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IBM Developer Model Asset Exchange: Fast Neural Style Transfer
This repository contains code to instantiate and deploy an image style transfer model. This model generates a new image that mixes the content of an input image with the style of another image. The model consists of a deep feed-forward convolutional net using a ResNet architecture, trained with a perceptual loss function between a dataset of content images and a given style image. The model was trained on the COCO 2014 data set and 4 different style images. The input to the model is an image, and the output is a stylized image.
The model is based on the Pytorch Fast Neural Style Transfer Example. The model files are hosted on IBM Cloud Object Storage. The code in this repository deploys the model as a web service in a Docker container. This repository was developed as part of the IBM Developer Model Asset Exchange and the public API is powered by IBM Cloud.
Model Metadata
| Domain | Application | Industry | Framework | Training Data | Input Data Format |
|---|---|---|---|---|---|
| Vision | Style Transfer | General | Pytorch | COCO 2014 | Image (PNG/JPG/TIFF) |
References
- J. Johnson, A. Alahi, L. Fei-Fei, "Perceptual Losses for Real-Time Style Transfer and Super-Resolution", 2016
- D. Ulyanov, A. Vedaldi, V. Lempitsky, "Instance Normalization", 2017
- D. Ulyanov, A. Vedaldi, V. Lempitsky, "Improved Texture Networks: Maximizing Quality and Diversity in Feed-forward Stylization and Texture Synthesis", 2017
- Pytorch Tutorial
- Pytorch Fast Neural Style Transfer Example
Licenses
| Component | License | Link |
|---|---|---|
| This repository | Apache 2.0 | LICENSE |
| Model Weights | BSD-3-Clause | Pytorch Examples LICENSE |
| Model Code (3rd party) | BSD-3-Clause | Pytorch Examples LICENSE |
| Test assets | Various | Samples README |
Pre-requisites:
Note: this model can be very memory intensive. If you experience crashes (such as the model API process terminating with a Killed message), ensure your docker container has sufficient resources allocated (for example you may need to increase the default memory limit on Mac or Windows).
docker: The Docker command-line interface. Follow the installation instructions for your system.- The minimum recommended resources for this model is 6 GB Memory and 4 CPUs.
Deployment options
Deploy from Quay
To run the docker image, which automatically starts the model serving API, run:
$ docker run -it -p 5000:5000 quay.io/codait/max-fast-neural-style-transfer
This will pull a pre-built image from the Quay.io container registry (or use an existing image if already cached locally) and run it. If you'd rather checkout and build the model locally you can follow the run locally steps below.
Deploy on Red Hat OpenShift
You can deploy the model-serving microservice on Red Hat OpenShift by following the instructions for the OpenShift web console or the OpenShift Container Platform CLI in this tutorial, specifying quay.io/codait/max-fast-neural-style-transfer as the image name.
Deploy on Kubernetes
You can also deploy the model on Kubernetes using the latest docker image on Quay.
On your Kubernetes cluster, run the following commands:
$ kubectl apply -f https://raw.githubusercontent.com/IBM/MAX-Fast-Neural-Style-Transfer/master/max-fast-neural-style-transfer.yaml
The model will be available internally at port 5000, but can also be accessed externally through the NodePort.
A more elaborate tutorial on how to deploy this MAX model to production on IBM Cloud can be found here.
Run Locally
1. Build the Model
Clone this repository locally. In a terminal, run the following command:
$ git clone https://github.com/IBM/MAX-Fast-Neural-Style-Transfer.git
Change directory into the repository base folder:
$ cd MAX-Fast-Neural-Style-Transfer
To build the docker image locally, run:
$ docker build -t max-fast-neural-style-transfer .
All required model assets will be downloaded during the build process. Note that currently this docker image is CPU only (we will add support for GPU images later).
2. Deploy the Model
To run the docker image, which automatically starts the model serving API, run:
$ docker run -it -p 5000:5000 max-fast-neural-style-transfer
3. Use the Model
The API server automatically generates an interactive Swagger documentation page. Go to http://localhost:5000 to load it. From there you can explore the API and also create test requests.
Use the model/predict endpoint to load a test image (you can use one of the test images from the samples folder) and get a stylized image back from the API. You can select the style model to use with the model querystring argument. The available options are mosaic (the default model), candy, rain_princess and udnie. See the Pytorch example for more details.
You can also test it on the command line, for example:
$ curl -F "image=@samples/bridge.jpg" -XPOST http://localhost:5000/model/predict?model=udnie > result.jpg
You can then open the stylized result image on your machine in the tool of your choice, which should look like the image below.
4. Development
To run the Flask API app in debug mode, edit config.py to set DEBUG = True under the application settings. You will then need to rebuild the docker image (see step 1).
5. Cleanup
To stop the Docker container, type CTRL + C in your terminal.
Resources and Contributions
If you are interested in contributing to the Model Asset Exchange project or have any queries, please follow the instructions here.