[PYTHON] Ich habe einen Anpanman-Maler diskriminiert
Einführung
Ich beschloss, eine einfache App mit der Technologie zu erstellen, die ich durch das Studium der KI gelernt hatte.
Ich habe so etwas wie das Folgende gemacht.
Der Grund, warum ich mich für Anpanman entschieden habe, war, dass es eine Zeichentrickfigur war, die ich leicht schreiben konnte.
Das Modell wollte mit GAN umgehen, und es war möglich, Anomalien zu erkennen, indem nur normale Bilder gelernt wurden.
Ich dachte, ich würde ANOGAN verwenden, aber als ich es nachschlug, hieß es EfficientGAN in der Hochgeschwindigkeitsversion von ANOGAN.
Es scheint etwas zu geben, also habe ich es gewählt.
Der Einfachheit halber habe ich es auch unter der Voraussetzung erstellt, dass nur Anpanmans Gesicht identifiziert wird.
fließen
- Lassen Sie AI das normale Bild von Anpanman lernen.
- Geben Sie die normale Bildgruppe und die abnormale Bildgruppe in AI ein und legen Sie den Zwischenwert des Durchschnitts jeder Punktzahl fest. Stellen Sie den Schwellenwert für die Unterscheidung abnormaler Bilder ein.
- Geben Sie tatsächlich ein handgeschriebenes Bild ein und lassen Sie AI feststellen, ob das Bild abnormal ist.
Was ich getan habe
Datensatzerstellung
・ Bild lernen
Ich sammelte Anpanman-Bilder aus dem Netz durch Schaben, verarbeitete die Bilder und schnitt nur das Gesicht aus. Wie später beschrieben, war der Hintergrund des Bilds der mobilen Kamera grau, um auch den Hintergrund zu lernen Die Graustufendaten wurden mit der folgenden Funktion erweitert.
from PIL import Image, ImageOps
import numpy as np
def make_gray_gradation(img, gradation_range=(230, 255)):
"""
Konvertieren Sie den Hintergrund des Eingabebildes in eine zufällige Graustufe
Input :Bilddatei(Farbe ist auch akzeptabel)
Output :Bilddatei(Bild mit grauem Abstufungsumwandlungshintergrund)
Pramater
img :Bild eingeben
gradation_range :Bereich der RGB-Werte, die gradient sein sollen
"""
gra_range = np.random.randint(*gradation_range)
gray = ImageOps.grayscale(img)
output = ImageOps.colorize(gray, black=(0, 0, 0), white=(gra_range, gra_range, gra_range))
return output
・ Normale Bildgruppe und abnormale Bildgruppe
Das normale Bild ist Anpanman, der das Illustrationsbild durch das Zeichenpapier geschrieben hat. Ich habe ein Bild von Anpanman verwendet, das von mir und meinen Alumni mit einem Mobiltelefon aufgenommen wurde.
Das abnormale Bild ist ein Bild einer Illustration von Bikinman, Dokin usw., die mit einem Mobiltelefon auf die gleiche Weise wie oben aufgenommen wurde. Ich habe ein schlechtes Anpanman-Bild verwendet, das im Netz gekratzt wurde.
Geben Sie nach dem Lernen die normale Bildgruppe und die abnormale Bildgruppe ein, vergleichen Sie die generierten Bilder und die Punkteverteilung
Da der Hintergrund des mit dem Mobiltelefon aufgenommenen Eingabebildes grau war, gibt es auch eine graue Abstufung im Lernbild
Ich habe es eingefügt, aber ich konnte es nicht gut reproduzieren.
Die Punktzahl hängt auch davon ab, ob der Hintergrund gut erzeugt wird und nicht, ob das Bild von Anpanman gut gezeichnet ist.
Es scheint, dass es entschieden wurde, und der Schwellenwert für die Unterscheidung abnormaler Bilder wurde nicht gut entschieden.
Gegenmaßnahmen Binärisierung
Machen Sie als Gegenmaßnahme den Hintergrund für alle Bilder weiß und stellen Sie sicher, dass der Umriss des Bildes Anpanman ähnelt. Ich habe es möglich gemacht festzustellen, ob es sich um ein abnormales Bild handelt. Das Folgende ist eine Funktion, die das Eingabebild binärisiert.
import os
import scipy.stats as stats
from PIL import Image
def image_binarization(path_in, path_out, th_zero_num=1400, width=100, height=100):
"""
Der Umriss des Eingabebildes wird in Schwarzweiß digitalisiert und ausgegeben.
Input :Ordnerpfad, in dem Bilddateien gespeichert sind(Das Ende ist/) (Es können nur Bilder in den Ordner gelegt werden)
Output :Speichern Sie das binärisierte Bild im angegebenen Ordner. 0 nach der Binärisierung(Gliederung)Geben Sie die Anzahl der Punkte aus.
Pramater
path_in :Verzeichnispfad mit Eingabebildern
path_out :Pfad des Ausgabeverzeichnisses
th_zero_num :0 im Bild(Gliederung)MIN Wert der Anzahl der Punkte von(Wenn der Umriss zu dunkel ist, verkleinern Sie ihn und passen Sie ihn an)
width :Bildbreite Größe
height :Vertikale Größe des Bildes
"""
list_in = os.listdir(path_in)
im_np_out = np.empty((0, width*height))
for img in list_in:
path_name = path_in+img
x_img = cv2.imread(path_name)
x_img = cv2.resize(x_img, (width, height))
x_img= cv2.cvtColor(x_img, cv2.COLOR_BGR2GRAY)
x_img = np.array(x_img)
x_img = x_img / 255.0
x_img = x_img.reshape((1, width, height))
x_img = x_img.reshape(1, width*height)
m = stats.mode(x_img)
max_hindo = m.mode[0][0]
for c in reversed(range(50)):
th = (c+1)*0.01
th_0_1 = max_hindo-th
x_img_ = np.where(x_img>th_0_1, 1, 0)
if (np.count_nonzero(x_img_ == 0))>th_zero_num:
break
display(np.count_nonzero(x_img_ == 0))
x_img = x_img_.reshape(width, height)
x_img = (x_img * 2.0) - 1.0
img_np_255 = (x_img + 1.0) * 127.5
img_np_255_mod1 = np.maximum(img_np_255, 0)
img_np_255_mod1 = np.minimum(img_np_255_mod1, 255)
img_np_uint8 = img_np_255_mod1.astype(np.uint8)
image = Image.fromarray(img_np_uint8)
image.save(path_out+img, quality=95)
Geben Sie nach Gegenmaßnahmen die normale Bildgruppe und die abnormale Bildgruppe ein, vergleichen Sie die generierten Bilder und die Punkteverteilung
Die Verteilung der Bewertungen war bis zu einem gewissen Grad zwischen dem korrekten Bild und dem abnormalen Bild aufgeteilt, also vorerst ungefähr
Es scheint, dass Sie den Schwellenwert bestimmen können, der normal und abnormal trennt. (Der Schwellenwert wurde auf 0,40372 eingestellt.)
Stellen Sie fest, ob das eingegebene Anpanman-Bild ein normales oder ein abnormales Bild ist
Bild eingeben
Geben Sie das Anpanman-Bild ein, das Sie durch das Illustrationsbild von nur 5 Blättern geschrieben haben. Andere betraten 14 arme Anpanman, die im Netz kratzten. (Eingabe nach der Binärisierung mit der obigen Binärisierungsfunktion)
Diskriminierungsergebnis
Das Beurteilungsergebnis wird durch die Hintergrundfarbe des Bewertungsanzeigerahmens dargestellt. Wenn der Hintergrund blau ist, ist es ein normales Bild, und wenn es rot ist, ist es ein abnormales Bild.
Infolgedessen wird 4/5 für Anpanman, der die Abbildung durchschaut, als normal eingestuft.
