utils.py

import torch
import logging
logger = logging.getLogger('Utils')


def l2_norm(input, axis=1):
    norm = torch.norm(input, 2, axis, True)
    output = torch.div(input, norm)
    return output

class AverageMeter(object):

    def __init__(self):
        self.reset()

    def reset(self):
        self.val = 0
        self.avg = 0
        self.sum = 0
        self.count = 0

    def update(self, val, n=1):
        self.val = val
        self.sum += val * n
        self.count += n
        self.avg = self.sum / self.count


def log_epoch(n_epochs=None, loss=None, acc=None, epoch=None, task=None, time=None, classification=False):
    acc_str = f"Task {task + 1}" if task is not None else f""
    acc_str += f" Epoch [{epoch + 1}]/[{n_epochs}]" if epoch is not None else f""
    acc_str += f"\t Training Loss: {loss:.4f}" if loss is not None else f""
    acc_str += f"\t Training Accuracy: {acc:.2f}" if acc is not None else f""
    acc_str += f"\t Time: {time:.2f}" if time is not None else f""
    if classification:
        acc_str = acc_str.replace("Training", "Classification")   
    logger.info(acc_str)