def fit(self, batch_size=128, nb_epochs=100, save_history=True, history_fn="Model History.txt") -> Model:
"""
Standard method to train any of the models.
"""
samples_per_epoch = img_utils.image_count()
val_count = img_utils.val_image_count()
if self.model == None: self.create_model(batch_size=batch_size)
callback_list = [callbacks.ModelCheckpoint(self.weight_path, monitor='val_PSNRLoss', save_best_only=True,
mode='max', save_weights_only=True, verbose=2)]
if save_history:
callback_list.append(HistoryCheckpoint(history_fn))
if K.backend() == 'tensorflow':
log_dir = './%s_logs/' % self.model_name
tensorboard = TensorBoardBatch(log_dir, batch_size=batch_size)
callback_list.append(tensorboard)
print("Training model : %s" % (self.__class__.__name__))
self.model.fit_generator(img_utils.image_generator(train_path, scale_factor=self.scale_factor,
small_train_images=self.type_true_upscaling,
batch_size=batch_size),
steps_per_epoch=samples_per_epoch // batch_size + 1,
epochs=nb_epochs, callbacks=callback_list,
validation_data=img_utils.image_generator(validation_path,
scale_factor=self.scale_factor,
small_train_images=self.type_true_upscaling,
batch_size=batch_size),
validation_steps=val_count // batch_size + 1)
return self.model
def fit(self, batch_size=128, nb_epochs=100, save_history=True, history_fn="Model History.txt") -> Model:
"""
Standard method to train any of the models.
"""
samples_per_epoch = img_utils.image_count()
val_count = img_utils.val_image_count()
if self.model == None: self.create_model(batch_size=batch_size)
callback_list = [callbacks.ModelCheckpoint(self.weight_path, monitor='val_PSNRLoss', save_best_only=True,
mode='max', save_weights_only=True)]
if save_history: callback_list.append(HistoryCheckpoint(history_fn))
print("Training model : %s" % (self.__class__.__name__))
self.model.fit_generator(img_utils.image_generator(train_path, scale_factor=self.scale_factor,
small_train_images=self.type_true_upscaling,
batch_size=batch_size),
samples_per_epoch=samples_per_epoch,
nb_epoch=nb_epochs, callbacks=callback_list,
validation_data=img_utils.image_generator(validation_path,
scale_factor=self.scale_factor,
small_train_images=self.type_true_upscaling,
batch_size=batch_size),
nb_val_samples=val_count)
return self.model
def fit(self, scale_factor, weight_fn, batch_size=128, nb_epochs=100, small_train_images=False,
save_history=True, history_fn="Model History.txt") -> Model:
"""
Standard method to train any of the models.
"""
samples_per_epoch = img_utils.image_count()
val_count = img_utils.val_image_count()
if self.model == None: self.create_model(batch_size=batch_size)
callback_list = [callbacks.ModelCheckpoint(weight_fn, monitor='val_PSNRLoss', save_best_only=True, mode='max', save_weights_only=True),]
if save_history: callback_list.append(HistoryCheckpoint(history_fn))
print("Training model : %s" % (self.__class__.__name__))
self.model.fit_generator(img_utils.image_generator(train_path, scale_factor=scale_factor,
small_train_images=small_train_images,
batch_size=batch_size),
samples_per_epoch=samples_per_epoch,
nb_epoch=nb_epochs, callbacks=callback_list,
validation_data=img_utils.image_generator(validation_path,
scale_factor=scale_factor,
small_train_images=small_train_images,
batch_size=batch_size),
nb_val_samples=val_count)
return self.model
def fit(self, nb_pretrain_samples=5000, batch_size=128, nb_epochs=100, disc_train_flip=0.1,
save_history=True, history_fn="GAN SRCNN History.txt"):
samples_per_epoch = img_utils.image_count()
meanaxis = (0, 2, 3) if K.image_dim_ordering() == 'th' else (0, 1, 2)
if self.model == None: self.create_model(mode='train', batch_size=batch_size)
if os.path.exists(self.gen_weight_path) and os.path.exists(self.disc_weight_path):
self.gen_model.load_weights(self.gen_weight_path)
self.disc_model.load_weights(self.disc_weight_path)
print("Pre-trained Generator and Discriminator network weights loaded")
else:
nb_train_samples = nb_pretrain_samples
print('Pre-training on %d images' % (nb_train_samples))
batchX, batchY = next(img_utils.image_generator(train_path, scale_factor=self.scale_factor,
small_train_images=self.type_true_upscaling,
batch_size=nb_train_samples))
# [-1, 1] scale conversion from [0, 1]
batchX = ((batchX * 255) - 127.5) / 127.5
batchY = ((batchY * 255) - 127.5) / 127.5
print("Pre-training Generator network")