8/14 ist ein abnormales Urteil für andere schlecht abgekratzte Anpanman
Die richtige Antwortrate war 12/19 = 63,15%. Ich muss die Genauigkeit verbessern.
Was ich gelernt habe
・ Obwohl ich ein Bild auf rein weißem Zeichenpapier aufgenommen habe, war der Hintergrund des tatsächlichen Bildes grau. Indem Sie das Ausmaß des Einflusses von Licht spüren und Methoden wie die Binarisierung entwickeln, beschränken Sie die Bedingungen auf diejenigen, die leichter zu erlernen sind. Ich habe gelernt, dass die Genauigkeit verbessert werden kann. ・ Um die Genauigkeit von GAN zu verbessern, fügen Sie Grobal Avarage Pooling, Leaky ReLu, Ebenen usw. Rauschen hinzu. Ich denke, es war gut, verschiedene Methoden zur Verbesserung der Genauigkeit ausprobieren zu können. (Obwohl sich das Ergebnis nicht wesentlich verbesserte)
von jetzt an
Ich möchte verschiedene Maßnahmen untersuchen und ausprobieren, um die Genauigkeit von GAN zu verbessern. Ich habe auch Google Colab und EC2 von AWS verwendet, aber in Zukunft verschiedene Dinge wie SageMaker von AWS und GCP Ich möchte mit der Cloud lernen.
Code
train_BiGAN.py
train_BiGAN.py
import numpy as np
import os
import tensorflow as tf
import utility as Utility
import argparse
import matplotlib.pyplot as plt
from model_BiGAN import BiGAN as Model
from make_datasets_TRAIN import Make_datasets_TRAIN as Make_datasets
def parser():
parser = argparse.ArgumentParser(description='train LSGAN')
parser.add_argument('--batch_size', '-b', type=int, default=300, help='Number of images in each mini-batch')
parser.add_argument('--log_file_name', '-lf', type=str, default='anpanman', help='log file name')
parser.add_argument('--epoch', '-e', type=int, default=1001, help='epoch')
parser.add_argument('--file_train_data', '-ftd', type=str, default='../Train_Data/191103/', help='train data')
parser.add_argument('--test_true_data', '-ttd', type=str, default='../Valid_True_Data/191103/', help='test of true_data')
parser.add_argument('--test_false_data', '-tfd', type=str, default='../Valid_False_Data/191103/', help='test of false_data')
parser.add_argument('--valid_span', '-vs', type=int, default=100, help='validation span')
return parser.parse_args()
args = parser()
#global variants
BATCH_SIZE = args.batch_size
LOGFILE_NAME = args.log_file_name
EPOCH = args.epoch
FILE_NAME = args.file_train_data
TRUE_DATA = args.test_true_data
FALSE_DATA = args.test_false_data
IMG_WIDTH = 100
IMG_HEIGHT = 100
IMG_CHANNEL = 1
BASE_CHANNEL = 32
NOISE_UNIT_NUM = 200
NOISE_MEAN = 0.0
NOISE_STDDEV = 1.0
TEST_DATA_SAMPLE = 5 * 5
L2_NORM = 0.001
KEEP_PROB_RATE = 0.5
SEED = 1234
SCORE_ALPHA = 0.9 # using for cost function
VALID_SPAN = args.valid_span
np.random.seed(seed=SEED)
BOARD_DIR_NAME = './tensorboard/' + LOGFILE_NAME
OUT_IMG_DIR = './out_images_BiGAN' #output image file
out_model_dir = './out_models_BiGAN/' #output model_ckpt file
#Load_model_dir = '../model_ckpt/' #Load model_ckpt file
OUT_HIST_DIR = './out_score_hist_BiGAN' #output histogram file
CYCLE_LAMBDA = 1.0
try:
os.mkdir('log')
os.mkdir('out_graph')
os.mkdir(OUT_IMG_DIR)
os.mkdir(out_model_dir)
os.mkdir(OUT_HIST_DIR)
os.mkdir('./out_images_Debug') #for debug
except:
pass
make_datasets = Make_datasets(FILE_NAME, TRUE_DATA, FALSE_DATA, IMG_WIDTH, IMG_HEIGHT, SEED)
model = Model(NOISE_UNIT_NUM, IMG_CHANNEL, SEED, BASE_CHANNEL, KEEP_PROB_RATE)
z_ = tf.placeholder(tf.float32, [None, NOISE_UNIT_NUM], name='z_') #noise to generator
x_ = tf.placeholder(tf.float32, [None, IMG_HEIGHT, IMG_WIDTH, IMG_CHANNEL], name='x_') #image to classifier
d_dis_f_ = tf.placeholder(tf.float32, [None, 1], name='d_dis_g_') #target of discriminator related to generator
d_dis_r_ = tf.placeholder(tf.float32, [None, 1], name='d_dis_r_') #target of discriminator related to real image
is_training_ = tf.placeholder(tf.bool, name = 'is_training')
with tf.variable_scope('encoder_model'):
z_enc = model.encoder(x_, reuse=False, is_training=is_training_)
with tf.variable_scope('decoder_model'):
x_dec = model.decoder(z_, reuse=False, is_training=is_training_)
x_z_x = model.decoder(z_enc, reuse=True, is_training=is_training_) # for cycle consistency
with tf.variable_scope('discriminator_model'):
#stream around discriminator
drop3_r, logits_r = model.discriminator(x_, z_enc, reuse=False, is_training=is_training_) #real pair
drop3_f, logits_f = model.discriminator(x_dec, z_, reuse=True, is_training=is_training_) #real pair
drop3_re, logits_re = model.discriminator(x_z_x, z_enc, reuse=True, is_training=is_training_) #fake pair
with tf.name_scope("loss"):
loss_dis_f = tf.reduce_mean(tf.square(logits_f - d_dis_f_), name='Loss_dis_gen') #loss related to generator
loss_dis_r = tf.reduce_mean(tf.square(logits_r - d_dis_r_), name='Loss_dis_rea') #loss related to real image
#total loss
loss_dis_total = loss_dis_f + loss_dis_r
loss_dec_total = loss_dis_f
loss_enc_total = loss_dis_r
with tf.name_scope("score"):
l_g = tf.reduce_mean(tf.abs(x_ - x_z_x), axis=(1,2,3))
l_FM = tf.reduce_mean(tf.abs(drop3_r - drop3_re), axis=1)
score_A = SCORE_ALPHA * l_g + (1.0 - SCORE_ALPHA) * l_FM
with tf.name_scope("optional_loss"):
loss_dec_opt = loss_dec_total + CYCLE_LAMBDA * l_g
loss_enc_opt = loss_enc_total + CYCLE_LAMBDA * l_g
tf.summary.scalar('loss_dis_total', loss_dis_total)
tf.summary.scalar('loss_dec_total', loss_dec_total)
tf.summary.scalar('loss_enc_total', loss_enc_total)
merged = tf.summary.merge_all()
# t_vars = tf.trainable_variables()
dec_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope="decoder")
enc_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope="encoder")
dis_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope="discriminator")
with tf.name_scope("train"):
train_dis = tf.train.AdamOptimizer(learning_rate=0.0001, beta1=0.5).minimize(loss_dis_total, var_list=dis_vars
, name='Adam_dis')
train_dec = tf.train.AdamOptimizer(learning_rate=0.01, beta1=0.5).minimize(loss_dec_total, var_list=dec_vars
, name='Adam_dec')
train_enc = tf.train.AdamOptimizer(learning_rate=0.005, beta1=0.5).minimize(loss_enc_total, var_list=enc_vars
