325 lines
17 KiB
Python
325 lines
17 KiB
Python
|
#
|
||
|
|
# 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
|
||
|
|
import torch.nn.functional as F
|
||
|
|
from random import randint
|
||
|
|
from utils.loss_utils import l1_loss, ssim, or_loss
|
||
|
|
from utils.general_utils import get_expon_lr_func
|
||
|
|
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, vis_orient
|
||
|
|
from argparse import ArgumentParser, Namespace
|
||
|
|
from arguments import ModelParams, PipelineParams, OptimizationParams
|
||
|
|
import pickle as pkl
|
||
|
|
try:
|
||
|
|
from torch.utils.tensorboard import SummaryWriter
|
||
|
|
TENSORBOARD_FOUND = True
|
||
|
|
except ImportError:
|
||
|
|
TENSORBOARD_FOUND = False
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
def training(dataset, opt, pipe, testing_iterations, saving_iterations, checkpoint_iterations, checkpoint, debug_from):
|
||
|
|
first_iter = 0
|
||
|
|
tb_writer = prepare_output_and_logger(dataset)
|
||
|
|
gaussians = GaussianModel(dataset.sh_degree)
|
||
|
|
scene = Scene(dataset, gaussians)
|
||
|
|
gaussians.training_setup(opt)
|
||
|
|
if checkpoint:
|
||
|
|
(model_params, first_iter) = torch.load(checkpoint)
|
||
|
|
gaussians.restore(model_params, opt)
|
||
|
|
if dataset.trainable_cameras or dataset.trainable_intrinsics:
|
||
|
|
params_cam_rotation = {}
|
||
|
|
params_cam_translation = {}
|
||
|
|
params_cam_fov = {}
|
||
|
|
for k in scene.train_cameras.keys():
|
||
|
|
for camera in scene.train_cameras[k]:
|
||
|
|
if dataset.trainable_cameras:
|
||
|
|
params_cam_rotation[camera.image_name] = camera._rotation_res
|
||
|
|
params_cam_translation[camera.image_name] = camera._translation_res
|
||
|
|
if dataset.trainable_intrinsics:
|
||
|
|
params_cam_fov[camera.image_name] = camera._fov_res
|
||
|
|
params_cam = list(params_cam_rotation.values()) + list(params_cam_translation.values()) + list(params_cam_fov.values())
|
||
|
|
l = [
|
||
|
|
{'params': list(params_cam_rotation.values()), 'lr': opt.cam_rotation_lr, "name": "rotation"},
|
||
|
|
{'params': list(params_cam_translation.values()), 'lr': opt.cam_translation_lr_init * gaussians.spatial_lr_scale, "name": "translation"},
|
||
|
|
{'params': list(params_cam_fov.values()), 'lr': opt.cam_fov_lr, "name": "fov"}
|
||
|
|
]
|
||
|
|
|
||
|
|
optimizer_cameras = torch.optim.Adam(l, lr=0.0, eps=1e-15)
|
||
|
|
translation_scheduler_args = get_expon_lr_func(lr_init=opt.cam_translation_lr_init * gaussians.spatial_lr_scale,
|
||
|
|
lr_final=opt.cam_translation_lr_final * gaussians.spatial_lr_scale,
|
||
|
|
max_steps=opt.cam_lr_max_steps)
|
||
|
|
|
||
|
|
bg_color = [1, 1, 1, 0, 0, 0, 0, 0, 0, 100] if dataset.white_background else [0, 0, 0, 0, 0, 0, 0, 0, 0, 100]
|
||
|
|
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="Training progress")
|
||
|
|
first_iter += 1
|
||
|
|
for iteration in range(first_iter, 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.convert_SHs_python, pipe.compute_cov3D_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()
|
||
|
|
|
||
|
|
gaussians.update_learning_rate(iteration)
|
||
|
|
|
||
|
|
# 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
|
||
|
|
render_pkg = render(viewpoint_cam, gaussians, pipe, background)
|
||
|
|
|
||
|
|
image = render_pkg["render"]
|
||
|
|
mask = render_pkg["mask"]
|
||
|
|
orient_angle = render_pkg["orient_angle"]
|
||
|
|
orient_conf = render_pkg["orient_conf"]
|
||
|
|
viewspace_point_tensor = render_pkg["viewspace_points"]
|
||
|
|
visibility_filter = render_pkg["visibility_filter"]
|
||
|
|
radii = render_pkg["radii"]
|
||
|
|
|
||
|
|
# Loss
|
||
|
|
gt_image = viewpoint_cam.original_image.cuda()
|
||
|
|
gt_mask = viewpoint_cam.original_mask.cuda()
|
||
|
|
gt_orient_angle = viewpoint_cam.original_orient_angle.cuda()
|
||
|
|
gt_orient_conf = viewpoint_cam.original_orient_conf.cuda()
|
||
|
|
|
||
|
|
Ll1 = l1_loss(image, gt_image, mask=gt_mask[1:].detach())
