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train_samm_cross.py
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import numpy as np
import sys
import math
import operator
import csv
import glob,os
import xlrd
import cv2
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.svm import SVC
from collections import Counter
from sklearn.metrics import confusion_matrix
import scipy.io as sio
import pydot, graphviz
from PIL import Image
from keras.models import Sequential, Model
from keras.utils import np_utils, plot_model
from keras import metrics
from keras import backend as K
from keras.models import model_from_json
from keras.layers import Dense, Dropout, Flatten, Activation, GlobalAveragePooling2D
from keras.layers import Conv2D, MaxPooling2D
from keras.preprocessing.sequence import pad_sequences
from keras import optimizers
from keras.applications.vgg16 import VGG16 as keras_vgg16
from keras.preprocessing.image import ImageDataGenerator, array_to_img
import keras
from keras.callbacks import EarlyStopping
from labelling import collectinglabel
from reordering import readinput
from evaluationmatrix import fpr
from utilities import Read_Input_Images, get_subfolders_num, standard_data_loader, label_matching, duplicate_channel
from utilities import record_scores, loading_smic_table, loading_casme_table, ignore_casme_samples, ignore_casmergb_samples, LossHistory
from utilities import loading_samm_table, loading_casme_objective_table, filter_objective_samples
from samm_utilitis import get_subfolders_num_crossdb, Read_Input_Images_SAMM_CASME, loading_samm_labels
from models import VGG_16, temporal_module, VGG_16_4_channels, convolutional_autoencoder
def train_samm_cross(batch_size, spatial_epochs, temporal_epochs, train_id, dB, spatial_size, flag, , objective_flag, tensorboard):
############## Path Preparation ######################
root_db_path = "/media/ice/OS/Datasets/"
workplace = root_db_path + dB + "/"
inputDir = root_db_path + dB + "/" + dB + "/"
######################################################
classes = 5
if dB == 'CASME2_TIM':
table = loading_casme_table(workplace + 'CASME2-ObjectiveClasses.xlsx')
listOfIgnoredSamples, IgnoredSamples_index = ignore_casme_samples(inputDir)
############## Variables ###################
r = w = spatial_size
subjects=2
n_exp = 5
# VidPerSubject = get_subfolders_num(inputDir, IgnoredSamples_index)
listOfIgnoredSamples = []
VidPerSubject = [2,1]
timesteps_TIM = 10
data_dim = r * w
pad_sequence = 10
channel = 3
############################################
os.remove(workplace + "Classification/CASME2_TIM_label.txt")
elif dB == 'CASME2_Optical':
table = loading_casme_table(workplace + 'CASME2-ObjectiveClasses.xlsx')
listOfIgnoredSamples, IgnoredSamples_index = ignore_casme_samples(inputDir)
############## Variables ###################
r = w = spatial_size
subjects=26
n_exp = 5
VidPerSubject = get_subfolders_num(inputDir, IgnoredSamples_index)
timesteps_TIM = 9
data_dim = r * w
pad_sequence = 9
channel = 3
############################################
# os.remove(workplace + "Classification/CASME2_TIM_label.txt")
elif dB == 'SAMM_TIM10':
table, table_objective = loading_samm_table(root_db_path, dB)
listOfIgnoredSamples = []
IgnoredSamples_index = np.empty([0])
################# Variables #############################
r = w = spatial_size
subjects = 29
n_exp = 8
VidPerSubject = get_subfolders_num(inputDir, IgnoredSamples_index)
timesteps_TIM = 10
data_dim = r * w
pad_sequence = 10
channel = 3
classes = 8
#########################################################
elif dB == 'SAMM_CASME_Optical':
# total amount of videos 253
# table, table_objective = loading_samm_table(root_db_path, dB)
# table = table_objective
table = loading_casme_objective_table(root_db_path, dB)
# merge samm and casme tables
# table = np.concatenate((table, table2), axis=1)
# print(table.shape)
# listOfIgnoredSamples, IgnoredSamples_index, sub_items = ignore_casme_samples(inputDir)
listOfIgnoredSamples = []
IgnoredSamples_index = np.empty([0])
sub_items = np.empty([0])
list_samples = filter_objective_samples(table)
r = w = spatial_size
subjects = 26 # some subjects were removed because of objective classes and ignore samples: 47
n_exp = 5
# TODO:
# 1) Further decrease the video amount, the one with objective classes >= 6
# list samples: samples with wanted objective class
VidPerSubject, list_samples = get_subfolders_num_crossdb(inputDir, IgnoredSamples_index, sub_items, table, list_samples)
# print(VidPerSubject)
# print(len(VidPerSubject))
# print(sum(VidPerSubject))
timesteps_TIM = 9
data_dim = r * w
channel = 3
classes = 5
if os.path.isfile(workplace + "Classification/SAMM_CASME_Optical_label.txt"):
os.remove(workplace + "Classification/SAMM_CASME_Optical_label.txt")
##################### Variables ######################
######################################################
############## 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 == 'st4':
train_spatial_flag = 1
train_temporal_flag = 1
channel_flag = 1
elif flag == 'st7':
train_spatial_flag = 1
train_temporal_flag = 1
channel_flag = 2
#########################################
############ Reading Images and Labels ################
SubperdB = Read_Input_Images_SAMM_CASME(inputDir, list_samples, listOfIgnoredSamples, dB, resizedFlag, table, workplace, spatial_size, channel)
print("Loaded Images into the tray...")
