mirror of
https://github.com/nerfstudio-project/nerfstudio.git
synced 2026-11-12 10:55:57 +08:00
Ethan WeberandJake Austin
0b042be956
* Model class done, pipeline class still needs more visualization stuffs * Renaming the BaseDataContainer to DataloaderOutputs and adding documentation * Pushing more documentation, removing some trivial functions * Updating dict typing * Removing config dicts for collider * Passing tests * Pushing shell new dataloader * Saving work, need to fix NGP model's get_loss_dict and Pipeline * V1 of the ngp pipeline should be done. Need train script and debugging * Updating with latest instant-ngp code * Syntax updates from latest ngp graph code * Linter, removing unneeded code in new NGP graph code * Style * Should be good now. Linter is happy and the code is verbatum current instant-ngp * Everything done except novel view testing code * Fixing some nitpicks * Debugging dataloader * merged master * WOP. integrating pipeline with configs and notebook demo * removing model folder * a lot of name changing from Graph to Model * in progress... changing trainer to use pipeline... broken right now * pipeline code runs * switched to val dataset * makes run_eval.py work * updated configs for pipeline change * fixing test_run_train script * fixing black and linting * fix docs * small updates to trainer Co-authored-by: Jake Austin <jake-austin@berkeley.edu>
56 lines
2.0 KiB
Python
56 lines
2.0 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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"""Collection of Ray Losses"""
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import torch
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from torchtyping import TensorType
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from nerfactory.cameras.rays import RaySamples
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def distortion_loss(
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ray_samples: RaySamples, densities: TensorType["bs":..., "num_samples", 1]
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) -> TensorType["bs":..., 1]:
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"""Ray baserd distortion loss proposed in MipNeRF-360.
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.. math::
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\\mathcal{L}(\\mathbf{s}, \\mathbf{w}) =\\iint\\limits_{-\\infty}^{\\,\\,\\,\\infty}
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\\mathbf{w}_\\mathbf{s}(u)\\mathbf{w}_\\mathbf{s}(v)|u - v|\\,d_{u}\\,d_{v}
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where :math:`\\mathbf{w}_\\mathbf{s}(u)=\\sum_i w_i \\mathbb{1}_{[\\mathbf{s}_i, \\mathbf{s}_{i+1})}(u)`
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is the weight at location :math:`u` between bin locations :math:`s_i` and :math:`s_{i+1}`.
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Args:
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ray_samples: Ray samples to compute loss over
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densities: Predicted sample densities
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Returns:
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Distortion Loss.
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"""
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# Compute the weight at each sample location
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weights = ray_samples.get_weights(densities)
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midpoints = (ray_samples.frustums.starts + ray_samples.frustums.ends) / 2.0 # (..., num_samples, 1)
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loss = (
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weights * weights[..., None, :, 0] * torch.abs(midpoints - midpoints[..., None, :, 0])
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) # (..., num_samples, num_samples)
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loss = torch.sum(loss, dim=(-1, -2))[..., None] # (..., num_samples)
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loss = loss + 1 / 3.0 * torch.sum(weights**2 * (ray_samples.frustums.ends - ray_samples.frustums.starts), dim=-2)
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return loss
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