def train_gen_zclass(generator_model, DCGAN_model, zclass_model, disc_type,
deterministic, noise_dim, noise_scale, batch_size, l_gen,
l_zclass, X_source, Y_source, n_classes):
X_gen = data_utils.sample_noise(noise_scale, batch_size, noise_dim)
X_source_batch2, Y_source_batch2, idx_source_batch2 = next(
data_utils.gen_batch(X_source, Y_source, batch_size))
if disc_type == "simple_disc":
gen_loss = DCGAN_model.train_on_batch(
[X_gen, X_source_batch2],
np.ones(X_gen.shape[0])) # TRYING SAME BATCH OF DISC
elif disc_type == ("nclass_disc"):
#(disc_p, class_p) = DCGAN_model.predict_on_batch(X_source_batch2)
#idx = np.argmax(class_p, axis=1)
#virtual_labels = (idx[:, None] == np.arange(n_classes)) * 1
virtual_labels = np.zeros([GAN.batch_size, GAN.n_classes])
gen_loss = DCGAN_model.train_on_batch(
[X_gen, X_source_batch2],
[np.ones(X_gen.shape[0]), virtual_labels]) # FIX :((
#gen_loss = gen_loss[0]
l_gen.appendleft(gen_loss)
if not deterministic:
zclass_loss = zclass_model.train_on_batch([X_gen, X_source_batch2],
[X_gen])
else:
zclass_loss = 0.0
l_zclass.appendleft(zclass_loss)
return l_gen, l_zclass
def visualize_save_stuffs(GANs, progbar, gen_iterations, batch_counter, n_batch_per_epoch, l_disc_real1, l_disc_gen1, l_gen_loss1,
l_class_loss1, l_disc_real2, l_disc_gen2, l_gen_loss2,l_class_loss2, A_data, A_labels, B_data, B_labels,start,e):
gen_iterations += 1
batch_counter += 1
image_dim_ordering = 'th'
#code.interact(local=locals())
progbar.add(GANs[0].batch_size, values=[("Loss_D_real1", np.mean(l_disc_real1)),
("Loss_D_gen1", np.mean(l_disc_gen1)),
("Loss_G1", np.mean(l_gen_loss1)),
("Loss_Classifier1",np.mean(l_class_loss1)),
("Loss_D_real2", np.mean(l_disc_real2)),
("Loss_D_gen2", np.mean(l_disc_gen2)),
("Loss_G2", np.mean(l_gen_loss2)),
("Loss_Classifier2",np.mean(l_class_loss2))
])
for GAN in GANs:
# plot images 1 times per epochs if GAN.dir == 'BtoA':
if GAN.dir == 'BtoA':
X_source=B_data
Y_source=B_labels
X_dest = A_data
Y_dest = A_labels
elif GAN.dir == 'AtoB':
X_source=A_data
Y_source=A_labels
X_dest = B_data
Y_dest = B_labels
if batch_counter == n_batch_per_epoch:
#if batch_counter % (n_batch_per_epoch) == 0:
X_source_batch_plot, Y_source_batch_plot, idx_source_plot = next(data_utils.gen_batch(X_source, Y_source, batch_size=GAN.batch_size))
returned_idx = data_utils.plot_generated_batch(X_dest, X_source, GAN.generator_model, GAN.noise_dim, image_dim_ordering, idx_source_plot,
batch_size=GAN.batch_size,different_idx=True, datagen=GAN.datagen, data_aug=GAN.data_aug)
print ("Dest labels:")
print (Y_dest[returned_idx].argmax(1))
print ("Source labels:")
print (Y_source_batch_plot.argmax(1))
print('\nEpoch %s, Time: %s' % (e + 1, time.time() - start))
else:
idx_source_plot = 0
Y_source_batch_plot = 0
#Save model weights (by default, every 5 epochs)
if batch_counter == n_batch_per_epoch:
data_utils.save_model_weights(GAN.generator_model, GAN.discriminator_model,
GAN.DCGAN_model, e, GAN.name, GAN.classificator_model, discriminator2=GAN.discriminator2)
return batch_counter, gen_iterations
def train(**kwargs):
"""
Train standard DCGAN model
args: **kwargs (dict) keyword arguments that specify the model hyperparameters
"""
# Roll out the parameters
generator = kwargs["generator"]
discriminator = kwargs["discriminator"]
dset = kwargs["dset"]
img_dim = kwargs["img_dim"]
nb_epoch = kwargs["nb_epoch"]
batch_size = kwargs["batch_size"]
n_batch_per_epoch = kwargs["n_batch_per_epoch"]
bn_mode = kwargs["bn_mode"]
noise_dim = kwargs["noise_dim"]
