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train_mesh_gaussian.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 numpy as np
import os
import jittor as jt
from random import randint
from utils.loss_utils import l1_loss, ssim, mesh_restrict_loss
from gaussian_renderer import render
import sys
from scene import Scene, GaussianModel, MeshBasedGaussianModel
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
try:
from tensorboardX import SummaryWriter
TENSORBOARD_FOUND = True
except ImportError:
TENSORBOARD_FOUND = False
jt.flags.use_cuda = 1
def training(dataset, opt, pipe, testing_iterations, saving_iterations, checkpoint_iterations, checkpoint, debug_from,mesh_path,is_exist_bg):
first_iter = 0
tb_writer = prepare_output_and_logger(dataset)
gaussians = MeshBasedGaussianModel(dataset.sh_degree,mesh_path)
scene = Scene(dataset, gaussians,is_exist_bg)
gaussians.training_setup(opt)
if checkpoint:
(model_params, first_iter) = jt.load(checkpoint)
gaussians.restore(model_params, opt)
bg_color = [1, 1, 1] if dataset.white_background else [0, 0, 0]
background = jt.array(bg_color, dtype=jt.float32)
# iter_start = torch.cuda.Event(enable_timing = True)
# iter_end = torch.cuda.Event(enable_timing = True)
# if opt.random_background:
if is_exist_bg:
print("use random background to train object, usually for object with background")
else:
print("use fixed background to train object, usually for blender object")
viewpoint_stack = None
ema_loss_for_log = 0.0
progress_bar = tqdm(range(first_iter, opt.iterations), desc="Training progress")
first_iter += 1
#init (all faces split)
while(gaussians.get_number <= 100000):
gaussians.densify_and_split_for_init()
# gaussians.densify_and_split_for_init()
for iteration in range(first_iter, opt.iterations + 1):
gaussians.update_learning_rate(iteration)
if iteration < opt.densify_until_iter:
gaussians.reset_viewspace_point()
# Every 1000 its we increase the levels of SH up to a maximum degree
if iteration % 1000 == 0:
gaussians.oneupSHdegree()
# 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
bg = jt.rand((3)) if is_exist_bg else background
render_pkg = render(viewpoint_cam, gaussians, pipe, bg)
image, viewspace_point_tensor, visibility_filter, radii = render_pkg["render"], render_pkg["viewspace_points"], render_pkg["visibility_filter"], render_pkg["radii"]
# Loss
gt_image = viewpoint_cam.original_image
if viewpoint_cam.mask is not None:
gt_image = gt_image * viewpoint_cam.mask + (jt.unsqueeze(jt.unsqueeze(bg,1),1)) * (1-viewpoint_cam.mask)
Ll1 = l1_loss(image, gt_image)
mrloss = mesh_restrict_loss(render_pkg["scale"],render_pkg["vertex1"],render_pkg["vertex2"],render_pkg["vertex3"],weight=opt.alpha_mrloss)
loss = (1.0 - opt.lambda_dssim) * Ll1 + opt.lambda_dssim * (1.0 - ssim(image, gt_image)) + mrloss
gaussians.optimizer.backward(loss)
if iteration < opt.densify_until_iter:
viewspace_point_tensor_grad = gaussians.get_viewspace_point_grad()
update_flag = False
with jt.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 and save
training_report(tb_writer, iteration, Ll1, loss, l1_loss, testing_iterations, scene, render, (pipe, bg))
if (iteration in saving_iterations):
print("\n[ITER {}] Saving Gaussians".format(iteration))
scene.save(iteration)
# Densification
if iteration < opt.densify_until_iter:
# Keep track of max radii in image-space for pruning
def max(a,b):
return jt.where(a>b,a,b)
gaussians.max_radii2D[visibility_filter] = max(gaussians.max_radii2D[visibility_filter], radii[visibility_filter])
gaussians.add_densification_stats(viewspace_point_tensor_grad, visibility_filter)
if iteration > opt.densify_from_iter and iteration % opt.densification_interval == 0:
size_threshold = 20 if iteration > opt.opacity_reset_interval else None
gaussians.densify_and_prune(opt.densify_grad_threshold, 0.005, scene.cameras_extent, size_threshold, 5)
#print(gaussians.get_number)
update_flag = True
