TorchACG is a Pytorch based framework for GAN based ACG applications.
TorchACG is a Pytorch based framework for designing and developing GAN based ACG applications. This framework has been designed to provide building blocks for popular GANs and also to allow customization for cutting edge research.
TorchACG should work on
- all Linux distributions no earlier than Ubuntu 16.04
- macOS X
- Windows 10
Using pip (for stable release):
pip install torchacg
Using pip (for latest master):
conda install -c tczhangzhi torchacg
git clone https://github.com/tczhangzhi/torchacg.git cd torchacg python setup.py install
The network architectures of popular GANs and cutting edge GAN based Neural Style Transfer methods are out of the box:
from torchacg.model.acgan import Generator, Discriminator g_model = Generator() d_model = Discriminator()
The diverse loss functions and complicated adversarial training methods are packed in different learners. It is convenient for users to reproduce the state-of-the-art training methods quickly without knowing implementation details:
from torchacg.trainer.acgan import ACGANTrainer trainer = ACGANTrainer(g_model, d_model, dataloader, device=device) trainer.start(epoch=10)
Run the test case:
PyTorch is MIT-style licensed, as found in the LICENSE file.
Copyright (c) 2020-present, Zhang Zhi
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