Augmented forward test function.

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

The key correspondence from bboxes to labels, masks and segmentations.

Builder of IoU calculator.

Build neck.

Classification-Aware Regression Loss (CARL). Args: cls_score (Tensor): Predicted classification scores. labels (Tensor): Targets

Async context manager that waits for work to complete on given CUDA streams.

see :func:`BaseInstanceMasks.crop`

See :func:`BaseInstanceMasks.crop_and_resize`.

Map from level to values.

FCOS Head Config.

Args: img (Tensor): Input images of shape (N, C, H, W). Typically these should be mean centered and std scaled.

FSAF Head Config.

Gather feature according to index. Args: feat (Tensor): Target feature map. ind (Tensor): Target coord index. mask (Tenso

Generate 2D gaussian heatmap. Args: heatmap (Tensor): Input heatmap, the gaussian kernel will cover on it and maintain the ma

Get category ids of repeat dataset by index. Args: idx (int): Index of data. Returns: list[int]: All categor

Get COCO category ids by index. Args: idx (int): Index of data. Returns: list[int]: All categories in the im

Get custom classification channels. Args: num_classes (int): The number of classes. Returns: int: The custom

Extract local maximum pixel with given kernal. Args: heat (Tensor): Target heatmap. kernel (int): Kernel size of max pooling. Def

Get root logger. Args: log_file (str, optional): File path of log. Defaults to None. log_level (int, optional): The level of logg

A wrapper for computing ATSS and FCOS targets for points in multiple images. Args: cls_scores (list[Tensor]): Box iou-awa

Get top k positions from heatmap. Args: scores (Tensor): Target heatmap with shape [batch, num_classes, height, width].

Initialize a detector from config file. Args: config (str or :obj:`mmcv.Config`): Config file path or the config object.

Inverse function of sigmoid. Args: x (Tensor): The tensor to do the inverse. eps (float): EPS avoid numerical

Importance-based Sample Reweighting (ISR_P), positive part. Args: cls_score (Tensor): Predicted classification scores. bbox_pred

Map from level to values based on max_value.

Test model with multiple gpus. This method tests model with multiple gpus and collects the results under two different modes: gpu and cpu mod

nn.Module: normalization layer after the first convolution layer

Get training data and annotations after pipeline. Args: idx (int): Index of data. Returns: dict: Training da

Randomly negate value based on random_negative_prob.

See :func:`BaseInstanceMasks.rescale`.

RetinanNet Head Config.

Sample positive and negative bboxes. This is a simple implementation of bbox sampling given candidates, assigning results and ground

Expand an array of boxes by a given scale. Args: bboxes (Tensor): Shape (m, 4) scale (float): The scale factor of bboxes Ret

Test only det bboxes without augmentation. Args: x (tuple[Tensor]): Feature maps of all scale level. img_metas (list[

str: a "nice" summary string describing this module

Check whether the `num_classes` in head matches the length of `CLASSSES` in `dataset`. Args: runner (obj:`EpochBasedRunne

Crop image with a given center and size, then paste the cropped image to a blank image with two centers align. This function is equiv

Paste instance masks according to boxes. This implementation is modified from https://github.com/facebookresearch/detectron2/ Args:

Check whether the center of each box is in the patch. Args: patch (list[int]): The cropped area, [left, top, right, bottom].

Fuse global context feats with roi feats.

Transform outputs for a single batch item into labeled boxes. This method is almost same as `ATSSHead._get_bboxes()`. We use sqrt(iou

Transform outputs for a batch item into bbox predictions. Args: mlvl_cls_scores (list[Tensor]): Each element in the list is

Get final border for the target size. This function generates a ``final_border`` according to image's shape. The area between ``final

Find the predefined detector config directory.

Load a configuration as a python module.

Sample fine grained feats from each level feature map and concatenate them together. Args: x (tuple[Tensor]): Feature map

Estimate uncertainty based on pred logits. We estimate uncertainty as L1 distance between 0.0 and the logits prediction in 'mask_pred

Mask refining process with point head in testing. Args: x (tuple[Tensor]): Feature maps of all scale level. rois (Ten

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

Add ground truth as assigned results. Args: gt_labels (torch.Tensor): Labels of gt boxes

Import a module from albumentations. It inherits some of :func:`build_from_cfg` logic. Args: cfg (dict): Config dict. It

Get offest for deformable conv based on anchor shape NOTE: currently support deformable kernel_size=3 and dilation=1 Args:

Attach a text label above each bar in *rects*, displaying its height.

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

Split batch bbox prediction back to each image.

Rescale bounding box w.r.t. scale_factor. Args: bboxes (Tensor): Shape (n, 4) for bboxes or (n, 5) for rois scale_factor (float):

Check whether the training dataset is compatible with head. Args: runner (obj:`EpochBasedRunner`): Epoch based Runner.

Check whether the dataset in val epoch is compatible with head. Args: runner (obj:`EpochBasedRunner`): Epoch based Runner.

Build a module. Args: cfg (dict, list[dict]): The config of modules, is is either a dict or a list of configs. registr

Build distiller.

Build a model from config and load the given checkpoint. Args: config_path (str): the OpenMMLab config for the model we want to

Builder for Transformer.

Collect the information of the running environments.

Convert various input in BGR order to normalized RGB matplotlib color tuples, Args: color (:obj:`Color`/str/tuple/int/ndarray): Color

match the detection results with the groundtruth in the whole database

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

Convert the torch forward outputs containing tuple or list to a list only containing torch.Tensor. Args: output (list(Tensor) | tuple

Crop predicted masks by zeroing out everything not in the predicted bbox. Args: masks (Tensor): shape [H, W, N].

see :func:`BaseInstanceMasks.crop_and_resize`

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

Encode bitmap mask to RLE code. Args: mask_results (list | tuple[list]): bitmap mask results. In mask scoring rcnn, mask_resu

Evaluate in VOC protocol. Args: results (list[list | tuple]): Testing results of the dataset. metric (str | list[str]