如何在PyTorch中找出图像分类器的混淆矩阵并作图

import torch
import torchvision
import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix

model = torch.load('path_to_model.pth')
test_dataset = torchvision.datasets.ImageFolder('path_to_test_data', transform=transform)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False)

model.eval()

def get_predictions(model, data_loader):
    predictions = []
    targets = []
    for images, labels in data_loader:
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        predictions.extend(predicted.tolist())
        targets.extend(labels.tolist())
    return predictions, targets

predictions, targets = get_predictions(model, test_loader)

confusion_mat = confusion_matrix(targets, predictions)

plt.figure(figsize=(num_classes, num_classes))
plt.imshow(confusion_mat, interpolation='nearest', cmap=plt.cm.Blues)
plt.title('Confusion Matrix')
plt.colorbar()
tick_marks = np.arange(num_classes)
plt.xticks(tick_marks, class_names, rotation=90)
plt.yticks(tick_marks, class_names)
plt.xlabel('Predicted')
plt.ylabel('True')
plt.show()