Construct 3D identity rotation given shape

imaginary components of the quaternion

imaginary components of the quaternion

make all the real part of the quaternion positive

make all the real part of the quaternion positive

normalized quaternion with norm of 1

normalized quaternion with norm of 1

Given a quaternion representing rotation, get the quaternion representing its inverse. Args: quaternion: Quaternions as tensor o

Convert rotations given as quaternions to axis/angle. Args: quaternions: quaternions with real part first, as tensor of

Generate random quaternions representing rotations, i.e. versors with nonnegative real part. Args: n: Number of quaternions in a

Generate random rotations as 3x3 rotation matrices. Args: n: Number of rotation matrices in a batch to return. dtype: Type t

Reduce loss as specified. Args: loss (Tensor): Elementwise loss tensor. reduction (str): Options are "none", "mean" and "sum".

Resets the environment to an initial state.

Resets the environment to an initial state.

Set random seed. Args: seed (int): Seed to be used. deterministic (bool): Whether to set the deterministic option for

Set requies_grad for all the networks. Args: nets (nn.Module | list[nn.Module]): A list of networks or a single network.

Set requies_grad for all the networks. Args: nets (nn.Module | list[nn.Module]): A list of networks or a single network.

Create a list of timesteps to use from an original diffusion process, given the number of timesteps we want to take from equally-sized portio

Convert a unit quaternion to a standard form: one in which the real part is non negative. Args: quaternions: Quaternions with re

Wrap models with separate MMDistributedDataParallel. It only wraps the modules with parameters.

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

Main api for training model.

Compute training losses for a single timestep. :param model: the model to evaluate loss on. :param x_start: the [N x C x ...

Update the reweighting using losses from a model. Sub-classes should override this method to update the reweighting using los

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

Get a numpy array of weights, one per diffusion step. The weights needn't be normalized, but must be positive.

Zero out the parameters of a module and return it.

Prepare data for the ``idx``-th data. As for video dataset, we can first parse raw data for each frame. Then we combine annotations fr