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Inpainting-TransGAN

Using ViTA Group's TransGAN to inpaint images.

Base code is here

Environment requirements

Our environment

Ubuntu 16.04 with 8 Core CPU, RTX 3090 24G*1 and 16G RAM

Python 3.10

Guide

Use pip to install all the needed packages. You can just copy these command to install.

First, install all the package without pytorch.

pip install imageio scipy==1.11.1 six numpy einops pillow python-dateutil==2.8.2 protobuf==3.19.0 tensorboard==1.12.2 tensorboardX==1.6 tqdm==4.29.1 opencv-python

Then, install pytorch 2.1.1. The command here is pytorch 2.1.1 with CUDA 11.8 version. If you are using different version of CUDA, you should search and download from pytorch's website.

pip install torch==2.1.1+cu118 torchvision==0.16.1+cu118 torchaudio==2.1.1+cu118 -f https://download.pytorch.org/whl/torch_stable.html

Noted

When you run the train script and it shows error below, please follow theses steps to install extra libraries.

  1. ImportError: libGL.so.1: cannot open shared object file: No such file or directory

    Ubuntu Linux command:

    apt-get update
    apt install libgl1-mesa-glx

Train or test

We try to use Trans-GAN to inpaint images on CelebA-HQ-256 and Place365. The original resolution of Place365 is 256*256, and we have resized them to 128*128, so if you want to use your own datasets to train Place365-inpainting, don't forget to resize them to 128 or write a new training script.

For training CelebA-HQ-256, run train_CelebA.py, for training Place365, run train_Place.py .

Before training or testing, don't forget to modify the dataset path and mask path in CelebAHQ_cfg.py and Place365_128_cfg.py.

For testing, you should modify the checkpoint path in CelebAHQ_cfg.py and Place365_128_cfg.py.

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Using ViTA Group's TransGAN to inpaint images.

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