Get a logging-friendly message to report parameter names (keys) that are in the model but not found in a checkpoint. Args: keys (

Get class palette (RGB) of a dataset.

Sample points for testing. Find ``num_points`` most uncertain points from ``uncertainty_map``. Args: seg_logits (Tensor)

Sample points for training. Sample points in [0, 1] x [0, 1] coordinate space based on their uncertainty. The uncertainties are calcu

Get the position from a period list. It will return the index of the right-closest number in the period list. For example, the cumulative_per

Get priority value. Args: priority (int or str or :obj:`Priority`): Priority. Returns: int: The priority value.

Given a URL, returns the URL it redirects to or the original URL in case of no indirection

Get the type and mode of points sampler. Args: sampler_type (str): The type of points sampler. The valid value are "D-FPS", "

Get spatial resolution of input feature map considering exporting to onnx mode. Args: x (torch.Tensor): Input tensor, shape (N, C, H,

Returns a dictionary containing a whole state of the module. Both parameters and persistent buffers (e.g. running averages) are included. Key

Returns a dictionary containing a whole state of the module. Both parameters and persistent buffers (e.g. running averages) are included. Key

Get the total training step/epoch.

Get a logging-friendly message to report parameter names (keys) that are in the checkpoint but not found in the model. Args: keys

Convert a grayscale image to BGR image. Args: img (ndarray): The input image. Returns: ndarray: The converted BGR image.

Hue distortion.

Inplace normalize an image with mean and std. Args: img (ndarray): Image to be normalized. mean (ndarray): The mean to be used fo

Pad the given image to a certain shape or pad on all sides with specified padding mode and padding value. Args: img (ndarray): Image

Import modules from the given list of strings. Args: imports (list | str | None): The given module names to be imported. allow_fa

Input: points: input points data, [B, N, C] idx: sample index data, [B, S] Return: new_points:, indexed points data,

Infer abbreviation from the class name. When we build a norm layer with `build_norm_layer()`, we want to preserve the norm type in variable n

Infer abbreviation from the class name. This method will infer the abbreviation to map class types to abbreviations. Rule 1: If the clas

Infer the scope of registry. The name of the package where registry is defined will be returned. Example: # in mmdet/mod

Infer the scope of registry. The name of the package where registry is defined will be returned. Example: # in mmdet/model

Inference image(s) with the segmentor. Args: model (nn.Module): The loaded segmentor. imgs (str/ndarray or list[str/ndarray]): Ei

Initialize a segmentor from config file. Args: config (str or :obj:`mmcv.Config`): Config file path or the config object.

Initialize the weights in backbone and heads. Args: pretrained (str, optional): Path to pre-trained weights. Defa

Initialize weight of later layer.

Default initialization for Parameters of Module.

Initialize a module. Args: module (``torch.nn.Module``): the module will be initialized. init_cfg (dict | list[dict]): initializa

Calculate intersection and Union. Args: pred_label (ndarray | str): Prediction segmentation map or predict result filename.

Tell the input variable is a scalar or not. Args: val: Input variable. include_np (bool): Whether include 0-d np.ndar

Check whether a file path is a file. Args: filepath (str or Path): Path to be checked whether it is a file. Returns:

Input: nsample: max sample number in local region xyz: all points, [B, N, C] new_xyz: query points, [B, S, C] Return: