def train_apex(batch_size, spatial_epochs, train_id, list_dB, spatial_size, objective_flag, tensorboard):
############## Path Preparation ######################
root_db_path = "/media/ostalo/MihaGarafolj/ME_data/"
#root_db_path = '/home/miha/Documents/ME_data/'
tensorboard_path = root_db_path + "tensorboard/"
if os.path.isdir(root_db_path + 'Weights/'+ str(train_id) ) == False:
os.mkdir(root_db_path + 'Weights/'+ str(train_id) )
######################################################
############## Variables ###################
dB = list_dB[0]
r, w, subjects, samples, n_exp, VidPerSubject, vidList, timesteps_TIM, data_dim, channel, table, listOfIgnoredSamples, db_home, db_images, cross_db_flag = load_db(root_db_path, list_dB, spatial_size, objective_flag)
# avoid confusion
if cross_db_flag == 1:
list_samples = listOfIgnoredSamples
# total confusion matrix to be used in the computation of f1 score
tot_mat = np.zeros((n_exp, n_exp))
history = LossHistory()
stopping = EarlyStopping(monitor='loss', min_delta = 0, mode = 'min', patience = 5)
############################################
############## Flags ####################
tensorboard_flag = tensorboard
resizedFlag = 1
#svm_flag = 0
#finetuning_flag = 0
cam_visualizer_flag = 0
#channel_flag = 0
#########################################
############ Reading Images and Labels ################
if cross_db_flag == 1:
SubperdB = Read_Input_Images_SAMM_CASME(db_images, list_samples, listOfIgnoredSamples, dB, resizedFlag, table, db_home, spatial_size, channel)
else:
SubperdB = Read_Input_Images_Apex(db_images, listOfIgnoredSamples, dB, resizedFlag, table, db_home, spatial_size, channel, objective_flag)
labelperSub = label_matching(db_home, dB, subjects, VidPerSubject)
print(labelperSub)
print("Loaded Images into the tray.")
print("Loaded Labels into the tray.")
#######################################################
# PREPROCESSING STEPS
# optical flow
#import ipdb
#ipdb.set_trace()
#SubperdB = optical_flow_2d(SubperdB, samples, r, w, timesteps_TIM)
########### Model Configurations #######################
#K.set_image_dim_ordering('th')
# config = tf.ConfigProto()
# config.gpu_options.allow_growth = True
# config.gpu_options.per_process_gpu_memory_fraction = 0.8
# K.tensorflow_backend.set_session(tf.Session(config=config))
########################################################
print("Beginning training process.")
########### Training Process ############
subjects_todo = read_subjects_todo(db_home, dB, train_id, subjects)
for sub in subjects_todo:
print("**** starting subject " + str(sub) + " ****")
# gpu_observer()
#spatial_weights_name = root_db_path + 'Weights/'+ str(train_id) + '/c3d_'+ str(train_id) + '_' + str(dB) + '_'
adam = optimizers.Adam(lr=0.00001, decay=0.000001)
############### Reinitialization & weights reset of models ########################
apex_model = apex_cnn(spatial_size=spatial_size, temporal_size=timesteps_TIM, classes=n_exp, channels=2, filters=16)
apex_model.compile(loss='categorical_crossentropy', optimizer=adam, metrics=[metrics.categorical_accuracy])
#svm_classifier = SVC(kernel='linear', C=1)
####################################################################################
############ for tensorboard ###############
if tensorboard_flag == 1:
cat_path2 = tensorboard_path + str(train_id) + str(sub) + "apex/"
if os.path.exists(cat_path2):
os.rmdir(cat_path2)
os.mkdir(cat_path2)
tbCallBack2 = keras.callbacks.TensorBoard(log_dir=cat_path2, write_graph=True)
#############################################
Train_X, Train_Y, Train_Y_gt, Test_X, Test_Y, Test_Y_gt = restructure_data_apex(sub, SubperdB, labelperSub, subjects, n_exp, r, w, timesteps_TIM, channel)
#Train_X, Train_Y, Train_Y_gt = upsample_training_set(Train_X, Train_Y, Train_Y_gt)
#Train_X, Test_X = optical_flow_2d_old(Train_X, Test_X, r, w, timesteps_TIM)
############### check gpu resources ####################
# gpu_observer()
########################################################
print("Beginning training & testing.")
##################### Training & Testing #########################
print("Beginning c3d training.")
# Spatial Training
input_u = Train_X[:,0,:,:].reshape(Train_X.shape[0],1,r,w)
input_v = Train_X[:,1,:,:].reshape(Train_X.shape[0],1,r,w)
if tensorboard_flag == 1:
apex_model.fit([input_u, input_v], Train_Y, batch_size=batch_size, epochs=spatial_epochs, shuffle=True, callbacks=[history,stopping,tbCallBack2])
else:
apex_model.fit([input_u, input_v], Train_Y, batch_size=batch_size, epochs=spatial_epochs, shuffle=True, callbacks=[history,stopping])
print(".record f1 and loss")
# record f1 and loss
record_loss_accuracy(db_home, train_id, dB, history)
print("Beginning testing.")
print(".predicting with c3d_model")
# Testing
input_u = Test_X[:,0,:,:].reshape(Test_X.shape[0],1,r,w)
input_v = Test_X[:,1,:,:].reshape(Test_X.shape[0],1,r,w)
predict_values = apex_model.predict([input_u, input_v], batch_size = batch_size)
predict = np.array([np.argmax(x) for x in predict_values])
##############################################################
#################### Confusion Matrix Construction #############
print (predict)
print (Test_Y_gt.astype(int))
print(".writing predicts to file")
file = open(db_home+'Classification/'+ 'Result/'+'/predicts_' + str(train_id) + '.txt', 'a')
for i in range(len(vidList[sub])):
file.write("sub_" + str(sub) + "," + str(vidList[sub][i]) + "," + str(predict.astype(list)[i]) + "," + str(Test_Y_gt.astype(int).astype(list)[i]) + "\n")
file.close()
file = open(db_home+'Classification/'+ 'Result/'+'/predictedvalues_' + str(train_id) + '.txt', 'a')
for i in range(len(vidList[sub])):
file.write("sub_" + str(sub) + "," + str(vidList[sub][i]) + "," + ','.join(str(e) for e in predict_values[i]) + "," + str(Test_Y_gt.astype(int).astype(list)[i]) + "\n")
file.close()
ct = confusion_matrix(Test_Y_gt,predict)
# check the order of the CT
order = np.unique(np.concatenate((predict,Test_Y_gt)))
# create an array to hold the CT for each CV
mat = np.zeros((n_exp,n_exp))
# put the order accordingly, in order to form the overall ConfusionMat
for m in range(len(order)):
for n in range(len(order)):
mat[int(order[m]),int(order[n])]=ct[m,n]
tot_mat = mat + tot_mat
################################################################
#################### cumulative f1 plotting ######################
microAcc = np.trace(tot_mat) / np.sum(tot_mat)
[f1,precision,recall] = fpr(tot_mat,n_exp)
file = open(db_home+'Classification/'+ 'Result/'+'/f1_' + str(train_id) + '.txt', 'a')
file.write(str(f1) + "\n")
file.close()
##################################################################
################# write each CT of each CV into .txt file #####################
record_scores(db_home, dB, ct, sub, order, tot_mat, n_exp, subjects)
war = weighted_average_recall(tot_mat, n_exp, samples)
uar = unweighted_average_recall(tot_mat, n_exp)
print("war: " + str(war))
print("uar: " + str(uar))
###############################################################################
################## free memory ####################
del apex_model
del Train_X, Test_X, Train_Y, Test_Y, input_u, input_v
K.get_session().close()
cfg = K.tf.ConfigProto()
cfg.gpu_options.allow_growth = True
K.set_session(K.tf.Session(config=cfg))
gc.collect()
def train_vgg_lstm(batch_size, spatial_epochs, temporal_epochs, train_id,
list_dB, spatial_size, flag, objective_flag, tensorboard):
############## Path Preparation ######################
root_db_path = "/media/ostalo/MihaGarafolj/ME_data/"
tensorboard_path = root_db_path + "tensorboard/"
if os.path.isdir(root_db_path + 'Weights/' + str(train_id)) == False:
os.mkdir(root_db_path + 'Weights/' + str(train_id))
######################################################
############## Variables ###################
dB = list_dB[0]
r, w, subjects, samples, n_exp, VidPerSubject, vidList, timesteps_TIM, data_dim, channel, table, listOfIgnoredSamples, db_home, db_images, cross_db_flag = load_db(
root_db_path, list_dB, spatial_size, objective_flag)
# avoid confusion
if cross_db_flag == 1:
list_samples = listOfIgnoredSamples
# total confusion matrix to be used in the computation of f1 score
tot_mat = np.zeros((n_exp, n_exp))
history = LossHistory()
stopping = EarlyStopping(monitor='loss',
min_delta=0,
mode='min',
patience=3)
############################################
############## Flags ####################
tensorboard_flag = tensorboard
resizedFlag = 1
train_spatial_flag = 0
train_temporal_flag = 0
svm_flag = 0
finetuning_flag = 0
cam_visualizer_flag = 0
channel_flag = 0
if flag == 'st':
train_spatial_flag = 1
train_temporal_flag = 1
finetuning_flag = 1
elif flag == 's':
train_spatial_flag = 1
finetuning_flag = 1
elif flag == 't':
train_temporal_flag = 1
elif flag == 'nofine':
svm_flag = 1
elif flag == 'scratch':
train_spatial_flag = 1
train_temporal_flag = 1
elif flag == 'st4se' or flag == 'st4se_cde':
train_spatial_flag = 1
train_temporal_flag = 1
channel_flag = 1
elif flag == 'st7se' or flag == 'st7se_cde':
train_spatial_flag = 1
train_temporal_flag = 1
channel_flag = 2
elif flag == 'st4te' or flag == 'st4te_cde':
train_spatial_flag = 1
train_temporal_flag = 1
channel_flag = 3
elif flag == 'st7te' or flag == 'st7te_cde':
train_spatial_flag = 1
train_temporal_flag = 1
channel_flag = 4
#########################################
############ Reading Images and Labels ################
if cross_db_flag == 1:
SubperdB = Read_Input_Images_SAMM_CASME(db_images, list_samples,
listOfIgnoredSamples, dB,
resizedFlag, table, db_home,
spatial_size, channel)
else:
SubperdB = Read_Input_Images(db_images, listOfIgnoredSamples, dB,
resizedFlag, table, db_home, spatial_size,
channel, objective_flag)
labelperSub = label_matching(db_home, dB, subjects, VidPerSubject)
print("Loaded Images into the tray.")