noise_scale = kwargs["noise_scale"]
lr_D = kwargs["lr_D"]
lr_G = kwargs["lr_G"]
opt_D = kwargs["opt_D"]
opt_G = kwargs["opt_G"]
use_mbd = kwargs["use_mbd"]
clamp_lower = kwargs["clamp_lower"]
clamp_upper = kwargs["clamp_upper"]
image_dim_ordering = kwargs["image_dim_ordering"]
epoch_size = n_batch_per_epoch * batch_size
deterministic = kwargs["deterministic"]
inject_noise = kwargs["inject_noise"]
model = kwargs["model"]
no_supertrain = kwargs["no_supertrain"]
pureGAN = kwargs["pureGAN"]
lsmooth = kwargs["lsmooth"]
simple_disc = kwargs["simple_disc"]
resume = kwargs["resume"]
name = kwargs["name"]
wd = kwargs["wd"]
history_size = kwargs["history_size"]
monsterClass = kwargs["monsterClass"]
print("\nExperiment parameters:")
for key in kwargs.keys():
print key, kwargs[key]
print("\n")
# Setup environment (logging directory etc)
general_utils.setup_logging("DCGAN")
# Load and normalize data
if dset == "mnistM":
X_source_train, Y_source_train, X_source_test, Y_source_test, n_classes1 = data_utils.load_image_dataset(
img_dim, image_dim_ordering, dset='mnist')
X_dest_train, Y_dest_train, X_dest_test, Y_dest_test, n_classes2 = data_utils.load_image_dataset(
img_dim, image_dim_ordering, dset='mnistM')
#code.interact(local=locals())
elif dset == "washington_vandal50k":
X_source_train = data_utils.load_image_dataset(img_dim,
image_dim_ordering,
dset='washington')
X_dest_train = data_utils.load_image_dataset(img_dim,
image_dim_ordering,
dset='vandal50k')
elif dset == "washington_vandal12classes":
X_source_train = data_utils.load_image_dataset(
img_dim, image_dim_ordering, dset='washington12classes')
X_dest_train = data_utils.load_image_dataset(img_dim,
image_dim_ordering,
dset='vandal12classes')
elif dset == "washington_vandal12classesNoBackground":
X_source_train, Y_source_train, n_classes1 = data_utils.load_image_dataset(
img_dim, image_dim_ordering, dset='washington12classes')
X_dest_train, Y_dest_train, n_classes2 = data_utils.load_image_dataset(
img_dim, image_dim_ordering, dset='vandal12classesNoBackground')
elif dset == "Wash_Vand_12class_LMDB":
X_source_train, Y_source_train, n_classes1 = data_utils.load_image_dataset(
img_dim, image_dim_ordering, dset='Wash_12class_LMDB')
elif dset == "OfficeDslrToAmazon":
X_source_train, Y_source_train, X_source_test, Y_source_test, n_classes1 = data_utils.load_image_dataset(
img_dim, image_dim_ordering, dset='OfficeDslr')
X_dest_train, Y_dest_train, X_dest_test, Y_dest_test, n_classes2 = data_utils.load_image_dataset(
img_dim, image_dim_ordering, dset='OfficeAmazon')
elif dset == "bedrooms":
X_source_train, Y_source_train, X_source_test, Y_source_test, n_classes1 = data_utils.load_image_dataset(
img_dim, image_dim_ordering, dset='bedrooms_small')
X_dest_train, Y_dest_train, X_dest_test, Y_dest_test, n_classes2 = data_utils.load_image_dataset(
img_dim, image_dim_ordering, dset='bedrooms')
elif dset == "Vand_Vand_12class_LMDB":
X_source_train, Y_source_train, X_source_test, Y_source_test, n_classes1 = data_utils.load_image_dataset(
img_dim, image_dim_ordering, dset='Vand_12class_LMDB_Background')
X_dest_train, Y_dest_train, X_dest_test, Y_dest_test, n_classes2 = data_utils.load_image_dataset(
img_dim, image_dim_ordering, dset='Vand_12class_LMDB')
else:
print "dataset not supported"
if n_classes1 != n_classes2: #sanity check
print "number of classes mismatch between source and dest domains"
n_classes = n_classes1 #
img_source_dim = X_source_train.shape[-3:] # is it backend agnostic?
