metrics.py

import numpy as np


class AverageMeter(object):
    """Computes and stores the average and current value"""

    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


class RecorderMeter(object):
    """Computes and stores the minimum loss value and its epoch index"""

    def __init__(self, total_epoch):
        self.reset(total_epoch)

    def reset(self, total_epoch):
        assert total_epoch > 0
        self.total_epoch = total_epoch
        self.current_epoch = 0
        self.epoch_losses = np.zeros((self.total_epoch, 2), dtype=np.float32)  # [epoch, train/val]
        self.epoch_losses = self.epoch_losses - 1

        self.epoch_accuracy = np.zeros((self.total_epoch, 2), dtype=np.float32)  # [epoch, train/val]
        self.epoch_accuracy = self.epoch_accuracy

    def update(self, idx, train_loss, train_acc, val_loss, val_acc):
        assert idx >= 0 and idx < self.total_epoch, 'total_epoch : {} , but update with the {} index'.format(
            self.total_epoch, idx)
        self.epoch_losses[idx, 0] = train_loss
        self.epoch_losses[idx, 1] = val_loss
        self.epoch_accuracy[idx, 0] = train_acc
        self.epoch_accuracy[idx, 1] = val_acc
        self.current_epoch = idx + 1
        return self.max_accuracy(False) == val_acc

    def max_accuracy(self, istrain):
        if self.current_epoch <= 0: return 0
        if istrain:
            return self.epoch_accuracy[:self.current_epoch, 0].max()
        else:
            return self.epoch_accuracy[:self.current_epoch, 1].max()