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 DistributedDataParallel module by device type. If device is cuda, return a MMDistributedDataParallel model; if device is mlu, return a

build DataParallel module by device type. if device is cuda, return a MMDataParallel model; if device is mlu, return a MLUDataParallel model.

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

Compute reweight_factor for regression and classification loss.

Collect the information of the running environments.

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

Evaluation for object detection. Args: dataset (Dataset): A PyTorch dataset. results (list): Object detection results

Draw labels on the axes. Args: ax (matplotlib.Axes): The input axes. labels (ndarray): The labels with the shape of (n, ).

Instance segmentation and object detection evaluation in COCO protocol. Args: results (list[list | tuple | dict]): Testin

Directly extract features from the backbone and neck.

Placeholder of forward function. Returns: dict[str, Tensor]: A dictionary, including features and predicted score

Forward features from the upstream network. Args: feats (tuple[Tensor]): Features from the upstream network, each is

Dummy forward function.

Forward for head part which is called after every decoder layer. Args: decoder_out (Tensor): in shape (num_queries, batch_size, c

Forward function for exporting to ONNX. Over-write `forward` because: `masks` is directly created with zero (valid position tag) and

r"""Generate 2D gaussian radius. This function is modified from the `official github repo <https://github.com/princeton-vl/CornerNet-Lite/blo

Generate base anchors. Returns: list(torch.Tensor): Base anchors of a feature grid in multiple \ feature levels.

Base on the previous bboxes and regression values, we compute the regressed bboxes and generate the grids on the bboxes. :param reg:

Generate base anchors of a single level. Args: base_size (int | float): Basic size of an anchor. scales (torch.Tensor

Get custom accuracy w.r.t. cls_score and labels. Args: cls_score (torch.Tensor): The prediction with shape (N, C + 2).

Get squares according to feature map sizes and guided anchors. Args: featmap_sizes (list[tuple]): Multi-level feature map sizes.

Get squares according to feature map sizes and guided anchors. Args: featmap_sizes (list[tuple]): Multi-level feature map sizes.

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

get_bboxes() is implemented in StageCascadeRPNHead.

Get name of caller method.

Get class names of a dataset.

Get classes name from file. Args: label_file (str): File path of the label description file that maps the classes

Get `gt_group_of` of a certain class, which is used in Open Images. Args: annotations (list[dict]): Same as `eval_map()`. class_i

Get the mask scores. mask_score = bbox_score * mask_iou

Get image metas from pipeline.

Get points according to feature map sizes. Args: featmap_sizes (list[tuple]): Multi-level feature map sizes. img_meta

Get ``num_points`` most uncertain points during test. Args: mask_pred (Tensor): A tensor of shape (num_rois, num_classes,

Get sampled approxs and inside flags according to feature map sizes. Args: featmap_sizes (list[tuple]): Multi-level feature map s

Resize, binarize, and format the instance mask predictions. Args: mask_pred (Tensor): shape (N, H, W). label_pred (Te

Compute corresponding GT box and classification targets for proposals. Args: proposals_list (list[list]): Multi level poi

Compute semantic segmentation targets for each image. Args: segm_pred (Tensor): Predicted semantic segmentation map

Get ``num_points`` most uncertain points with random points during train. Sample points in [0, 1] x [0, 1] coordinate space based on their

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

Get a tensor of bboxes centered at (0, 0). Returns: Tensor: Tensor of bboxes with shape (num_bboxes, 4) in [xmin, ymi

Are points located in bboxes. Args: points (Tensor): Points, shape: (m, 2). bboxes (Tensor): Bounding boxes, shape: (n, 4). Retu

Maximization part of EM algorithm(Expectation-Maximization)

Concat multi-level feature maps by image. [feature_level0, feature_level1...] -> [feature_image0, feature_image1...] Convert the shape of eac

Load annotation from annotation file.

Load image level annotations from csv style ann_file. Args: image_level_ann_file (str): CSV style image level annotation

Load proposal from proposal file.

Load state_dict to a module. This method is modified from :meth:`torch.nn.Module.load_state_dict`. Default value for ``strict`` is set to ``Fa

A combination of the func:``AnchorHead.loss`` and func:``SSDHead.loss``. When ``self.use_ohem == True``, it functions like ``SSDHead.

Compute loss of the head. Args: segm_pred (list[Tensor]): Predicted semantic segmentation map with shape (N, num_

Compute loss of the head. Args: mask_pred (list[Tensor]): Predicted prototypes with shape (num_classes, H, W).

Compute loss of the head. Args: cls_scores (list[Tensor]): Box scores for each scale level Has shape (N, num_poin

Pack all blocks in a stage into a ``ResLayer``.

Dictionary mapper. Renames keys according to keymap provided. Args: d (dict): old dict keymap (dict): {'old_key':'new

Compute mask target for positive proposals in multiple images. Args: pos_proposals_list (list[Tensor]): Positive proposals in multiple

Returns: list: Contains all values in meta_info_fields.

Apply same name function to all tensors in data_fields.

nn.Module: normalization layer after the second convolution layer

Change from point offset to point coordinate.

Test without augmentation.

Test without augmentation.

padding has no effect on polygons`

Convert palette to matplotlib palette. Args: palette List[tuple]: A list of color tuples. Returns: List[tuple[float]]: A lis

The single core function to evaluate the metric of Panoptic Segmentation. Same as the function with the same name in `panopticapi`. Only the

Print time spent by CPU and GPU. Useful as a temporary context manager to find sweet spots of code suitable for async implementation.

Simple version of ``kwimage.Boxes.random`` Returns: Tensor: shape (n, 4) in x1, y1, x2, y2 format. References: https://gitla

Randomly sample an img_scale when ``multiscale_mode=='range'``. Args: img_scales (list[tuple]): Images scale range for sampling.

Refine bboxes through stages.

Predict bucketing estimation (cls_pred) and fine regression (offset pred) with side-aware features.