Introduction

This project is an adaptation of srez project for floydhub. You will need a Floydhub account and have floyd-cli installed. See https://www.floydhub.com/welcome.

Setup project

$ git clone https://github.com/yuntai/srez
$ cd srez
$ floyd init srez

Training

The model is trained with Large-scale CelebFaces Attributes (CelebA) Dataset. The dataset is available in Floydhub with ID (yo5Gx9CA8AkAo4XL54fgJX). With '--data ', the data volume containing the dataset is mounted on /input. The jpeg files are packed inside /input/data/img_align_celebra.zip. The zip file is first deflated into a local temporary directory and then used by the model training.

To run training with the CelebA dataset available in Floydhub with 10 minutes of training time:

floyd run --env tensorflow-1.0 --gpu --data yo5Gx9CA8AkAo4XL54fgJX "python srez_main.py --run train --train_time 10 --dataset_zip /input/data/img_align_celeba.zip"

If you want to use your own image dataset, you need to specify --dataset instead of --dataset_zip pointing to the directory containing jpeg files.

To check the progress,

floyd logs <RUN ID> -t

When the training is finished, you can check the output with 'floyd data' command. First, check the data id for the previous run with

floyd data status

floyd data output <data id>

In train directory, you can see the list of png files which are generated by the model training for every 100 batches.

From left to right, the first column is the 16x16 input image, the second one is what you would get from a standard bicubic interpolation, the third is the output generated by the neural net, and on the right is the ground truth. As the training preogress, you can find more plausible reconsturction is produced.

The project comes with a simple utility program to a generate video file combining these png files.

floyd run --env tensorflow-1.0 --data <data id> "python srez_main.py --run=demo --train_dir=/input/train"

You can check the produced video file in the output.

In data volume prepared in Floydhub (yo5Gx9CA8AkAo4XL54fgJX) does also contain the pnf files generated during training with 130,000 batches.

To generate a video file,

floyd run --env tensorflow-1.0 --data yo5Gx9CA8AkAo4XL54fgJX "python srez_main.py --run=demo --train_dir=/input/train"

Here is the link to the movie file hosted in Floydhub. Training Video

Serve mode

You can now host this model as a REST API. This means you can send any image to this API as a HTTP request and its resolution will be enhanced.

Floyd run command has a serve mode. This will upload the files in the current directory and run a special command - python app.py. Floyd expects this file to contain the code to run a web server and listen on port 5000. You can see the app.py file in the repository. This file handles the incoming request, load the checkpoint from the pre-trained model (`/input/checkpoint/checkpoint_new.txt') and apply the generative operation.

The Flask is used. The dependency is specified in floyd_requirements.txt file.

Note that this feature is in preview mode and is not production ready yet

$ floyd run --env tensorflow-1.0 --data yo5Gx9CA8AkAo4XL54fgJX --mode serve
Creating project run. Total upload size: 297.3KiB
Syncing code ...
RUN ID                  NAME                VERSION
----------------------  ----------------  ---------
niSuYzjT7hmqxAVP6M68bG  kobi2000/srez:48         48

Path to service endpoint: https://www.floydhub.com:8000/xtH75NqswHKuu3fVKWJ4La

To view logs enter:
    floyd logs niSuYzjT7hmqxAVP6M68bG

Sending requests to the REST API

The app support two run modes. With run=downsample the image is downsampled by the factor of 4.

curl -o 000001_downsampled.jpg -F "file=@./000001.jpg" -F "run=downsample" https://www.floydhub.com:8000/xtH75NqswHKuu3fVKWJ4La

We are going to send this downscaled image back to the app to get a reconstructed image.

curl -o output.jpg -F "file=@./000001_downsampled.jpg" https://www.floydhub.com:8000/xtH75NqswHKuu3fVKWJ4La