def train(self):
#Train Alternating
if random() < mixed_prob:
style = mixedList(BATCH_SIZE)
else:
style = noiseList(BATCH_SIZE)
a, b, c = self.train_step(
self.im.get_batch(BATCH_SIZE).astype('float32'), style,
nImage(BATCH_SIZE), self.gp_weight)
if self.GAN.steps % 10 == 0:
self.GAN.EMA()
if self.GAN.steps <= 10000 and self.GAN.steps % 1000 == 2:
self.GAN.MAinit()
new_weight = 5 / (np.array(c) + 1e-7)
self.gp_weight = self.gp_weight[0] * 0.9 + 0.1 * new_weight
self.gp_weight = np.clip([self.gp_weight] * BATCH_SIZE, 0.01,
10000.0).astype('float32')
#Print info
if self.GAN.steps % 100 == 0 and not self.silent:
print("\n\nRound " + str(self.GAN.steps) + ":")
print("D:", np.array(a))
print("G:", np.array(b))
print("GP:", self.gp_weight[0])
s = round((time.clock() - self.lastblip), 4)
self.lastblip = time.clock()
steps_per_second = 100 / s
steps_per_minute = steps_per_second * 60
steps_per_hour = steps_per_minute * 60
print("Steps/Second: " + str(round(steps_per_second, 2)))
print("Steps/Hour: " + str(round(steps_per_hour)))
min1k = floor(1000 / steps_per_minute)
sec1k = floor(1000 / steps_per_second) % 60
print("1k Steps: " + str(min1k) + ":" + str(sec1k))
steps_left = 200000 - self.GAN.steps + 1e-7
hours_left = steps_left // steps_per_hour
minutes_left = (steps_left // steps_per_minute) % 60
print("Til Completion: " + str(int(hours_left)) + "h" +
str(int(minutes_left)) + "m")
print()
#Save Model
if self.GAN.steps % 500 == 0:
self.save(floor(self.GAN.steps / 10000))
if self.GAN.steps % 1000 == 0 or (self.GAN.steps % 100 == 0
and self.GAN.steps < 1000):
self.evaluate(floor(self.GAN.steps / 1000))
printProgressBar(self.GAN.steps % 100, 99, decimals=0)
self.GAN.steps = self.GAN.steps + 1
def train(self, directory=''):
if self.im is None:
# Data generator (my own code, not from TF 2.0)
self.im = dataGenerator(directory, im_size, flip=True)
# Train Alternating
if random() < mixed_prob:
style = mixedList(BATCH_SIZE)
else:
style = noiseList(BATCH_SIZE)
# Apply penalties every 16 steps
apply_gradient_penalty = self.GAN.steps % 2 == 0 or self.GAN.steps < 10000 # noqa
apply_path_penalty = self.GAN.steps % 16 == 0
a, b, c, d = self.train_step(
self.im.get_batch(BATCH_SIZE).astype('float32'), style,
nImage(BATCH_SIZE), apply_gradient_penalty, apply_path_penalty)
# Adjust path length penalty mean
# d = pl_mean when no penalty is applied
if self.pl_mean == 0:
self.pl_mean = np.mean(d)
self.pl_mean = 0.99 * self.pl_mean + 0.01 * np.mean(d)
if self.GAN.steps % 10 == 0 and self.GAN.steps > 20000:
self.GAN.EMA()
if self.GAN.steps <= 25000 and self.GAN.steps % 1000 == 2:
self.GAN.MAinit()
if np.isnan(a):
print("NaN Value Error.")
exit()
# Print info
if self.GAN.steps % 100 == 0 and not self.silent:
print("\n\nRound " + str(self.GAN.steps) + ":")
print("D:", np.array(a))
print("G:", np.array(b))
print("PL:", self.pl_mean)
s = round((time.clock() - self.lastblip), 4)
self.lastblip = time.clock()
steps_per_second = 100 / s
steps_per_minute = steps_per_second * 60
steps_per_hour = steps_per_minute * 60
print("Steps/Second: " + str(round(steps_per_second, 2)))
print("Steps/Hour: " + str(round(steps_per_hour)))
min1k = floor(1000 / steps_per_minute)
sec1k = floor(1000 / steps_per_second) % 60
print("1k Steps: " + str(min1k) + ":" + str(sec1k))
steps_left = 200000 - self.GAN.steps + 1e-7
hours_left = steps_left // steps_per_hour
minutes_left = (steps_left // steps_per_minute) % 60
print("Til Completion: " + str(int(hours_left)) + "h" +
str(int(minutes_left)) + "m")
print()
# Save Model
if self.GAN.steps % 500 == 0:
self.save(floor(self.GAN.steps / 10000))
if self.GAN.steps % 1000 == 0 or (self.GAN.steps % 100 == 0
and self.GAN.steps < 2500):
self.evaluate(floor(self.GAN.steps / 1000))
printProgressBar(self.GAN.steps % 100, 99, decimals=0)
self.GAN.steps = self.GAN.steps + 1
def train(self):
source_class = 0
target_class = 1
source_images = self.im[source_class].get_batch(BATCH_SIZE).astype('float32')
target_images = self.im[target_class].get_batch(BATCH_SIZE).astype('float32')
apply_gradient_penalty = self.GAN.steps % 4 == 0
a, b, c = self.train_step(source_images, target_images, apply_gradient_penalty)
#Adjust path length penalty mean
#d = pl_mean when no penalty is applied
if self.GAN.steps % 10 == 0 and self.GAN.steps > 20000:
self.GAN.EMA()
if self.GAN.steps <= 25000 and self.GAN.steps % 1000 == 2:
self.GAN.MAinit()
if np.isnan(a):
print("NaN Value Error.")
exit()
#Print info
if self.GAN.steps % 100 == 0 or self.GAN.steps < 10 and not self.silent:
print("\n\nRound " + str(self.GAN.steps) + ":")
print("D:", np.array(a))
print("G:", np.array(b))
print("R:", np.array(c))
s = round((time.clock() - self.lastblip), 4)
self.lastblip = time.clock()
steps_per_second = 100 / s
steps_per_minute = steps_per_second * 60
steps_per_hour = steps_per_minute * 60
print("Steps/Second: " + str(round(steps_per_second, 2)))
print("Steps/Hour: " + str(round(steps_per_hour)))
min1k = floor(1000/steps_per_minute)
sec1k = floor(1000/steps_per_second) % 60
print("1k Steps: " + str(min1k) + ":" + str(sec1k))
steps_left = 200000 - self.GAN.steps + 1e-7
hours_left = steps_left // steps_per_hour
minutes_left = (steps_left // steps_per_minute) % 60
print("Til Completion: " + str(int(hours_left)) + "h" + str(int(minutes_left)) + "m")
print()
#Save Model
if self.GAN.steps % 500 == 0:
self.save(floor(self.GAN.steps / 10000))
if self.GAN.steps % 1000 == 0 or (self.GAN.steps % 100 == 0 and self.GAN.steps < 2500):
self.evaluate(floor(self.GAN.steps / 1000))
printProgressBar(self.GAN.steps % 100, 99, decimals = 0)
self.GAN.steps = self.GAN.steps + 1
