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train_single.py
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train_single.py
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#
# Copyright (C) 2023 - 2024, 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 os
import torch
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, get_expon_lr_func
import uuid
from tqdm import tqdm
from torch.utils.data import DataLoader
from argparse import ArgumentParser, Namespace
from arguments import ModelParams, PipelineParams, OptimizationParams
def direct_collate(x):
return x
def training(dataset, opt, pipe, saving_iterations, checkpoint_iterations, checkpoint, debug_from):
first_iter = 0
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)
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)
depth_l1_weight = get_expon_lr_func(opt.depth_l1_weight_init, opt.depth_l1_weight_final, max_steps=opt.iterations)
ema_loss_for_log = 0.0
ema_Ll1depth_for_log = 0.0
progress_bar = tqdm(range(first_iter, opt.iterations), desc="Training progress")
first_iter += 1
indices = None
training_generator = DataLoader(scene.getTrainCameras(), num_workers = 8, prefetch_factor = 1, persistent_workers = True, collate_fn=direct_collate)
iteration = first_iter
while iteration < opt.iterations + 1:
for viewpoint_batch in training_generator:
for viewpoint_cam in viewpoint_batch:
background = torch.rand((3), dtype=torch.float32, device="cuda")
viewpoint_cam.world_view_transform = viewpoint_cam.world_view_transform.cuda()
viewpoint_cam.projection_matrix = viewpoint_cam.projection_matrix.cuda()
viewpoint_cam.full_proj_transform = viewpoint_cam.full_proj_transform.cuda()
viewpoint_cam.camera_center = viewpoint_cam.camera_center.cuda()
if not args.disable_viewer:
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:
if keep_alive:
net_image = render(custom_cam, gaussians, pipe, background, scaling_modifer, indices = indices)["render"]
else:
net_image = render(custom_cam, gaussians, pipe, background, scaling_modifer, indices = indices)["depth"].repeat(3, 1, 1)
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()
# Render
if (iteration - 1) == debug_from:
pipe.debug = True
render_pkg = render(viewpoint_cam, gaussians, pipe, background, indices = indices, use_trained_exp=True)
image, invDepth, viewspace_point_tensor, visibility_filter, radii = render_pkg["render"], render_pkg["depth"], render_pkg["viewspace_points"], render_pkg["visibility_filter"], render_pkg["radii"]
# Loss
gt_image = viewpoint_cam.original_image.cuda()
if viewpoint_cam.alpha_mask is not None:
alpha_mask = viewpoint_cam.alpha_mask.cuda()
image *= alpha_mask
Ll1 = l1_loss(image, gt_image)
Lssim = (1.0 - ssim(image, gt_image))
photo_loss = (1.0 - opt.lambda_dssim) * Ll1 + opt.lambda_dssim * Lssim
loss = photo_loss.clone()
Ll1depth_pure = 0.0
if depth_l1_weight(iteration) > 0 and viewpoint_cam.depth_reliable:
mono_invdepth = viewpoint_cam.invdepthmap.cuda()
depth_mask = viewpoint_cam.depth_mask.cuda()
Ll1depth_pure = torch.abs((invDepth - mono_invdepth) * depth_mask).mean()
Ll1depth = depth_l1_weight(iteration) * Ll1depth_pure
loss += Ll1depth
Ll1depth = Ll1depth.item()
else:
Ll1depth = 0
loss.backward()
iter_end.record()
with torch.no_grad():
# Progress bar
ema_loss_for_log = 0.4 * photo_loss.item() + 0.6 * ema_loss_for_log
ema_Ll1depth_for_log = 0.4 * Ll1depth + 0.6 * ema_Ll1depth_for_log
if iteration % 10 == 0:
progress_bar.set_postfix({"Loss": f"{ema_loss_for_log:.{7}f}", "Depth Loss": f"{ema_Ll1depth_for_log:.{7}f}", "Size": f"{gaussians._xyz.size(0)}"})
progress_bar.update(10)
# Log and save
if (iteration in saving_iterations):
print("\n[ITER {}] Saving Gaussians".format(iteration))
scene.save(iteration)
print("peak memory: ", torch.cuda.max_memory_allocated(device='cuda'))
if iteration == opt.iterations:
progress_bar.close()
return
# 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)
gaussians.add_densification_stats(viewspace_point_tensor, visibility_filter)
if iteration > opt.densify_from_iter and iteration % opt.densification_interval == 0:
gaussians.densify_and_prune(opt.densify_grad_threshold, 0.005, scene.cameras_extent)
if iteration % opt.opacity_reset_interval == 0 or (dataset.white_background and iteration == opt.densify_from_iter):
print("-----------------RESET OPACITY!-------------")
gaussians.reset_opacity()
# Optimizer step
if iteration < opt.iterations:
gaussians.exposure_optimizer.step()
gaussians.exposure_optimizer.zero_grad(set_to_none = True)
if gaussians._xyz.grad != None and gaussians.skybox_locked:
gaussians._xyz.grad[:gaussians.skybox_points, :] = 0
gaussians._rotation.grad[:gaussians.skybox_points, :] = 0
gaussians._features_dc.grad[:gaussians.skybox_points, :, :] = 0
gaussians._features_rest.grad[:gaussians.skybox_points, :, :] = 0
gaussians._opacity.grad[:gaussians.skybox_points, :] = 0
gaussians._scaling.grad[:gaussians.skybox_points, :] = 0
if gaussians._opacity.grad != None:
relevant = (gaussians._opacity.grad.flatten() != 0).nonzero()
relevant = relevant.flatten().long()
if(relevant.size(0) > 0):
gaussians.optimizer.step(relevant)
else:
gaussians.optimizer.step(relevant)
print("No grads!")
gaussians.optimizer.zero_grad(set_to_none = True)
if not args.skip_scale_big_gauss:
with torch.no_grad():
vals, _ = gaussians.get_scaling.max(dim=1)
violators = vals > scene.cameras_extent * 0.02
if gaussians.scaffold_points is not None:
violators[:gaussians.scaffold_points] = False
gaussians._scaling[violators] = gaussians.scaling_inverse_activation(gaussians.get_scaling[violators] * 0.8)
if (iteration in checkpoint_iterations):
print("\n[ITER {}] Saving Checkpoint".format(iteration))
torch.save((gaussians.capture(), iteration), scene.model_path + "/chkpnt" + str(iteration) + ".pth")
iteration += 1
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))))
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('--disable_viewer', action='store_true', default=False)
parser.add_argument('--debug_from', type=int, default=-1)
parser.add_argument('--detect_anomaly', action='store_true', default=False)
parser.add_argument("--save_iterations", nargs="+", type=int, default=[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)
args = parser.parse_args(sys.argv[1:])
args.save_iterations.append(args.iterations)
print("Optimizing " + args.model_path)
if args.eval and args.exposure_lr_init > 0 and not args.train_test_exp:
print("Reconstructing for evaluation (--eval) with exposure optimization on the train set but not for the test set.")
print("This will lead to high error when computing metrics. To optimize exposure on the left half of the test images, use --train_test_exp")
# Initialize system state (RNG)
safe_state(args.quiet)
# Start GUI server, configure and run training
if not args.disable_viewer:
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.save_iterations, args.checkpoint_iterations, args.start_checkpoint, args.debug_from)
# All done
print("\nTraining complete.")