Rezise the sample to ensure the given size. Keeps aspect ratio. Args: sample (dict): sample size (tuple): image size Returns

Compute metrics for 'pred' compared to 'gt' Args: gt (numpy.ndarray): Ground truth values pred (numpy.ndarray): Predicted values

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

Reverses the imagenet normalization applied to the input. Args: x (torch.Tensor - shape(N,3,H,W)): input tensor Returns: tor

Reverses the imagenet normalization applied to the input. Args: x (torch.Tensor - shape(N,3,H,W)): input tensor Returns: tor

Implements the "standard" process for log regression evaluation: The value of C is chosen by training on train_dataset and evaluating on

Fix random seed for reproducibility Args: seed (int): random seed

x_list contains a list of tensors to nest together and run

Intrinsics for a pinhole camera model. Assume fov of 55 degrees and central principal point.

Learning rate configuration for different layers of the model Args: lr (float) : Base learning rate Returns:

Find and parse the .json config file for the model. Args: model_name (str): name of the model. The config file should be named config_{mo