mirror of
https://github.com/nerfstudio-project/nerfstudio.git
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268 lines
8.6 KiB
Python
268 lines
8.6 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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Collection of renderers
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Example:
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.. code-block:: python
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field_outputs = field(ray_sampler)
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weights = ray_sampler.get_weights(field_outputs[FieldHeadNames.DENSITY])
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rgb_renderer = RGBRenderer()
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rgb = rgb_renderer(rgb=field_outputs[FieldHeadNames.RGB], weights=weights)
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"""
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import math
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from typing import Literal, Optional
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import nerfacc
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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 RaySamples
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from nerfactory.utils.math import components_from_spherical_harmonics
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class RGBRenderer(nn.Module):
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"""Standard volumetic rendering.
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Args:
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background_color: Background color as RGB. Uses random colors if None.
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"""
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def __init__(self, background_color: Optional[TensorType[3]] = None) -> None:
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super().__init__()
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self.background_color = background_color
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@classmethod
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def combine_rgb(
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cls,
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rgb: TensorType["bs":..., "num_samples", 3],
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weights: TensorType["bs":..., "num_samples", 1],
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background_color: Optional[TensorType[3]] = None,
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ray_indices: Optional[TensorType["num_samples"]] = None,
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num_rays: Optional[int] = None,
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) -> TensorType["bs":..., 3]:
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"""Composite samples along ray and render color image
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Args:
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rgb: RGB for each sample
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weights: Weights for each sample
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background_color: Background color as RGB. Uses random colors if None.
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ray_indices: Ray index for each sample, used when samples are packed.
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num_rays: Number of rays, used when samples are packed.
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Returns:
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Outputs rgb values.
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"""
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if ray_indices is not None and num_rays is not None:
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rgb = nerfacc.volumetric_rendering_accumulate(weights, ray_indices, rgb, num_rays)
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accumulated_weight = nerfacc.volumetric_rendering_accumulate(weights, ray_indices, None, num_rays)
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else:
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rgb = torch.sum(weights * rgb, dim=-2)
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accumulated_weight = torch.sum(weights, dim=-2)
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if background_color is None:
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background_color = torch.rand_like(rgb).to(rgb.device)
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rgb = rgb + background_color.to(weights.device) * (1.0 - accumulated_weight)
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return rgb
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def forward(
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self,
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rgb: TensorType["bs":..., "num_samples", 3],
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weights: TensorType["bs":..., "num_samples", 1],
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ray_indices: Optional[TensorType["num_samples"]] = None,
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num_rays: Optional[int] = None,
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) -> TensorType["bs":..., 3]:
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"""Composite samples along ray and render color image
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Args:
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rgb: RGB for each sample
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weights: Weights for each sample
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ray_indices: Ray index for each sample, used when samples are packed.
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num_rays: Number of rays, used when samples are packed.
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Returns:
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Outputs of rgb values.
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"""
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rgb = self.combine_rgb(
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rgb, weights, background_color=self.background_color, ray_indices=ray_indices, num_rays=num_rays
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)
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if not self.training:
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torch.clamp_(rgb, min=0.0, max=1.0)
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return rgb
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class SHRenderer(nn.Module):
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"""Render RGB value from spherical harmonics.
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Args:
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background_color: Background color as RGB. Uses random colors if None
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activation: Output activation.
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"""
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def __init__(
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self, background_color: Optional[TensorType[3]] = None, activation: Optional[nn.Module] = nn.Sigmoid()
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) -> None:
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super().__init__()
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self.background_color = background_color
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self.activation = activation
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def forward(
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self,
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sh: TensorType[..., "num_samples", "coeffs"],
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directions: TensorType[..., "num_samples", 3],
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weights: TensorType[..., "num_samples", 1],
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) -> TensorType[..., 3]:
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"""Composite samples along ray and render color image
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Args:
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sh: Spherical hamonics coefficients for each sample
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directions: Sample direction
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weights: Weights for each sample
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Returns:
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Outputs of rgb values.
