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FERPlus_Vision_FaceChannel_Frame.py
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FERPlus_Vision_FaceChannel_Frame.py
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# -*- coding: utf-8 -*-
"""Experiments with the FER+ Dataset using the Face Channel
More information: Barros, P., Jirak, D., Weber, C., & Wermter, S. (2015). Multimodal emotional state recognition using sequence-dependent deep hierarchical features. Neural Networks, 72, 140-151.
Parameters:
baseDirectory (String): Base directory where the experiment will be saved.
datasetFolderTrain (String): Folder where the audios used for training the model are stored
datasetFolderTest (String): Folder where the audios used for testing the model are stored
experimentName (String): Name of the experiment.
logManager (LogManager):
Author: Pablo Barros
Created on: 02.05.2018
Last Update: 16.06.2018
"""
import matplotlib
matplotlib.use('Agg')
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "1"
from keras import backend as K
def set_keras_backend(backend):
if K.backend() != backend:
os.environ['KERAS_BACKEND'] = backend
reload(K)
assert K.backend() == backend
def runModel():
from KEF.Controllers import ExperimentManager
# from KEF.DataLoaders import FER2013PlusLoader
from KEF.DataLoaders import FER2013PlusLoader
from KEF.Implementations import Vision_CNN_FER2013
dataDirectory = "/data/VisionFER+/"
#
datasetFolderTrain = "/data/datasets/fer2013Plus/Images/FER2013Train/" # FER2013
datasetFolderTest = "/data/datasets/fer2013Plus/Images/FER2013Test/" # FER2013
datasetFolderValidation = "/data/datasets/fer2013Plus/Images/FER2013Valid/" # FER2013
labelFolderTrain = "/data/datasets/fer2013Plus/labels/FER2013Train/label.csv"
labelFolderTest = "/data/datasets/fer2013Plus/labels/FER2013Test/label.csv"
labelFolderValidation = "/data/datasets/fer2013Plus/labels/FER2013Valid/label.csv"
""" Initianize all the parameters and modules necessary
image size: 64,64
"""
experimentManager = ExperimentManager.ExperimentManager(dataDirectory,
"EMOTIW_PReTrained_With_FER_Experiment_GPU_CategoricalCrossentropy_2",
verbose=True)
grayScale = True
preProcessingProperties = [(64, 64), grayScale]
""" Loading the training and testing data
"""
dataLoader = FER2013PlusLoader.FER2013PlusLoader(experimentManager.logManager, preProcessingProperties)
#
dataLoader.loadTrainData(datasetFolderTrain, labelFolderTrain)
#
dataLoader.loadTestData(datasetFolderTest, labelFolderTest)
dataLoader.loadValidationData(datasetFolderValidation, labelFolderValidation)
# """ Creating and tuning the CNN
# """
cnnModel = Vision_CNN_FER2013.CNN_FER2013(experimentManager, "CNN", experimentManager.plotManager)
cnnModel.buildModel(dataLoader.dataTest.dataX.shape[1:], len(dataLoader.dataTest.labelDictionary))
##
cnnModel.train(dataLoader.dataTrain, dataLoader.dataValidation, False)
##
cnnModel.save(experimentManager.modelDirectory)
set_keras_backend("tensorflow")
print K.backend
if K.backend == "tensorflow":
import tensorflow as tf
sess = tf.Session()
# from keras import backend as K
K.set_session(sess)
with tf.device('/gpu:1'):
runModel()
else:
runModel()