.bbox_heads.bbox_head.BBoxHead.refine_boxes`` method.

Convert detection results to COCO json style.

Convert detection results to COCO json style.

Evaluation in Cityscapes protocol. Args: results (list): Testing results of the dataset. txtfile_prefix (str | None):

Filter images too small.

Filter bboxes and corresponding masks too small after shear augmentation.

Filter bboxes and corresponding masks too small after rotate augmentation.

Filter bboxes and masks too small or translated out of image.

Forward function for classification and regression parts.

Forward function for shared part.

Transform outputs for a single batch item into bbox predictions. Args: pred_maps_list (list[Tensor]): Prediction maps for differe

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 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 iou-aware scores for a sing

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

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 labeled boxes. Args: cls_scores (list[Tensor]): Box scores for a single scale leve

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: tl_heat (Tensor): Top-left corner heatmap for current leve

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

Find the predefined detector config directory.

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

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

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 layers of the head.

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

Initialize layers of the transformer head.

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

use deconv layers to upsample backbone's output.

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.