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"""
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sh = sh.view(*sh.shape[:-1], 3, sh.shape[-1] // 3)
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levels = int(math.sqrt(sh.shape[-1]))
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components = components_from_spherical_harmonics(levels=levels, directions=directions)
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rgb = sh * components[..., None, :] # [..., num_samples, 3, sh_components]
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rgb = torch.sum(sh, dim=-1) + 0.5 # [..., num_samples, 3]
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if self.activation is not None:
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self.activation(rgb)
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rgb = RGBRenderer.combine_rgb(rgb, weights, background_color=self.background_color)
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return rgb
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class AccumulationRenderer(nn.Module):
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"""Accumulated value along a ray."""
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@classmethod
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def forward(
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cls,
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weights: TensorType["bs":..., "num_samples", 1],
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ray_indices: Optional[TensorType["num_samples"]] = None,
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num_rays: Optional[int] = None,
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) -> TensorType["bs":..., 1]:
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"""Composite samples along ray and calculate accumulation.
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Args:
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weights: Weights for each sample
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ray_indices: Ray index for each sample, used when samples are packed.
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num_rays: Number of rays, used when samples are packed.
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Returns:
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Outputs of accumulated values.
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"""
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if ray_indices is not None and num_rays is not None:
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accumulation = nerfacc.volumetric_rendering_accumulate(weights, ray_indices, None, num_rays)
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else:
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accumulation = torch.sum(weights, dim=-2)
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return accumulation
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class DepthRenderer(nn.Module):
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"""Calculate depth along ray.
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Args:
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method (str, optional): Depth calculation method.
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"""
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def __init__(self, method: Literal["expected"] = "expected") -> None:
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super().__init__()
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self.method = method
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def forward(
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self,
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weights: TensorType[..., "num_samples", 1],
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ray_samples: RaySamples,
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ray_indices: Optional[TensorType["num_samples"]] = None,
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num_rays: Optional[int] = None,
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) -> TensorType[..., 1]:
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"""Composite samples along ray and calculate disparities.
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Args:
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weights: Weights for each sample.
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ray_samples: Set of ray samples.
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ray_indices: Ray index for each sample, used when samples are packed.
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num_rays: Number of rays, used when samples are packed.
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Returns:
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Outputs of depth values.
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"""
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if self.method == "expected":
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eps = 1e-10
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steps = (ray_samples.frustums.starts + ray_samples.frustums.ends) / 2
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if ray_indices is not None and num_rays is not None:
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depth = nerfacc.volumetric_rendering_accumulate(weights, ray_indices, steps, num_rays)
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else:
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depth = torch.sum(weights * steps, dim=-2) / (torch.sum(weights, -2) + eps)
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depth = torch.clip(depth, steps[..., 0, :], steps[..., -1, :])
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return depth
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raise NotImplementedError(f"Method {self.method} not implemented")
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class UncertaintyRenderer(nn.Module):
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"""Calculate uncertainty along the ray."""
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@classmethod
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def forward(
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cls, betas: TensorType["bs":..., "num_samples", 1], weights: TensorType["bs":..., "num_samples", 1]
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) -> TensorType["bs":..., 1]:
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"""Calculate uncertainty along the ray.
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Args:
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betas: Uncertainty betas for each sample.
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weights: Weights of each sample.
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Returns:
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Rendering of uncertainty.
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"""
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uncertainty = torch.sum(weights * betas, dim=-2)
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return uncertainty
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class SemanticRenderer(nn.Module):
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"""Calculate semantics along the ray."""
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@classmethod
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def forward(
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cls,
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semantics: TensorType["bs":..., "num_samples", "num_classes"],
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weights: TensorType["bs":..., "num_samples", 1],
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) -> TensorType["bs":..., "num_classes"]:
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"""_summary_"""
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sem = torch.sum(weights * semantics, dim=-2)
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return sem
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