r"""Produce output given segmentation and other conditioning inputs. random style will be used if neither z nor style_img is provided.

r"""Get rank of the thread.

r"""Performs a single optimization step. Args: closure (callable, optional): A closure that reevaluates the model

r"""Return an activation normalization layer. Args: num_features (int): Number of feature channels. norm_type (str):

r"""Get world size. How many GPUs are available in this job.

r"""check if current process is the master

r"""Compute visualization images and save them to the disk. Args: path (str): Location of the file. data (dict): Data

r""" Set a model to require gradient or not. Args: model (nn.Module): Neural network model. require (bool): Whether the network r

r"""Record the value

r"""Return a nonlinearity layer. Args: nonlinearity_type (str): Type of nonlinear activation function. ``'none'``

r"""Get number of channels for the input image. Args: data_cfg (obj): Data configuration structure. Returns: num_channels (in

r"""Set random seeds for everything. Args: seed (int): Random seed. by_rank (bool):

r"""Move all tensors inside data to device. Args: data (dict, list, or tensor): Input data. device (str): 'cpu' or 'cuda'.

r"""Forward the CNN Args: net_out (N x C5 x H x W tensor): Intermediate feature maps. z (N x C3 tensor): Intermediate

r"""Sample points along rays, forwarding the per-point MLP and aggregate pixel features Args: blk_feats (K x C1 tensor): Deprecat

r"""Return data loader . Args: cfg (obj): Global configuration file. dataset (obj): PyTorch dataset object. batch_size (i

r"""Get the image save path. Args: subdir (str): Sub-directory under the main directory for saving the outputs.

r"""Wrap a model for distributed data parallel training. Args: model (obj): PyTorch network model. Returns: (obj): Wrapped P

r"""Get number of channels for the input label map. Args: data_cfg (obj): Data configuration structure. video (bool): Whether we

r"""Return the target vector for the binary cross entropy loss computation. Args: dis_output (tensor): Discriminator outp

r"""Initialize the cudnn module. The two things to consider is whether to use cudnn benchmark and whether to use cudnn deterministic. If cudnn

r"""Save network weights, optimizer parameters, scheduler parameters to a checkpoint.

r"""Calculate number of parameters in a PyTorch network. Args: model (obj): PyTorch network. Returns: (int): Number of param

r"""Detach all logging variables to prevent potential memory leak.

r"""Flush all meters using the current iteration.

r"""Perform same data augmentation on all inputs. Args: inputs (dict): Keys are from self.augmentable_data_types. Values are

r""" Record time before applying backward.

r""" Record time before applying forward.

r""" Record time before computing loss.

r""" Record time before updating the weights

r"""Write values to meters.

r"""Normalize using ImageNet mean and std. Args: input (4D tensor NxCxHxW): The input images, assuming to be [-1, 1]. Returns:

Returns the average value of data inside every voronoi cell

Returns the average value of data inside every voronoi cell

r""" Reduce to all ranks

r"""Hyper Conv2d forward. Convolve x using the provided weight and bias. Args: x (N x C x H x W tensor): Input tensor.

r"""Return the networks, the optimizers, and the schedulers. We will first set the random seed to a fixed value so that each GPU copy will be

r"""Return the scheduler object. Args: cfg_opt (obj): Config for the specific optimization module (gen/dis). net (obj): PyTorch n

Return the scheduler object. Args: cfg_opt (obj): Config for the specific optimization module (gen/dis). opt (obj): PyTorch optim

r"""Return weight normalization. Args: norm_type (str): Type of weight normalization. ``'none'``, ``'spectral'``,

r"""Compute results images for a batch of input data and save the results in the specified folder. Args: data (dict):

r"""Compute result images according to the provided camera trajectory and save the results in the specified folder. The full image is evaluate

r"""Create log directory for storing checkpoints and output images. Args: config_path (str): Path to the configuration file. logd

r"""N+1 label GAN loss computation. Args: input_x (tensor): Output values. t_real (boolean): Is this output value for

r"""GAN loss computation. Args: dis_output (tensor): Discriminator outputs. t_real (bool): If ``True``, uses the real

r"""Slice all tensors from start to end. Args: data (dict, list or tensor): Input data.

r"""Split concatenated labels into their parts. Args: labels (torch.Tensor): Labels obtained through concatenation. label_lengths

r"""Things to do before an iteration. Args: data (dict): Data used for the current iteration. current_iteration (int)

r"""Convert segmentation mask tensor to color image. Args: segmap (tensor) of If tensor then (NxCxHxW) or (NxTxCxHxW) or (CxHxW).

r"""3-dimensional Homogeneous matrix and regular vector multiplication Convert v to homogeneous vector, perform M-V multiplication, and convert ba

r"""Initialize weights in the network. Args: init_type (str): The name of the initialization scheme. gain (float): The parameter

r"""Write metrics to the tensorboard.