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evaluate.py
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#
# Copyright (C) 2023, Inria
# GRAPHDECO research group, https://team.inria.fr/graphdeco
# All rights reserved.
#
# This software is free for non-commercial, research and evaluation use
# under the terms of the LICENSE.md file.
#
# For inquiries contact [email protected]
#
import glob
import json
import os
import torch
import torch.nn.functional as F
from utils.loss_utils import psnr, ssim
from gaussian_renderer import render
from scene import Scene, GaussianModel, EnvLight
from utils.general_utils import seed_everything, visualize_depth
from tqdm import tqdm
from argparse import ArgumentParser
from torchvision.utils import make_grid, save_image
from omegaconf import OmegaConf
EPS = 1e-5
@torch.no_grad()
def evaluation(iteration, scene : Scene, renderFunc, renderArgs, env_map=None):
from lpipsPyTorch import lpips
scale = scene.resolution_scales[0]
if "kitti" in args.model_path:
# follow NSG: https://github.com/princeton-computational-imaging/neural-scene-graphs/blob/8d3d9ce9064ded8231a1374c3866f004a4a281f8/data_loader/load_kitti.py#L766
num = len(scene.getTrainCameras())//2
eval_train_frame = num//5
traincamera = sorted(scene.getTrainCameras(), key =lambda x: x.colmap_id)
validation_configs = ({'name': 'test', 'cameras': scene.getTestCameras(scale=scale)},
{'name': 'train', 'cameras': traincamera[:num][-eval_train_frame:]+traincamera[num:][-eval_train_frame:]})
else:
validation_configs = ({'name': 'test', 'cameras': scene.getTestCameras(scale=scale)},
{'name': 'train', 'cameras': scene.getTrainCameras()})
for config in validation_configs:
if config['cameras'] and len(config['cameras']) > 0:
l1_test = 0.0
psnr_test = 0.0
ssim_test = 0.0
lpips_test = 0.0
outdir = os.path.join(args.model_path, "eval", config['name'] + f"_{iteration}" + "_render")
os.makedirs(outdir,exist_ok=True)
for idx, viewpoint in enumerate(tqdm(config['cameras'])):
render_pkg = renderFunc(viewpoint, scene.gaussians, *renderArgs, env_map=env_map)
image = torch.clamp(render_pkg["render"], 0.0, 1.0)
gt_image = torch.clamp(viewpoint.original_image.to("cuda"), 0.0, 1.0)
depth = render_pkg['depth']
alpha = render_pkg['alpha']
sky_depth = 900
depth = depth / alpha.clamp_min(EPS)
if env_map is not None:
if args.depth_blend_mode == 0: # harmonic mean
depth = 1 / (alpha / depth.clamp_min(EPS) + (1 - alpha) / sky_depth).clamp_min(EPS)
elif args.depth_blend_mode == 1:
depth = alpha * depth + (1 - alpha) * sky_depth
depth = visualize_depth(depth)
alpha = alpha.repeat(3, 1, 1)
grid = [gt_image, image, alpha, depth]
grid = make_grid(grid, nrow=2)
save_image(grid, os.path.join(outdir, f"{viewpoint.colmap_id:03d}.png"))
l1_test += F.l1_loss(image, gt_image).double()
psnr_test += psnr(image, gt_image).double()
ssim_test += ssim(image, gt_image).double()
lpips_test += lpips(image, gt_image, net_type='vgg').double() # very slow
psnr_test /= len(config['cameras'])
l1_test /= len(config['cameras'])
ssim_test /= len(config['cameras'])
lpips_test /= len(config['cameras'])
print("\n[ITER {}] Evaluating {}: L1 {} PSNR {} SSIM {} LPIPS {}".format(iteration, config['name'], l1_test, psnr_test, ssim_test, lpips_test))
with open(os.path.join(outdir, "metrics.json"), "w") as f:
json.dump({"split": config['name'], "iteration": iteration, "psnr": psnr_test.item(), "ssim": ssim_test.item(), "lpips": lpips_test.item()}, f)
if __name__ == "__main__":
# Set up command line argument parser
parser = ArgumentParser(description="Training script parameters")
parser.add_argument("--config", type=str, required=True)
parser.add_argument("--base_config", type=str, default = "configs/base.yaml")
args, _ = parser.parse_known_args()
base_conf = OmegaConf.load(args.base_config)
second_conf = OmegaConf.load(args.config)
cli_conf = OmegaConf.from_cli()
args = OmegaConf.merge(base_conf, second_conf, cli_conf)
args.resolution_scales = args.resolution_scales[:1]
print(args)
seed_everything(args.seed)
sep_path = os.path.join(args.model_path, 'separation')
os.makedirs(sep_path, exist_ok=True)
gaussians = GaussianModel(args)
scene = Scene(args, gaussians, shuffle=False)
if args.env_map_res > 0:
env_map = EnvLight(resolution=args.env_map_res).cuda()
env_map.training_setup(args)
else:
env_map = None
checkpoints = glob.glob(os.path.join(args.model_path, "chkpnt*.pth"))
assert len(checkpoints) > 0, "No checkpoints found."
checkpoint = sorted(checkpoints, key=lambda x: int(x.split("chkpnt")[-1].split(".")[0]))[-1]
(model_params, first_iter) = torch.load(checkpoint)
gaussians.restore(model_params, args)
if env_map is not None:
env_checkpoint = os.path.join(os.path.dirname(checkpoint),
os.path.basename(checkpoint).replace("chkpnt", "env_light_chkpnt"))
(light_params, _) = torch.load(env_checkpoint)
env_map.restore(light_params)
bg_color = [1, 1, 1] if args.white_background else [0, 0, 0]
background = torch.tensor(bg_color, dtype=torch.float32, device="cuda")
evaluation(first_iter, scene, render, (args, background), env_map=env_map)
print("Evaluation complete.")