, name='Adam_enc')
train_dec_opt = tf.train.AdamOptimizer(learning_rate=0.005, beta1=0.5).minimize(loss_dec_opt, var_list=dec_vars
, name='Adam_dec')
train_enc_opt = tf.train.AdamOptimizer(learning_rate=0.005, beta1=0.5).minimize(loss_enc_opt, var_list=enc_vars
, name='Adam_enc')
sess = tf.Session()
ckpt = tf.train.get_checkpoint_state(out_model_dir)
saver = tf.train.Saver()
if ckpt: #Wenn es einen Kontrollpunkt gibt
last_model = ckpt.model_checkpoint_path #Pfad zum zuletzt gespeicherten Modell
saver.restore(sess, last_model) #Variable Daten lesen
print("load " + last_model)
else: #Wenn keine Daten gespeichert sind
#init = tf.initialize_all_variables()
sess.run(tf.global_variables_initializer())
summary_writer = tf.summary.FileWriter(BOARD_DIR_NAME, sess.graph)
log_list = []
log_list.append(['epoch', 'AUC'])
#training loop
for epoch in range(0, EPOCH):
sum_loss_dis_f = np.float32(0)
sum_loss_dis_r = np.float32(0)
sum_loss_dis_total = np.float32(0)
sum_loss_dec_total = np.float32(0)
sum_loss_enc_total = np.float32(0)
len_data = make_datasets.make_data_for_1_epoch()
for i in range(0, len_data, BATCH_SIZE):
img_batch = make_datasets.get_data_for_1_batch(i, BATCH_SIZE)
z = make_datasets.make_random_z_with_norm(NOISE_MEAN, NOISE_STDDEV, len(img_batch), NOISE_UNIT_NUM)
tar_g_1 = make_datasets.make_target_1_0(1.0, len(img_batch)) #1 -> real
tar_g_0 = make_datasets.make_target_1_0(0.0, len(img_batch)) #0 -> fake
#train discriminator
sess.run(train_dis, feed_dict={z_:z, x_: img_batch, d_dis_f_: tar_g_0, d_dis_r_: tar_g_1, is_training_:True})
#train decoder
sess.run(train_dec, feed_dict={z_:z, d_dis_f_: tar_g_1, is_training_:True})
# sess.run(train_dec_opt, feed_dict={z_:z, x_: img_batch, d_dis_f_: tar_g_1, is_training_:True})
#train encoder
sess.run(train_enc, feed_dict={x_:img_batch, d_dis_r_: tar_g_0, is_training_:True})
# sess.run(train_enc_opt, feed_dict={x_:img_batch, d_dis_r_: tar_g_0, is_training_:True})
# loss for discriminator
loss_dis_total_, loss_dis_r_, loss_dis_f_ = sess.run([loss_dis_total, loss_dis_r, loss_dis_f],
feed_dict={z_: z, x_: img_batch, d_dis_f_: tar_g_0,
d_dis_r_: tar_g_1, is_training_:False})
#loss for decoder
loss_dec_total_ = sess.run(loss_dec_total, feed_dict={z_: z, d_dis_f_: tar_g_1, is_training_:False})
#loss for encoder
loss_enc_total_ = sess.run(loss_enc_total, feed_dict={x_: img_batch, d_dis_r_: tar_g_0, is_training_:False})
#for tensorboard
merged_ = sess.run(merged, feed_dict={z_:z, x_: img_batch, d_dis_f_: tar_g_0, d_dis_r_: tar_g_1, is_training_:False})
summary_writer.add_summary(merged_, epoch)
sum_loss_dis_f += loss_dis_f_
sum_loss_dis_r += loss_dis_r_
sum_loss_dis_total += loss_dis_total_
sum_loss_dec_total += loss_dec_total_
sum_loss_enc_total += loss_enc_total_
print("----------------------------------------------------------------------")
print("epoch = {:}, Encoder Total Loss = {:.4f}, Decoder Total Loss = {:.4f}, Discriminator Total Loss = {:.4f}".format(
epoch, sum_loss_enc_total / len_data, sum_loss_dec_total / len_data, sum_loss_dis_total / len_data))
print("Discriminator Real Loss = {:.4f}, Discriminator Generated Loss = {:.4f}".format(
sum_loss_dis_r / len_data, sum_loss_dis_r / len_data))
if epoch % VALID_SPAN == 0:
# score_A_list = []
score_A_np = np.zeros((0, 2), dtype=np.float32)
val_data_num = len(make_datasets.valid_data)
val_true_data_num = len(make_datasets.valid_true_np)
val_false_data_num = len(make_datasets.valid_false_np)
img_batch_1, _ = make_datasets.get_valid_data_for_1_batch(0, val_true_data_num)
img_batch_0, _ = make_datasets.get_valid_data_for_1_batch(val_data_num - val_false_data_num, val_true_data_num)
x_z_x_1 = sess.run(x_z_x, feed_dict={x_:img_batch_1, is_training_:False})
x_z_x_0 = sess.run(x_z_x, feed_dict={x_:img_batch_0, is_training_:False})
score_A_1 = sess.run(score_A, feed_dict={x_:img_batch_1, is_training_:False})
score_A_0 = sess.run(score_A, feed_dict={x_:img_batch_0, is_training_:False})
score_A_re_1 = np.reshape(score_A_1, (-1, 1))
score_A_re_0 = np.reshape(score_A_0, (-1, 1))
tars_batch_1 = np.ones(val_true_data_num)
tars_batch_0 = np.zeros(val_false_data_num)
tars_batch_re_1 = np.reshape(tars_batch_1, (-1, 1))
tars_batch_re_0 = np.reshape(tars_batch_0, (-1, 1))
score_A_np_1_tmp = np.concatenate((score_A_re_1, tars_batch_re_1), axis=1)
score_A_np_0_tmp = np.concatenate((score_A_re_0, tars_batch_re_0), axis=1)
score_A_np = np.concatenate((score_A_np_1_tmp, score_A_np_0_tmp), axis=0)
#print(score_A_np)
tp, fp, tn, fn, precision, recall = Utility.compute_precision_recall(score_A_np)
auc = Utility.make_ROC_graph(score_A_np, 'out_graph/' + LOGFILE_NAME, epoch)
print("tp:{}, fp:{}, tn:{}, fn:{}, precision:{:.4f}, recall:{:.4f}, AUC:{:.4f}".format(tp, fp, tn, fn, precision, recall, auc))
log_list.append([epoch, auc])
Utility.make_score_hist(score_A_1, score_A_0, epoch, LOGFILE_NAME, OUT_HIST_DIR)
Utility.make_output_img(img_batch_1, img_batch_0, x_z_x_1, x_z_x_0, score_A_0, score_A_1, epoch, LOGFILE_NAME, OUT_IMG_DIR)
#after learning
Utility.save_list_to_csv(log_list, 'log/' + LOGFILE_NAME + '_auc.csv')
#saver2 = tf.train.Saver()
save_path = saver.save(sess, out_model_dir + 'anpanman_weight.ckpt')
print("Model saved in file: ", save_path)
model_BiGAN.py
model_BiGAN.py
import numpy as np
# import os
import tensorflow as tf
# from PIL import Image
# import utility as Utility
# import argparse
class BiGAN():
def __init__(self, noise_unit_num, img_channel, seed, base_channel, keep_prob):
self.NOISE_UNIT_NUM = noise_unit_num # 200
self.IMG_CHANNEL = img_channel # 1
self.SEED = seed
np.random.seed(seed=self.SEED)
self.BASE_CHANNEL = base_channel # 32
self.KEEP_PROB = keep_prob
def leaky_relu(self, x, alpha):
return tf.nn.relu(x) - alpha * tf.nn.relu(-x)
def gaussian_noise(self, input, std): #used at discriminator
noise = tf.random_normal(shape=tf.shape(input), mean=0.0, stddev=std, dtype=tf.float32, seed=self.SEED)
return input + noise
def conv2d(self, input, in_channel, out_channel, k_size, stride, seed):
