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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
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
import pandas as pd
models_configuration = {
'baseline': {
'quantised': False,
'half_float': False,
'name': 'point_cloud.ply'
},
'quantised': {
'quantised': True,
'half_float': False,
'name': 'point_cloud_quantised.ply'
},
'quantised_half': {
'quantised': True,
'half_float': True,
'name': 'point_cloud_quantised_half.ply'
},
}
def measure_fps(iteration, views, gaussians, pipeline, background, pcd_name):
fps = 0
for _, view in enumerate(views):
render(view, gaussians, pipeline, background, measure_fps=False)
for _, view in enumerate(views):
fps += render(view, gaussians, pipeline, background, measure_fps=True)["FPS"]
fps *= 1000 / len(views)
return pd.Series([fps], index=["FPS"], name=f"{pcd_name}_{iteration}")
def render_set(model_path,
name,
iteration,
views,
gaussians,
pipeline,
background,
pcd_name):
render_path = os.path.join(model_path, name, f"{pcd_name}_{iteration}", "renders")
gts_path = os.path.join(model_path, name, f"{pcd_name}_{iteration}", "gt")
makedirs(render_path, exist_ok=True)
makedirs(gts_path, exist_ok=True)
for idx, view in enumerate(tqdm(views, desc="Rendering progress")):
rendering = render(view, gaussians, pipeline, background)["render"]
gt = view.original_image[0:3, :, :]
torchvision.utils.save_image(rendering, os.path.join(render_path, '{0:05d}'.format(idx) + ".png"))
torchvision.utils.save_image(gt, os.path.join(gts_path, '{0:05d}'.format(idx) + ".png"))
def render_sets(dataset : ModelParams,
iteration : int,
pipeline : PipelineParams,
skip_train : bool,
skip_test : bool,
skip_measure_fps : bool):
with torch.no_grad():
gaussians = GaussianModel(dataset.sh_degree)
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")
configurations = {}
if not skip_train:
configurations["train"] = scene.getTrainCameras()
if not skip_test:
configurations["test"] = scene.getTestCameras()
for model in args.models:
name = models_configuration[model]['name']
quantised = models_configuration[model]['quantised']
half_float = models_configuration[model]['half_float']
try:
scene.gaussians.load_ply(os.path.join(scene.model_path,
"point_cloud",
"iteration_" + str(scene.loaded_iter),
name), quantised=quantised, half_float=half_float)
except:
raise RuntimeError(f"Configuration {model} with name {name} not found!")
for k,v in configurations.items():
df = pd.DataFrame()
render_set(dataset.model_path, k, scene.loaded_iter, v, gaussians, pipeline, background, name)
if not skip_measure_fps:
df = df.append(measure_fps(
scene.loaded_iter,
scene.getTrainCameras() + scene.getTestCameras(),
gaussians,
pipeline,
background,
name
))
with open(os.path.join(dataset.model_path, f"fps_results.json"), 'w') as f:
f.write(df.T.to_json())
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("--models",
help="Types of models to test",
choices=models_configuration.keys(),
default=['baseline', 'quantised_half'],
nargs="+")
parser.add_argument("--skip_measure_fps", 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, args.skip_measure_fps)