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model-dbb.py
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import tensorflow as tf
import os
import numpy as np
import time
import sys
from random import randint, sample
from collections import Counter, OrderedDict
import subprocess
import io
import scipy.io
import math
import matplotlib.pyplot as plt
from keras.layers import Input, LSTM, TimeDistributed, Dense, Bidirectional, Layer
from keras.models import Sequential, load_model
from keras.layers.core import Dropout
from keras import initializers, optimizers, regularizers, constraints
from keras import backend as K
from keras.callbacks import TensorBoard, ModelCheckpoint, CSVLogger, Callback
from keras.utils import to_categorical, multi_gpu_model
baseDrive = '/home/istvan/2019_dec_paper/capgMyo/'
NUMBER_OF_FEATURES = 128
recurrent_dropout = 0.5
dropout = 0.5
number_of_classes = 8
cellNeurons = 512
denseNeurons = 512
def toMultiGpuModel(model):
try:
gpu_model = multi_gpu_model(model)
return gpu_model
except:
print("gpu_model error")
return None
def download(db='b', num=20):
for i in range(num):
name = "http://zju-capg.org/myo/data/db"+db+"-preprocessed-" + '{:03d}'.format(i+1) + ".zip"
call(["wget", name])
def unzip(db='b', num=20):
for i in range(num):
name = "db"+db+"-preprocessed-" + '{:03d}'.format(i+1) + ".zip"
call(["unzip", name, "-d", "unzipped-db"+db])
def convert(db='b'):
directory = "/home/istvan/capgMyo/unzipped-db"+db
directory_out = "/home/istvan/capgMyo/data/db"+db
for root, dirs, files in os.walk(directory):
for file in files:
if file.endswith(".mat"):
aFile = os.path.join(root, file)
matFile = scipy.io.loadmat(aFile)
np.save(os.path.join(directory_out, '{:03d}-{:03d}-{:03d}.npy'.format(matFile['subject'][0][0], matFile['gesture'][0][0], matFile['trial'][0][0])), matFile['data'])
def unisonShuffle(X, y):
s = np.arange(X.shape[0])
np.random.shuffle(s)
return X[s], y[s]
def sequenceBatchGeneratorAbsMean2(batchSize,
seq_len,
indexes,
stride,
allNumOfSamples,
meanOf,
shuffling,
standardize,
amountOfRepetitions,
amountOfGestures,
totalGestures,
totalRepetitions,
directory,
dataset,
repetitions,
number_of_classes):
X = []
y = []
# DBB:
# 1st half's mean: -1.494554769724577e-06
# 1st half's std: 0.014456551081024154
# 2nd half's mean: -3.342415416859754e-07
# 2nd half's std: 0.012518382863642336
# All's mean: -9.143981557052827e-07
# All's std: 0.013522236859191582
if dataset == 'dbb-1st':
mean = -1.494554769724577e-06
std = 0.014456551081024154
elif dataset == 'dbb-2nd':
mean = -3.342415416859754e-07
std = 0.012518382863642336
elif dataset == 'dbb-all':
mean = -3.342415416859754e-07
std = 0.012518382863642336
if shuffling:
range_i = sample(indexes, len(indexes))
range_j = sample(range(1, totalGestures+1), amountOfGestures)
range_k = sample(range(1, totalRepetitions+1), amountOfRepetitions)
else:
range_i = indexes
range_j = range(1, amountOfGestures+1)
range_k = range(1, amountOfRepetitions+1)
if repetitions is not None: # intra-session
if len(repetitions) == 5: # intra-session
if shuffling:
range_k = sample(repetitions, amountOfRepetitions)
else:
range_k = repetitions[:amountOfRepetitions]
counter = 0
while True:
for i in range_i: # users
for j in range_j: # 8 gestures
for k in range_k: # 10 repetitions
fileName = '{:03d}-{:03d}-{:03d}.npy'.format(i, j, k)
aFile = np.load(os.path.join(directory, fileName))
if standardize:
aFile = (aFile-mean)/std
aFile = np.abs(aFile)
absMeanFile = np.apply_along_axis(lambda m: np.convolve(m, np.ones((meanOf,))/meanOf, mode='valid'), axis=0, arr=aFile)
del aFile
for l in range(0, absMeanFile.shape[0]-seq_len+1, stride):
X.append(absMeanFile[l:l+seq_len, :])
y.append(j-1)
counter += 1
if counter % allNumOfSamples == 0:
if shuffling:
yield unisonShuffle(np.array(X), to_categorical(y, num_classes=number_of_classes))
else:
yield np.array(X), to_categorical(y, num_classes=number_of_classes)
del X, y
X = []
y = []
counter = 0
elif counter % batchSize == 0:
if shuffling:
yield unisonShuffle(np.array(X), to_categorical(y, num_classes=number_of_classes))
