mirror of
https://github.com/graphdeco-inria/gaussian-splatting
synced 2024-11-22 08:18:17 +00:00
123 lines
3.6 KiB
Python
123 lines
3.6 KiB
Python
import torch
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import sys
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from datetime import datetime
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import numpy as np
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import random
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def inverse_sigmoid(x):
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return torch.log(x/(1-x))
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def PILtoTorch(pil_image, resolution):
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resized_image_PIL = pil_image.resize(resolution)
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resized_image = torch.from_numpy(np.array(resized_image_PIL)) / 255.0
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if len(resized_image.shape) == 3:
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return resized_image.permute(2, 0, 1)
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else:
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return resized_image.unsqueeze(dim=-1).permute(2, 0, 1)
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def get_expon_lr_func(
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lr_init, lr_final, lr_delay_steps=0, lr_delay_mult=1.0, max_steps=1000000
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):
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"""
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Copied from Plenoxels
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Continuous learning rate decay function. Adapted from JaxNeRF
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The returned rate is lr_init when step=0 and lr_final when step=max_steps, and
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is log-linearly interpolated elsewhere (equivalent to exponential decay).
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If lr_delay_steps>0 then the learning rate will be scaled by some smooth
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function of lr_delay_mult, such that the initial learning rate is
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lr_init*lr_delay_mult at the beginning of optimization but will be eased back
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to the normal learning rate when steps>lr_delay_steps.
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:param conf: config subtree 'lr' or similar
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:param max_steps: int, the number of steps during optimization.
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:return HoF which takes step as input
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"""
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def helper(step):
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if step < 0 or (lr_init == 0.0 and lr_final == 0.0):
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# Disable this parameter
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return 0.0
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if lr_delay_steps > 0:
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# A kind of reverse cosine decay.
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delay_rate = lr_delay_mult + (1 - lr_delay_mult) * np.sin(
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0.5 * np.pi * np.clip(step / lr_delay_steps, 0, 1)
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)
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else:
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delay_rate = 1.0
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t = np.clip(step / max_steps, 0, 1)
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log_lerp = np.exp(np.log(lr_init) * (1 - t) + np.log(lr_final) * t)
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return delay_rate * log_lerp
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return helper
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def strip_lowerdiag(L):
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uncertainty = torch.zeros((L.shape[0], 6), dtype=torch.float, device="cuda")
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uncertainty[:, 0] = L[:, 0, 0]
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uncertainty[:, 1] = L[:, 0, 1]
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uncertainty[:, 2] = L[:, 0, 2]
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uncertainty[:, 3] = L[:, 1, 1]
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uncertainty[:, 4] = L[:, 1, 2]
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uncertainty[:, 5] = L[:, 2, 2]
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return uncertainty
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def strip_symmetric(sym):
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return strip_lowerdiag(sym)
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def build_rotation(r):
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norm = torch.sqrt(r[:,0]*r[:,0] + r[:,1]*r[:,1] + r[:,2]*r[:,2] + r[:,3]*r[:,3])
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q = r / norm[:, None]
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R = torch.zeros((q.size(0), 3, 3), device='cuda')
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r = q[:, 0]
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x = q[:, 1]
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y = q[:, 2]
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z = q[:, 3]
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R[:, 0, 0] = 1 - 2 * (y*y + z*z)
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R[:, 0, 1] = 2 * (x*y - r*z)
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R[:, 0, 2] = 2 * (x*z + r*y)
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R[:, 1, 0] = 2 * (x*y + r*z)
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R[:, 1, 1] = 1 - 2 * (x*x + z*z)
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R[:, 1, 2] = 2 * (y*z - r*x)
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R[:, 2, 0] = 2 * (x*z - r*y)
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R[:, 2, 1] = 2 * (y*z + r*x)
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R[:, 2, 2] = 1 - 2 * (x*x + y*y)
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return R
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def build_scaling_rotation(s, r):
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L = torch.zeros((s.shape[0], 3, 3), dtype=torch.float, device="cuda")
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R = build_rotation(r)
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L[:,0,0] = s[:,0]
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L[:,1,1] = s[:,1]
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L[:,2,2] = s[:,2]
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L = R @ L
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return L
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def safe_state(silent):
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old_f = sys.stdout
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class F:
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def __init__(self, silent):
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self.silent = silent
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def write(self, x):
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if not self.silent:
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if x.endswith("\n"):
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old_f.write(x.replace("\n", " [{}]\n".format(str(datetime.now().strftime("%d/%m %H:%M:%S")))))
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else:
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old_f.write(x)
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def flush(self):
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old_f.flush()
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sys.stdout = F(silent)
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random.seed(0)
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np.random.seed(0)
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torch.manual_seed(0)
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torch.cuda.set_device(torch.device("cuda:0"))
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