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train.py
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train.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, cos_loss, bce_loss, knn_smooth_loss
from gaussian_renderer import render, network_gui
import numpy as np
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, depth2rgb, normal2rgb, depth2normal, match_depth, normal2curv, resize_image, cross_sample
from torchvision.utils import save_image
from argparse import ArgumentParser, Namespace
import time
import os
from arguments import ModelParams, PipelineParams, OptimizationParams
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)
scene = Scene(dataset, gaussians, opt.camera_lr, shuffle=False, resolution_scales=[1, 2, 4])
use_mask = dataset.use_mask
gaussians.training_setup(opt)
if checkpoint:
(model_params, first_iter) = torch.load(checkpoint)
gaussians.restore(model_params, opt)
elif use_mask: # visual hull init
gaussians.mask_prune(scene.getTrainCameras(), 4)
None
opt.densification_interval = max(opt.densification_interval, len(scene.getTrainCameras()))
background = torch.tensor([1, 1, 1] if dataset.white_background else [0, 0, 0], dtype=torch.float32, device="cuda")
iter_start = torch.cuda.Event(enable_timing = True)
iter_end = torch.cuda.Event(enable_timing = True)
pool = torch.nn.MaxPool2d(9, stride=1, padding=4)
viewpoint_stack = None
ema_loss_for_log = 0.0
progress_bar = tqdm(range(first_iter, opt.iterations), desc="Training progress")
first_iter += 1
count = -1
for iteration in range(first_iter, opt.iterations + 2):
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()
if iteration - 1 == 0:
scale = 4
elif iteration - 1 == 2000 + 1:
scale = 2
elif iteration - 1 == 5000 + 1:
scale = 1
# scale = 1
# Pick a random Camera
if not viewpoint_stack:
viewpoint_stack = scene.getTrainCameras(scale).copy()[:]
viewpoint_cam = viewpoint_stack.pop(randint(0, len(viewpoint_stack) - 1))
# viewpoint_cam = scene.getTrainCameras(scale)[0]
# Render
if (iteration - 1) == debug_from:
pipe.debug = True
background = torch.rand((3), dtype=torch.float32, device="cuda") if dataset.random_background else background
patch_size = [float('inf'), float('inf')]
render_pkg = render(viewpoint_cam, gaussians, pipe, background, patch_size)
image, normal, depth, opac, viewspace_point_tensor, visibility_filter, radii = \
render_pkg["render"], render_pkg["normal"], render_pkg["depth"], render_pkg["opac"], \
render_pkg["viewspace_points"], render_pkg["visibility_filter"], render_pkg["radii"]
mask_gt = viewpoint_cam.get_gtMask(use_mask)
gt_image = viewpoint_cam.get_gtImage(background, use_mask)
mask_vis = (opac.detach() > 1e-5)
normal = torch.nn.functional.normalize(normal, dim=0) * mask_vis
d2n = depth2normal(depth, mask_vis, viewpoint_cam)
mono = viewpoint_cam.mono if dataset.mono_normal else None
if mono is not None:
mono *= mask_gt
monoN = mono[:3]
# monoD = mono[3:]
# monoD_match, mask_match = match_depth(monoD, depth, mask_gt * mask_vis, 256, [viewpoint_cam.image_height, viewpoint_cam.image_width])
# Loss
Ll1 = l1_loss(image, gt_image)
loss_rgb = (1.0 - opt.lambda_dssim) * Ll1 + opt.lambda_dssim * (1.0 - ssim(image, gt_image))
loss_mask = (opac * (1 - pool(mask_gt))).mean()
if mono is not None:
loss_monoN = cos_loss(normal, monoN, weight=mask_gt)
# loss_depth = l1_loss(depth * mask_match, monoD_match)
loss_surface = cos_loss(normal, d2n)
opac_ = gaussians.get_opacity
