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render.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 george.drettakis@inria.fr
#
import gc
import os
from os import makedirs
import json
import time
import torch
import torchvision
from tqdm import tqdm
from skimage.metrics import structural_similarity as sk_ssim
from lpipsPyTorch import lpips
import joblib
from scene import Scene
from gaussian_renderer import render
# from gaussian_renderer.faster import render
from utils.general_utils import safe_state
from utils.image_utils import psnr
from argparse import ArgumentParser
from arguments import ModelParams, PipelineParams, OptimizationParams, get_combined_args
from scene.c_gaussian_model import CGaussianModel as GaussianModel
from utils.loss_utils import ssim
def render_set(model_path, name, iteration, scene, gaussians, pipeline, background, inverval=1, near=0.2, far=100.0, save_img=False):
gts_path = os.path.join(model_path, name, "itrs_{}".format(iteration), "gt")
render_path = os.path.join(model_path, name, "itrs_{}".format(iteration), "renders")
if save_img:
makedirs(gts_path, exist_ok=True)
makedirs(render_path, exist_ok=True)
if name == "train":
viewpoint_stack, images = scene.getTrainCameras(return_as='generator', shuffle=False)
viewpoint_stack = viewpoint_stack.copy()
else:
viewpoint_stack, images = scene.getTestCameras(return_as='generator', shuffle=False)
viewpoint_stack = viewpoint_stack.copy()
idx = 0
count = 0
psnr_sum = 0
psnrs = []
ssims = []
skssims = []
skssims2 = []
lpipss = []
lpipssvggs = []
image_names = []
times = []
while(len(viewpoint_stack)):
cam = viewpoint_stack.pop(0)
gt = next(images).cuda()
if idx % inverval == 0:
rendering_dict = render(cam, gaussians, pipeline, background, near=near, far=far)
rendering = rendering_dict["render"]
img_name = cam.image_name
if save_img:
torchvision.utils.save_image(rendering, os.path.join(render_path, img_name))
psnrs.append(psnr(rendering.unsqueeze(0), gt.unsqueeze(0)))
ssims.append(ssim(rendering.unsqueeze(0), gt.unsqueeze(0)))
skssims.append(sk_ssim(rendering.detach().cpu().numpy(), gt.detach().cpu().numpy(), data_range=1, multichannel=True, channel_axis=0))
skssims2.append(sk_ssim(rendering.detach().cpu().numpy(), gt.detach().cpu().numpy(), data_range=2, multichannel=True, channel_axis=0))
lpipss.append(lpips(rendering.unsqueeze(0), gt.unsqueeze(0), net_type='alex')) #
lpipssvggs.append(lpips(rendering.unsqueeze(0), gt.unsqueeze(0), net_type='vgg'))
image_names.append(img_name)
psnr_sum += psnr(rendering.unsqueeze(0), gt.unsqueeze(0)).mean().detach().item()
count += 1
idx += 1
# start timing
for _ in range(20):
for idx in range(500):
st = time.time()
rendering_dict = render(cam, gaussians, pipeline, background, near=near, far=far)
if idx > 100: #warm up
times.append(time.time() - st)
mean_results = {
"SSIM": torch.tensor(ssims).mean().item(),
"SKSSIM": torch.tensor(skssims).mean().item(),
"SKSSIM2": torch.tensor(skssims2).mean().item(),
"PSNR": torch.tensor(psnrs).mean().item(),
"LPIPS": torch.tensor(lpipss).mean().item(),
"LPIPSVGG": torch.tensor(lpipssvggs).mean().item(),
"times": torch.tensor(times).mean().item(),
}
with open(model_path + "/" + "mean_metrics.json", 'w') as fp:
json.dump(mean_results, fp, indent=True)
per_view_results = {
"SSIM": {name: ssim for ssim, name in zip(torch.tensor(ssims).tolist(), image_names)},
"SKSSIM": {name: ssim for ssim, name in zip(torch.tensor(skssims).tolist(), image_names)},
"SKSSIM2": {name: ssim for ssim, name in zip(torch.tensor(skssims2).tolist(), image_names)},
"PSNR": {name: psnr for psnr, name in zip(torch.tensor(psnrs).tolist(), image_names)},
"LPIPS": {name: lp for lp, name in zip(torch.tensor(lpipss).tolist(), image_names)},
"LPIPSVGG": {name: lpipssvgg for lpipssvgg, name in zip(torch.tensor(lpipssvggs).tolist(), image_names)},
}
with open(model_path + "/" + "all_metrics.json", 'w') as fp:
json.dump(per_view_results, fp, indent=True)
print("Set " + name + ", PSNR: ", psnr_sum / count, ", Count: ", count)
def render_sets(dataset : ModelParams, iteration : int, opt : OptimizationParams, pipeline : PipelineParams, skip_train : bool, train_inverval : int, skip_test : bool, save_img : bool):
with torch.no_grad():
gaussians = GaussianModel(dataset.sh_degree, dataset.duration, dataset.time_interval, dataset.time_pad, interp_type=dataset.interp_type, time_pad_type=dataset.time_pad_type)
scene = Scene(dataset, gaussians, load_iteration=iteration, shuffle=False, opt=opt)
bg_color = [1,1,1]
background = torch.tensor(bg_color, dtype=torch.float32, device="cuda")
if not skip_train:
render_set(dataset.model_path, "train", scene.loaded_iter, scene, gaussians, pipeline, background, train_inverval, near=dataset.near, far=dataset.far, save_img=save_img)
if not skip_test:
render_set(dataset.model_path, "test", scene.loaded_iter, scene, gaussians, pipeline, background, near=dataset.near, far=dataset.far, save_img=save_img)
if __name__ == "__main__":
# Set up command line argument parser
parser = ArgumentParser(description="Testing script parameters")
model = ModelParams(parser, sentinel=True)
opt = OptimizationParams(parser)
pipeline = PipelineParams(parser)
parser.add_argument("--iteration", default=-1, type=int)
parser.add_argument("--skip_train", action="store_true")
parser.add_argument("--train_inverval", default=1, type=int)
parser.add_argument("--skip_test", action="store_true")
parser.add_argument("--quiet", action="store_true")
parser.add_argument("--save_img", action="store_true")
args = get_combined_args(parser)
print("Rendering " + args.model_path)
# Initialize system state (RNG)
safe_state(args.quiet)
# args.start_timestamp = 0
# args.end_timestamp = 300
render_sets(model.extract(args), args.iteration, opt.extract(args), pipeline.extract(args), args.skip_train, args.train_inverval, args.skip_test, args.save_img)