img_dest_dim = X_dest_train.shape[-3:]
# Create optimizers
opt_D = data_utils.get_optimizer(opt_D, lr_D)
opt_G = data_utils.get_optimizer(opt_G, lr_G)
opt_C = data_utils.get_optimizer('SGD', 0.01)
#######################
# Load models
#######################
noise_dim = (noise_dim, )
if generator == "upsampling":
generator_model = models.generator_upsampling_mnistM(noise_dim,
img_source_dim,
img_dest_dim,
bn_mode,
deterministic,
pureGAN,
inject_noise,
wd,
dset=dset)
else:
generator_model = models.generator_deconv(noise_dim,
img_dest_dim,
bn_mode,
batch_size,
dset=dset)
if simple_disc:
discriminator_model = models.discriminator_naive(
img_dest_dim, bn_mode, model, wd, inject_noise, n_classes, use_mbd)
DCGAN_model = models.DCGAN_naive(generator_model, discriminator_model,
noise_dim, img_source_dim)
elif discriminator == "disc_resnet":
discriminator_model = models.discriminatorResNet(
img_dest_dim, bn_mode, model, wd, monsterClass, inject_noise,
n_classes, use_mbd)
DCGAN_model = models.DCGAN(generator_model, discriminator_model,
noise_dim, img_source_dim, img_dest_dim,
monsterClass)
else:
discriminator_model = models.disc1(img_dest_dim, bn_mode, model, wd,
monsterClass, inject_noise,
n_classes, use_mbd)
DCGAN_model = models.DCGAN(generator_model, discriminator_model,
noise_dim, img_source_dim, img_dest_dim,
monsterClass)
####special options for bedrooms dataset:
if dset == "bedrooms":
generator_model = models.generator_dcgan(noise_dim, img_source_dim,
img_dest_dim, bn_mode,
deterministic, pureGAN,
inject_noise, wd)
discriminator_model = models.discriminator_naive(
img_dest_dim,
bn_mode,
model,
wd,
inject_noise,
n_classes,
use_mbd,
model_name="discriminator_naive")
DCGAN_model = models.DCGAN_naive(generator_model, discriminator_model,
noise_dim, img_source_dim)
############################
# Compile models
############################
generator_model.compile(loss='mse', optimizer=opt_G)
models.make_trainable(discriminator_model, False)
#discriminator_model.trainable = False
if model == 'wgan':
DCGAN_model.compile(loss=models.wasserstein, optimizer=opt_G)
models.make_trainable(discriminator_model, True)
discriminator_model.compile(loss=models.wasserstein, optimizer=opt_D)
if model == 'lsgan':
if simple_disc:
DCGAN_model.compile(loss=['mse'], optimizer=opt_G)
models.make_trainable(discriminator_model, True)
discriminator_model.compile(loss=['mse'], optimizer=opt_D)
elif monsterClass:
DCGAN_model.compile(loss=['categorical_crossentropy'],
optimizer=opt_G)
models.make_trainable(discriminator_model, True)
discriminator_model.compile(loss=['categorical_crossentropy'],
optimizer=opt_D)
else:
DCGAN_model.compile(loss=['mse', 'categorical_crossentropy'],
loss_weights=[1.0, 1.0],
optimizer=opt_G)
models.make_trainable(discriminator_model, True)
discriminator_model.compile(
loss=['mse', 'categorical_crossentropy'],
loss_weights=[1.0, 1.0],
optimizer=opt_D)
visualize = True
if resume: ########loading previous saved model weights
data_utils.load_model_weights(generator_model, discriminator_model,
DCGAN_model, name)
#####################
###classifier
#####################
if not ((dset == 'mnistM') or (dset == 'bedrooms')):
classifier, GenToClassifierModel = classifier_build_test(
img_dest_dim,
n_classes,
generator_model,
noise_dim,
noise_scale,
img_source_dim,
opt_C,
X_source_test,
Y_source_test,
X_dest_test,
Y_dest_test,
wd=0.0001)
gen_iterations = 0
max_history_size = int(history_size * batch_size)
img_buffer = ImageHistoryBuffer((0, ) + img_source_dim, max_history_size,