labelperSub = label_matching(workplace, dB, subjects, VidPerSubject)
print("Loaded Labels into the tray...")
if channel_flag == 1:
SubperdB_strain = Read_Input_Images(inputDir, listOfIgnoredSamples, 'CASME2_Strain_TIM10', resizedFlag, table, workplace, spatial_size, 1)
elif channel_flag == 2:
SubperdB_strain = Read_Input_Images(inputDir, listOfIgnoredSamples, 'CASME2_Strain_TIM10', resizedFlag, table, workplace, spatial_size, 1)
SubperdB_gray = Read_Input_Images(inputDir, listOfIgnoredSamples, 'CASME2_TIM', resizedFlag, table, workplace, spatial_size, 3)
#######################################################
########### 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)
adam2 = optimizers.Adam(lr= 0.00075, decay= 0.0001)
# 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
else:
data_dim = 4096
########################################################
########### Training Process ############
# total confusion matrix to be used in the computation of f1 score
tot_mat = np.zeros((n_exp,n_exp))
# model checkpoint
spatial_weights_name = 'vgg_spatial_'+ str(train_id) + '_casme2_'
temporal_weights_name = 'temporal_ID_' + str(train_id) + '_casme2_'
history = LossHistory()
stopping = EarlyStopping(monitor='loss', min_delta = 0, mode = 'min')
############### Reinitialization & weights reset of models ########################
vgg_model_cam = VGG_16(spatial_size=spatial_size, classes=classes, weights_path='VGG_Face_Deep_16.h5')
temporal_model = temporal_module(data_dim=data_dim, classes=classes, timesteps_TIM=timesteps_TIM)
temporal_model.compile(loss='categorical_crossentropy', optimizer=adam, metrics=[metrics.categorical_accuracy])
conv_ae = convolutional_autoencoder(spatial_size = spatial_size, classes = classes)
conv_ae.compile(loss='binary_crossentropy', optimizer=adam)
if channel_flag == 1 or channel_flag == 2:
vgg_model = VGG_16_4_channels(classes=classes, spatial_size = spatial_size)
vgg_model.compile(loss='categorical_crossentropy', optimizer=adam, metrics=[metrics.categorical_accuracy])
else:
vgg_model = VGG_16(spatial_size = spatial_size, classes=classes, 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)
#############################################
image_label_mapping = np.empty([0])
Train_X, Train_Y= standard_data_loader(SubperdB, labelperSub, subjects, classes)
# Rearrange Training labels into a vector of images, breaking sequence
Train_X_spatial = Train_X.reshape(Train_X.shape[0]*timesteps_TIM, r, w, channel)
# Test_X_spatial = Test_X.reshape(Test_X.shape[0]* timesteps_TIM, r, w, channel)
# Special Loading for 4-Channel
if channel_flag == 1:
Train_X_Strain, _, Test_X_Strain, _, _ = data_loader_with_LOSO(sub, SubperdB_strain, labelperSub, subjects, classes)
Train_X_Strain = Train_X_Strain.reshape(Train_X_Strain.shape[0]*timesteps_TIM, r, w, 1)
Test_X_Strain = Test_X_Strain.reshape(Test_X.shape[0]*timesteps_TIM, r, w, 1)
# Concatenate Train X & Train_X_Strain
Train_X_spatial = np.concatenate((Train_X_spatial, Train_X_Strain), axis=3)
Test_X_spatial = np.concatenate((Test_X_spatial, Test_X_Strain), axis=3)
channel = 4
elif channel_flag == 2:
Train_X_Strain, _, Test_X_Strain, _, _ = data_loader_with_LOSO(sub, SubperdB_strain, labelperSub, subjects, classes)
Train_X_gray, _, Test_X_gray, _, _ = data_loader_with_LOSO(sub, SubperdB_gray, labelperSub, subjects)
Train_X_Strain = Train_X_Strain.reshape(Train_X_Strain.shape[0]*timesteps_TIM, r, w, 1)
Test_X_Strain = Test_X_Strain.reshape(Test_X_Strain.shape[0]*timesteps_TIM, r, w, 1)
Train_X_gray = Train_X_gray.reshape(Train_X_gray.shape[0]*timesteps_TIM, r, w, 3)
Test_X_gray = Test_X_gray.reshape(Test_X_gray.shape[0]*timesteps_TIM, r, w, 3)
# Concatenate Train_X_Strain & Train_X & Train_X_gray
Train_X_spatial = np.concatenate((Train_X_spatial, Train_X_Strain, Train_X_gray), axis=3)
Test_X_spatial = np.concatenate((Test_X_spatial, Test_X_Strain, Test_X_gray), axis=3)
channel = 7
if channel == 1:
# Duplicate channel of input image
Train_X_spatial = duplicate_channel(Train_X_spatial)
# Test_X_spatial = duplicate_channel(Test_X_spatial)
# Extend Y labels 10 fold, so that all images have labels
Train_Y_spatial = np.repeat(Train_Y, timesteps_TIM, axis=0)
# Test_Y_spatial = np.repeat(Test_Y, timesteps_TIM, axis=0)
# print ("Train_X_shape: " + str(np.shape(Train_X_spatial)))
# print ("Train_Y_shape: " + str(np.shape(Train_Y_spatial)))
# print ("Test_X_shape: " + str(np.shape(Test_X_spatial)))
# print ("Test_Y_shape: " + str(np.shape(Test_Y_spatial)))
# print(Train_X_spatial)
##################### Training & Testing #########################
X = Train_X_spatial.reshape(Train_X_spatial.shape[0], channel, r, w)
y = Train_Y_spatial.reshape(Train_Y_spatial.shape[0], classes)
normalized_X = X.astype('float32') / 255.
