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

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

Flatten an object so it can be used for PyTorch tracing. Also returns how to rebuild the original object from the flattened outputs. Ret

Load result file and return a result object. :param resFile (str) : file name of result file :return: res (obj) : resul

A 'as_tuple=True' version of torch.nonzero to support torchscript. because of https://github.com/pytorch/pytorch/issues/38718

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 order m

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

Preparation for a new round of evaluation. Should be called before starting a round of evaluation.

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

RoiAlign with scale 1.0.

Equivalent of ``AugmentationList(augmentations)(self)``

Build a batched dataloader. The main differences from `torch.utils.data.DataLoader` are: 1. support aspect ratio grouping options 2. use

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

Complete Intersection over Union Loss (Zhaohui Zheng et. al) https://arxiv.org/abs/1911.08287 Args: boxes1, boxes2 (Tensor): box

Distance Intersection over Union Loss (Zhaohui Zheng et. al) https://arxiv.org/abs/1911.08287 Args: boxes1, boxes2 (Tensor): box

Args: binary_mask (ndarray): numpy array of shape (H, W), where H is the image height and W is the image width. E

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

Fix the __qualname__ of module members to be their exported api name, so when they are referenced in docs, sphinx can find them. Reference:

Use the given boxes in `instances` to produce other (non-box) per-ROI outputs. Args: features: same as in `forward()`

Prepare some proposals to be used to train the ROI heads. It performs box matching between `proposals` and `targets`, and assigns

Locate and return an object ``x`` using an input string ``{x.__module__}.{x.__qualname__}``, such as "module.submodule.class_name". Rais

Normalize, pad and batch the input images.

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

This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, num_key_value_heads, seqlen, he

Args: img: same as in __init__

Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`]. Returns: `Dict[str,

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

Depending on the brightness_factor, gives a lighter or darker color i.e. a color with less or more saturation than the original color

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

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

coords should be a N * 2 array-like, containing N couples of (x, y) points

img should be a numpy array, formatted as Height * Width * Nchannels

In-place override contents of cfg. Args: cfg: an omegaconf config object overrides: list of strings in the f

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

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

Returns the config for a default multi-step LR scheduler such as "1x", "3x", commonly referred to in papers, where every 1x has the total len

Dump IR of a TracedModule/ScriptModule/Function in various format (code, graph, inlined graph). Useful for debugging. Args: mode

Returns: Boxes: tight bounding boxes around bitmasks. If a mask is empty, it's bounding box will be all zero.

Like model_zoo.get, but do not load any weights (even pretrained)

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

Launch multi-gpu or distributed training. This function must be called on all machines involved in the training. It will spawn child proc

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

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

Patch the builtin len() function of a few detectron2 modules to use __len__ instead, because __len__ does not convert values to integers

Args: polygons (list[ndarray]): each array has shape (Nx2,) height, width (int) Returns: ndarray: a bool mask of sha

Args: rgb (bool): whether to return RGB colors or BGR colors. maximum (int): either 255 or 1 Returns: ndarray: a vec

Read an image into the given format. Will apply rotation and flipping if the image has such exif information. Args: file_name (s

Makes a function retry itself after encountering pytorch's CUDA OOM error. It will first retry after calling `torch.cuda.empty_cache()`.

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

Hooks are stateless by default, but can be made checkpointable by implementing `state_dict` and `load_state_dict`.

Compute and display summary metrics for evaluation results given a custom value for max_dets_per_image

Args: img_size (int): Input image size. patch_size (int): Patch size. in_chans (int): Number of input ima

Args: stem (nn.Module): a stem module stages (list[list[CNNBlockBase]]): several (typically 4) stages,

Args: op (Callable): operation to be applied to the image, which takes in an ndarray and returns an ndarray.

Args: stem (nn.Module): a stem module stages (list[list[CNNBlockBase]]): several (typically 4) stages,

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

Add mult proposal scores at testing