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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 sys | |
| import random | |
| from datetime import datetime | |
| import numpy as np | |
| import torch | |
| 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")) | |