有人能解释一下如何在pytorch中放大CNN训练中的小图像吗？

import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.transforms as transforms

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1)
        self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1)
        self.fc1 = nn.Linear(32 * 8 * 8, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = F.max_pool2d(x, 2)
        x = F.relu(self.conv2(x))
        x = F.max_pool2d(x, 2)
        x = x.view(-1, 32 * 8 * 8)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return x

model = Net()

model.load_state_dict(torch.load('model_weights.pth'))

transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Resize((32, 32)),  # 将小图像放大到指定大小
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])

image = Image.open('small_image.jpg')
image = transform(image).unsqueeze(0)

output = model(image)

probabilities = F.softmax(output, dim=1)