TorchOptics is an open-source Python library for differentiable Fourier optics simulations with PyTorch.
- Differentiable Fourier Optics Simulations: A comprehensive framework for modeling, analyzing, and designing optical systems using differentiable Fourier optics.
- Built on PyTorch: Leverages PyTorch for GPU acceleration, batch processing, automatic differentiation, and efficient gradient-based optimization.
- End-to-End Optimization: Enables optimization of optical hardware and deep learning models within a unified, differentiable pipeline.
- Wide Range of Optical Elements and Spatial Profiles: Includes standard elements like lenses and modulators, along with commonly used spatial profiles such as Hermite-Gaussian and Laguerre-Gaussian beams.
- Polarized Light Simulation: Simulates polarized light interactions using matrix Fourier optics with Jones calculus.
- Spatial Coherence Support: Models optical fields with arbitrary spatial coherence through the mutual coherence function.
Our research paper, available on arXiv, introduces the TorchOptics library and provides a comprehensive review of its features and applications.
Access the latest documentation at torchoptics.readthedocs.io.
TorchOptics and its dependencies can be installed using pip:
pip install torchoptics
To install the library in development mode, first clone the GitHub repository and then use pip to install it in editable mode:
git clone https://github.com/MatthewFilipovich/torchoptics
pip install -e ./torchoptics
This example demonstrates simulating a 4f imaging system using TorchOptics. The field at each focal plane along the z-axis is computed and visualized:
import torch
import torchoptics
from torchoptics import Field, System
from torchoptics.elements import Lens
from torchoptics.profiles import checkerboard
# Set simulation properties
shape = 1000 # Number of grid points in each dimension
spacing = 10e-6 # Spacing between grid points (m)
wavelength = 700e-9 # Field wavelength (m)
focal_length = 200e-3 # Lens focal length (m)
tile_length = 400e-6 # Checkerboard tile length (m)
num_tiles = 15 # Number of tiles in each dimension
# Determine device
device = "cuda" if torch.cuda.is_available() else "cpu"
# Configure torchoptics default properties
torchoptics.set_default_spacing(spacing)
torchoptics.set_default_wavelength(wavelength)
# Initialize input field with checkerboard pattern
field_data = checkerboard(shape, tile_length, num_tiles)
input_field = Field(field_data).to(device)
# Define 4f optical system with two lenses
system = System(
Lens(shape, focal_length, z=1 * focal_length),
Lens(shape, focal_length, z=3 * focal_length),
).to(device)
# Measure field at focal planes along the z-axis
measurements = [
system.measure_at_z(input_field, z=i * focal_length)
for i in range(5)
]
# Visualize the measured intensity distributions
for i, measurement in enumerate(measurements):
measurement.visualize(title=f"z={i}f", vmax=1)
Intensity distributions at different focal planes in the 4f system.
Propagation of the intensity distribution.
For more examples and detailed usage, please refer to the documentation.
We welcome all bug reports and suggestions for future features and enhancements, which can be filed as GitHub issues. To contribute a feature:
- Fork it (https://github.com/MatthewFilipovich/torchoptics/fork)
- Create your feature branch (
git checkout -b feature/fooBar
) - Commit your changes (
git commit -am 'Add some fooBar'
) - Push to the branch (
git push origin feature/fooBar
) - Submit a Pull Request
If you are using TorchOptics for research purposes, we kindly request that you cite the following paper:
M.J. Filipovich and A.I. Lvovsky, TorchOptics: An open-source Python library for differentiable Fourier optics simulations, arXiv preprint arXiv:2411.18591 (2024).
TorchOptics is distributed under the MIT License. See the LICENSE file for more details.