model-ninapro-g12.py

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

onTpu = False
if 'COLAB_TPU_ADDR' in os.environ:
    onTpu = True
    tpu = os.environ['COLAB_TPU_ADDR']
    print(tpu)
    
if onTpu: # in Colab TPU
    from google.colab import drive
    from IPython.lib import backgroundjobs as bg
    from tensorflow.python.keras.layers import Input, LSTM, TimeDistributed, Dense, Bidirectional, GRU, Layer
    from tensorflow.python.keras.models import Sequential, load_model
    from tensorflow.python.keras.layers.core import Dropout
    from tensorflow.python.keras import initializers, optimizers, regularizers, constraints
    from tensorflow.python.keras import backend as K
    from tensorflow.python.keras.callbacks import TensorBoard, ModelCheckpoint, CSVLogger, Callback
    from tensorflow.python.keras.utils import to_categorical, multi_gpu_model
    baseDrive = '/content/drive/My Drive/data/ninapro/'
    drive.mount('/content/drive')
else: # on GPU
    import os
    if "10.4.17.191" in os.environ["SSH_CONNECTION"]: # MH 8 GPU server / 7
        os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
        os.environ["CUDA_VISIBLE_DEVICES"]="1,2,3,4,5,6,7"
    from keras.layers import Input, LSTM, TimeDistributed, Dense, Bidirectional, GRU, 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/ninapro/'

NUMBER_OF_FEATURES = 10 # Sparse system
recurrent_dropout = 0.5
dropout = 0.5
number_of_classes = 12
cellNeurons = 512
denseNeurons = 512

def toTpuModel(model):
  # This address identifies the TPU we'll use when configuring TensorFlow.
  TPU_WORKER = 'grpc://' + tpu
  tf.logging.set_verbosity(tf.logging.INFO)

  tpu_model = tf.contrib.tpu.keras_to_tpu_model(
    model,
    strategy=tf.contrib.tpu.TPUDistributionStrategy(
      tf.contrib.cluster_resolver.TPUClusterResolver(TPU_WORKER)))

  #tpu_model.summary()
  
  return tpu_model

def toMultiGpuModel(model):
    try:
        gpu_model = multi_gpu_model(model)
        return gpu_model
    except:
        print("gpu_model error")
        return None

def dot_product(x, kernel):
    """
    Wrapper for dot product operation, in order to be compatible with both
    Theano and Tensorflow
    Args:
        x (): input
        kernel (): weights
    Returns:
    """
    if K.backend() == 'tensorflow':
        # todo: check that this is correct
        return K.squeeze(K.dot(x, K.expand_dims(kernel)), axis=-1)
    else:
        return K.dot(x, kernel)


class Attention(Layer):
    def __init__(self,
                 W_regularizer=None, b_regularizer=None,
                 W_constraint=None, b_constraint=None,
                 bias=True,
                 return_attention=False,
                 **kwargs):
        """
        Keras Layer that implements an Attention mechanism for temporal data.
        Supports Masking.
        Follows the work of Raffel et al. [https://arxiv.org/abs/1512.08756]
        # Input shape
            3D tensor with shape: `(samples, steps, features)`.
        # Output shape
            2D tensor with shape: `(samples, features)`.
        :param kwargs:
        Just put it on top of an RNN Layer (GRU/LSTM/SimpleRNN) with return_sequences=True.
        The dimensions are inferred based on the output shape of the RNN.
        Note: The layer has been tested with Keras 1.x
        Example:
        
            # 1
            model.add(LSTM(64, return_sequences=True))
            model.add(Attention())
            # next add a Dense layer (for classification/regression) or whatever...
            # 2 - Get the attention scores
            hidden = LSTM(64, return_sequences=True)(words)
            sentence, word_scores = Attention(return_attention=True)(hidden)
        """
        self.supports_masking = True
        self.return_attention = return_attention
        self.init = initializers.get('glorot_uniform')

        self.W_regularizer = regularizers.get(W_regularizer)
        self.b_regularizer = regularizers.get(b_regularizer)

        self.W_constraint = constraints.get(W_constraint)
        self.b_constraint = constraints.get(b_constraint)

        self.bias = bias
        super(Attention, self).__init__(**kwargs)

    def build(self, input_shape):
        assert len(input_shape) == 3

        self.W = self.add_weight(shape=(int(input_shape[-1]),), #, self.output_dim)
                                 initializer=self.init,
                                 name='{}_W'.format(self.name),
                                 regularizer=self.W_regularizer,
                                 constraint=self.W_constraint)
        if self.bias:
            self.b = self.add_weight(shape=(int(input_shape[1]),),
                                     initializer='zero',
                                     name='{}_b'.format(self.name),
                                     regularizer=self.b_regularizer,
                                     constraint=self.b_constraint)
        else:
            self.b = None

        self.built = True

    def compute_mask(self, input, input_mask=None):
        # do not pass the mask to the next layers
        return None

    def call(self, x, mask=None):
        eij = dot_product(x, self.W)

        if self.bias:
            eij += self.b

        eij = K.tanh(eij)

        a = K.exp(eij)