|
||
|
|
Lssim = (1.0 - ssim(image * gt_mask[1:], gt_image * gt_mask[1:]))
|
||
|
|
Lmask = l1_loss(mask, gt_mask)
|
||
|
|
|
||
|
|
orient_weight = torch.ones_like(gt_mask[:1]) * gt_orient_conf
|
||
|
|
Lorient = or_loss(orient_angle, gt_orient_angle, orient_conf, weight=orient_weight, mask=gt_mask[:1])
|
||
|
|
|
||
|
|
if torch.isnan(Lorient).any(): Lorient = torch.zeros_like(Ll1)
|
||
|
|
|
||
|
|
loss = (
|
||
|
|
Ll1 * opt.lambda_dl1 +
|
||
|
|
Lssim * opt.lambda_dssim +
|
||
|
|
Lmask * opt.lambda_dmask +
|
||
|
|
Lorient * opt.lambda_dorient
|
||
|
|
)
|
||
|
|
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 and save
|
||
|
|
training_report(tb_writer, iteration, Ll1, Lmask, Lorient, loss, l1_loss, or_loss, iter_start.elapsed_time(iter_end), testing_iterations, scene, render, (pipe, background))
|
||
|
|
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
|
||
|
|
gaussians.max_radii2D[visibility_filter] = torch.max(gaussians.max_radii2D[visibility_filter], radii[visibility_filter])
|
||
|
|
gaussians.add_densification_stats(viewspace_point_tensor, 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()
|
||
|
|
|
||
|
|
# Optimizer step
|
||
|
|
if iteration < opt.iterations:
|
||
|
|
for param in [gaussians._xyz, gaussians._features_dc, gaussians._features_rest, gaussians._opacity, gaussians._label, gaussians._scaling, gaussians._rotation]:
|
||
|
|
if param.grad is not None and param.grad.isnan().any():
|
||
|
|
gaussians.optimizer.zero_grad(set_to_none = True)
|
||
|
|
print('NaN during backprop was found, skipping iteration...')
|
||
|
|
|
||
|
|
gaussians.optimizer.step()
|
||
|
|
gaussians.optimizer.zero_grad(set_to_none = True)
|
||
|
|
|
||
|
|
if iteration < opt.iterations_cam and dataset.trainable_cameras:
|
||
|
|
''' Learning rate scheduling per step '''
|
||
|
|
for param_group in optimizer_cameras.param_groups:
|
||
|
|
if param_group["name"] == "translation":
|
||
|
|
lr = translation_scheduler_args(iteration)
|
||
|
|
param_group['lr'] = lr
|
||
|
|
|
||
|
|
for param in params_cam:
|
||
|
|
if param.grad is not None and param.grad.isnan().any():
|
||
|
|
optimizer_cameras.zero_grad(set_to_none = True)
|
||
|
|
print('NaN during backprop was found, skipping iteration...')
|
||
|
|
|
||
|
|
optimizer_cameras.step()
|
||
|
|
optimizer_cameras.zero_grad()
|
||
|
|
|
||
|
|
if (iteration in checkpoint_iterations):
|
||
|
|
print("\n[ITER {}] Saving Checkpoint".format(iteration))
|
||
|
|
os.makedirs(scene.model_path + "/checkpoints", exist_ok=True)
|
||
|
|
os.makedirs(scene.model_path + "/cameras", exist_ok=True)
|
||
|
|
torch.save((gaussians.capture(), iteration), scene.model_path + "/checkpoints/" + str(iteration) + ".pth")
|
||
|
|
if dataset.trainable_cameras:
|
||
|
|
pkl.dump((params_cam_rotation, params_cam_translation, params_cam_fov), open(scene.model_path + "/cameras/" + str(iteration) + ".pkl", 'wb'))
|
||
|
|
projection_all = {}
|
||
|
|
for camera in scene.train_cameras[1.0]:
|
||
|
|
projection_all[camera.image_name] = camera.full_proj_transform.cpu()
|
||
|
|
pkl.dump(projection_all, open(scene.model_path + "/cameras/" + str(iteration) + "_matrices.pkl", 'wb'))
|
||
|
|
|
||
|
|
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, Lmask, Lorient, loss, l1_loss, or_loss, elapsed, 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/ce_loss', Lmask.item(), iteration)
|
||
|
|
tb_writer.add_scalar('train_loss_patches/or_loss', Lorient.item(), iteration)
|
||
|
|
tb_writer.add_scalar('train_loss_patches/total_loss', loss.item(), iteration)
|
||
|
|
tb_writer.add_scalar('iter_time', elapsed, iteration)
|
||
|
|
|
||
|
|
# 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:
|
||
|
|
Ll1_test = 0.0
|
||
|
|
Lmask_test = 0.0
|
||
|
|
Lorient_test = 0.0
|
||
|
|
psnr_test = 0.0
|
||
|
|
for idx, viewpoint in enumerate(config['cameras']):
|
||
|
|
render_pkg = renderFunc(viewpoint, scene.gaussians, *renderArgs)
|
||
|
|
