DL Projekt: Style Transfer

Setup

File Structure

.
├── data 
│   ╰── train2017
│       ╰── train2017
│           ├── 000000000009.jpg
│           ╽   ...
├── src 
│   ├── architecture.py         <- model architecture
│   ├── dataset.py              <- dataloading
│   ├── train.py                <- actual training
│   ├── trainer.py              <- specific training loop
│   ├── loss.py                 <- perceptual loss function
│   ├── config.py
│   ╰── utils.py                <- (TODO: the video stuff)
├── style_images                <- put all style images here
│   ├── style1.jpg
│   ╽   ...
├── test_images                 <- put all test images here
├── checkpoints                 <- create this directory (models will be saved here)
├── environment.yml             <- for conda (if desired)
├── demo.ipynb                  <- demonstration of our results
├── README.md
╽

Setup

1. Download the COCO Dataset and extract it into the data folder. (for curl use curl http://images.cocodataset.org/zips/train2017.zip --output data/train2017.zip and then unzip data/train2017.zip -d train2017, then the directory structure should be correct (if not just adjust the DATA_DIR in src/config.py))

• If you want to use the conda environment, run conda env create -f environment.yml and then conda activate style-transfer in order to activate it.
• NOTE: Depending on your OS, you may need to change the pytorch related packages and channels (see here (channels are added in the command with -c))
• as it was not possible to install openCV with conda, it is installed with pip (see here)
• a list of all needed packages can be found below if anything goes wrong (or you just want to install them manually)

3. Specify the style and test images in src/config.py. (Check all other parameters as well (one may want to change the len of the dataset to adjust the number of training images))
4. Run python -m src.train to start training.
5. The saved model can be found in checkpoints and the generated output images in test_images.
6. You can use utils.py to apply the model to your webcam stream (see utils.py for more information). Alternatively a very similar demo can be found in demo.ipynb.

Exercise

Build a model, that takes a style image and mixes it with a content image. Demonstrate your results by creating a simple application, which takes a webcam frame and stylises it.

Paper: https://cs.stanford.edu/people/jcjohns/papers/eccv16/JohnsonECCV16.pdf

Dataset for content images: http://cocodataset.org/#download

 

Packages

We used Python 3.10 and the following packages:

• torchaudio
• pytorch-cuda
• torchvision
• pytorch
• wandb
• pandas
• torchmetrics
• matplotlib
• tqdm
• numpy
• pillow
• datetime