#
# 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, kl_divergence
from gaussian_renderer import render, network_gui
import sys
from scene import Scene, GaussianModel, DeformModel
from utils.general_utils import safe_state, get_linear_noise_func
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 torch.utils.tensorboard import SummaryWriter
TENSORBOARD_FOUND = True
except ImportError:
TENSORBOARD_FOUND = False
# batch_timeformer = 4
# n_layer = 1
# weight_reg = 0
# weight_t = 0.8
from TimeFormer import TimeFormer
# timeFormer = TimeFormer(input_dims=4, multires=4, nhead=4, hidden_dims=36, num_layer=n_layer).to('cuda')
def trans(xyz, t):
temp = torch.zeros((xyz.shape[0], 1)).to('cuda')
temp[:, :] = 2 * t - 1
temp = torch.cat([xyz, temp], dim=1)
return temp
def training(dataset, opt, pipe, testing_iterations, saving_iterations):
tb_writer = prepare_output_and_logger(dataset)
gaussians = GaussianModel(dataset.sh_degree)
deform = DeformModel(dataset.is_blender, dataset.is_6dof)
deform.train_setting(opt)
n_layer = dataset.n_layer
weight_reg = dataset.weight_reg
weight_t = dataset.weight_t
batch_timeformer = dataset.batch
timeFormer = TimeFormer(input_dims=4, multires=4, nhead=4, hidden_dims=36, num_layer=n_layer).to('cuda')
timeFormer.train_setting(opt)
scene = Scene(dataset, gaussians)
gaussians.training_setup(opt)
bg_color = [1, 1, 1] if dataset.white_background else [0, 0, 0]
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
best_psnr = 0.0
best_iteration = 0
progress_bar = tqdm(range(opt.iterations), desc="Training progress")
smooth_term = get_linear_noise_func(lr_init=0.1, lr_final=1e-15, lr_delay_mult=0.01, max_steps=20000)
for iteration in range(1, opt.iterations + 1):
if network_gui.conn == None:
network_gui.try_connect()
while network_gui.conn != None:
try:
net_image_bytes = None
custom_cam, do_training, pipe.do_shs_python, pipe.do_cov_python, keep_alive, scaling_modifer = network_gui.receive()
if custom_cam != None:
net_image = render(custom_cam, gaussians, pipe, background, scaling_modifer)["render"]
net_image_bytes = memoryview((torch.clamp(net_image, min=0, max=1.0) * 255).byte().permute(1, 2,
0).contiguous().cpu().numpy())
network_gui.send(net_image_bytes, dataset.source_path)
if do_training and ((iteration < int(opt.iterations)) or not keep_alive):
break
except Exception as e:
network_gui.conn = None
iter_start.record()
# 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 or len(viewpoint_stack) < batch_timeformer:
viewpoint_stack = scene.getTrainCameras().copy()
total_frame = len(viewpoint_stack)
time_interval = 1 / total_frame
viewpoint_cams = []
for _ in range(batch_timeformer) :
viewpoint_cam = viewpoint_stack.pop(randint(0,len(viewpoint_stack)-1))
viewpoint_cams.append(viewpoint_cam)
# breakpoint()
time_ori = torch.cat([trans(gaussians.get_xyz, viewpoint_cam.fid).unsqueeze(0) for viewpoint_cam in viewpoint_cams], dim=0)
time_offset = timeFormer(time_ori)
images_gt = []
images = []
images_t = []
for i in range(len(viewpoint_cams)):
viewpoint_cam = viewpoint_cams[i]
# viewpoint_cam = viewpoint_stack.pop(randint(0, len(viewpoint_stack) - 1))
if dataset.load2gpu_on_the_fly:
viewpoint_cam.load2device()
fid = viewpoint_cam.fid
# w/ time offset
if iteration < opt.warm_up:
d_xyz, d_rotation, d_scaling = 0.0, 0.0, 0.0
else:
N = gaussians.get_xyz.shape[0]
time_input = fid.unsqueeze(0).expand(N, -1)
ast_noise = 0 if dataset.is_blender else torch.randn(1, 1, device='cuda').expand(N, -1) * time_interval * smooth_term(iteration)