print("Loaded Labels into the tray.")
#######################################################
# PREPROCESSING STEPS
# optical flow
#SubperdB = optical_flow_2d(SubperdB, samples, r, w, timesteps_TIM)
gc.collect()
if channel_flag == 1:
aux_db1 = list_dB[1]
db_strain_img = root_db_path + aux_db1 + "/" + aux_db1 + "/"
if cross_db_flag == 1:
SubperdB = Read_Input_Images_SAMM_CASME(
db_strain_img, list_samples, listOfIgnoredSamples, aux_db1,
resizedFlag, table, db_home, spatial_size, 1)
else:
SubperdB_strain = Read_Input_Images(db_strain_img,
listOfIgnoredSamples, aux_db1,
resizedFlag, table, db_home,
spatial_size, 1,
objective_flag)
elif channel_flag == 2:
aux_db1 = list_dB[1]
aux_db2 = list_dB[2]
db_strain_img = root_db_path + aux_db1 + "/" + aux_db1 + "/"
db_gray_img = root_db_path + aux_db2 + "/" + aux_db2 + "/"
if cross_db_flag == 1:
SubperdB_strain = Read_Input_Images_SAMM_CASME(
db_strain_img, list_samples, listOfIgnoredSamples, aux_db1,
resizedFlag, table, db_home, spatial_size, 1)
SubperdB_gray = Read_Input_Images_SAMM_CASME(
db_gray_img, list_samples, listOfIgnoredSamples, aux_db2,
resizedFlag, table, db_home, spatial_size, 1)
else:
SubperdB_strain = Read_Input_Images(db_strain_img,
listOfIgnoredSamples, aux_db1,
resizedFlag, table, db_home,
spatial_size, 1,
objective_flag)
SubperdB_gray = Read_Input_Images(db_gray_img,
listOfIgnoredSamples, aux_db2,
resizedFlag, table, db_home,
spatial_size, 1, objective_flag)
elif channel_flag == 3:
aux_db1 = list_dB[1]
db_strain_img = root_db_path + aux_db1 + "/" + aux_db1 + "/"
if cross_db_flag == 1:
SubperdB = Read_Input_Images_SAMM_CASME(
db_strain_img, list_samples, listOfIgnoredSamples, aux_db1,
resizedFlag, table, db_home, spatial_size, 3)
else:
SubperdB_strain = Read_Input_Images(db_strain_img,
listOfIgnoredSamples, aux_db1,
resizedFlag, table, db_home,
spatial_size, 3,
objective_flag)
elif channel_flag == 4:
aux_db1 = list_dB[1]
aux_db2 = list_dB[2]
db_strain_img = root_db_path + aux_db1 + "/" + aux_db1 + "/"
db_gray_img = root_db_path + aux_db2 + "/" + aux_db2 + "/"
if cross_db_flag == 1:
SubperdB_strain = Read_Input_Images_SAMM_CASME(
db_strain_img, list_samples, listOfIgnoredSamples, aux_db1,
resizedFlag, table, db_home, spatial_size, 3)
SubperdB_gray = Read_Input_Images_SAMM_CASME(
db_gray_img, list_samples, listOfIgnoredSamples, aux_db2,
resizedFlag, table, db_home, spatial_size, 3)
else:
SubperdB_strain = Read_Input_Images(db_strain_img,
listOfIgnoredSamples, aux_db1,
resizedFlag, table, db_home,
spatial_size, 3,
objective_flag)
SubperdB_gray = Read_Input_Images(db_gray_img,
listOfIgnoredSamples, aux_db2,
resizedFlag, table, db_home,
spatial_size, 3, objective_flag)
#######################################################
########### Model Configurations #######################
#K.set_image_dim_ordering('th')
# config = tf.ConfigProto()
# config.gpu_options.allow_growth = True
# config.gpu_options.per_process_gpu_memory_fraction = 0.8
# K.tensorflow_backend.set_session(tf.Session(config=config))
# Different Conditions for Temporal Learning ONLY
if train_spatial_flag == 0 and train_temporal_flag == 1 and dB != 'CASME2_Optical':
data_dim = spatial_size * spatial_size
elif train_spatial_flag == 0 and train_temporal_flag == 1 and dB == 'CASME2_Optical':
data_dim = spatial_size * spatial_size * 3
elif channel_flag == 3:
data_dim = 8192
elif channel_flag == 4:
data_dim = 12288
else:
data_dim = 4096
########################################################
print("Beginning training process.")
########### Training Process ############
subjects_todo = read_subjects_todo(db_home, dB, train_id, subjects)
for sub in subjects_todo:
#sgd = optimizers.SGD(lr=0.0001, decay=1e-7, momentum=0.9, nesterov=True)
adam = optimizers.Adam(lr=0.00001, decay=0.000001)
print("**** starting subject " + str(sub) + " ****")
#gpu_obgpu_observer()
spatial_weights_name = root_db_path + 'Weights/' + str(
train_id) + '/vgg_spatial_' + str(train_id) + '_' + str(dB) + '_'
spatial_weights_name_strain = root_db_path + 'Weights/' + str(
train_id) + '/vgg_spatial_strain_' + str(train_id) + '_' + str(
dB) + '_'
spatial_weights_name_gray = root_db_path + 'Weights/' + str(
train_id) + '/vgg_spatial_gray_' + str(train_id) + '_' + str(
dB) + '_'
temporal_weights_name = root_db_path + 'Weights/' + str(
train_id) + '/temporal_ID_' + str(train_id) + '_' + str(dB) + '_'
ae_weights_name = root_db_path + 'Weights/' + str(
train_id) + '/autoencoder_' + str(train_id) + '_' + str(dB) + '_'
ae_weights_name_strain = root_db_path + 'Weights/' + str(
train_id) + '/autoencoder_strain_' + str(train_id) + '_' + str(
dB) + '_'
############### Reinitialization & weights reset of models ########################
temporal_model = temporal_module(data_dim=data_dim,
timesteps_TIM=timesteps_TIM,
lstm1_size=3000,
classes=n_exp)
temporal_model.compile(loss='categorical_crossentropy',
optimizer=adam,
metrics=[metrics.categorical_accuracy])
if channel_flag == 1:
vgg_model = VGG_16_4_channels(classes=n_exp,
channels=4,
spatial_size=spatial_size)
if finetuning_flag == 1:
for layer in vgg_model.layers[:33]:
layer.trainable = False
vgg_model.compile(loss='categorical_crossentropy',
optimizer=adam,
metrics=[metrics.categorical_accuracy])
elif channel_flag == 2:
vgg_model = VGG_16_4_channels(classes=n_exp,
channels=5,
spatial_size=spatial_size)
if finetuning_flag == 1:
for layer in vgg_model.layers[:33]:
layer.trainable = False
vgg_model.compile(loss='categorical_crossentropy',
optimizer=adam,
metrics=[metrics.categorical_accuracy])
elif channel_flag == 3 or channel_flag == 4:
vgg_model = VGG_16(spatial_size=spatial_size,
classes=n_exp,
channels=3,
weights_path='VGG_Face_Deep_16.h5')
vgg_model_strain = VGG_16(spatial_size=spatial_size,
classes=n_exp,
channels=3,
weights_path='VGG_Face_Deep_16.h5')
if finetuning_flag == 1:
for layer in vgg_model.layers[:33]:
layer.trainable = False
for layer in vgg_model_strain.layers[:33]:
layer.trainable = False
