def process(self):
images_A = get_image_paths(self.arguments.input_A)
images_B = get_image_paths(self.arguments.input_B)
images_A = load_images(images_A) / 255.0
images_B = load_images(images_B) / 255.0
images_A += images_B.mean(axis=(0, 1, 2)) - \
images_A.mean(axis=(0, 1, 2))
print('press "q" to stop training and save model')
BATCH_SIZE = 64
for epoch in range(1000000):
warped_A, target_A = get_training_data(images_A, BATCH_SIZE)
warped_B, target_B = get_training_data(images_B, BATCH_SIZE)
loss_A = autoencoder_A.train_on_batch(warped_A, target_A)
loss_B = autoencoder_B.train_on_batch(warped_B, target_B)
print(loss_A, loss_B)
if epoch % 100 == 0:
self.save_model_weights()
self.show_sample(target_A[0:14], target_B[0:14])
key = cv2.waitKey(1)
if key == ord('q'):
self.save_model_weights()
exit()
def process(self):
print('Starting, this may take a while...')
images_A = get_image_paths(self.arguments.input_A)
images_B = get_image_paths(self.arguments.input_B)
images_A = load_images(images_A) / 255.0
images_B = load_images(images_B) / 255.0
images_A += images_B.mean(axis=(0, 1, 2)) - images_A.mean(axis=(0, 1,
2))
print('press "q" to stop training and save model')
BATCH_SIZE = 64
for epoch in range(1000000):
if self.arguments.verbose:
print("Iteration number {}".format(epoch + 1))
start_time = time.time()
warped_A, target_A = get_training_data(images_A, BATCH_SIZE)
warped_B, target_B = get_training_data(images_B, BATCH_SIZE)
loss_A = autoencoder_A.train_on_batch(warped_A, target_A)
loss_B = autoencoder_B.train_on_batch(warped_B, target_B)
print(loss_A, loss_B)
if epoch % self.arguments.save_interval == 0:
self.save_model_weights()
self.show_sample(target_A[0:14], target_B[0:14])
key = cv2.waitKey(1)
if key == ord('q'):
self.save_model_weights()
exit()
if self.arguments.verbose:
end_time = time.time()
time_elapsed = int(round((end_time - start_time)))
m, s = divmod(time_elapsed, 60)
h, m = divmod(m, 60)
print("Iteration done in {:02d}h{:02d}m{:02d}s".format(
h, m, s))
cv2.imwrite( '_sample.jpg', figure )
images_A = get_image_paths( 'data/trump' )
images_B = get_image_paths( 'data/cage' )
images_A = load_images( images_A ) / 255.0
images_B = load_images( images_B ) / 255.0
images_A += images_B.mean( axis=(0,1,2) ) - images_A.mean( axis=(0,1,2) )
print( 'press "q" to stop training and save model' )
BATCH_SIZE = 64
for epoch in range(1000000):
warped_A, target_A = get_training_data( images_A, BATCH_SIZE )
warped_B, target_B = get_training_data( images_B, BATCH_SIZE )
loss_A = autoencoder_A.train_on_batch( warped_A, target_A )
loss_B = autoencoder_B.train_on_batch( warped_B, target_B )
print( loss_A, loss_B )
if epoch % 100 == 0:
save_model_weights()
show_sample(target_A[0:14], target_B[0:14])
key = cv2.waitKey(1)
if key == ord('q'):
save_model_weights()
exit()