w = tf.get_variable('w', [k_size, k_size, in_channel, out_channel],
initializer=tf.random_normal_initializer
(mean=0.0, stddev=0.02, seed=seed), dtype=tf.float32)
b = tf.get_variable('b', [out_channel], initializer=tf.constant_initializer(0.0))
conv = tf.nn.conv2d(input, w, strides=[1, stride, stride, 1], padding="SAME", name='conv') + b
return conv
def conv2d_transpose(self, input, in_channel, out_channel, k_size, stride, seed):
w = tf.get_variable('w', [k_size, k_size, out_channel, in_channel],
initializer=tf.random_normal_initializer
(mean=0.0, stddev=0.02, seed=seed), dtype=tf.float32)
b = tf.get_variable('b', [out_channel], initializer=tf.constant_initializer(0.0))
out_shape = tf.stack(
[tf.shape(input)[0], tf.shape(input)[1] * 2, tf.shape(input)[2] * 2, tf.constant(out_channel)])
deconv = tf.nn.conv2d_transpose(input, w, output_shape=out_shape, strides=[1, stride, stride, 1],
padding="SAME") + b
return deconv
def batch_norm(self, input):
shape = input.get_shape().as_list()
n_out = shape[-1]
scale = tf.get_variable('scale', [n_out], initializer=tf.constant_initializer(1.0))
beta = tf.get_variable('beta', [n_out], initializer=tf.constant_initializer(0.0))
batch_mean, batch_var = tf.nn.moments(input, [0])
bn = tf.nn.batch_normalization(input, batch_mean, batch_var, beta, scale, 0.0001, name='batch_norm')
return bn
def fully_connect(self, input, in_num, out_num, seed):
w = tf.get_variable('w', [in_num, out_num], initializer=tf.random_normal_initializer
(mean=0.0, stddev=0.02, seed=seed), dtype=tf.float32)
b = tf.get_variable('b', [out_num], initializer=tf.constant_initializer(0.0))
fc = tf.matmul(input, w, name='fc') + b
return fc
def encoder(self, x, reuse=False, is_training=False): #x is expected [n, 28, 28, 1]
with tf.variable_scope('encoder', reuse=reuse):
with tf.variable_scope("layer1"): # layer1 conv nx28x28x1 -> nx14x14x32
conv1 = self.conv2d(x, self.IMG_CHANNEL, self.BASE_CHANNEL, 3, 2, self.SEED)
with tf.variable_scope("layer2"): # layer2 conv nx14x14x32 -> nx7x7x64
conv2 = self.conv2d(conv1, self.BASE_CHANNEL, self.BASE_CHANNEL*2, 3, 2, self.SEED)
bn2 = self.batch_norm(conv2)
lr2 = self.leaky_relu(bn2, alpha=0.1)
with tf.variable_scope("layer3"): # layer3 conv nx7x7x64 -> nx4x4x128
conv3 = self.conv2d(lr2, self.BASE_CHANNEL*2, self.BASE_CHANNEL*4, 3, 2, self.SEED)
bn3 = self.batch_norm(conv3)
lr3 = self.leaky_relu(bn3, alpha=0.1)
with tf.variable_scope("layer4"): # layer4 fc nx4x4x128 -> nx200
shape = tf.shape(lr3)
print(shape[1])
reshape4 = tf.reshape(lr3, [shape[0], shape[1]*shape[2]*shape[3]])
fc4 = self.fully_connect(reshape4, 21632, self.NOISE_UNIT_NUM, self.SEED)
return fc4
def decoder(self, z, reuse=False, is_training=False): # z is expected [n, 200]
with tf.variable_scope('decoder', reuse=reuse):
with tf.variable_scope("layer1"): # layer1 fc nx200 -> nx1024
fc1 = self.fully_connect(z, self.NOISE_UNIT_NUM, 1024, self.SEED)
bn1 = self.batch_norm(fc1)
rl1 = tf.nn.relu(bn1)
with tf.variable_scope("layer2"): # layer2 fc nx1024 -> nx6272
fc2 = self.fully_connect(rl1, 1024, 25*25*self.BASE_CHANNEL*4, self.SEED)
bn2 = self.batch_norm(fc2)
rl2 = tf.nn.relu(bn2)
with tf.variable_scope("layer3"): # layer3 deconv nx6272 -> nx7x7x128 -> nx14x14x64
shape = tf.shape(rl2)
reshape3 = tf.reshape(rl2, [shape[0], 25, 25, 128])
deconv3 = self.conv2d_transpose(reshape3, self.BASE_CHANNEL*4, self.BASE_CHANNEL*2, 4, 2, self.SEED)
bn3 = self.batch_norm(deconv3)
rl3 = tf.nn.relu(bn3)
with tf.variable_scope("layer4"): # layer3 deconv nx14x14x64 -> nx28x28x1
deconv4 = self.conv2d_transpose(rl3, self.BASE_CHANNEL*2, self.IMG_CHANNEL, 4, 2, self.SEED)
tanh4 = tf.tanh(deconv4)
return tanh4
def discriminator(self, x, z, reuse=False, is_training=True): #z[n, 200], x[n, 28, 28, 1]
with tf.variable_scope('discriminator', reuse=reuse):
with tf.variable_scope("x_layer1"): # layer x1 conv [n, 28, 28, 1] -> [n, 14, 14, 64]
convx1 = self.conv2d(x, self.IMG_CHANNEL, self.BASE_CHANNEL*2, 4, 2, self.SEED)
lrx1 = self.leaky_relu(convx1, alpha=0.1)
dropx1 = tf.layers.dropout(lrx1, rate=1.0 - self.KEEP_PROB, name='dropout', training=is_training)
with tf.variable_scope("x_layer2"): # layer x2 conv [n, 14, 14, 64] -> [n, 7, 7, 64] -> [n, 3136]
convx2 = self.conv2d(dropx1, self.BASE_CHANNEL*2, self.BASE_CHANNEL*2, 4, 2, self.SEED)
bnx2 = self.batch_norm(convx2)
lrx2 = self.leaky_relu(bnx2, alpha=0.1)
dropx2 = tf.layers.dropout(lrx2, rate=1.0 - self.KEEP_PROB, name='dropout', training=is_training)
shapex2 = tf.shape(dropx2)
reshape3 = tf.reshape(dropx2, [shapex2[0], shapex2[1]*shapex2[2]*shapex2[3]])
with tf.variable_scope("z_layer1"): # layer1 fc [n, 200] -> [n, 512]
fcz1 = self.fully_connect(z, self.NOISE_UNIT_NUM, 512, self.SEED)
lrz1 = self.leaky_relu(fcz1, alpha=0.1)
dropz1 = tf.layers.dropout(lrz1, rate=1.0 - self.KEEP_PROB, name='dropout', training=is_training)
with tf.variable_scope("y_layer3"): # layer1 fc [n, 6272], [n, 1024]
con3 = tf.concat([reshape3, dropz1], axis=1)
fc3 = self.fully_connect(con3, 40000+512, 1024, self.SEED)
lr3 = self.leaky_relu(fc3, alpha=0.1)
self.drop3 = tf.layers.dropout(lr3, rate=1.0 - self.KEEP_PROB, name='dropout', training=is_training)
with tf.variable_scope("y_fc_logits"):
self.logits = self.fully_connect(self.drop3, 1024, 1, self.SEED)
return self.drop3, self.logits
make_datasets_TRAIN.py
make_datasets_TRAIN.py
import numpy as np
import os
import glob
import re
import random
#import cv2
from PIL import Image
from keras.preprocessing import image
class Make_datasets_TRAIN():
def __init__(self, filename, true_data, false_data, img_width, img_height, seed):
self.filename = filename
self.true_data = true_data
self.false_data = false_data
self.img_width = img_width
self.img_height = img_height
self.seed = seed
x_train, x_valid_true, x_valid_false, y_train, y_valid_true, y_valid_false = self.read_DATASET(self.filename, self.true_data, self.false_data)
self.train_np = np.concatenate((y_train.reshape(-1,1), x_train), axis=1).astype(np.float32)