vgg_model.compile(loss='categorical_crossentropy',
optimizer=adam,
metrics=[metrics.categorical_accuracy])
vgg_model_strain.compile(loss='categorical_crossentropy',
optimizer=adam,
metrics=[metrics.categorical_accuracy])
if channel_flag == 4:
vgg_model_gray = VGG_16(spatial_size=spatial_size,
classes=n_exp,
channels=3,
weights_path='VGG_Face_Deep_16.h5')
if finetuning_flag == 1:
for layer in vgg_model_gray.layers[:33]:
layer.trainable = False
vgg_model_gray.compile(loss='categorical_crossentropy',
optimizer=adam,
metrics=[metrics.categorical_accuracy])
else:
vgg_model = VGG_16(spatial_size=spatial_size,
classes=n_exp,
channels=channel,
channel_first=False,
weights_path='VGG_Face_Deep_16.h5')
if finetuning_flag == 1:
for layer in vgg_model.layers[:33]:
layer.trainable = False
vgg_model.compile(loss='categorical_crossentropy',
optimizer=adam,
metrics=[metrics.categorical_accuracy])
#svm_classifier = SVC(kernel='linear', C=1)
####################################################################################
############ for tensorboard ###############
if tensorboard_flag == 1:
cat_path = tensorboard_path + str(train_id) + str(sub) + "/"
if os.path.exists(cat_path):
os.rmdir(cat_path)
os.mkdir(cat_path)
tbCallBack = keras.callbacks.TensorBoard(log_dir=cat_path,
write_graph=True)
cat_path2 = tensorboard_path + str(train_id) + str(
sub) + "spatial/"
if os.path.exists(cat_path2):
os.rmdir(cat_path2)
os.mkdir(cat_path2)
tbCallBack2 = keras.callbacks.TensorBoard(log_dir=cat_path2,
write_graph=True)
#############################################
#import ipdb; ipdb.set_trace()
Train_X, Train_Y, Test_X, Test_Y, Test_Y_gt, X, y, test_X, test_y = restructure_data(
sub, SubperdB, labelperSub, subjects, n_exp, r, w, timesteps_TIM,
channel)
# Special Loading for 4-Channel
if channel_flag == 1:
_, _, _, _, _, Train_X_Strain, Train_Y_Strain, Test_X_Strain, Test_Y_Strain = restructure_data(
sub, SubperdB_strain, labelperSub, subjects, n_exp, r, w,
timesteps_TIM, 1)
# verify
# sanity_check_image(Test_X_Strain, 1, spatial_size)
# Concatenate Train X & Train_X_Strain
X = np.concatenate((X, Train_X_Strain), axis=1)
test_X = np.concatenate((test_X, Test_X_Strain), axis=1)
total_channel = 4
elif channel_flag == 2:
_, _, _, _, _, Train_X_Strain, Train_Y_Strain, Test_X_Strain, Test_Y_Strain = restructure_data(
sub, SubperdB_strain, labelperSub, subjects, n_exp, r, w,
timesteps_TIM, 1)
_, _, _, _, _, Train_X_Gray, Train_Y_Gray, Test_X_Gray, Test_Y_Gray = restructure_data(
sub, SubperdB_gray, labelperSub, subjects, n_exp, r, w,
timesteps_TIM, 1)
# Concatenate Train_X_Strain & Train_X & Train_X_Gray
X = np.concatenate((X, Train_X_Strain, Train_X_Gray), axis=1)
test_X = np.concatenate((test_X, Test_X_Strain, Test_X_Gray),
axis=1)
total_channel = 5
elif channel_flag == 3:
_, _, _, _, _, Train_X_Strain, Train_Y_Strain, Test_X_Strain, Test_Y_Strain = restructure_data(
sub, SubperdB_strain, labelperSub, subjects, n_exp, r, w,
timesteps_TIM, 3)
elif channel_flag == 4:
_, _, _, _, _, Train_X_Strain, Train_Y_Strain, Test_X_Strain, Test_Y_Strain = restructure_data(
sub, SubperdB_strain, labelperSub, subjects, n_exp, r, w,
timesteps_TIM, 3)
_, _, _, _, _, Train_X_Gray, Train_Y_Gray, Test_X_Gray, Test_Y_Gray = restructure_data(
sub, SubperdB_gray, labelperSub, subjects, n_exp, r, w,
timesteps_TIM, 3)
############### check gpu resources ####################
#gpu_observer()
########################################################
print("Beginning training & testing.")
##################### Training & Testing #########################
if train_spatial_flag == 1 and train_temporal_flag == 1:
print("Beginning spatial training.")
# Spatial Training
if tensorboard_flag == 1:
vgg_model.fit(X,
y,
batch_size=batch_size,
epochs=spatial_epochs,
shuffle=True,
callbacks=[history, stopping, tbCallBack2])
elif channel_flag == 3 or channel_flag == 4:
vgg_model.fit(X,
y,
batch_size=batch_size,
epochs=spatial_epochs,
shuffle=True,
callbacks=[history, stopping])
vgg_model_strain.fit(Train_X_Strain,
y,
batch_size=batch_size,
epochs=spatial_epochs,
shuffle=True,
callbacks=[stopping])
model_strain = record_weights(vgg_model_strain,
spatial_weights_name_strain, sub,
flag)
output_strain = model_strain.predict(Train_X_Strain,
batch_size=batch_size)
if channel_flag == 4:
vgg_model_gray.fit(Train_X_Gray,
y,
batch_size=batch_size,
epochs=spatial_epochs,
shuffle=True,
callbacks=[stopping])
model_gray = record_weights(vgg_model_gray,
spatial_weights_name_gray, sub,
flag)
output_gray = model_gray.predict(Train_X_Gray,
batch_size=batch_size)
else:
#import ipdb; ipdb.set_trace()
vgg_model.fit(X,
y,
batch_size=batch_size,
epochs=spatial_epochs,
shuffle=True,
callbacks=[history, stopping])
print(".record f1 and loss")
# record f1 and loss
record_loss_accuracy(db_home, train_id, dB, history)
print(".save vgg weights")
# save vgg weights
# model = record_weights(vgg_model, spatial_weights_name, sub, flag)
print(".spatial encoding")
# Spatial Encoding
model_int = Model(inputs=vgg_model.input,
outputs=vgg_model.get_layer('dense_prvi').output)
#model = record_weights(cnn_model, spatial_weights_name, sub, flag)
features = model_int.predict(X, batch_size=batch_size)
features = features.reshape(int(Train_X.shape[0]), timesteps_TIM,
features.shape[1])
# concatenate features for temporal enrichment
if channel_flag == 3:
output = np.concatenate((output, output_strain), axis=1)
elif channel_flag == 4:
output = np.concatenate((output, output_strain, output_gray),
axis=1)
print("Beginning temporal training.")
# Temporal Training
if tensorboard_flag == 1:
temporal_model.fit(features,
Train_Y,
batch_size=batch_size,
epochs=temporal_epochs,
callbacks=[tbCallBack])
else:
temporal_model.fit(features,
Train_Y,
batch_size=batch_size,
epochs=temporal_epochs)
print(".save temportal weights")
# save temporal weights
temporal_model = record_weights(temporal_model,
temporal_weights_name, sub,
't') # let the flag be t
print("Beginning testing.")
