MIT License

项目介绍

MatthewFilipovich/torchoptics 是一个托管在 GitHub 上的开源项目,主要使用 Python 编写,采用 MIT License 许可证,目前获得 143 个 Star、20 次 Fork。

仓库全名
MatthewFilipovich/torchoptics
开源许可证
MIT License
Star 数
143
Fork 数
20
未关闭 Issue
1

README 摘要

TorchOptics is an open-source Python library for simulating optical systems using Fourier optics, built on PyTorch. It provides GPU-accelerated, fully differentiable wave optics simulations, enabling end-to-end optimization of optical hardware jointly with machine learning models. Learn more in our paper on arXiv. Key Features 🌊 Differentiable Wave Optics: Model, analyze, and optimize optical systems using Fourier…

完整内容见 GitHub 原始 README 或本页 。

常见问题

torchoptics 用什么语言开发?

主要使用 Python 编写,采用 MIT License 许可证,目前有 143 个 Star。

torchoptics 可以免费商用吗?

仓库采用 MIT License 许可证,可免费使用与修改,商业使用请遵守许可证条款(例如保留版权声明与许可声明)。

如何获取 torchoptics 的源码?

可以直接克隆仓库:git clone https://github.com/MatthewFilipovich/torchoptics.git,默认分支为 main;安装与运行步骤见仓库 README。

torchoptics 最近还在维护吗?

仓库最近一次更新为 5月14日,当前有 1 个未关闭 Issue;Star 数为 143,可作为社区活跃度的参考。

关于

GitHub 原始描述(英文)

Differentiable wave optics simulation library built on PyTorch

描述取自 GitHub 官方 API,保持仓库作者书写的原文。

语言占比

Python

Release

README

build Codecov Documentation Status PyPI version Python Version License

TorchOptics is an open-source Python library for simulating optical systems using Fourier optics, built on PyTorch. It provides GPU-accelerated, fully differentiable wave optics simulations, enabling end-to-end optimization of optical hardware jointly with machine learning models.

Learn more in our paper on arXiv.

Key Features

  • 🌊 Differentiable Wave Optics: Model, analyze, and optimize optical systems using Fourier optics.
  • 🔥 Built on PyTorch: GPU acceleration, batch processing, and automatic differentiation.
  • 🛠️ End-to-End Optimization: Joint optimization of optical hardware and machine learning models.
  • 🔬 Optical Elements: Lenses, phase/amplitude modulators, detectors, polarizers, and more.
  • 🖼️ Spatial Profiles: Hermite-Gaussian, Laguerre-Gaussian, Zernike modes, gratings, and others.
  • 🔆 Polarization and Coherence: Simulate polarized light and fields with arbitrary spatial coherence.

Installation

pip install torchoptics

Documentation

Full documentation is available at torchoptics.readthedocs.io.

Examples

Wave Propagation

Simulate free-space propagation of an octagonal aperture (full example):

import torch
import torchoptics
from torchoptics import Field
from torchoptics.profiles import octagon

device = "cuda" if torch.cuda.is_available() else "cpu"
torchoptics.set_default_spacing(10e-6)
torchoptics.set_default_wavelength(700e-9)

field = Field(octagon(shape=500, radius=150e-5)).to(device)

for z in torch.linspace(0, 2, 11):
    field.propagate_to_z(z).visualize(title=f"z = {z:.2f} m")

4f Imaging System

Simulate a 4f system with a high-pass spatial filter (full example):

import torch
import torchoptics
from torchoptics import Field, System
from torchoptics.elements import AmplitudeModulator, Lens
from torchoptics.profiles import checkerboard, circle

device = "cuda" if torch.cuda.is_available() else "cpu"
torchoptics.set_default_spacing(10e-6)
torchoptics.set_default_wavelength(700e-9)

shape = 500
f = 50e-3

input_field = Field(checkerboard(shape, tile_length=200e-6, num_tiles=15)).to(device)

system = System(
    Lens(shape, f, z=1 * f),
    AmplitudeModulator(1 - circle(shape, radius=200e-6), z=2 * f),
    Lens(shape, f, z=3 * f),
).to(device)

for i in range(5):
    system.measure_at_z(input_field, z=i * f).visualize(title=f"z={i}f", vmax=1)

Inverse Design

Train a diffractive optical system to convert a Gaussian beam into a petal beam (full example):

import torch
import torchoptics
from torch.nn import Parameter
from torchoptics import Field, System
from torchoptics.elements import PhaseModulator
from torchoptics.profiles import gaussian, laguerre_gaussian

device = "cuda" if torch.cuda.is_available() else "cpu"
torchoptics.set_default_spacing(10e-6)
torchoptics.set_default_wavelength(700e-9)

shape = 250
waist_radius = 300e-6

input_field = Field(gaussian(shape, waist_radius=waist_radius), z=0).to(device)

petal_profile = laguerre_gaussian(shape, p=0, l=4, waist_radius=waist_radius)
petal_profile += laguerre_gaussian(shape, p=0, l=-4, waist_radius=waist_radius)
target_field = Field(petal_profile, z=0.8).normalize().to(device)

system = System(
    PhaseModulator(Parameter(torch.zeros(shape, shape)), z=0.2),
    PhaseModulator(Parameter(torch.zeros(shape, shape)), z=0.4),
    PhaseModulator(Parameter(torch.zeros(shape, shape)), z=0.6),
).to(device)

optimizer = torch.optim.Adam(system.parameters(), lr=0.05)
for iteration in range(100):
    optimizer.zero_grad()
    output_field = system.measure_at_z(input_field, 0.8)
    loss = 1 - output_field.inner(target_field).abs().square()
    loss.backward()
    optimizer.step()

For more examples, see the examples gallery.

Contributing

Contributions are welcome! See the Contributing Guide for details.

Citing TorchOptics

If you use TorchOptics in your research, please cite our paper:

@misc{filipovich2024torchoptics,
      title={TorchOptics: An open-source Python library for differentiable Fourier optics simulations},
      author={Matthew J. Filipovich and A. I. Lvovsky},
      year={2024},
      eprint={2411.18591},
      archivePrefix={arXiv},
      primaryClass={physics.optics},
      url={https://arxiv.org/abs/2411.18591},
}

License

Distributed under the MIT License. See LICENSE for details.

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