self.valid_true_np = np.concatenate((y_valid_true.reshape(-1,1), x_valid_true), axis=1).astype(np.float32)
self.valid_false_np = np.concatenate((y_valid_false.reshape(-1,1), x_valid_false), axis=1).astype(np.float32)
print("self.train_np.shape, ", self.train_np.shape)
print("self.valid_true_np.shape, ", self.valid_true_np.shape)
print("self.valid_false_np.shape, ", self.valid_false_np.shape)
print("np.max(x_train), ", np.max(x_train))
print("np.min(x_train), ", np.min(x_train))
self.valid_data = np.concatenate((self.valid_true_np, self.valid_false_np))
random.seed(self.seed)
np.random.seed(self.seed)
def read_DATASET(self, train_path, true_path, false_path):
train_list = os.listdir(train_path)
y_train = np.ones(len(train_list))
x_train = np.empty((0, self.img_width*self.img_height))
for img in train_list:
path_name = train_path+img
x_img = Image.open(path_name)
#Richten Sie die Größe aus
x_img = x_img.resize((self.img_width, self.img_height))
#Konvertieren Sie 3ch in 1ch
x_img= x_img.convert('L')
# PIL.Image.Vom Bild zum numpy Array
x_img = np.array(x_img)
#Normalisierung
x_img = x_img / 255.0
#Achse hinzufügen
x_img = x_img.reshape((1,self.img_width, self.img_height))
# flatten
x_img = x_img.reshape(1, self.img_width*self.img_height)
x_train = np.concatenate([x_train, x_img], axis = 0)
print("x_train.shape, ", x_train.shape)
print("y_train.shape, ", y_train.shape)
test_true_list = os.listdir(true_path)
y_test_true = np.ones(len(test_true_list))
x_test_true = np.empty((0, self.img_width*self.img_height))
for img in test_true_list:
path_name = true_path+img
x_img = Image.open(path_name)
x_img = x_img.resize((self.img_width, self.img_height))
x_img= x_img.convert('L')
x_img = np.array(x_img)
x_img = x_img / 255.0
x_img = x_img.reshape((1,self.img_width, self.img_height))
x_img = x_img.reshape(1, self.img_width*self.img_height)
x_test_true = np.concatenate([x_test_true, x_img], axis = 0)
print("x_test_true.shape, ", x_test_true.shape)
print("y_test_true.shape, ", y_test_true.shape)
test_false_list = os.listdir(false_path)
y_test_false = np.zeros(len(test_false_list))
x_test_false = np.empty((0, self.img_width*self.img_height))
for img in test_false_list:
path_name = false_path+img
x_img = Image.open(path_name)
x_img = x_img.resize((self.img_width, self.img_height))
x_img= x_img.convert('L')
x_img = np.array(x_img)
x_img = x_img / 255.0
x_img = x_img.reshape((1,self.img_width, self.img_height))
x_img = x_img.reshape(1, self.img_width*self.img_height)
x_test_false = np.concatenate([x_test_false, x_img], axis = 0)
print("x_test_false.shape, ", x_test_false.shape)
print("y_test_false.shape, ", y_test_false.shape)
return x_train, x_test_true, x_test_false, y_train, y_test_true, y_test_false
def get_file_names(self, dir_name):
target_files = []
for root, dirs, files in os.walk(dir_name):
targets = [os.path.join(root, f) for f in files]
target_files.extend(targets)
return target_files
def read_data(self, d_y_np, width, height):
tars = []
images = []
for num, d_y_1 in enumerate(d_y_np):
image = d_y_1[1:].reshape(width, height, 1)
tar = d_y_1[0]
images.append(image)
tars.append(tar)
return np.asarray(images), np.asarray(tars)
def normalize_data(self, data):
# data0_2 = data / 127.5
# data_norm = data0_2 - 1.0
data_norm = (data * 2.0) - 1.0 #applied for tanh
return data_norm
def make_data_for_1_epoch(self):
self.filename_1_epoch = np.random.permutation(self.train_np)
return len(self.filename_1_epoch)
def get_data_for_1_batch(self, i, batchsize):
filename_batch = self.filename_1_epoch[i:i + batchsize]
images, _ = self.read_data(filename_batch, self.img_width, self.img_height)
images_n = self.normalize_data(images)
return images_n
def get_valid_data_for_1_batch(self, i, batchsize):
filename_batch = self.valid_data[i:i + batchsize]
images, tars = self.read_data(filename_batch, self.img_width, self.img_height)
images_n = self.normalize_data(images)
return images_n, tars
def make_random_z_with_norm(self, mean, stddev, data_num, unit_num):
norms = np.random.normal(mean, stddev, (data_num, unit_num))
# tars = np.zeros((data_num, 1), dtype=np.float32)
return norms
def make_target_1_0(self, value, data_num):
if value == 0.0:
target = np.zeros((data_num, 1), dtype=np.float32)
elif value == 1.0:
target = np.ones((data_num, 1), dtype=np.float32)
else:
print("target value error")
return target
utility.py
utility.py
import numpy as np
# import os
from PIL import Image
import matplotlib.pyplot as plt
import sklearn.metrics as sm
import csv
import seaborn as sns
def compute_precision_recall(score_A_np, ):
array_1 = np.where(score_A_np[:, 1] == 1.0)
array_0 = np.where(score_A_np[:, 1] == 0.0)
mean_1 = np.mean((score_A_np[array_1])[:, 0])
mean_0 = np.mean((score_A_np[array_0])[:, 0])
medium = (mean_1 + mean_0) / 2.0
print("mean_positive_score, ", mean_1)
print("mean_negative_score, ", mean_0)
print("score_threshold(pos_neg middle), ", medium)
np.save('./score_threshold.npy', medium)
array_upper = np.where(score_A_np[:, 0] >= medium)[0]
array_lower = np.where(score_A_np[:, 0] < medium)[0]
#print(array_upper)
print("negative_predict_num, ", array_upper.shape)
print("positive_predict_num, ", array_lower.shape)
array_1_tf = np.where(score_A_np[:, 1] == 1.0)[0]
array_0_tf = np.where(score_A_np[:, 1] == 0.0)[0]
#print(array_1_tf)
print("negative_fact_num, ", array_0_tf.shape)
print("positive_fact_num, ", array_1_tf.shape)
tn = len(set(array_lower)&set(array_1_tf))
tp = len(set(array_upper)&set(array_0_tf))
fp = len(set(array_lower)&set(array_0_tf))
fn = len(set(array_upper)&set(array_1_tf))
precision = tp / (tp + fp + 0.00001)
recall = tp / (tp + fn + 0.00001)
return tp, fp, tn, fn, precision, recall
def score_divide(score_A_np):
array_1 = np.where(score_A_np[:, 1] == 1.0)[0]
array_0 = np.where(score_A_np[:, 1] == 0.0)[0]
print("positive_predict_num, ", array_1.shape)
print("negative_predict_num, ", array_0.shape)
array_1_np = score_A_np[array_1][:, 0]
array_0_np = score_A_np[array_0][:, 0]
#print(array_1_np)
#print(array_0_np)
return array_1_np, array_0_np