# test_X = Test_X_spatial.reshape(Test_X_spatial.shape[0], channel, r, w)
# test_y = Test_Y_spatial.reshape(Test_Y_spatial.shape[0], classes)
# normalized_test_X = test_X.astype('float32') / 255.
print(X.shape)
###### conv weights must be freezed for transfer learning ######
if finetuning_flag == 1:
for layer in vgg_model.layers[:33]:
layer.trainable = False
for layer in vgg_model_cam.layers[:31]:
layer.trainable = False
if train_spatial_flag == 1 and train_temporal_flag == 1:
# Autoencoder features
# conv_ae.fit(normalized_X, normalized_X, batch_size=batch_size, epochs=spatial_epochs, shuffle=True)
# Spatial Training
if tensorboard_flag == 1:
vgg_model.fit(X, y, batch_size=batch_size, epochs=spatial_epochs, shuffle=True, callbacks=[tbCallBack2])
else:
vgg_model.fit(X, y, batch_size=batch_size, epochs=spatial_epochs, shuffle=True, callbacks=[history, stopping])
# record f1 and loss
file_loss = open(workplace+'Classification/'+ 'Result/'+dB+'/loss_' + str(train_id) + '.txt', 'a')
file_loss.write(str(history.losses) + "\n")
file_loss.close()
file_loss = open(workplace+'Classification/'+ 'Result/'+dB+'/accuracy_' + str(train_id) + '.txt', 'a')
file_loss.write(str(history.accuracy) + "\n")
file_loss.close()
vgg_model.save_weights(spatial_weights_name + 'HDE'+ ".h5")
model = Model(inputs=vgg_model.input, outputs=vgg_model.layers[35].output)
plot_model(model, to_file="spatial_module_FULL_TRAINING.png", show_shapes=True)
model_ae = Model(inputs=conv_ae.input, outputs=conv_ae.output)
plot_model(model_ae, to_file='autoencoders.png', show_shapes=True)
# Autoencoding
output_ae = model_ae.predict(normalized_X, batch_size = batch_size)
for i in range(batch_size):
visual_ae = output_ae[i].reshape(224,224,channel)
# de-normalize
visual_ae = ( ( visual_ae - min(visual_ae) ) / ( max(visual_ae) - min(visual_ae) ) ) * 255
fname = '{prefix}_{index}_{hash}.{format}'.format(prefix='AE_output', index=str(sub),
hash=np.random.randint(1e7), format='png')
cv2.imwrite(workplace+'Classification/Result/ae_train/'+fname, visual_ae)
output_ae = model.predict(output_ae, batch_size = batch_size)
# Spatial Encoding
output = model.predict(X, batch_size = batch_size)
# features = output.reshape(int(Train_X.shape[0]), timesteps_TIM, output.shape[1])
# merging autoencoded features and spatial features
output = np.concatenate((output, output_ae), axis=1)
# print(output.shape)
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)
temporal_model.save_weights(temporal_weights_name + 'HDE' + ".h5")
# # Testing
# output = model.predict(test_X, batch_size = batch_size)
# output_ae = model_ae.predict(normalized_test_X, batch_size = batch_size)
# for i in range(batch_size):
# visual_ae = output_ae[i].reshape(224,224,channel)
# # de-normalize
# visual_ae = ( ( visual_ae - min(visual_ae) ) / ( max(visual_ae) - min(visual_ae) ) ) * 255
# fname = '{prefix}_{index}_{hash}.{format}'.format(prefix='AE_output', index=str(sub),
# hash=np.random.randint(1e7), format='png')
# cv2.imwrite(workplace+'Classification/Result/ae_train/'+fname, visual_ae)
# output_ae = model.predict(output_ae, batch_size = batch_size)
# output = np.concatenate((output, output_ae), 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(workplace+'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(workplace, dB, ct, sub, order, tot_mat, n_exp, subjects)
###############################################################################