我正在尝试使用AlexNet对为3S音频片段生成的声谱图图像进行分类。我已经成功地训练了我的数据集，并正在尝试识别模型错误分类的图像。
我可以通过调用www.example.com _df.adressfname来获取图像的文件名iterator.dataset.data，但是我不确定如何在get_predictions函数的for循环中使用此语句。如果我尝试使用**iterator.dataset.data_df.adressfname[i]**检索文件名，则会出现以下错误：参数0中的元素0应为Tensor，但得到的是str
最后，我想创建一个包含文件名、实际标签和预测标签的 Dataframe 。有人有什么建议吗？

class CustomDataset(Dataset):
  def __init__(self, img_path, csv_file, transforms):
    self.imgs_path = img_path
    self.csv_train_file = csv_file
    file_list = glob.glob(self.imgs_path + "*")
    self.data = []
    self.data_df = pd.read_csv(self.csv_train_file)
    self.transforms = transforms

    for ind in self.data_df.index:
      img_path = self.data_df['spectrogramSegFilename'][ind]
      class_name = self.data_df['dx'][ind]
      self.data.append([img_path, class_name])
    self.class_map = {"ProbableAD" : 0, "Control": 1}
    self.img_dim = (256, 256)

  def __len__(self):
    return len(self.data)

  def __getitem__(self, idx):
    img_path, class_name = self.data[idx]
    img = cv2.imread(img_path)
    img = cv2.resize(img, self.img_dim)
    class_id = self.class_map[class_name]
    img_tensor = torch.from_numpy(img)
    img_tensor = img_tensor.permute(2, 0, 1)
    data = self.transforms(img_tensor)
    class_id = torch.tensor([class_id])
    return data, class_id

if __name__ == "__main__":
    transformations = transforms.Compose([transforms.ToPILImage(), transforms.Resize(256), transforms.CenterCrop(256), transforms.ToTensor(), transforms.Normalize((0.49966475, 0.1840554, 0.34930056), (0.35317238, 0.17343724, 0.1894943))])
    train_dataset = CustomDataset("/spectrogram_images/spectrogram_train/", "train_features_segmented.csv", transformations)
    test_dataset = CustomDataset("/spectrogram_images/spectrogram_test/", "test_features_segmented.csv", transformations)
    train_dataset = CustomDataset("spectrogram_images/spectrogram_train/", "/train_features_segmented.csv")
    test_dataset = CustomDataset("spectrogram_images/spectrogram_test/", "/test_features_segmented.csv")
    train_data_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
    test_data_loader = DataLoader(test_dataset, batch_size=64, shuffle=True)

def get_predictions(model, iterator, device):

    model.eval()

    images = []
    labels = []
    probs = []
    participant_ids = []

    with torch.no_grad():

      for i, (x, y) in enumerate(iterator): 

            x = x.to(device)
            y_pred = model(x)
            y_prob = F.softmax(y_pred, dim=-1)
            participant_ids.append(iterator.dataset.data_df.adressfname[i])
            images.append(x.cpu())
            labels.append(y.cpu())
            probs.append(y_prob.cpu())
            
    images = torch.cat(images, dim=0)
    labels = torch.cat(labels, dim=0)
    probs = torch.cat(probs, dim=0)
    participant_ids = torch.cat(participant_ids, dim=0)

    return images, labels, probs, participant_ids