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 = 0.0 self.avg = 0.0 self.sum = 0.0 self.count = 0.0 def update(self, val, n=1): self.val = val self.sum += val * n self.count += n self.avg = self.sum / self.count def __repr__(self): return ('{name}(val={val}, avg={avg}, count={count})'.format(name=self.__class__.__name__, **self.__dict__)) 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, 'total_epoch should be greater than 0 vs {:}'.format(total_epoch) 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) == self.epoch_accuracy[idx, 1] 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() def plot_curve(self, save_path): import matplotlib matplotlib.use('agg') import matplotlib.pyplot as plt title = 'the accuracy/loss curve of train/val' dpi = 100 width, height = 1600, 1000 legend_fontsize = 10 figsize = width / float(dpi), height / float(dpi) fig = plt.figure(figsize=figsize) x_axis = np.array([i for i in range(self.total_epoch)]) # epochs y_axis = np.zeros(self.total_epoch) plt.xlim(0, self.total_epoch) plt.ylim(0, 100) interval_y = 5 interval_x = 5 plt.xticks(np.arange(0, self.total_epoch + interval_x, interval_x)) plt.yticks(np.arange(0, 100 + interval_y, interval_y)) plt.grid() plt.title(title, fontsize=20) plt.xlabel('the training epoch', fontsize=16) plt.ylabel('accuracy', fontsize=16) y_axis[:] = self.epoch_accuracy[:, 0] plt.plot(x_axis, y_axis, color='g', linestyle='-', label='train-accuracy', lw=2) plt.legend(loc=4, fontsize=legend_fontsize) y_axis[:] = self.epoch_accuracy[:, 1] plt.plot(x_axis, y_axis, color='y', linestyle='-', label='valid-accuracy', lw=2) plt.legend(loc=4, fontsize=legend_fontsize) y_axis[:] = self.epoch_losses[:, 0] plt.plot(x_axis, y_axis*50, color='g', linestyle=':', label='train-loss-x50', lw=2) plt.legend(loc=4, fontsize=legend_fontsize) y_axis[:] = self.epoch_losses[:, 1] plt.plot(x_axis, y_axis*50, color='y', linestyle=':', label='valid-loss-x50', lw=2) plt.legend(loc=4, fontsize=legend_fontsize) if save_path is not None: fig.savefig(save_path, dpi=dpi, bbox_inches='tight') print ('---- save figure {} into {}'.format(title, save_path)) plt.close(fig)