mirror of
https://github.com/nerfstudio-project/nerfstudio.git
synced 2026-11-11 02:24:38 +08:00
121 lines
4.6 KiB
Python
121 lines
4.6 KiB
Python
# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
"""
|
|
Proposal network field.
|
|
"""
|
|
|
|
from typing import Literal, Optional, Tuple
|
|
|
|
import torch
|
|
from torch import Tensor, nn
|
|
|
|
from nerfstudio.cameras.rays import RaySamples
|
|
from nerfstudio.data.scene_box import SceneBox
|
|
from nerfstudio.field_components.activations import trunc_exp
|
|
from nerfstudio.field_components.encodings import HashEncoding
|
|
from nerfstudio.field_components.mlp import MLP
|
|
from nerfstudio.field_components.spatial_distortions import SpatialDistortion
|
|
from nerfstudio.fields.base_field import Field
|
|
|
|
|
|
class HashMLPDensityField(Field):
|
|
"""A lightweight density field module.
|
|
|
|
Args:
|
|
aabb: parameters of scene aabb bounds
|
|
num_layers: number of hidden layers
|
|
hidden_dim: dimension of hidden layers
|
|
spatial_distortion: spatial distortion module
|
|
use_linear: whether to skip the MLP and use a single linear layer instead
|
|
"""
|
|
|
|
aabb: Tensor
|
|
|
|
def __init__(
|
|
self,
|
|
aabb: Tensor,
|
|
num_layers: int = 2,
|
|
hidden_dim: int = 64,
|
|
spatial_distortion: Optional[SpatialDistortion] = None,
|
|
use_linear: bool = False,
|
|
num_levels: int = 8,
|
|
max_res: int = 1024,
|
|
base_res: int = 16,
|
|
log2_hashmap_size: int = 18,
|
|
features_per_level: int = 2,
|
|
average_init_density: float = 1.0,
|
|
implementation: Literal["tcnn", "torch"] = "tcnn",
|
|
) -> None:
|
|
super().__init__()
|
|
self.register_buffer("aabb", aabb)
|
|
self.spatial_distortion = spatial_distortion
|
|
self.use_linear = use_linear
|
|
self.average_init_density = average_init_density
|
|
|
|
self.register_buffer("max_res", torch.tensor(max_res))
|
|
self.register_buffer("num_levels", torch.tensor(num_levels))
|
|
self.register_buffer("log2_hashmap_size", torch.tensor(log2_hashmap_size))
|
|
|
|
self.encoding = HashEncoding(
|
|
num_levels=num_levels,
|
|
min_res=base_res,
|
|
max_res=max_res,
|
|
log2_hashmap_size=log2_hashmap_size,
|
|
features_per_level=features_per_level,
|
|
implementation=implementation,
|
|
)
|
|
|
|
if not self.use_linear:
|
|
network = MLP(
|
|
in_dim=self.encoding.get_out_dim(),
|
|
num_layers=num_layers,
|
|
layer_width=hidden_dim,
|
|
out_dim=1,
|
|
activation=nn.ReLU(),
|
|
out_activation=None,
|
|
implementation=implementation,
|
|
)
|
|
self.mlp_base = torch.nn.Sequential(self.encoding, network)
|
|
else:
|
|
self.linear = torch.nn.Linear(self.encoding.get_out_dim(), 1)
|
|
|
|
def get_density(self, ray_samples: RaySamples) -> Tuple[Tensor, None]:
|
|
if self.spatial_distortion is not None:
|
|
positions = self.spatial_distortion(ray_samples.frustums.get_positions())
|
|
positions = (positions + 2.0) / 4.0
|
|
else:
|
|
positions = SceneBox.get_normalized_positions(ray_samples.frustums.get_positions(), self.aabb)
|
|
# Make sure the tcnn gets inputs between 0 and 1.
|
|
selector = ((positions > 0.0) & (positions < 1.0)).all(dim=-1)
|
|
positions = positions * selector[..., None]
|
|
positions_flat = positions.view(-1, 3)
|
|
if not self.use_linear:
|
|
density_before_activation = (
|
|
self.mlp_base(positions_flat).view(*ray_samples.frustums.shape, -1).to(positions)
|
|
)
|
|
else:
|
|
x = self.encoding(positions_flat).to(positions)
|
|
density_before_activation = self.linear(x).view(*ray_samples.frustums.shape, -1)
|
|
|
|
# Rectifying the density with an exponential is much more stable than a ReLU or
|
|
# softplus, because it enables high post-activation (float32) density outputs
|
|
# from smaller internal (float16) parameters.
|
|
density = self.average_init_density * trunc_exp(density_before_activation)
|
|
density = density * selector[..., None]
|
|
return density, None
|
|
|
|
def get_outputs(self, ray_samples: RaySamples, density_embedding: Optional[Tensor] = None) -> dict:
|
|
return {}
|