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implicit.py
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implicit.py
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import torch
import torch.nn.functional as F
from torch import autograd
from ray_utils import RayBundle
# Sphere SDF class
class SphereSDF(torch.nn.Module):
def __init__(
self,
cfg
):
super().__init__()
self.radius = torch.nn.Parameter(
torch.tensor(cfg.radius.val).float(), requires_grad=cfg.radius.opt
)
self.center = torch.nn.Parameter(
torch.tensor(cfg.center.val).float().unsqueeze(0), requires_grad=cfg.center.opt
)
def forward(self, points):
points = points.view(-1, 3)
return torch.linalg.norm(
points - self.center,
dim=-1,
keepdim=True
) - self.radius
# Box SDF class
class BoxSDF(torch.nn.Module):
def __init__(
self,
cfg
):
super().__init__()
self.center = torch.nn.Parameter(
torch.tensor(cfg.center.val).float().unsqueeze(0), requires_grad=cfg.center.opt
)
self.side_lengths = torch.nn.Parameter(
torch.tensor(cfg.side_lengths.val).float().unsqueeze(0), requires_grad=cfg.side_lengths.opt
)
def forward(self, points):
points = points.view(-1, 3)
diff = torch.abs(points - self.center) - self.side_lengths / 2.0
signed_distance = torch.linalg.norm(
torch.maximum(diff, torch.zeros_like(diff)),
dim=-1
) + torch.minimum(torch.max(diff, dim=-1)[0], torch.zeros_like(diff[..., 0]))
return signed_distance.unsqueeze(-1)
# Torus SDF class
class TorusSDF(torch.nn.Module):
def __init__(
self,
cfg
):
super().__init__()
self.center = torch.nn.Parameter(
torch.tensor(cfg.center.val).float().unsqueeze(0), requires_grad=cfg.center.opt
)
self.radii = torch.nn.Parameter(
torch.tensor(cfg.radii.val).float().unsqueeze(0), requires_grad=cfg.radii.opt
)
def forward(self, points):
points = points.view(-1, 3)
diff = points - self.center
q = torch.stack(
[
torch.linalg.norm(diff[..., :2], dim=-1) - self.radii[..., 0],
diff[..., -1],
],
dim=-1
)
return (torch.linalg.norm(q, dim=-1) - self.radii[..., 1]).unsqueeze(-1)
sdf_dict = {
'sphere': SphereSDF,
'box': BoxSDF,
'torus': TorusSDF,
}
# Converts SDF into density/feature volume
class SDFVolume(torch.nn.Module):
def __init__(
self,
cfg
):
super().__init__()
self.sdf = sdf_dict[cfg.sdf.type](
cfg.sdf
)
self.rainbow = cfg.feature.rainbow if 'rainbow' in cfg.feature else False
self.feature = torch.nn.Parameter(
torch.ones_like(torch.tensor(cfg.feature.val).float().unsqueeze(0)), requires_grad=cfg.feature.opt
)
self.alpha = torch.nn.Parameter(
torch.tensor(cfg.alpha.val).float(), requires_grad=cfg.alpha.opt
)
self.beta = torch.nn.Parameter(
torch.tensor(cfg.beta.val).float(), requires_grad=cfg.beta.opt
)
def _sdf_to_density(self, signed_distance):
# Convert signed distance to density with alpha, beta parameters
return torch.where(
signed_distance > 0,
0.5 * torch.exp(-signed_distance / self.beta),
1 - 0.5 * torch.exp(signed_distance / self.beta),
) * self.alpha
def forward(self, ray_bundle):
sample_points = ray_bundle.sample_points.view(-1, 3)
depth_values = ray_bundle.sample_lengths[..., 0]
deltas = torch.cat(
(
depth_values[..., 1:] - depth_values[..., :-1],
1e10 * torch.ones_like(depth_values[..., :1]),
),
dim=-1,
).view(-1, 1)
# Transform SDF to density
signed_distance = self.sdf(ray_bundle.sample_points)
density = self._sdf_to_density(signed_distance)
# Outputs
if self.rainbow:
base_color = torch.clamp(
torch.abs(sample_points - self.sdf.center),
0.02,
0.98
)
else:
base_color = 1.0
out = {
'density': -torch.log(1.0 - density) / deltas,
'feature': base_color * self.feature * density.new_ones(sample_points.shape[0], 1)
}
return out
# Converts SDF into density/feature volume
class SDFSurface(torch.nn.Module):
def __init__(
self,
cfg
):
super().__init__()
self.sdf = sdf_dict[cfg.sdf.type](
cfg.sdf
)
self.rainbow = cfg.feature.rainbow if 'rainbow' in cfg.feature else False
self.feature = torch.nn.Parameter(
torch.ones_like(torch.tensor(cfg.feature.val).float().unsqueeze(0)), requires_grad=cfg.feature.opt
)
def get_distance(self, points):
points = points.view(-1, 3)
return self.sdf(points)
def get_color(self, points):
