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train.py
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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 utils.loss_utils import l1_loss, l2_loss, ssim
from gaussian_renderer import render, network_gui
import sys
from scene import Scene, GaussianModel
from utils.general_utils import safe_state
import numpy as np
import cv2
from tqdm import tqdm
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 lpips
loss_fn_vgg = lpips.LPIPS(net='vgg').to(torch.device('cuda', torch.cuda.current_device()))
from datasets.wildavatar_dataset import WildAvatarDatasetBatch
import time
from utils.loader_utils import InfiniteSampler, collate_fn, data_to_device
from test import test_single
def training(dataset, opt, pipe, testing_iterations, saving_iterations, checkpoint, debug_from):
first_iter = 0
dataset.dataset_name = "WildAvatar"
data_root = os.path.join("data/WildAvatar", dataset.source_path.split("/")[-1])
train_dataset = WildAvatarDatasetBatch(data_root=data_root, poses_start=0, poses_interval=2, poses_num=10, white_back=dataset.white_background)
train_dataloader = InfiniteSampler(dataset=train_dataset, rank=0, num_replicas=1, shuffle=True, seed=0)
training_set_iterator = iter(torch.utils.data.DataLoader(dataset=train_dataset, sampler=train_dataloader, batch_size=1, collate_fn=collate_fn, num_workers=12))
tb_writer = prepare_output_and_logger(dataset)
gaussians = GaussianModel(dataset.sh_degree, dataset.smpl_type, dataset.motion_offset_flag, dataset.actor_gender)
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")
iter_start = torch.cuda.Event(enable_timing = True)
iter_end = torch.cuda.Event(enable_timing = True)
ema_loss_for_log = 0.0
Ll1_loss_for_log = 0.0
mask_loss_for_log = 0.0
ssim_loss_for_log = 0.0
lpips_loss_for_log = 0.0
progress_bar = tqdm(range(first_iter, opt.iterations), desc="Training progress")
elapsed_time = 0
first_iter += 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()
# Start timer
start_time = time.time()
# Pick a random Camera
viewpoint_cam = next(training_set_iterator)
viewpoint_cam = data_to_device(viewpoint_cam)
# Render
if iteration == debug_from:
pipe.debug = True
render_pkg = render(viewpoint_cam, gaussians, pipe, background)
image, alpha, viewspace_point_tensor, visibility_filter, radii = render_pkg["render"], render_pkg["render_alpha"], render_pkg["viewspace_points"], render_pkg["visibility_filter"], render_pkg["radii"]
# Loss
gt_image = viewpoint_cam.original_image.cuda()
bkgd_mask = viewpoint_cam.bkgd_mask.cuda()
bound_mask = viewpoint_cam.bound_mask.cuda()
Ll1 = l1_loss(image.permute(1,2,0)[bound_mask[0]==1], gt_image.permute(1,2,0)[bound_mask[0]==1])
mask_loss = l2_loss(alpha[bound_mask==1], bkgd_mask[bound_mask==1])
# crop the object region
x, y, w, h = cv2.boundingRect(bound_mask[0].cpu().numpy().astype(np.uint8))
img_pred = image[:, y:y + h, x:x + w].unsqueeze(0)
img_gt = gt_image[:, y:y + h, x:x + w].unsqueeze(0)
# ssim loss
ssim_loss = ssim(img_pred, img_gt)
# lipis loss
lpips_loss = loss_fn_vgg(img_pred, img_gt).reshape(-1)
loss = Ll1 + 0.1 * mask_loss + 0.01 * (1.0 - ssim_loss) + 0.01 * lpips_loss
loss.backward()
# end time
end_time = time.time()
# Calculate elapsed time
elapsed_time += (end_time - start_time)
iter_end.record()
with torch.no_grad():
# Progress bar
ema_loss_for_log = 0.4 * loss.item() + 0.6 * ema_loss_for_log
Ll1_loss_for_log = 0.4 * Ll1.item() + 0.6 * Ll1_loss_for_log
mask_loss_for_log = 0.4 * mask_loss.item() + 0.6 * mask_loss_for_log
ssim_loss_for_log = 0.4 * ssim_loss.item() + 0.6 * ssim_loss_for_log
lpips_loss_for_log = 0.4 * lpips_loss.item() + 0.6 * lpips_loss_for_log
if iteration % 10 == 0:
progress_bar.set_postfix({"#pts": gaussians._xyz.shape[0], "Ll1 Loss": f"{Ll1_loss_for_log:.{3}f}", "mask Loss": f"{mask_loss_for_log:.{2}f}",
"ssim": f"{ssim_loss_for_log:.{2}f}", "lpips": f"{lpips_loss_for_log:.{2}f}"})
progress_bar.update(10)
if iteration == opt.iterations:
progress_bar.close()
# Log and save
if iteration in testing_iterations:
with torch.no_grad():
test_single(tb_writer, scene, render, (args, background), visualing=True, args=args)
if (iteration in saving_iterations):
print("\n[ITER {}] Saving Gaussians".format(iteration))
scene.save(iteration)
print("\n[ITER {}] Saving Checkpoint".format(iteration))
torch.save((gaussians.capture(), iteration), scene.model_path + "/chkpnt" + str(iteration) + ".pth")
# Start timer
start_time = time.time()
# 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, kl_threshold=0.4, t_vertices=viewpoint_cam.big_pose_world_vertex, iter=iteration)
# gaussians.densify_and_prune(opt.densify_grad_threshold, 0.01, scene.cameras_extent, 1)
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)
# end time
end_time = time.time()
# Calculate elapsed time
elapsed_time += (end_time - start_time)
def prepare_output_and_logger(args):
if not args.model_path:
args.model_path = os.path.join("./output/", args.exp_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
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])
parser.add_argument("--save_iterations", nargs="+", type=int, default=[2_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)
if args.exp_name == "":
args.exp_name = args.source_path.replace("data/", "")
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.start_checkpoint, args.debug_from)
# All done
print("\nTraining complete.")