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143 lines
5.0 KiB
Python
143 lines
5.0 KiB
Python
# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Base class for the graphs.
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"""
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from abc import abstractmethod
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from dataclasses import dataclass, field
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from typing import Dict, Optional, Tuple, Type
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import torch
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from jaxtyping import Float, Shaped
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from torch import Tensor, nn
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from nerfstudio.cameras.rays import Frustums, RaySamples
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from nerfstudio.configs.base_config import InstantiateConfig
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from nerfstudio.field_components.field_heads import FieldHeadNames
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@dataclass
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class FieldConfig(InstantiateConfig):
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"""Configuration for field instantiation"""
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_target: Type = field(default_factory=lambda: Field)
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"""target class to instantiate"""
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class Field(nn.Module):
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"""Base class for fields."""
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def __init__(self) -> None:
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super().__init__()
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self._sample_locations = None
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self._density_before_activation = None
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def density_fn(
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self, positions: Shaped[Tensor, "*bs 3"], times: Optional[Shaped[Tensor, "*bs 1"]] = None
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) -> Shaped[Tensor, "*bs 1"]:
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"""Returns only the density. Used primarily with the density grid.
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Args:
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positions: the origin of the samples/frustums
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"""
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del times
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# Need to figure out a better way to describe positions with a ray.
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ray_samples = RaySamples(
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frustums=Frustums(
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origins=positions,
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directions=torch.ones_like(positions),
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starts=torch.zeros_like(positions[..., :1]),
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ends=torch.zeros_like(positions[..., :1]),
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pixel_area=torch.ones_like(positions[..., :1]),
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)
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)
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density, _ = self.get_density(ray_samples)
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return density
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@abstractmethod
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def get_density(
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self, ray_samples: RaySamples
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) -> Tuple[Shaped[Tensor, "*batch 1"], Float[Tensor, "*batch num_features"]]:
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"""Computes and returns the densities. Returns a tensor of densities and a tensor of features.
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Args:
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ray_samples: Samples locations to compute density.
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"""
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def get_normals(self) -> Float[Tensor, "*batch 3"]:
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"""Computes and returns a tensor of normals.
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Args:
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density: Tensor of densities.
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"""
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assert self._sample_locations is not None, "Sample locations must be set before calling get_normals."
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assert self._density_before_activation is not None, "Density must be set before calling get_normals."
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assert (
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self._sample_locations.shape[:-1] == self._density_before_activation.shape[:-1]
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), "Sample locations and density must have the same shape besides the last dimension."
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normals = torch.autograd.grad(
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self._density_before_activation,
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self._sample_locations,
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grad_outputs=torch.ones_like(self._density_before_activation),
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retain_graph=True,
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)[0]
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normals = -torch.nn.functional.normalize(normals, dim=-1)
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return normals
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@abstractmethod
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def get_outputs(
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self, ray_samples: RaySamples, density_embedding: Optional[Tensor] = None
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) -> Dict[FieldHeadNames, Tensor]:
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"""Computes and returns the colors. Returns output field values.
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Args:
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ray_samples: Samples locations to compute outputs.
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density_embedding: Density embeddings to condition on.
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"""
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def forward(self, ray_samples: RaySamples, compute_normals: bool = False) -> Dict[FieldHeadNames, Tensor]:
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"""Evaluates the field at points along the ray.
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Args:
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ray_samples: Samples to evaluate field on.
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"""
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if compute_normals:
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with torch.enable_grad():
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density, density_embedding = self.get_density(ray_samples)
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else:
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density, density_embedding = self.get_density(ray_samples)
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field_outputs = self.get_outputs(ray_samples, density_embedding=density_embedding)
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field_outputs[FieldHeadNames.DENSITY] = density # type: ignore
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if compute_normals:
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with torch.enable_grad():
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normals = self.get_normals()
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field_outputs[FieldHeadNames.NORMALS] = normals # type: ignore
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return field_outputs
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def get_normalized_directions(directions: Float[Tensor, "*bs 3"]) -> Float[Tensor, "*bs 3"]:
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"""SH encoding must be in the range [0, 1]
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Args:
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directions: batch of directions
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"""
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return (directions + 1.0) / 2.0
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