batch_size, n_classes)
#################
# Start training
################
for e in range(nb_epoch):
# Initialize progbar and batch counter
progbar = generic_utils.Progbar(epoch_size)
batch_counter = 1
start = time.time()
while batch_counter < n_batch_per_epoch:
if no_supertrain is None:
if (gen_iterations < 25) and (not resume):
disc_iterations = 100
if gen_iterations % 500 == 0:
disc_iterations = 10
else:
disc_iterations = kwargs["disc_iterations"]
else:
if (gen_iterations < 25) and (not resume):
disc_iterations = 100
else:
disc_iterations = kwargs["disc_iterations"]
###################################
# 1) Train the critic / discriminator
###################################
list_disc_loss_real = []
list_disc_loss_gen = []
list_gen_loss = []
for disc_it in range(disc_iterations):
# Clip discriminator weights
#for l in discriminator_model.layers:
# weights = l.get_weights()
# weights = [np.clip(w, clamp_lower, clamp_upper) for w in weights]
# l.set_weights(weights)
X_dest_batch, Y_dest_batch, idx_dest_batch = next(
data_utils.gen_batch(X_dest_train, Y_dest_train,
batch_size))
X_source_batch, Y_source_batch, idx_source_batch = next(
data_utils.gen_batch(X_source_train, Y_source_train,
batch_size))
# Create a batch to feed the discriminator model
X_disc_real, X_disc_gen = data_utils.get_disc_batch(
X_dest_batch,
generator_model,
batch_counter,
batch_size,
noise_dim,
X_source_batch,
noise_scale=noise_scale)
if model == 'wgan':
# Update the discriminator
current_labels_real = -np.ones(X_disc_real.shape[0])
current_labels_gen = np.ones(X_disc_gen.shape[0])
if model == 'lsgan':
if simple_disc: #for real domain I put [labels 0 0 0...0], for fake domain I put [0 0...0 labels]
current_labels_real = np.ones(X_disc_real.shape[0])
#current_labels_gen = -np.ones(X_disc_gen.shape[0])
current_labels_gen = np.zeros(X_disc_gen.shape[0])
elif monsterClass: #for real domain I put [labels 0 0 0...0], for fake domain I put [0 0...0 labels]
current_labels_real = np.concatenate(
(Y_dest_batch,
np.zeros((X_disc_real.shape[0], n_classes))),
axis=1)
current_labels_gen = np.concatenate((np.zeros(
(X_disc_real.shape[0], n_classes)),
Y_source_batch),
axis=1)
else:
current_labels_real = [
np.ones(X_disc_real.shape[0]), Y_dest_batch
]
Y_fake_batch = (1.0 / n_classes) * np.ones(
[X_disc_gen.shape[0], n_classes])
current_labels_gen = [
np.zeros(X_disc_gen.shape[0]), Y_fake_batch
]
#label smoothing
#current_labels_real = np.multiply(current_labels_real, lsmooth) #usually lsmooth = 0.7
disc_loss_real = discriminator_model.train_on_batch(
X_disc_real, current_labels_real)
img_buffer.add_to_buffer(X_disc_gen, current_labels_gen,
batch_size)
bufferImages, bufferLabels = img_buffer.get_from_buffer(
batch_size)
disc_loss_gen = discriminator_model.train_on_batch(
bufferImages, bufferLabels)
list_disc_loss_real.append(disc_loss_real)
list_disc_loss_gen.append(disc_loss_gen)
#######################
# 2) Train the generator
#######################
X_gen = data_utils.sample_noise(noise_scale, batch_size, noise_dim)
X_source_batch2, Y_source_batch2, idx_source_batch2 = next(
data_utils.gen_batch(X_source_train, Y_source_train,
batch_size))
# w1 = classifier.get_weights() #FOR DEBUG
if model == 'wgan':
gen_loss = DCGAN_model.train_on_batch([X_gen, X_source_batch2],
-np.ones(X_gen.shape[0]))
if model == 'lsgan':
if simple_disc:
gen_loss = DCGAN_model.train_on_batch(
[X_gen, X_source_batch2],
np.ones(X_gen.shape[0])) #TRYING SAME BATCH OF DISC?