image = torch.clamp(render_pkg["render"], 0.0, 1.0)
|
||
|
|
mask = torch.clamp(render_pkg["mask"], 0.0, 1.0)
|
||
|
|
orient_angle = torch.clamp(render_pkg["orient_angle"], 0.0, 1.0)
|
||
|
|
orient_conf = render_pkg["orient_conf"]
|
||
|
|
orient_conf_vis = (1 - 1 / (orient_conf + 1)) * mask[:1]
|
||
|
|
gt_image = torch.clamp(viewpoint.original_image.to("cuda"), 0.0, 1.0)
|
||
|
|
gt_mask = torch.clamp(viewpoint.original_mask.to("cuda"), 0.0, 1.0)
|
||
|
|
gt_orient_angle = torch.clamp(viewpoint.original_orient_angle.to("cuda"), 0.0, 1.0)
|
||
|
|
gt_orient_conf = viewpoint.original_orient_conf.to("cuda")
|
||
|
|
gt_orient_conf_vis = (1 - 1 / (gt_orient_conf + 1)) * gt_mask[:1]
|
||
|
|
if tb_writer and (idx < 5):
|
||
|
|
tb_writer.add_images(config['name'] + "_view_{}/render".format(viewpoint.image_name), image[None], global_step=iteration)
|
||
|
|
tb_writer.add_images(config['name'] + "_view_{}/render_mask".format(viewpoint.image_name), F.pad(mask, (0, 0, 0, 0, 0, 3-mask.shape[0]), 'constant', 0)[None], global_step=iteration)
|
||
|
|
tb_writer.add_images(config['name'] + "_view_{}/render_orient".format(viewpoint.image_name), vis_orient(orient_angle, mask[:1])[None], global_step=iteration)
|
||
|
|
tb_writer.add_images(config['name'] + "_view_{}/render_orient_conf".format(viewpoint.image_name), vis_orient(orient_angle, orient_conf_vis)[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)
|
||
|
|
tb_writer.add_images(config['name'] + "_view_{}/ground_truth_mask".format(viewpoint.image_name), F.pad(gt_mask, (0, 0, 0, 0, 0, 3-gt_mask.shape[0]), 'constant', 0)[None], global_step=iteration)
|
||
|
|
tb_writer.add_images(config['name'] + "_view_{}/ground_truth_orient".format(viewpoint.image_name), vis_orient(gt_orient_angle, gt_mask[:1])[None], global_step=iteration)
|
||
|
|
tb_writer.add_images(config['name'] + "_view_{}/ground_truth_orient_conf".format(viewpoint.image_name), vis_orient(gt_orient_angle, gt_orient_conf_vis)[None], global_step=iteration)
|
||
|
|
Ll1_test += l1_loss(image, gt_image).mean().double()
|
||
|
|
Lmask_test += l1_loss(mask, gt_mask).mean().double()
|
||
|
|
Lorient_test += or_loss(orient_angle, gt_orient_angle, mask=gt_mask[:1], weight=gt_orient_conf).mean().double()
|
||
|
|
psnr_test += psnr(image, gt_image).mean().double()
|
||
|
|
Ll1_test /= len(config['cameras'])
|
||
|
|
Lmask_test /= len(config['cameras'])
|
||
|
|
Lorient_test /= len(config['cameras'])
|
||
|
|
psnr_test /= len(config['cameras'])
|
||
|
|
print("\n[ITER {}] Evaluating {}: L1 {} CE {} OR {} PSNR {}".format(iteration, config['name'], Ll1_test, Lmask_test, Lorient_test, psnr_test))
|
||
|
|
if tb_writer:
|
||
|
|
tb_writer.add_scalar(config['name'] + '/loss_viewpoint - l1_loss', Ll1_test, iteration)
|
||
|
|
tb_writer.add_scalar(config['name'] + '/loss_viewpoint - ce_loss', Lmask_test, iteration)
|
||
|
|
tb_writer.add_scalar(config['name'] + '/loss_viewpoint - or_loss', Lorient_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, iteration)
|
||
|
|
tb_writer.add_histogram("scene/label_histogram", scene.gaussians.get_label, iteration)
|
||
|
|
tb_writer.add_scalar('total_points', scene.gaussians.get_xyz.shape[0], iteration)
|
||
|
|
torch.cuda.empty_cache()
|
||
|
|
|
||
|
|
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=[1_000, 5_000, 15_000, 30_000])
|
||
|
|
parser.add_argument("--save_iterations", nargs="+", type=int, default=[1_000, 5_000, 15_000, 30_000])
|
||
|
|
parser.add_argument("--quiet", action="store_true")
|
||
|
|
parser.add_argument("--checkpoint_iterations", nargs="+", type=int, default=[1_000, 5_000, 15_000, 30_000])
|
||
|
|
parser.add_argument("--start_checkpoint", type=str, default = None)
|
||
|
|
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)
|
||
|
|
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.checkpoint_iterations, args.start_checkpoint, args.debug_from)
|
||
|
|
|
||
|
|
# All done
|
||
|
|
print("\nTraining complete.")
|