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  • Install PyTorch and other dependencies (e.g., torchvision, visdom and dominate).

    • For Conda users, create an environment and then run the installation script ./scripts/conda_deps.sh

The code for this model is based on CycleGan which was written by Jun-Yan Zhu and Taesung Park, and supported by Tongzhou Wang.

Relevant files are: feature_gan_model.py networks.py

To train: First download the VGG-19 model from the following link:

"https://drive.google.com/file/d/1pjWYa-5_PCpwlNNsa5Rfbb8am7k6vF2I/view?usp=sharing"

Then paste it in the main folder:

To start training:

"python train.py --dataroot ./datasets/naos --name naos --model feature_gan --netG unet_256 --load_size 296 --crop_size 256 --lr 0.00005 --n_epochs 100 --n_epochs_decay 150 --print_freq 10 --display_freq 10"

To infer and trace:

Use your own weights or download a pre-trained model from the following link:

"https://drive.google.com/file/d/1wTNH7gB3ujb3AwebK8AarIoiD7GmrORY/view?usp=sharing"

place the weights file on: "checkpoints/SimRobot"

Then:

"python test.py --dataroot ./datasets/SimRobot --name SimRobot --model feature_gan --netG unet_512 --num_test 10 --load_size 512 --crop_size 512"

The generated images are placed in the folder "results", the traced model "traced_unet_512" is on the main folder.