Create a grayscale version of the original image. The colors in masked area, if given, will be kept.

Args: classes (list[int] or None): scores (list[float] or None): class_names (list[str] or None): is_crowd (list[

Compute loss for dense multi-level box regression. Loss is accumulated over ``fg_mask``. Args: anchors: #lvl anchor boxes, each

Extract per-box feature. This is similar to RoIAlign(sampling_ratio=1) except: 1. It's implemented by point_sample 2. It pool

RoiAlignRotated with scale 1.0 and 0 sample ratio.

Transpose the dense per-level predictions. Args: predictions: a list of outputs, each is a list of per-level

Accumulate per image evaluation results and store the result in self.eval. Does not support changing parameter settings from those u

Run all_gather on arbitrary picklable data (not necessarily tensors). Args: data: any picklable object group: a torch proces

Benchmark the dataloader in each distributed worker, and log results of all workers. This helps understand the final performance as w

Built an anchor generator from `cfg.MODEL.ANCHOR_GENERATOR.NAME`.

Build embedder used to embed mesh vertices into an embedding space. Embedder contains sub-embedders, one for each mesh ID. Args:

Build a LR scheduler from config.

Build an optimizer from config.

Check that the datasets have consistent metadata. Args: key (str): a metadata key dataset_names (list[str]): a list of datas

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

Convert a scripted Instances object to a regular :class:`Instances` object

Build a list of :class:`EventWriter` to be used. It now consists of a :class:`CommonMetricPrinter`, :class:`TensorboardXWriter` and :clas

Args: x_data (list[int]): a list containing x values of all the points being drawn. Length of list should match t

Draw semantic segmentation predictions/labels. Args: sem_seg (Tensor or ndarray): the segmentation of shape (H, W).

Evaluate/summarize the performance, after processing all input/output pairs. Returns: dict: A new evalua

Implement operator-level flops counting using jit. This is a wrapper of :func:`fvcore.nn.flop_count` and adds supports for standard detec

Args: input: NCHW images rois: Bx5 boxes. First column is the index into N. The other 4 columns are xyxy.

A context manager that annotates the "training" attribute of every submodule to constant, so that the training codepath in these modules can

Get box regression transformation deltas (dx, dy, dw, dh) that can be used to transform the `src_boxes` into the `target_boxes`. That

Convert box-normalized [0, 1] x [0, 1] point cooordinates to image-level coordinates. Args: boxes_coords (Tensor): A tensor of shape

Returns: dict[str -> (float, int)]: mapping from the name of each scalar to the most recent value and the iterati

Load a json file with COCO's instances annotation format. Currently supports instance detection, instance segmentation, and person keypoi

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

Args: predictions: return values of :meth:`forward()`. proposals (list[Instances]): proposals that match the features

If gradient clipping is enabled through config options, wraps the existing optimizer type to become a new dynamically created class Optimizer

Compare the "keep" result of two nms call. They are allowed to be different in terms of edit distance due to floating point precision iss

Args: predictions: return values of :meth:`forward()`. proposals (list[Instances]): proposals that match the features

Normalize, pad and batch the input images.

Args: dataset_type (DatasetType): a DatasetType e.g. DatasetType.VIDEO_LIST factory (Callable[Metadata, CfgNode]): a

Save an object by LazyConfig.save and load it back. This is used to test that a config still works the same after serialization/deseriali

Run :func:`torch.jit.script` on a model that uses the :class:`Instances` class. Since attributes of :class:`Instances` are "dynamically" adde

Given a list of N Instances (for N images), each containing a `gt_classes` field, return a list of Instances that contain only instances with

Transfers all tensors to the given device

In-place transform all attributes of this class. By "in-place", it means after calling this method, accessing an attribute such

Check that the model must be called with the given input/output dtype

Args: optimizer, last_iter: See ``torch.optim.lr_scheduler.LRScheduler``. ``last_iter`` is the same as ``last_epo

Returns: str: module name of the caller tuple: a hashable key to be used to identify different callers

Forward logic of the keypoint prediction branch. Args: features (dict[str, Tensor]): mapping from feature map names to t

Forward logic of the mask prediction branch. Args: features (dict[str, Tensor]): mapping from feature map names to tenso

Return the learning rate warmup factor at a specific iteration. See :paper:`ImageNet in 1h` for more details. Args: method (str)

Randomly modifies given color to produce a slightly different color than the color given. Args: color (tuple[double]): a

Internal function used to log the usage of different detectron2 components inside facebook's infra.