src/Baselines/radarcam-depth/utils/log_utils.py

import os
import torch
import numpy as np
from matplotlib import pyplot as plt


def log(s, filepath=None, to_console=True):
    '''
    Logs a string to either file or console
    Arg(s):
        s : str
            string to log
        filepath
            output filepath for logging
        to_console : bool
            log to console
    '''

    if to_console:
        print(s)

    if filepath is not None:
        if not os.path.isdir(os.path.dirname(filepath)):
            os.makedirs(os.path.dirname(filepath))
            with open(filepath, 'w+') as o:
                o.write(s + '\n')
        else:
            with open(filepath, 'a+') as o:
                o.write(s + '\n')


def colorize(T, colormap='magma', return_numpy=False):
    '''
    Colorizes a 1-channel tensor with matplotlib colormaps
    Arg(s):
        T : torch.Tensor[float32]
            1-channel tensor
        colormap : str
            matplotlib colormap
    '''

    cm = plt.cm.get_cmap(colormap)
    shape = T.shape

    # Convert to numpy array and transpose
    if shape[0] > 1:
        T = np.squeeze(np.transpose(T.cpu().numpy(), (0, 2, 3, 1)))
    else:
        T = np.squeeze(np.transpose(T.cpu().numpy(), (0, 2, 3, 1)), axis=-1)

    # Colorize using colormap
    color = np.concatenate([
        np.expand_dims(cm(T[n, ...])[..., 0:3], 0) for n in range(T.shape[0])],
        axis=0)

    if return_numpy:
        return color
    else:
        # Transpose back to torch format
        color = np.transpose(color, (0, 3, 1, 2))

        # Convert back to tensor
        return torch.from_numpy(color.astype(np.float32))



def log_params(log_path, params_dict):
    with open(log_path, 'w') as log_file:
        for param_name, param_value in params_dict.items():
            log_file.write(f"{param_name}: {param_value}\n")