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https://github.com/nerfstudio-project/nerfstudio.git
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109 lines
4.1 KiB
Python
109 lines
4.1 KiB
Python
# Copyright 2022 The Plenoptix Team. 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 typing import Dict, Optional, Tuple
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import torch
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from torch import nn
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from torchtyping import TensorType
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from nerfactory.cameras.rays import Frustums, RaySamples
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from nerfactory.fields.modules.field_heads import FieldHeadNames
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class Field(nn.Module):
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"""Base class for fields."""
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def density_fn(self, positions):
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"""Returns only the density. Used primarily with the density grid."""
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# Need to figure out a better way to descibe 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(self, ray_samples: RaySamples) -> Tuple[TensorType[..., 1], TensorType[..., "num_features"]]:
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"""Computes and returns the densities.
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Args:
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ray_samples (RaySamples): Samples locations to compute density.
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Returns:
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Tuple[TensorType[...,1], TensorType[...,"num_features"]]: A tensor of densities and a tensor of features.
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"""
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@abstractmethod
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def get_outputs(
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self, ray_samples: RaySamples, density_embedding: Optional[TensorType] = None
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) -> Dict[FieldHeadNames, TensorType]:
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"""Computes and returns the colors.
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Args:
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ray_samples (RaySamples): Samples locations to compute outputs.
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density_embedding (TensorType, optional): Density embeddings to condition on.
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Returns:
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Dict[FieldHeadNames, TensorType]: Output field values.
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"""
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def forward(self, ray_samples: RaySamples):
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"""Evaluates the field at points along the ray.
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Args:
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ray_samples (RaySamples): Samples to evaluate field on.
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"""
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valid_mask = ray_samples.valid_mask
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if valid_mask is not None:
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# Hacky handling of empty masks. Tests on a single ray but doesn't use results
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if not valid_mask.any():
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ray_samples = RaySamples(frustums=Frustums.get_mock_frustum().to(valid_mask.device))
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else:
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ray_samples = ray_samples.apply_masks()
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density_masked, density_embedding_masked = self.get_density(ray_samples)
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field_outputs_masked = self.get_outputs(ray_samples, density_embedding=density_embedding_masked)
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field_outputs = {}
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for k, value in field_outputs_masked.items():
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zeros = torch.zeros(
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*valid_mask.shape[:-1], value.shape[-1], dtype=value.dtype, device=valid_mask.device
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)
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if valid_mask.any():
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zeros[valid_mask[..., 0]] = value
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else:
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zeros[0, :] = value
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field_outputs[k] = zeros
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density = torch.zeros(valid_mask.shape, dtype=density_masked.dtype, device=valid_mask.device)
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if valid_mask.any():
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density[valid_mask[..., 0]] = density_masked
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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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return field_outputs
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