How can i use torchmetrics.image.fid to evaluate the quality of my generated images?

[yuanxiqd]

Based on DCGAN, I write my gan code as follows:

import basic_libraries
from torchmetrics.image.fid import FrechetInceptionDistance

transform = transforms.Compose([
    transforms.Grayscale(num_output_channels=3),
    transforms.Resize(args.image_size),
    transforms.CenterCrop(args.image_size),
    transforms.ToTensor(),
    transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))
])
trainset = torchvision.datasets.MNIST(root=args.dataroot, train=True, download=True, transform=transform)
testset = torchvision.datasets.MNIST(root=args.dataroot, train=False, download=True, transform=transform)

trainloader = torch.utils.data.DataLoader(
    trainset,
    batch_size=args.batch_size,
    shuffle=True,
    num_workers=args.n_workers
)

testloader = torch.utils.data.DataLoader(
    testset,
    batch_size=args.batch_size,
    shuffle=False,
    num_workers=args.n_workers
)

...

netD = NetD() # discriminator
netG = NetG() # generator
fid = FrechetInceptionDistance(feature=64)
netG.fid=fid
...

fid_scores = []
for epoch in range(args.n_epochs):
    # Step 1: train gan model using trainloader
    ...
    # Step 2: evaluate gan model using testloader
    for image, _ in tqdm(testloader):
        noise = torch.randn(args.batch_size, args.nz, 1, 1, device=device)
        with torch.no_grad():
            fake = netG(noise)
        fake.data = fake.data.mul(0.5).add(0.5)
        image.data = image.data.mul(0.5).add(0.5)
        netG.fid.update(convert_image_dtype(fake, torch.uint8), real=False)
        netG.fid.update(convert_image_dtype(image.to(device),torch.uint8),real=True)
  
    current_fid = netG.fid.compute().item()
    netG.fid.reset()
    fid_scores.append(current_fid)
    print(f'FID = {current_fid:.4f}')

I want to ask whether this code is correct for my purpose: use torchmetrics to evaluate my gan model on MNIST.
Thanks you very much.