mirror of
https://github.com/nerfstudio-project/nerfstudio.git
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250 lines
9.9 KiB
Python
250 lines
9.9 KiB
Python
"""
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Implementation of vanilla nerf.
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"""
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from typing import Dict, List, Tuple
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import torch
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from torch import nn
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from torch.nn import Parameter
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from torchmetrics import PeakSignalNoiseRatio, StructuralSimilarityIndexMeasure
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from torchmetrics.image.lpip import LearnedPerceptualImagePatchSimilarity
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from mattport.nerf.field_modules.encoding import NeRFEncoding
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from mattport.nerf.field_modules.field_heads import DensityFieldHead, FieldHeadNames, RGBFieldHead
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from mattport.nerf.field_modules.mlp import MLP
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from mattport.nerf.graph.base import Graph
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from mattport.nerf.loss import MSELoss
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from mattport.nerf.renderers import AccumulationRenderer, DepthRenderer, RGBRenderer
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from mattport.nerf.sampler import PDFSampler, UniformSampler
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from mattport.structures import colors
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from mattport.structures.rays import RayBundle, RaySamples
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from mattport.utils import stats_tracker, visualization, writer
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class NeRFField(nn.Module):
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"""NeRF module"""
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def __init__(self, num_layers=8, layer_width=256, skip_connections: Tuple = (4,)) -> None:
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super().__init__()
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self.num_layers = num_layers
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self.layer_width = layer_width
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self.skip_connections = skip_connections
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self.encoding_xyz = NeRFEncoding(
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in_dim=3, num_frequencies=10, min_freq_exp=0.0, max_freq_exp=8.0, include_input=True
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)
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self.encoding_dir = NeRFEncoding(
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in_dim=3, num_frequencies=4, min_freq_exp=0.0, max_freq_exp=4.0, include_input=True
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)
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self.mlp_base = MLP(
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in_dim=self.encoding_xyz.get_out_dim(),
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out_dim=self.layer_width,
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num_layers=self.num_layers,
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layer_width=self.layer_width,
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skip_connections=self.skip_connections,
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activation=nn.ReLU(),
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)
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self.mlp_rgb = MLP(
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in_dim=self.mlp_base.get_out_dim() + self.encoding_dir.get_out_dim(),
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out_dim=self.layer_width // 2,
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num_layers=2,
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layer_width=self.layer_width // 2,
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activation=nn.ReLU(),
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)
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self.field_output_rgb = RGBFieldHead(in_dim=self.mlp_rgb.get_out_dim())
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self.field_output_density = DensityFieldHead(in_dim=self.mlp_base.get_out_dim())
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def forward(self, ray_samples: RaySamples):
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"""Evaluates the field at points along the ray
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Args:
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xyz: ()
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# TODO(ethan): change the input to be something more abstracted
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e.g., a FieldInput structure
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"""
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positions = ray_samples.positions
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directions = ray_samples.directions
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encoded_xyz = self.encoding_xyz(positions)
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encoded_dir = self.encoding_dir(directions)
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base_mlp_out = self.mlp_base(encoded_xyz)
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rgb_mlp_out = self.mlp_rgb(torch.cat([encoded_dir, base_mlp_out], dim=-1))
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field_rgb_output = self.field_output_rgb(rgb_mlp_out)
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field_density_out = self.field_output_density(base_mlp_out)
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field_outputs = {}
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field_outputs.update(field_rgb_output)
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field_outputs.update(field_density_out)
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return field_outputs
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class NeRFGraph(Graph):
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"""Vanilla NeRF graph"""
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def __init__(
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self,
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intrinsics=None,
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camera_to_world=None,
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near_plane=2.0,
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far_plane=6.0,
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num_coarse_samples=64,
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num_importance_samples=128,
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**kwargs,
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) -> None:
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self.near_plane = near_plane
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self.far_plane = far_plane
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self.num_coarse_samples = num_coarse_samples
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self.num_importance_samples = num_importance_samples
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self.field_coarse = None
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self.field_fine = None
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super().__init__(intrinsics=intrinsics, camera_to_world=camera_to_world, **kwargs)
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def populate_fields(self):
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"""Set the fields."""