else:
yield np.array(X), to_categorical(y, num_classes=number_of_classes)
del X, y
X = []
y = []
del absMeanFile
def buildModel(classes, features, cellNeurons, cellDropout, denseDropout, denseNeurons, sequenceLength, stacked=False, bidirectional=False, l2=0.0):
model = Sequential()
model.add(TimeDistributed(Dense(features,
kernel_initializer='identity',
bias_initializer='zeros',
name='customNn',
activation=None), input_shape=(sequenceLength, features), name='td', trainable=False))
if bidirectional:
if stacked:
model.add(Bidirectional(LSTM(cellNeurons, recurrent_dropout=cellDropout, name='rnn', trainable=True, return_sequences=True, kernel_regularizer=regularizers.l2(l2)), merge_mode='concat'))
model.add(Bidirectional(LSTM(cellNeurons, recurrent_dropout=cellDropout, name='rnn_2nd_layer', trainable=True, kernel_regularizer=regularizers.l2(l2)), merge_mode='concat'))
else:
model.add(Bidirectional(LSTM(cellNeurons, recurrent_dropout=cellDropout, name='rnn', trainable=True, kernel_regularizer=regularizers.l2(l2)), merge_mode='concat'))
else:
if stacked:
model.add(LSTM(cellNeurons, recurrent_dropout=cellDropout, name='rnn', trainable=True, return_sequences=True, kernel_regularizer=regularizers.l2(l2)))
model.add(LSTM(cellNeurons, recurrent_dropout=cellDropout, name='rnn_2nd_layer', trainable=True, kernel_regularizer=regularizers.l2(l2)))
#model.add(Attention(name='attention', trainable=True))
else:
model.add(LSTM(cellNeurons, recurrent_dropout=cellDropout, name='rnn', trainable=True, kernel_regularizer=regularizers.l2(l2)))
model.add(Dense(denseNeurons, name='nn', trainable=True, kernel_regularizer=regularizers.l2(l2)))
model.add(Dropout(denseDropout, name='nn_dropout', trainable=True))
model.add(Dense(classes, activation="softmax", name='output_softmax', trainable=True, kernel_regularizer=regularizers.l2(l2)))
#model.summary()
if onTpu:
model.compile(loss="categorical_crossentropy",
optimizer=tf.train.AdamOptimizer(learning_rate=0.001),
metrics=["accuracy"])
multi_model = toTpuModel(model)
else:
multi_model = toMultiGpuModel(model)
multi_model.compile(loss="categorical_crossentropy",
optimizer=optimizers.Adam(lr=0.001, decay=0.0001),
metrics=["accuracy"])
return model, multi_model
class AltModelCheckpoint(ModelCheckpoint):
def __init__(self, filepath, alternate_model, **kwargs):
"""
Additional keyword args are passed to ModelCheckpoint; see those docs for information on what args are accepted.
:param filepath:
:param alternate_model: Keras model to save instead of the default. This is used especially when training multi-
gpu models built with Keras multi_gpu_model(). In that case, you would pass the original
"template model" to be saved each checkpoint.
:param kwargs: Passed to ModelCheckpoint.
"""
self.alternate_model = alternate_model
super().__init__(filepath, **kwargs)
def on_epoch_end(self, epoch, logs=None):
model_before = self.model
self.model = self.alternate_model
super().on_epoch_end(epoch, logs)
self.model = model_before
def getAdaptationModel(modelPath, adaptationVersion, features, seqLen):
fineTuneModel = load_model(modelPath)
fineTuneModel.get_layer('td').trainable = True
if adaptationVersion == 21:
fineTuneModel.get_layer('td').activation = 'relu'
if adaptationVersion == 22:
fineTuneModel.get_layer('td').activation = 'exponential'
if adaptationVersion == 23:
fineTuneModel.get_layer('td').activation = 'elu'
if adaptationVersion == 24:
fineTuneModel.get_layer('td').activation = 'sigmoid'
fineTuneModel.get_layer('rnn').trainable = False
if fineTuneModel.get_layer('rnn_2nd_layer') != None:
fineTuneModel.get_layer('rnn_2nd_layer').trainable = False
fineTuneModel.get_layer('nn').trainable = False
fineTuneModel.get_layer('nn_dropout').trainable = False
fineTuneModel.get_layer('output_softmax').trainable = False
if adaptationVersion == 31:
fineTuneModel.get_layer('td').activation = 'relu'
fineTuneModel.name = "existingModel"
newModel = Sequential()
newModel.add(TimeDistributed(Dense(features,
kernel_initializer='identity',
