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large_distill_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
from gaussian_renderer import render, network_gui
import sys
from scene import LargeScene, GaussianModel
from scene.cameras import MiniCamPlus
from scene.datasets import GSDataset, CacheDataLoader
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 utils.graphics_utils import getWorld2View2
from utils.pose_utils import gaussian_poses
from icecream import ic
import random
import copy
import json
import numpy as np
from utils.camera_utils import loadCam
from utils.logger_utils import prepare_output_and_logger, training_report_large
from torch.optim.lr_scheduler import ExponentialLR
try:
from torch.utils.tensorboard import SummaryWriter
TENSORBOARD_FOUND = True
except ImportError:
TENSORBOARD_FOUND = False
class NumpyArrayEncoder(json.JSONEncoder):
def default(self, obj):
if isinstance(obj, np.integer):
return int(obj)
elif isinstance(obj, np.floating):
return float(obj)
elif isinstance(obj, np.ndarray):
return obj.tolist()
else:
return super(NumpyArrayEncoder, self).default(obj)
to_tensor = lambda x: x.to("cuda") if isinstance(
x, torch.Tensor) else torch.Tensor(x).to("cuda")
img2mse = lambda x, y: torch.mean((x - y)**2)
mse2psnr = lambda x: -10. * torch.log(x) / torch.log(to_tensor([10.]))
def training(args, dataset, opt, pipe, testing_iterations, saving_iterations, checkpoint_iterations, checkpoint, debug_from, new_max_sh):
first_iter = 0
old_sh_degree = dataset.sh_degree
dataset.sh_degree = new_max_sh
tb_writer = prepare_output_and_logger(dataset)
with torch.no_grad():
teacher_gaussians = GaussianModel(old_sh_degree)
# teacher_gaussians.training_setup(opt)
student_gaussians = GaussianModel(old_sh_degree)
student_scene = LargeScene(dataset, student_gaussians)
gs_dataset = GSDataset(student_scene.getTrainCameras(), student_scene, dataset, pipe)
data_loader = CacheDataLoader(gs_dataset, max_cache_num=opt.max_cache_num, seed=42, batch_size=1, shuffle=True, num_workers=8)
if checkpoint:
(teacher_model_params, _) = torch.load(args.teacher_model)
(model_params, first_iter) = torch.load(checkpoint)
teacher_gaussians.restore(teacher_model_params, copy.deepcopy(opt))
student_gaussians.restore(model_params, opt)
student_gaussians.max_sh_degree = new_max_sh
student_gaussians.onedownSHdegree()
student_gaussians.training_setup(opt)
student_gaussians.scheduler = ExponentialLR(student_gaussians.optimizer, gamma=0.90)
# if !args.enable
if (not args.enable_covariance):
student_gaussians._scaling.requires_grad = False
student_gaussians._rotation.requires_grad = False
if (not args.enable_opacity):
student_gaussians._opacity.requires_grad = False
teacher_gaussians.optimizer = None
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
print("Number of student points at initialisation : ", student_gaussians._opacity.shape[0])
# os.makedirs(student_scene.model_path + "/vis_data", exist_ok=True)
iteration = first_iter
while iteration <= opt.iterations:
for dataset_index, (cam_info, _) in enumerate(data_loader):
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, student_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()
student_gaussians.update_learning_rate(iteration)
# Every 500 iterations step in scheduler
if iteration % 500 == 0:
# student_gaussians.oneupSHdegree()
student_gaussians.scheduler.step()
viewpoint_cam_org = MiniCamPlus(
cam_info["R"],
cam_info["T"],
cam_info["image_width"],
cam_info["image_height"],
cam_info["FoVy"],
cam_info["FoVx"],
0.01,
100,
cam_info["world_view_transform"],
cam_info["projection_matrix"],
cam_info["full_proj_transform"],
)
viewpoint_cam = copy.deepcopy(viewpoint_cam_org)
if (iteration - 1) == debug_from:
pipe.debug = True
if args.augmented_view:
viewpoint_cam = gaussian_poses(viewpoint_cam, mean= 0, std_dev_translation=0.05, std_dev_rotation=0)
student_render_pkg = render(viewpoint_cam, student_gaussians, pipe, background)
student_image = student_render_pkg["render"]
teacher_render_pkg = render(viewpoint_cam, teacher_gaussians, pipe, background)
teacher_image = teacher_render_pkg["render"].detach()
else:
render_pkg = render(viewpoint_cam, student_gaussians, pipe, background)
student_image = render_pkg["render"]
teacher_image = render(viewpoint_cam, teacher_gaussians, pipe, background)["render"].detach()
Ll1 = l1_loss(student_image, teacher_image)
# Ll1 = img2mse(student_image, teacher_image)
loss = (1.0 - opt.lambda_dssim) * Ll1 + opt.lambda_dssim * (1.0 - ssim(student_image, teacher_image))
loss.backward()
iter_end.record()
with torch.no_grad():
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()
if (iteration in saving_iterations):
print("\n[ITER {}] Saving Gaussians".format(iteration))
ic(student_gaussians._features_rest.detach().shape)
student_scene.save(iteration)
training_report_large(tb_writer, iteration, Ll1, loss, l1_loss, iter_start.elapsed_time(iter_end), testing_iterations, dataset, student_scene, render, (pipe, background))
if (iteration in checkpoint_iterations):
print("\n[ITER {}] Saving Checkpoint".format(iteration))
if not os.path.exists(student_scene.model_path):
os.makedirs(student_scene.model_path)
torch.save((student_gaussians.capture(), iteration), student_scene.model_path + "/chkpnt" + str(iteration) + ".pth")
# Optimizer step
if iteration < opt.iterations:
student_gaussians.optimizer.step()
student_gaussians.optimizer.zero_grad(set_to_none = True)
iteration += 1
else:
return
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=[35_001, 40_000])
parser.add_argument("--save_iterations", nargs="+", type=int, default=[40_000])
parser.add_argument("--quiet", action="store_true")
parser.add_argument("--checkpoint_iterations", nargs="+", type=int, default=[40_000])
parser.add_argument("--start_checkpoint", type=str, default = None)
parser.add_argument("--new_max_sh", type=int, default = 2)
parser.add_argument("--augmented_view", action="store_true")
parser.add_argument("--enable_covariance", action="store_true")
parser.add_argument("--enable_opacity", action="store_true")
parser.add_argument("--opacity_prune", type=float, default = 0)
parser.add_argument("--teacher_model", type=str)
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(args, 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.new_max_sh)
# All done
print("\nTraining complete.")