Dump model and checkpointables to a file. Args: name (str): name of the file. kwargs (dict): extra arbitrary

Creates a dataset with the specified parameters. Args: dataset_str: A dataset string description (e.g. ImageNet:split=TRAIN).

Creates a data loader with the specified parameters. Args: dataset: A dataset (third party, LaViDa or WebDataset). batch_siz

Setup logging. Args: output: A file name or a directory to save log files. If None, log files will not be saved. If outp

Builds a model from a config. The model is specified by the model name and version in the config. The model is then constructed using the build_from_c

Converts a depth map to a color image. Args: value (torch.Tensor, numpy.ndarry): Input depth map. Shape: (H, W) or (1, H, W) or (1, 1, H,

Main entry point to get the config for the model. Args: model_name (str): name of the desired model. mode (str, optional): "train

Returns: True if distributed training is enabled

Projector MLP Args: in_features (int): input channels out_features (int): output channels mlp_dim (int, o

Inference interface for the model with padding augmentation Padding augmentation fixes the boundary artifacts in the output depth map

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

Create configs and perform basic setups.

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 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