print(".predicting with spatial model")
# Testing
features = model_int.predict(test_X, batch_size=batch_size)
features = features.reshape(int(Test_X.shape[0]), timesteps_TIM,
features.shape[1])
if channel_flag == 3 or channel_flag == 4:
output_strain = model_strain.predict(Test_X_Strain,
batch_size=batch_size)
if channel_flag == 4:
output_gray = model_gray.predict(Test_X_Gray,
batch_size=batch_size)
# concatenate features for temporal enrichment
if channel_flag == 3:
output = np.concatenate((output, output_strain), axis=1)
elif channel_flag == 4:
output = np.concatenate((output, output_strain, output_gray),
axis=1)
print(".outputing features")
print(".predicting with temporal model")
predict_values = temporal_model.predict(features,
batch_size=batch_size)
predict = np.array([np.argmax(x) for x in predict_values])
##############################################################
#################### Confusion Matrix Construction #############
print(predict)
print(Test_Y_gt.astype(int))
print(".writing predicts to file")
file = open(
db_home + 'Classification/' + 'Result/' + '/predicts_' +
str(train_id) + '.txt', 'a')
for i in range(len(vidList[sub])):
file.write("sub_" + str(sub) + "," + str(vidList[sub][i]) + "," +
str(predict.astype(list)[i]) + "," +
str(Test_Y_gt.astype(int).astype(list)[i]) + "\n")
file.close()
file = open(
db_home + 'Classification/' + 'Result/' + '/predictedvalues_' +
str(train_id) + '.txt', 'a')
for i in range(len(vidList[sub])):
file.write("sub_" + str(sub) + "," + str(vidList[sub][i]) + "," +
','.join(str(e) for e in predict_values[i]) + "," +
str(Test_Y_gt.astype(int).astype(list)[i]) + "\n")
file.close()
ct = confusion_matrix(Test_Y_gt, predict)
# check the order of the CT
order = np.unique(np.concatenate((predict, Test_Y_gt)))
# create an array to hold the CT for each CV
mat = np.zeros((n_exp, n_exp))
# put the order accordingly, in order to form the overall ConfusionMat
for m in range(len(order)):
for n in range(len(order)):
mat[int(order[m]), int(order[n])] = ct[m, n]
tot_mat = mat + tot_mat
################################################################
#################### cumulative f1 plotting ######################
microAcc = np.trace(tot_mat) / np.sum(tot_mat)
[f1, precision, recall] = fpr(tot_mat, n_exp)
file = open(
db_home + 'Classification/' + 'Result/' + '/f1_' + str(train_id) +
'.txt', 'a')
file.write(str(f1) + "\n")
file.close()
##################################################################
################# write each CT of each CV into .txt file #####################
record_scores(db_home, dB, ct, sub, order, tot_mat, n_exp, subjects)
war = weighted_average_recall(tot_mat, n_exp, samples)
uar = unweighted_average_recall(tot_mat, n_exp)
print("war: " + str(war))
print("uar: " + str(uar))
###############################################################################
################## free memory ####################
del vgg_model
del temporal_model
del model_int
del Train_X, Test_X, X, y
if channel_flag == 1:
del Train_X_Strain, Test_X_Strain, Train_Y_Strain, Test_Y_Strain
elif channel_flag == 2:
del Train_X_Strain, Test_X_Strain, Train_Y_Strain, Test_Y_Strain, Train_X_Gray, Test_X_Gray, Train_Y_Gray, Test_Y_Gray
elif channel_flag == 3:
del vgg_model_strain, model_strain
del Train_X_Strain, Test_X_Strain, Train_Y_Strain, Test_Y_Strain
elif channel_flag == 4:
del Train_X_Strain, Test_X_Strain, Train_Y_Strain, Test_Y_Strain, Train_X_Gray, Test_X_Gray, Train_Y_Gray, Test_Y_Gray
del vgg_model_gray, vgg_model_strain, model_gray, model_strain
K.get_session().close()
cfg = K.tf.ConfigProto()
cfg.gpu_options.allow_growth = True
K.set_session(K.tf.Session(config=cfg))
gc.collect()
def train(batch_size, spatial_epochs, temporal_epochs, train_id, list_dB,
spatial_size, flag, objective_flag, tensorboard):
############## Path Preparation ######################
root_db_path = "/media/ice/OS/Datasets/"
tensorboard_path = "/home/ice/Documents/Micro-Expression/tensorboard/"
if os.path.isdir(root_db_path + 'Weights/' + str(train_id)) == False:
os.mkdir(root_db_path + 'Weights/' + str(train_id))
######################################################
############## Variables ###################
dB = list_dB[0]
r, w, subjects, samples, n_exp, VidPerSubject, timesteps_TIM, data_dim, channel, table, listOfIgnoredSamples, db_home, db_images, cross_db_flag = load_db(
root_db_path, list_dB, spatial_size, objective_flag)
# avoid confusion
if cross_db_flag == 1:
list_samples = listOfIgnoredSamples
# total confusion matrix to be used in the computation of f1 score
tot_mat = np.zeros((n_exp, n_exp))
history = LossHistory()
stopping = EarlyStopping(monitor='loss', min_delta=0, mode='min')
############################################
############## Flags ####################
tensorboard_flag = tensorboard
resizedFlag = 1
train_spatial_flag = 0
train_temporal_flag = 0
svm_flag = 0
finetuning_flag = 0
cam_visualizer_flag = 0
channel_flag = 0
if flag == 'st':
train_spatial_flag = 1
train_temporal_flag = 1
finetuning_flag = 1
elif flag == 's':
train_spatial_flag = 1
finetuning_flag = 1
elif flag == 't':
train_temporal_flag = 1
elif flag == 'nofine':
svm_flag = 1
elif flag == 'scratch':
train_spatial_flag = 1
train_temporal_flag = 1
elif flag == 'st4se' or flag == 'st4se_cde':
train_spatial_flag = 1
train_temporal_flag = 1
channel_flag = 1
elif flag == 'st7se' or flag == 'st7se_cde':
train_spatial_flag = 1
train_temporal_flag = 1
channel_flag = 2
elif flag == 'st4te' or flag == 'st4te_cde':
train_spatial_flag = 1
train_temporal_flag = 1
channel_flag = 3
elif flag == 'st7te' or flag == 'st7te_cde':
train_spatial_flag = 1
train_temporal_flag = 1
channel_flag = 4
#########################################
############ Reading Images and Labels ################
if cross_db_flag == 1:
SubperdB = Read_Input_Images_SAMM_CASME(db_images, list_samples,
listOfIgnoredSamples, dB,
resizedFlag, table, db_home,
spatial_size, channel)
else:
SubperdB = Read_Input_Images(db_images, listOfIgnoredSamples, dB,
resizedFlag, table, db_home, spatial_size,
channel, objective_flag)
labelperSub = label_matching(db_home, dB, subjects, VidPerSubject)
print("Loaded Images into the tray...")
print("Loaded Labels into the tray...")
if channel_flag == 1:
aux_db1 = list_dB[1]
db_strain_img = root_db_path + aux_db1 + "/" + aux_db1 + "/"
if cross_db_flag == 1:
SubperdB = Read_Input_Images_SAMM_CASME(
db_strain_img, list_samples, listOfIgnoredSamples, aux_db1,
resizedFlag, table, db_home, spatial_size, 1)
else:
SubperdB_strain = Read_Input_Images(db_strain_img,
listOfIgnoredSamples, aux_db1,
resizedFlag, table, db_home,
spatial_size, 1,
objective_flag)
elif channel_flag == 2:
aux_db1 = list_dB[1]
aux_db2 = list_dB[2]
db_strain_img = root_db_path + aux_db1 + "/" + aux_db1 + "/"
db_gray_img = root_db_path + aux_db2 + "/" + aux_db2 + "/"
if cross_db_flag == 1:
SubperdB_strain = Read_Input_Images_SAMM_CASME(
db_strain_img, list_samples, listOfIgnoredSamples, aux_db1,
resizedFlag, table, db_home, spatial_size, 1)
SubperdB_gray = Read_Input_Images_SAMM_CASME(
db_gray_img, list_samples, listOfIgnoredSamples, aux_db2,
resizedFlag, table, db_home, spatial_size, 1)
else:
SubperdB_strain = Read_Input_Images(db_strain_img,
listOfIgnoredSamples, aux_db1,
resizedFlag, table, db_home,
spatial_size, 1,
objective_flag)
SubperdB_gray = Read_Input_Images(db_gray_img,
listOfIgnoredSamples, aux_db2,
resizedFlag, table, db_home,
spatial_size, 1, objective_flag)
elif channel_flag == 3:
aux_db1 = list_dB[1]
db_strain_img = root_db_path + aux_db1 + "/" + aux_db1 + "/"
if cross_db_flag == 1:
SubperdB = Read_Input_Images_SAMM_CASME(
