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

Create a linear module.

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

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.

Evaluate a function without caching intermediate activations, allowing for reduced memory at the expense of extra compute in the backward pas

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``

Compute the intermediate time steps for sampling. Args: skip_type: A `str`. The type for the spacing of the time steps. We support

Compute the continuous-time label t in [0, T] of a given half-logSNR lambda_t.

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.

Make a standard normalization layer. :param channels: number of input channels. :return: an nn.Module for normalization.

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

Average latest n values or all values.

Build projection layer for key/query/value/out.

Calculate evaluation metrics Args: results (list[ndarray] | list[str]): List of prediction segmentation maps or list of predic

Evaluate the results. Args: runner (:obj:`mmcv.Runner`): The underlined training runner. results (list): Output resul

Get the registry record. Args: key (str): The class name in string format. Returns: class: The corresponding

Initialize and get a logger by name. If the logger has not been initialized, this method will initialize the logger by adding one or two hand

Read an image. Args: img_or_path (ndarray or str or Path): Either a numpy array or str or pathlib.Path. If it is a numpy arra

Make divisible function. This function rounds the channel number to the nearest value that can be divisible by the divisor. It is taken from

nn.Module: normalization layer after the third convolution layer

Save ndarray to local numpy file. Args: array (ndarray): Ndarray to save. temp_file_name (str): Numpy file name. If 'temp_file_na

Scatter with support for kwargs dictionary.

Convert objects of various python types to :obj:`torch.Tensor`. Supported types are: :class:`numpy.ndarray`, :class:`torch.Tensor`, :class:`S

r"""Fills the input Tensor with values drawn from a truncated normal distribution. The values are effectively drawn from the normal distributi

Load checkpoint from somewhere (modelzoo, file, url). Args: filename (str): Accept local filepath, URL, ``torchvision://xxx``,

This function is taken from the original tf repo. It ensures that all layers have a channel number that is divisible by 8 It can be seen

This function is taken from the original tf repo. It ensures that all layers have a channel number that is divisible by 8 It can be seen

Make each stage.

Make transition layer.