mirror of
https://github.com/graphdeco-inria/gaussian-splatting
synced 2024-11-25 05:16:33 +00:00
134 lines
3.9 KiB
Python
134 lines
3.9 KiB
Python
#
|
|
# 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 george.drettakis@inria.fr
|
|
#
|
|
|
|
import torch
|
|
import sys
|
|
from datetime import datetime
|
|
import numpy as np
|
|
import random
|
|
|
|
def inverse_sigmoid(x):
|
|
return torch.log(x/(1-x))
|
|
|
|
def PILtoTorch(pil_image, resolution):
|
|
resized_image_PIL = pil_image.resize(resolution)
|
|
resized_image = torch.from_numpy(np.array(resized_image_PIL)) / 255.0
|
|
if len(resized_image.shape) == 3:
|
|
return resized_image.permute(2, 0, 1)
|
|
else:
|
|
return resized_image.unsqueeze(dim=-1).permute(2, 0, 1)
|
|
|
|
def get_expon_lr_func(
|
|
lr_init, lr_final, lr_delay_steps=0, lr_delay_mult=1.0, max_steps=1000000
|
|
):
|
|
"""
|
|
Copied from Plenoxels
|
|
|
|
Continuous learning rate decay function. Adapted from JaxNeRF
|
|
The returned rate is lr_init when step=0 and lr_final when step=max_steps, and
|
|
is log-linearly interpolated elsewhere (equivalent to exponential decay).
|
|
If lr_delay_steps>0 then the learning rate will be scaled by some smooth
|
|
function of lr_delay_mult, such that the initial learning rate is
|
|
lr_init*lr_delay_mult at the beginning of optimization but will be eased back
|
|
to the normal learning rate when steps>lr_delay_steps.
|
|
:param conf: config subtree 'lr' or similar
|
|
:param max_steps: int, the number of steps during optimization.
|
|
:return HoF which takes step as input
|
|
"""
|
|
|
|
def helper(step):
|
|
if step < 0 or (lr_init == 0.0 and lr_final == 0.0):
|
|
# Disable this parameter
|
|
return 0.0
|
|
if lr_delay_steps > 0:
|
|
# A kind of reverse cosine decay.
|
|
delay_rate = lr_delay_mult + (1 - lr_delay_mult) * np.sin(
|
|
0.5 * np.pi * np.clip(step / lr_delay_steps, 0, 1)
|
|
)
|
|
else:
|
|
delay_rate = 1.0
|
|
t = np.clip(step / max_steps, 0, 1)
|
|
log_lerp = np.exp(np.log(lr_init) * (1 - t) + np.log(lr_final) * t)
|
|
return delay_rate * log_lerp
|
|
|
|
return helper
|
|
|
|
def strip_lowerdiag(L):
|
|
uncertainty = torch.zeros((L.shape[0], 6), dtype=torch.float, device="cuda")
|
|
|
|
uncertainty[:, 0] = L[:, 0, 0]
|
|
uncertainty[:, 1] = L[:, 0, 1]
|
|
uncertainty[:, 2] = L[:, 0, 2]
|
|
uncertainty[:, 3] = L[:, 1, 1]
|
|
uncertainty[:, 4] = L[:, 1, 2]
|
|
uncertainty[:, 5] = L[:, 2, 2]
|
|
return uncertainty
|
|
|
|
def strip_symmetric(sym):
|
|
return strip_lowerdiag(sym)
|
|
|
|
def build_rotation(r):
|
|
norm = torch.sqrt(r[:,0]*r[:,0] + r[:,1]*r[:,1] + r[:,2]*r[:,2] + r[:,3]*r[:,3])
|
|
|
|
q = r / norm[:, None]
|
|
|
|
R = torch.zeros((q.size(0), 3, 3), device='cuda')
|
|
|
|
r = q[:, 0]
|
|
x = q[:, 1]
|
|
y = q[:, 2]
|
|
z = q[:, 3]
|
|
|
|
R[:, 0, 0] = 1 - 2 * (y*y + z*z)
|
|
R[:, 0, 1] = 2 * (x*y - r*z)
|
|
R[:, 0, 2] = 2 * (x*z + r*y)
|
|
R[:, 1, 0] = 2 * (x*y + r*z)
|
|
R[:, 1, 1] = 1 - 2 * (x*x + z*z)
|
|
R[:, 1, 2] = 2 * (y*z - r*x)
|
|
R[:, 2, 0] = 2 * (x*z - r*y)
|
|
R[:, 2, 1] = 2 * (y*z + r*x)
|
|
R[:, 2, 2] = 1 - 2 * (x*x + y*y)
|
|
return R
|
|
|
|
def build_scaling_rotation(s, r):
|
|
L = torch.zeros((s.shape[0], 3, 3), dtype=torch.float, device="cuda")
|
|
R = build_rotation(r)
|
|
|
|
L[:,0,0] = s[:,0]
|
|
L[:,1,1] = s[:,1]
|
|
L[:,2,2] = s[:,2]
|
|
|
|
L = R @ L
|
|
return L
|
|
|
|
def safe_state(silent):
|
|
old_f = sys.stdout
|
|
class F:
|
|
def __init__(self, silent):
|
|
self.silent = silent
|
|
|
|
def write(self, x):
|
|
if not self.silent:
|
|
if x.endswith("\n"):
|
|
old_f.write(x.replace("\n", " [{}]\n".format(str(datetime.now().strftime("%d/%m %H:%M:%S")))))
|
|
else:
|
|
old_f.write(x)
|
|
|
|
def flush(self):
|
|
old_f.flush()
|
|
|
|
sys.stdout = F(silent)
|
|
|
|
random.seed(0)
|
|
np.random.seed(0)
|
|
torch.manual_seed(0)
|
|
torch.cuda.set_device(torch.device("cuda:0"))
|