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Running on Zero
File size: 3,669 Bytes
7405e85 | 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 | import torch
from torch import nn
class GaussianLatentDecoder(nn.Module):
def __init__(
self,
latent_channels: int,
hidden_channels: int,
num_gaussian_parameters: int,
init_sh_input_img: bool = True,
) -> None:
super().__init__()
in_channels = latent_channels + 1 # out_hat + depth
self.decoder = nn.Sequential(
nn.Conv2d(in_channels, hidden_channels, 3, 1, 1, padding_mode="replicate"),
nn.GELU(),
nn.Conv2d(
hidden_channels,
num_gaussian_parameters,
3,
1,
1,
padding_mode="replicate",
),
nn.GELU(),
nn.Conv2d(
num_gaussian_parameters,
num_gaussian_parameters,
3,
1,
1,
padding_mode="replicate",
),
)
self._init_output_layer(init_sh_input_img)
def _init_output_layer(self, init_sh_input_img: bool) -> None:
# Keep the Gaussian head initially close to identity-like residuals.
# Channel order: opacity, offset_xy, scales, quaternion, SH residuals.
output_layer = self.decoder[-1]
if init_sh_input_img:
nn.init.zeros_(output_layer.weight[10:])
nn.init.zeros_(output_layer.bias[10:])
nn.init.zeros_(output_layer.weight[3:6])
nn.init.zeros_(output_layer.bias[3:6])
def forward(
self,
latent: torch.Tensor,
depth: torch.Tensor,
) -> torch.Tensor:
x = torch.cat((latent, depth), dim=1)
return self.decoder(x)
class GaussianLatentDepthDecoder(nn.Module):
def __init__(
self,
latent_channels: int,
hidden_channels: int,
) -> None:
super().__init__()
self.inv_depth_logits = nn.Sequential(
nn.Conv2d(latent_channels, hidden_channels, 3, 1, 1, padding_mode="replicate"),
nn.GELU(),
nn.Conv2d(hidden_channels, hidden_channels, 3, 1, 1, padding_mode="replicate"),
nn.GELU(),
nn.Conv2d(hidden_channels, 1, 3, 1, 1, padding_mode="replicate"),
)
# Predict the midpoint of the valid inverse-depth interval at initialization.
nn.init.zeros_(self.inv_depth_logits[-1].weight)
nn.init.zeros_(self.inv_depth_logits[-1].bias)
def forward(
self,
latent: torch.Tensor,
near: torch.Tensor,
far: torch.Tensor,
return_inverse: bool = False,
return_normalized_inverse: bool = False,
) -> torch.Tensor | tuple[torch.Tensor, ...]:
with torch.autocast(device_type=latent.device.type, enabled=False):
inv_depth_logits = self.inv_depth_logits(latent.float())
near = near.float().clamp_min(torch.finfo(torch.float32).eps)
far = far.float().clamp_min(torch.finfo(torch.float32).eps)
inv_min = far.reciprocal()
inv_max = near.reciprocal()
inv_range = (inv_max - inv_min).clamp_min(torch.finfo(torch.float32).eps)
normalized_inv_depth = torch.sigmoid(inv_depth_logits.float())
inv_depth = inv_min + normalized_inv_depth * inv_range
depth = inv_depth.clamp_min(torch.finfo(torch.float32).eps).reciprocal()
outputs: list[torch.Tensor] = [depth]
if return_inverse:
outputs.append(inv_depth)
if return_normalized_inverse:
outputs.append(normalized_inv_depth)
if len(outputs) == 1:
return outputs[0]
return tuple(outputs)
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