utils.py

import torch
import numpy as np
import logging
from mmcv.utils import get_logger


def get_total_grad_norm(parameters, norm_type=2):
    parameters = list(filter(lambda p: p.grad is not None, parameters))
    norm_type = float(norm_type)
    device = parameters[0].grad.device
    total_norm = torch.norm(torch.stack([torch.norm(p.grad.detach(), norm_type).to(device) for p in parameters]),
                            norm_type)
    return total_norm

def image_graph_collate_scene_graph(batch):
    batch = list(zip(*batch))
    return tuple(batch)


def get_root_logger(log_file=None, log_level=logging.INFO):
    """Use ``get_logger`` method in mmcv to get the root logger.
    The logger will be initialized if it has not been initialized. By default a
    StreamHandler will be added. If ``log_file`` is specified, a FileHandler
    will also be added. The name of the root logger is the top-level package
    name, e.g., "mmaction".
    Args:
        log_file (str | None): The log filename. If specified, a FileHandler
            will be added to the root logger.
        log_level (int): The root logger level. Note that only the process of
            rank 0 is affected, while other processes will set the level to
            "Error" and be silent most of the time.
    Returns:
        :obj:`logging.Logger`: The root logger.
    """
    return get_logger(__name__.split('.')[0], log_file, log_level)