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

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

Directly extract features from the backbone+neck.

see :func:`BaseInstanceMasks.flip`

Get annotation of concatenated dataset by index. Args: idx (int): Index of data. Returns: dict: Annotation i

Get k of TopK for onnx exporting. The K of TopK in TensorRT should not be a Tensor, while in ONNX Runtime it could be a Tensor.Due to dynam

Make plugins for ResNet ``stage_idx`` th stage. Currently we support to insert ``context_block``, ``empirical_attention_block``, ``no

Obtain tight bounding boxes of binary masks. Args: masks (Tensor): Binary mask of shape (n, h, w). Returns: Tensor: Bboxe wi

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

nn.Module: the normalization layer named "norm2"

Converting the points set into bounding box. :param pts: the input points sets (fields), each points set (fields) is represented

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

Prepare an example input image for ``generate_inputs_and_wrap_model``. Args: input_config (dict): customized config describing the exampl

See :func:`BaseInstanceMasks.resize`.

Downsample the first feat and upsample last feat in feats.

Dump the detection results to a COCO style json file. There are 3 types of results: proposals, bbox predictions, mask predictions, an

Split side-aware features aligned with orders of bucketing targets.

Test det bboxes without test-time augmentation. Args: feats (tuple[torch.Tensor]): Multi-level features from the

Generate sparse points according to the ``prior_idxs``. Args: prior_idxs (Tensor): The index of corresponding anchors

Make sure different ranks share the same seed. All workers must call this function, otherwise it will deadlock. This method is generally used

Check if detected bboxes are true positive or false positive. Args: det_bbox (ndarray): Detected bboxes of this image, of shape (m, 5).

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

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.

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.

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,

Async inference image(s) with the detector. Args: model (nn.Module): The loaded detector. img (str | ndarray): Either image files

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.

Builder for Transformer.

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

Calculate the centroid coordinates of the mask. Args: mask (Tensor): The mask to be calculated, shape (h, w). esp (float): Avoid

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.

see :func:`BaseInstanceMasks.crop`

See :func:`BaseInstanceMasks.crop_and_resize`.

Map from level to values.

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

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

FCOS Head Config.

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

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

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

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

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

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

Returns an available device, cpu, cuda or mlu.

Get label assignment (from teacher). Args: cls_scores (list[Tensor]): Box scores for each scale level. Has shape

Only keep loading image and annotations related configuration. Args: pipeline (list[dict]): Data pipeline configs. Returns:

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 hierarchy for classes. Args: hierarchy_file (sty): File path to the hierarchy for classes. Returns: ndar

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].

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

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.

nn.Module: normalization layer after the first convolution layer

Randomly negate value based on random_negative_prob.

See :func:`BaseInstanceMasks.rescale`.