points = points.view(-1, 3)
# Outputs
if self.rainbow:
base_color = torch.clamp(
torch.abs(points - self.sdf.center),
0.02,
0.98
)
else:
base_color = 1.0
return base_color * self.feature * points.new_ones(points.shape[0], 1)
def forward(self, points):
return self.get_distance(points)
class HarmonicEmbedding(torch.nn.Module):
def __init__(
self,
in_channels: int = 3,
n_harmonic_functions: int = 6,
omega0: float = 1.0,
logspace: bool = True,
include_input: bool = True,
) -> None:
super().__init__()
if logspace:
frequencies = 2.0 ** torch.arange(
n_harmonic_functions,
dtype=torch.float32,
)
else:
frequencies = torch.linspace(
1.0,
2.0 ** (n_harmonic_functions - 1),
n_harmonic_functions,
dtype=torch.float32,
)
self.register_buffer("_frequencies", omega0 * frequencies, persistent=False)
self.include_input = include_input
self.output_dim = n_harmonic_functions * 2 * in_channels
if self.include_input:
self.output_dim += in_channels
def forward(self, x: torch.Tensor):
embed = (x[..., None] * self._frequencies).view(*x.shape[:-1], -1)
if self.include_input:
return torch.cat((embed.sin(), embed.cos(), x), dim=-1)
else:
return torch.cat((embed.sin(), embed.cos()), dim=-1)
class LinearWithRepeat(torch.nn.Linear):
def forward(self, input):
n1 = input[0].shape[-1]
output1 = F.linear(input[0], self.weight[:, :n1], self.bias)
output2 = F.linear(input[1], self.weight[:, n1:], None)
return output1 + output2.unsqueeze(-2)
class MLPWithInputSkips(torch.nn.Module):
def __init__(
self,
n_layers: int,
input_dim: int,
output_dim: int,
skip_dim: int,
hidden_dim: int,
input_skips,
):
super().__init__()
layers = []
for layeri in range(n_layers):
if layeri == 0:
dimin = input_dim
dimout = hidden_dim
elif layeri in input_skips:
dimin = hidden_dim + skip_dim
dimout = hidden_dim
else:
dimin = hidden_dim
dimout = hidden_dim
linear = torch.nn.Linear(dimin, dimout)
layers.append(torch.nn.Sequential(linear, torch.nn.ReLU(True)))
self.mlp = torch.nn.ModuleList(layers)
self._input_skips = set(input_skips)
def forward(self, x: torch.Tensor, z: torch.Tensor) -> torch.Tensor:
y = x
for li, layer in enumerate(self.mlp):
if li in self._input_skips:
y = torch.cat((y, z), dim=-1)
y = layer(y)
return y
# TODO (Q3.1): Implement NeRF MLP
class NeuralRadianceField(torch.nn.Module):
def __init__(
self,
cfg,
):
super().__init__()
self.harmonic_embedding_xyz = HarmonicEmbedding(3, cfg.n_harmonic_functions_xyz)
self.harmonic_embedding_dir = HarmonicEmbedding(3, cfg.n_harmonic_functions_dir)
embedding_dim_xyz = self.harmonic_embedding_xyz.output_dim
embedding_dim_dir = self.harmonic_embedding_dir.output_dim
pass
class NeuralSurface(torch.nn.Module):
def __init__(
self,
cfg,
):
super().__init__()
# TODO (Q6): Implement Neural Surface MLP to output per-point SDF
# TODO (Q7): Implement Neural Surface MLP to output per-point color
def get_distance(
self,
points
):
'''
TODO: Q6
Output:
distance: N X 1 Tensor, where N is number of input points
'''
points = points.view(-1, 3)
pass
def get_color(
self,
points
):
'''
TODO: Q7
Output:
distance: N X 3 Tensor, where N is number of input points
'''
points = points.view(-1, 3)
pass
def get_distance_color(
self,
points
):
'''
TODO: Q7
Output:
distance, points: N X 1, N X 3 Tensors, where N is number of input points
You may just implement this by independent calls to get_distance, get_color
but, depending on your MLP implementation, it maybe more efficient to share some computation
'''
def forward(self, points):
return self.get_distance(points)
def get_distance_and_gradient(
self,
points
):
has_grad = torch.is_grad_enabled()
points = points.view(-1, 3)
# Calculate gradient with respect to points
with torch.enable_grad():
points = points.requires_grad_(True)
distance = self.get_distance(points)
gradient = autograd.grad(
distance,
points,
torch.ones_like(distance, device=points.device),
create_graph=has_grad,
retain_graph=has_grad,
only_inputs=True
)[0]
return distance, gradient
implicit_dict = {
'sdf_volume': SDFVolume,
'nerf': NeuralRadianceField,
'sdf_surface': SDFSurface,
'neural_surface': NeuralSurface,
}