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fb440fedf3
* occupancy grid in progress * visualizing occupancy grid. currently restructing field and renderer head outputs * black and pylint. broke nerfw and semantic nerf but it's okay * get_project_root
194 lines
6.7 KiB
Python
194 lines
6.7 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 mattport.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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positions: TensorType[..., 3] = None
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directions: TensorType[..., 3] = None
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camera_indices: TensorType[..., 1] = None
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@dataclass
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class RayBundle:
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"""_summary_
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Returns:
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_type_: _description_
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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"]] = 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 to_camera_ray_bundle(self, image_height, image_width) -> "CameraRayBundle":
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"""Returns a CameraRayBundle from this object."""
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camera_ray_bundle = CameraRayBundle(
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origins=self.origins.view(image_height, image_width, 3),
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directions=self.directions.view(image_height, image_width, 3),
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camera_indices=self.camera_indices.view(image_height, image_width)
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if not isinstance(self.camera_indices, type(None))
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else None,
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)
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return camera_ray_bundle
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def move_to_device(self, device):
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"""Move to a device."""
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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 __len__(self):
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num_rays = self.origins.shape[0]
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return num_rays
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def sample(self, num_rays: int):
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"""Returns a RayBundle as a subset of rays.
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Args:
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num_rays (int):
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Returns:
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RayBundle: _description_
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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):
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"""Return a masked instance of the ray bundle."""
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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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@dataclass
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class CameraRayBundle:
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"""_summary_"""
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origins: TensorType["image_height", "image_width", 3]
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directions: TensorType["image_height", "image_width", 3]
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camera_indices: Optional[TensorType["image_height", "image_width", 3]] = None
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camera_index: int = None
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def __post_init__(self):
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if not isinstance(self.camera_index, type(None)):
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self.set_camera_indices(self.camera_index)
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def set_camera_indices(self, camera_index: int):
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"""Sets the camera indices for a specific camera index."""
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self.camera_index = camera_index
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self.camera_indices = torch.ones_like(self.origins[..., 0]).long() * camera_index
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def get_num_rays(self):
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"""Return the number of rays in this bundle."""
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image_height, image_width = self.origins.shape[:2]
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num_rays = image_height * image_width
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return num_rays
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def to_ray_bundle(self) -> RayBundle:
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"""_summary_
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Returns:
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RayBundle: _description_
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"""
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# TODO(ethan): handle camera_index
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ray_bundle = RayBundle(origins=self.origins.view(-1, 3), directions=self.directions.view(-1, 3))
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return ray_bundle
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def get_row_major_sliced_ray_bundle(self, start_idx, end_idx):
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"""Return a RayBundle"""
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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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class RaySamples:
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"""_summary_"""
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def __init__(
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self,
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ts: TensorType["num_rays", "num_samples"],
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ray_bundle: RayBundle,
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) -> None:
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self.ray_bundle = ray_bundle
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self.ts = ts
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self.positions = self.get_positions(ray_bundle)
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self.directions = ray_bundle.directions.unsqueeze(1).repeat(1, self.positions.shape[1], 1)
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self.deltas = self.get_deltas()
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def to_point_samples(self):
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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)
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def get_camera_indices(self):
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"""Returns camera indices."""
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assert not isinstance(self.ray_bundle.camera_indices, type(None)), "Camera indices cannot be None here."
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camera_indices = self.ray_bundle.camera_indices.unsqueeze(1).repeat(1, self.positions.shape[1])
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return camera_indices
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def get_positions(self, ray_bundle: RayBundle) -> TensorType["num_rays", "num_samples", 3]:
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"""Returns positions."""
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return ray_bundle.origins[:, None] + self.ts[:, :, None] * ray_bundle.directions[:, None]
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def get_deltas(self) -> TensorType[..., "num_samples"]:
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"""Returns deltas."""
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dists = self.ts[..., 1:] - self.ts[..., :-1]
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dist_inf = 1e10 * torch.ones_like(dists[..., -1:])
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dists = torch.cat([dists, dist_inf], -1) # [N_rays, N_samples]
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deltas = dists * torch.norm(self.ray_bundle.directions[..., None, :], dim=-1)
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return deltas
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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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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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weights = alphas * transmittance # [..., "num_samples"]
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return weights
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