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https://github.com/IBM/MAX-Fast-Neural-Style-Transfer

Generate a new image that mixes the content of a source image with the style of another image.
https://github.com/IBM/MAX-Fast-Neural-Style-Transfer

docker-image machine-learning machine-learning-models pytorch

Last synced: 2 months ago
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Generate a new image that mixes the content of a source image with the style of another image.

Host: GitHub
URL: https://github.com/IBM/MAX-Fast-Neural-Style-Transfer
Owner: IBM
License: apache-2.0
Created: 2018-03-09T23:08:47.000Z (over 6 years ago)
Default Branch: master
Last Pushed: 2023-05-23T00:40:48.000Z (about 1 year ago)
Last Synced: 2024-04-10T20:48:29.831Z (3 months ago)
Topics: docker-image, machine-learning, machine-learning-models, pytorch
Language: Python
Homepage: https://developer.ibm.com/code/exchanges/models/
Size: 6.34 MB
Stars: 50
Watchers: 26
Forks: 21
Open Issues: 5
Metadata Files:
- Readme: README.md
- License: LICENSE

Lists

awesome-ibmcloud - max-fast-neural-style-transfer - Generate a new image that mixes the content of a source image with the style of another image. (Data & AI)

README

        [![Build Status](https://travis-ci.org/IBM/MAX-Fast-Neural-Style-Transfer.svg?branch=master)](https://travis-ci.org/IBM/MAX-Fast-Neural-Style-Transfer) [![Website Status](https://img.shields.io/website/http/max-fast-neural-style-transfer.codait-prod-41208c73af8fca213512856c7a09db52-0000.us-east.containers.appdomain.cloud/swagger.json.svg?label=api+demo)](http://max-fast-neural-style-transfer.codait-prod-41208c73af8fca213512856c7a09db52-0000.us-east.containers.appdomain.cloud)

[](http://ibm.biz/max-to-ibm-cloud-tutorial)

# 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](http://mscoco.org/dataset/#download) 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](https://github.com/pytorch/examples/tree/master/fast_neural_style). The model files are hosted on [IBM Cloud Object Storage](https://max-cdn.cdn.appdomain.cloud/max-fast-neural-style-transfer/1.0.1/assets.tar.gz). 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](https://developer.ibm.com/code/exchanges/models/) and the public API is powered by [IBM Cloud](https://ibm.biz/Bdz2XM).

## Model Metadata

| Domain | Application | Industry  | Framework | Training Data | Input Data Format |

| ------------- | --------  | -------- | --------- | --------- | -------------- |

| Vision | Style Transfer | General | Pytorch | [COCO 2014](http://mscoco.org/dataset/#download) | Image (PNG/JPG/TIFF)|

## References

* _J. Johnson, A. Alahi, L. Fei-Fei_, ["Perceptual Losses for Real-Time Style Transfer and Super-Resolution"](https://arxiv.org/pdf/1603.08155.pdf), 2016

* _D. Ulyanov, A. Vedaldi, V. Lempitsky_, ["Instance Normalization"](https://arxiv.org/pdf/1607.08022.pdf), 2017

* _D. Ulyanov, A. Vedaldi, V. Lempitsky_, ["Improved Texture Networks: Maximizing Quality and Diversity in Feed-forward Stylization and Texture Synthesis"](https://arxiv.org/pdf/1701.02096.pdf), 2017

* [Pytorch Tutorial](http://pytorch.org/tutorials/advanced/neural_style_tutorial.html)

* [Pytorch Fast Neural Style Transfer Example](https://github.com/pytorch/examples/tree/master/fast_neural_style)

## Licenses

| Component | License | Link  |

| ------------- | --------  | -------- |

| This repository | [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) | [LICENSE](LICENSE) |

| Model Weights | [BSD-3-Clause](https://opensource.org/licenses/BSD-3-Clause) | [Pytorch Examples LICENSE](https://github.com/pytorch/examples/blob/master/LICENSE) |

| Model Code (3rd party) | [BSD-3-Clause](https://opensource.org/licenses/BSD-3-Clause) | [Pytorch Examples LICENSE](https://github.com/pytorch/examples/blob/master/LICENSE) |

| Test assets | Various | [Samples README](samples/README.md) |

## 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](https://docs.docker.com/docker-for-mac/#advanced-tab) or [Windows](https://docs.docker.com/docker-for-windows/#advanced)). 

* `docker`: The [Docker](https://www.docker.com/) command-line interface. Follow the [installation instructions](https://docs.docker.com/install/) for your system.

* The minimum recommended resources for this model is 6 GB Memory and 4 CPUs.

# Deployment options

* [Deploy from Quay](#deploy-from-quay)

* [Deploy on Red Hat OpenShift](#deploy-on-red-hat-openshift)

* [Deploy on Kubernetes](#deploy-on-kubernetes)

* [Run Locally](#run-locally)

## Deploy from Quay

To run the docker image, which automatically starts the model serving API, run:

```bash

$ 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](#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](https://developer.ibm.com/tutorials/deploy-a-model-asset-exchange-microservice-on-red-hat-openshift/), 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:

```bash

$ 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](https://ibm.biz/Bdz2XM) can be found [here](http://ibm.biz/max-to-ibm-cloud-tutorial).

## Run Locally

1. [Build the Model](#1-build-the-model)

2. [Deploy the Model](#2-deploy-the-model)

3. [Use the Model](#3-use-the-model)

4. [Development](#4-development)

5. [Cleanup](#5-cleanup)

### 1. Build the Model

Clone this repository locally. In a terminal, run the following command:

```bash

$ git clone https://github.com/IBM/MAX-Fast-Neural-Style-Transfer.git

```

Change directory into the repository base folder:

```bash

$ cd MAX-Fast-Neural-Style-Transfer

```

To build the docker image locally, run: 

```bash

$ 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:

```bash

$ 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](https://github.com/pytorch/examples/tree/master/fast_neural_style#models) for more details.

![Swagger Doc Screenshot](docs/swagger-screenshot.png)

You can also test it on the command line, for example:

```bash

$ 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.

![CLI Screenshot](docs/cli-screenshot.jpg)

### 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](#1-build-the-model)).

### 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](https://github.com/CODAIT/max-central-repo).