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train_feature.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 os
import torch
from random import randint
from utils.loss_utils import l1_loss, ssim
from gaussian_renderer import render, network_gui
import sys
from scene import Scene, GaussianModel
from utils.general_utils import safe_state
import uuid
from tqdm import tqdm
from utils.image_utils import psnr
from argparse import ArgumentParser, Namespace
from arguments import ModelParams, PipelineParams, OptimizationParams
from torch.utils.tensorboard import SummaryWriter
from scene.VGG import VGGEncoder
def training(dataset, opt, pipe, testing_iterations, saving_iterations, ply_path, debug_from):
first_iter = 0
tb_writer = prepare_output_and_logger(dataset)
gaussians = GaussianModel(dataset.sh_degree)
vgg_encoder = VGGEncoder().cuda()
# load the rgb reconstructed gaussians ply file
scene = Scene(dataset, gaussians, load_path=ply_path, vgg_encoder=vgg_encoder)
gaussians.training_setup_feature(opt)
bg_color = [1]*32 if dataset.white_background else [0]*32
background = torch.tensor(bg_color, dtype=torch.float32, device="cuda")
iter_start = torch.cuda.Event(enable_timing = True)
iter_end = torch.cuda.Event(enable_timing = True)
viewpoint_stack = None
ema_loss_for_log = 0.0
progress_bar = tqdm(range(first_iter, opt.iterations), desc="Feature training", bar_format='{l_bar}{r_bar}')
first_iter += 1
for iteration in range(first_iter, opt.iterations + 1):
iter_start.record()
# Pick a random Camera
if not viewpoint_stack:
viewpoint_stack = scene.getTrainCameras().copy()
viewpoint_cam = viewpoint_stack.pop(randint(0, len(viewpoint_stack)-1))
# Render
if (iteration - 1) == debug_from:
pipe.debug = True
render_pkg = render(viewpoint_cam, gaussians, pipe, background, feature_linear=gaussians.feature_linear)
rendered_feature, viewspace_point_tensor, visibility_filter, radii = render_pkg["render"], render_pkg["viewspace_points"], render_pkg["visibility_filter"], render_pkg["radii"]
# Loss
gt_feature = viewpoint_cam.vgg_features
loss = l1_loss(rendered_feature, gt_feature)
loss.backward()
iter_end.record()
with torch.no_grad():
# Progress bar
ema_loss_for_log = 0.4 * loss.item() + 0.6 * ema_loss_for_log
if iteration % 10 == 0:
progress_bar.set_postfix({"Loss": f"{ema_loss_for_log:.{7}f}"})
progress_bar.update(10)
if iteration == opt.iterations:
progress_bar.close()
# Log
tb_writer.add_scalar('train_loss/l1_loss', loss.item(), iteration)
# Optimizer step
if iteration < opt.iterations:
gaussians.optimizer.step()
gaussians.optimizer.zero_grad(set_to_none = True)
# Save model
os.makedirs(args.model_path + "/chkpnt", exist_ok = True)
torch.save(gaussians.capture(is_feature_model=True), args.model_path + "/chkpnt" + "/feature.pth")
def prepare_output_and_logger(args):
if not args.model_path:
if os.getenv('OAR_JOB_ID'):
unique_str=os.getenv('OAR_JOB_ID')
else:
unique_str = str(uuid.uuid4())
args.model_path = os.path.join("./output/", unique_str[0:10])
# Set up output folder
print("Output folder: {}".format(args.model_path))
os.makedirs(args.model_path, exist_ok = True)
with open(os.path.join(args.model_path, "cfg_args"), 'w') as cfg_log_f:
cfg_log_f.write(str(Namespace(**vars(args))))
# Create Tensorboard writer
tb_writer = SummaryWriter(args.model_path)
return tb_writer
if __name__ == "__main__":
# Set up command line argument parser
parser = ArgumentParser(description="Training script parameters")
lp = ModelParams(parser)
op = OptimizationParams(parser)
pp = PipelineParams(parser)
parser.add_argument('--debug_from', type=int, default=-1)
parser.add_argument('--detect_anomaly', action='store_true', default=False)
parser.add_argument("--test_iterations", nargs="+", type=int, default=[7_000, 30_000])
parser.add_argument("--save_iterations", nargs="+", type=int, default=[7_000, 30_000])
parser.add_argument("--quiet", action="store_true")
parser.add_argument("--ply_path", type=str, required=True)
parser.add_argument("--exp_name", type=str, default='default')
args = parser.parse_args(sys.argv[1:])
args.save_iterations.append(args.iterations)
if args.source_path[-1] == '/':
args.source_path = args.source_path[:-1]
args.model_path = os.path.join("./output", os.path.basename(args.source_path), "feature", args.exp_name)
print("Optimizing " + args.model_path)
# Initialize system state (RNG)
safe_state(args.quiet)
# configure and run training
torch.autograd.set_detect_anomaly(args.detect_anomaly)
training(lp.extract(args), op.extract(args), pp.extract(args), args.test_iterations, args.save_iterations, args.ply_path, args.debug_from)
# All done
print("\nFeature training complete.")