Mask head forward function used in both training and testing.

Mask head forward function used in both training and testing.

Get the flag indicate whether anchor centers are inside regions.

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

Map bboxes from testing scale to original image scale.

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,

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

Load anns with the specified ids. self.anns is a list of annotation lists instead of a list of annotations. Args:

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.

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

Visualize the detection results on the image. Args: model (nn.Module): The loaded detector. img (str or np.ndarray): Image filena

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

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.

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:

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,

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`

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 proposals from encoded memory. Args: memory (Tensor) : The output of encoder, has shape (bs, num_key, em

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

Transform network outputs for a batch into bbox predictions. Args: all_cls_scores (Tensor): Classification score of all

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.