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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 json
import os
import glob
from collections import defaultdict
import torch
import torch.nn.functional as F
import random
from random import randint
from utils.loss_utils import psnr, ssim, tv_loss
from gaussian_renderer import get_renderer
from scene import Scene, GaussianModel, EnvLight
from utils.general_utils import seed_everything, visualize_depth, init_logging
from tqdm import tqdm
from argparse import ArgumentParser
from torchvision.utils import make_grid, save_image
import logging
from omegaconf import OmegaConf, DictConfig
from pprint import pprint, pformat
from texttable import Texttable
try:
from torch.utils.tensorboard import SummaryWriter
TENSORBOARD_FOUND = True
except ImportError:
TENSORBOARD_FOUND = False
EPS = 1e-5
non_zero_mean = (
lambda x: sum(x) / len(x) if len(x) > 0 else -1
)
def training(args):
if TENSORBOARD_FOUND:
tb_writer = SummaryWriter(args.model_path)
logging.info("Logging progress to Tensorboard")
else:
tb_writer = None
logging.info("Tensorboard not available: not logging progress")
vis_path = os.path.join(args.model_path, 'visualization')
os.makedirs(vis_path, exist_ok=True)
render_func, render_wrapper = get_renderer(args.render_type)
gaussians = GaussianModel(args)
scene = Scene(args, gaussians)
gaussians.training_setup(args)
if args.env_map_res > 0:
env_map = EnvLight(resolution=args.env_map_res).cuda()
env_map.training_setup(args)
else:
env_map = None
first_iter = 0
if args.resume:
checkpoints = glob.glob(os.path.join(args.model_path, "chkpnt*.pth"))
assert len(checkpoints) > 0, "No checkpoints found."
checkpoint = sorted(checkpoints, key=lambda x: int(x.split("chkpnt")[-1].split(".")[0]))[-1]
logging.info(f"Loading checkpoint {checkpoint}")
(model_params, first_iter) = torch.load(checkpoint)
gaussians.restore(model_params, args)
order = first_iter // args.sh_increase_interval
for _ in range(order):
scene.upScale()
if env_map is not None:
env_checkpoint = os.path.join(os.path.dirname(checkpoint),
os.path.basename(checkpoint).replace("chkpnt", "env_light_chkpnt"))
(light_params, _) = torch.load(env_checkpoint)
env_map.restore(light_params)
bg_color = [1, 1, 1] if args.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_dict_for_log = defaultdict(int)
progress_bar = tqdm(range(1, args.iterations + 1), desc="Training", bar_format='{l_bar}{bar:50}{r_bar}')
# update the progress bar to the first iteration
progress_bar.update(first_iter + 1)
for iteration in range(first_iter + 1, args.iterations + 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 % args.sh_increase_interval == 0:
gaussians.oneupSHdegree()
if not viewpoint_stack:
viewpoint_stack = list(range(len(scene.getTrainCameras())))
camera_id = viewpoint_stack.pop(randint(0, len(viewpoint_stack) - 1))
viewpoint_cam = scene.getTrainCameras()[camera_id]
nearest_cam = None if len(viewpoint_cam.nearest_id) == 0 else scene.getTrainCameras()[random.sample(viewpoint_cam.nearest_id,1)[0]]
loss, log_dict, render_pkg = render_wrapper(args, viewpoint_cam, gaussians, background, scene.time_interval, env_map, iteration, camera_id, nearest_cam=nearest_cam)
loss.backward()
log_dict['loss'] = loss.item()
# iter_end.record()
with torch.no_grad():
for key in ["psnr"]: # 'loss', "loss_l1",
ema_dict_for_log[key] = 0.4 * log_dict[key] + 0.6 * ema_dict_for_log[key]
# log_dict['iter_time'] = iter_start.elapsed_time(iter_end)
log_dict['total_points'] = gaussians.get_xyz.shape[0]
