Convert targets by image to targets by feature level. [target_img0, target_img1] -> [target_level0, target_level1, ...]

Inference image(s) with the detector. Args: model (nn.Module): The loaded detector. imgs (str/ndarray or list[str/ndarray]): Eith

Merge augmented detection bboxes and scores. Args: aug_bboxes (list[Tensor]): shape (n, 4*#class) aug_scores (list[Tensor] or Non

Visualize the detection results on the image. Args: img (str or np.ndarray): Image filename or loaded image. result (tuple[list]

Create sample results that can be passed to BBoxHead.get_target

Import a module from albumentations. Inherits some of `build_from_cfg` logic. Args: cfg (dict): Config dict. It should at

Flip bboxes horizontally. Args: bboxes(Tensor or ndarray): Shape (..., 4*k) img_shape(tuple): Image shape. Returns:

Flip bboxes horizontally. Args: bboxes(ndarray): shape (..., 4*k) img_shape(tuple): (height, width)

Build optimizer from configs. Args: model (:obj:`nn.Module`): The model with parameters to be optimized. optimizer_cfg (dict): Th

Run code concurrently in different streams. :param streamqueue: asyncio.Queue instance. Queue tasks define the pool of streams used for conc

Simple version of the ``kwarray.ensure_rng`` Args: rng (int | numpy.random.RandomState | None): if None, then defaults t

Base on the previous bboxes and regression values, we compute the regressed bboxes and generate the grids on the bboxes.

Get squares according to feature map sizes and guided anchors. Args: featmap_sizes (list[tuple]): Multi-level feature map

Get points according to feature map sizes. Args: featmap_sizes (list[tuple]): Multi-level feature map sizes. img_meta

Get points according to feature map sizes. Args: featmap_sizes (list[tuple]): Multi-level feature map sizes. dtype (t

Dictionary mapper. Renames keys according to keymap provided. Args: d (dict): old dict keymap (dict)

Change from point offset to point coordinate.

Compute corresponding GT box and classification targets for proposals. Args: points_list (list[list]): Multi level points of each image.

Print time spent by CPU and GPU. Useful as a temporary context manager to find sweet spots of code suitable for async implementation.

Simple version of ``kwimage.Boxes.random`` Returns: Tensor: shape (n, 4) in x1, y1, x2, y2 format. References: https://

Set random seed. Args: seed (int): Seed to be used. deterministic (bool): Whether to set the deterministic option for

Show RPN proposals on the image. Although we assume batch size is 1, this method supports arbitrary batch size.

Visualize the instance segmentation results on the image. Args: img (str or np.ndarray): Image filename or loaded image. result (

Test only det bboxes without augmentation.

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

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

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

Filter images too small.

Find the predefined detector config directory

Find the predefined detector config directory

Load a configuration as a python module