ChaLearn Looking at People Workshop

Transcription

1 ChaLearn Looking at People Workshop Cultural Event Recognition (Demo) Junior Fabian, CVC, Hugo Escalante, INAOE, Xavier Baró, UOC, Sergio Escalera, CVC/UB, Jordi González, CVC, Pablo Pardo, UB, Marc Simon, UB, Isabelle Guyon, Chalearn

2 Contents Introduction Overview AWS instructions Running a basic deep learning model on challenge data GPU vs CPU Final remarks

3 Cultural eventrecognitionchallenge The Cultural Event Recognition challenge aims to investigate the performance of recognition methods based on several cues like garments, human poses, objects, background, etc. To this end, our dataset contains significant variability in terms of clothes, actions, illumination, localization and context. This is the second round for this track. We have significantly incremented the number of images and classes

4 Data

5 Cultural eventrecognitionchallenge

6 Demo overview The goal of the demo is to show the benefits of using GPUs over CPUs when training a basic deep learning method for recognizing cultural events. Demo in a nutshell: Use Amazon Web Services for launching a server with GPUs Run a basic of-the-shell model using Theano A subset of the challenge data will be used Prerequisites: createanaccountin AWS

7 Go to Login [create your own account]

8 Spotrequests

9 Request Spot Instances

10 Real stuff: Choose North California

11 Image for the demo

12 Select server configuration

13 * It depends on the current price Select price*

14 Launch server

15 Select Key Pair If you already have one

16 OR: Create new key pair Choose name Save demohack15.pem In directory where saved, at terminal do: chmod 0400 demohack15.pem ssh-add -K demohack15.pem Put key in key ring, so you can omit -i option

17 View spot requests Wait until server is created, then select it.

18 Connect to your server ssh -i demohack15.pem Omit -i option if the key is in the key ring.

19 Create VM image

20 Terminateinstance Do not forget to terminate the instances!

21 Runningthedemo In the server we already have the Code and Data to run the demo This is the script to run: >python run.py

22 Runningthedemo This script shows how training and testing a simple CNN model for the cultural event recognition data set. The script is based on the code of convolutional neural networks(cnns) from the Theano tutorial. For the demo we only use 50 classes and we have resized the images to 50x50. The code from Theano tutorial was used to deal with the MNIST dataset. For our cultural event dataset our goal is not to look for the best model but for its computational requirements in CPU and GPU, which can also generalize to other more complex models.

23 Runningthedemo The most important in this demo is to solve the problem using CNN and to compare the execution time in GPU vs. CPU.

24 Runningthedemo [Code] cnn.py defines 3 classes: hidden layer, convolutional layer and the whole CNN logistic_sgd.py auxiliary file, which contains the logistic regression class cnn_training_computation.py Contains the definition of the training and prediction process. Defines: l l l l l - the Theano shared (shared memory on the GPU) variables, storing the datasets and labels - CNN structural parameters - training parameters - training flow (method) and its auxiliary functions - prediction flow and its auxiliary functions run.py the main program that executes our deep learning example. It reads the datasets, performs normalization, trains the CNN and do predictions.

25 Results TIME (minutes) 50x50 acc = 15,08% GPU 11.45m CPU m TIME (minutes) 100x100 acc = 18,36% GPU 30.11m CPU m ~8h 200x200 acc = 28,07% TIME (minutes) GPU 85.21m CPU ~23h

26 Final remarks A very basic DL model was evaluated in the context of the cultural-event recognition challenge This demo is illustrative of the benefits of using GPUs for deep learning More complex models, based on theano/python could be run following the same instructions herein described.

27 Sponsors & organizers

28 Questions?

29