if iteration % 10 == 0:
postfix = {k[5:] if k.startswith("loss_") else k:f"{ema_dict_for_log[k]:.{5}f}" for k, v in ema_dict_for_log.items()}
postfix["scale"] = scene.resolution_scales[scene.scale_index]
postfix["pts"] = gaussians.get_xyz.shape[0]
progress_bar.set_postfix(postfix)
progress_bar.update(10)
# Log and save
complete_eval(tb_writer, iteration, args.test_iterations, scene, render_func, (args, background),
log_dict, env_map=env_map)
# Densification
if iteration > args.densify_until_iter * args.time_split_frac:
gaussians.no_time_split = False
if iteration < args.densify_until_iter and (args.densify_until_num_points < 0 or gaussians.get_xyz.shape[0] < args.densify_until_num_points):
viewspace_point_tensor = render_pkg["viewspace_points"]
visibility_filter = render_pkg["visibility_filter"]
radii = render_pkg["radii"]
# 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 > args.densify_from_iter and iteration % args.densification_interval == 0:
size_threshold = args.size_threshold if (iteration > args.opacity_reset_interval and args.prune_big_point > 0) else None
if size_threshold is not None:
size_threshold = size_threshold // scene.resolution_scales[0]
gaussians.densify_and_prune(args.densify_grad_threshold, args.thresh_opa_prune, scene.cameras_extent, size_threshold, args.densify_grad_t_threshold)
if iteration % args.opacity_reset_interval == 0 or (args.white_background and iteration == args.densify_from_iter):
gaussians.reset_opacity()
gaussians.optimizer.step()
gaussians.optimizer.zero_grad(set_to_none = True)
if env_map is not None and iteration < args.env_optimize_until:
env_map.optimizer.step()
env_map.optimizer.zero_grad(set_to_none = True)
torch.cuda.empty_cache()
if iteration % args.vis_step == 0 or iteration == 1:
save_visualizations(render_pkg, viewpoint_cam, vis_path, iteration)
if iteration % args.scale_increase_interval == 0:
scene.upScale()
if iteration in args.checkpoint_iterations:
logging.info("[ITER {}] Saving Checkpoint".format(iteration))
torch.save((gaussians.capture(), iteration), scene.model_path + "/chkpnt" + str(iteration) + ".pth")
torch.save((env_map.capture(), iteration), scene.model_path + "/env_light_chkpnt" + str(iteration) + ".pth")
if gaussians.uncertainty_model is not None:
gaussians.uncertainty_model.save(scene.model_path + "/uncertainty_model" + str(iteration) + ".pth")
def complete_eval(tb_writer, iteration, test_iterations, scene : Scene, renderFunc, renderArgs, log_dict, env_map=None):
from lpipsPyTorch import lpips
if tb_writer and iteration % 10 == 0:
for key, value in log_dict.items():
tb_writer.add_scalar(f'train/{key}', value, iteration)
if iteration in test_iterations:
scale = scene.resolution_scales[scene.scale_index]
if iteration < args.iterations:
validation_configs = ({'name': 'test', 'cameras': scene.getTestCameras(scale=scale)}, {'name': 'train', 'cameras': scene.getTrainCameras()})
else:
if "kitti" in args.model_path:
# follow NSG: https://github.com/princeton-computational-imaging/neural-scene-graphs/blob/8d3d9ce9064ded8231a1374c3866f004a4a281f8/data_loader/load_kitti.py#L766
num = len(scene.getTrainCameras())//2
eval_train_frame = num//5
traincamera = sorted(scene.getTrainCameras(), key =lambda x: x.colmap_id)
validation_configs = ({'name': 'test', 'cameras': scene.getTestCameras(scale=scale)},
{'name': 'train', 'cameras': traincamera[:num][-eval_train_frame:]+traincamera[num:][-eval_train_frame:]})
else:
validation_configs = ({'name': 'test', 'cameras': scene.getTestCameras(scale=scale)},
