Delete metrics.py

metrics.py

@@ -1,160 +0,0 @@
-import os
-import json
-import torch
-import numpy as np
-import torchvision
-from tqdm import tqdm
-from PIL import Image
-
-from gaussian_renderer import render
-from gaussian_renderer import GaussianModel
-from scene.cameras import Camera
-from utils.graphics_utils import getWorld2View2, getProjectionMatrix
-
-import lpips
-import piq
-from pytorch_fid import fid_score
-from skimage.metrics import structural_similarity as ssim_fn
-
-# ----------------------------
-# Utils
-# ----------------------------
-def load_cameras(camera_json):
-    with open(camera_json, 'r') as f:
-        cams = json.load(f)
-
-    cameras = []
-    for cam in cams:
-        W2C = np.array(cam["world_view_transform"])
-        P = np.array(cam["projection_matrix"])
-
-        camera = Camera(
-            colmap_id=cam["id"],
-            R=W2C[:3, :3].T,
-            T=W2C[:3, 3],
-            FoVx=cam["FoVx"],
-            FoVy=cam["FoVy"],
-            image=torch.zeros(3, cam["height"], cam["width"]),
-            image_name=cam["image_name"],
-            uid=cam["id"]
-        )
-
-        camera.world_view_transform = torch.tensor(W2C).cuda()
-        camera.projection_matrix = torch.tensor(P).cuda()
-        camera.full_proj_transform = camera.world_view_transform @ camera.projection_matrix
-        camera.camera_center = camera.world_view_transform.inverse()[3, :3]
-
-        cameras.append(camera)
-
-    return cameras
-
-
-def load_gt(gt_dir, name):
-    img = Image.open(os.path.join(gt_dir, name + ".png")).convert("RGB")
-    img = torchvision.transforms.ToTensor()(img)
-    return img.cuda()
-
-
-# ----------------------------
-# Render function
-# ----------------------------
-@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.tensor([0, 0, 0], device="cuda", dtype=torch.float32)
-
-    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
-# ----------------------------
-def compute_metrics(preds, gt_dir):
-    psnr, ssim, lp, niqe = [], [], [], []
-
-    lpips_fn = lpips.LPIPS(net='alex').cuda()
-
-    for name, pred in preds.items():
-        gt = load_gt(gt_dir, 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
-            )
-        )
-        lp.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(lp),
-        "NIQE": np.mean(niqe)
-    }
-
-
-def compute_fid(pred_dir, gt_dir):
-    return fid_score.calculate_fid_given_paths(
-        [pred_dir, gt_dir],
-        batch_size=8,
-        device="cuda",
-        dims=2048
-    )
-
-
-# ----------------------------
-# Main
-# ----------------------------
-if __name__ == "__main__":
-
-    ply_a = "ply_a.ply"
-    ply_b = "ply_b.ply"
-    camera_json = "cameras.json"
-    gt_dir = "gt"
-
-    cameras = load_cameras(camera_json)
-
-    preds_a = render_ply(ply_a, cameras, "renders_a")
-    preds_b = render_ply(ply_b, cameras, "renders_b")
-
-    metrics_a = compute_metrics(preds_a, gt_dir)
-    metrics_b = compute_metrics(preds_b, gt_dir)
-
-    fid_a = compute_fid("renders_a", gt_dir)
-    fid_b = compute_fid("renders_b", gt_dir)
-
-    print("\n====== Model A ======")
-    for k, v in metrics_a.items():
-        print(f"{k}: {v:.4f}")
-    print(f"FID: {fid_a:.4f}")
-
-    print("\n====== Model B ======")
-    for k, v in metrics_b.items():
-        print(f"{k}: {v:.4f}")
-    print(f"FID: {fid_b:.4f}")