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prune_finetune.py
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prune_finetune.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 lpipsPyTorch import lpips
from gaussian_renderer import render, network_gui, count_render
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
import numpy as np
try:
from torch.utils.tensorboard import SummaryWriter
TENSORBOARD_FOUND = True
except ImportError:
TENSORBOARD_FOUND = False
from icecream import ic
import random
import copy
import gc
from os import makedirs
from prune import prune_list, calculate_v_imp_score
import torchvision
from torch.optim.lr_scheduler import ExponentialLR
import csv
from utils.logger_utils import training_report, prepare_output_and_logger
to_tensor = (
lambda x: x.to("cuda")
if isinstance(x, torch.Tensor)
else torch.Tensor(x).to("cuda")
)
img2mse = lambda x, y: torch.mean((x - y) ** 2)
mse2psnr = lambda x: -10.0 * torch.log(x) / torch.log(to_tensor([10.0]))
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)
if checkpoint:
gaussians.training_setup(opt)
(model_params, first_iter) = torch.load(checkpoint)
gaussians.restore(model_params, opt)
elif args.start_pointcloud:
gaussians.load_ply(args.start_pointcloud)
ic(gaussians.get_xyz.shape)
# ic(gaussians.optimizer.param_groups["xyz"].shape)
gaussians.training_setup(opt)
gaussians.max_radii2D = torch.zeros((gaussians.get_xyz.shape[0]), device="cuda")
else:
raise ValueError("A checkpoint file or a pointcloud is required to proceed.")
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
gaussians.scheduler = ExponentialLR(gaussians.optimizer, gamma=0.95)
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()
if iteration % 400 == 0:
gaussians.scheduler.step()
# 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)
)
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 % 1000 == 0:
progress_bar.set_postfix({"Loss": f"{ema_loss_for_log:.{7}f}"})
progress_bar.update(1000)
if iteration == opt.iterations:
progress_bar.close()
# Log and save
if iteration in saving_iterations:
print("\n[ITER {}] Saving Gaussians".format(iteration))
scene.save(iteration)
if iteration in checkpoint_iterations:
print("\n[ITER {}] Saving Checkpoint".format(iteration))
if not os.path.exists(scene.model_path):
os.makedirs(scene.model_path)
torch.save(
(gaussians.capture(), iteration),
scene.model_path + "/chkpnt" + str(iteration) + ".pth",
)
if iteration == checkpoint_iterations[-1]:
gaussian_list, imp_list = prune_list(gaussians, scene, pipe, background)
v_list = calculate_v_imp_score(gaussians, imp_list, args.v_pow)
np.savez(os.path.join(scene.model_path,"imp_score"), v_list.cpu().detach().numpy())
training_report(
tb_writer,
iteration,
Ll1,
loss,
l1_loss,
iter_start.elapsed_time(iter_end),
testing_iterations,
scene,
render,
(pipe, background),
)
if iteration in args.prune_iterations:
ic("Before prune iteration, number of gaussians: " + str(len(gaussians.get_xyz)))
i = args.prune_iterations.index(iteration)
gaussian_list, imp_list = prune_list(gaussians, scene, pipe, background)
if args.prune_type == "important_score":
gaussians.prune_gaussians(
(args.prune_decay**i) * args.prune_percent, imp_list
)
elif args.prune_type == "v_important_score":
# normalize scale
v_list = calculate_v_imp_score(gaussians, imp_list, args.v_pow)
gaussians.prune_gaussians(
(args.prune_decay**i) * args.prune_percent, v_list
)
elif args.prune_type == "max_v_important_score":
v_list = imp_list * torch.max(gaussians.get_scaling, dim=1)[0]
gaussians.prune_gaussians(
(args.prune_decay**i) * args.prune_percent, v_list
)
elif args.prune_type == "count":
gaussians.prune_gaussians(
(args.prune_decay**i) * args.prune_percent, gaussian_list
)
elif args.prune_type == "opacity":
gaussians.prune_gaussians(
(args.prune_decay**i) * args.prune_percent,
gaussians.get_opacity.detach(),
)
# TODO(release different pruning method)
# elif args.prune_type == "HDBSCAN":
# masks = HDBSCAN_prune(gaussians, imp_list, (args.prune_decay**i)*args.prune_percent)
# gaussians.prune_points(masks)
# # elif args.prune_type == "v_important_score":
# # imp_list *
# elif args.prune_type == "two_step":
# if i == 0:
# volume = torch.prod(gaussians.get_scaling, dim = 1)
# index = int(len(volume) * 0.9)
# sorted_volume, sorted_indices = torch.sort(volume, descending=True, dim=0)
# kth_percent_largest = sorted_volume[index]
# v_list = torch.pow(volume/kth_percent_largest, args.v_pow)
# v_list = v_list * imp_list
# gaussians.prune_gaussians((args.prune_decay**i)*args.prune_percent, v_list)
# else:
# k = 5^(1*i) * 100
# masks = uniform_prune(gaussians, k, imp_list, 0.3, "k_mean")
# gaussians.prune_points(masks)
# else:
# k = len(gaussians.get_xyz)//500 * i
# masks = uniform_prune(gaussians, k, imp_list, (args.prune_decay**i)*args.prune_percent, args.prune_type)
# gaussians.prune_points(masks)
# gaussians.prune_gaussians(args.prune_percent, imp_list)
# gaussians.optimizer.zero_grad(set_to_none = True) #hachy way to maintain grad
# if (iteration in args.opacity_prune_iterations):
# gaussians.prune_opacity(0.05)
else:
raise Exception("Unsupportive pruning method")
ic("After prune iteration, number of gaussians: " + str(len(gaussians.get_xyz)))
# if iteration in args.densify_iteration:
# 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.densify(opt.densify_grad_threshold, scene.cameras_extent)
ic("after")
ic(gaussians.get_xyz.shape)
ic(len(gaussians.optimizer.param_groups[0]['params'][0]))
if iteration < opt.iterations:
gaussians.optimizer.step()
gaussians.optimizer.zero_grad(set_to_none=True)
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=[30_001, 30_002, 35_000]
)
parser.add_argument(
"--save_iterations", nargs="+", type=int, default=[35_000]
)
parser.add_argument("--quiet", action="store_true")
parser.add_argument(
"--checkpoint_iterations", nargs="+", type=int, default=[35_000]
)
parser.add_argument("--prune_iterations", nargs="+", type=int, default=[30_001])
parser.add_argument("--start_checkpoint", type=str, default=None)
parser.add_argument("--start_pointcloud", type=str, default=None)
parser.add_argument("--prune_percent", type=float, default=0.1)
parser.add_argument("--prune_decay", type=float, default=1)
parser.add_argument(
"--prune_type", type=str, default="important_score"
) # k_mean, farther_point_sample, important_score
parser.add_argument("--v_pow", type=float, default=0.1)
parser.add_argument("--densify_iteration", nargs="+", type=int, default=[-1])
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,
args,
)
# All done
print("\nTraining complete.")