evaluate_fid.py

import torch.nn.functional as F

from torchmetrics.image.fid import FrechetInceptionDistance

from PIL import Image

import os

import torch

import numpy as np


streetview_path = "Generated Images/streetview_clip"

sd_path = "Generated Images/sd_clip"

ldm3d_path = "Generated Images/ldm3d_clip"

device = torch.device('cuda')


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dataset_path = "real_images"

image_paths = [os.path.join(dataset_path, x) for x in file_names]



real_images = [np.array(Image.open(path).convert("RGB")) for path in image_paths]



def preprocess_image(image):

    image = torch.tensor(image, device=device, dtype=torch.float32).unsqueeze(0)

    image = image.permute(0, 3, 1, 2) / 255.0

    resized_image = F.interpolate(image, size=(768, 1536), mode='bilinear', align_corners=False)

    return resized_image



real_images = torch.cat([preprocess_image(image) for image in real_images])

print(real_images.shape)



# read in generated images

models = [

	(streetview_path, "StreetView360X"),

	(sd_path, "Stable Diffusion 2.1"),

	(ldm3d_path, "LDM3D-pano"),

]



for path, name in models:

    image_paths = sorted([os.path.join(path, x) for x in os.listdir(path)], key=lambda a: int(a.replace(path, "").split("_")[0][1:]))

    assert(len(image_paths) == len(real_images))

    fake_images = [np.array(Image.open(path).convert("RGB")) for path in image_paths]
    
    fake_images = np.stack(fake_images, axis=0)
    fake_images = torch.tensor(fake_images, device=device, dtype=torch.float32)

    fake_images = fake_images.permute(0, 3, 1, 2) / 255.0

    print(fake_images.shape)

    fid = FrechetInceptionDistance(normalize=True).to(device)

    fid.update(real_images, real=True)

    fid.update(fake_images, real=False)

    print(f"FID for {name}: {float(fid.compute())}")