在PyTorch中進行模型評估通常需要以下步驟：

1. 導入所需的庫和模型：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
from torchvision import transforms, datasets

2. 加載測試數據集：

transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])

test_dataset = datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=64, shuffle=False)

3. 加載模型：

model = YourModel()
model.load_state_dict(torch.load('model.pth'))
model.eval()

4. 定義評估函數：

def evaluate_model(model, test_loader):
    correct = 0
    total = 0
    with torch.no_grad():
        for images, labels in test_loader:
            outputs = model(images)
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()
    accuracy = correct / total
    print('Accuracy of the model on the test set: {:.2f}%'.format(accuracy * 100))

5. 調用評估函數：

evaluate_model(model, test_loader)

這樣你就可以在PyTorch中對模型進行評估了。