        # apply mask after the exp. will be re-normalized next
        if mask is not None:
            # Cast the mask to floatX to avoid float64 upcasting in theano
            a *= K.cast(mask, K.floatx())

        # in some cases especially in the early stages of training the sum may be almost zero
        # and this results in NaN's. A workaround is to add a very small positive number ε to the sum.
        # a /= K.cast(K.sum(a, axis=1, keepdims=True), K.floatx())
        a /= K.cast(K.sum(a, axis=1, keepdims=True) + K.epsilon(), K.floatx())

        weighted_input = x * K.expand_dims(a)

        result = K.sum(weighted_input, axis=1)

        if self.return_attention:
            return [result, a]
        return result

    def compute_output_shape(self, input_shape):
        if self.return_attention:
            return [(input_shape[0], input_shape[-1]),
                    (input_shape[0], input_shape[1])]
        else:
            return input_shape[0], input_shape[-1]

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 = []
    
    # Ninapro DB1:
    # All's mean:		NO NEED!
    # All's std:		NO NEED!
    if dataset == 'db1':
      mean = 0.0
      std = 1.0
    
    if shuffling:
        range_i = sample(indexes, len(indexes))
        range_j = sample(range(1, totalGestures+1), amountOfGestures)
        # range_k = sample(range(0, totalRepetitions), amountOfRepetitions)
        # For dynamic set of repetitions
        range_k = sample(repetitions, len(repetitions))
    else:
        range_i = indexes
        range_j = range(1, amountOfGestures+1)
        # range_k = range(0, amountOfRepetitions)
        # For dynamic set of repetitions
        range_k = repetitions
    
    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:
            for j in range_j:
                for k in range_k:
                    fileName = '{:03d}_{:03d}_{:03d}.mat'.format(i, j, k)
                    aFile = scipy.io.loadmat(os.path.join(directory, fileName))
                    if standardize:
                        print("NO need")
                        aFile = (aFile-mean)/std
                    #aFile = np.abs(aFile) #No Need
                    #absMeanFile = np.apply_along_axis(lambda m: np.convolve(m, np.ones((meanOf,))/meanOf, mode='valid'), axis=0, arr=aFile) #No Need
                    absMeanFile = aFile['data'][aFile['shape'][0, 0]//2-90:aFile['shape'][0, 0]//2+90, :]
                    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),
                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)
    
    # Test optimizer's state:
    #print(fineTuneModel.optimizer.get_config())
    #print(dir(fineTuneModel.optimizer))
    #print(fineTuneModel.optimizer.lr)
    
    fineTuneModel.get_layer('td').trainable = True
    if adaptationVersion == 2:
        fineTuneModel.get_layer('td').activation = 'relu'
    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 == 3:
        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 == 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
    
    if onTpu:
        multiFineTuneModel.compile(loss="categorical_crossentropy",
                    optimizer=tf.train.AdamOptimizer(lr=0.001),
                    metrics=["accuracy"])
        multiFineTuneModel = toTpuModel(fineTuneModel)
    else:
        multiFineTuneModel = toMultiGpuModel(fineTuneModel)
        multiFineTuneModel.compile(loss="categorical_crossentropy",
                    optimizer=optimizers.Adam(lr=0.001),
                    metrics=["accuracy"])

    # Test optimizer's state:
    #print(fineTuneModel.optimizer.get_config())
    #print(dir(fineTuneModel.optimizer))
    #print(fineTuneModel.optimizer.lr)
    
    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

class WeightsNorm(Callback):
    def on_batch_end(self, batch, logs={}):
        # Norm clipping:
        print(str(math.sqrt(sum(np.sum(K.get_value(w)) for w in self.model.optimizer.weights))) + '\n')
        return

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=False,
                                                            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=False,
                                                            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"
    
    if saveCheckpoints == True:
        if onTpu:
            modelCheckpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=0, save_best_only=True, mode='max')
        else:
            modelCheckpoint = AltModelCheckpoint(filepath, alternate_model=base_model, monitor='val_acc', 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=True)