elif monsterClass:
labels_gen = np.concatenate(
(Y_source_batch2,
np.zeros((X_disc_real.shape[0], n_classes))),
axis=1)
gen_loss = DCGAN_model.train_on_batch(
[X_gen, X_source_batch2], labels_gen)
else:
gen_loss = DCGAN_model.train_on_batch(
[X_gen, X_source_batch2],
[np.ones(X_gen.shape[0]), Y_source_batch2])
# gen_loss2 = GenToClassifierModel.train_on_batch([X_gen,X_source_batch2], Y_source_batch2)
# w2 = classifier.get_weights() #FOR DEBUG
# for a,b in zip(w1, w2):
# if np.all(a == b):
# print "no bug in GEN model update"
# else:
# print "BUG IN GEN MODEL UPDATE"
list_gen_loss.append(gen_loss)
gen_iterations += 1
batch_counter += 1
progbar.add(batch_size,
values=[("Loss_D", 0.5 * np.mean(list_disc_loss_real) +
0.5 * np.mean(list_disc_loss_gen)),
("Loss_D_real", np.mean(list_disc_loss_real)),
("Loss_D_gen", np.mean(list_disc_loss_gen)),
("Loss_G", np.mean(list_gen_loss))])
# plot images 1 times per epoch
if batch_counter % (n_batch_per_epoch) == 0:
X_source_batch_plot, Y_source_batch_plot, idx_source_plot = next(
data_utils.gen_batch(X_source_test,
Y_source_test,
batch_size=32))
data_utils.plot_generated_batch(X_dest_test,
X_source_test,
generator_model,
noise_dim,
image_dim_ordering,
idx_source_plot,
batch_size=32)
if gen_iterations % (n_batch_per_epoch * 5) == 0:
if visualize:
BIG_ASS_VISUALIZATION_slerp(X_source_train[1],
generator_model, noise_dim)
print("Dest labels:")
print(Y_dest_test[idx_source_plot].argmax(1))
print("Source labels:")
print(Y_source_batch_plot.argmax(1))
print('\nEpoch %s/%s, Time: %s' %
(e + 1, nb_epoch, time.time() - start))
# Save model weights (by default, every 5 epochs)
data_utils.save_model_weights(generator_model, discriminator_model,
DCGAN_model, e, name)
def train(**kwargs):
"""
Train model
Load the whole train data in memory for faster operations
args: **kwargs (dict) keyword arguments that specify the model hyperparameters
"""
# Roll out the parameters
batch_size = kwargs["batch_size"]
n_batch_per_epoch = kwargs["n_batch_per_epoch"]
nb_epoch = kwargs["nb_epoch"]
model_name = kwargs["model_name"]
generator = kwargs["generator"]
image_dim_ordering = kwargs["image_dim_ordering"]
img_dim = kwargs["img_dim"]
patch_size = kwargs["patch_size"]
bn_mode = kwargs["bn_mode"]
label_smoothing = kwargs["use_label_smoothing"]
label_flipping = kwargs["label_flipping"]
dset = kwargs["dset"]
use_mbd = kwargs["use_mbd"]
epoch_size = n_batch_per_epoch * batch_size
# Setup environment (logging directory etc)
general_utils.setup_logging(model_name)
print "hi"
# Load and rescale data
X_full_train, X_sketch_train, X_full_val, X_sketch_val = data_utils.load_data(
dset, image_dim_ordering)
img_dim = X_full_train.shape[-3:]
print "data loaded in memory"
# Get the number of non overlapping patch and the size of input image to the discriminator
nb_patch, img_dim_disc = data_utils.get_nb_patch(img_dim, patch_size,
image_dim_ordering)
try:
# Create optimizers
opt_dcgan = Adam(lr=1E-3, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
# opt_discriminator = SGD(lr=1E-3, momentum=0.9, nesterov=True)
opt_discriminator = Adam(lr=1E-3,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-08)
# Load generator model
generator_model = models.load("generator_unet_%s" % generator, img_dim,
nb_patch, bn_mode, use_mbd, batch_size)
# Load discriminator model
discriminator_model = models.load("DCGAN_discriminator", img_dim_disc,
nb_patch, bn_mode, use_mbd,
batch_size)
generator_model.compile(loss='mae', optimizer=opt_discriminator)
discriminator_model.trainable = False
DCGAN_model = models.DCGAN(generator_model, discriminator_model,
img_dim, patch_size, image_dim_ordering)
