Args: samples (dict): A dictionary containing the following keys: - image (torch.Tensor): A tensor of shape (batc

Concatenate all file paths. Join one or more filepath components intelligently. The return value is the concatenation of filepath and

Forward function for plugins.

Check whether the object has a method. Args: method (str): The method name to check. obj (object): The object to check. Retu

A wrapper around :func:`grid_sample` to support 3D point_coords tensors Unlike :func:`torch.nn.functional.grid_sample` it assumes point_coords to

Args: samples (dict): A dictionary containing the following keys: - image (torch.Tensor): A tensor of shape (batc

Print a log message. Args: msg (str): The message to be logged. logger (logging.Logger | str | None): The logger to be used.

Sample pixels that have high loss or with low prediction confidence. Args: seg_logit (torch.Tensor): segmentation logits, shape (

Calculate annealing cos learning rate. Cosine anneal from `weight * start + (1 - weight) * end` to `end` as percentage goes from 0.0 to 1.0.

Build activation layer. Args: cfg (dict): The activation layer config, which should contain: - type (str): Layer type.

Args: imgs (List[Tensor]): the outer list indicates test-time augmentations and inner Tensor should have a shape

Read data from a given ``filepath`` with 'rb' mode. Note: There are two types of return values for ``get``, one is ``bytes``

Check whether it is a sequence of some type. Args: seq (Sequence): The sequence to be checked. expected_type (type): Expected typ

Check whether it is a tuple of some type. A partial method of :func:`is_seq_of`.

In distributed setting, this function only download checkpoint at local rank 0.

Print a log message. Args: msg (str): The message to be logged. logger (logging.Logger | str | None): The logger to be used.

r"""Register a path to registry with key 'name' Args: name: Key with which the path will be registered. Usage:

Apply element-wise weight and reduce loss. Args: loss (Tensor): Element-wise loss. weight (Tensor): Element-wise weights.

Initialize BaseModule, inherited from `torch.nn.Module`

Init. Args: scale_factor (float): scaling mode (str): interpolation mode

Convert the type and range of the input image. It converts the input image to np.float32 type and range of [0, 1]. It is mainly used for pre-

Convert the type and range of the image according to dst_type. It converts the image to desired type and range. If `dst_type` is np.uint8, im