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train_iter_prune.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
from utils.S3IM import S3IM
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 torch.utils.tensorboard import SummaryWriter
from NN_Comp.UNet import UNet
# from NN_Comp.swin_unet import SUNet_model
import yaml
import pdb
TENSORBOARD_FOUND = True
def training(dataset,
opt,
pipe,
testing_iterations,
saving_iterations,
checkpoint_iterations,
checkpoint,
debug_from,
no_xyz=False,
sample_mode="FPRC",
sample_schedule='every 3000',
sample_rate=0.1,
kernal=103):
first_iter = 0
tb_writer = prepare_output_and_logger(dataset)
gaussians = GaussianModel(dataset.sh_degree)
scene = Scene(dataset, gaussians)
NN_Comp = UNet(3, 3).cuda()
with open('NN_Comp/NN_comp.yaml', 'r') as config:
nn_comp_config = yaml.safe_load(config)
# NN_Comp = SUNet_model(nn_comp_config).cuda()
s3im = S3IM(s3im_patch_height=kernal)
optimizer = torch.optim.Adam(NN_Comp.parameters(), lr=0.001, betas=(0.9, 0.999))
lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones = [40000, 60000], gamma = 0.8)
gaussians.training_setup(opt)
if checkpoint:
(model_params, _) = torch.load(checkpoint)
if no_xyz:
gaussians.restore_no_xyz(model_params, opt)
else:
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)
# import pdb; pdb.set_trace()
if 'every' in sample_schedule:
sample_only_once = False
sample_freq = int(sample_schedule.split('-')[-1]) # NOTE: sample_schedule format should be: [every-N]
else:
sample_only_once = True
sample_freq = 8000 # TODO:
viewpoint_stack = None
ema_loss_for_log = 0.0
progress_bar = tqdm(range(first_iter, opt.iterations), desc="Training progress")
first_iter += 1
scene.save(0)
for iteration in range(first_iter, opt.iterations + 1):
iter_start.record()
optimizer.zero_grad()
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, viewspace_point_tensor, visibility_filter, radii = render_pkg["render"], render_pkg["viewspace_points"], render_pkg["visibility_filter"], render_pkg["radii"]
gt_image = viewpoint_cam.original_image.cuda()
if iteration <= opt.densify_until_iter:
Ll1 = l1_loss(image, gt_image)
ssim_loss = 1.0 - ssim(image, gt_image)
loss = (1.0 - opt.lambda_dssim) * Ll1 + opt.lambda_dssim * ssim_loss
loss.backward()
if iteration > opt.densify_until_iter * 2.0:
image = NN_Comp(image.unsqueeze(0)).squeeze(0)
Ll1 = l1_loss(image, gt_image)
ssim_loss = 1.0 - ssim(image, gt_image)
loss = (1.0 - opt.lambda_dssim) * Ll1 + opt.lambda_dssim * ssim_loss
loss.backward()
optimizer.step()
lr_scheduler.step()
# if iteration > opt.densify_until_iter*2:
# image = NN_Comp(image.unsqueeze(0)).squeeze(0)
# Ll1 = l1_loss(image, gt_image)
# ssim_loss = 1.0 - ssim(image, gt_image)
# s3im_loss = s3im(image.flatten(1), gt_image.flatten(1))
# loss = (1.0 - opt.lambda_dssim) * Ll1 + opt.lambda_dssim * ssim_loss
# loss.backward()
# optimizer.step()
# lr_scheduler.step()
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), NN_Comp)
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()
# Graph Downsampling # TODO: add strategy for different sample frequencies
if not sample_only_once:
if iteration % sample_freq == 0:
print("\n[ITER {}] Graph Downsampling with Sample Rate: {}".format(iteration, sample_rate))
gaussians.graph_downsampling(sample_mode, sample_rate)
else:
if iteration == sample_freq:
print("\n[ITER {}] Graph Downsampling with Sample Rate: {}".format(iteration, sample_rate))
gaussians.graph_downsampling(sample_mode, sample_rate)
# 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")
torch.save(NN_Comp.state_dict(), scene.model_path + "/NN_Comp" + 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, NN_Comp):
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)
tb_writer.add_scalar('cuda_memory', torch.cuda.memory_allocated() / 1024**2, 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)
if iteration > 30000:
image = NN_Comp(image.unsqueeze(0)).squeeze(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('--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=[10000, 15000, 20000, 30000, 40000, 50000, 60000])
parser.add_argument("--save_iterations", nargs="+", type=int, default=[10000, 15000, 20000, 30000, 40000, 50000, 60000])
parser.add_argument("--quiet", action="store_true")
parser.add_argument("--checkpoint_iterations", nargs="+", type=int, default=[10000, 15000, 20000, 30000, 40000, 50000, 60000])
parser.add_argument("--start_checkpoint", type=str, default = None)
parser.add_argument("--no_xyz", type=bool, default=False, help="restore point cloud?")
parser.add_argument("--ply_dir", type=str, default = None)
parser.add_argument("--sample_rate", default=0.1, type=float, help="number of samples")
parser.add_argument("--sample_mode", default="FPRC", type=str, help="sample mode")
parser.add_argument("--sample_schedule", default="8k", type=str, help="sample schedule")
parser.add_argument("--kernal", type=int, default=103)
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
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, args.no_xyz, args.sample_mode, args.sample_schedule,
args.sample_rate, args.kernal)
# All done
print("\nTraining complete.")