def save_graph(x, y, filename, epoch):
plt.figure(figsize=(7, 5))
plt.plot(x, y)
plt.title('ROC curve ' + filename + ' epoch:' + str(epoch))
# x axis label
plt.xlabel("FP / (FP + TN)")
# y axis label
plt.ylabel("TP / (TP + FN)")
# save
plt.savefig(filename + '_ROC_curve_epoch' + str(epoch) +'.png')
plt.close()
def make_ROC_graph(score_A_np, filename, epoch):
argsort = np.argsort(score_A_np, axis=0)[:, 0]
value_1_0 = score_A_np[argsort][::-1].astype(np.float32)
#value_1_0 = (np.where(score_A_np_sort[:, 1] == 7., 1., 0.)).astype(np.float32)
# score_A_np_sort_0_1 = np.concatenate((score_A_np_sort, value_1_0), axis=1)
sum_1 = np.sum(value_1_0)
len_s = len(score_A_np)
sum_0 = len_s - sum_1
tp = np.cumsum(value_1_0[:, 1]).astype(np.float32)
index = np.arange(1, len_s + 1, 1).astype(np.float32)
fp = index - tp
fn = sum_1 - tp
tn = sum_0 - fp
tp_ratio = tp / (tp + fn + 0.00001)
fp_ratio = fp / (fp + tn + 0.00001)
save_graph(fp_ratio, tp_ratio, filename, epoch)
auc = sm.auc(fp_ratio, tp_ratio)
return auc
def unnorm_img(img_np):
img_np_255 = (img_np + 1.0) * 127.5
img_np_255_mod1 = np.maximum(img_np_255, 0)
img_np_255_mod1 = np.minimum(img_np_255_mod1, 255)
img_np_uint8 = img_np_255_mod1.astype(np.uint8)
return img_np_uint8
def convert_np2pil(images_255):
list_images_PIL = []
for num, images_255_1 in enumerate(images_255):
# img_255_tile = np.tile(images_255_1, (1, 1, 3))
image_1_PIL = Image.fromarray(images_255_1)
list_images_PIL.append(image_1_PIL)
return list_images_PIL
def make_score_hist(score_a_1, score_a_0, epoch, LOGFILE_NAME, OUT_HIST_DIR):
list_1 = score_a_1.tolist()
list_0 = score_a_0.tolist()
#print(list_1)
#print(list_0)
plt.figure(figsize=(7, 5))
plt.title("Histgram of Score")
plt.xlabel("Score")
plt.ylabel("freq")
plt.hist(list_1, bins=40, alpha=0.3, histtype='stepfilled', color='r', label="1")
plt.hist(list_0, bins=40, alpha=0.3, histtype='stepfilled', color='b', label='0')
plt.legend(loc=1)
plt.savefig(OUT_HIST_DIR + "/resultScoreHist_"+ LOGFILE_NAME + '_' + str(epoch) + ".png ")
plt.show()
def make_score_hist_test(score_a_1, score_a_0, score_th, LOGFILE_NAME, OUT_HIST_DIR):
list_1 = score_a_1.tolist()
list_0 = score_a_0.tolist()
#print(list_1)
#print(list_0)
plt.figure(figsize=(7, 5))
plt.title("Histgram of Score")
plt.xlabel("Score")
plt.ylabel("freq")
plt.hist(list_1, bins=40, alpha=0.3, histtype='stepfilled', color='r', label="1")
plt.hist(list_0, bins=40, alpha=0.3, histtype='stepfilled', color='b', label='0')
plt.legend(loc=1)
plt.savefig(OUT_HIST_DIR + "/resultScoreHist_"+ LOGFILE_NAME + "_test.png ")
plt.show()
def make_score_bar(score_a):
score_a = score_a.tolist()
list_images_PIL = []
for score in score_a:
x="score"
plt.bar(x,score,label=score)
fig, ax = plt.subplots(figsize=(1, 1))
ax.bar(x,score,label=round(score,3))
ax.legend(loc='center', fontsize=12)
fig.canvas.draw()
#im = np.array(fig.canvas.renderer.buffer_rgba()) #matplotlib ist 3.Nach 1
im = np.array(fig.canvas.renderer._renderer)
image_1_PIL = Image.fromarray(im)
list_images_PIL.append(image_1_PIL)
return list_images_PIL
def make_score_bar_predict(score_A_np_tmp):
score_a = score_A_np_tmp.tolist()
list_images_PIL = []
for score in score_a:
x="score"
#plt.bar(x,score[0],label=score)
fig, ax = plt.subplots(figsize=(1, 1))
if score[1]==0:
ax.bar(x,score[0], color='red',label=round(score[0],3))
else:
ax.bar(x,score[0], color='blue',label=round(score[0],3))
ax.legend(loc='center', fontsize=12)
fig.canvas.draw()
#im = np.array(fig.canvas.renderer.buffer_rgba()) #matplotlib ist 3.Nach 1
im = np.array(fig.canvas.renderer._renderer)
image_1_PIL = Image.fromarray(im)
list_images_PIL.append(image_1_PIL)
return list_images_PIL
def make_output_img(img_batch_1, img_batch_0, x_z_x_1, x_z_x_0, score_a_0, score_a_1, epoch, log_file_name, out_img_dir):
(data_num, img1_h, img1_w, _) = img_batch_1.shape
img_batch_1_unn = np.tile(unnorm_img(img_batch_1), (1, 1, 3))
img_batch_0_unn = np.tile(unnorm_img(img_batch_0), (1, 1, 3))
x_z_x_1_unn = np.tile(unnorm_img(x_z_x_1), (1, 1, 3))
x_z_x_0_unn = np.tile(unnorm_img(x_z_x_0), (1, 1, 3))
diff_1 = img_batch_1 - x_z_x_1
diff_1_r = (2.0 * np.maximum(diff_1, 0.0)) - 1.0 #(0.0, 1.0) -> (-1.0, 1.0)
diff_1_b = (2.0 * np.abs(np.minimum(diff_1, 0.0))) - 1.0 #(-1.0, 0.0) -> (1.0, 0.0) -> (1.0, -1.0)
diff_1_g = diff_1_b * 0.0 - 1.0
diff_1_r_unnorm = unnorm_img(diff_1_r)
diff_1_b_unnorm = unnorm_img(diff_1_b)
diff_1_g_unnorm = unnorm_img(diff_1_g)
diff_1_np = np.concatenate((diff_1_r_unnorm, diff_1_g_unnorm, diff_1_b_unnorm), axis=3)
diff_0 = img_batch_0 - x_z_x_0
diff_0_r = (2.0 * np.maximum(diff_0, 0.0)) - 1.0 #(0.0, 1.0) -> (-1.0, 1.0)
diff_0_b = (2.0 * np.abs(np.minimum(diff_0, 0.0))) - 1.0 #(-1.0, 0.0) -> (1.0, 0.0) -> (1.0, -1.0)
diff_0_g = diff_0_b * 0.0 - 1.0
diff_0_r_unnorm = unnorm_img(diff_0_r)
diff_0_b_unnorm = unnorm_img(diff_0_b)
diff_0_g_unnorm = unnorm_img(diff_0_g)
diff_0_np = np.concatenate((diff_0_r_unnorm, diff_0_g_unnorm, diff_0_b_unnorm), axis=3)
img_batch_1_PIL = convert_np2pil(img_batch_1_unn)
img_batch_0_PIL = convert_np2pil(img_batch_0_unn)
x_z_x_1_PIL = convert_np2pil(x_z_x_1_unn)
x_z_x_0_PIL = convert_np2pil(x_z_x_0_unn)
diff_1_PIL = convert_np2pil(diff_1_np)
diff_0_PIL = convert_np2pil(diff_0_np)
score_a_1_PIL = make_score_bar(score_a_1)
score_a_0_PIL = make_score_bar(score_a_0)
wide_image_np = np.ones(((img1_h + 1) * data_num - 1, (img1_w + 1) * 8 - 1, 3), dtype=np.uint8) * 255
wide_image_PIL = Image.fromarray(wide_image_np)
for num, (ori_1, ori_0, xzx1, xzx0, diff1, diff0, score_1, score_0) in enumerate(zip(img_batch_1_PIL, img_batch_0_PIL ,x_z_x_1_PIL, x_z_x_0_PIL, diff_1_PIL, diff_0_PIL, score_a_1_PIL, score_a_0_PIL)):
wide_image_PIL.paste(ori_1, (0, num * (img1_h + 1)))
wide_image_PIL.paste(xzx1, (img1_w + 1, num * (img1_h + 1)))
wide_image_PIL.paste(diff1, ((img1_w + 1) * 2, num * (img1_h + 1)))
wide_image_PIL.paste(score_1, ((img1_w + 1) * 3, num * (img1_h + 1)))
wide_image_PIL.paste(ori_0, ((img1_w + 1) * 4, num * (img1_h + 1)))
