Concatenates a list of Boxes into a single Boxes Arguments: boxes_list (list[Boxes]) Returns: Boxes

Access an attribute and return its value if exists. Otherwise return default.

Efficient version of torch.cat that avoids a copy if there is only a single element in a list

Clone the Boxes. Returns: Boxes

Get a copy of the default config. Returns: a detectron2 CfgNode instance.

Clip (in place) the boxes by limiting x coordinates to the range [0, width] and y coordinates to the range [0, height]. Args

Add a scalar `value` to the `HistoryBuffer` associated with `name`. Args: smoothing_hint (bool): a 'hint' on whether thi

Args: filepath (str): a string that contains the absolute path, including the file name, where the visualized ima

Args: box: can be a k-tuple, k-list or an Nxk array/tensor, where k = 4 or 5 from_mode, to_mode (BoxMode) Re

Args: norm (str or callable): either one of BN, SyncBN, FrozenBN, GN; or a callable that takes a channel number and returns

This function is a replacement of .to(another_device) such that it allows the casting to be traced properly by explicitly calling the underly

Initialize the detectron2 logger and set its verbosity level to "DEBUG". Args: output (str): a file name or a directory to save log.

Returns: The :class:`EventStorage` object that's currently being used. Throws an error if no :class:`EventStorage` is currently e

Make this block not trainable. This method sets all parameters to `requires_grad=False`, and convert all BatchNorm layers to

If `resume==True`, and last checkpoint exists, resume from it and load all checkpointables (eg. optimizer and scheduler). Ot

Args: start_iter, max_iter (int): See docs above

Returns: ndarray: the visualized image of shape (H, W, 3) (RGB) in uint8 type. The shape is scale

User should call this function at the beginning of each iteration, to notify the storage of the start of a new iteration. The

Build the whole model architecture, defined by ``cfg.MODEL.META_ARCHITECTURE``. Note that it does not load any weights from ``cfg``.

Args: boxes (Boxes, RotatedBoxes or ndarray): either a :class:`Boxes`, or an Nx4 numpy array of XYXY_ABS format f

Returns: str: a yaml string representation of the config

Set multiple metadata with kwargs.

Return intersection-over-union (Jaccard index) of boxes. Both sets of boxes are expected to be in (x_center, y_center, width, height, an

Args: tensors: a tuple or list of `torch.Tensors`, each of shape (Hi, Wi) or (C_1, ..., C_K, Hi, Wi) where K >= 1

Args: shape: (h, w) tuple or a int interp: PIL interpolation method

Computes the area of all the boxes. Returns: torch.Tensor: a vector with areas of each box.

Build a backbone from `cfg.MODEL.BACKBONE.NAME`. Returns: an instance of :class:`Backbone`

Create a parser with some common arguments used by detectron2 users. Args: epilog (str): epilog passed to ArgumentParser describing

A wrapper around :function:`torch.nn.functional.grid_sample` to support 3D point_coords tensors. Unlike :function:`torch.nn.functional.grid_s

Args: name (str): the name that identifies a dataset, e.g. "coco_2014_train". func (callable): a callable which takes

Set the field named `name` to `value`. The length of `value` must be the number of instances, and must agree with other exist

Perform some basic common setups at the beginning of a job, including: 1. Set up the detectron2 logger 2. Log basic information about en

Args: main_func: a function that will be called by `main_func(*args)` num_machines (int): the total number of machines ma

Remove the field called `name`.

Same as torchvision.ops.boxes.batched_nms, but safer.

Returns: torch.nn.Module: It now calls :func:`detectron2.modeling.build_model`. Overwrite it if you'd like a dif

Log only for the first n times. Args: lvl (int): the logging level msg (str): n (int): name (str): name of t

Find boxes that are non-empty. A box is considered empty, if either of its side is no larger than threshold. Returns:

Similar to `build_detection_train_loader`. But this function uses the given `dataset_name` argument (instead of the names in cfg), and us

Build a proposal generator from `cfg.MODEL.PROPOSAL_GENERATOR.NAME`. The name can be "PrecomputedProposals" to use no proposal generator.

Resize the output instances. The input images are often resized when entering an object detector. As a result, we often need the outputs

Given two lists of rotated boxes of size N and M, compute the IoU (intersection over union) between __all__ N x M pairs of boxes. The

Register a dataset in COCO's json annotation format for instance detection, instance segmentation and keypoint detection. (i.e., Type 1 a

Args: results (OrderedDict[dict]): task_name -> {metric -> score} Returns: bool: whether the verification succeeds or not

Extract ground truth values z_gt for valid point indices and estimated values z_est using bilinear interpolation over top-left (y_lo, x_lo),

Create a small table using the keys of small_dict as headers. This is only suitable for small dictionaries. Args: small_dict (di

Draw annotations/segmentaions in Detectron2 Dataset format. Args: dic (dict): annotation/segmentation data of one image,

Draw instance-level prediction results on an image. Args: predictions (Instances): the output of an instance detection/s

Performs non-maximum suppression (NMS) on the rotated boxes according to their intersection-over-union (IoU). Rotated NMS iteratively re

Given two lists of boxes of size N and M, compute the IoU (intersection over union) between __all__ N x M pairs of boxes. The box ord

Transpose/reshape a tensor from (N, (A x K), H, W) to (N, (HxWxA), K)

If one size (or aspect ratio) is specified and there are multiple feature maps, we "broadcast" anchors of that single size (or aspect ratio)

Return the base directory for configurations

Prepends the provided path with a base path prefix if: 1) base path is not None; 2) path is a local path

RoiAlign with scale 1.0 and 0 sample ratio.

Apply transformation `deltas` (dx, dy, dw, dh) to `boxes`. Args: deltas (Tensor): transformation deltas of shape (N, k*4

A data loader is created by the following steps: 1. Use the dataset names in config to query :class:`DatasetCatalog`, and obtain a list of d

3x3 convolution with padding