bias_initializer='zeros',
activation='relu'), input_shape=(seqLen, features), name='td0', trainable=True))
newModel.add(fineTuneModel)
fineTuneModel = newModel
if adaptationVersion == 32:
fineTuneModel.get_layer('td').activation = 'exponential'
fineTuneModel.name = "existingModel"
newModel = Sequential()
newModel.add(TimeDistributed(Dense(features,
kernel_initializer='identity',
bias_initializer='zeros',
activation='exponential'), input_shape=(seqLen, features), name='td0', trainable=True))
newModel.add(fineTuneModel)
fineTuneModel = newModel
if adaptationVersion == 34:
fineTuneModel.get_layer('td').activation = 'sigmoid'
fineTuneModel.name = "existingModel"
newModel = Sequential()
newModel.add(TimeDistributed(Dense(features,
kernel_initializer='identity',
bias_initializer='zeros',
activation='sigmoid'), input_shape=(seqLen, features), name='td0', trainable=True))
newModel.add(fineTuneModel)
fineTuneModel = newModel
if adaptationVersion == 4: # initializer does not work with this initializer cause it is not square
fineTuneModel.get_layer('td').activation = 'relu'
fineTuneModel.name = "existingModel"
newModel = Sequential()
newModel.add(TimeDistributed(Dense(10*features,
kernel_initializer='identity',
bias_initializer='zeros',
activation='relu'), input_shape=(seqLen, features), name='td0', trainable=True))
newModel.add(fineTuneModel)
fineTuneModel = newModel
multiFineTuneModel = toMultiGpuModel(fineTuneModel)
multiFineTuneModel.compile(loss="categorical_crossentropy",
optimizer=optimizers.Adam(lr=0.001, decay=0.0001),
metrics=["accuracy"])
return fineTuneModel, multiFineTuneModel
def validationAccuracyValues(x):
return(x[-10:-5])
def getBestModel(testUser, workingDirectory):
file_list = os.listdir(workingDirectory+str(testUser))
best = max(file_list, key=validationAccuracyValues)
print('\nBest pre-trained model to start with: ' + str(best))
return workingDirectory+str(testUser) + '/' + best
def checkdataStatistics():
directory = "/home/istvan/capgMyo/data/dbb"
bigArray = None
isFirst = True
for i in range (1, 21): # second session
for j in range (1, 9): # 8 gestures
for k in range (1, 11): # 10 repetitions
fileName = '{:03d}-{:03d}-{:03d}.npy'.format(i, j, k)
aFile = np.load(os.path.join(directory, fileName))
if isFirst:
bigArray = aFile
isFirst = False
else:
bigArray = np.concatenate((bigArray, aFile), axis=0)
mean, std = np.mean(bigArray), np.std(bigArray)
print(mean)
print(std)
# 2nd half's mean: -3.342415416859754e-07
# 2nd half's std: 0.012518382863642336
# All's mean: -9.143981557052827e-07
# All's std: 0.013522236859191582
print(np.max(bigArray))
print(np.min(bigArray))
newArray = (bigArray - mean)/std
mean2, std2 = np.mean(newArray), np.std(newArray)
print(mean2)
print(std2)
print(np.max(newArray))
print(np.min(newArray))
plt.hist(bigArray, bins=10)
plt.title("Histogram")
plt.savefig('histogram-all.png')
plt.close()
plt.hist(newArray, bins=10)
plt.title("Histogram")
plt.savefig('histogram-new-all.png')
def preTrainingModel(trainingUsers,
testUsers,
allNumOfTrainingSamples,
trainingStepsPerEpoch,
allNumOfValidationSamples,
validationStepsPerEpoch,
amountOfRepetitions,
amountOfGestures,
preTrainingNumOfEpochs,
trial,
batchSize,
totalGestures,
totalRepetitions,
directory,
testUser,
workingDirectory,
trainingDataset,
testDataset,
trainingRepetitions,
testRepetitions,
number_of_classes,
saveCheckpoints):
base_model, multi_model = buildModel(classes=number_of_classes,
features=NUMBER_OF_FEATURES,
cellNeurons=cellNeurons,
cellDropout=recurrent_dropout,
denseDropout=dropout,
denseNeurons=denseNeurons,
sequenceLength=seq_len,
stacked=True,
bidirectional=False,
l2=0.0)
histories = {}
path = workingDirectory+str(testUser)
if not os.path.exists(path):
os.makedirs(path)
my_training_batch_generator = sequenceBatchGeneratorAbsMean2(batchSize=batchSize,
seq_len=seq_len,
indexes=trainingUsers,
stride=stride,
allNumOfSamples=allNumOfTrainingSamples,
meanOf=mean,
shuffling=True,
standardize=True,
amountOfRepetitions=totalRepetitions,
amountOfGestures=totalGestures,
totalGestures=totalGestures,
