mirror of
https://github.com/nerfstudio-project/nerfstudio.git
synced 2026-11-13 03:14:00 +08:00
Ethan Weber
457788d689
* renaming dataloader to data. adding some dataloader classes * restructuring configs * fixed instant ngp tcnn direction rescaling * working through test cases * black and lint and tests
90 lines
3.4 KiB
Python
90 lines
3.4 KiB
Python
"""
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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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from torch import nn
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import torch
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from torchtyping import TensorType
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from pyrad.fields.modules.field_heads import FieldHeadNames
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from pyrad.cameras.rays import Frustums, PointSamples
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from pyrad.utils.misc import is_not_none
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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 occupancy grid."""
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# Need to figure out a better way to descibe positions with a ray.
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point_samples = PointSamples(
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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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frustum_starts=torch.zeros((*positions.shape[:-1], 1)),
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frustum_ends=torch.zeros((*positions.shape[:-1], 1)),
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pixel_area=torch.ones((*positions.shape[:-1], 1)),
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)
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)
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density, _ = self.get_density(point_samples)
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return density
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@abstractmethod
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def get_density(self, point_samples: PointSamples) -> Tuple[TensorType[..., 1], TensorType[..., "num_features"]]:
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"""Computes and returns the densities.
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Args:
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point_samples (PointSamples): 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, point_samples: PointSamples, 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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point_samples (PointSamples): 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, point_samples: PointSamples):
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"""Evaluates the field at points along the ray."""
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valid_mask = point_samples.valid_mask
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if is_not_none(valid_mask):
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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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point_samples = PointSamples(frustums=Frustums.get_mock_frustum())
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else:
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point_samples = point_samples.apply_masks()
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density_masked, density_embedding_masked = self.get_density(point_samples)
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field_outputs_masked = self.get_outputs(point_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(*valid_mask.shape, value.shape[-1], dtype=torch.float32, device=valid_mask.device)
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if valid_mask.any():
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zeros[valid_mask] = 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, 1, dtype=torch.float32, device=valid_mask.device)
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if valid_mask.any():
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density[valid_mask] = density_masked
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else:
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density, density_embedding = self.get_density(point_samples)
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field_outputs = self.get_outputs(point_samples, density_embedding=density_embedding)
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field_outputs[FieldHeadNames.DENSITY] = density
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return field_outputs
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