mirror of
https://github.com/nerfstudio-project/nerfstudio.git
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195 lines
8.1 KiB
Python
195 lines
8.1 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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Implementation of vanilla nerf.
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"""
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from typing import Dict, List
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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.image.lpip import LearnedPerceptualImagePatchSimilarity
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from torchmetrics.functional import structural_similarity_index_measure
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from pyrad.fields.modules.encoding import NeRFEncoding
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from pyrad.fields.modules.field_heads import FieldHeadNames
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from pyrad.fields.nerf_field import NeRFField
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from pyrad.graphs.base import Graph
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from pyrad.optimizers.loss import MSELoss
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from pyrad.graphs.modules.ray_sampler import PDFSampler, UniformSampler
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from pyrad.renderers.renderers import AccumulationRenderer, DepthRenderer, RGBRenderer
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from pyrad.utils import colors
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from pyrad.cameras.rays import RayBundle
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from pyrad.utils import visualization, writer
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class NeRFGraph(Graph):
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"""Vanilla NeRF graph
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Args:
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intrinsics (torch.Tensor): Camera intrinsics.
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camera_to_world (torch.Tensor): Camera to world transformation.
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near_plane (float, optional): Where to start sampling points. Defaults to a distance of 2,
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far_plane (float, optional): Where to stop sampling points. Defaults to a distance of 6,
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num_coarse_samples (int, optional): Number of samples in coarse field evaluation. Defaults to 64,
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num_importance_samples(int, optional): Number of samples in fine field evaluation. Defaults to 64,
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"""
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def __init__(
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self,
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intrinsics: torch.Tensor = None,
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camera_to_world: torch.Tensor = None,
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near_plane: float = 2.0,
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far_plane: float = 6.0,
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num_coarse_samples: int = 64,
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num_importance_samples: int = 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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position_encoding = 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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direction_encoding = 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.field_coarse = NeRFField(position_encoding=position_encoding, direction_encoding=direction_encoding)
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self.field_fine = NeRFField(position_encoding=position_encoding, direction_encoding=direction_encoding)
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def populate_misc_modules(self):
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# samplers
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self.sampler_uniform = UniformSampler(num_samples=self.num_coarse_samples)
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self.sampler_pdf = PDFSampler(num_samples=self.num_importance_samples)
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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 = structural_similarity_index_measure
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self.lpips = LearnedPerceptualImagePatchSimilarity()
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def get_param_groups(self) -> Dict[str, List[Parameter]]:
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param_groups = {}
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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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# uniform sampling
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ray_samples_uniform = self.sampler_uniform(ray_bundle)
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# coarse field:
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field_outputs_coarse = self.field_coarse.forward(ray_samples_uniform)
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weights_coarse = ray_samples_uniform.get_weights(field_outputs_coarse[FieldHeadNames.DENSITY])
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rgb_coarse = self.renderer_rgb(
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rgb=field_outputs_coarse[FieldHeadNames.RGB],
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weights=weights_coarse,
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)
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accumulation_coarse = self.renderer_accumulation(weights_coarse)
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depth_coarse = self.renderer_depth(weights_coarse, ray_samples_uniform)
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# pdf sampling
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ray_samples_pdf = self.sampler_pdf(ray_bundle, ray_samples_uniform, weights_coarse)
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# fine field:
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field_outputs_fine = self.field_fine.forward(ray_samples_pdf)
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weights_fine = ray_samples_pdf.get_weights(field_outputs_fine[FieldHeadNames.DENSITY])
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rgb_fine = self.renderer_rgb(
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rgb=field_outputs_fine[FieldHeadNames.RGB],
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weights=weights_fine,
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)
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accumulation_fine = self.renderer_accumulation(weights_fine)
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depth_fine = self.renderer_depth(weights_fine, ray_samples_pdf)
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outputs = {
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"rgb_coarse": rgb_coarse,
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"rgb_fine": rgb_fine,
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"accumulation_coarse": accumulation_coarse,
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"accumulation_fine": accumulation_fine,
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"depth_coarse": depth_coarse,
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"depth_fine": depth_fine,
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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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image = batch["image"]
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rgb_loss_coarse = self.rgb_loss(image, outputs["rgb_coarse"])
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rgb_loss_fine = self.rgb_loss(image, 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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return loss_dict
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def log_test_image_outputs(self, image_idx, step, batch, outputs):
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image = batch["image"]
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rgb_coarse = outputs["rgb_coarse"]
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rgb_fine = outputs["rgb_fine"]
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acc_coarse = visualization.apply_colormap(outputs["accumulation_coarse"])
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acc_fine = visualization.apply_colormap(outputs["accumulation_fine"])
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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_rgb = torch.cat([image, rgb_coarse, rgb_fine], dim=1)
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combined_acc = torch.cat([acc_coarse, acc_fine], dim=1)
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combined_depth = torch.cat([depth_coarse, depth_fine], dim=1)
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writer.put_image(name=f"img/image_idx_{image_idx}", image=combined_rgb, step=step)
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writer.put_image(name=f"accumulation/image_idx_{image_idx}", image=combined_acc, step=step)
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writer.put_image(name=f"depth/image_idx_{image_idx}", image=combined_depth, step=step)
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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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fine_psnr = self.psnr(image, rgb_fine)
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fine_ssim = self.ssim(image, rgb_fine)
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fine_lpips = self.lpips(image, rgb_fine)
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writer.put_scalar(name=f"psnr/val_{image_idx}-coarse", scalar=float(coarse_psnr), step=step)
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writer.put_scalar(name=f"psnr/val_{image_idx}-fine", scalar=float(fine_psnr), step=step)
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writer.put_scalar(name=f"ssim/val_{image_idx}", scalar=float(fine_ssim), step=step)
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writer.put_scalar(name=f"lpips/val_{image_idx}", scalar=float(fine_lpips), step=step)
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writer.put_scalar(name=writer.EventName.CURR_TEST_PSNR, scalar=float(fine_psnr), step=step)
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return fine_psnr.item()
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