totalRepetitions=totalRepetitions,
directory=directory,
dataset=trainingDataset,
repetitions=trainingRepetitions,
number_of_classes=number_of_classes)
my_validation_batch_generator = sequenceBatchGeneratorAbsMean2(batchSize=batchSize,
seq_len=seq_len,
indexes=testUsers,
stride=stride,
allNumOfSamples=allNumOfValidationSamples,
meanOf=mean,
shuffling=False,
standardize=True,
amountOfRepetitions=totalRepetitions,
amountOfGestures=totalGestures,
totalGestures=totalGestures,
totalRepetitions=totalRepetitions,
directory=directory,
dataset=testDataset,
repetitions=testRepetitions,
number_of_classes=number_of_classes)
filepath=path + "/e{epoch:03d}-a{val_accuracy:.3f}.hdf5"
if saveCheckpoints == True:
if onTpu:
modelCheckpoint = ModelCheckpoint(filepath, monitor='val_accuracy', verbose=0, save_best_only=True, mode='max')
else:
modelCheckpoint = AltModelCheckpoint(filepath, alternate_model=base_model, monitor='val_accuracy', verbose=0, save_best_only=True, mode='max')
callbacks_list = [modelCheckpoint]
else:
callbacks_list = []
startTime = int(round(time.time()))
print("\n##### Start Time with test user "+str(testUser)+": "+str(startTime))
histories[testUser] = multi_model.fit_generator(generator=my_training_batch_generator,
steps_per_epoch=trainingStepsPerEpoch,
epochs=preTrainingNumOfEpochs,
#max_queue_size=5,
verbose=2,
callbacks=callbacks_list,
validation_data=my_validation_batch_generator,
validation_steps=validationStepsPerEpoch,
use_multiprocessing=False)
endTime = int(round(time.time()))
print("\n##### End Time with test user "+str(testUser)+": "+str(endTime))
toLog = str(preTrainingNumOfEpochs) + ',' + str(seq_len) + ',' + str(stride) + ',' + str(batchSize) + ',' + str(mean)
with open(workingDirectory+"history.csv", "a") as myfile:
myfile.write(str(endTime)\
+ ',' + str(trial)\
+ ',' + str(testUser)\
+ ',' + str(max(histories[testUser].history['accuracy']))\
+ ',' + str(max(histories[testUser].history['val_accuracy']))\
+ ',' + str(endTime-startTime)\
+ ',' + toLog\
+ ',' + str(amountOfRepetitions)\
+ ',' + str(amountOfGestures) + '\n')
del histories
del base_model, multi_model
del my_training_batch_generator, my_validation_batch_generator
def adaptModel(fineTuneUsers, testUsers, allNumOfFineTuningSamples, fineTuningStepsPerEpoch, amountOfRepetitions, amountOfGestures,
allNumOfValidationSamples,
validationStepsPerEpoch,
numberOfFineTuningEpochs,
trial,
batchSize,
totalGestures,
totalRepetitions,
directory,
testUser,
workingDirectory,
trainingDataset,
testDataset,
trainingRepetitions,
testRepetitions,
number_of_classes,
adaptationVersion):
base_model, multi_model = getAdaptationModel(modelPath=getBestModel(testUser, workingDirectory), adaptationVersion=adaptationVersion, features=NUMBER_OF_FEATURES, seqLen=seq_len)
histories = {}
path = workingDirectory+str(testUser)+'-adapted'
if not os.path.exists(path):
os.makedirs(path)
my_training_batch_generator = sequenceBatchGeneratorAbsMean2(batchSize=batchSize,
seq_len=seq_len,
indexes=fineTuneUsers,
stride=stride,
allNumOfSamples=allNumOfFineTuningSamples,
meanOf=mean,
shuffling=True,
standardize=True,
amountOfRepetitions=amountOfRepetitions,
amountOfGestures=amountOfGestures,
totalGestures=totalGestures,
totalRepetitions=totalRepetitions,
directory=directory,
dataset=trainingDataset,
repetitions=trainingRepetitions,
number_of_classes=number_of_classes)
my_validation_batch_generator = sequenceBatchGeneratorAbsMean2(batchSize=batchSize,
seq_len=seq_len,
indexes=testUsers,
stride=stride,
allNumOfSamples=allNumOfValidationSamples,
meanOf=mean,
shuffling=False,
standardize=True,
amountOfRepetitions=totalRepetitions,
amountOfGestures=totalGestures,
totalGestures=totalGestures,
totalRepetitions=totalRepetitions,
directory=directory,
dataset=testDataset,
repetitions=testRepetitions,
number_of_classes=number_of_classes)
filepath=path + "/e{epoch:03d}-a{val_acc:.3f}.hdf5"
#modelCheckpoint = AltModelCheckpoint(filepath, alternate_model=base_model, monitor='val_acc', verbose=0, save_best_only=True, mode='max')
