53 lines
1.6 KiB
Markdown
53 lines
1.6 KiB
Markdown
# Deep JSCC
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This implements training of deep JSCC models for wireless image transmission as described in the paper [Deep Joint Source-Channel Coding for Wireless Image Transmission](https://ieeexplore.ieee.org/abstract/document/8723589) by Pytorch. And there has been a [Tensorflow and keras implementations ](https://github.com/irdanish11/DJSCC-for-Wireless-Image-Transmission).
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This is my first time to use PyTorch and git to reproduce a paper, so there may be some mistakes. If you find any, please let me know. Thanks!
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## Architecture
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## Demo
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## Installation
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conda or other virtual environment is recommended.
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```
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git clone https://github.com/chunbaobao/Deep-JSCC-PyTorch.git
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pip install requirements.txt
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```
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## Usage
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### Training Model
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Run(example presented in paper)
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```
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cd ./Deep-JSCC-PyTorch
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```
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```
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python train.py --lr 10e-4 --epochs 100 --batch_size 32 --channel 'AWGN' --saved ./saved --snr_list 1 4 7 13 19 --ratio_list 1/6 1/12 --dataset imagenet
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```
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or
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```
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python train.py --lr 10e-3 --epochs 100 --batch_size 512 --channel 'AWGN' --saved ./saved --dataset cifar10 --num_workers 4 --parallel True
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```
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### Evaluation
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Run(example presented in paper)
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```
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python eval.py --channel 'AWGN' --saved ./saved/${mode_path} --snr 20 --ratio_list 1/3 --test_img ./test_image ./demo/kodim08.png
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```
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## Citation
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If you find (part of) this code useful for your research, please consider citing
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```
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@misc{chunhang_Deep-JSCC,
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author = {chunhang},
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title = {a pytorch implementation of Deep JSCC},
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url ={https://github.com/chunbaobao/Deep-JSCC-PyTorch},
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year = {2023}
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}
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