d_xyz, d_rotation, d_scaling = deform.step(gaussians.get_xyz.detach()+time_offset[i, :, :], time_input + ast_noise)
render_pkg_re_t = render(viewpoint_cam, gaussians, pipe, background, d_xyz, d_rotation, d_scaling, dataset.is_6dof)
image_t, _, _, _ = render_pkg_re_t["render"], render_pkg_re_t[
"viewspace_points"], render_pkg_re_t["visibility_filter"], render_pkg_re_t["radii"]
# w/o time offset
if iteration < opt.warm_up:
d_xyz, d_rotation, d_scaling = 0.0, 0.0, 0.0
else:
N = gaussians.get_xyz.shape[0]
time_input = fid.unsqueeze(0).expand(N, -1)
ast_noise = 0 if dataset.is_blender else torch.randn(1, 1, device='cuda').expand(N, -1) * time_interval * smooth_term(iteration)
d_xyz, d_rotation, d_scaling = deform.step(gaussians.get_xyz.detach(), time_input + ast_noise)
render_pkg_re = render(viewpoint_cam, gaussians, pipe, background, d_xyz, d_rotation, d_scaling, dataset.is_6dof)
image, viewspace_point_tensor, visibility_filter, radii = render_pkg_re["render"], render_pkg_re[
"viewspace_points"], render_pkg_re["visibility_filter"], render_pkg_re["radii"]
# Loss
gt_image = viewpoint_cam.original_image.cuda()
images.append(image.unsqueeze(0))
images_t.append(image_t.unsqueeze(0))
images_gt.append(gt_image.unsqueeze(0))
image_tensor = torch.cat(images, 0)
image_tensor_t = torch.cat(images_t, 0)
gt_image_tensor = torch.cat(images_gt, 0)
# breakpoint()
Ll1 = l1_loss(image_tensor, gt_image_tensor[:,:3,:,:])
Ll1_t = l1_loss(image_tensor_t, gt_image_tensor[:, :3, :, :])
l_reg = l1_loss(time_offset, 0)
loss = Ll1 + weight_t * Ll1_t + weight_reg * (l_reg if iteration >= opt.warm_up else 0.0)
# loss += (1.0 - opt.lambda_dssim) * Ll1 + opt.lambda_dssim * (1.0 - ssim(image, gt_image))
loss.backward()
iter_end.record()
if dataset.load2gpu_on_the_fly:
viewpoint_cam.load2device('cpu')
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()
# Keep track of max radii in image-space for pruning
gaussians.max_radii2D[visibility_filter] = torch.max(gaussians.max_radii2D[visibility_filter],
radii[visibility_filter])
# Log and save
if iteration % 10 == 0:
cur_psnr = training_report(tb_writer, iteration, Ll1, loss, l1_loss, iter_start.elapsed_time(iter_end),
testing_iterations, scene, render, (pipe, background), deform,
dataset.load2gpu_on_the_fly, dataset.is_6dof, Ll1_t, l_reg)
if iteration in testing_iterations:
if cur_psnr.item() > best_psnr:
best_psnr = cur_psnr.item()
best_iteration = iteration
if iteration in saving_iterations:
print("\n[ITER {}] Saving Gaussians".format(iteration))
scene.save(iteration)
deform.save_weights(args.model_path, iteration)
# Densification
if iteration < opt.densify_until_iter:
viewspace_point_tensor_densify = render_pkg_re["viewspace_points_densify"]
gaussians.add_densification_stats(viewspace_point_tensor_densify, 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)
if iteration % opt.opacity_reset_interval == 0 or (
dataset.white_background and iteration == opt.densify_from_iter):
# gaussians.reset_opacity()
pass
# Optimizer step
if iteration < opt.iterations:
gaussians.optimizer.step()
gaussians.update_learning_rate(iteration)
deform.optimizer.step()
gaussians.optimizer.zero_grad(set_to_none=True)
deform.optimizer.zero_grad()
deform.update_learning_rate(iteration)
timeFormer.optimizer.step()
timeFormer.optimizer.zero_grad()
timeFormer.update_learning_rate(iteration)
# def print_model_param_values(model):
# for name, param in model.named_parameters():
# print(name, param.data)
# print_model_param_values(timeFormer.output_layer)
print("Best PSNR = {} in Iteration {}".format(best_psnr, best_iteration))