{'name': 'train', 'cameras': scene.getTrainCameras()})
for config in validation_configs:
if config['cameras'] and len(config['cameras']) > 0:
l1_test = []
psnr_test = []
ssim_test = []
lpips_test = []
masked_psnr_test = []
masked_ssim_test = []
depth_error = []
outdir = os.path.join(args.model_path, "eval", config['name'] + f"_{iteration}" + "_render")
os.makedirs(outdir,exist_ok=True)
for idx, viewpoint in enumerate(tqdm(config['cameras'], desc="Evaluating", bar_format='{l_bar}{bar:50}{r_bar}')):
v = scene.gaussians.get_inst_velocity
t_scale = scene.gaussians.get_scaling_t.clamp_max(2)
other = [t_scale, v]
render_pkg = renderFunc(viewpoint, scene.gaussians, *renderArgs, env_map=env_map, other=other, is_training=False)
image = torch.clamp(render_pkg["render"], 0.0, 1.0)
gt_image = torch.clamp(viewpoint.original_image.to("cuda"), 0.0, 1.0)
depth = render_pkg['depth']
alpha = render_pkg['alpha']
normal = render_pkg['normal']
sky_depth = 900
depth = depth / alpha.clamp_min(EPS)
if env_map is not None:
if args.depth_blend_mode == 0: # harmonic mean
depth = 1 / (alpha / depth.clamp_min(EPS) + (1 - alpha) / sky_depth).clamp_min(EPS)
elif args.depth_blend_mode == 1:
depth = alpha * depth + (1 - alpha) * sky_depth
pts_depth = viewpoint.pts_depth.cuda()
mask = (pts_depth > 0)
depth_error.append(F.l1_loss(depth[mask], pts_depth[mask]).double().item())
feature = render_pkg['feature'] / alpha.clamp_min(1e-5)
t_map = feature[0:1]
v_map = feature[1:]
v_norm_map = v_map.norm(dim=0, keepdim=True)
et_color = visualize_depth(t_map, near=0.01, far=1)
v_color = visualize_depth(v_norm_map, near=0.01, far=1)
sky_mask = viewpoint.sky_mask.to("cuda")
if scene.gaussians.uncertainty_model is not None:
_, _, dynamic_mask, dino_cosine = scene.gaussians.uncertainty_model.get_loss(gt_image, image.detach(), sky_mask)
else:
dynamic_mask = viewpoint.dynamic_mask.to("cuda") if viewpoint.dynamic_mask is not None else torch.zeros_like(alpha, dtype=torch.bool)
dino_cosine = torch.ones_like(depth)
dynamic_mask = (dynamic_mask > 1 - EPS).to(dtype=dynamic_mask.dtype)
depth = visualize_depth(depth)
alpha = alpha.repeat(3, 1, 1)
grid = [image, alpha, depth, et_color, \
gt_image, normal, dynamic_mask.float().repeat(3, 1, 1), v_color]
grid = make_grid(grid, nrow=4)
save_image(grid, os.path.join(outdir, f"{viewpoint.colmap_id:03d}.png"))
l1_test.append(F.l1_loss(image, gt_image).double().item())
psnr_test.append(psnr(image, gt_image).double().item())
ssim_test.append(ssim(image, gt_image).double().item())
lpips_test.append(lpips(image, gt_image, net_type='vgg').double().item()) # very slow
if dynamic_mask.sum() > 0:
dynamic_mask = dynamic_mask.repeat(3, 1, 1) > 0 # (C, H, W)
masked_psnr_test.append(psnr(image[dynamic_mask], gt_image[dynamic_mask]).double().item())
unaveraged_ssim = ssim(image, gt_image, size_average=False) # (C, H, W)
masked_ssim_test.append(unaveraged_ssim[dynamic_mask].mean().double().item())
psnr_test = non_zero_mean(psnr_test)
l1_test = non_zero_mean(l1_test)
ssim_test = non_zero_mean(ssim_test)
lpips_test = non_zero_mean(lpips_test)
masked_psnr_test = non_zero_mean(masked_psnr_test)
masked_ssim_test = non_zero_mean(masked_ssim_test)
depth_error = non_zero_mean(depth_error)
t = Texttable()
t.add_rows([["PSNR", "SSIM", "LPIPS", "L1", "PSNR (dynamic)", "SSIM (dynamic)", "Depth Error"],
[f"{psnr_test:.4f}", f"{ssim_test:.4f}", f"{lpips_test:.4f}", f"{l1_test:.4f}", f"{masked_psnr_test:.4f}", f"{masked_ssim_test:.4f}", f"{depth_error:.4f}"]])
logging.info(t.draw())
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)
