Apply transformation `pred_bboxes` to `boxes`. Args: bboxes (torch.Tensor): Basic boxes.Shape (B, N, 4) or (N, 4) pre

Get custom activation of cls_score. Args: cls_score (torch.Tensor): The prediction with shape (N, C + 2). Returns:

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

Refine bboxes during training. Args: rois (Tensor): Shape (n*bs, 5), where n is image number per GPU, and bs is t

see :func:`BaseInstanceMasks.resize`

Box head forward function used in both training and testing.

Create sample results that can be passed to BBoxHead.get_targets.

Create a dummy onnx::NonMaxSuppression op while exporting to ONNX. This function helps exporting to onnx with batch and multiclass NMS op. It

Check if norm layer is in correct train state.

See :func:`BaseInstanceMasks.crop`.

Directly extract features from the backbone+neck.

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

Get user-defined property.

dict: a dictionary of info about the object

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

nn.Module: normalization layer after the third convolution layer

Simple forward test function.

Convert objects of various python types to :obj:`torch.Tensor`. Supported types are: :class:`numpy.ndarray`, :class:`torch.Tensor`, :class:`S

Box head forward function used in both training and testing.

Box head forward function used in both training and testing.

Mask head forward function used in both training and testing.

Mask head forward function used in both training and testing.

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

Get the flag indicate whether anchor centers are inside regions.

Map bboxes from testing scale to original image scale.

Build backbone.

Build linear layer. Args: cfg (None or dict): The linear layer config, which should contain: - type (str): Layer type.

Calculate region of the box defined by the ratio, the ratio is from the center of the box to every edge.

Calculate a proportional bbox region. The bbox center are fixed and the new h' and w' is h * ratio and w * ratio. Args: bbox (Tensor

Clip boxes dynamically for onnx. Since torch.clamp cannot have dynamic `min` and `max`, we scale the boxes by 1/max_shape and clamp in the

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,

Evaluate mAP of a dataset. Args: det_results (list[list]): [[cls1_det, cls2_det, ...], ...]. The outer list indicates images,

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

Directly extract features from the backbone+neck.

Extract features from multiple images. Args: imgs (list[torch.Tensor]): A list of images. The images are augmente

see :func:`BaseInstanceMasks.flip`

Format the results to json (standard format for COCO evaluation). Args: results (list[tuple | numpy.ndarray]): Testing results of

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

Generate grid anchors in multiple feature levels. Args: featmap_sizes (list[tuple]): List of feature map sizes in

Draw bboxes and class labels (with scores) on an image. Args: img (str or ndarray): The image to be displayed. bboxes (ndarray):

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

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

Compute a maximum-weighted matching in the general undirected weighted graph given by "edges". If "maxcardinality" is true, only maximum-card

See :func:`BaseInstanceMasks.pad`.

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

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

See :func:`BaseInstanceMasks.resize`.

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.

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

See :func:`BaseInstanceMasks.to_tensor`.

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:

Transform outputs for a single batch item into bbox predictions. Args: cls_scores (list[Tensor]): Box scores of all scale level

Initialize classification conv layers of the head.

Initialize bbox regression conv layers of the head.

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.

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``,