db_strain_img, list_samples, listOfIgnoredSamples, aux_db1,
resizedFlag, table, db_home, spatial_size, 3)
else:
SubperdB_strain = Read_Input_Images(db_strain_img,
listOfIgnoredSamples, aux_db1,
resizedFlag, table, db_home,
spatial_size, 3,
objective_flag)
elif channel_flag == 4:
aux_db1 = list_dB[1]
aux_db2 = list_dB[2]
db_strain_img = root_db_path + aux_db1 + "/" + aux_db1 + "/"
db_gray_img = root_db_path + aux_db2 + "/" + aux_db2 + "/"
if cross_db_flag == 1:
SubperdB_strain = Read_Input_Images_SAMM_CASME(
db_strain_img, list_samples, listOfIgnoredSamples, aux_db1,
resizedFlag, table, db_home, spatial_size, 3)
SubperdB_gray = Read_Input_Images_SAMM_CASME(
db_gray_img, list_samples, listOfIgnoredSamples, aux_db2,
resizedFlag, table, db_home, spatial_size, 3)
else:
SubperdB_strain = Read_Input_Images(db_strain_img,
listOfIgnoredSamples, aux_db1,
resizedFlag, table, db_home,
spatial_size, 3,
objective_flag)
SubperdB_gray = Read_Input_Images(db_gray_img,
listOfIgnoredSamples, aux_db2,
resizedFlag, table, db_home,
spatial_size, 3, objective_flag)
#######################################################
########### Model Configurations #######################
sgd = optimizers.SGD(lr=0.0001, decay=1e-7, momentum=0.9, nesterov=True)
adam = optimizers.Adam(lr=0.00001, decay=0.000001)
# Different Conditions for Temporal Learning ONLY
if train_spatial_flag == 0 and train_temporal_flag == 1 and dB != 'CASME2_Optical':
data_dim = spatial_size * spatial_size
elif train_spatial_flag == 0 and train_temporal_flag == 1 and dB == 'CASME2_Optical':
data_dim = spatial_size * spatial_size * 3
elif channel_flag == 3:
data_dim = 8192
elif channel_flag == 4:
data_dim = 12288
else:
data_dim = 4096
########################################################
########### Training Process ############
for sub in range(subjects):
spatial_weights_name = root_db_path + 'Weights/' + str(
train_id) + '/vgg_spatial_' + str(train_id) + '_' + str(dB) + '_'
spatial_weights_name_strain = root_db_path + 'Weights/' + str(
train_id) + '/vgg_spatial_strain_' + str(train_id) + '_' + str(
dB) + '_'
spatial_weights_name_gray = root_db_path + 'Weights/' + str(
train_id) + '/vgg_spatial_gray_' + str(train_id) + '_' + str(
dB) + '_'
temporal_weights_name = root_db_path + 'Weights/' + str(
train_id) + '/temporal_ID_' + str(train_id) + '_' + str(dB) + '_'
ae_weights_name = root_db_path + 'Weights/' + str(
train_id) + '/autoencoder_' + str(train_id) + '_' + str(dB) + '_'
ae_weights_name_strain = root_db_path + 'Weights/' + str(
train_id) + '/autoencoder_strain_' + str(train_id) + '_' + str(
dB) + '_'
############### Reinitialization & weights reset of models ########################
temporal_model = temporal_module(data_dim=data_dim,
timesteps_TIM=timesteps_TIM,
classes=n_exp)
temporal_model.compile(loss='categorical_crossentropy',
optimizer=adam,
metrics=[metrics.categorical_accuracy])
conv_ae = convolutional_autoencoder(spatial_size=spatial_size,
classes=n_exp)
conv_ae.compile(loss='binary_crossentropy', optimizer=adam)
if channel_flag == 1:
vgg_model = VGG_16_4_channels(classes=n_exp,
channels=4,
spatial_size=spatial_size)
vgg_model.compile(loss='categorical_crossentropy',
optimizer=adam,
metrics=[metrics.categorical_accuracy])
elif channel_flag == 2:
vgg_model = VGG_16_4_channels(classes=n_exp,
channels=5,
spatial_size=spatial_size)
vgg_model.compile(loss='categorical_crossentropy',
optimizer=adam,
metrics=[metrics.categorical_accuracy])
elif channel_flag == 3 or channel_flag == 4:
vgg_model = VGG_16(spatial_size=spatial_size,
classes=n_exp,
channels=3,
weights_path='VGG_Face_Deep_16.h5')
vgg_model.compile(loss='categorical_crossentropy',
optimizer=adam,
metrics=[metrics.categorical_accuracy])
vgg_model_strain = VGG_16(spatial_size=spatial_size,
classes=n_exp,
channels=3,
weights_path='VGG_Face_Deep_16.h5')
vgg_model_strain.compile(loss='categorical_crossentropy',
optimizer=adam,
metrics=[metrics.categorical_accuracy])
if channel_flag == 4:
vgg_model_gray = VGG_16(spatial_size=spatial_size,
classes=n_exp,
channels=3,
weights_path='VGG_Face_Deep_16.h5')
vgg_model_gray.compile(loss='categorical_crossentropy',
optimizer=adam,
metrics=[metrics.categorical_accuracy])
else:
vgg_model = VGG_16(spatial_size=spatial_size,
classes=n_exp,
channels=3,
weights_path='VGG_Face_Deep_16.h5')
vgg_model.compile(loss='categorical_crossentropy',
optimizer=adam,
metrics=[metrics.categorical_accuracy])
svm_classifier = SVC(kernel='linear', C=1)
####################################################################################
############ for tensorboard ###############
if tensorboard_flag == 1:
cat_path = tensorboard_path + str(sub) + "/"
os.mkdir(cat_path)
tbCallBack = keras.callbacks.TensorBoard(log_dir=cat_path,
write_graph=True)
cat_path2 = tensorboard_path + str(sub) + "spat/"
os.mkdir(cat_path2)
tbCallBack2 = keras.callbacks.TensorBoard(log_dir=cat_path2,
write_graph=True)
#############################################
Train_X, Train_Y, Test_X, Test_Y, Test_Y_gt, X, y, test_X, test_y = restructure_data(
sub, SubperdB, labelperSub, subjects, n_exp, r, w, timesteps_TIM,
channel)
# Special Loading for 4-Channel
if channel_flag == 1:
_, _, _, _, _, Train_X_Strain, Train_Y_Strain, Test_X_Strain, Test_Y_Strain = restructure_data(
sub, SubperdB_strain, labelperSub, subjects, n_exp, r, w,
timesteps_TIM, 1)
# verify
# sanity_check_image(Test_X_Strain, 1, spatial_size)
# Concatenate Train X & Train_X_Strain
X = np.concatenate((X, Train_X_Strain), axis=1)
test_X = np.concatenate((test_X, Test_X_Strain), axis=1)
total_channel = 4
elif channel_flag == 2:
_, _, _, _, _, Train_X_Strain, Train_Y_Strain, Test_X_Strain, Test_Y_Strain = restructure_data(
sub, SubperdB_strain, labelperSub, subjects, n_exp, r, w,
timesteps_TIM, 1)
_, _, _, _, _, Train_X_Gray, Train_Y_Gray, Test_X_Gray, Test_Y_Gray = restructure_data(
sub, SubperdB_gray, labelperSub, subjects, n_exp, r, w,
timesteps_TIM, 1)
# Concatenate Train_X_Strain & Train_X & Train_X_gray
X = np.concatenate((X, Train_X_Strain, Train_X_gray), axis=1)
test_X = np.concatenate((test_X, Test_X_Strain, Test_X_gray),
axis=1)
total_channel = 5
elif channel_flag == 3:
_, _, _, _, _, Train_X_Strain, Train_Y_Strain, Test_X_Strain, Test_Y_Strain = restructure_data(
sub, SubperdB_strain, labelperSub, subjects, n_exp, r, w,
timesteps_TIM, 3)
elif channel_flag == 4:
_, _, _, _, _, Train_X_Strain, Train_Y_Strain, Test_X_Strain, Test_Y_Strain = restructure_data(
sub, SubperdB_strain, labelperSub, subjects, n_exp, r, w,
timesteps_TIM, 3)
_, _, _, _, _, Train_X_Gray, Train_Y_Gray, Test_X_Gray, Test_Y_Gray = restructure_data(
sub, SubperdB_gray, labelperSub, subjects, n_exp, r, w,
timesteps_TIM, 3)
############### check gpu resources ####################
gpu_observer()
########################################################
##################### Training & Testing #########################
# conv weights must be freezed for transfer learning
if finetuning_flag == 1:
for layer in vgg_model.layers[:33]:
layer.trainable = False
if channel_flag == 3 or channel_flag == 4:
for layer in vgg_model_strain.layers[:33]:
layer.trainable = False
if channel_flag == 4:
for layer in vgg_model_gray.layers[:33]:
layer.trainable = False
if train_spatial_flag == 1 and train_temporal_flag == 1:
# Spatial Training
if tensorboard_flag == 1:
vgg_model.fit(X,
y,
batch_size=batch_size,
epochs=spatial_epochs,
shuffle=True,
callbacks=[tbCallBack2])
elif channel_flag == 3 or channel_flag == 4:
vgg_model.fit(X,
y,
batch_size=batch_size,
epochs=spatial_epochs,
shuffle=True,
callbacks=[history, stopping])
vgg_model_strain.fit(Train_X_Strain,
