Project sponsored by Kontron
Project Lead and related articles by Samuel Cozannet
Code and programming by Ronan Delacroix
This small demo aims to provides a simple dummy web interface allowing to upload images and send them to Tensorflow for prediction.
Also provides an API to upload your image and do Tensorflow prediction using kind of Rest/Json format.
The API actually sends the image to a Tensorflow server set for prediction. The API also resizes original images into different lower resolutions, make the same Tensorflow prediction and create metrics out of results. It returns statistics about timings, performances, accuracy and prediction precision.
The aim is to compare Tensorflow results and processing times when varying image sizes, and/or server kind (CPU, GPU, cores).
This small project is not production grade and is made for Mobile World Congress as a demo.
Read https://medium.com/@samnco/fun-with-kubernetes-tensorflow-serving-4fef8d7502b9 to know more.
bin/tensorflow-image-resizer
First set required ENV variable (see below), then :
gunicorn -b 0.0.0.0:5051 --access-logfile - --error-logfile - tensorflow-image-resizer.web:app
or
bin/tensorflow-image-resizer.sh
docker build . -t mwc-tensorflow-image-resizer:latest
docker run \
--env TF_SERVERS="Default CPU Server:tf-serving-server-cpu:9000;GPU Server:tf-serving-server-gpu:9000" \
-p 5051:5051 -t -i \
mwc-tensorflow-image-resizer:latest
Finally go to http://localhost:5051/
- TF_SERVERS <required> - contains info about TF servers :
TF_SERVERS="<server_name>:<server_host>:<server_port>"You can input multiple TF servers (separated with semicolons if there are more than one). Example :TF_SERVERS="My CPU TF Server:192.168.0.3:9000;Someone else GPU TF Server:192.168.1.148:8000".
- MODEL_NAME - default :
'inception'- Name of the TF Model to use. - MODEL_SIGNATURE_NAME - default :
'predict_images'- Signature Name of the TF Model to use. - MODEL_INPUT_KEY - default :
'images'- Input key of the TF Model to upload an image to.
Pay attention that your TF server should be accessible on the network of your docker container.
You can use docker-compose and the docker-compose.yaml file to run the full project, both TF server and the client at once.
Copy the docker-compose.yaml in a folder of your choice and simply type :
docker-compose up
Also, you only need to download the TF image inception model and place it in /tmp/model-data:
> mkdir /tmp/model-data
> cd /tmp/model-data
# copy your exported model data here, so that it contains a folder named "1" like this :
> tree
.
└── 1
├── saved_model.pb
└── variables
├── variables.data-00000-of-00001
└── variables.index
You can also use this project as an API endpoint to post your image from your own app.
Simply upload your image through a HTTP POST request on /upload url, using file input file field.
Here is an example :
curl -F "file=@/Users/ronan/Pictures/cat.jpg" http://localhost:5051/upload
The result is presented as JSON. Dummy result example follows:
{
"files": [
{
"filename": "moi.jpg",
"filesize": 118707,
"content_type": "image\/jpeg",
"server_names": [
"default",
"gpu"
],
"sizes": [
"Orig. 550x636",
"2048x2048",
"1024x1024",
"512x512",
"256x256",
"128x128"
],
"derivatives": {
"Orig. 550x636": {
"average_duration": 6.6138181686401,
"predictions": {
"gpu": {
"results": {
"comic book": 8.89501953125,
"book jacket, dust cover, dust jacket, dust wrapper": 8.2884063720703,
"envelope": 4.5581865310669,
"tray": 3.6763899326324,
"packet": 3.4327256679535
},
"precision": 100,
"predict_duration": 0.99241065979004,
"total_duration": 0.99241065979004
},
"default": {
"results": {
"comic book": 8.89501953125,
"book jacket, dust cover, dust jacket, dust wrapper": 8.2884063720703,
"envelope": 4.5581865310669,
"tray": 3.6763899326324,
"packet": 3.4327256679535
},
"precision": 100,
"predict_duration": 12.23522567749,
"total_duration": 12.23522567749
}
},
"resize_duration": 0,
"format": "JPEG",
"average_precision": 100,
"filesize": 118707,
"filename": "moi.jpg",
"image_size": "550x636",
"resized": "Reference",
"filesize_percent": 100
},
"2048x2048": {
"average_duration": 1.1133260726929,
"predictions": {
"gpu": {
"results": {
"comic book": 8.8598012924194,
"book jacket, dust cover, dust jacket, dust wrapper": 8.6930170059204,
"envelope": 4.5749850273132,
"tray": 3.6941545009613,
"puck, hockey puck": 3.4707596302032
},
"precision": 81.067491018236,
"predict_duration": 1.0252320766449,
"total_duration": 1.1765704154968
},
"default": {
"results": {
"comic book": 8.8598012924194,
"book jacket, dust cover, dust jacket, dust wrapper": 8.6930170059204,
"envelope": 4.5749850273132,