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self.field_coarse = NeRFField()
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self.field_fine = NeRFField()
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def populate_modules(self):
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# samplers
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self.sampler_uniform = UniformSampler(
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near_plane=self.near_plane, far_plane=self.far_plane, num_samples=self.num_coarse_samples
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)
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self.sampler_pdf = PDFSampler(num_samples=self.num_importance_samples)
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# field
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self.populate_fields()
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# renderers
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self.renderer_rgb = RGBRenderer(background_color=colors.WHITE)
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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 = StructuralSimilarityIndexMeasure()
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self.lpips = LearnedPerceptualImagePatchSimilarity()
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def get_param_groups(self) -> Dict[str, List[Parameter]]:
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"""Obtain the parameter groups for the optimizers
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Returns:
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Dict[str, List[Parameter]]: Mapping of different parameter groups
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"""
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param_groups = {}
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# param_groups["cameras"] = list(self.ray_generator.parameters())
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param_groups["fields"] = list(self.field_coarse.parameters()) + list(self.field_fine.parameters())
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return param_groups
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def get_outputs(self, ray_bundle: RayBundle):
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# coarse network:
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uniform_ray_samples = self.sampler_uniform(ray_bundle) # RaySamples
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# time coarse network # xyz -> asd
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coarse_field_outputs = self.field_coarse(uniform_ray_samples) # FieldOutputs
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# time end coarse network
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coarse_weights = uniform_ray_samples.get_weights(coarse_field_outputs[FieldHeadNames.DENSITY])
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coarse_renderer_outputs = self.renderer_rgb(
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rgb=coarse_field_outputs[FieldHeadNames.RGB],
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weights=coarse_weights,
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) # RendererOutputs
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coarse_renderer_accumulation = self.renderer_accumulation(coarse_weights) # RendererOutputs
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coarse_renderer_depth = self.renderer_depth(coarse_weights, uniform_ray_samples.ts)
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# fine network:
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pdf_ray_samples = self.sampler_pdf(uniform_ray_samples, coarse_weights) # RaySamples
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fine_field_outputs = self.field_fine(pdf_ray_samples) # FieldOutputs
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fine_weights = pdf_ray_samples.get_weights(fine_field_outputs[FieldHeadNames.DENSITY])
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fine_renderer_outputs = self.renderer_rgb(
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rgb=fine_field_outputs[FieldHeadNames.RGB],
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weights=fine_weights,
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) # RendererOutputs
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fine_renderer_accumulation = self.renderer_accumulation(fine_weights) # RendererOutputs
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fine_renderer_depth = self.renderer_depth(fine_weights, pdf_ray_samples.ts)
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# outputs:
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outputs = {
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"rgb_coarse": coarse_renderer_outputs.rgb,
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"rgb_fine": fine_renderer_outputs.rgb,
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"accumulation_coarse": coarse_renderer_accumulation.accumulation,
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"accumulation_fine": fine_renderer_accumulation.accumulation,
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"depth_coarse": coarse_renderer_depth.depth,
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"depth_fine": fine_renderer_depth.depth,
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}
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return outputs
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def get_loss_dict(self, outputs, batch):
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device = outputs["rgb_coarse"].device
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pixels = batch["pixels"].to(device)
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rgb_loss_coarse = self.rgb_loss(pixels, outputs["rgb_coarse"])
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rgb_loss_fine = self.rgb_loss(pixels, outputs["rgb_fine"])
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loss_dict = {"rgb_loss_coarse": rgb_loss_coarse, "rgb_loss_fine": rgb_loss_fine}
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loss_dict["aggregated_loss"] = self.get_aggregated_loss_from_loss_dict(loss_dict)
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return loss_dict
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def log_test_image_outputs(self, image_idx, step, image, outputs):
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rgb_coarse = outputs["rgb_coarse"]
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rgb_fine = outputs["rgb_fine"]
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combined_image = torch.cat([image, rgb_coarse, rgb_fine], dim=1)
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writer.write_event(
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{"name": f"image_idx_{image_idx}-rgb_coarse_fine", "x": combined_image, "step": step, "group": "val_img"}
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)
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accumulation_coarse = visualization.apply_colormap(outputs["accumulation_coarse"])
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accumulation_fine = visualization.apply_colormap(outputs["accumulation_fine"])
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combined_image = torch.cat([accumulation_coarse, accumulation_fine], dim=1)
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writer.write_event(
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{"name": f"image_idx_{image_idx}", "x": combined_image, "step": step, "group": "val_accumulation"}
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)
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depth_coarse = visualization.apply_depth_colormap(
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outputs["depth_coarse"],
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accumulation=outputs["accumulation_coarse"],
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near_plane=self.near_plane,
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far_plane=self.far_plane,
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)
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depth_fine = visualization.apply_depth_colormap(
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outputs["depth_fine"],
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accumulation=outputs["accumulation_fine"],
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near_plane=self.near_plane,
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far_plane=self.far_plane,
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)
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combined_image = torch.cat([depth_coarse, depth_fine], dim=1)
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writer.write_event({"name": f"image_idx_{image_idx}", "x": combined_image, "step": step, "group": "val_depth"})
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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_coarse = torch.moveaxis(rgb_coarse, -1, 0)[None, ...]
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rgb_fine = torch.moveaxis(rgb_fine, -1, 0)[None, ...]
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coarse_psnr = self.psnr(image, rgb_coarse)
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writer.write_event(
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{"name": f"val_{image_idx}-coarse", "scalar": float(coarse_psnr), "step": step, "group": "psnr"}
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)
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fine_psnr = self.psnr(image, rgb_fine)
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stats_tracker.update_stats(
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{"name": stats_tracker.Stats.CURR_TEST_PSNR, "value": float(fine_psnr), "step": step}
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)
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writer.write_event(
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{"name": f"val_idx_{image_idx}-fine", "scalar": float(fine_psnr), "step": step, "group": "psnr"}
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)
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fine_ssim = self.ssim(image, rgb_fine)
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writer.write_event({"name": f"val_idx_{image_idx}", "scalar": float(fine_ssim), "step": step, "group": "ssim"})
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fine_lpips = self.lpips(image, rgb_fine)
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writer.write_event(
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{"name": f"val_idx_{image_idx}", "scalar": float(fine_lpips), "step": step, "group": "lpips"}
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)
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return fine_psnr.item()
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