loss = [l1_loss, 'binary_crossentropy']
loss_weights = [1E1, 1]
DCGAN_model.compile(loss=loss,
loss_weights=loss_weights,
optimizer=opt_dcgan)
discriminator_model.trainable = True
discriminator_model.compile(loss='binary_crossentropy',
optimizer=opt_discriminator)
gen_loss = None
disc_loss = None
iter_num = 102
weights_path = "/home/abhik/pix2pix/src/model/weights/gen_weights_iter%s_epoch30.h5" % (
str(iter_num - 1))
print weights_path
generator_model.load_weights(weights_path)
#discriminator_model.load_weights("disc_weights1.2.h5")
#DCGAN_model.load_weights("DCGAN_weights1.2.h5")
print("Weights Loaded for iter - %d" % iter_num)
# Running average
losses_list = list()
# loss_list = list()
# prev_avg = 0
# Start training
print("Start training")
for e in range(nb_epoch):
# Initialize progbar and batch counter
progbar = generic_utils.Progbar(epoch_size)
batch_counter = 1
start = time.time()
# global disc_n, disc_prev_avg, gen1_n, gen1_prev_avg, gen2_n, gen2_prev_avg, gen3_n, gen3_prev_avg
# disc_n = 1
# disc_prev_avg = 0
# gen1_n = 1
# gen1_prev_avg = 0
# gen2_n = 1
# gen2_prev_avg = 0
# gen3_n = 1
# gen3_prev_avg = 0
for X_full_batch, X_sketch_batch in data_utils.gen_batch(
X_full_train, X_sketch_train, batch_size):
# Create a batch to feed the discriminator model
X_disc, y_disc = data_utils.get_disc_batch(
X_full_batch,
X_sketch_batch,
generator_model,
batch_counter,
patch_size,
image_dim_ordering,
label_smoothing=label_smoothing,
label_flipping=label_flipping)
# Update the discriminator
disc_loss = discriminator_model.train_on_batch(X_disc, y_disc)
# Create a batch to feed the generator model
X_gen_target, X_gen = next(
data_utils.gen_batch(X_full_train, X_sketch_train,
batch_size))
y_gen = np.zeros((X_gen.shape[0], 2), dtype=np.uint8)
y_gen[:, 1] = 1
# Freeze the discriminator
discriminator_model.trainable = False
gen_loss = DCGAN_model.train_on_batch(X_gen,
[X_gen_target, y_gen])
# Unfreeze the discriminator
discriminator_model.trainable = True
# Running average
# loss_list.append(disc_loss)
# loss_list_n = len(loss_list)
# new_avg = ((loss_list_n-1)*prev_avg + disc_loss)/loss_list_n
# prev_avg = new_avg
# disc_avg, gen1_avg, gen2_avg, gen3_avg = running_avg(disc_loss, gen_loss[0], gen_loss[1], gen_loss[2])
# print("running disc loss", new_avg)
# print(disc_loss, gen_loss)
# print ("all losses", disc_avg, gen1_avg, gen2_avg, gen3_avg)
# print("")
batch_counter += 1
progbar.add(batch_size,
values=[("D logloss", disc_loss),
("G tot", gen_loss[0]),
("G L1", gen_loss[1]),
("G logloss", gen_loss[2])])
# Saving data for plotting
# losses = [e+1, batch_counter, disc_loss, gen_loss[0], gen_loss[1], gen_loss[2], disc_avg, gen1_avg, gen2_avg, gen3_avg, iter_num]
# losses_list.append(losses)
# Save images for visualization
if batch_counter % (n_batch_per_epoch / 2) == 0:
# Get new images from validation
data_utils.plot_generated_batch(
X_full_batch, X_sketch_batch, generator_model,
batch_size, image_dim_ordering, "training", iter_num)
X_full_batch, X_sketch_batch = next(
data_utils.gen_batch(X_full_val, X_sketch_val,
batch_size))
data_utils.plot_generated_batch(
X_full_batch, X_sketch_batch, generator_model,
batch_size, image_dim_ordering, "validation", iter_num)
if batch_counter >= n_batch_per_epoch:
break
print("")
print('Epoch %s/%s, Time: %s' %
(e + 1, nb_epoch, time.time() - start))
#Running average
disc_avg, gen1_avg, gen2_avg, gen3_avg = running_avg(
disc_loss, gen_loss[0], gen_loss[1], gen_loss[2])
#Validation loss
y_gen_val = np.zeros((X_sketch_batch.shape[0], 2), dtype=np.uint8)
y_gen_val[:, 1] = 1
val_loss = DCGAN_model.test_on_batch(X_full_batch,
[X_sketch_batch, y_gen_val])
