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https://github.com/alisiahkoohi/xonv
xonv: Extended convolutional layers
https://github.com/alisiahkoohi/xonv
convolutional-neural-networks deep-learning deep-neural-networks
Last synced: about 2 months ago
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xonv: Extended convolutional layers
- Host: GitHub
- URL: https://github.com/alisiahkoohi/xonv
- Owner: alisiahkoohi
- License: mit
- Created: 2024-07-06T02:10:43.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2024-08-19T21:39:31.000Z (4 months ago)
- Last Synced: 2024-09-20T02:18:08.505Z (3 months ago)
- Topics: convolutional-neural-networks, deep-learning, deep-neural-networks
- Language: Python
- Homepage: https://pypi.org/project/xonv/
- Size: 84 KB
- Stars: 0
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
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.
```bash
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.**
```bash
# 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:
```python
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:
```bash
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:
```bash
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:
```bash
python scripts/regression_convergence_comparison.py
```
## Questions
Please contact [email protected] for questions.
## Author
Ali Siahkoohi