Build a LR scheduler from config.

Build a mask head defined by `cfg.MODEL.ROI_MASK_HEAD.NAME`.

Build ROIHeads defined by `cfg.MODEL.ROI_HEADS.NAME`.

Returns: iterable It now calls :func:`detectron2.data.build_detection_train_loader`. Overwrite it if you'd like

equivalent to FastRCNNOutputLayers.inference

equivalent to mask_head.mask_rcnn_inference

We estimate uncerainty as L1 distance between 0.0 and the logit prediction in 'logits' for the foreground class in `classes`. Args:

Concatenates a list of RotatedBoxes into a single RotatedBoxes Arguments: boxes_list (list[RotatedBoxes]) Retur

Args: instance_lists (list[Instances]) Returns: Instances

Colorize depth maps.

Load and prepare dataset dicts for training / testing Args: dataset_names (Collection[str]): a list of dataset names keep_in

3x3 convolution with padding

Convert all BatchNorm/SyncBatchNorm in module into FrozenBatchNorm. Args: module (torch.nn.Module): Returns:

Convert an image from given format to RGB. Args: image (np.ndarray or Tensor): an HWC image format (str): the format of inpu

When a dependency of a class is not available, create a dummy class which throws ImportError when used. Args: klass (str): name

When a dependency of a function is not available, create a dummy function which throws ImportError when used. Args: func (str):

Convert a version string into a tuple of integers. This method is usually used for comparing two versions. For pre-release versions: alpha <

Downgrade a config from its current version to an older version. Args: cfg (CfgNode): to_version (int): Note: A

Args: segment: numpy array of shape Nx2, containing all the points in the polygon. color: color of the polygon. Refer

Args: soft_mask (ndarray): float array of shape (H, W), each value in [0, 1]. color: color of the mask. Refer to `mat

Args: img_ids: a list of image IDs to evaluate on. Default to None for the whole dataset

Export the model to Caffe2's protobuf format. The returned object can be saved with its :meth:`.save_protobuf()` method. The

Export the model to a ``torch.jit.TracedModule`` by tracing. The returned object can be saved to a file by ``.save()``. Retu

Extract data for mask loss from instances that contain matched GT and estimated bounding boxes. Args: proposals_targets: Iterable

Call `fast_rcnn_inference_single_image` for all images. Args: boxes (list[Tensor]): A list of Tensors of predicted class-specific or

Single-image inference. Return bounding-box detection results by thresholding on scores and applying non-maximum suppression (NMS). Args

Filter out images with too few number of keypoints. Args: dataset_dicts (list[dict]): annotations in Detectron2 Dataset format.

Expand a hierarchical dict of scalars into a flat dict of scalars. If results[k1][k2][k3] = v, the returned dict will have the entry {"k1

Args: input: NCHW images rois: Bx6 boxes. First column is the index into N. The other 5 columns are (

Args: packed_annotations: annotations packed into tensors, the following attributes are required:

Generate config from config str. Args: cfg_str (str): Config str. file_format (str): Config file format corresponding

Calculate absolute positional embeddings. If needed, resize embeddings and remove cls_token dimension for the original embeddings. Ar

this will perform the index select, cat the tensors, and provide the attn_bias from cache

this will perform the index select, cat the tensors, and provide the attn_bias from cache

this will perform the index select, cat the tensors, and provide the attn_bias from cache

this will perform the index select, cat the tensors, and provide the attn_bias from cache

Returns: The box centers in a Nx2 array of (x, y).

Returns the URL to the model trained using the given config Args: config_path (str): config file name relative to detectron2's "conf

Interpolate Embeddings and Segmentations to the size of a given bounding box, and compute closest vertices and the segmentation mask Arg

Args: image_size (tuple): height, width Returns: crop_size (tuple): height, width in absolute pixels

Returns metadata associated with COCO DensePose datasets Args: base_path: Optional[str] Base path used to load metadata from

Load and return the configuration from the specified file (relative to the base configuration directory)

Compute the output size given input size and target short edge length.

Load point coords and their corresponding labels from point annotation. Args: instances (list[Instances]): A list of N Instances, wh

Get relative positional embeddings according to the relative positions of query and key sizes. Args: q_size (int): size of qu

Return the list of external input/output of sub-graph, each element is tuple of the name and corresponding version in predict_net. exter

Find `num_points` most uncertain points from `uncertainty_map` grid. Args: uncertainty_map (Tensor): A tensor of shape (N, 1, H, W)

Sample points in [0, 1] x [0, 1] coordinate space based on their uncertainty. The unceratinties are calculated for each point using 'unce