#csv_logger = CSVLogger(workingDirectory+'log_u' + str(testUser) + '-adapted.csv', append=True, separator=',')
#callbacks_list = [modelCheckpoint]
startTime = int(round(time.time()))
print("\n##### Start Time with test user "+str(testUser)+": "+str(startTime))
histories[testUser] = multi_model.fit_generator(generator=my_training_batch_generator,
steps_per_epoch=fineTuningStepsPerEpoch,
epochs=numberOfFineTuningEpochs,
#max_queue_size=5,
verbose=2,
#callbacks=callbacks_list,
validation_data=my_validation_batch_generator,
validation_steps=validationStepsPerEpoch,
use_multiprocessing=False)
endTime = int(round(time.time()))
print("\n##### End Time with test user "+str(testUser)+": "+str(endTime))
toLog = str(numberOfFineTuningEpochs) + ',' + str(seq_len) + ',' + str(stride) + ',' + str(batchSize) + ',' + str(mean)
with open(workingDirectory+"history-adapted.csv", "a") as myfile:
myfile.write(str(endTime)\
+ ',' + str(trial)\
+ ',' + str(testUser)\
+ ',' + str(max(histories[testUser].history['accuracy']))\
+ ',' + str(max(histories[testUser].history['val_accuracy']))\
+ ',' + str(endTime-startTime)\
+ ',' + toLog\
+ ',' + str(amountOfRepetitions)\
+ ',' + str(amountOfGestures) + '\n')
del histories
del base_model, multi_model
del my_training_batch_generator, my_validation_batch_generator
fineTuningEpochList = [100]
fineTuningRepetitionList = [10]
fineTuningGestureList = [8]
preTrainingNumOfEpochs = 100
seq_len = 150
stride = 70
mean = 11
#seq_len = 40
#stride = 19
#mean=11
totalGestures=8
totalRepetitions=10
def generalScenario(validation, training, adaptationVersion):
for trial in range(70, 71):
for numberOfFineTuningEpochs in fineTuningEpochList:
for amountOfGestures in fineTuningGestureList:
for amountOfRepetitions in fineTuningRepetitionList:
batchSize = 520
if validation == 'inter-subject':
allNumOfFineTuningSamples = 1 * amountOfGestures * amountOfRepetitions * ((1000-mean+1-seq_len)//stride + 1)
fineTuningStepsPerEpoch = allNumOfFineTuningSamples // batchSize
allNumOfValidationSamples = 1 * 8 * 10 * ((1000-mean+1-seq_len)//stride + 1)
validationStepsPerEpoch = allNumOfValidationSamples // batchSize
allNumOfTrainingSamples = 9 * 8 * 10 * ((1000-mean+1-seq_len)//stride + 1)
trainingStepsPerEpoch = allNumOfTrainingSamples // batchSize
directory = baseDrive+"data/dbb"
workingDirectory = baseDrive+'data/inter-subject/dbb/'
trainingUsers = list(range(11, 21))
for testUser in trainingUsers: # LOSOCV for inter-subject validation
fineTuneUsers = [testUser]
testUsers = [testUser]
currentTrainingUsers = trainingUsers.copy()
currentTrainingUsers.remove(testUser)
if training == 'pre-training':
preTrainingModel(trainingUsers=currentTrainingUsers,
testUsers=testUsers,
allNumOfTrainingSamples=allNumOfTrainingSamples,
trainingStepsPerEpoch=trainingStepsPerEpoch,
allNumOfValidationSamples=allNumOfValidationSamples,
validationStepsPerEpoch=validationStepsPerEpoch,
amountOfRepetitions=amountOfRepetitions,
amountOfGestures=amountOfGestures,
preTrainingNumOfEpochs=preTrainingNumOfEpochs,
trial=trial,
batchSize=batchSize,
totalGestures=totalGestures,
totalRepetitions=totalRepetitions,
directory=directory,
testUser=testUser,
workingDirectory=workingDirectory,
trainingDataset='dbb-2nd',
testDataset='dbb-2nd',
trainingRepetitions=None,
testRepetitions=None,
number_of_classes=number_of_classes,
saveCheckpoints=True)
elif training == 'fine-tuning':
adaptModel(fineTuneUsers=fineTuneUsers,
testUsers=testUsers,
allNumOfFineTuningSamples=allNumOfFineTuningSamples,
fineTuningStepsPerEpoch=fineTuningStepsPerEpoch,
allNumOfValidationSamples=allNumOfValidationSamples,
validationStepsPerEpoch=validationStepsPerEpoch,
amountOfRepetitions=amountOfRepetitions,
amountOfGestures=amountOfGestures,
numberOfFineTuningEpochs=numberOfFineTuningEpochs,
trial=trial,
batchSize=batchSize,
totalGestures=totalGestures,
totalRepetitions=totalRepetitions,
directory=directory,
testUser=testUser,
workingDirectory=workingDirectory,
trainingDataset='dbb-2nd',
testDataset='dbb-2nd',
trainingRepetitions=None,
testRepetitions=None,
