xonv 0.2.5

xonv: Extended convolutional layers

xonv: Extended convolutional layers

This repository contains the code for extended convolutional layers. These layers are akin to the convolutional layers in PyTorch, but with the key difference that they have spatially varying kernels.

Since the kernels are spatially varying, the convolutional layers in this repository offer more expressive power than the standard convolutional layers while having $\mathcal{O}(n)$ parameters, where $n$ is the input size. The implementation is based on matrix-vector products, which allows for scalable training and inference on GPUs.

Below is a comparison between the toeplitz-like matrix associated with a regular convolutional layer vs the extended convolutional layer (xonv):

Installation

Run the command below to install the package to be used in your Python environment.

pip install xonv

For further development and to run the examples, clone the repository and install the package in editable mode. Make sure to adapt CUDA version in setup.cfg to the one installed on your system.

# Create a new conda environment.
conda create --name xonv "python<=3.12"
conda activate xonv

# Clone the repository and install the package in editable mode.
git clone ttps://github.com/alisiahkoohi/xonv
cd xonv/
pip install -e .

Usage

The extended convolutional layers can be used as a drop-in replacement for the PyTorch convolutional layers. The following example demonstrates how to use the extended convolutional layers:

from xonv.layer import Xonv2D

input_size = (32, 32)  # Height, Width of input
in_channels = 3
out_channels = 16
kernel_size = 3
stride = 2

layer = Xonv2D(
    in_channels,
    out_channels,
    kernel_size,
    input_size,
    stride=stride,
)

input_tensor = torch.randn(1, in_channels, *input_size)
output = layer(input_tensor)

print(layer)  # Xonv2D(in_channels=3, out_channels=16, kernel_size=3, input_size=(32, 32), stride=2)
print(output.shape)  # Should be [1, 16, 16, 16]

Examples

Visualizing the associated linear matrix

To visualize the toeplitz-like matrix associated with the convolutional layer, run the following command:

python scripts/create_toeplitz_like_matrix.py

Comparing the loss landscape of Xonv2d vs torch.nn.Conv2d for a regression task

To compare the loss landscape of the extended convolutional layer with the standard convolutional layer, run the following command:

python scripts/regression_loss_landscape_comparison.py

Comparing convergence rate of Xonv2d vs torch.nn.Conv2d for a regression task

To compare the convergence rate of the extended convolutional layer with the standard convolutional layer, run the following command:

python scripts/regression_convergence_comparison.py

Questions

Please contact [e-mail] for questions.

Author

Ali Siahkoohi