Forward function used in test time. This function wraps <forward> function in no_grad() so we don't save intermediate steps for backprop

Compute metrics of predicted depth maps. Applies cropping and masking as necessary or specified via arguments. Refer to compute_errors for more detail

Calculate additional output images for visdom and HTML visualization

3x3 convolution with padding

Create a dataset given the option. This function wraps the class CustomDatasetDataLoader. This is the main interface between this package

Create a model given the option. This function warps the class CustomDatasetDataLoader. This is the main interface between this package and '

Reference: https://github.com/google-research/google-research/blob/e96197de06613f1b027d20328e06d69829fa5a89/infinite_nature/render_utils.py#L68

Crops the images according to the crop parameters. Args: images: RGB or depth images, shape (H, W, 3) or (H, W). crop_params: Cro

This is what should be passed into the function that is called by gradio

Must be the last element of the bundle for unbundling to work

Unbundle: get a dictionary with stuff after it is sent bby the gradio to the function. Enkey format: bunch of Gradio components, then

Import the module "data/[dataset_name]_dataset.py". In the file, the class called DatasetNameDataset() will be instantiated. It has to be a s

Import the module "models/[model_name]_model.py". In the file, the class called DatasetNameModel() will be instantiated. It has to be a subcl

this will perform the index select, cat the tensors, and provide the attn_bias from cache

this will perform the index select, cat the tensors, and provide the attn_bias from cache

Crops the black border of the RGB. Args: rgb: RGB image, shape (H, W, 3). Returns: Crop parameters.

Returns parameters of all the ModuleDicts children that are exclusively used for the given bin configuration

Return a normalization layer Parameters: norm_type (str) -- the name of the normalization layer: batch | instance | none For BatchNo

Crops the white border of the RGB. Args: rgb: RGB image, shape (H, W, 3). Returns: Crop parameters.

Normalizes objs, but uses (a, b) instead of (minimum, maximum) value of objs, if supplied

Initialize a network: 1. register CPU/GPU device (with multi-GPU support); 2. initialize the network weights Parameters: net (network)

initialize network's weights init_type: normal | xavier | kaiming | orthogonal https://github.com/junyanz/pytorch-CycleGAN-an

Load all the networks from the disk. Parameters: epoch (int) -- current epoch; used in the file name '%s_net_%s.pth' % (epoch, na

Load state_dict into model, handling DataParallel and DistributedDataParallel. Also checks for "model" key in state_dict. DataParallel prefixes s

Loads weights to the model from a given resource. A resource can be of following types: 1. URL. Prefixed with "url::" e.g. url

create a single empty directory if it didn't exist Parameters: path (str) -- a single directory path

Takes the filepath, returns (fps, frames). Every frame is a Pillow Image object

Parse our options, create checkpoints directory suffix, and set up gpu device.