if iteration % opt.opacity_reset_interval == 0 or (dataset.white_background and iteration == opt.densify_from_iter):
gaussians.reset_opacity()
if (iteration in checkpoint_iterations):
print("\n[ITER {}] Saving Checkpoint".format(iteration))
jt.save((gaussians.capture(), iteration), scene.model_path + "/chkpnt" + str(iteration) + ".pth")
# Optimizer step
if iteration < opt.iterations:
# if iteration >= 600:
# points_old = gaussians.optimizer.param_groups[0]['params'][0].clone()
if not update_flag:
gaussians.optimizer.step()
# if iteration >= 600:
# points_new = gaussians.optimizer.param_groups[0]['params'][0].clone()
# print((points_new - points_old).nonzero().shape)
# breakpoint()
gaussians.optimizer.zero_grad()
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 = None
if TENSORBOARD_FOUND:
tb_writer = SummaryWriter(args.model_path)
else:
print("Tensorboard not available: not logging progress")
return tb_writer
def training_report(tb_writer, iteration, Ll1, loss, l1_loss, testing_iterations, scene : Scene, renderFunc, renderArgs):
if tb_writer:
tb_writer.add_scalar('train_loss_patches/l1_loss', Ll1.item(), iteration)
tb_writer.add_scalar('train_loss_patches/total_loss', loss.item(), iteration)
# Report test and samples of training set
if iteration in testing_iterations:
validation_configs = ({'name': 'test', 'cameras' : scene.getTestCameras()},
{'name': 'train', 'cameras' : [scene.getTrainCameras()[idx % len(scene.getTrainCameras())] for idx in range(5, 30, 5)]})
for config in validation_configs:
if config['cameras'] and len(config['cameras']) > 0:
l1_test = 0.0
psnr_test = 0.0
for idx, viewpoint in enumerate(config['cameras']):
image = jt.clamp(renderFunc(viewpoint, scene.gaussians, *renderArgs)["render"], 0.0, 1.0)
gt_image = jt.clamp(viewpoint.original_image, 0.0, 1.0)
bg = renderArgs[1]
gt_image = jt.clamp(gt_image * viewpoint.mask + (jt.unsqueeze(jt.unsqueeze(bg,1),1)) * (1-viewpoint.mask))
if tb_writer and (idx < 5):
tb_writer.add_images(config['name'] + "_view_{}/render".format(viewpoint.image_name), image[None].numpy(), global_step=iteration)
if iteration == testing_iterations[0]:
tb_writer.add_images(config['name'] + "_view_{}/ground_truth".format(viewpoint.image_name), gt_image[None].numpy(), global_step=iteration)
l1_test += l1_loss(image, gt_image).mean().double().item()
psnr_test += psnr(image, gt_image).mean().double().item()
psnr_test /= len(config['cameras'])
l1_test /= len(config['cameras'])
print("\n[ITER {}] Evaluating {}: L1 {} PSNR {}".format(iteration, config['name'], l1_test, psnr_test))
if tb_writer:
# breakpoint()
tb_writer.add_scalar(config['name'] + '/loss_viewpoint - l1_loss', l1_test, iteration)
tb_writer.add_scalar(config['name'] + '/loss_viewpoint - psnr', psnr_test, iteration)
if tb_writer:
tb_writer.add_histogram("scene/opacity_histogram", scene.gaussians.get_opacity.numpy(), iteration)
tb_writer.add_scalar('total_points', scene.gaussians.get_xyz.shape[0], iteration)
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('--ip', type=str, default="127.0.0.1")
parser.add_argument('--port', type=int, default=6009)
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("--checkpoint_iterations", nargs="+", type=int, default=[])
parser.add_argument("--start_checkpoint", type=str, default = None)
parser.add_argument("--input_mesh", type=str, default ="no mesh")
parser.add_argument("--is_exist_bg",action='store_true', default=False)
args = parser.parse_args(sys.argv[1:])
args.save_iterations.append(args.iterations)
print("Optimizing " + args.model_path)
# Initialize system state (RNG)
# safe_state(args.quiet)
# Start GUI server, configure and run training
# network_gui.init(args.ip, args.port)
training(lp.extract(args), op.extract(args), pp.extract(args), args.test_iterations, args.save_iterations, args.checkpoint_iterations, args.start_checkpoint, args.debug_from,
args.input_mesh,args.is_exist_bg)
# All done
print("\nTraining complete.")