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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 [email protected]
#
import os
import torch
from random import randint
from utils.loss_utils import l1_loss, ssim
from gaussian_renderer import render
import sys
from scene import RobotScene, 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
import itertools
import numpy as np
try:
from torch.utils.tensorboard import SummaryWriter
TENSORBOARD_FOUND = True
except ImportError:
TENSORBOARD_FOUND = False
from lbs.nn import train_lrs
from torch.utils.data import DataLoader
def training(gaussians, scene, stage, tb_writer, dataset, opt, pipe, test_every, save_every, checkpoint_every, checkpoint, debug_from):
first_iter = 0
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")
print("bg color", background)
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
max_iterations = opt.canonical_training_iterations if stage == 'canonical' else opt.pose_conditioned_training_iterations
progress_bar = tqdm(range(first_iter, max_iterations), desc="Training progress")
first_iter += 1
# for iteration in range(first_iter, opt.iterations + 1):
viewpoint_stack = scene.getTrainCameras(stage=stage)
iteration = 0
for viewpoint_cam in itertools.cycle(viewpoint_stack):
iteration += 1
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
if opt.random_background:
background = torch.rand_like(background)
render_pkg = render(viewpoint_cam, gaussians, background, stage=stage)
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 opt.random_background:
robot_mask = viewpoint_cam.robot_mask.cuda()
gt_image = gt_image * robot_mask + background.reshape(-1, 1, 1) * ~robot_mask
Ll1 = l1_loss(image, gt_image)
loss = (1.0 - opt.lambda_dssim) * Ll1 + opt.lambda_dssim * (1.0 - ssim(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
background = torch.ones_like(background) if opt.random_background else torch.zeros_like(background)
training_report(tb_writer, iteration, Ll1, loss, l1_loss, iter_start.elapsed_time(iter_end), test_every, scene, render, background, stage)
if (iteration % save_every == 0):
print("\n[ITER {}] Saving Gaussians".format(iteration))
mem = torch.cuda.max_memory_allocated() / 1024**3
print(f"Max memory used: {mem:.2f} GB")
scene.save(iteration, stage)
# 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)
gaussians.add_densification_stats(viewspace_point_tensor, visibility_filter, image.shape[2], image.shape[1])
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()
# CLIP gradient norm so weird camera angles can't mess this up
for idx, param_group in enumerate(gaussians.optimizer.param_groups):
grad_norm = torch.nn.utils.clip_grad_norm_(param_group['params'], max_norm=1.0)
tb_writer.add_scalar(f"grad_norm_{idx}", grad_norm, iteration)
if stage == 'pose_conditioned':
grad_norm_lrs = torch.nn.utils.clip_grad_norm_(gaussians.lrs_model.parameters(), max_norm=1.0)
tb_writer.add_scalar(f"grad_norm_lrs", grad_norm_lrs, iteration)
grad_norm_appearance_deform = torch.nn.utils.clip_grad_norm_(gaussians.appearance_deformation_model.parameters(), max_norm=1.0)
tb_writer.add_scalar(f"grad_norm_appearance_deform", grad_norm_appearance_deform, iteration)
gaussians.optimizer.step()
if stage == 'pose_conditioned':
gaussians.optimizer_appearance_deformation.step()
gaussians.optimizer_lrs.step()
gaussians.optimizer.zero_grad()
if stage == 'pose_conditioned':
gaussians.optimizer_appearance_deformation.zero_grad()
gaussians.optimizer_lrs.zero_grad()
if (iteration % checkpoint_every == 0):
print("\n[ITER {}] Saving Checkpoint".format(iteration))
torch.save((gaussians.capture(), iteration), scene.model_path + "/chkpnt" + f"_{stage}_" + str(iteration) + ".pth")
assert stage in ['canonical', 'pose_conditioned']
if iteration > max_iterations:
break
def prepare_output_and_logger(args, experiment_name):
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/", experiment_name)
# 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 : RobotScene, renderFunc, background, stage):
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
test_cameras = scene.getSampleCameras(stage=stage)
if iteration % args.test_every == 0:
torch.cuda.empty_cache()
validation_configs = [{'name': 'test',
'cameras': test_cameras}]
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, background, stage=stage)["render"], 0.0, 1.0)
gt_image = torch.clamp(viewpoint.original_image.to("cuda"), 0.0, 1.0)
if tb_writer and (idx < 16):
tb_writer.add_images(config['name'] + f"_view_{idx}/{stage}_render", image[None], global_step=iteration)
tb_writer.add_images(config['name'] + f"_view_{idx}/{stage}_ground_truth", 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_scalar('total_points', scene.gaussians.get_xyz.shape[0], iteration)
torch.cuda.empty_cache()
def three_stage_training(dataset, hyper, opt, pipe, test_every, save_every, checkpoint_iterations, checkpoint, debug_from, experiment_name):
tb_writer = prepare_output_and_logger(dataset, experiment_name)
gaussians = GaussianModel(dataset.sh_degree, opt)
scene = RobotScene(dataset, gaussians, opt_params=opt)
print("============ Training canonical Gaussian ============")
# first stage, train the canonical gaussian
training(gaussians, scene, 'canonical', tb_writer, dataset, opt, pipe, test_every, save_every, checkpoint_iterations, checkpoint, debug_from)
print("============ Training LBS model ============")
# second stage
train_lrs(gaussians)
print("============ Training pose conditioned Gaussian ============")
#first stage, train the canonical gaussian
training(gaussians, scene, 'pose_conditioned', tb_writer, dataset, opt, pipe, test_every, save_every, checkpoint_iterations, checkpoint, debug_from)
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=randint(10000, 65535))
parser.add_argument('--debug_from', type=int, default=-1)
parser.add_argument('--detect_anomaly', action='store_true', default=False)
parser.add_argument("--test_every", type=int, default=400)
parser.add_argument("--save_every", type=int, default=4000)
parser.add_argument("--quiet", action="store_true")
parser.add_argument("--checkpoint_every", type=int, default=4000)
parser.add_argument("--start_checkpoint", type=str, default = None)
parser.add_argument("--experiment_name", type=str, default = None)
args = parser.parse_args(sys.argv[1:])
if args.experiment_name is None:
import time
args.experiment_name = time.strftime("%Y-%m-%d-%H-%M-%S")
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)
three_stage_training(lp.extract(args), None, op.extract(args), pp.extract(args), args.test_every, args.save_every, args.checkpoint_every, args.start_checkpoint, args.debug_from, args.experiment_name)
# All done
print("\nTraining complete.")