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

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

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

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

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

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

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

Returns a config object for a model in model zoo. Args: config_path (str): config file name relative to detectron2's "configs/"

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

Get default param list for optimizer, with support for a few types of overrides. If no overrides needed, this is equivalent to `model.paramet

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

Get frequency weight for each class sorted by class id. We now calcualte freqency weight using image_count to the power freq_weight_power.

Returns: dict: a dict which maps names (str) to data of the fields Modifying the returned dict will modify this instance

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

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

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

Run model on the data_loader and evaluate the metrics with evaluator. Also benchmark the inference speed of `model.__call__` accurately.

Dump an "Instances" object to a COCO-format json that's used for evaluation. Args: instances (Instances): img_id (int): the

Recursively instantiate objects defined in dictionaries by "_target_" and arguments. Args: cfg: a dict-like object with "_target

Bilinear interpolation method to be used for upscaling Args: tensor_nchw (tensor): tensor of shape (N, C, H, W)

Returns whether execution is currently in Torch FX tracing mode

Similar to :meth:`latest`, but the returned values are either the un-smoothed original latest value, or a median of the given

List all registered datasets. Returns: list[str]

Load data from a file Args: fpath (str): file path to load data from

Load data from a file Args: fpath (str): file path to load data from

Args: image_dir (str): path to the raw dataset. e.g., "~/cityscapes/leftImg8bit/train". gt_dir (str): path to the raw annotations

Args: image_dir (str): path to the raw dataset. e.g., "~/cityscapes/leftImg8bit/train". gt_dir (str): path to the raw annotations

Args: image_dir (str): path to the raw dataset. e.g., "~/coco/train2017". gt_dir (str): path to the raw annotations. e.g., "~/coc

Load a json file in LVIS's annotation format. Args: json_file (str): full path to the LVIS json annotation file. image_root

Load precomputed object proposals into the dataset. The proposal file should be a pickled dict with the following keys: - "ids": list[i

Convert pred_mask_logits to estimated foreground probability masks while also extracting only the masks for the predicted classes in pred_ins

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

Load content from the given config file and merge it into self. Args: cfg_filename: config filename allow_un

Returns: (H, W) array, a mask for all pixels that have a prediction

Convert a numpy image or a batch of images to a PIL image.

Pack data into tensors

Given two lists of boxes of size N and M, compute the intersection area between __all__ N x M pairs of boxes. The box order must be (xmin

recursively (post-order) update all modules with the target type and its subclasses, make a initialization/composition/inheritance/... via th

Print main metrics in a format similar to Detectron, so that they are easy to copypaste into a spreadsheet. Args: results (Order

Args: dataset_dicts (list[dict]): list of dataset dicts. class_names (list[str]): list of class names (zero-indexed).

Compute (fractional) per-image repeat factors based on category frequency. The repeat factor for an image is a function of the freque

Resample coarse segmentation tensor to the given bounding box and derive labels for each pixel of the bounding box Args: coarse_

Resample fine and coarse segmentation tensors to the given bounding box and derive labels for each pixel of the bounding box Args:

Resamples U and V coordinate estimates for the given bounding box Args: u (tensor [1, C, H, W] of float): U coordinates v (t

Resamples U and V coordinate estimates for the given bounding box Args: predictor_output (DensePoseChartPredictorOutput): DensePose

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

Scale the box with horizontal and vertical scaling factors

Set the random seed for the RNG in torch, numpy and python. Args: seed (int): if None, will use a strong random seed.

Return semantic segmentation predictions in the original resolution. The input images are often resized when entering semantic segmentor. Mo

Turn a list of integer scalars or integer Tensor scalars into a vector, in a way that's both traceable and scriptable. In tracing, `x` s

Return `num_samples` (or fewer, if not enough found) random samples from `labels` which is a mixture of positives & negatives. It will tr

Compute and display summary metrics for evaluation results. Note this function can *only* be applied on the default parameter setting

Helper function to synchronize (barrier) among all processes when using distributed training

Args: see documentation of :func:`paste_masks_in_image`.

Convert InstancesList to List[Instances]. The input `instances_list` can also be a List[Instances], in this case this method is a non