wide_image_PIL.paste(xzx0, ((img1_w + 1) * 5, num * (img1_h + 1)))
wide_image_PIL.paste(diff0, ((img1_w + 1) * 6, num * (img1_h + 1)))
wide_image_PIL.paste(score_0, ((img1_w + 1) * 7, num * (img1_h + 1)))
wide_image_PIL.save(out_img_dir + "/resultImage_"+ log_file_name + '_' + str(epoch) + ".png ")
def make_output_img_test(img_batch_test, x_z_x_test, score_A_np_tmp, log_file_name, out_img_dir):
(data_num, img1_h, img1_w, _) = img_batch_test.shape
img_batch_test_unn = np.tile(unnorm_img(img_batch_test), (1, 1, 3))
x_z_x_test_unn = np.tile(unnorm_img(x_z_x_test), (1, 1, 3))
diff_test = img_batch_test - x_z_x_test
diff_test_r = (2.0 * np.maximum(diff_test, 0.0)) - 1.0 #(0.0, 1.0) -> (-1.0, 1.0)
diff_test_b = (2.0 * np.abs(np.minimum(diff_test, 0.0))) - 1.0 #(-1.0, 0.0) -> (1.0, 0.0) -> (1.0, -1.0)
diff_test_g = diff_test_b * 0.0 - 1.0
diff_test_r_unnorm = unnorm_img(diff_test_r)
diff_test_b_unnorm = unnorm_img(diff_test_b)
diff_test_g_unnorm = unnorm_img(diff_test_g)
diff_test_np = np.concatenate((diff_test_r_unnorm, diff_test_g_unnorm, diff_test_b_unnorm), axis=3)
img_batch_test_PIL = convert_np2pil(img_batch_test_unn)
x_z_x_test_PIL = convert_np2pil(x_z_x_test_unn)
diff_test_PIL = convert_np2pil(diff_test_np)
score_a = score_A_np_tmp[:, 1:]
#tars = score_A_np_tmp[:, 0]
score_a_PIL = make_score_bar_predict(score_A_np_tmp)
wide_image_np = np.ones(((img1_h + 1) * data_num - 1, (img1_w + 1) * 8 - 1, 3), dtype=np.uint8) * 255
wide_image_PIL = Image.fromarray(wide_image_np)
for num, (ori_test, xzx_test, diff_test, score_test) in enumerate(zip(img_batch_test_PIL, x_z_x_test_PIL, diff_test_PIL, score_a_PIL)):
wide_image_PIL.paste(ori_test, (0, num * (img1_h + 1)))
wide_image_PIL.paste(xzx_test, (img1_w + 1, num * (img1_h + 1)))
wide_image_PIL.paste(diff_test, ((img1_w + 1) * 2, num * (img1_h + 1)))
wide_image_PIL.paste(score_test, ((img1_w + 1) * 3, num * (img1_h + 1)))
wide_image_PIL.save(out_img_dir + "/resultImage_"+ log_file_name + "_test.png ")
def save_list_to_csv(list, filename):
f = open(filename, 'w')
writer = csv.writer(f, lineterminator='\n')
writer.writerows(list)
f.close()
predict_BiGAN.py
predict_BiGAN.py
import numpy as np
import os
import tensorflow as tf
import utility as Utility
import argparse
import matplotlib.pyplot as plt
from model_BiGAN import BiGAN as Model
from make_datasets_predict import Make_datasets_predict as Make_datasets
def parser():
parser = argparse.ArgumentParser(description='train LSGAN')
parser.add_argument('--batch_size', '-b', type=int, default=300, help='Number of images in each mini-batch')
parser.add_argument('--log_file_name', '-lf', type=str, default='anpanman', help='log file name')
parser.add_argument('--epoch', '-e', type=int, default=1, help='epoch')
#parser.add_argument('--file_train_data', '-ftd', type=str, default='./mnist.npz', help='train data')
#parser.add_argument('--test_true_data', '-ttd', type=str, default='./mnist.npz', help='test of true_data')
#parser.add_argument('--test_false_data', '-tfd', type=str, default='./mnist.npz', help='test of false_data')
parser.add_argument('--test_data', '-td', type=str, default='../Test_Data/200112/', help='test of false_data')
parser.add_argument('--valid_span', '-vs', type=int, default=1, help='validation span')
parser.add_argument('--score_th', '-st', type=float, default=np.load('./score_threshold.npy'), help='validation span')
return parser.parse_args()
args = parser()
#global variants
BATCH_SIZE = args.batch_size
LOGFILE_NAME = args.log_file_name
EPOCH = args.epoch
#FILE_NAME = args.file_train_data
#TRUE_DATA = args.test_true_data
#FALSE_DATA = args.test_false_data
TEST_DATA = args.test_data
IMG_WIDTH = 100
IMG_HEIGHT = 100
IMG_CHANNEL = 1
BASE_CHANNEL = 32
NOISE_UNIT_NUM = 200
NOISE_MEAN = 0.0
NOISE_STDDEV = 1.0
TEST_DATA_SAMPLE = 5 * 5
L2_NORM = 0.001
KEEP_PROB_RATE = 0.5
SEED = 1234
SCORE_ALPHA = 0.9 # using for cost function
VALID_SPAN = args.valid_span
np.random.seed(seed=SEED)
BOARD_DIR_NAME = './tensorboard/' + LOGFILE_NAME
OUT_IMG_DIR = './out_images_BiGAN' #output image file
out_model_dir = './out_models_BiGAN/' #output model_ckpt file
#Load_model_dir = '../model_ckpt/' #Load model_ckpt file
OUT_HIST_DIR = './out_score_hist_BiGAN' #output histogram file
CYCLE_LAMBDA = 1.0
SCORE_TH = args.score_th
make_datasets = Make_datasets(TEST_DATA, IMG_WIDTH, IMG_HEIGHT, SEED)
model = Model(NOISE_UNIT_NUM, IMG_CHANNEL, SEED, BASE_CHANNEL, KEEP_PROB_RATE)
z_ = tf.placeholder(tf.float32, [None, NOISE_UNIT_NUM], name='z_') #noise to generator
x_ = tf.placeholder(tf.float32, [None, IMG_HEIGHT, IMG_WIDTH, IMG_CHANNEL], name='x_') #image to classifier
d_dis_f_ = tf.placeholder(tf.float32, [None, 1], name='d_dis_g_') #target of discriminator related to generator
d_dis_r_ = tf.placeholder(tf.float32, [None, 1], name='d_dis_r_') #target of discriminator related to real image
is_training_ = tf.placeholder(tf.bool, name = 'is_training')
with tf.variable_scope('encoder_model'):
z_enc = model.encoder(x_, reuse=False, is_training=is_training_)
with tf.variable_scope('decoder_model'):
x_dec = model.decoder(z_, reuse=False, is_training=is_training_)
x_z_x = model.decoder(z_enc, reuse=True, is_training=is_training_) # for cycle consistency
with tf.variable_scope('discriminator_model'):
#stream around discriminator
drop3_r, logits_r = model.discriminator(x_, z_enc, reuse=False, is_training=is_training_) #real pair
drop3_f, logits_f = model.discriminator(x_dec, z_, reuse=True, is_training=is_training_) #real pair
drop3_re, logits_re = model.discriminator(x_z_x, z_enc, reuse=True, is_training=is_training_) #fake pair
with tf.name_scope("loss"):
loss_dis_f = tf.reduce_mean(tf.square(logits_f - d_dis_f_), name='Loss_dis_gen') #loss related to generator
loss_dis_r = tf.reduce_mean(tf.square(logits_r - d_dis_r_), name='Loss_dis_rea') #loss related to real image
#total loss
loss_dis_total = loss_dis_f + loss_dis_r
loss_dec_total = loss_dis_f
loss_enc_total = loss_dis_r
with tf.name_scope("score"):
l_g = tf.reduce_mean(tf.abs(x_ - x_z_x), axis=(1,2,3))
