See :func:`BaseInstanceMasks.to_tensor`.

Convert ``xyxy`` style bounding boxes to ``xywh`` style for COCO evaluation. Args: bbox (numpy.ndarray): The bounding box

Add shared or separable branch. convs -> avg pool (optional) -> fcs

Box head forward function used in both training and testing. Returns all regression, classification results and a intermediate feature.

Box head forward function used in both training and testing.

Build head for each branch.

Decode init_proposal_bboxes according to the size of images and expand dimension of init_proposal_features to batch_size. Args:

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

Initialize classification conv layers of the head.

Initialize bbox regression conv layers of the head.

Make each stage.

Make transition layer.

Mask head forward function used in both training and testing.

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

Generate mesh grid of x and y. Args: x (torch.Tensor): Grids of x dimension. y (torch.Tensor): Grids of y dimension.

Calculate accuracy according to the prediction and target. Args: pred (torch.Tensor): The model prediction, shape (N, num_class, ...)

Get anchor centers from anchors. Args: anchors (Tensor): Anchor list with shape (N, 4), "xyxy" format. Returns:

Assign w.r.t. the overlaps of bboxes with gts. Args: overlaps (Tensor): Overlaps between k gt_bboxes and n bboxes,

Test det bboxes with test time augmentation, can be applied in DenseHead except for ``RPNHead`` and its variants, e.g., ``GARPNHead``,

Augmented forward test function.

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

Build backbone.

Build loss.

Builder for Transformer.

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

This function would modify some filed to keep the compatibility of config. For example, it will move some args which will be deprecated to th

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

Crop from ``img``

Encode images with backbone and decode into a semantic segmentation map of the same size as input.

Map from level to values.

Coerces input into a random number generator. If the input is None, then a global random state is returned. If the input is a numeric value,

Calculate recalls. Args: gts (list[ndarray]): a list of arrays of shape (n, 4) proposals (list[ndarray]): a list of arrays of sha

Evaluation in COCO protocol. Args: results (list[list | tuple]): Testing results of the dataset. metric (str | list[s

Find bboxes as long as a part of bboxes is inside the image. Args: bboxes (Tensor): Shape (N, 4). img_h (int): Image height.

Placeholder for Forward function for training.

Forward function for training. Args: img (Tensor): Input images. img_metas (list[dict]): List of image info dict wher

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 annotation by index. Args: idx (int): Index of data. Returns: dict: Annotation info of specified index.

Get annotation by index. Args: idx (int): Index of data. Returns: dict: Annotation info of specified index.

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

Get a yacs CfgNode object with default values.

Returns an available device, cpu, cuda or mlu.

Returns an available device, cpu, cuda or mlu.

Get one ground truth segmentation map for evaluation.

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

Get palette from various inputs. Args: palette (list[tuple] | str | tuple | :obj:`Color`): palette inputs. num_classes (int): the

Get multi-image mask results. Args: mlvl_mask_preds (list[Tensor]): Multi-level mask prediction. Each element in

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].

Inference with slide/whole style. Args: img (Tensor): The input image of shape (N, 3, H, W). img_meta (dict): Image i

Initialize a segmentor 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

Checks whether x has any class *named* cls_name in its ancestry. Doesn't require access to the class's implementation. Useful for pa

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.

Log image size. Args: img_scale (tuple): Image size to be logged. shape_order (str, optional): The order of image shape.

make plugins for block. Args: in_channels (int): Input channels of plugin. plugins (list[dict]): List of plugins cfg

Matrix NMS for multi-class masks. Args: masks (Tensor): Has shape (num_instances, h, w) labels (Tensor): Labels of corresponding

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

Flatten [N, C, H, W] shape tensor to [N, L, C] shape tensor. Args: x (Tensor): The input tensor of shape [N, C, H, W] before conversion.