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https://github.com/nerfstudio-project/nerfstudio.git
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245 lines
8.7 KiB
Python
245 lines
8.7 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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""" Compound model that integrates multiple NeRF Advancements
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Features:
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NGP Hash encoding for fast training
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NGP ray sampler for fast training
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Spatial contraction for training on large scenes
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Appearance conditioning
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TODO:
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Pose optimization
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"""
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from __future__ import annotations
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from dataclasses import dataclass, field
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from typing import Dict, List, Literal, Optional, Tuple, Type
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import nerfacc
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import torch
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from torch.nn import Parameter
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from torchmetrics import PeakSignalNoiseRatio
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from torchmetrics.functional import structural_similarity_index_measure
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from torchmetrics.image.lpip import LearnedPerceptualImagePatchSimilarity
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from nerfactory.cameras.rays import RayBundle
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from nerfactory.fields.compound_field import field_implementation_to_class
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from nerfactory.fields.modules.field_heads import FieldHeadNames
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from nerfactory.models.base import Model, ModelConfig
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from nerfactory.models.modules.ray_sampler import VolumetricSampler
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from nerfactory.optimizers.loss import MSELoss
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from nerfactory.renderers.renderers import (
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AccumulationRenderer,
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DepthRenderer,
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RGBRenderer,
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)
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from nerfactory.utils import colors, visualization
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from nerfactory.utils.callbacks import (
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TrainingCallback,
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TrainingCallbackAttributes,
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TrainingCallbackLocation,
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)
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@dataclass
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class CompoundModelConfig(ModelConfig):
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"""Compound Model Config"""
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_target: Type = field(
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default_factory=lambda: CompoundModel
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) # We can't write `CompoundModel` directly, because `CompoundModel` doesn't exist yet
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"""target class to instantiate"""
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enable_collider: bool = False
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"""Whether to create a scene collider to filter rays."""
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collider_params: Optional[Dict[str, float]] = None
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"""Instant NGP doesn't use a collider."""
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field_implementation: Literal["torch", "tcnn"] = "tcnn"
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"""one of "torch" or "tcnn", or other fields in 'field_implementation_to_class'"""
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num_samples: int = 1024
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"""Number of max samples per ray"""
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grid_resolution: int = 128
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"""Resolution of the grid used for the field."""
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cone_angle: float = 0.0
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"""Should be set to 0.0 for blender scenes but 1./256 for real scenes."""
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near_plane: float = 0.05
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"""How far along ray to start sampling."""
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randomize_background: bool = False
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"""Whether to randomize the background color."""
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class CompoundModel(Model):
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"""Compound model
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Args:
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config: Compound model configuration to instantiate model
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"""
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config: CompoundModelConfig
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def __init__(self, config: CompoundModelConfig, **kwargs) -> None:
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assert config.field_implementation in field_implementation_to_class
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self.field = None
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super().__init__(config=config, **kwargs)
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def populate_modules(self):
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"""Set the fields and modules."""
