def processThread(self):
variant = "Original" # TODO Pass as argument
print('Loading data, this may take a while...')
model = PluginLoader.get_model(variant)(self.arguments.model_dir)
model.load(swapped=False)
images_A = get_image_paths(self.arguments.input_A)
images_B = get_image_paths(self.arguments.input_B)
trainer = PluginLoader.get_trainer(variant)(model, images_A, images_B)
try:
print('Starting. Press "Enter" to stop training and save model')
for epoch in range(0, 1000000):
save_iteration = epoch % self.arguments.save_interval == 0
trainer.train_one_step(epoch,
self.show if save_iteration else None)
if save_iteration:
model.save_weights()
if self.stop:
model.save_weights()
exit()
except KeyboardInterrupt:
try:
model.save_weights()
except KeyboardInterrupt:
print('Saving model weights has been cancelled!')
exit(0)
def load_trainer(self, model):
""" Load the trainer requested for training """
images_a, images_b = self.images
trainer = PluginLoader.get_trainer(self.trainer_name)
trainer = trainer(model, images_a, images_b, self.args.batch_size,
self.args.perceptual_loss)
return trainer
def load_trainer(self, model):
""" Load the trainer requested for training """
images_a, images_b = self.images
trainer = PluginLoader.get_trainer(self.trainer_name)
trainer = trainer(model,
images_a,
images_b,
self.args.batch_size,
self.args.perceptual_loss)
return trainer
def processThread(self):
try:
if self.arguments.allow_growth:
self.set_tf_allow_growth()
print('Loading data, this may take a while...')
# this is so that you can enter case insensitive values for trainer
trainer = self.arguments.trainer
trainer = "LowMem" if trainer.lower() == "lowmem" else trainer
model = PluginLoader.get_model(trainer)(get_folder(
self.arguments.model_dir))
model.load(swapped=False)
images_A = get_image_paths(self.arguments.input_A)
images_B = get_image_paths(self.arguments.input_B)
trainer = PluginLoader.get_trainer(trainer)
trainer = trainer(model, images_A, images_B,
self.arguments.batch_size,
self.arguments.perceptual_loss)
print('Starting. Press "Enter" to stop training and save model')
for epoch in range(0, self.arguments.epochs):
save_iteration = epoch % self.arguments.save_interval == 0
trainer.train_one_step(
epoch, self.show if
(save_iteration or self.save_now) else None,
self.arguments.save_interval)
if save_iteration:
model.save_weights()
if self.stop:
model.save_weights()
exit()
if self.save_now:
model.save_weights()
self.save_now = False
except KeyboardInterrupt:
try:
model.save_weights()
except KeyboardInterrupt:
print('Saving model weights has been cancelled!')
exit(0)
except Exception as e:
print(e)
exit(1)
def processThread(self):
print("Loading Data..! This may take a while")
trainer = self.arguments.trainer
trainer = "LowMem" if trainer.lower() == "lowmem" else trainer
model = PluginLoader.get_model(trainer)(get_folder(
self.arguments.model_dir))
model.load(swapped=False)
images_A = get_image_paths(self.arguments.input_A)
images_B = get_image_paths(self.arguments.input_B)
trainer = PluginLoader.get_trainer(trainer)
trainer = trainer(model,
images_A,
images_B,
batch_size=self.arguments.batch_size)
try:
print("Starting. Press Enter to stop Training and Save model")
for epoch in range(0, 100000):
save_iteration = epoch % self.arguments.save_interval == 0
trainer.train_one_step(
epoch, self.show if
(save_iteration or self.save_now) else None)
if save_iteration:
model.save_weights()
if self.stop:
model.save_weights()
exit()
if self.save_now:
model.save_weights()
self.save_now = False
except KeyboardInterrupt:
try:
model.save_weights()
except KeyboardInterrupt:
print("Saving model weights has been cancelled...!")
exit(0)
def processThread(self):
if self.arguments.allow_growth:
self.set_tf_allow_growth()
print('Loading data, this may take a while...')
# this is so that you can enter case insensitive values for trainer
trainer = self.arguments.trainer
trainer = "LowMem" if trainer.lower() == "lowmem" else trainer
model = PluginLoader.get_model(trainer)(get_folder(self.arguments.model_dir))
model.load(swapped=False)
images_A = get_image_paths(self.arguments.input_A)
images_B = get_image_paths(self.arguments.input_B)
trainer = PluginLoader.get_trainer(trainer)
trainer = trainer(model, images_A, images_B, batch_size=self.arguments.batch_size)
try:
print('Starting. Press "Enter" to stop training and save model')
for epoch in range(0, self.arguments.epochs):
save_iteration = epoch % self.arguments.save_interval == 0
trainer.train_one_step(epoch, self.show if (save_iteration or self.save_now) else None)
if save_iteration:
model.save_weights()
if self.stop:
model.save_weights()
exit()
if self.save_now:
model.save_weights()
self.save_now = False
except KeyboardInterrupt:
try:
model.save_weights()
except KeyboardInterrupt:
print('Saving model weights has been cancelled!')
exit(0)
except Exception as e:
print(e)
exit(1)
def processThread(self):
print('Loading data, this may take a while...')
# this is so that you can enter case insensitive values for trainer
trainer = self.arguments.trainer
trainer = trainer if trainer != "Lowmem" else "LowMem"
model = PluginLoader.get_model(trainer)(self.arguments.model_dir)
model.load(swapped=False)
images_A = get_image_paths(self.arguments.input_A)
images_B = get_image_paths(self.arguments.input_B)
trainer = PluginLoader.get_trainer(trainer)(
model, images_A, images_B, batch_size=self.arguments.batch_size)
try:
print('Starting. Press "Enter" to stop training and save model')
for epoch in range(0, 1000000):
save_iteration = epoch % self.arguments.save_interval == 0
trainer.train_one_step(
epoch, self.show if
(save_iteration or self.save_now) else None)
if save_iteration:
model.save_weights()
if self.stop:
model.save_weights()
exit()
if self.save_now:
model.save_weights()
self.save_now = False
except KeyboardInterrupt:
try:
model.save_weights()
except KeyboardInterrupt:
print('Saving model weights has been cancelled!')
exit(0)
