ConformalLayers: A non-linear sequential neural network with associative layers

ConformalLayers is a conformal embedding of sequential layers of Convolutional Neural Networks (CNNs) that allows associativity between operations like convolution, average pooling, dropout, flattening, padding, dilation, grouping, and stride. Such embedding allows associativity between layers of CNNs, considerably reducing the number of operations to perform inference in neural networks.

This repository is a implementation of ConformalLayers written in Python using Minkowski Engine and PyTorch as backend. This implementation is a first step into the usage of activation functions, like ReSPro, that can be represented as tensors, depending on the geometry model.

Please cite our SIBGRAPI'21 paper if you use this code in your research. The paper presents a complete description of the library:

@InProceedings{sousa_et_al-sibgrapi-2021,
  author    = {Sousa, Eduardo V. and Fernandes, Leandro A. F. and Vasconcelos, Cristina N.},
  title     = {{C}onformal{L}ayers: a non-linear sequential neural network with associative layers},
  booktitle = {Proceedings of the 2021 34th SIBGRAPI Conference on Graphics, Patterns and Images},
  year      = {2021},
  pages     = {386--393},
  doi       = {https://doi.org/10.1109/SIBGRAPI54419.2021.00059}
}

Please, let Eduardo Vera Sousa, Leandro A. F. Fernandes and Cristina Nader Vasconcelos know if you want to contribute to this project. Also, do not hesitate to contact them if you encounter any problems.

Contents:

• ConformalLayers: A non-linear sequential neural network with associative layers
  • Requirements
  • Installation
    • Python System
    • Docker
  • Running Examples
  • Running Unit Tests
  • Documentation
    • Modules
      • Activation
      • Convolution
      • Pooling
      • Regularization
      • Utility
  • License

Requirements

Make sure that you have the following tools and Python modules before attempting to use ConformalLayers:

• Python 3.8+ interpreter
• PyTorch (version >= 1.10.1) with CUDA
• Minkowski Engine (version >= 0.5.4)

The following tool is optional:

• Docker

The following Python modules are optional, but necessary for running the experiments:

• CodeCarbon (version >= 1.2.0)
• PyTorch Lightning (version >= 1.5.9)
• scikit-learn
• SciPy (version >= 1.8.0)
• TorchMetrics (version >= 0.7.0)
• tqdm (version >= 4.61.2)
• tzlocal (version == 2.1)
• Weights & Biases (version >= 0.12.9)

The complete set of required Python modules will be installed automatically by following the instructions presented below.

Installation

Python System

No magic needed here. Just run:

git clone https://github.com/Prograf-UFF/ConformalLayers
cd ConformalLayers
python setup.py install

But the Minkowski Engine may need some special libraries like MKL or OpenBLAS. See its Quick Start Tutorial for details. Also, make sure that Minkowski Engine is not using the CPU_ONLY build set! It happens when you have installed the CPU version of PyTorch. So, first you have to install the CUDA Toolkit/SDK and PyTorch with CUDA according to your computer platform.

Docker

Just run:

git clone https://github.com/Prograf-UFF/ConformalLayers
cd ConformalLayers
docker build -t clayers .

Once the docker is built, check it loads ConformalLayers correctly:

docker run clayers python3 -c "import cl; print(cl.__version__)"

and run it (detached) with GPUs:

docker run --gpus all --env NVIDIA_DISABLE_REQUIRE=1 -it -d clayers

Running Experiments

First, you have to go to the folder <ConformalLayers-dir>/experiments. Run all experiments using the following commands:

python run_all_bechmarks.py --wandb_entity NAME
python run_all_sweeps.py --wandb_entity NAME

Results will be available after summarization:

python summarize_bechmarks.py --wandb_entity NAME
python summarize_sweeps.py --wandb_entity NAME

You have to set NAME to the name of your entity in Weights & Biases.

Individual benchmarks and sweeps can be run using the scripts benchmark.py and sweep.py. Use --help with these scripts to get help on the available arguments.

The files in the <ConformalLayers-dir>/experiments/stuff/networks folder contains the description of each architecture used in our experiments and present the usage of the classes and methods of our library.

Running Unit Tests

First, you have to go to the folder <ConformalLayers-dir>/tests. Run all tests using the following command:

python run_all_tests.py

To run the tests for each module, run:

python test-<module_name>.py

Documentation

Here you find a brief description of the classes available for the user. The detailed documentation is not ready yet.

Contents:

• Modules
  • Convolution
  • Pooling
  • Activation
  • Regularization

Modules

Here we present the main modules implemented in our framework. They can be found inside cl. Most of the modules are used just like in PyTorch, so users with some background on this framework benefits from this implementation. For users not familiar with PyTorch, the usage still quite simple and intuitive.