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super().populate_modules()
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# torch or tiny-cuda-nn version
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self.field = field_implementation_to_class[self.config.field_implementation](
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aabb=self.scene_bounds.aabb, num_images=self.num_train_data
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)
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self.scene_aabb = Parameter(self.scene_bounds.aabb.flatten(), requires_grad=False)
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# Sampler
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self.sampler = VolumetricSampler(
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aabb=self.scene_aabb,
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density_fn=self.field.density_fn,
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grid_resolution=self.config.grid_resolution,
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num_samples=self.config.num_samples,
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)
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# renderers
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background_color = None if self.config.randomize_background else colors.WHITE
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self.renderer_rgb = RGBRenderer(background_color=background_color)
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self.renderer_accumulation = AccumulationRenderer()
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self.renderer_depth = DepthRenderer()
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# losses
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self.rgb_loss = MSELoss()
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# metrics
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self.psnr = PeakSignalNoiseRatio(data_range=1.0)
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self.ssim = structural_similarity_index_measure
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self.lpips = LearnedPerceptualImagePatchSimilarity()
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# no colliders default
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def get_training_callbacks(
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self, training_callback_attributes: TrainingCallbackAttributes
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) -> List[TrainingCallback]:
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return [
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TrainingCallback(
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where_to_run=[TrainingCallbackLocation.BEFORE_TRAIN_ITERATION],
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update_every_num_iters=1,
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func=self.sampler.occ_field.every_n_step,
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)
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]
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def get_param_groups(self) -> Dict[str, List[Parameter]]:
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param_groups = {}
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if self.field is None:
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raise ValueError("populate_fields() must be called before get_param_groups")
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param_groups["fields"] = list(self.field.parameters())
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return param_groups
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def get_outputs(self, ray_bundle: RayBundle):
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assert self.field is not None
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num_rays = len(ray_bundle)
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with torch.no_grad():
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ray_samples, packed_info, ray_indices = self.sampler(
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ray_bundle=ray_bundle,
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near_plane=self.config.near_plane,
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)
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field_outputs = self.field.forward(ray_samples)
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# accumulation
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weights = nerfacc.volumetric_rendering_weights(
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packed_info=packed_info,
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sigmas=field_outputs[FieldHeadNames.DENSITY],
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frustum_starts=ray_samples.frustums.starts,
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frustum_ends=ray_samples.frustums.ends,
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)
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rgb = self.renderer_rgb(
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rgb=field_outputs[FieldHeadNames.RGB],
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weights=weights,
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ray_indices=ray_indices,
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num_rays=num_rays,
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)
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depth = self.renderer_depth(
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weights=weights, ray_samples=ray_samples, ray_indices=ray_indices, num_rays=num_rays
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)
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accumulation = self.renderer_accumulation(weights=weights, ray_indices=ray_indices, num_rays=num_rays)
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alive_ray_mask = accumulation.squeeze(-1) > 0
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outputs = {
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"rgb": rgb,
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"accumulation": accumulation,
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"depth": depth,
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"alive_ray_mask": alive_ray_mask, # the rays we kept from sampler
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"num_samples_per_ray": packed_info[:, 1],
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}
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return outputs
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def get_metrics_dict(self, outputs, batch):
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image = batch["image"].to(self.device)
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metrics_dict = {}
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metrics_dict["psnr"] = self.psnr(outputs["rgb"], image)
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metrics_dict["num_samples_per_batch"] = outputs["num_samples_per_ray"].sum()
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return metrics_dict
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def get_loss_dict(self, outputs, batch, metrics_dict=None):
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image = batch["image"].to(self.device)
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mask = outputs["alive_ray_mask"]
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rgb_loss = self.rgb_loss(image[mask], outputs["rgb"][mask])
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loss_dict = {"rgb_loss": rgb_loss}
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return loss_dict
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def get_image_metrics_and_images(
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self, outputs: Dict[str, torch.Tensor], batch: Dict[str, torch.Tensor]
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) -> Tuple[Dict[str, float], Dict[str, torch.Tensor]]:
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image = batch["image"].to(self.device)
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rgb = outputs["rgb"]
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acc = visualization.apply_colormap(outputs["accumulation"])
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depth = visualization.apply_depth_colormap(
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outputs["depth"],
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accumulation=outputs["accumulation"],
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)
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alive_ray_mask = visualization.apply_colormap(outputs["alive_ray_mask"])
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combined_rgb = torch.cat([image, rgb], dim=1)
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combined_acc = torch.cat([acc], dim=1)
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combined_depth = torch.cat([depth], dim=1)
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combined_alive_ray_mask = torch.cat([alive_ray_mask], dim=1)
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# Switch images from [H, W, C] to [1, C, H, W] for metrics computations
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image = torch.moveaxis(image, -1, 0)[None, ...]
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rgb = torch.moveaxis(rgb, -1, 0)[None, ...]
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psnr = self.psnr(image, rgb)
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ssim = self.ssim(image, rgb)
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lpips = self.lpips(image, rgb)
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# all of these metrics will be logged as scalars
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metrics_dict = {"psnr": float(psnr.item()), "ssim": float(ssim), "lpips": float(lpips)} # type: ignore
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# TODO(ethan): return an image dictionary
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images_dict = {
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"img": combined_rgb,
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"accumulation": combined_acc,
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"depth": combined_depth,
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"alive_ray_mask": combined_alive_ray_mask,
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}
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return metrics_dict, images_dict
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