def Train(Odir, TAdir, Mdir, ep, sv):
imgsA = loadImgs(Odir) / 255.0
imgsB = loadImgs(TAdir) / 255.0
imgsA += imgsB.mean(axis=(0, 1, 2)) - imgsA.mean(axis=(0, 1, 2))
try:
encoder.load_weights(Mdir + "/encoder.h5")
decoder_A.load_weights(Mdir + "/decoder_A.h5")
decoder_B.load_weights(Mdir + "/decoder_B.h5")
print("loaded existing model")
except:
print("No existing model")
for epoch in range(int(ep)):
# get next training batch
batch_size = 64
warped_A, target_A = get_training_data(imgsA, batch_size)
warped_B, target_B = get_training_data(imgsB, batch_size)
# train and calculate loss
loss_A = autoencoder_A.train_on_batch(warped_A, target_A)
loss_B = autoencoder_B.train_on_batch(warped_B, target_B)
if epoch % int(sv) == 0:
print("Training loss " + str(epoch) + " :")
print(loss_A, loss_B)
# save model every 100 steps
save_model_weights(Mdir)
test_A = target_A[0:14]
test_B = target_B[0:14]
# create image and write to disk
# save our model after training has finished
save_model_weights(Mdir)
while 1:
pbar = tqdm(range(1000000))
for epoch in pbar:
warped_A, target_A, mask_A = get_training_data( images_A, landmarks_A,landmarks_B, batch_size )
warped_B, target_B, mask_B = get_training_data( images_B, landmarks_B,landmarks_A, batch_size )
omask = numpy.ones((target_A.shape[0],64,64,1),float)
loss_A = autoencoder_A.train_on_batch([warped_A,mask_A], [target_A,mask_A])
loss_B = autoencoder_B.train_on_batch([warped_B,mask_B], [target_B,mask_B])
pbar.set_description("Loss A [{}] Loss B [{}]".format(loss_A,loss_B))
if epoch % 100 == 0:
save_model_weights()
test_A = target_A[0:8,:,:,:3]
test_B = target_B[0:8,:,:,:3]
test_A_i = []
test_B_i = []
for i in test_A:
images_A = get_image_paths( "/input/data/data/trump" )
images_B = get_image_paths( "/input/data/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" )
for epoch in range(1000000):
batch_size = 64
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()
test_A = target_A[0:14]
test_B = target_B[0:14]
figure_A = numpy.stack([
test_A,
autoencoder_A.predict( test_A ),
autoencoder_B.predict( test_A ),
], axis=1 )
figure_B = numpy.stack([
test_B,
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" )
for epoch in range(1000000):
batch_size = 64
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()
test_A = target_A[0:14]
test_B = target_B[0:14]
figure_A = numpy.stack([
test_A,
autoencoder_A.predict( test_A ),
autoencoder_B.predict( test_A ),
], axis=1 )
figure_B = numpy.stack([
test_B,