"tray": 3.6941545009613,
"puck, hockey puck": 3.4707596302032
},
"precision": 81.067491018236,
"predict_duration": 1.2014200687408,
"total_duration": 1.3527584075928
}
},
"resize_duration": 0.15133833885193,
"format": "JPEG",
"average_precision": 81.067491018236,
"filesize": 71679,
"filename": "moi.2048x2048.jpg",
"image_size": "550x636",
"resized": "2048x2048",
"filesize_percent": 60.383128206424
},
"1024x1024": {
"average_duration": 0.89396262168884,
"predictions": {
"gpu": {
"results": {
"comic book": 8.8598012924194,
"book jacket, dust cover, dust jacket, dust wrapper": 8.6930170059204,
"envelope": 4.5749850273132,
"tray": 3.6941545009613,
"puck, hockey puck": 3.4707596302032
},
"precision": 81.067491018236,
"predict_duration": 0.83424687385559,
"total_duration": 0.85841846466064
},
"default": {
"results": {
"comic book": 8.8598012924194,
"book jacket, dust cover, dust jacket, dust wrapper": 8.6930170059204,
"envelope": 4.5749850273132,
"tray": 3.6941545009613,
"puck, hockey puck": 3.4707596302032
},
"precision": 81.067491018236,
"predict_duration": 0.95367836952209,
"total_duration": 0.97784996032715
}
},
"resize_duration": 0.024171590805054,
"format": "JPEG",
"average_precision": 81.067491018236,
"filesize": 71679,
"filename": "moi.1024x1024.jpg",
"image_size": "550x636",
"resized": "1024x1024",
"filesize_percent": 60.383128206424
},
"512x512": {
"average_duration": 0.98880505561829,
"predictions": {
"gpu": {
"results": {
"comic book": 8.9899501800537,
"book jacket, dust cover, dust jacket, dust wrapper": 8.4078044891357,
"envelope": 4.4274864196777,
"tray": 3.9461207389832,
"puck, hockey puck": 3.2541999816895
},
"precision": 81.395449099462,
"predict_duration": 1.0694222450256,
"total_duration": 1.2666203975677
},
"default": {
"results": {
"comic book": 8.9899501800537,
"book jacket, dust cover, dust jacket, dust wrapper": 8.4078044891357,
"envelope": 4.4274864196777,
"tray": 3.9461207389832,
"puck, hockey puck": 3.2541999816895
},
"precision": 81.395449099462,
"predict_duration": 0.90818786621094,
"total_duration": 1.1053860187531
}
},
"resize_duration": 0.19719815254211,
"format": "JPEG",
"average_precision": 81.395449099462,
"filesize": 47200,
"filename": "moi.512x512.jpg",
"image_size": "442x512",
"resized": "512x512",
"filesize_percent": 39.761766365926
},
"256x256": {
"average_duration": 1.4369288682938,
"predictions": {
"gpu": {
"results": {
"comic book": 9.7475137710571,
"book jacket, dust cover, dust jacket, dust wrapper": 8.5457496643066,
"jersey, T-shirt, tee shirt": 4.631067276001,
"tray": 4.5579609870911,
"envelope": 4.5538229942322
},
"precision": 87.314467556298,
"predict_duration": 0.88055467605591,
"total_duration": 0.90344023704529
},
"default": {
"results": {
"comic book": 9.7475137710571,
"book jacket, dust cover, dust jacket, dust wrapper": 8.5457496643066,
"jersey, T-shirt, tee shirt": 4.631067276001,
"tray": 4.5579609870911,
"envelope": 4.5538229942322
},
"precision": 87.314467556298,
"predict_duration": 1.9933030605316,
"total_duration": 2.016188621521
}
},
"resize_duration": 0.02288556098938,
"format": "JPEG",
"average_precision": 87.314467556298,
"filesize": 15173,
"filename": "moi.256x256.jpg",
"image_size": "221x256",
"resized": "256x256",
"filesize_percent": 12.781891548097
},
"128x128": {
"average_duration": 0.8400958776474,
"predictions": {
"gpu": {
"results": {
"comic book": 9.6742382049561,
"book jacket, dust cover, dust jacket, dust wrapper": 7.1120657920837,
"jersey, T-shirt, tee shirt": 5.7149996757507,
"binder, ring-binder": 5.0224323272705,
"handkerchief, hankie, hanky, hankey": 4.6893925666809
},
"precision": 38.913513118862,
"predict_duration": 0.82929587364197,
"total_duration": 0.90497159957886
},
"default": {
"results": {
"comic book": 9.6742382049561,
"book jacket, dust cover, dust jacket, dust wrapper": 7.1120657920837,
"jersey, T-shirt, tee shirt": 5.7149996757507,
"binder, ring-binder": 5.0224323272705,
"handkerchief, hankie, hanky, hankey": 4.6893925666809
},
"precision": 38.913513118862,
"predict_duration": 0.85089588165283,
"total_duration": 0.92657160758972
}
},
"resize_duration": 0.07567572593689,
"format": "JPEG",
"average_precision": 38.913513118862,
"filesize": 4933,
"filename": "moi.128x128.jpg",
"image_size": "110x128",
"resized": "128x128",
"filesize_percent": 4.1556100314219
}
}
}
]
}
This API can be used on Linux or MacOS systems.
Mainly tested on Python 3.5 and 3.6.
Project url : https://github.com/ronhanson/crypto-miner-webui
Copyright © 2018 Ronan Delacroix
This program is released under MIT Licence. Feel free to use it or part of it anywhere you want.