Module	Description
cl.ConformalLayers	This class is equivalent to the nn.Sequential module from PyTorch
cl.Conv1d, cl.Conv2d, cl.Conv3d	Convolution operation implemented for n-D signals
cl.AvgPool1d, cl.AvgPool2d, cl.AvgPool3d	Average pooling operation implemented for n-D signals
cl.BaseActivation	The abstract class for the activation function layer. To extend the library, one shall implement this class
cl.Dropout	In this version, cl.Dropout is only regularization available. In this approach, during the training phase, we randomly shut down some neurons with a probability p, passed as argument to this module
cl.Flatten	Flattens a contiguous range of dims into a tensor.
cl.ReSPro	The layer that corresponds to the ReSPro activation function. Such function is a linear function with non-linear behavior that can be encoded as a tensor. The non-linearity of this function is controlled by a parameter α (passed as argument) that can be provided or inferred from the data

To define a sequential network, you need to queue the layers in an instance of cl.ConformalLayers. This class in a very similar to the nn.Sequential module from PyTorch and plays an important role in this task, as you can see by comparing the code snippets below:

# This one is built with pure PyTorch
import torch.nn as nn

class D3ModNet(nn.Module):
    
    def __init__(self) -> None:
        super(D3ModNet, self).__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 32, kernel_size=3),
            nn.ReLU(),
            nn.AvgPool2d(kernel_size=2, stride=2),
            nn.Conv2d(32, 32, kernel_size=3),
            nn.ReLU(),
            nn.AvgPool2d(kernel_size=2, stride=2),
            nn.Conv2d(32, 32, kernel_size=3),
            nn.ReLU(),
            nn.AvgPool2d(kernel_size=2, stride=2),
        )
        self.classifier = nn.Linear(128, 10)

    def forward(self, images: ImageBatch) -> Logits:
        x = self.features(images)
        x = torch.flatten(x, 1)
        return self.classifier(x)

# This one is built with ConformalLayers
import cl
import torch.nn as nn

class D3ModNetCL(nn.Module):

    def __init__(self) -> None:
        super(D3ModNetCL, self).__init__()
        self.features = cl.ConformalLayers(
            cl.Conv2d(3, 32, kernel_size=3),
            cl.ReSPro(),
            cl.AvgPool2d(kernel_size=2, stride=2),
            cl.Conv2d(32, 32, kernel_size=3),
            cl.ReSPro(),
            cl.AvgPool2d(kernel_size=2, stride=2),
            cl.Conv2d(32, 32, kernel_size=3),
            cl.ReSPro(),
            cl.AvgPool2d(kernel_size=2, stride=2),
        )
        self.classifier = nn.Linear(128, 10)

    def forward(self, images: ImageBatch) -> Logits:
        x = self.features(images)
        x = torch.flatten(x, 1)
        return self.classifier(x)

They look pretty much the same code, right? That's because we've implemented cl.ConformalLayers to be a transition smoothest as possible to the PyTorch user. Most of the modules has almost the same method signatures of the ones provided by PyTorch.

Activation

Our activation module has cl.ReSPro activation function implemented natively. By using Reflections, Scalings and Projections on an hypersphere in higher dimensions, we created a non-linear differentiable associative activation function that can be represented in tensor form. It has only one parameter, that controls how close to linear or non-linear is the curve. More details are available on the main paper.

• alpha (float, optional) - controls the non-linearity of the curve. If it is not provided, it's automatically estimated.

Convolution

The convolution operation implemented in ConformalLayers on the modules cl.Conv1d, cl.Conv2d and cl.Conv3d is almost the same one implemented on PyTorch but we do not allow bias. This is mostly due to the construction of our logic when building the representation with tensors. Although we have a few ideas on how to include bias on this representation, they are not included in the current version. The parameters are detailed below and are originally available in PyTorch convolution documentation page. The exception here relies on the padding_mode parameter, that is always set to 'zeros' in our implementation.

• in_channels (int) – Number of channels in the input image.
• out_channels (int) – Number of channels produced by the convolution.
• kernel_size (int or tuple) – Size of the convolving kernel.
• stride (int or tuple, optional) – Stride of the convolution. Default: 1.
• padding (int, tuple or str, optional) – Padding added to both sides of the input. Default: 0.
• (int or tuple, optional) – Spacing between kernel elements. Default: 1.
• groups (int, optional) – Number of blocked connections from input channels to output channels. Default: 1.

Pooling

In our current implementation, we only support average pooling, which is implemented on modules cl.AvgPool1d, cl.AvgPool2d and cl.AvgPool3d. The parameters list, originally available in PyTorch average pooling documentation page, is described below:

• kernel_size – the size of the window.
• stride – the stride of the window. Default value is kernel_size.
• padding – implicit zero padding to be added on both sides.
• ceil_mode – when True, will use ceil instead of floor to compute the output shape.
• count_include_pad – when True, will include the zero-padding in the averaging calculation.

Regularization

On regularization module we have cl.Dropout implemented in this version. It is based on the idea of randomly shutting down some neurons in order to prevent overfitting. It takes only two parameters, listed below. This list was originally available in PyTorch documentation page.

• p – probability of an element to be zeroed. Default: 0.5.
• inplace – If set to True, will do this operation in-place. Default: False.

Utility

The cl.Flatten class behaves like nn.Flatten(start_dim=1, end_dim=-1). See PyTorch documentation page.

License

This software is licensed under the GNU General Public License v3.0. See the LICENSE file for details.