https://github.com/muhd-umer/aecc
AeCC: Autoencoders for compressed communication
https://github.com/muhd-umer/aecc
autoencoder cnn communication compression computer-vision python pytorch vision-transformer
Last synced: about 1 year ago
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AeCC: Autoencoders for compressed communication
- Host: GitHub
- URL: https://github.com/muhd-umer/aecc
- Owner: muhd-umer
- License: mit
- Created: 2023-11-24T16:25:03.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-12-30T09:40:33.000Z (over 2 years ago)
- Last Synced: 2025-03-28T18:12:06.891Z (about 1 year ago)
- Topics: autoencoder, cnn, communication, compression, computer-vision, python, pytorch, vision-transformer
- Language: Python
- Homepage:
- Size: 208 MB
- Stars: 4
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Codeowners: .github/CODEOWNERS
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README
# AeCC: Autoencoders for Compressed Communication
[](https://opensource.org/licenses/MIT)
[](https://pytorch.org/)
In the field of communication systems, the transmission of images over noisy channels poses a significant challenge. To address this challenge, a novel communication system is proposed that employs a vision transformer-based autoencoder for image compression and a denoising network for noise removal. The proposed system operates by first encoding the input image into a lower-dimensional latent space representation using the vision transformer-based autoencoder. This compressed representation is then transmitted through a noisy channel, where it is inevitably corrupted by noise. At the receiver, the denoising network is employed to reconstruct the original image from the received, noisy representation.
## Block Diagram
## Installation
To get started with this project, follow the steps below:
- Clone the repository to your local machine using the following command:
```fish
git clone https://github.com/muhd-umer/aecc.git
```
- It is recommended to create a new virtual environment so that updates/downgrades of packages do not break other projects. To create a new virtual environment, run the following command:
```fish
conda env create -f environment.yml
```
- Alternatively, you can use `mamba` (faster than conda) package manager to create a new virtual environment:
```fish
wget -O miniforge.sh \
"https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh"
bash miniforge.sh -b -p "${HOME}/conda"
source "${HOME}/conda/etc/profile.d/conda.sh"
# For mamba support also run the following command
source "${HOME}/conda/etc/profile.d/mamba.sh"
conda activate
mamba env create -f environment.yml
```
- Activate the newly created environment:
```fish
conda activate aecc
```
- Install the PyTorch Ecosystem:
```fish
# pip will take care of necessary CUDA packages
pip3 install torch torchvision torchaudio
# additional packages (already included in environment.yml)
pip3 install einops python-box timm torchinfo \
lightning rich wandb rawpy
```
## Dataset
A custom Imagenette dataset is used for training and testing the model. The dataset can be downloaded from [here](https://github.com/muhd-umer/aecc/releases/tag/v0.0.1).
Or, you can use the following commands to download the dataset:
```fish
wget -O imagenette.zip \
"https://github.com/muhd-umer/aecc/releases/download/v0.0.1/imagenette320p.zip"
# unzip the dataset
unzip -q data/imagenette.zip -d data/
```
CIFAR100 can be downloaded from [here](https://www.cs.toronto.edu/~kriz/cifar.html). CIFAR10 can also be downloaded in a similar manner.
Or, you can use the following commands to download the dataset:
```fish
# download as python pickle
cd data
curl -O https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz
tar -xvzf cifar-100-python.tar.gz
# download as ImageNet format
pip3 install cifar2png
cifar2png cifar100 data/cifar100
```
For MNIST, `torchvision` can be used to download the dataset:
```fish
# download MNIST dataset
python3 -c "import torchvision; torchvision.datasets.MNIST('data/', download=True)"
```
## Training
To train the model from scratch, run the following command:
```fish
# train the model from scratch using default config
python3 train.py --model-name MODEL_NAME \ # name of the model
--dataset DATASET # name of the dataset
# Train the model from scratch using overrides
python3 train.py --model-name MODEL_NAME \ # name of the model
--dataset DATASET \ # name of the dataset
--model-cfg MODEL_CFG \ # path to the model config file
--data-cfg DATA_CFG \ # path to the data config file
--data-dir DATA_DIR \ # directory containing data
--model-dir MODEL_DIR \ # directory to save model
--batch-size BATCH_SIZE \ # batch size
--num-workers NUM_WORKERS \ # number of workers
--num-epochs NUM_EPOCHS \ # number of epochs
--lr LR \ # learning rate
--val-size VAL_SIZE \ # validation size
--noise-factor NOISE_FACTOR \ # noise factor
--rich-progress \ # use rich progress bar
--accelerator ACCELERATOR \ # type of accelerator
--devices DEVICES \ # devices to use for training
--weights WEIGHTS \ # path to weights file
--resume \ # resume training from the provided weights
--test-only \ # only test the model, do not train
--logger-backend LOGGER_BACKEND \ # logger backend (tensorboard, wandb)
--normalize NORMALIZE \ # normalize the data (default, standard, neg1to1)
--val-freq VAL_FREQ # validate every n epochs
```
## Project Structure
The project is structured as follows:
```shell
aecc
├── config/ # configuration directory
├── data/ # data directory
├── models/ # model directory
├── resources/ # resources directory
├── utils/ # utility directory
├── LICENSE # license file
├── README.md # readme file
├── environment.yml # conda environment file
├── upscale.py # upscaling script
└── train.py # training script
```
## Contributing ❤️
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.