opac_mask0 = torch.gt(opac_, 0.01) * torch.le(opac_, 0.5)
opac_mask1 = torch.gt(opac_, 0.5) * torch.le(opac_, 0.99)
opac_mask = opac_mask0 * 0.01 + opac_mask1
loss_opac = (torch.exp(-(opac_ - 0.5)**2 * 20) * opac_mask).mean()
# loss_opac = bce_loss(opac_, torch.gt(opac_, 0.01) * torch.le(opac_, 0.99)) * 0.01
curv_n = normal2curv(normal, mask_vis)
# curv_d2n = normal2curv(d2n, mask_vis_2)
loss_curv = l1_loss(curv_n * 1, 0) #+ 1 * l1_loss(curv_d2n, 0)
loss = 1 * loss_rgb
loss += 0.1 * loss_mask
loss += (0.01 + 0.1 * min(2 * iteration / opt.iterations, 1)) * loss_surface
# loss += (0.00 + 0.1 * min(1 * iteration / opt.iterations, 1)) * loss_surface
loss += 0.005 * loss_curv
loss += 0.01* loss_opac
# mono = None
if mono is not None:
loss += (0.04 - ((iteration / opt.iterations)) * 0.02) * loss_monoN
# loss += 0.01 * loss_depth
loss.backward()
iter_end.record()
with torch.no_grad():
# Progress bar
ema_loss_for_log = 0.4 * loss_rgb.item() + 0.6 * ema_loss_for_log
if iteration % 10 == 0:
progress_bar.set_postfix({"Loss": f"{ema_loss_for_log:.{7}f}, Pts={len(gaussians._xyz)}"})
progress_bar.update(10)
if iteration == opt.iterations:
progress_bar.close()
# Log and save
test_background = torch.tensor([1, 1, 1] if dataset.white_background else [0, 0, 0], dtype=torch.float32, device="cuda")
training_report(tb_writer, iteration, Ll1, loss, l1_loss, iter_start.elapsed_time(iter_end), testing_iterations, scene, pipe, test_background, use_mask)
if (iteration in saving_iterations):
print("\n[ITER {}] Saving Gaussians".format(iteration))
scene.save(iteration)
# Densification
if iteration > opt.densify_from_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)
min_opac = 0.1
if iteration % opt.densification_interval == 0:
gaussians.adaptive_prune(min_opac, scene.cameras_extent)
gaussians.adaptive_densify(opt.densify_grad_threshold, scene.cameras_extent)
if (iteration - 1) % opt.opacity_reset_interval == 0 and opt.opacity_lr > 0:
gaussians.reset_opacity(0.12, iteration)
if (iteration - 1) % 1000 == 0:
normal_wrt = normal2rgb(normal, mask_vis)
depth_wrt = depth2rgb(depth, mask_vis)
img_wrt = torch.cat([gt_image, image, normal_wrt * opac, depth_wrt * opac], 2)
save_image(img_wrt.cpu(), f'test/test.png')
if iteration < opt.iterations:
gaussians.optimizer.step()
gaussians.optimizer.zero_grad()
# viewpoint_cam.optimizer.step()
# viewpoint_cam.optimizer.zero_grad()
if (iteration in checkpoint_iterations):
# gaussians.adaptive_prune(min_opac, scene.cameras_extent)
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", f"{args.source_path.split('/')[-1]}_{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, pipe, bg, use_mask):
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()[::8]})
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(render(viewpoint, scene.gaussians, pipe, bg, [float('inf'), float('inf')])["render"], 0.0, 1.0)
gt_image = torch.clamp(viewpoint.get_gtImage(bg, with_mask=use_mask), 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('--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=[5_000, 10_000, 15_000, 20_000, 25_000, 30_000])
parser.add_argument("--save_iterations", nargs="+", type=int, default=[5_000, 15_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)
args = parser.parse_args(sys.argv[1:])
args.save_iterations.append(args.iterations)
print("Optimizing " + args.model_path)
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.")