V3D / recon /train_512.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, lpips
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 scripts.sampling.simple_mv_latent_sample import sample_one
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)
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
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
bg = torch.rand((3), device="cuda") if opt.random_background 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.cuda()
Ll1 = l1_loss(image, gt_image)
loss = (1.0 - opt.lambda_dssim) * Ll1 + opt.lambda_dssim * (
1.0 - ssim(image, gt_image)
)
if opt.lambda_lpips > 0:
loss += opt.lambda_lpips * lpips(image, gt_image)
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,
loss,
l1_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:
gaussians.optimizer.step()
gaussians.optimizer.zero_grad(set_to_none=True)
if iteration in checkpoint_iterations:
print("\n[ITER {}] Saving Checkpoint".format(iteration))
torch.save(
(gaussians.capture(), iteration),
scene.model_path + "/chkpnt" + str(iteration) + ".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 = 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,
):
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("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:
l1_test = 0.0
psnr_test = 0.0
for idx, viewpoint in enumerate(config["cameras"]):
image = torch.clamp(
renderFunc(viewpoint, scene.gaussians, *renderArgs)["render"],
0.0,
1.0,
)
gt_image = torch.clamp(
viewpoint.original_image.to("cuda"), 0.0, 1.0
)
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(image, gt_image).mean().double()
psnr_test += psnr(image, gt_image).mean().double()
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:
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, 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("--seed", type=int, default=42)
parser.add_argument("--image", type=str, default="assets/images/ceramic.png")
parser.add_argument("--ckpt_path", type=str, required=True)
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("--border_ratio", type=float, default=0.3)
parser.add_argument("--min_guidance_scale", type=float, default=1.0)
parser.add_argument("--max_guidance_scale", type=float, default=2.5)
parser.add_argument("--sigma_max", type=float, 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)
print("=====Start generating MV Images=====")
images, _ = sample_one(
args.image,
args.ckpt_path,
seed=args.seed,
border_ratio=args.border_ratio,
min_guidance_scale=args.min_guidance_scale,
max_guidance_scale=args.max_guidance_scale,
sigma_max=args.sigma_max,
)
print("=====Finish generating MV Images=====")
lp = lp.extract(args)
lp.images = images
training(
lp,
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.")