styleTransfer_spell

Style Transfer example with ml5.js, training the model with Spell.run

Demo: https://yining1023.github.io/styleTransfer_spell/

Here are some slides that introduce what is style transfer and how does it work.

Training a style transfer model with Spell!

Check out Transferring Style Tutorial from Spell for more info about steps 1 - 3.

1. Preparing your environment
2. Downloading Datasets
3. Training with style.py
4. Converting model to ml5js (Read more at reiinakano's fast-style-transfer-deeplearnjs)

Credits

I used the TensorFlow implementation of fast style tranfer developed by Logan Engstrom. And the fast-style-transfer-deeplearnjs by Reiichiro Nakano to convert the tensforflow model to a tf.js model that can used in ml5.js

0. Setup Spell.run

Sign up on Spell.run, login

You can skip the following two steps if you have pip installed already.

$ curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py`
$ python get-pip.py

Install spell, and log into spell

$ pip install spell
$ spell
$ spell login

1. Preparing your environment

Clone the fast-style-transfer git repo from github.

$ git clone https://github.com/lengstrom/fast-style-transfer
$ cd fast-style-transfer

Create some folders and files

$ mkdir ckpt/
$ touch ckpt/.gitignore
$ mkdir images
$ mkdir images/style

Put a "style" image into the images/style directory. There needs to be at least one image in this folder.

Add the changes and commit it to git.

$ git add images ckpt
$ git commit -m "Added required folders and images"

2. Downloading Datasets

$ spell run --machine-type CPU ./setup.sh

It took me 1.5 hours to finish this run. The dataset is large, it takes time to save to Spell.

3. Training with style.py

spell run --mount runs/THE_RUN_NUMBER_OF_YOUR_SETUP_RUN/data:datasets \
            --machine-type V100 \
            --framework tensorflow \
            --apt ffmpeg \
            --pip moviepy \
  "python style.py \
  --checkpoint-dir ckpt \
  --style images/style/YOUR_STYLE_IMAGE_NAME.jpg \
  --style-weight 1.5e2 \
  --train-path datasets/train2014 \
  --vgg-path datasets/imagenet-vgg-verydeep-19.mat"
  

Remember to replace the THE_RUN_NUMBER_OF_YOUR_SETUP_RUN and YOUR_STYLE_IMAGE_NAME. I used V100 machine. This run took me ~2 hours. And it created files in the ckpt folder.

To list and download these resulting checkpoint files use the spell ls and spell cp commands. You can go to any directory that you want to save the files in and run -

spell ls runs/YOUR_RUN_NUMBER
spell ls runs/YOUR_RUN_NUMBER/ckpt
spell cp runs/YOUR_RUN_NUMBER/ckpt

Remember to replace YOUR_RUN_NUMBER.

4. Converting model to ml5js

Go to a new directory,

git clone https://github.com/reiinakano/fast-style-transfer-deeplearnjs.git
cd fast-style-transfer-deeplearnjs

Put the checkpoint files we downloaded from spell into the current directory,

python scripts/dump_checkpoint_vars.py --output_dir=src/ckpts/YOUR_FOLDER_NAME --checkpoint_file=./YOUR_FOLDER_NAME/fns.ckpt

python scripts/remove_optimizer_variables.py --output_dir=src/ckpts/YOUR_FOLDER_NAME

Remember to replace YOUR_FOLDER_NAME, the folder that holds all the checkpoint files. It will create a new folder in src/ckpts with 49 items including a manifest.json file.

5. Run the model in ml5js

Copy the folder we got from step 4 and put it into /models. Change style = ml5.styleTransfer('models/fuchun', modelLoaded); to your model file path. Run the code

python -m SimpleHTTPServer

Go to localhost:8000, you should be able to see the model working!