Returns: bool: whether the field called `name` exists.

Normalize N D-dimensional embedding vectors arranged in a tensor [N, D] Args: embeddings (tensor [N, D]): N D-dimensional embedding

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

Registers a converter for the specified type. Can be used as a decorator (if converter is None), or called as a method. Args

Args: img: same as in __init__

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

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

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

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.

Execute inference on a list of inputs, using batch size = self.batch_size, instead of the length of the list. Inputs & outpu

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

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

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

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

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

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

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

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

Args: images (torch.Tensor): image to be encoded vae (torch.nn.Module): vae model Returns: torch.Tensor: latent enco

Return the number of samples logged in the past `window_size` iterations.

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

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

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

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

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

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

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

Create a list of blocks of the same type that forms one ResNet stage. Args: block_class (type): a subclass of CNNBlockBa

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

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

Args: cfg (CfgNode): model (nn.Module): evaluators (DatasetEvaluator, list[DatasetEvaluator] or None): if

Transfers all tensors to the given device

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

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.

Rescale the output instances to the target size.

Uniform float random number between low and high.

In given proto, rename all blobs with matched version