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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
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/capgMyo/'
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/capgMyo/'
NUMBER_OF_FEATURES = 128
recurrent_dropout = 0.5
dropout = 0.5
number_of_classes = 8
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 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 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 = []
# 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)
# 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, decay=0.0001),
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 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_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=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['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=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['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 = [1, 2, 3, 4, 5, 6, 7, 8]
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):
for trial in range(700, 701):
for numberOfFineTuningEpochs in fineTuningEpochList:
for amountOfGestures in fineTuningGestureList:
for amountOfRepetitions in fineTuningRepetitionList:
###################################################################################
# seq_len = 40
# stride = 19
# mean = 11
# -> allNumOfFineTuningSamples is a multiple of 51
# 1020 worked pretty well on GPU
###################################################################################
# seq_len = 150
# stride = 70
# mean = 11
# -> allNumOfFineTuningSamples is a multiple of 13
# 8*5 * 13 (520) would work pretty well on TPU both for training and validation as well
###################################################################################
# seq_len = 150
# stride = 53
# mean = 3
# -> allNumOfFineTuningSamples is a multiple of 17
# 8*10 * 17 (1360) would work pretty well on TPU both for training and validation as well
###################################################################################
# seq_len = 150
# stride = 10
# mean = 21
# -> allNumOfFineTuningSamples is a multiple of 84
# 10 * 84 (840) would work pretty well on TPU both for training and validation as well
###################################################################################
#if amountOfGestures * amountOfRepetitions >= 84: # this is dynamic (20 for: sl 40, 51 for sl 150)
batchSize = 520
#else:
# batchSize = amountOfGestures * amountOfRepetitions * 84
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)
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)
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/dbb/'
trainingUsers = list(range(1, 11))
testUsers = list(range(11, 21))
fineTuneUsers = list(range(11, 21))
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)
elif validation == 'inter-session-split':
trainingUsers = list(range(1, 11))
testUsers = list(range(11, 21))
directory = baseDrive+"data/dbb"
workingDirectory = baseDrive+'data/inter-session-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':
fineTuneUsers = list(range(11, 21))
allNumOfFineTuningSamples = 10 * amountOfGestures * 5 * ((1000-mean+1-seq_len)//stride + 1)
fineTuningStepsPerEpoch = allNumOfValidationSamples // batchSize
allNumOfValidationSamples = 10 * 8 * 5 * ((1000-mean+1-seq_len)//stride + 1)
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)
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,