121 lines
3.9 KiB
Python
121 lines
3.9 KiB
Python
import numpy as np
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class AverageMeter(object):
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"""Computes and stores the average and current value"""
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def __init__(self):
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self.reset()
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def reset(self):
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self.val = 0.0
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self.avg = 0.0
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self.sum = 0.0
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self.count = 0.0
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def update(self, val, n=1):
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self.val = val
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self.sum += val * n
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self.count += n
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self.avg = self.sum / self.count
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def __repr__(self):
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return "{name}(val={val}, avg={avg}, count={count})".format(
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name=self.__class__.__name__, **self.__dict__
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)
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class RecorderMeter(object):
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"""Computes and stores the minimum loss value and its epoch index"""
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def __init__(self, total_epoch):
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self.reset(total_epoch)
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def reset(self, total_epoch):
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assert total_epoch > 0, "total_epoch should be greater than 0 vs {:}".format(
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total_epoch
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)
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self.total_epoch = total_epoch
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self.current_epoch = 0
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self.epoch_losses = np.zeros(
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(self.total_epoch, 2), dtype=np.float32
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) # [epoch, train/val]
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self.epoch_losses = self.epoch_losses - 1
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self.epoch_accuracy = np.zeros(
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(self.total_epoch, 2), dtype=np.float32
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) # [epoch, train/val]
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self.epoch_accuracy = self.epoch_accuracy
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def update(self, idx, train_loss, train_acc, val_loss, val_acc):
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assert (
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idx >= 0 and idx < self.total_epoch
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), "total_epoch : {} , but update with the {} index".format(
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self.total_epoch, idx
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)
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self.epoch_losses[idx, 0] = train_loss
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self.epoch_losses[idx, 1] = val_loss
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self.epoch_accuracy[idx, 0] = train_acc
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self.epoch_accuracy[idx, 1] = val_acc
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self.current_epoch = idx + 1
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return self.max_accuracy(False) == self.epoch_accuracy[idx, 1]
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def max_accuracy(self, istrain):
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if self.current_epoch <= 0:
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return 0
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if istrain:
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return self.epoch_accuracy[: self.current_epoch, 0].max()
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else:
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return self.epoch_accuracy[: self.current_epoch, 1].max()
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def plot_curve(self, save_path):
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import matplotlib
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matplotlib.use("agg")
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import matplotlib.pyplot as plt
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title = "the accuracy/loss curve of train/val"
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dpi = 100
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width, height = 1600, 1000
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legend_fontsize = 10
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figsize = width / float(dpi), height / float(dpi)
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fig = plt.figure(figsize=figsize)
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x_axis = np.array([i for i in range(self.total_epoch)]) # epochs
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y_axis = np.zeros(self.total_epoch)
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plt.xlim(0, self.total_epoch)
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plt.ylim(0, 100)
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interval_y = 5
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interval_x = 5
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plt.xticks(np.arange(0, self.total_epoch + interval_x, interval_x))
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plt.yticks(np.arange(0, 100 + interval_y, interval_y))
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plt.grid()
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plt.title(title, fontsize=20)
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plt.xlabel("the training epoch", fontsize=16)
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plt.ylabel("accuracy", fontsize=16)
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y_axis[:] = self.epoch_accuracy[:, 0]
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plt.plot(x_axis, y_axis, color="g", linestyle="-", label="train-accuracy", lw=2)
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plt.legend(loc=4, fontsize=legend_fontsize)
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y_axis[:] = self.epoch_accuracy[:, 1]
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plt.plot(x_axis, y_axis, color="y", linestyle="-", label="valid-accuracy", lw=2)
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plt.legend(loc=4, fontsize=legend_fontsize)
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y_axis[:] = self.epoch_losses[:, 0]
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plt.plot(
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x_axis, y_axis * 50, color="g", linestyle=":", label="train-loss-x50", lw=2
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)
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plt.legend(loc=4, fontsize=legend_fontsize)
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y_axis[:] = self.epoch_losses[:, 1]
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plt.plot(
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x_axis, y_axis * 50, color="y", linestyle=":", label="valid-loss-x50", lw=2
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)
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plt.legend(loc=4, fontsize=legend_fontsize)
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if save_path is not None:
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fig.savefig(save_path, dpi=dpi, bbox_inches="tight")
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print("---- save figure {} into {}".format(title, save_path))
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plt.close(fig)
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