It is recommended to run this project on a Deep Learning with Linux Google Cloud VM instance preinstalled with NVIDIA drivers, CUDA 11, and PyTorch 1.8.
Once the VM is set up, clone the repo with git clone https://github.com/colinpcurtis/datares_GANs.git
and cd datares_GANs.
Run make dataset. This downloads and unzips the dataset zip files into the /datasets directory.
Then run ./move.sh to move the dataset into a format so that the PyTorch dataset class can read
the images.
There are numerous command line arguments to run the model. For simplicity purposes they can be found and changed in
the Makefile. To run the model, simply invoke the make command and training will start.
There may be some CUDA out of memory errors depending on the GPU that is used for training. In that case, change the BATCH_SIZE variable to something smaller in Models/cycleGAN/CycleGAN.py
In a new terminal window, run tensorboard --logdir ./logs to open the TensorBoard logs. This has to be
port forwarded to a local computer, so run
gcloud compute ssh user@instance_name -- -NfL 6006:localhost:6006
in the local terminal to access the logs. Then visiting http://localhost:6006 in a web browser
will display the training logs.
It takes approximately 3 hours to train the Cycle GAN for 10 epochs using 5 residual layers and float 32.
Google Cloud SDK (if using a gcloud VM) must be installed for port forwarding.
The final iteration of the model will allow users to upload images onto a web app, but some initial results can be seen below.
This large image of Royce Hall has to be cropped to a 512x512 size in order to fit in the model.
Then a generated image from the Cycle GAN can be seen below.