hist = self.gen_model.fit(batchX, batchY, batch_size, nb_epoch=200, verbose=2)
print("Generator pretrain final PSNR : ", hist.history['PSNRLoss'][-1])
print("Pre-training Discriminator network")
genX = self.gen_model.predict(batchX, batch_size=batch_size)
print('GenX Output mean (per channel) :', np.mean(genX, axis=meanaxis))
print('BatchX mean (per channel) :', np.mean(batchX, axis=meanaxis))
X = np.concatenate((genX, batchX))
# Using soft and noisy labels
if np.random.uniform() > disc_train_flip:
# give correct classifications
y = [0] * nb_train_samples + [1] * nb_train_samples
else:
# give wrong classifications (noisy labels)
y = [1] * nb_train_samples + [0] * nb_train_samples
y = np.asarray(y, dtype=np.float32).reshape(-1, 1)
y = to_categorical(y, nb_classes=2)
y = img_utils.smooth_gan_labels(y)
hist = self.disc_model.fit(X, y, batch_size=batch_size,
nb_epoch=1, verbose=0)
print('Discriminator History :', hist.history)
print()
self.gen_model.save_weights(self.gen_weight_path, overwrite=True)
self.disc_model.save_weights(self.disc_weight_path, overwrite=True)
iteration = 0
save_index = 1
print("Training full model : %s" % (self.__class__.__name__))
for i in range(nb_epochs):
print("Epoch : %d" % (i + 1))
print()
for x, _ in img_utils.image_generator(train_path, scale_factor=self.scale_factor,
small_train_images=self.type_true_upscaling, batch_size=batch_size):
t1 = time.time()
x = ((x * 255) - 127.5) / 127.5
X_pred = self.gen_model.predict(x, batch_size)
print("Input batchX mean (per channel) :", np.mean(x, axis=meanaxis))
print("X_pred mean (per channel) :", np.mean(X_pred, axis=meanaxis))
X = np.concatenate((X_pred, x))
# Using soft and noisy labels
if np.random.uniform() > disc_train_flip:
# give correct classifications
y_disc = [0] * nb_train_samples + [1] * nb_train_samples
else:
# give wrong classifications (noisy labels)
y_disc = [1] * nb_train_samples + [0] * nb_train_samples
y_disc = np.asarray(y_disc, dtype=np.float32).reshape(-1, 1)
y_disc = to_categorical(y_disc, nb_classes=2)
y_disc = img_utils.smooth_gan_labels(y_disc)
hist = self.disc_model.fit(X, y_disc, verbose=0, batch_size=batch_size, nb_epoch=1)
discriminator_loss = hist.history['loss'][0]
discriminator_acc = hist.history['acc'][0]
# Using soft labels
y_model = [1] * nb_train_samples
y_model = np.asarray(y_model, dtype=np.int).reshape(-1, 1)
y_model = to_categorical(y_model, nb_classes=2)
y_model = img_utils.smooth_gan_labels(y_model)
hist = self.model.fit(x, y_model, batch_size, nb_epoch=1, verbose=0)
generative_loss = hist.history['loss'][0]
iteration += batch_size
save_index += 1
t2 = time.time()
print("Iter : %d / %d | Time required : %0.2f seconds | Discriminator Loss / Acc : %0.6f / %0.3f | "
"Generative Loss : %0.6f" % (iteration, samples_per_epoch, t2 - t1,
discriminator_loss, discriminator_acc, generative_loss))
# Validate at end of epoch
if iteration >= samples_per_epoch:
print("Evaluating generator model...")
# losses = self.gen_model.evaluate_generator(generator=img_utils.image_generator(train_path,
# scale_factor=self.scale_factor,
# small_train_images=self.type_true_upscaling,
# batch_size=batch_size),
# val_samples=samples_per_epoch)
#
# print('Generator Loss (PSNR):', losses[-1])
self.evaluate('val_images/')
# Save weights every 100 iterations
if save_index % 100 == 0:
print("Saving generator weights")
self.gen_model.save_weights(self.weight_path, overwrite=True)
if iteration >= samples_per_epoch:
break
iteration = 0
save_index = 1
return self.model
student_output_tensor)) # l2 norm of difference
joint_model.add_loss(l2_weight * teacher_student_loss)
# perceptual loss
with K.name_scope('perceptual_loss'):
perceptual_weight = 2.
perceptual_loss = K.sum(
K.square(
gram_matrix(teacher_output_tensor) -
gram_matrix(student_output_tensor)))
joint_model.add_loss(perceptual_weight * perceptual_loss)
joint_model.compile(optimizer='adam', loss=zero_loss)
# train student model using teacher model
samples_per_epoch = img_utils.image_count()
val_count = img_utils.val_image_count()
weight_path = 'weights/joint_model (%s) %dX.h5' % (teacher_model.model_name,
scale_factor)
history_fn = 'Joint_model_training.txt'
train_path = img_utils.output_path
validation_path = img_utils.validation_output_path
path_X = img_utils.output_path + "X/"
path_Y = img_utils.output_path + "y/"
callback_list = [
ModelCheckpoint(weight_path,
monitor='val_loss',
save_best_only=True,
