This is a Deep Dream implementation using PyTorch with pretrained GoogLeNet weights. The algorithm is very simple (doesn'b't use octaves) and aims to be fast. As of now, in a MBP with an i7 it runs in ~2s. The deployed version in Heroku tends to be slower and is not always available.

Here are some of the available styles:

Original	Painting
Mixed	Trippy
Syd Barret	Whatever

This API is hosted in heroku. Here is a snippet to quickly try the API using the trippy style:

curl \
    -X POST \
    -F 'image=@/path/to/source/image.jpeg' \
    http://deep-api-23.herokuapp.com/deep/dream/trippy \
    --output /path/to/output/image.jpeg

Heroku requests have a timeout of 30s. This time should be enough to run the algorithm, but it's not consistent at all! So it won't work every time.

The Github repo performs a workflow to make sure the Unit Tests are running and checks the PyCodeStyle.

The Heroku app is linked to this repo to perform continous deployment over the master branch.

• Dev Requirements can be installed using: pip install -r ./support/requirements/dev.txt.
• Heroku Requirements are prepared to install the CPU-only PyTorch version and will work in Linux only.

uwsgi --ini uwsgi.ini 

python deep_api