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train_joint.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 numpy as np
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 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 scene.cameras import Camera
from utils.graphics_utils import getWorld2View2_torch
from utils.pose_utils import get_camera_from_tensor
from utils.camera_utils import generate_interpolated_path
from utils.camera_utils import visualizer
import torchvision
try:
from torch.utils.tensorboard import SummaryWriter
TENSORBOARD_FOUND = True
except ImportError:
TENSORBOARD_FOUND = False
from time import perf_counter
def save_pose(path, quat_pose, train_cams, llffhold=2):
output_poses=[]
index_colmap = [cam.colmap_id for cam in train_cams]
for quat_t in quat_pose:
w2c = get_camera_from_tensor(quat_t)
output_poses.append(w2c)
colmap_poses = []
for i in range(len(index_colmap)):
ind = index_colmap.index(i+1)
bb=output_poses[ind]
bb = bb#.inverse()
colmap_poses.append(bb)
colmap_poses = torch.stack(colmap_poses).detach().cpu().numpy()
np.save(path, colmap_poses)
def training(dataset, opt, pipe, testing_iterations, saving_iterations, checkpoint_iterations, checkpoint, debug_from, args):
first_iter = 0
tb_writer = prepare_output_and_logger(dataset)
gaussians = GaussianModel(dataset.sh_degree)
scene = Scene(dataset, gaussians, opt=args, shuffle=True)
gaussians.training_setup(opt)
if checkpoint:
(model_params, first_iter) = torch.load(checkpoint)
gaussians.restore(model_params, opt)
train_cams_init = scene.getTrainCameras().copy()
os.makedirs(scene.model_path + 'pose', exist_ok=True)
save_pose(scene.model_path + 'pose' + "/pose_org.npy", gaussians.P, train_cams_init)
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
start = perf_counter()
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)
if args.optim_pose==False:
gaussians.P.requires_grad_(False)
# 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))
pose = gaussians.get_RT(viewpoint_cam.uid)
# 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, camera_pose=pose)
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))
loss.backward()
# for param_group in gaussians.optimizer.param_groups:
# for param in param_group['params']:
# if param is gaussians.P:
# print(viewpoint_cam.uid, param.grad)
# break
# print("Gradient of self.P:", gaussians.P.grad)
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)
save_pose(scene.model_path + 'pose' + f"/pose_{iteration}.npy", gaussians.P, train_cams_init)
# 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")
end = perf_counter()
train_time = end - start
# We commented out log&save operations, and then calculate train time.
# train_time = np.array(train_time)
# print("total_test_time_epoch: ", 1)
# print("instantsplat_train_time_mean: ", train_time.mean())
# print("instantsplat_train_time_median: ", np.median(train_time))
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(len(scene.getTrainCameras()))]})
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']):
if config['name']=="train":
pose = scene.gaussians.get_RT(viewpoint.uid)
else:
pose = scene.gaussians.get_RT_test(viewpoint.uid)
image = torch.clamp(renderFunc(viewpoint, scene.gaussians, *renderArgs, camera_pose=pose)["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('--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=[500, 800, 1000, 1500, 2000, 3000, 4000, 5000, 6000, 7_000, 30_000])
parser.add_argument("--save_iterations", nargs="+", type=int, default=[])
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("--scene", type=str, default=None)
parser.add_argument("--n_views", type=int, default=None)
parser.add_argument("--get_video", action="store_true")
parser.add_argument("--optim_pose", action="store_true")
args = parser.parse_args(sys.argv[1:])
args.save_iterations.append(args.iterations)
os.makedirs(args.model_path, exist_ok=True)
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(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)
# All done
print("\nTraining complete.")