y,
batch_size=batch_size,
epochs=spatial_epochs,
shuffle=True,
callbacks=[stopping])
model_strain = record_weights(vgg_model_strain,
spatial_weights_name_strain, sub,
flag)
output_strain = model_strain.predict(Train_X_Strain,
batch_size=batch_size)
if channel_flag == 4:
vgg_model_gray.fit(Train_X_Gray,
y,
batch_size=batch_size,
epochs=spatial_epochs,
shuffle=True,
callbacks=[stopping])
model_gray = record_weights(vgg_model_gray,
spatial_weights_name_gray, sub,
flag)
output_gray = model_gray.predict(Train_X_Gray,
batch_size=batch_size)
else:
vgg_model.fit(X,
y,
batch_size=batch_size,
epochs=spatial_epochs,
shuffle=True,
callbacks=[history, stopping])
# record f1 and loss
record_loss_accuracy(db_home, train_id, dB, history)
# save vgg weights
model = record_weights(vgg_model, spatial_weights_name, sub, flag)
# Spatial Encoding
output = model.predict(X, batch_size=batch_size)
# concatenate features for temporal enrichment
if channel_flag == 3:
output = np.concatenate((output, output_strain), axis=1)
elif channel_flag == 4:
output = np.concatenate((output, output_strain, output_gray),
axis=1)
features = output.reshape(int(Train_X.shape[0]), timesteps_TIM,
output.shape[1])
# Temporal Training
if tensorboard_flag == 1:
temporal_model.fit(features,
Train_Y,
batch_size=batch_size,
epochs=temporal_epochs,
callbacks=[tbCallBack])
else:
temporal_model.fit(features,
Train_Y,
batch_size=batch_size,
epochs=temporal_epochs)
# save temporal weights
temporal_model = record_weights(temporal_model,
temporal_weights_name, sub,
't') # let the flag be t
# Testing
output = model.predict(test_X, batch_size=batch_size)
if channel_flag == 3 or channel_flag == 4:
output_strain = model_strain.predict(Test_X_Strain,
batch_size=batch_size)
if channel_flag == 4:
output_gray = model_gray.predict(Test_X_Gray,
batch_size=batch_size)
# concatenate features for temporal enrichment
if channel_flag == 3:
output = np.concatenate((output, output_strain), axis=1)
elif channel_flag == 4:
output = np.concatenate((output, output_strain, output_gray),
axis=1)
features = output.reshape(Test_X.shape[0], timesteps_TIM,
output.shape[1])
predict = temporal_model.predict_classes(features,
batch_size=batch_size)
##############################################################
#################### Confusion Matrix Construction #############
print(predict)
print(Test_Y_gt)
ct = confusion_matrix(Test_Y_gt, predict)
# check the order of the CT
order = np.unique(np.concatenate((predict, Test_Y_gt)))
# create an array to hold the CT for each CV
mat = np.zeros((n_exp, n_exp))
# put the order accordingly, in order to form the overall ConfusionMat
for m in range(len(order)):
for n in range(len(order)):
mat[int(order[m]), int(order[n])] = ct[m, n]
tot_mat = mat + tot_mat
################################################################
#################### cumulative f1 plotting ######################
microAcc = np.trace(tot_mat) / np.sum(tot_mat)
[f1, precision, recall] = fpr(tot_mat, n_exp)
file = open(
db_home + 'Classification/' + 'Result/' + dB + '/f1_' +
str(train_id) + '.txt', 'a')
file.write(str(f1) + "\n")
file.close()
##################################################################
################# write each CT of each CV into .txt file #####################
record_scores(db_home, dB, ct, sub, order, tot_mat, n_exp, subjects)
war = weighted_average_recall(tot_mat, n_exp, samples)
uar = unweighted_average_recall(tot_mat, n_exp)
print("war: " + str(war))
print("uar: " + str(uar))
###############################################################################
################## free memory ####################
del vgg_model
del temporal_model
del model
del Train_X, Test_X, X, y
if channel_flag == 1:
del Train_X_Strain, Test_X_Strain, Train_Y_Strain, Train_Y_Strain
elif channel_flag == 2:
del Train_X_Strain, Test_X_Strain, Train_Y_Strain, Train_Y_Strain, Train_X_Gray, Test_X_Gray, Train_Y_Gray, Test_Y_Gray
elif channel_flag == 3:
del vgg_model_strain, model_strain
del Train_X_Strain, Test_X_Strain, Train_Y_Strain, Train_Y_Strain
elif channel_flag == 4:
del Train_X_Strain, Test_X_Strain, Train_Y_Strain, Train_Y_Strain, Train_X_Gray, Test_X_Gray, Train_Y_Gray, Test_Y_Gray
del vgg_model_gray, vgg_model_strain, model_gray, model_strain
gc.collect()
###################################################
from utils_DTSCNN import augmentation_casme
from list_databases import load_db
###
# data augmentation and sampling as done in the DTSCNN paper
###
#root_db_path = "/home/miha/Documents/ME_data/"
root_db_path = "/media/ostalo/MihaGarafolj/ME_data/"
######################################################
list_dB = ['CASME2_Optical']
objective_flag = 'st'
spatial_size = 224
r, w, subjects, samples, n_exp, VidPerSubject, vidList, timesteps_TIM, data_dim, channel, table, listOfIgnoredSamples, db_home, db_images, cross_db_flag = load_db(
root_db_path, list_dB, spatial_size, objective_flag)
resizedFlag = 0
#import ipdb; ipdb.set_trace()
augmentation_casme(db_images, db_images + "../" + "augmentation1/", 150, table,
resizedFlag, r, w)
def train_ram(batch_size, spatial_epochs, temporal_epochs, train_id, list_dB,
spatial_size, flag, objective_flag, tensorboard):
############## Path Preparation ######################
root_db_path = "/media/ice/OS/Datasets/"
tensorboard_path = "/home/ice/Documents/Micro-Expression/tensorboard/"
if os.path.isdir(root_db_path + 'Weights/' + str(train_id)) == False:
os.mkdir(root_db_path + 'Weights/' + str(train_id))
######################################################
############## Variables ###################
dB = list_dB[0]
r, w, subjects, samples, n_exp, VidPerSubject, timesteps_TIM, data_dim, channel, table, listOfIgnoredSamples, db_home, db_images, cross_db_flag = load_db(
root_db_path, list_dB, spatial_size, objective_flag)
# avoid confusion
if cross_db_flag == 1:
list_samples = listOfIgnoredSamples
# total confusion matrix to be used in the computation of f1 score
tot_mat = np.zeros((n_exp, n_exp))
history = LossHistory()
stopping = EarlyStopping(monitor='loss', min_delta=0, mode='min')
############################################
############## Flags ####################
tensorboard_flag = tensorboard
resizedFlag = 1
train_spatial_flag = 0
train_temporal_flag = 0
svm_flag = 0
finetuning_flag = 0
cam_visualizer_flag = 0
channel_flag = 0
if flag == 'st':
train_spatial_flag = 1
train_temporal_flag = 1
finetuning_flag = 1
elif flag == 's':
train_spatial_flag = 1
finetuning_flag = 1
elif flag == 't':
train_temporal_flag = 1
elif flag == 'nofine':
svm_flag = 1
elif flag == 'scratch':
train_spatial_flag = 1
train_temporal_flag = 1
elif flag == 'st4se' or flag == 'st4se_cde':
train_spatial_flag = 1
train_temporal_flag = 1
channel_flag = 1
elif flag == 'st7se' or flag == 'st7se_cde':
train_spatial_flag = 1
train_temporal_flag = 1
channel_flag = 2
elif flag == 'st4te' or flag == 'st4te_cde':
train_spatial_flag = 1
train_temporal_flag = 1
channel_flag = 3
elif flag == 'st7te' or flag == 'st7te_cde':
train_spatial_flag = 1
train_temporal_flag = 1
channel_flag = 4
#########################################
############ Reading Images and Labels ################
if cross_db_flag == 1:
SubperdB = Read_Input_Images_SAMM_CASME(db_images, list_samples,
listOfIgnoredSamples, dB,
resizedFlag, table, db_home,
spatial_size, channel)
else:
SubperdB = Read_Input_Images(db_images, listOfIgnoredSamples, dB,
resizedFlag, table, db_home, spatial_size,
channel, objective_flag)
labelperSub = label_matching(db_home, dB, subjects, VidPerSubject)
print("Loaded Images into the tray...")
print("Loaded Labels into the tray...")
