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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, 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
try:
from torch.utils.tensorboard import SummaryWriter
TENSORBOARD_FOUND = True
except ImportError:
TENSORBOARD_FOUND = False
import cv2
import matplotlib.pyplot as plt
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
# from torchmetrics.functional.regression import pearson_corrcoef
def training(dataset, opt, pipe, testing_iterations, saving_iterations, checkpoint_iterations, checkpoint, debug_from):
first_iter = 0
tb_writer = prepare_output_and_logger(dataset)
gaussians = GaussianModel(dataset.sh_degree)
scene = Scene(dataset, gaussians)
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:0")
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
depth_weight = 0.005
face_prior_weight = 1
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)
# 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"]
# 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))
# set all the flags here for various experiment
render_depth = True
use_norm_depth_loss = False
face_prior_loss = False
use_abs_depth_loss = True
multiscale_loss = False
huber_loss = False
# visualize depth
if render_depth:
pred_depth = render_pkg["depth"][0]
pred_depth = (1 - ((pred_depth - pred_depth.min()) / (pred_depth.max() - pred_depth.min())))*255.0
if (iteration%1000 == 0):
depth_dir = os.path.join(dataset.model_path, 'depth_rendering')
if (not os.path.exists(depth_dir)):
os.makedirs(depth_dir)
depth_render_path = os.path.join(depth_dir, 'depth_' + viewpoint_cam.image_name + str(iteration) + '.png')
pred_depth_np = pred_depth.detach().cpu().numpy()
cv2.imwrite(depth_render_path, pred_depth_np)
pred_depth = pred_depth.unsqueeze(0)
# scale invariant depth loss
if use_abs_depth_loss:
prior_depth_dir = os.path.join(dataset.source_path, 'abs_depth')
if iteration % 2000 == 0:
depth_weight = depth_weight/2
if (not os.path.exists(prior_depth_dir)):
print("depth data missing. make sure you have depth data at abs_depth folder")
else:
prior_depth_path = os.path.join(prior_depth_dir, viewpoint_cam.image_name + '.npy')
prior_depth = torch.tensor(np.load(prior_depth_path)).cuda()
n = pred_depth.shape[1] * pred_depth.shape[2]
eps = 0.000001
pred_depth = pred_depth + eps
prior_depth = prior_depth + eps
rendered_depth = pred_depth.reshape(-1, 1)
midas_depth = prior_depth.reshape(-1, 1)
log_prior_depth = torch.log(prior_depth)
log_pred_depth = torch.log(pred_depth)
rescale_depth_error = torch.sum(log_prior_depth - log_pred_depth)/n
overall_depth_error = torch.sum(torch.square(log_pred_depth - log_prior_depth + rescale_depth_error))/(2*n)
loss += depth_weight*overall_depth_error
if multiscale_loss:
def compute_multi_scale_loss(pred_depth, gt_depth, scales=[1, 2, 4, 8], weight=0.5):
nloss = 0.0
for scale in scales:
scaled_pred_depth = F.interpolate(pred_depth, scale_factor=1/scale, mode='bilinear', align_corners=False)
scaled_gt_depth = F.interpolate(gt_depth, scale_factor=1/scale, mode='nearest')
current_loss = F.l1_loss(scaled_pred_depth, scaled_gt_depth)
nloss += 0.001 * current_loss
return nloss
multi = compute_multi_scale_loss(log_pred_depth.unsqueeze(0), log_prior_depth.unsqueeze(0).unsqueeze(0))
loss += 0.001 * multi
if huber_loss:
huber_loss = nn.SmoothL1Loss()
hloss = huber_loss(log_pred_depth, log_prior_depth.unsqueeze(0))
loss += 0.01*hloss
if use_norm_depth_loss:
prior_depth_dir = os.path.join(dataset.source_path, 'depth')
if (not os.path.exists(prior_depth_dir)):
print("depth dir missing")
else:
prior_depth_path = os.path.join(prior_depth_dir, viewpoint_cam.image_name + '.png')
prior_depth = torch.tensor(cv2.imread(prior_depth_path, cv2.IMREAD_GRAYSCALE)).cuda()
prior_depth = prior_depth.unsqueeze(0).float()
if iteration % 2000 == 0:
depth_weight = depth_weight/2
Ll1_depth = l1_loss(pred_depth, prior_depth)
loss += depth_weight*Ll1_depth
# loss = Ll1_depth
if face_prior_loss:
prior_v, prior_f, _ = load_obj(os.path.join(dataset.source_path, 'sparse/0/points3D.obj'), device="cuda:0")
prior = Meshes(verts=[prior_v], faces=[prior_f.verts_idx])
loss_prior, p2f = point_mesh_face_distance(prior, Pointclouds(points=[gaussians._xyz]))
loss += face_prior_weight*loss_prior
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
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)
# 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")
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(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)
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=[2_000, 5_000, 10_000])
parser.add_argument("--save_iterations", nargs="+", type=int, default=[2_000, 5_000, 10_000])
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)
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(lp.extract(args), op.extract(args), pp.extract(args), args.test_iterations, args.save_iterations, args.checkpoint_iterations, args.start_checkpoint, args.debug_from)
# All done
print("\nTraining complete.")