l_FM = tf.reduce_mean(tf.abs(drop3_r - drop3_re), axis=1)
score_A = SCORE_ALPHA * l_g + (1.0 - SCORE_ALPHA) * l_FM
with tf.name_scope("optional_loss"):
loss_dec_opt = loss_dec_total + CYCLE_LAMBDA * l_g
loss_enc_opt = loss_enc_total + CYCLE_LAMBDA * l_g
tf.summary.scalar('loss_dis_total', loss_dis_total)
tf.summary.scalar('loss_dec_total', loss_dec_total)
tf.summary.scalar('loss_enc_total', loss_enc_total)
merged = tf.summary.merge_all()
# t_vars = tf.trainable_variables()
dec_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope="decoder")
enc_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope="encoder")
dis_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope="discriminator")
with tf.name_scope("train"):
train_dis = tf.train.AdamOptimizer(learning_rate=0.00005, beta1=0.5).minimize(loss_dis_total, var_list=dis_vars
, name='Adam_dis')
train_dec = tf.train.AdamOptimizer(learning_rate=0.005, beta1=0.5).minimize(loss_dec_total, var_list=dec_vars
, name='Adam_dec')
train_enc = tf.train.AdamOptimizer(learning_rate=0.005, beta1=0.5).minimize(loss_enc_total, var_list=enc_vars
, name='Adam_enc')
train_dec_opt = tf.train.AdamOptimizer(learning_rate=0.005, beta1=0.5).minimize(loss_dec_opt, var_list=dec_vars
, name='Adam_dec')
train_enc_opt = tf.train.AdamOptimizer(learning_rate=0.005, beta1=0.5).minimize(loss_enc_opt, var_list=enc_vars
, name='Adam_enc')
sess = tf.Session()
ckpt = tf.train.get_checkpoint_state(out_model_dir)
saver = tf.train.Saver()
if ckpt: #Wenn es einen Kontrollpunkt gibt
last_model = ckpt.model_checkpoint_path #Pfad zum zuletzt gespeicherten Modell
saver.restore(sess, last_model) #Variable Daten lesen
print("load " + last_model)
else: #Wenn keine gespeicherten Daten vorhanden sind
#init = tf.initialize_all_variables()
sess.run(tf.global_variables_initializer())
summary_writer = tf.summary.FileWriter(BOARD_DIR_NAME, sess.graph)
log_list = []
log_list.append(['epoch', 'AUC'])
#training loop
for epoch in range(1):
if epoch % VALID_SPAN == 0:
score_A_np = np.zeros((0, 2), dtype=np.float32)
val_data_num = len(make_datasets.valid_data)
img_batch_test = make_datasets.get_valid_data_for_1_batch(0, val_data_num)
score_A_ = sess.run(score_A, feed_dict={x_:img_batch_test, is_training_:False})
score_A_re = np.reshape(score_A_, (-1, 1))
tars_batch_re = np.where(score_A_re < SCORE_TH, 1, 0) #np.reshape(tars_batch, (-1, 1))
score_A_np_tmp = np.concatenate((score_A_re, tars_batch_re), axis=1)
x_z_x_test = sess.run(x_z_x, feed_dict={x_:img_batch_test, is_training_:False})
#print(score_A_np_tmp)
array_1_np, array_0_np = Utility.score_divide(score_A_np_tmp)
Utility.make_score_hist_test(array_1_np, array_0_np, SCORE_TH, LOGFILE_NAME, OUT_HIST_DIR)
Utility.make_output_img_test(img_batch_test, x_z_x_test, score_A_np_tmp, LOGFILE_NAME, OUT_IMG_DIR)
make_datasets_predict.py
make_datasets_predict.py
import numpy as np
import os
import glob
import re
import random
#import cv2
from PIL import Image
from keras.preprocessing import image
class Make_datasets_predict():
def __init__(self, test_data, img_width, img_height, seed):
self.filename = test_data
self.img_width = img_width
self.img_height = img_height
self.seed = seed
x_test = self.read_DATASET(self.filename)
self.valid_data = x_test
random.seed(self.seed)
np.random.seed(self.seed)
def read_DATASET(self, test_path):
test_list = os.listdir(test_path)
x_test = np.empty((0, self.img_width*self.img_height))
for img in test_list:
path_name = test_path+img
x_img = Image.open(path_name)
#Richten Sie die Größe aus
x_img = x_img.resize((self.img_width, self.img_height))
#Konvertieren Sie 3ch in 1ch
x_img= x_img.convert('L')
# PIL.Image.Vom Bild zum numpy Array
x_img = np.array(x_img)
#Normalisierung
x_img = x_img / 255.0
#Achse hinzufügen
x_img = x_img.reshape((1,self.img_width, self.img_height))
# flatten
x_img = x_img.reshape(1, self.img_width*self.img_height)
x_test = np.concatenate([x_test, x_img], axis = 0)
print("x_test.shape, ", x_test.shape)
return x_test
def get_file_names(self, dir_name):
target_files = []
for root, dirs, files in os.walk(dir_name):
targets = [os.path.join(root, f) for f in files]
target_files.extend(targets)
return target_files
def divide_MNIST_by_digit(self, train_np, data1_num, data2_num):
data_1 = train_np[train_np[:,0] == data1_num]
data_2 = train_np[train_np[:,0] == data2_num]
return data_1, data_2
def read_data(self, d_y_np, width, height):
#tars = []
images = []
for num, d_y_1 in enumerate(d_y_np):
image = d_y_1.reshape(width, height, 1)
#tar = d_y_1[0]
images.append(image)
#tars.append(tar)
return np.asarray(images)#, np.asarray(tars)
def normalize_data(self, data):
# data0_2 = data / 127.5
# data_norm = data0_2 - 1.0
data_norm = (data * 2.0) - 1.0 #applied for tanh
return data_norm
def make_data_for_1_epoch(self):
self.filename_1_epoch = np.random.permutation(self.train_np)
return len(self.filename_1_epoch)
def get_data_for_1_batch(self, i, batchsize):
filename_batch = self.filename_1_epoch[i:i + batchsize]
images, _ = self.read_data(filename_batch, self.img_width, self.img_height)
images_n = self.normalize_data(images)
return images_n
def get_valid_data_for_1_batch(self, i, batchsize):
filename_batch = self.valid_data[i:i + batchsize]
images = self.read_data(filename_batch, self.img_width, self.img_height)
images_n = self.normalize_data(images)
return images_n#, tars
def make_random_z_with_norm(self, mean, stddev, data_num, unit_num):
norms = np.random.normal(mean, stddev, (data_num, unit_num))
# tars = np.zeros((data_num, 1), dtype=np.float32)
return norms
def make_target_1_0(self, value, data_num):
if value == 0.0:
target = np.zeros((data_num, 1), dtype=np.float32)
elif value == 1.0:
target = np.ones((data_num, 1), dtype=np.float32)
else:
print("target value error")
return target
GitHub-Link
https://github.com/YousukeAnai/Dic_Graduation_Assignment
Artikel, auf die verwiesen wird
https://qiita.com/masataka46/items/49dba2790fa59c29126b https://qiita.com/underfitting/items/a0cbb035568dea33b2d7
Recommended Posts