resnet50测试函数‘

以下是一个基于PyTorch实现的在ResNet-50上进行测试的函数：

import torch
import torchvision.models as models
import torchvision.transforms as transforms

def test_resnet50():
    # Load the pre-trained ResNet-50 model
    model = models.resnet50(pretrained=True)

    # Set the model to evaluation mode
    model.eval()

    # Define the image preprocessing steps
    preprocess = transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406],
                             std=[0.229, 0.224, 0.225])
    ])

    # Load the test image
    img = Image.open('test_image.jpg')

    # Preprocess the image
    img_tensor = preprocess(img)

    # Add a batch dimension to the tensor
    img_tensor = img_tensor.unsqueeze(0)

    # Pass the tensor through the model
    output = model(img_tensor)

    # Get the predicted class index
    _, pred = torch.max(output, 1)

    # Print the predicted class index
    print(pred.item())

这个函数首先加载了预训练的ResNet-50模型，并将其设置为评估模式。然后，它定义了用于预处理图像的步骤，加载测试图像，对其进行预处理，将其传递给模型并获取预测类别。最后，它打印了预测类别的索引。注意，这个函数需要 Pillow 和 torchvision 库。