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78d2329 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 | import torch
import torch.nn.functional as F
from jaxtyping import Float
from torch import Tensor, nn
from optgs.model.types import Gaussians
from optgs.scene_trainer.common.gaussians import build_covariance
class GaussiansModule(nn.Module):
def __init__(
self,
means: Float[Tensor, "gaussian 3"],
harmonics: Float[Tensor, "gaussian 3 d_sh"],
opacities: Float[Tensor, "gaussian"],
scales: Float[Tensor, "gaussian 3"],
rotations_unnorm: Float[Tensor, "gaussian 4"]
):
# all gaussians parameters are post-activation
super().__init__()
def _register_param(name, value):
if value is None:
setattr(self, name, None)
else:
param = nn.Parameter(value)
setattr(self, name, param)
self.scaling_activation = torch.exp
self.scaling_inverse_activation = torch.log
self.covariance_activation = build_covariance
self.opacity_activation = torch.sigmoid
self.inverse_opacity_activation = torch.logit
self.rotation_activation = F.normalize
# Register parameters
means = means.detach().clone()
means.requires_grad_(True)
harmonics = harmonics.detach().clone() # [G, sh_d, C]
d_sh = harmonics.shape[-1]
sh0 = harmonics[..., 0:1] # [G, 3, 1]
if d_sh == 1:
# sh_degree = 0
shN = None
else:
# sh_degree > 0
shN = harmonics[..., 1:] # [G, 3, d_sh-1]
sh0.requires_grad_(True)
if shN is not None:
shN.requires_grad_(True)
# Invert the opacity to optimize in the unconstrained space
opacities_raw = self.inverse_opacity_activation(opacities.detach().clone(), eps=1e-6)
opacities_raw.requires_grad_(True)
# Invert the scales
scales_raw = self.scaling_inverse_activation(scales.detach().clone())
scales_raw.requires_grad_(True)
# Rotations in xyzw (scalar last)
# remember to convert to wxyz (scalar first) before rendering and saving to ply
rotations_unnorm = rotations_unnorm.detach().clone()
rotations_unnorm.requires_grad_(True)
_register_param("opacities_raw", opacities_raw)
_register_param("scales_raw", scales_raw)
_register_param("means", means)
_register_param("rotations_unnorm", rotations_unnorm)
_register_param("sh0", sh0)
if shN is not None:
_register_param("shN", shN)
for name, param in self.named_parameters():
print(f"Registered parameter: {name}, shape: {param.shape}, dtype: {param.dtype}, min: {param.min()}, max: {param.max()}, requires_grad: {param.requires_grad}")
@property
def scales(self):
scales = self.scaling_activation(self.scales_raw)
return scales
@property
def opacities(self):
opacities = self.opacity_activation(self.opacities_raw)
return opacities
@property
def rotations(self):
rotations = self.rotation_activation(self.rotations_unnorm, dim=-1)
return rotations
@property
def harmonics(self):
# returns [G, 3, d_sh]
shN = getattr(self, "shN", None)
if shN is not None:
harmonics_ = torch.cat([self.sh0, shN], dim=-1)
else:
harmonics_ = self.sh0
return harmonics_
@property
def covariances(self):
rotation_xyzw = self.rotations
covariances = self.covariance_activation(self.scales, rotation_xyzw) # [G, 3, 3]
return covariances
def reset_opacity(self, optimizer):
opacities_old = self.opacity_activation(self.opacities_raw)
opacities_raw_new = self.inverse_opacity_activation(torch.min(opacities_old, torch.ones_like(opacities_old)*0.01), eps=1e-6)
# optimizable_tensors = self.replace_tensor_to_optimizer(optimizer, opacities_raw_new, "opacity")
# self.opacities_raw = optimizable_tensors["opacity"]
def gaussians2module(gaussians: Gaussians, device: torch.device) -> GaussiansModule:
bs = gaussians.means.shape[0]
assert bs == 1, "Batch size > 1 not supported for post-processing"
# bs = 1
# convert Gaussians to GaussiansModule
gaussian_module = GaussiansModule(
means=gaussians.means[0].to(device),
harmonics=gaussians.harmonics[0].to(device),
opacities=gaussians.opacities[0].to(device),
scales=gaussians.scales[0].to(device),
rotations_unnorm=gaussians.rotations_unnorm[0].to(device),
)
return gaussian_module
def module2gaussians(gaussian_module: GaussiansModule) -> Gaussians:
gaussians = Gaussians(
means=gaussian_module.means.unsqueeze(0), # [1, G, 3]
covariances=gaussian_module.covariances.unsqueeze(0), # [1, G, 3, 3]
harmonics=gaussian_module.harmonics.unsqueeze(0), # [1, G, sh_d, C]
opacities=gaussian_module.opacities.unsqueeze(0), # [1, G]
scales=gaussian_module.scales.unsqueeze(0), # [1, G, 3]
rotations=gaussian_module.rotations.unsqueeze(0), # [1, G, 4]
rotations_unnorm=gaussian_module.rotations.unsqueeze(0),
)
return gaussians
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