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TorchACG is a Pytorch based framework for GAN based ACG applications.

Project description

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.

System requirements

TorchACG should work on

  • all Linux distributions no earlier than Ubuntu 16.04
  • macOS X
  • Windows 10

Installation

Using pip (for stable release):

pip install torchacg

Using pip (for latest master):

conda install -c tczhangzhi torchacg

From source:

git clone https://github.com/tczhangzhi/torchacg.git
cd torchacg
python setup.py install

Getting Started

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)

Test

Setup environments:

export PYTHONPATH=./

Run the test case:

python tests/test_acgan.py

Documentation

Coming soon.

License

PyTorch is MIT-style licensed, as found in the LICENSE file.

Copyright (c) 2020-present, Zhang Zhi

Project details


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This version

0.1.0

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