number_of_classes=number_of_classes,
adaptationVersion=adaptationVersion)
if validation == 'inter-subject-split':
directory = baseDrive+"data/dbb"
workingDirectory = baseDrive+'data/inter-subject-split/dbb/'
trainingUsers = list(range(11, 21))
for testUser in trainingUsers: # LOSOCV for inter-subject validation
testUsers = [testUser]
if training == 'pre-training':
currentTrainingUsers = trainingUsers.copy()
currentTrainingUsers.remove(testUser)
allNumOfTrainingSamples = 9 * 8 * 10 * ((1000-mean+1-seq_len)//stride + 1)
trainingStepsPerEpoch = allNumOfTrainingSamples // batchSize
allNumOfValidationSamples = 1 * 8 * 10 * ((1000-mean+1-seq_len)//stride + 1)
validationStepsPerEpoch = allNumOfValidationSamples // batchSize
preTrainingModel(trainingUsers=currentTrainingUsers,
testUsers=testUsers,
allNumOfTrainingSamples=allNumOfTrainingSamples,
trainingStepsPerEpoch=trainingStepsPerEpoch,
allNumOfValidationSamples=allNumOfValidationSamples,
validationStepsPerEpoch=validationStepsPerEpoch,
amountOfRepetitions=10,
amountOfGestures=8,
preTrainingNumOfEpochs=preTrainingNumOfEpochs,
trial=trial,
batchSize=batchSize,
totalGestures=8,
totalRepetitions=10,
directory=directory,
testUser=testUser,
workingDirectory=workingDirectory,
trainingDataset='dbb-2nd',
testDataset='dbb-2nd',
trainingRepetitions=None,
testRepetitions=None,
number_of_classes=number_of_classes,
saveCheckpoints=True)
elif training == 'fine-tuning':
trainingRepetitions = [1, 3, 5, 7, 9]
testRepetitions = [2, 4, 6, 8, 10]
fineTuneUsers = [testUser]
allNumOfFineTuningSamples = 1 * amountOfGestures * 5 * ((1000-mean+1-seq_len)//stride + 1)
fineTuningStepsPerEpoch = allNumOfFineTuningSamples // batchSize
allNumOfValidationSamples = 1 * 8 * 5 * ((1000-mean+1-seq_len)//stride + 1)
validationStepsPerEpoch = allNumOfValidationSamples // batchSize
adaptModel(fineTuneUsers=fineTuneUsers,
testUsers=testUsers,
allNumOfFineTuningSamples=allNumOfFineTuningSamples,
fineTuningStepsPerEpoch=fineTuningStepsPerEpoch,
allNumOfValidationSamples=allNumOfValidationSamples,
validationStepsPerEpoch=validationStepsPerEpoch,
amountOfRepetitions=5,
amountOfGestures=amountOfGestures,
numberOfFineTuningEpochs=numberOfFineTuningEpochs,
trial=trial,
batchSize=batchSize,
totalGestures=totalGestures,
totalRepetitions=5,
directory=directory,
testUser=testUser,
workingDirectory=workingDirectory,
trainingDataset='dbb-2nd',
testDataset='dbb-2nd',
trainingRepetitions=trainingRepetitions,
testRepetitions=testRepetitions,
number_of_classes=number_of_classes,
adaptationVersion=adaptationVersion)
elif validation == 'inter-session':
allNumOfFineTuningSamples = 10 * amountOfGestures * amountOfRepetitions * ((1000-mean+1-seq_len)//stride + 1)
fineTuningStepsPerEpoch = allNumOfFineTuningSamples // batchSize
allNumOfValidationSamples = 10 * 8 * 10 * ((1000-mean+1-seq_len)//stride + 1)
validationStepsPerEpoch = allNumOfValidationSamples // batchSize
allNumOfTrainingSamples = 10 * 8 * 10 * ((1000-mean+1-seq_len)//stride + 1)
trainingStepsPerEpoch = allNumOfTrainingSamples // batchSize
directory = baseDrive+"data/dbb"
workingDirectory = baseDrive+'data/inter-session-correct/dbb/'
trainingUsers = [1, 3, 5, 7, 9, 11, 13, 15, 17, 19]
testUsers = [2, 4, 6, 8, 10, 12, 14, 16, 18, 20]
fineTuneUsers = [2, 4, 6, 8, 10, 12, 14, 16, 18, 20]
testUser='2ndSession'
if training == 'pre-training':
preTrainingModel(trainingUsers=trainingUsers,
testUsers=testUsers,
allNumOfTrainingSamples=allNumOfTrainingSamples,
trainingStepsPerEpoch=trainingStepsPerEpoch,
allNumOfValidationSamples=allNumOfValidationSamples,
validationStepsPerEpoch=validationStepsPerEpoch,
amountOfRepetitions=amountOfRepetitions,
amountOfGestures=amountOfGestures,
preTrainingNumOfEpochs=preTrainingNumOfEpochs,
trial=trial,
batchSize=batchSize,
totalGestures=totalGestures,
totalRepetitions=totalRepetitions,
directory=directory,
testUser=testUser,
workingDirectory=workingDirectory,
trainingDataset='dbb-1st',
testDataset='dbb-2nd',
trainingRepetitions=None,
testRepetitions=None,
number_of_classes=number_of_classes,
