我是AI/ML的新手，在创建自定义数据集时遇到了问题。我尝试按照https://pytorch.org/tutorials/beginner/data_loading_tutorial.html操作，但我的数据集没有正确创建，或者我做错了什么。我会感谢任何反馈。
当我运行这段代码时，我得到错误：TypeError: conv2d(): argument 'input' (position 1) must be Tensor, not str
这似乎是因为当animal_trainset变量被创建为所有image时，标签对现在被设置为字符串'image'和'label'。我已经检查了MyDataset函数和ToTensor函数，图像和标签似乎正确，但当组合起来时，有些东西会中断。
下面是我的代码：

import os
import torch
import pandas as pd
from skimage import io, transform
import numpy as np
import matplotlib.pyplot as plt
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, utils
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim

class MyDataset(Dataset):

    def __init__(self, csv_file, transform=None):
        self.animals = pd.read_csv(csv_file)
        self.transform = transform

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

    def __getitem__(self, idx):
        if torch.is_tensor(idx):
            idx = idx.tolist()

        img_name = self.animals.iloc[idx, 0]
        if 'cat' in img_name:
            label = 'cat'
        if 'dog' in img_name:
            label = 'dog'
        if 'rat' in img_name:
            label = 'rat'
        if 'bat' in img_name:
            label = 'bat'
        else:
            label = 'lizard'
        image = io.imread(img_name)
        sample = {'image': image, 'label': label}
        print(label)

        if self.transform:
            sample = self.transform(sample)

        return sample

class ToTensor(object):
    def __call__(self, sample):
        image, label = sample['image'], sample['label']

        # swap color axis because
        # numpy image: H x W x C
        # torch image: C x H x W
        image = image.transpose((2, 0, 1))
        print(label)
        return {'image': torch.from_numpy(image),'label': label}

class Net(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = torch.flatten(x, 1) # flatten all dimensions except batch
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

fig = plt.figure()

animal_trainset = MyDataset(csv_file = '/Classifier/CombinedCSV.csv', transform=ToTensor())
animal_trainloader = DataLoader(animal_trainset, batch_size=4)

animal_testset = MyDataset(csv_file = '/Classifier/CombinedCSV.csv', transform=ToTensor())
animal_testloader = DataLoader(animal_testset, batch_size=4)

classes = ('dog', 'cat', 'bat', 'rat', 'lizard')

net = Net()

criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

for epoch in range(2):  # loop over the dataset multiple times

    running_loss = 0.0
    for i, data in enumerate(animal_trainloader, 0):
        # get the inputs; data is a list of [inputs, labels]
        inputs, labels = data

        # zero the parameter gradients
        optimizer.zero_grad()

        # forward + backward + optimize
        print(inputs)
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        # print statistics
        running_loss += loss.item()
        if i % 2000 == 1999:    # print every 2000 mini-batches
            print(f'[{epoch + 1}, {i + 1:5d}] loss: {running_loss / 2000:.3f}')
            running_loss = 0.0

print('Finished Training')