Delete metrics.py

metrics.py

@@ -1,139 +0,0 @@
-import os
-import json
-import torch
-import numpy as np
-import torchvision
-from tqdm import tqdm
-
-from gaussian_renderer import render, GaussianModel
-from scene.cameras import Camera
-
-import lpips
-import piq
-from pytorch_fid import fid_score
-from skimage.metrics import structural_similarity as ssim_fn
-
-# --------------------------------------------------
-# Camera loading (same cameras for both ply)
-# --------------------------------------------------
-def load_cameras(camera_json):
-    with open(camera_json, 'r') as f:
-        cam_data = json.load(f)
-
-    cameras = []
-    for cam in cam_data:
-        camera = Camera(
-            colmap_id=cam["id"],
-            R=np.array(cam["R"]),
-            T=np.array(cam["T"]),
-            FoVx=cam["FoVx"],
-            FoVy=cam["FoVy"],
-            image=torch.zeros(3, cam["height"], cam["width"]),
-            image_name=cam["image_name"],
-            uid=cam["id"]
-        )
-        cameras.append(camera.cuda())
-
-    return cameras
-
-
-# --------------------------------------------------
-# Render a ply under fixed cameras
-# --------------------------------------------------
-@torch.no_grad()
-def render_ply(ply_path, cameras, out_dir, sh_degree=3):
-    os.makedirs(out_dir, exist_ok=True)
-
-    gaussians = GaussianModel(sh_degree)
-    gaussians.load_ply(ply_path)
-    gaussians = gaussians.cuda()
-
-    bg = torch.zeros(3, device="cuda")
-
-    rendered = {}
-
-    for cam in tqdm(cameras, desc=f"Rendering {os.path.basename(ply_path)}"):
-        img = render(
-            cam,
-            gaussians,
-            pipeline=None,
-            background=bg
-        )["render"].clamp(0, 1)
-
-        torchvision.utils.save_image(
-            img, os.path.join(out_dir, cam.image_name + ".png")
-        )
-        rendered[cam.image_name] = img
-
-    return rendered
-
-
-# --------------------------------------------------
-# Metrics: A = GT, B = Pred
-# --------------------------------------------------
-def compute_metrics(gt_imgs, pred_imgs):
-    psnr, ssim, lpips_v, niqe = [], [], [], []
-
-    lpips_fn = lpips.LPIPS(net='alex').cuda()
-
-    for name in gt_imgs.keys():
-        gt = gt_imgs[name]
-        pred = pred_imgs[name]
-
-        psnr.append(piq.psnr(pred, gt).item())
-
-        ssim.append(
-            ssim_fn(
-                gt.permute(1,2,0).cpu().numpy(),
-                pred.permute(1,2,0).cpu().numpy(),
-                channel_axis=2,
-                data_range=1.0
-            )
-        )
-
-        lpips_v.append(
-            lpips_fn(pred.unsqueeze(0), gt.unsqueeze(0)).item()
-        )
-
-        niqe.append(
-            piq.niqe(pred.unsqueeze(0)).item()
-        )
-
-    return {
-        "PSNR": np.mean(psnr),
-        "SSIM": np.mean(ssim),
-        "LPIPS": np.mean(lpips_v),
-        "NIQE": np.mean(niqe)
-    }
-
-
-def compute_fid(dir_a, dir_b):
-    return fid_score.calculate_fid_given_paths(
-        [dir_a, dir_b],
-        batch_size=8,
-        device="cuda",
-        dims=2048
-    )
-
-
-# --------------------------------------------------
-# Main
-# --------------------------------------------------
-if __name__ == "__main__":
-
-    ply_gt = "A.ply"     # reference
-    ply_pred = "B.ply"   # compared model
-    camera_json = "cameras.json"
-
-    cameras = load_cameras(camera_json)
-
-    gt_imgs = render_ply(ply_gt, cameras, "render_A")
-    pred_imgs = render_ply(ply_pred, cameras, "render_B")
-
-    metrics = compute_metrics(gt_imgs, pred_imgs)
-    fid = compute_fid("render_A", "render_B")
-
-    print("\n===== A (GT) vs B (Pred) =====")
-    for k, v in metrics.items():
-        print(f"{k}: {v:.4f}")
-    print(f"FID: {fid:.4f}")