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render.py
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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 [email protected]
#
import torch
from scene import Scene
import os
import time
import pickle
from tqdm import tqdm
from os import makedirs
from gaussian_renderer import render
import torchvision
from utils.general_utils import safe_state
from argparse import ArgumentParser
from arguments import ModelParams, PipelineParams, get_combined_args
from gaussian_renderer import GaussianModel
from utils.image_utils import psnr
from utils.loss_utils import ssim
import lpips
loss_fn_vgg = lpips.LPIPS(net='vgg').to(torch.device('cuda', torch.cuda.current_device()))
def render_set(model_path, name, iteration, views, gaussians, pipeline, background):
render_path = os.path.join(model_path, name, "ours_{}".format(iteration), "renders")
gts_path = os.path.join(model_path, name, "ours_{}".format(iteration), "gt")
makedirs(render_path, exist_ok=True)
makedirs(gts_path, exist_ok=True)
# Load data (deserialize)
with open(model_path + '/smpl_rot/' + f'iteration_{iteration}/' + 'smpl_rot.pickle', 'rb') as handle:
smpl_rot = pickle.load(handle)
rgbs = []
rgbs_gt = []
elapsed_time = 0
for _, view in enumerate(tqdm(views, desc="Rendering progress")):
gt = view.original_image[0:3, :, :].cuda()
bound_mask = view.bound_mask
transforms, translation = smpl_rot[name][view.pose_id]['transforms'], smpl_rot[name][view.pose_id]['translation']
# Start timer
start_time = time.time()
render_output = render(view, gaussians, pipeline, background, transforms=transforms, translation=translation)
rendering = render_output["render"]
# end time
end_time = time.time()
# Calculate elapsed time
elapsed_time += end_time - start_time
rendering.permute(1,2,0)[bound_mask[0]==0] = 0 if background.sum().item() == 0 else 1
rgbs.append(rendering)
rgbs_gt.append(gt)
# Calculate elapsed time
print("Elapsed time: ", elapsed_time, " FPS: ", len(views)/elapsed_time)
psnrs = 0.0
ssims = 0.0
lpipss = 0.0
for id in range(len(views)):
rendering = rgbs[id]
gt = rgbs_gt[id]
rendering = torch.clamp(rendering, 0.0, 1.0)
gt = torch.clamp(gt, 0.0, 1.0)
torchvision.utils.save_image(rendering, os.path.join(render_path, '{0:05d}'.format(id) + ".png"))
torchvision.utils.save_image(gt, os.path.join(gts_path, '{0:05d}'.format(id) + ".png"))
# metrics
psnrs += psnr(rendering, gt).mean().double()
ssims += ssim(rendering, gt).mean().double()
lpipss += loss_fn_vgg(rendering, gt).mean().double()
psnrs /= len(views)
ssims /= len(views)
lpipss /= len(views)
# evalution metrics
print("\n[ITER {}] Evaluating {} #{}: PSNR {} SSIM {} LPIPS {}".format(iteration, name, len(views), psnrs, ssims, lpipss))
def render_sets(dataset : ModelParams, iteration : int, pipeline : PipelineParams, skip_train : bool, skip_test : bool):
with torch.no_grad():
gaussians = GaussianModel(dataset.sh_degree, dataset.smpl_type, dataset.motion_offset_flag, dataset.actor_gender)
scene = Scene(dataset, gaussians, load_iteration=iteration, shuffle=False)
bg_color = [1,1,1] if dataset.white_background else [0, 0, 0]
background = torch.tensor(bg_color, dtype=torch.float32, device="cuda")
if not skip_train:
render_set(dataset.model_path, "train", scene.loaded_iter, scene.getTrainCameras(), gaussians, pipeline, background)
if not skip_test:
render_set(dataset.model_path, "test", scene.loaded_iter, scene.getTestCameras(), gaussians, pipeline, background)
if __name__ == "__main__":
# Set up command line argument parser
parser = ArgumentParser(description="Testing script parameters")
model = ModelParams(parser, sentinel=True)
pipeline = PipelineParams(parser)
parser.add_argument("--iteration", default=-1, type=int)
parser.add_argument("--skip_train", action="store_true")
parser.add_argument("--skip_test", action="store_true")
parser.add_argument("--quiet", action="store_true")
args = get_combined_args(parser)
print("Rendering " + args.model_path)
# Initialize system state (RNG)
safe_state(args.quiet)
render_sets(model.extract(args), args.iteration, pipeline.extract(args), args.skip_train, args.skip_test)