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245 lines
8.6 KiB
Python
245 lines
8.6 KiB
Python
"""
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Some ray datastructures.
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"""
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import random
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from dataclasses import dataclass
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from typing import Optional
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import torch
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from torchtyping import TensorType
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from radiance.utils.misc import is_not_none
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@dataclass
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class PointSamples:
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"""Samples in space.
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Args:
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positions (TensorType[..., 3]): XYZ locations
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directions (TensorType[..., 3]): Unit direction vector
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camera_indices (TensorType[..., 1]): Camera index
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valid_mask (TensorType[...]): Rays that are valid
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"""
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positions: TensorType[..., 3]
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directions: TensorType[..., 3] = None
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camera_indices: TensorType[..., 1] = None
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valid_mask: TensorType[...] = None
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def apply_masks(self) -> "PointSamples":
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"""Use valid_mask to mask samples.
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Returns:
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PointSamples: New set of masked samples.
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"""
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if is_not_none(self.valid_mask):
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positions = self.positions[self.valid_mask]
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directions = self.directions[self.valid_mask] if is_not_none(self.directions) else self.directions
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camera_indices = (
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self.camera_indices[self.valid_mask] if is_not_none(self.camera_indices) else self.camera_indices
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)
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return PointSamples(
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positions=positions,
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directions=directions,
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camera_indices=camera_indices,
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)
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return PointSamples(
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positions=self.positions,
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directions=self.directions,
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camera_indices=self.camera_indices,
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)
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@dataclass
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class RaySamples:
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"""Samples along a ray
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Args:
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positions (TensorType[..., 3]): XYZ locations
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directions (TensorType[..., 3]): Unit direction vector
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camera_indices (TensorType[..., 1]): Camera index
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valid_mask (TensorType[...]): Rays that are valid
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ts (TensorType[..., 1]): "time steps", distances along ray
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deltas )TensorType[..., 1]): "width" of each sample
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"""
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positions: TensorType[..., 3]
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directions: TensorType[..., 3] = None
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camera_indices: TensorType[..., 1] = None
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valid_mask: TensorType[...] = None
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ts: TensorType[..., 1] = None
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deltas: TensorType[..., 1] = None
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def to_point_samples(self) -> PointSamples:
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"""Convert to PointSamples instance and return."""
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# TODO: make this more interpretable
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return PointSamples(positions=self.positions, directions=self.directions, valid_mask=self.valid_mask)
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def get_weights(self, densities: TensorType[..., "num_samples", 1]) -> TensorType[..., "num_samples"]:
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"""Return weights based on predicted densities
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Args:
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densities (TensorType[..., "num_samples", 1]): Predicted densities for samples along ray
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Returns:
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TensorType[..., "num_samples"]: Weights for each sample
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"""
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delta_density = self.deltas * densities[..., 0]
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alphas = 1 - torch.exp(-delta_density)
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# mip-nerf version of transmittance calculation:
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transmittance = torch.cumsum(delta_density[..., :-1], dim=-1)
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transmittance = torch.cat(
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[torch.zeros((*transmittance.shape[:1], 1)).to(densities.device), transmittance], axis=-1
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)
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transmittance = torch.exp(-transmittance) # [..., "num_samples"]
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# most nerf codebases do the following:
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# transmittance = torch.cat(
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# [torch.ones((*alphas.shape[:1], 1)).to(densities.device), 1.0 - alphas + 1e-10], dim=-1
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# )
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# transmittance = torch.cumprod(transmittance, dim=-1)[..., :-1] # [..., "num_samples"]
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weights = alphas * transmittance # [..., "num_samples"]
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return weights
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def set_valid_mask(self, valid_mask: TensorType[..., "num_samples"]) -> None:
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"""Sets valid mask"""
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self.valid_mask = valid_mask
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@dataclass
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class RayBundle:
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"""A bundle of ray parameters.