# print "val_loss ===" + str(val_loss)
#logging
# Saving data for plotting
losses = [
e + 1, iter_num, disc_loss, gen_loss[0], gen_loss[1],
gen_loss[2], disc_avg, gen1_avg, gen2_avg, gen3_avg,
val_loss[0], val_loss[1], val_loss[2]
]
losses_list.append(losses)
if (e + 1) % 5 == 0:
gen_weights_path = os.path.join(
'../../models/%s/gen_weights_iter%s_epoch%s.h5' %
(model_name, iter_num, e + 1))
generator_model.save_weights(gen_weights_path, overwrite=True)
disc_weights_path = os.path.join(
'../../models/%s/disc_weights_iter%s_epoch%s.h5' %
(model_name, iter_num, e + 1))
discriminator_model.save_weights(disc_weights_path,
overwrite=True)
DCGAN_weights_path = os.path.join(
'../../models/%s/DCGAN_weights_iter%s_epoch%s.h5' %
(model_name, iter_num, e + 1))
DCGAN_model.save_weights(DCGAN_weights_path, overwrite=True)
loss_array = np.asarray(losses_list)
print(loss_array.shape) # 10 element vector
loss_path = os.path.join(
'../../losses/loss_iter%s_epoch%s.csv' % (iter_num, e + 1))
np.savetxt(loss_path, loss_array, fmt='%.5f', delimiter=',')
np.savetxt('test.csv', loss_array, fmt='%.5f', delimiter=',')
except KeyboardInterrupt:
pass
def train_toy(**kwargs):
"""
Train model
args: **kwargs (dict) keyword arguments that specify the model hyperparameters
"""
# Roll out the parameters
batch_size = kwargs["batch_size"]
n_batch_per_epoch = kwargs["n_batch_per_epoch"]
nb_epoch = kwargs["nb_epoch"]
noise_dim = kwargs["noise_dim"]
noise_scale = kwargs["noise_scale"]
lr_D = kwargs["lr_D"]
lr_G = kwargs["lr_G"]
opt_D = kwargs["opt_D"]
opt_G = kwargs["opt_G"]
clamp_lower = kwargs["clamp_lower"]
clamp_upper = kwargs["clamp_upper"]
epoch_size = n_batch_per_epoch * batch_size
print("\nExperiment parameters:")
for key in kwargs.keys():
print key, kwargs[key]
print("\n")
# Setup environment (logging directory etc)
general_utils.setup_logging("toy_MLP")
# Load and rescale data
X_real_train = data_utils.load_toy()
# Create optimizers
opt_G = data_utils.get_optimizer(opt_G, lr_G)
opt_D = data_utils.get_optimizer(opt_D, lr_D)
#######################
# Load models
#######################
noise_dim = (noise_dim,)
generator_model = models.generator_toy(noise_dim)
discriminator_model = models.discriminator_toy()
GAN_model = models.GAN_toy(generator_model, discriminator_model, noise_dim)
############################
# Compile models
############################
generator_model.compile(loss='mse', optimizer=opt_G)
discriminator_model.trainable = False
GAN_model.compile(loss=models.wasserstein, optimizer=opt_G)
discriminator_model.trainable = True
discriminator_model.compile(loss=models.wasserstein, optimizer=opt_D)
# Global iteration counter for generator updates
gen_iterations = 0
#################
# Start training
#################
for e in range(nb_epoch):
# Initialize progbar and batch counter
progbar = generic_utils.Progbar(epoch_size)
batch_counter = 1
start = time.time()
while batch_counter < n_batch_per_epoch:
disc_iterations = kwargs["disc_iterations"]
###################################
# 1) Train the critic / discriminator
###################################
list_disc_loss_real = []
list_disc_loss_gen = []
for disc_it in range(disc_iterations):
# Clip discriminator weights
for l in discriminator_model.layers:
weights = l.get_weights()
weights = [np.clip(w, clamp_lower, clamp_upper) for w in weights]
l.set_weights(weights)
X_real_batch = next(data_utils.gen_batch(X_real_train, batch_size))
# Create a batch to feed the discriminator model
X_disc_real, X_disc_gen = data_utils.get_disc_batch(X_real_batch,
generator_model,
batch_counter,
batch_size,
noise_dim,
noise_scale=noise_scale)
