This repository is the PyTorch implementation of Hyper-AJSCC, as proposed in the paper My Paper Title.
The codes are compatible with the packages:
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pytorch 2.0.1
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torchvision 0.15.2
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numpy 1.25.2
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tensorboardX 2.6.2.2
The code can be run on the datasets such as CIFAR-10 . One should download the datasets in a directory (e.g., ./data/) and change the root parameter in datasets/dataloader.py, e.g.,
root = r'./data/Additionally, since there is no official implementation of Generalized divisive normalization layer (GDN) in PyTorch, one may need to use the PyTorch implementation at GitHub - jorge-pessoa/pytorch-gdn: PyTorch implementation of the Generalized divisive normalization non-linearity layer and put the corresponding source code in models/pytorch_gdn.py.
To train the model(s) for image transmission, run this command:
python main.py --train 1 --sc 16 --type recon --epoches 800 --lr 1e-4 --step 400To train the model(s) for image classification, run this command:
python main.py --train 1 --sc 8 --type task --epoches 400 --lr 1e-3 --step 150Then, the log files logs_recon\ and logs_task\ records the information such as the values of loss in the training stage. And the trained models are automatically stored at the directory trained_models\.
To evaluate the model trained for image transmission , run:
python main.py --train 0 --sc 16 --type recon --epoches 800 --lr 1e-4 --step 400Or similarly run the following command to evaluate the models trained for image classification,
python main.py --train 0 --sc 8 --type task --epoches 400 --lr 1e-3 --step 150Then, the evaluation results are recorded in the tensorboard files in directories result_recon\ or result_task\