    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['acc']))\
            + ',' + str(max(histories[testUser].history['val_acc']))\
            + ',' + 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):
    
    base_model, multi_model = getAdaptationModel(modelPath=getBestModel(testUser, workingDirectory), adaptationVersion=1, 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=False,
                                                            amountOfRepetitions=amountOfRepetitions,
                                                            amountOfGestures=amountOfGestures,
                                                            totalGestures=totalGestures,
                                                            totalRepetitions=totalRepetitions,
                                                            directory=directory,
															dataset=trainingDataset,
															repetitions=testRepetitions,
                                                            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=False,
                                                            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['acc']))\
            + ',' + str(max(histories[testUser].history['val_acc']))\
            + ',' + 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 = [1, 2, 4, 8, 16, 32, 64]
#fineTuningRepetitionList = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
#fineTuningGestureList = []
fineTuningEpochList = [100]
fineTuningRepetitionList = [10]
fineTuningGestureList = [12]

preTrainingNumOfEpochs = 100
seq_len = 150
stride = 15
mean = 1 # No need
#seq_len = 40
#stride = 1
#mean=1 # No need
totalGestures=12
totalRepetitions=10

def generalScenario(validation, training):
    for trial in range(750, 751):
        for numberOfFineTuningEpochs in fineTuningEpochList:
            for amountOfGestures in fineTuningGestureList:
                for amountOfRepetitions in fineTuningRepetitionList:
                    ###################################################################################
                    # seq_len = 40
                    # stride = 1
                    # mean = 1
                    # -> allNumOfFineTuningSamples is a multiple of 141
                    # 2*5 * 141 (1410?) would work pretty well on GPU
                    ###################################################################################
                    # seq_len = 150
                    # stride = 1
                    # mean = 1
                    # -> allNumOfFineTuningSamples is a multiple of 31
                    # 12 * 31 (?) would work pretty well on TPU both for training and validation as well
                    ###################################################################################
                    # seq_len = 40
                    # stride = 20
                    # mean = 1
                    # -> allNumOfFineTuningSamples is a multiple of 8
                    # 12*5 * 8 (480) would work pretty well on GPU
                    ###################################################################################
                    # seq_len = 150
                    # stride = 15
                    # mean = 1
                    # -> allNumOfFineTuningSamples is a multiple of 3
                    # 12*5 * 3 (180) would work pretty well on TPU both for training and validation as well
                    ###################################################################################
                    