saveCheckpoints=True)
elif training == 'fine-tuning':
adaptModel(fineTuneUsers=fineTuneUsers,
testUsers=testUsers,
allNumOfFineTuningSamples=allNumOfFineTuningSamples,
fineTuningStepsPerEpoch=fineTuningStepsPerEpoch,
allNumOfValidationSamples=allNumOfValidationSamples,
validationStepsPerEpoch=validationStepsPerEpoch,
amountOfRepetitions=amountOfRepetitions,
amountOfGestures=amountOfGestures,
numberOfFineTuningEpochs=numberOfFineTuningEpochs,
trial=trial,
batchSize=batchSize,
totalGestures=totalGestures,
totalRepetitions=totalRepetitions,
directory=directory,
testUser=testUser,
workingDirectory=workingDirectory,
trainingDataset='dbb-2nd',
testDataset='dbb-2nd',
trainingRepetitions=None,
testRepetitions=None,
number_of_classes=number_of_classes,
adaptationVersion=adaptationVersion)
elif validation == 'inter-session-split':
trainingUsers = [1, 3, 5, 7, 9, 11, 13, 15, 17, 19]
testUsers = [2, 4, 6, 8, 10, 12, 14, 16, 18, 20]
fineTuneUsers = [2, 4, 6, 8, 10, 12, 14, 16, 18, 20]
directory = baseDrive+"data/dbb"
workingDirectory = baseDrive+'data/inter-session-correct-split/dbb/'
testUser='2ndSession'
if training == 'pre-training':
allNumOfTrainingSamples = 10 * 8 * 10 * ((1000-mean+1-seq_len)//stride + 1)
trainingStepsPerEpoch = allNumOfTrainingSamples // batchSize
allNumOfValidationSamples = 10 * 8 * 10 * ((1000-mean+1-seq_len)//stride + 1)
validationStepsPerEpoch = allNumOfValidationSamples // batchSize
preTrainingModel(trainingUsers=trainingUsers,
testUsers=testUsers,
allNumOfTrainingSamples=allNumOfTrainingSamples,
trainingStepsPerEpoch=trainingStepsPerEpoch,
allNumOfValidationSamples=allNumOfValidationSamples,
validationStepsPerEpoch=validationStepsPerEpoch,
amountOfRepetitions=10,
amountOfGestures=8,
preTrainingNumOfEpochs=preTrainingNumOfEpochs,
trial=trial,
batchSize=batchSize,
totalGestures=8,
totalRepetitions=10,
directory=directory,
testUser=testUser,
workingDirectory=workingDirectory,
trainingDataset='dbb-1st',
testDataset='dbb-2nd',
trainingRepetitions=None,
testRepetitions=None,
number_of_classes=number_of_classes,
saveCheckpoints=True)
elif training == 'fine-tuning':
allNumOfFineTuningSamples = 10 * amountOfGestures * 5 * ((1000-mean+1-seq_len)//stride + 1)
allNumOfValidationSamples = 10 * 8 * 5 * ((1000-mean+1-seq_len)//stride + 1)
fineTuningStepsPerEpoch = allNumOfValidationSamples // batchSize
validationStepsPerEpoch = allNumOfValidationSamples // batchSize
fineTuneRepetitions = [1, 3, 5, 7, 9]
testRepetitions = [2, 4, 6, 8, 10]
adaptModel(fineTuneUsers=fineTuneUsers,
testUsers=testUsers,
allNumOfFineTuningSamples=allNumOfFineTuningSamples,
fineTuningStepsPerEpoch=fineTuningStepsPerEpoch,
allNumOfValidationSamples=allNumOfValidationSamples,
validationStepsPerEpoch=validationStepsPerEpoch,
amountOfRepetitions=5,
amountOfGestures=amountOfGestures,
numberOfFineTuningEpochs=numberOfFineTuningEpochs,
trial=trial,
batchSize=batchSize,
totalGestures=totalGestures,
totalRepetitions=5,
directory=directory,
testUser=testUser,
workingDirectory=workingDirectory,
trainingDataset='dbb-2nd',
testDataset='dbb-2nd',
trainingRepetitions=fineTuneRepetitions,
testRepetitions=testRepetitions,
number_of_classes=number_of_classes,
adaptationVersion=adaptationVersion)
elif validation == 'intra-session':
allNumOfValidationSamples = 10 * 8 * 5 * ((1000-mean+1-seq_len)//stride + 1)
validationStepsPerEpoch = allNumOfValidationSamples // batchSize
allNumOfTrainingSamples = 10 * 8 * 5 * ((1000-mean+1-seq_len)//stride + 1)
trainingStepsPerEpoch = allNumOfTrainingSamples // batchSize
directory = baseDrive+"data/dbb"
workingDirectory = baseDrive+'data/intra-session/dbb/'
trainingUsers = list(range(11, 21))
testUsers = list(range(11, 21))
trainingRepetitions = [1, 3, 5, 7, 9]
testRepetitions = [2, 4, 6, 8, 10]
testUser='evenRepetitions'
if training == 'pre-training':
preTrainingModel(trainingUsers=trainingUsers,
testUsers=testUsers,
allNumOfTrainingSamples=allNumOfTrainingSamples,
trainingStepsPerEpoch=trainingStepsPerEpoch,
allNumOfValidationSamples=allNumOfValidationSamples,
