Print all variables.

r"""Get alexnet layers

r"""Builds sequence of augmentation ops. Returns: (list of alb.ops): List of augmentation ops.

r"""Builds sequence of augmentation ops that will be applied to each frame in the video independently. Returns: (list of alb.ops):

r"""Choose key to replace with 'image' for input to albumentations. Returns: key (str): Chosen key to be replace with 'image'

r"""Choose key to replace with 'keypoints' for input to albumentations. Returns: key (str): Chosen key to be replace with 'keypoin

r"""Collate separated images back into sequence, grouped by keys. Args: augmented (dict): Dict containing frames with keys of the

r"""FID computation function to be overloaded.

r"""Create additional targets as required by the albumentation library. Args: inputs (dict): Keys are from self.augmentable_data_

r"""Make input one-hot. Args: label_map (torch.Tensor): (C, H, W) tensor containing indices. num_classes (int): Number of labels

r"""Operations to do after an epoch. Args: data (dict): Data used for the current iteration. current_epoch (int): Cur

r"""Operations to do after an iteration. Args: data (dict): Data used for the current iteration. current_epoch (int):

r"""Per-pixel rendering forwarding Args: blk_feats: Deprecated worldcoord2 (N x H x W x L x 3 tensor): 3D world coord

r"""Forwarding the MLP. Args: blk_feats (K x C1 tensor): Sparse block features. worldcoord2 (N x H x W x L x 3 tensor

r"""Sample camera poses and perform ray-voxel intersection. Args: batch_size (int): Expected batch size of the current batch

r"""Get height and width to resize to, given smallest side. Args: height (int): Input image height. width (int): Inpu

r"""Return dataset object for the test set Args: cfg (obj): Global configuration file. Returns: (obj): PyTorch dataset objec

r"""Return dataset objects for the training and validation sets. Args: cfg (obj): Global configuration file. Returns: (dict)

r"""Compute visualization outputs. Args: data (dict): Data used for the current iteration.

r"""Get inception v3 layers

r"""Initialize a dictionary of hyperparameters that we want to monitor in the HParams dashboard in tensorBoard.

r"""Every trainer should implement its own init loss function.

r"""Initialize the tensorboard. Different algorithms might require different performance metrics. Hence, custom tensorboard initializa

r"""Perform different data augmentation on different image inputs. Note that this operation only works Args: inputs (dict): Keys

r"""Implement the things you want to do before saving a checkpoint. For example, you can compute the K-mean features (pix2pixHD) before

r"""Get resnet50 layers

r"""Get robust resnet50 layers

r"""Save network weights, optimizer parameters, scheduler parameters in the checkpoint. Args: cfg (obj): Global configuration.

r"""Operations to do before starting an epoch. Args: current_epoch (int): Current number of epoch.

r"""Operations to do before starting an iteration. Args: data (dict): Data used for the current iteration. current_it

r""" Record forward, backward, loss time for the discriminator update.

r""" Record forward, backward, loss, and model average time for the generator update.

r""" Record time before applying model average.

r"""Get vgg16 layers

r"""Get vgg19 layers

r"""Dummy member function to be overloaded by the child class. In the child class, you can write down whatever you want to track.

r"""Write all loss values to tensorboard.

r"""Write values to tensorboard. By default, we will log the time used per iteration, time used per epoch, generator learning rate, and

r"""Write out lmdb containing (key, contents of filepath) to file. Args: filepaths (list): List of filepath strings. keys (list):

r"""Add filepath and key to list of filepaths and keys. Args: filepath (str): Filepath to add. key (str): LMDB key for this filep

Get file path for our dataset structure.

r"""Returns the output of the residual branch. Args: x (tensor): Input tensor. cond_inputs (list of tensors) : Condit

r""" Copy state_dictionary from source to target. Here source is the regular module and the target is the moving average module. Basic

r"""Main function. Args: data_root (str): Location of dataset root. cfg (object): Loaded config object. paired (bool): Pa

r"""Weight initialization.

r"""Every trainer should implement its own discriminator forward.

r"""Update the discriminator. Args: data (dict): Data used for the current iteration.

r"""Download a file from google drive or pbss by using the url. Args: URL: GDrive URL or PBSS pre-signed URL for the checkpoint.

r"""Things to do after an epoch. Args: data (dict): Data used for the current iteration. current_epoch (int): Curren

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

r"""Write the value in the tensorboard. Args: step (int): Epoch or iteration number.

r"""Fuse operation min mean for hinge loss computation of negative samples

r"""Fuse operation min mean for hinge loss computation of positive samples

r"""Every trainer should implement its own generator forward.

r"""Update the generator. Args: data (dict): Data used for the current iteration.

r"""Get CPU affinity

r"""Get all filenames from list. Args: list_name (str): Path to filename list. Returns: all_filenames (dict): Folder name for

r"""Get confirm token Args: response: Check if the file exists. Returns:

r"""Get height and width of crop. Args: augmentation (dict): Dict of applied augmentations. Returns: (dict): - cro

Generate a unique id based on date. Returns: str: Return uid string, e.g. '20171122171307111552'.

r"""Get the data types which should be put in LMDB. Args: cfg: Configuration object.

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