                    
                    if validation == 'inter-subject':
                        batchSize = 180
                        allNumOfFineTuningSamples = 1 * amountOfGestures * amountOfRepetitions * ((180-mean+1-seq_len)//stride + 1)
                        fineTuningStepsPerEpoch = allNumOfFineTuningSamples // batchSize
                        allNumOfValidationSamples = 1 * 12 * 10 * ((180-mean+1-seq_len)//stride + 1)
                        validationStepsPerEpoch = allNumOfValidationSamples // batchSize
                        allNumOfTrainingSamples = 26 * 12 * 10 * ((180-mean+1-seq_len)//stride + 1)
                        trainingStepsPerEpoch = allNumOfTrainingSamples // batchSize
                        directory = baseDrive+"data/db1"
                        workingDirectory = baseDrive+'data/inter-subject-sl150-g12/db1/'
                        trainingUsers = list(range(0, 27))
                        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='db1',
									testDataset='db1',
									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='db1',
									testDataset='db1',
									trainingRepetitions=None,
									testRepetitions=None,
                                    number_of_classes=number_of_classes)
                    if validation == 'inter-subject-split':
                        directory = baseDrive+"data/db1"
                        workingDirectory = baseDrive+'data/inter-subject-split-sl150-g12/db1/'
                        trainingUsers = list(range(0, 27))
                        for testUser in trainingUsers: # LOSOCV for inter-subject validation
                            testUsers = [testUser]
                            if training == 'pre-training':
                                batchSize = 360
                                currentTrainingUsers = trainingUsers.copy()
                                currentTrainingUsers.remove(testUser)
                                allNumOfTrainingSamples = 26 * 12 * 10 * ((180-mean+1-seq_len)//stride + 1)
                                trainingStepsPerEpoch = allNumOfTrainingSamples // batchSize
                                allNumOfValidationSamples = 1 * 12 * 10 * ((180-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=12,
                                    preTrainingNumOfEpochs=preTrainingNumOfEpochs,
                                    trial=trial,
                                    batchSize=batchSize,
                                    totalGestures=12,
                                    totalRepetitions=10,
                                    directory=directory,
                                    testUser=testUser,
                                    workingDirectory=workingDirectory,
									trainingDataset='db1',
									testDataset='db1',
									trainingRepetitions=None,
									testRepetitions=None,
                                    number_of_classes=number_of_classes,
                                    saveCheckpoints=True)
                            elif training == 'fine-tuning':
                                batchSize = 180
                                trainingRepetitions = [0, 2, 4, 6, 8]
                                testRepetitions = [1, 3, 5, 7, 9]
                                # From the Chinese paper:
                                #trainingRepetitions = [0, 2, 3, 4, 8]
                                #testRepetitions = [1, 5, 6, 7, 9]
                                fineTuneUsers = [testUser]
                                allNumOfFineTuningSamples = 1 * amountOfGestures * 5 * ((180-mean+1-seq_len)//stride + 1)
                                fineTuningStepsPerEpoch = allNumOfFineTuningSamples // batchSize
                                allNumOfValidationSamples = 1 * 12 * 5 * ((180-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='db1',
									testDataset='db1',
									trainingRepetitions=trainingRepetitions,
									testRepetitions=testRepetitions,
                                    number_of_classes=number_of_classes)
                    elif validation == 'intra-session':
                        batchSize = 1080
                        allNumOfValidationSamples = 27 * 12 * 5 * ((180-mean+1-seq_len)//stride + 1)
                        validationStepsPerEpoch = allNumOfValidationSamples // batchSize
                        allNumOfTrainingSamples = 27 * 12 * 5 * ((180-mean+1-seq_len)//stride + 1)
                        trainingStepsPerEpoch = allNumOfTrainingSamples // batchSize
                        directory = baseDrive+"data/db1"
                        workingDirectory = baseDrive+'data/intra-session-sl150-g12/db1/'
                        trainingUsers = list(range(0, 27))
                        trainingUsers = list(range(0, 27))
                        trainingRepetitions = [0, 2, 4, 6, 8]
                        testRepetitions = [1, 3, 5, 7, 9]
                        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='db1',
								testDataset='db1',
								trainingRepetitions=trainingRepetitions,
								testRepetitions=testRepetitions,
                                number_of_classes=number_of_classes,
                                saveCheckpoints=False)
                    elif validation == 'intra-session-separated':
                        batchSize = 180
                        allNumOfValidationSamples = 1 * 12 * 5 * ((180-mean+1-seq_len)//stride + 1)
                        validationStepsPerEpoch = allNumOfValidationSamples // batchSize
                        allNumOfTrainingSamples = 1 * 12 * 5 * ((180-mean+1-seq_len)//stride + 1)
                        trainingStepsPerEpoch = allNumOfTrainingSamples // batchSize
                        directory = baseDrive+"data/db1"
                        workingDirectory = baseDrive+'data/intra-session-separated-sl150-g12/db1/'
                        trainingUsers = list(range(0, 27))
                        for subject in trainingUsers:
                            print("subject: " + str(subject))
                            trainingUsers = [subject]
                            testUsers = [subject]
                            # Kinaiake:
                            # [0, 2, 3, 5, 7, 8, 9], [1, 4, 6]
                            trainingRepetitions = [0, 2, 4, 6, 8]
                            testRepetitions = [1, 3, 5, 7, 9]
                            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='db1',
                                    testDataset='db1',
                                    trainingRepetitions=trainingRepetitions,
                                    testRepetitions=testRepetitions,
                                    number_of_classes=number_of_classes,
                                    saveCheckpoints=False)
                    elif validation == 'intra-session-separated-70t':
                        batchSize = 108
                        allNumOfValidationSamples = 1 * 12 * 7 * ((180-mean+1-seq_len)//stride + 1)
                        validationStepsPerEpoch = allNumOfValidationSamples // batchSize
                        allNumOfTrainingSamples = 1 * 12 * 3 * ((180-mean+1-seq_len)//stride + 1)
                        trainingStepsPerEpoch = allNumOfTrainingSamples // batchSize
                        directory = baseDrive+"data/db1"
                        workingDirectory = baseDrive+'data/intra-session-separated-sl150-g12-70t/db1/'
                        trainingUsers = list(range(0, 27))
                        for subject in trainingUsers:
                            print("subject: " + str(subject))
                            trainingUsers = [subject]
                            testUsers = [subject]
                            # Kinaiake:
                            # [0, 2, 3, 5, 7, 8, 9], [1, 4, 6]
                            trainingRepetitions = [0, 1, 2, 3, 4, 5, 6]
                            testRepetitions = [7, 8, 9]
                            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='db1',
                                    testDataset='db1',
                                    trainingRepetitions=trainingRepetitions,
                                    testRepetitions=testRepetitions,
                                    number_of_classes=number_of_classes,
                                    saveCheckpoints=False)
                        
generalScenario(validation='inter-subject-split', training='pre-training')
generalScenario(validation='inter-subject-split', training='fine-tuning')
#generalScenario(validation='inter-subject', training='pre-training')
#generalScenario(validation='inter-subject', training='fine-tuning')
#generalScenario(validation='intra-session-separated', training='pre-training')