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Args:
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origins (TensorType[..., 3]): Ray origins (XYZ)
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directions (TensorType[..., 3]): Unit ray direction vector
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camera_indices (TensorType[..., 1]): Camera indices
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nears (TensorType[..., 1]): Distance along ray to start sampling
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fars (TensorType[..., 1]): Rays Distance along ray to stop sampling
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valid_mask (TensorType[...]): Rays that are valid
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"""
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origins: TensorType["num_rays", 3]
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directions: TensorType["num_rays", 3]
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camera_indices: Optional[TensorType["num_rays", 1]] = None
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nears: Optional[TensorType["num_rays"]] = None
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fars: Optional[TensorType["num_rays"]] = None
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valid_mask: Optional[TensorType["num_rays"]] = None
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def move_to_device(self, device: torch.device) -> None:
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"""Move bundle data to a device.
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Args:
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device (torch.device): Device to move tensors to.
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"""
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self.origins = self.origins.to(device)
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self.directions = self.directions.to(device)
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if not isinstance(self.camera_indices, type(None)):
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self.camera_indices = self.camera_indices.to(device)
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def set_camera_indices(self, camera_index: int) -> None:
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"""Sets all of the the camera indices to a specific camera index.
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Args:
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camera_index (int): Camera index.
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"""
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self.camera_indices = torch.ones_like(self.origins[..., 0]).long() * camera_index
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def __len__(self):
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num_rays = torch.numel(self.origins) // self.origins.shape[-1]
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return num_rays
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def sample(self, num_rays: int) -> "RayBundle":
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"""Returns a RayBundle as a subset of rays.
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Args:
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num_rays (int): Number of rays in output RayBundle
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Returns:
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RayBundle: RayBundle with subset of rays.
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"""
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assert num_rays <= len(self)
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indices = random.sample(range(len(self)), k=num_rays)
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return RayBundle(
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origins=self.origins[indices],
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directions=self.directions[indices],
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camera_indices=self.camera_indices[indices],
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)
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def get_masked_ray_bundle(self, valid_mask: TensorType) -> "RayBundle":
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"""Return a masked instance of the ray bundle.
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Args:
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valid_mask (TensorType): Mask of rays to keep.
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Returns:
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RayBundle: RayBundle of masked rays.
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"""
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return RayBundle(
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origins=self.origins[valid_mask],
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directions=self.directions[valid_mask],
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camera_indices=self.camera_indices[valid_mask] if is_not_none(self.camera_indices) else None,
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nears=self.nears[valid_mask] if is_not_none(self.nears) else None,
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fars=self.fars[valid_mask] if is_not_none(self.fars) else None,
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valid_mask=self.valid_mask[valid_mask] if is_not_none(self.valid_mask) else None,
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)
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def get_row_major_sliced_ray_bundle(self, start_idx: int, end_idx: int) -> "RayBundle":
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"""Flattens RayBundle and extracts chunk given start and end indicies.
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Args:
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start_idx (int): Start index of RayBundle chunk.
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end_idx (int): End index of RayBundle chunk.
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Returns:
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RayBundle: Flattened RayBundle with end_idx-start_idx rays.
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"""
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camera_indices = (
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self.camera_indices.view(-1)[start_idx:end_idx] if not isinstance(self.camera_indices, type(None)) else None
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)
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return RayBundle(
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origins=self.origins.view(-1, 3)[start_idx:end_idx],
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directions=self.directions.view(-1, 3)[start_idx:end_idx],
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camera_indices=camera_indices,
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)
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def get_ray_samples(self, ts: TensorType[..., "num_samples"]) -> RaySamples:
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"""Produces samples for each ray by projection points along the ray direction.
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Args:
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ts (TensorType[..., "num_samples"]): Distance from origin of sample points.
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Returns:
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RaySamples: Samples projected along ray.
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"""
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positions = self.origins[:, None] + ts[:, :, None] * self.directions[:, None]
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directions = self.directions.unsqueeze(1).repeat(1, positions.shape[1], 1)
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valid_mask = torch.ones_like(ts, dtype=torch.bool)
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dists = ts[..., 1:] - ts[..., :-1]
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dists = torch.cat([dists, dists[..., -1:]], -1) # [N_rays, N_samples]
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deltas = dists * torch.norm(self.directions[..., None, :], dim=-1)
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if self.camera_indices is not None:
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camera_indices = self.camera_indices.unsqueeze(1).repeat(1, positions.shape[1])
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else:
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camera_indices = None
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ray_samples = RaySamples(
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positions=positions,
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directions=directions,
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camera_indices=camera_indices,
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valid_mask=valid_mask,
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ts=ts,
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deltas=deltas,
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)
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return ray_samples
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