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

Ramdom jitter positive proposals for training.

Register a module. Args: module (:obj:`nn.Module`): Module to be registered.

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

Async test only det bboxes without augmentation.

almost the same with anchor_target, with a little modification, here we need return the anchor

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

Improving Object Localization with Fitness NMS and Bounded IoU Loss, https://arxiv.org/abs/1711.00164. Args: pred (tensor): Predicted

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

Get bboxes according to center points. Only used in MaxIOUAssigner.

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

Convert keys in detectron pretrained ResNet models to pytorch style.

Copy gradients from fp16 model to fp32 weight copy.

Copy updated params from fp32 weight copy to fp16 model.

Calculate recalls. Args: gts(list or ndarray): a list of arrays of shape (n, 4) proposals(list or ndarray): a list of arrays of s

Args: imgs (List[Tensor]): the outer list indicates test-time augmentations and inner Tensor should have a shape

Args: img (list[Tensor]): list of tensors of shape (1, C, H, W). Typically these should be mean centered and std

Compute location targets for guided anchoring. Each feature map is divided into positive, negative and ignore regions. - positive regions: ta

Compute guided anchoring targets. Args: approx_list (list[list]): Multi level approxs of each image. inside_flag_list (list[list]

Transform outputs for a single batch item into labeled boxes.

Get det results and gt information of a certain class. Args: det_results (list[list]): Same as `eval_map()`. annotations (list[di