if channel_flag == 1:
aux_db1 = list_dB[1]
db_strain_img = root_db_path + aux_db1 + "/" + aux_db1 + "/"
if cross_db_flag == 1:
SubperdB = Read_Input_Images_SAMM_CASME(
db_strain_img, list_samples, listOfIgnoredSamples, aux_db1,
resizedFlag, table, db_home, spatial_size, 1)
else:
SubperdB_strain = Read_Input_Images(db_strain_img,
listOfIgnoredSamples, aux_db1,
resizedFlag, table, db_home,
spatial_size, 1,
objective_flag)
elif channel_flag == 2:
aux_db1 = list_dB[1]
aux_db2 = list_dB[2]
db_strain_img = root_db_path + aux_db1 + "/" + aux_db1 + "/"
db_gray_img = root_db_path + aux_db2 + "/" + aux_db2 + "/"
if cross_db_flag == 1:
SubperdB_strain = Read_Input_Images_SAMM_CASME(
db_strain_img, list_samples, listOfIgnoredSamples, aux_db1,
resizedFlag, table, db_home, spatial_size, 1)
SubperdB_gray = Read_Input_Images_SAMM_CASME(
db_gray_img, list_samples, listOfIgnoredSamples, aux_db2,
resizedFlag, table, db_home, spatial_size, 1)
else:
SubperdB_strain = Read_Input_Images(db_strain_img,
listOfIgnoredSamples, aux_db1,
resizedFlag, table, db_home,
spatial_size, 1,
objective_flag)
SubperdB_gray = Read_Input_Images(db_gray_img,
listOfIgnoredSamples, aux_db2,
resizedFlag, table, db_home,
spatial_size, 1, objective_flag)
elif channel_flag == 3:
aux_db1 = list_dB[1]
db_strain_img = root_db_path + aux_db1 + "/" + aux_db1 + "/"
if cross_db_flag == 1:
SubperdB = Read_Input_Images_SAMM_CASME(
db_strain_img, list_samples, listOfIgnoredSamples, aux_db1,
resizedFlag, table, db_home, spatial_size, 3)
else:
SubperdB_strain = Read_Input_Images(db_strain_img,
listOfIgnoredSamples, aux_db1,
resizedFlag, table, db_home,
spatial_size, 3,
objective_flag)
elif channel_flag == 4:
aux_db1 = list_dB[1]
aux_db2 = list_dB[2]
db_strain_img = root_db_path + aux_db1 + "/" + aux_db1 + "/"
db_gray_img = root_db_path + aux_db2 + "/" + aux_db2 + "/"
if cross_db_flag == 1:
SubperdB_strain = Read_Input_Images_SAMM_CASME(
db_strain_img, list_samples, listOfIgnoredSamples, aux_db1,
resizedFlag, table, db_home, spatial_size, 3)
SubperdB_gray = Read_Input_Images_SAMM_CASME(
db_gray_img, list_samples, listOfIgnoredSamples, aux_db2,
resizedFlag, table, db_home, spatial_size, 3)
else:
SubperdB_strain = Read_Input_Images(db_strain_img,
listOfIgnoredSamples, aux_db1,
resizedFlag, table, db_home,
spatial_size, 3,
objective_flag)
SubperdB_gray = Read_Input_Images(db_gray_img,
listOfIgnoredSamples, aux_db2,
resizedFlag, table, db_home,
spatial_size, 3, objective_flag)
#######################################################
########### Model Configurations #######################
sgd = optimizers.SGD(lr=0.0001, decay=1e-7, momentum=0.9, nesterov=True)
adam = optimizers.Adam(lr=0.00001, decay=0.000001)
# Different Conditions for Temporal Learning ONLY
if train_spatial_flag == 0 and train_temporal_flag == 1 and dB != 'CASME2_Optical':
data_dim = spatial_size * spatial_size
elif train_spatial_flag == 0 and train_temporal_flag == 1 and dB == 'CASME2_Optical':
data_dim = spatial_size * spatial_size * 3
elif channel_flag == 3:
data_dim = 8192
elif channel_flag == 4:
data_dim = 12288
else:
data_dim = 4096
########################################################
########### Training Process ############
baseline_history = LossHistory()
action_history = LossHistory()
for sub in range(subjects):
spatial_weights_name = root_db_path + 'Weights/' + str(
train_id) + '/vgg_spatial_' + str(train_id) + '_' + str(dB) + '_'
spatial_weights_name_strain = root_db_path + 'Weights/' + str(
train_id) + '/vgg_spatial_strain_' + str(train_id) + '_' + str(
dB) + '_'
spatial_weights_name_gray = root_db_path + 'Weights/' + str(
train_id) + '/vgg_spatial_gray_' + str(train_id) + '_' + str(
dB) + '_'
temporal_weights_name = root_db_path + 'Weights/' + str(
train_id) + '/temporal_ID_' + str(train_id) + '_' + str(dB) + '_'
ae_weights_name = root_db_path + 'Weights/' + str(
train_id) + '/autoencoder_' + str(train_id) + '_' + str(dB) + '_'
ae_weights_name_strain = root_db_path + 'Weights/' + str(
train_id) + '/autoencoder_strain_' + str(train_id) + '_' + str(
dB) + '_'
############ for tensorboard ###############
#############################################
Train_X, Train_Y, Test_X, Test_Y, Test_Y_gt, X, y, test_X, test_y = restructure_data(
sub, SubperdB, labelperSub, subjects, n_exp, r, w, timesteps_TIM,
channel)
# Special Loading for 4-Channel
if channel_flag == 1:
_, _, _, _, _, Train_X_Strain, Train_Y_Strain, Test_X_Strain, Test_Y_Strain = restructure_data(
sub, SubperdB_strain, labelperSub, subjects, n_exp, r, w,
timesteps_TIM, 1)
# verify
# sanity_check_image(Test_X_Strain, 1, spatial_size)
# Concatenate Train X & Train_X_Strain
X = np.concatenate((X, Train_X_Strain), axis=1)
test_X = np.concatenate((test_X, Test_X_Strain), axis=1)
total_channel = 4
elif channel_flag == 2:
_, _, _, _, _, Train_X_Strain, Train_Y_Strain, Test_X_Strain, Test_Y_Strain = restructure_data(
sub, SubperdB_strain, labelperSub, subjects, n_exp, r, w,
timesteps_TIM, 1)
_, _, _, _, _, Train_X_Gray, Train_Y_Gray, Test_X_Gray, Test_Y_Gray = restructure_data(
sub, SubperdB_gray, labelperSub, subjects, n_exp, r, w,
timesteps_TIM, 1)
# Concatenate Train_X_Strain & Train_X & Train_X_gray
X = np.concatenate((X, Train_X_Strain, Train_X_gray), axis=1)
test_X = np.concatenate((test_X, Test_X_Strain, Test_X_gray),
axis=1)
total_channel = 5
elif channel_flag == 3:
_, _, _, _, _, Train_X_Strain, Train_Y_Strain, Test_X_Strain, Test_Y_Strain = restructure_data(
sub, SubperdB_strain, labelperSub, subjects, n_exp, r, w,
timesteps_TIM, 3)
elif channel_flag == 4:
_, _, _, _, _, Train_X_Strain, Train_Y_Strain, Test_X_Strain, Test_Y_Strain = restructure_data(
sub, SubperdB_strain, labelperSub, subjects, n_exp, r, w,
timesteps_TIM, 3)
_, _, _, _, _, Train_X_Gray, Train_Y_Gray, Test_X_Gray, Test_Y_Gray = restructure_data(
sub, SubperdB_gray, labelperSub, subjects, n_exp, r, w,
timesteps_TIM, 3)
############### Reinitialization & weights reset of models ########################
lt = np.array((1, 100))
no_patches = 4
scale = (224 - lt[0]) / no_patches
print(lt.shape)
print(X[0].shape)
glimpse_seq = []
for item in X:
# print(item.shape)
glimpse_seq_temp = create_glimpse(item, scale, no_patches, lt)
glimpse_seq += [glimpse_seq_temp]
# print(glimpse_seq.shape)
# glimpse_seq = create_glimpse(X[0], scale, no_patches, lt)
# print(glimpse_seq.shape)
glimpse_seq = np.asarray(glimpse_seq)
glimpse_seq = glimpse_seq.reshape(glimpse_seq.shape[0],
glimpse_seq.shape[2],
glimpse_seq.shape[3],
glimpse_seq.shape[4])
# print(glimpse_seq.shape)
model = Recurrent_Attention_Network(glimpse_seq, lt, 256, 1, 112, 256,
56)
baseline_model = Recurrent_Attention_Network(glimpse_seq, lt, 256, 1,
112, 256, 56)
####################################################################################
############### check gpu resources ####################
gpu_observer()
########################################################
##################### Training & Testing #########################
lt = lt.reshape(1, 2)
lt = np.repeat(lt, glimpse_seq.shape[0], axis=0)
model.fit([glimpse_seq, lt], [y],
callbacks=[action_history],
epochs=spatial_epochs)
ht_model = Model(inputs=model.input, outputs=model.layers[8].output)
out, ht = ht_model.predict([glimpse_seq, lt])
lt, log_pi = location_network(ht)
# reward
p_class = model.predict([glimpse_seq, lt])
p_class = (p_class == p_class[:].max(axis=1)[:, None]).astype(int)
print(p_class)
reward = K.cast((p_class == y), dtype='float32')
reward = reward.eval()
print(reward)
# train baseline with reward as labels
baseline_model = Model(inputs=baseline_model.input,
outputs=baseline_model.layers[10].output)
baseline_model.compile(loss=['mean_squared_error'],
optimizer=adam,
metrics=[metrics.categorical_accuracy])
baseline_model.fit([glimpse_seq, lt], [reward],
callbacks=[baseline_history],
epochs=spatial_epochs)
# adjusted reward based on baselines
adjusted_reward = reward - baseline_model.predict([glimpse_seq, lt])
# reinforce_loss = sum(-log_pi * adjusted_reward, axis = 1)
# log_pi = log_pi.reshape(2)
log_pi = np.repeat(log_pi, 5, axis=1)
log_pi = log_pi.T
reinforce_loss = np.mean(np.dot(adjusted_reward, -log_pi))
print(reinforce_loss)
hybrid_loss = float(action_history.losses[0]) + float(
baseline_history.losses[0]) + reinforce_loss
print(hybrid_loss)
##############################################################
#################### Confusion Matrix Construction #############
# print (predict)
# print (Test_Y_gt)
# ct = confusion_matrix(Test_Y_gt,predict)
# # check the order of the CT
# order = np.unique(np.concatenate((predict,Test_Y_gt)))
# # create an array to hold the CT for each CV
# mat = np.zeros((n_exp,n_exp))
# # put the order accordingly, in order to form the overall ConfusionMat
# for m in range(len(order)):
# for n in range(len(order)):
# mat[int(order[m]),int(order[n])]=ct[m,n]
# tot_mat = mat + tot_mat
################################################################
#################### cumulative f1 plotting ######################
# microAcc = np.trace(tot_mat) / np.sum(tot_mat)
# [f1,precision,recall] = fpr(tot_mat,n_exp)
# file = open(db_home+'Classification/'+ 'Result/'+dB+'/f1_' + str(train_id) + '.txt', 'a')
# file.write(str(f1) + "\n")
# file.close()
##################################################################
################# write each CT of each CV into .txt file #####################
# record_scores(db_home, dB, ct, sub, order, tot_mat, n_exp, subjects)
# war = weighted_average_recall(tot_mat, n_exp, samples)
# uar = unweighted_average_recall(tot_mat, n_exp)
# print("war: " + str(war))
# print("uar: " + str(uar))
###############################################################################
################## free memory ####################
gc.collect()
###################################################
# python main.py --train='./train_ram.py' --batch_size=30 --dB='CASME2_TIM' --flag='st' --spatial_size=224 --objective_flag=0
def train(batch_size, spatial_epochs, temporal_epochs, train_id, list_dB,
spatial_size, flag, objective_flag, tensorboard):
############## Path Preparation ######################
root_db_path = "/home/zhangke/database/"
tensorboard_path = root_db_path + "tensorboard/"
if os.path.isdir(root_db_path + 'Weights/' + str(train_id)) == False:
os.mkdir(root_db_path + 'Weights/' + str(train_id))
######################################################
############## Variables ###################
dB = list_dB[0]
r, w, subjects, samples, n_exp, VidPerSubject, timesteps_TIM, data_dim, channel, table, listOfIgnoredSamples, db_home, db_images, cross_db_flag = load_db(
root_db_path, list_dB, spatial_size, objective_flag)
# avoid confusion
if cross_db_flag == 1:
list_samples = listOfIgnoredSamples
# total confusion matrix to be used in the computation of f1 score
tot_mat = np.zeros((n_exp, n_exp))
history = LossHistory()
stopping = EarlyStopping(monitor='loss', min_delta=0, mode='min')
############################################
############## Flags ####################
tensorboard_flag = tensorboard
resizedFlag = 1
train_spatial_flag = 0
train_temporal_flag = 0
svm_flag = 0
finetuning_flag = 0
cam_visualizer_flag = 0
channel_flag = 0
if flag == 'st':
train_spatial_flag = 1
train_temporal_flag = 1
finetuning_flag = 1
#########################################
############ Reading Images and Labels ################
if cross_db_flag == 1:
SubperdB = Read_Input_Images_SAMM_CASME(db_images, list_samples,
listOfIgnoredSamples, dB,
resizedFlag, table, db_home,
spatial_size, channel)
else:
SubperdB = Read_Input_Images(db_images, listOfIgnoredSamples, dB,
resizedFlag, table, db_home, spatial_size,
channel, objective_flag)
labelperSub = label_matching(db_home, dB, subjects, VidPerSubject)
print("Loaded Images into the tray.subjects", subjects)
print("Loaded Labels into the tray.")