tb_writer.add_scalar(config['name'] + '/loss_viewpoint - ssim', ssim_test, iteration)
with open(os.path.join(args.model_path, "eval", "metrics_{}.json".format(iteration)), "a+") as f:
json.dump({"split": config['name'], "iteration": iteration,
"psnr": psnr_test, "ssim": ssim_test, "lpips": lpips_test, "masked_psnr": masked_psnr_test, "masked_ssim": masked_ssim_test, "depth_error": depth_error,
}, f)
torch.cuda.empty_cache()
def save_visualizations(render_pkg, viewpoint_cam, vis_path, iteration):
alpha = torch.clamp(render_pkg["alpha"], 0.0, 1.0)
depth = render_pkg["depth"]
image = render_pkg["render"]
rendered_normal = torch.clamp(render_pkg.get("normal", torch.zeros_like(image)), 0.0, 1.0)
sky_mask = viewpoint_cam.sky_mask.cuda() if viewpoint_cam.sky_mask is not None else torch.zeros_like(alpha, dtype=torch.bool)
gt_image = viewpoint_cam.original_image.cuda()
gt_normal = viewpoint_cam.normal_map.cuda() if viewpoint_cam.normal_map is not None else torch.zeros_like(rendered_normal)
pseudo_normal = torch.clamp(render_pkg.get("depth_normal", gt_normal), 0.0, 1.0)
# other_img = []
feature = render_pkg['feature'] / alpha.clamp_min(1e-5)
t_map = feature[0:1] # (1, H, W)
v_map = feature[1:] # (1, H, W)
v_norm_map = v_map.norm(dim=0, keepdim=True)
et_color = visualize_depth(t_map, near=0.01, far=1)
v_color = visualize_depth(v_norm_map, near=0.01, far=1)
# other_img.append(et_color)
# other_img.append(v_color)
dynamic_mask = render_pkg['dynamic_mask']
if viewpoint_cam.pts_depth is not None:
pts_depth = viewpoint_cam.pts_depth
# pts_depth[pts_depth == 0] = 900
pts_depth_vis = visualize_depth(pts_depth)
# other_img.append(pts_depth_vis)
not_sky_mask = torch.logical_not(sky_mask[:1]).float()
rendered_distance = render_pkg.get("rendered_distance", torch.zeros_like(alpha))
# d_mask = render_pkg.get("d_mask", torch.zeros_like(alpha))
grid = make_grid([
image,
alpha.repeat(3, 1, 1),
visualize_depth(depth),
rendered_normal,
et_color,
visualize_depth(rendered_distance),
# dino_cosine.repeat(3, 1, 1),
gt_image,
not_sky_mask.repeat(3, 1, 1),
pts_depth_vis,
# gt_normal * not_sky_mask,
pseudo_normal,
dynamic_mask.repeat(3, 1, 1),
v_color,
# d_mask.repeat(3, 1, 1),
], nrow=6)
save_image(grid, os.path.join(vis_path, f"{iteration:05d}_{viewpoint_cam.colmap_id:03d}.png"))
if __name__ == "__main__":
# Set up command line argument parser
parser = ArgumentParser(description="Training script parameters")
parser.add_argument("--config", type=str, required=True)
parser.add_argument("--base_config", type=str, default = "configs/base.yaml")
args, _ = parser.parse_known_args()
base_conf = OmegaConf.load(args.base_config)
second_conf = OmegaConf.load(args.config)
cli_conf = OmegaConf.from_cli()
args = OmegaConf.merge(base_conf, second_conf, cli_conf)
args.save_iterations.append(args.iterations)
args.checkpoint_iterations.append(args.iterations)
args.test_iterations.append(args.iterations)
if args.exhaust_test:
args.test_iterations += [i for i in range(0,args.iterations, args.test_interval)]
os.makedirs(args.model_path, exist_ok=True)
init_logging(os.path.join(args.model_path, "train.log"))
logging.info("PID:{}".format(os.getpid()))
logging.info("Optimizing " + args.model_path)
logging.info('Configurations:\n {}'.format(pformat(OmegaConf.to_container(args, resolve=True, throw_on_missing=True))))
# write config to yaml file
OmegaConf.save(args, os.path.join(args.model_path, "config.yaml"))
seed_everything(args.seed)
training(args)
# All done
logging.info("Training complete.")