validationStepsPerEpoch=validationStepsPerEpoch,
amountOfRepetitions=5,
amountOfGestures=amountOfGestures,
preTrainingNumOfEpochs=preTrainingNumOfEpochs,
trial=trial,
batchSize=batchSize,
totalGestures=totalGestures,
totalRepetitions=5,
directory=directory,
testUser=testUser,
workingDirectory=workingDirectory,
trainingDataset='dbb-2nd',
testDataset='dbb-2nd',
trainingRepetitions=trainingRepetitions,
testRepetitions=testRepetitions,
number_of_classes=number_of_classes,
saveCheckpoints=False)
elif validation == 'intra-session-separated':
allNumOfValidationSamples = 1 * 8 * 5 * ((1000-mean+1-seq_len)//stride + 1)
validationStepsPerEpoch = allNumOfValidationSamples // batchSize
allNumOfTrainingSamples = 1 * 8 * 5 * ((1000-mean+1-seq_len)//stride + 1)
trainingStepsPerEpoch = allNumOfTrainingSamples // batchSize
directory = baseDrive+"data/dbb"
workingDirectory = baseDrive+'data/intra-session-separated/dbb/'
trainingUsers = [1, 3, 5, 7, 9, 11, 13, 15, 17, 19]
for subject in trainingUsers:
print("subject: " + str(subject))
trainingUsers = [subject]
testUsers = [subject]
trainingRepetitions = [1, 3, 5, 7, 9]
testRepetitions = [2, 4, 6, 8, 10]
testUser=subject
if training == 'pre-training':
preTrainingModel(trainingUsers=trainingUsers,
testUsers=testUsers,
allNumOfTrainingSamples=allNumOfTrainingSamples,
trainingStepsPerEpoch=trainingStepsPerEpoch,
allNumOfValidationSamples=allNumOfValidationSamples,
validationStepsPerEpoch=validationStepsPerEpoch,
amountOfRepetitions=5,
amountOfGestures=amountOfGestures,
preTrainingNumOfEpochs=preTrainingNumOfEpochs,
trial=trial,
batchSize=batchSize,
totalGestures=totalGestures,
totalRepetitions=5,
directory=directory,
testUser=testUser,
workingDirectory=workingDirectory,
trainingDataset='dbb-all',
testDataset='dbb-all',
trainingRepetitions=trainingRepetitions,
testRepetitions=testRepetitions,
number_of_classes=number_of_classes)
# Istvan Titan V
'''
#szombat ejjel:
generalScenario(validation='inter-session-split', training='fine-tuning', adaptationVersion=23)
generalScenario(validation='inter-session', training='fine-tuning', adaptationVersion=23)
generalScenario(validation='inter-session-split', training='fine-tuning', adaptationVersion=1000)
generalScenario(validation='inter-session', training='fine-tuning', adaptationVersion=1000)
generalScenario(validation='inter-session-split', training='fine-tuning', adaptationVersion=21)
generalScenario(validation='inter-session', training='fine-tuning', adaptationVersion=21)
generalScenario(validation='inter-session-split', training='fine-tuning', adaptationVersion=22)
generalScenario(validation='inter-session', training='fine-tuning', adaptationVersion=22)
generalScenario(validation='inter-session-split', training='fine-tuning', adaptationVersion=24)
generalScenario(validation='inter-session', training='fine-tuning', adaptationVersion=24)
#vasarnap ejjel:
generalScenario(validation='inter-session-split', training='fine-tuning', adaptationVersion=31)
generalScenario(validation='inter-session', training='fine-tuning', adaptationVersion=31)
generalScenario(validation='inter-session-split', training='fine-tuning', adaptationVersion=32)
generalScenario(validation='inter-session', training='fine-tuning', adaptationVersion=32)
generalScenario(validation='inter-session-split', training='fine-tuning', adaptationVersion=34)
generalScenario(validation='inter-session', training='fine-tuning', adaptationVersion=34)
#hetfo delben:
generalScenario(validation='inter-subject-split', training='fine-tuning', adaptationVersion=22)
generalScenario(validation='inter-subject', training='fine-tuning', adaptationVersion=22)
#idaig jutottunk.
#hetfo este:
#relu:
generalScenario(validation='inter-session-split', training='fine-tuning', adaptationVersion=21)
generalScenario(validation='inter-session', training='fine-tuning', adaptationVersion=21)
#deep-relu:
generalScenario(validation='inter-session-split', training='fine-tuning', adaptationVersion=31)
generalScenario(validation='inter-session', training='fine-tuning', adaptationVersion=31)
'''