complextorch 1.1.10

A lightweight complex-valued neural network package built on PyTorch

ComplexTorch

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Complex PyTorch

(Available on PyPI)

Author: Josiah W. Smith, Ph.D.

A lightweight complex-valued neural network package built on PyTorch.

This is a package built on PyTorch with the intention of implementing light-weight interfaces for common complex-valued neural network operations and architectures. Notably, we include efficient implementations for linear, convolution, and attention modules in addition to activation functions and normalization layers such as batchnorm and layernorm.

Although there is an emphasis on 1-D data tensors, due to a focus on signal processing, communications, and radar data, many of the routines are implemented for 2-D and 3-D data as well.

Version 1.1 Release Notes:

• Methods have been renamed to reflect identical names in PyTorch, e.g., complextorch.nn.CVConv1d was renamed to complextorch.nn.Conv1d. This change was implemented for quick conversion from PyTorch to complextorch.
• Use of torch.Tensor is now recommended over complextorch.CVTensor. Previous speed advantages of complextorch.CVTensor are no longer present if using a version of PyTorch newer than 2.1.0.
• Similarly, previous implementations of complextorch.nn.Conv1d (for 1-D, 2-D, 3-D, and transposed convolution) and complextorch.nn.Lienar have been renamed with the prefix Slow as PyTorch's native convolution and linear operators now outperform that of complextorch. Now, complextorch.nn.Conv1d, for example, uses torch.nn.Conv1d with dtype=torch.float for maximum efficiency.

Documentation

Please see Read the Docs or our arXiv paper, which is also located at docs/complextorch_paper.pdf.

Dependencies

This library requires numpy and PyTorch.PyTorch should be installed to your environment using the compute platform (CPU/GPU) settings for your machine. PyTorch will not be automatically installed with the installation of complextorch and MUST be installed manually by the user.

Installation:

IMPORTANT: Prior to installation, install PyTorch to your environment using your preferred method using the compute platform (CPU/GPU) settings for your machine.

Using pip

pip install complextorch

From the source:

git clone https://github.com/josiahwsmith10/complextorch.git
cd complextorch
pip install -r requirements.txt
pip install . --use-pep517

Basic Usage

import complextorch as cT

x = cT.randn(64, 5, 7)