2021-03-26 20:54:33,596 - Model - INFO - Epoch 1 (1/200): 2021-03-26 20:57:40,380 - Model - INFO - Train Instance Accuracy: 0.571037 2021-03-26 20:58:16,623 - Model - INFO - Test Instance Accuracy: 0.718528, Class Accuracy: 0.627357 2021-03-26 20:58:16,623 - Model - INFO - Best Instance Accuracy: 0.718528, Class Accuracy: 0.627357 2021-03-26 20:58:16,623 - Model - INFO - Save model... 2021-03-26 20:58:16,623 - Model - INFO - Saving at log/classification/pointnet2_msg_normals/checkpoints/best_model.pth 2021-03-26 20:58:16,698 - Model - INFO - Epoch 2 (2/200): 2021-03-26 21:01:26,685 - Model - INFO - Train Instance Accuracy: 0.727947 2021-03-26 21:02:03,642 - Model - INFO - Test Instance Accuracy: 0.790858, Class Accuracy: 0.702316 2021-03-26 21:02:03,642 - Model - INFO - Best Instance Accuracy: 0.790858, Class Accuracy: 0.702316 2021-03-26 21:02:03,642 - Model - INFO - Save model... 2021-03-26 21:02:03,643 - Model - INFO - Saving at log/classification/pointnet2_msg_normals/checkpoints/best_model.pth 2021-03-26 21:02:03,746 - Model - INFO - Epoch 3 (3/200): 2021-03-26 21:05:15,349 - Model - INFO - Train Instance Accuracy: 0.781606 2021-03-26 21:05:51,538 - Model - INFO - Test Instance Accuracy: 0.803641, Class Accuracy: 0.738575 2021-03-26 21:05:51,538 - Model - INFO - Best Instance Accuracy: 0.803641, Class Accuracy: 0.738575 2021-03-26 21:05:51,539 - Model - INFO - Save model... 2021-03-26 21:05:51,539 - Model - INFO - Saving at log/classification/pointnet2_msg_normals/checkpoints/best_model.pth 我有类似于这样的一段txt文件，请你帮我写一段代码来可视化这些训练结果

你好，根据你提供的信息，我可以帮助你写一个 Python 代码来可视化训练结果。首先，你需要读入这个 txt 文件，并将其转换为 Python 可以处理的数据结构，例如列表或字典。接下来，你可以使用 Python 的可视化库，例如 Matplotlib 或 Seaborn，来绘制出训练和测试的精度曲线，并将其保存到文件中。

下面是一个示例代码，你可以根据自己的需要进行修改：

import re
import matplotlib.pyplot as plt

# 读取 txt 文件
with open('log.txt', 'r') as f:
    log_text = f.read()

# 使用正则表达式提取训练和测试的精度数据
train_accuracies = []
test_accuracies = []
class_accuracies = []
for match in re.finditer(r'Train Instance Accuracy: (\d+\.\d+).*Test Instance Accuracy: (\d+\.\d+).*Class Accuracy: (\d+\.\d+)', log_text):
    train_accuracies.append(float(match.group(1)))
    test_accuracies.append(float(match.group(2)))
    class_accuracies.append(float(match.group(3)))

# 绘制训练和测试精度曲线
plt.plot(train_accuracies, label='Train Accuracy')
plt.plot(test_accuracies, label='Test Accuracy')
plt.plot(class_accuracies, label='Class Accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.legend()

# 保存图片
plt.savefig('accuracies.png')

希望这个示例代码能帮助你。如果你有任何疑问，请随时追问。