########### Model Configurations #######################
K.set_image_dim_ordering('th')
sgd = optimizers.SGD(lr=0.0001, decay=1e-7, momentum=0.9, nesterov=True)
adam = optimizers.Adam(lr=0.00001, decay=0.000001)
data_dim = 4096
########################################################
print("Beginning training process.")
########### Training Process ############
aaa = 1
for sub in range(subjects):
print(".starting subject" + str(sub))
gpu_observer()
spatial_weights_name = root_db_path + 'Weights/' + str(
train_id) + '/vgg_spatial_' + str(train_id) + '_' + str(dB) + '_'
spatial_weights_name_strain = root_db_path + 'Weights/' + str(
train_id) + '/vgg_spatial_strain_' + str(train_id) + '_' + str(
dB) + '_'
spatial_weights_name_gray = root_db_path + 'Weights/' + str(
train_id) + '/vgg_spatial_gray_' + str(train_id) + '_' + str(
dB) + '_'
temporal_weights_name = root_db_path + 'Weights/' + str(
train_id) + '/temporal_ID_' + str(train_id) + '_' + str(dB) + '_'
ae_weights_name = root_db_path + 'Weights/' + str(
train_id) + '/autoencoder_' + str(train_id) + '_' + str(dB) + '_'
ae_weights_name_strain = root_db_path + 'Weights/' + str(
train_id) + '/autoencoder_strain_' + str(train_id) + '_' + str(
dB) + '_'
############### Reinitialization & weights reset of models ########################
temporal_model = temporal_module(data_dim=data_dim,
timesteps_TIM=timesteps_TIM,
classes=n_exp)
temporal_model.compile(loss='categorical_crossentropy',
optimizer=adam,
metrics=[metrics.categorical_accuracy])
if channel_flag == 0:
vgg_model = VGG_16(spatial_size=spatial_size,
classes=n_exp,
channels=3,
weights_path='VGG_Face_Deep_16.h5')
if finetuning_flag == 1:
for layer in vgg_model.layers[:33]:
layer.trainable = False
vgg_model.compile(loss='categorical_crossentropy',
optimizer=adam,
metrics=[metrics.categorical_accuracy])
############ for tensorboard ###############
if tensorboard_flag == 1:
cat_path = tensorboard_path + str(sub) + "/"
os.mkdir(cat_path)
tbCallBack = keras.callbacks.TensorBoard(log_dir=cat_path,
write_graph=True)
cat_path2 = tensorboard_path + str(sub) + "spatial/"
os.mkdir(cat_path2)
tbCallBack2 = keras.callbacks.TensorBoard(log_dir=cat_path2,
write_graph=True)
#############################################
Train_X, Train_Y, Test_X, Test_Y, Test_Y_gt, X, y, test_X, test_y = restructure_data(
sub, SubperdB, labelperSub, subjects, n_exp, r, w, timesteps_TIM,
channel)
############### check gpu resources ####################
gpu_observer()
########################################################
print("Beginning training & testing.")
##################### Training & Testing #########################
if train_spatial_flag == 1 and train_temporal_flag == 1:
print("Beginning spatial training.")
# Spatial Training
if tensorboard_flag == 1:
vgg_model.fit(X,
y,
batch_size=batch_size,
epochs=spatial_epochs,
shuffle=True,
callbacks=[history, stopping, tbCallBack2])
else:
vgg_model.fit(X,
y,
batch_size=batch_size,
epochs=spatial_epochs,
shuffle=True,
callbacks=[history, stopping])
print(".record f1 and loss")
# record f1 and loss
record_loss_accuracy(db_home, train_id, dB, history)
print(".save vgg weights")
# save vgg weights
model = record_weights(vgg_model, spatial_weights_name, sub, flag)
print(".spatial encoding")
# Spatial Encoding
output = model.predict(X, batch_size=batch_size)
features = output.reshape(int(Train_X.shape[0]), timesteps_TIM,
output.shape[1])
print("Beginning temporal training.")
# Temporal Training
if tensorboard_flag == 1:
temporal_model.fit(features,
Train_Y,
batch_size=batch_size,
epochs=temporal_epochs,
callbacks=[tbCallBack])
else:
temporal_model.fit(features,
Train_Y,
batch_size=batch_size,
epochs=temporal_epochs)
print(".save temportal weights")
# save temporal weights
temporal_model = record_weights(temporal_model,
temporal_weights_name, sub,
't') # let the flag be t
print("Beginning testing.")
print(".predicting with spatial model")
# Testing
output = model.predict(test_X, batch_size=batch_size)
print(".outputing features")
features = output.reshape(Test_X.shape[0], timesteps_TIM,
output.shape[1])
print(".predicting with temporal model")
predict = temporal_model.predict(features)
predict = np.argmax(predict, axis=1)
#predict = temporal_model.predict_classes(features, batch_size=batch_size)
#################### Confusion Matrix Construction #############
print(predict)
print(Test_Y_gt.astype(int))
print(".writing predicts to file")
file = open(
db_home + 'Classification/' + 'Result/' + dB + '/predicts_' +
str(train_id) + '.txt', 'a')
file.write("predicts_sub_" + str(sub) + "," +
(",".join(repr(e) for e in predict.astype(list))) + "\n")
file.write("actuals_sub_" + str(sub) + "," + (",".join(
repr(e) for e in Test_Y_gt.astype(int).astype(list))) + "\n")
file.close()
ct = confusion_matrix(Test_Y_gt, predict)
# check the order of the CT
order = np.unique(np.concatenate((predict, Test_Y_gt)))
# create an array to hold the CT for each CV
mat = np.zeros((n_exp, n_exp))
# put the order accordingly, in order to form the overall ConfusionMat
for m in range(len(order)):
for n in range(len(order)):
mat[int(order[m]), int(order[n])] = ct[m, n]
tot_mat = mat + tot_mat
#################### cumulative f1 plotting ######################
microAcc = np.trace(tot_mat) / np.sum(tot_mat)
[f1, precision, recall] = fpr(tot_mat, n_exp)
file = open(
db_home + 'Classification/' + 'Result/' + dB + '/f1_' +
str(train_id) + '.txt', 'a')
file.write(str(f1) + "\n")
file.close()
##################################################################
################# write each CT of each CV into .txt file #####################
record_scores(db_home, dB, ct, sub, order, tot_mat, n_exp, subjects)
war = weighted_average_recall(tot_mat, n_exp, samples)
uar = unweighted_average_recall(tot_mat, n_exp)
print("war: " + str(war))
print("uar: " + str(uar))
###############################################################################
################## free memory ####################
del vgg_model
del temporal_model
del model
del Train_X, Test_X, X, y
gc.collect()