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, elapsed, testing_iterations, scene: Scene, renderFunc,
renderArgs, deform, load2gpu_on_the_fly, is_6dof=False, loss_t=0.0, loss_reg=0.0):
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)
tb_writer.add_scalar('train_loss_patches/iter_time', elapsed, iteration)
tb_writer.add_scalar(f'train_loss_patches/l1_timeformer_loss', loss_t.item(), iteration)
tb_writer.add_scalar(f'train_loss_patches/reg_loss', loss_reg.item(), iteration)
if tb_writer:
# tb_writer.add_histogram("scene/opacity_histogram", scene.gaussians.get_opacity, iteration)
tb_writer.add_scalar('train_loss_patches/total_points', scene.gaussians.get_xyz.shape[0], iteration)
torch.cuda.empty_cache()
test_psnr = 0.0
# Report test and samples of training set
if iteration in testing_iterations:
torch.cuda.empty_cache()
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:
images = torch.tensor([], device="cuda")
gts = torch.tensor([], device="cuda")
for idx, viewpoint in enumerate(config['cameras']):
if load2gpu_on_the_fly:
viewpoint.load2device()
fid = viewpoint.fid
xyz = scene.gaussians.get_xyz
time_input = fid.unsqueeze(0).expand(xyz.shape[0], -1)
d_xyz, d_rotation, d_scaling = deform.step(xyz.detach(), time_input)
image = torch.clamp(
renderFunc(viewpoint, scene.gaussians, *renderArgs, d_xyz, d_rotation, d_scaling, is_6dof)["render"],
0.0, 1.0)
gt_image = torch.clamp(viewpoint.original_image.to("cuda"), 0.0, 1.0)
images = torch.cat((images, image.unsqueeze(0)), dim=0)
gts = torch.cat((gts, gt_image.unsqueeze(0)), dim=0)
if load2gpu_on_the_fly:
viewpoint.load2device('cpu')
if tb_writer and (idx < 5):
tb_writer.add_images(config['name'] + "_view_{}/render".format(viewpoint.image_name),
image[None], global_step=iteration)
if iteration == testing_iterations[0]:
tb_writer.add_images(config['name'] + "_view_{}/ground_truth".format(viewpoint.image_name),
gt_image[None], global_step=iteration)
l1_test = l1_loss(images, gts)
psnr_test = psnr(images, gts).mean()
if config['name'] == 'test' or len(validation_configs[0]['cameras']) == 0:
test_psnr = psnr_test
print("\n[ITER {}] Evaluating {}: L1 {} PSNR {}".format(iteration, config['name'], l1_test, psnr_test))
if tb_writer:
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)
return test_psnr
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('--n_layer', type=int, default=4)
parser.add_argument('--batch', type=int, default=4)
parser.add_argument('--weight_reg', type=float, default=0.0001)
parser.add_argument('--weight_t', type=float, default=0.8)
parser.add_argument('--detect_anomaly', action='store_true', default=False)
parser.add_argument("--test_iterations", nargs="+", type=int,
default=[2_000, 3_000, 4_000, 5000, 6000, 7_000, 8_000, 9_000] + list(range(10000, 40001, 1000)))
parser.add_argument("--save_iterations", nargs="+", type=int, default=[5_000, 10_000, 15_000, 20_000, 40000]+ list(range(3000, 20001, 1000)))
parser.add_argument("--quiet", action="store_true")
args = parser.parse_args(sys.argv[1:])
args.save_iterations.append(args.iterations)
weight_t_str = str(args.weight_t)
weight_reg_str = str(args.weight_reg)
args.model_path += f'_batch_{args.batch}_trans_{weight_t_str}_reg_{weight_reg_str}_layer_{args.n_layer}_random'
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)
torch.autograd.set_detect_anomaly(args.detect_anomaly)
training(lp.extract(args), op.extract(args), pp.extract(args), args.test_iterations, args.save_iterations)
# All done
print("\nTraining complete.")