# Update the discriminator
disc_loss_real = discriminator_model.train_on_batch(X_disc_real, -np.ones(X_disc_real.shape[0]))
disc_loss_gen = discriminator_model.train_on_batch(X_disc_gen, np.ones(X_disc_gen.shape[0]))
list_disc_loss_real.append(disc_loss_real)
list_disc_loss_gen.append(disc_loss_gen)
#######################
# 2) Train the generator
#######################
X_gen = data_utils.sample_noise(noise_scale, batch_size, noise_dim)
# Freeze the discriminator
discriminator_model.trainable = False
gen_loss = GAN_model.train_on_batch(X_gen, -np.ones(X_gen.shape[0]))
# Unfreeze the discriminator
discriminator_model.trainable = True
batch_counter += 1
progbar.add(batch_size, values=[("Loss_D", -np.mean(list_disc_loss_real) - np.mean(list_disc_loss_gen)),
("Loss_D_real", -np.mean(list_disc_loss_real)),
("Loss_D_gen", np.mean(list_disc_loss_gen)),
("Loss_G", -gen_loss)])
# # Save images for visualization
if gen_iterations % 50 == 0:
data_utils.plot_generated_toy_batch(X_real_train, generator_model,
discriminator_model, noise_dim, gen_iterations)
gen_iterations += 1
print('\nEpoch %s/%s, Time: %s' % (e + 1, nb_epoch, time.time() - start))
def train(**kwargs):
"""
Train standard DCGAN model
args: **kwargs (dict) keyword arguments that specify the model hyperparameters
"""
# Roll out the parameters
generator = kwargs["generator"]
discriminator = kwargs["discriminator"]
dset = kwargs["dset"]
img_dim = kwargs["img_dim"]
nb_epoch = kwargs["nb_epoch"]
batch_size = kwargs["batch_size"]
n_batch_per_epoch = kwargs["n_batch_per_epoch"]
bn_mode = kwargs["bn_mode"]
noise_dim = kwargs["noise_dim"]
noise_scale = kwargs["noise_scale"]
lr_D = kwargs["lr_D"]
lr_G = kwargs["lr_G"]
opt_D = kwargs["opt_D"]
opt_G = kwargs["opt_G"]
use_mbd = kwargs["use_mbd"]
image_dim_ordering = kwargs["image_dim_ordering"]
epoch_size = n_batch_per_epoch * batch_size
deterministic = kwargs["deterministic"]
inject_noise = kwargs["inject_noise"]
model = kwargs["model"]
no_supertrain = kwargs["no_supertrain"]
pureGAN = kwargs["pureGAN"]
lsmooth = kwargs["lsmooth"]
disc_type = kwargs["disc_type"]
resume = kwargs["resume"]
name = kwargs["name"]
wd = kwargs["wd"]
history_size = kwargs["history_size"]
monsterClass = kwargs["monsterClass"]
print("\nExperiment parameters:")
for key in kwargs.keys():
print key, kwargs[key]
print("\n")
# Setup environment (logging directory etc)
general_utils.setup_logging("DCGAN")
# Load and normalize data
if dset == "mnistM":
X_source_train, Y_source_train, X_source_test, Y_source_test, n_classes1 = data_utils.load_image_dataset(
img_dim, image_dim_ordering, dset='mnist')
# X_source_train=np.concatenate([X_source_train,X_source_train,X_source_train], axis=1)
# X_source_test=np.concatenate([X_source_test,X_source_test,X_source_test], axis=1)
X_dest_train, Y_dest_train, X_dest_test, Y_dest_test, n_classes2 = data_utils.load_image_dataset(
img_dim, image_dim_ordering, dset='mnistM')
elif dset == "OfficeDslrToAmazon":
X_source_train, Y_source_train, X_source_test, Y_source_test, n_classes1 = data_utils.load_image_dataset(
img_dim, image_dim_ordering, dset='OfficeDslr')
X_dest_train, Y_dest_train, X_dest_test, Y_dest_test, n_classes2 = data_utils.load_image_dataset(
img_dim, image_dim_ordering, dset='OfficeAmazon')
else:
print "dataset not supported"
if n_classes1 != n_classes2: #sanity check
print "number of classes mismatch between source and dest domains"
n_classes = n_classes1 #
img_source_dim = X_source_train.shape[-3:] # is it backend agnostic?
img_dest_dim = X_dest_train.shape[-3:]
# Create optimizers
opt_D = data_utils.get_optimizer(opt_D, lr_D)
opt_G = data_utils.get_optimizer(opt_G, lr_G)
opt_GC = data_utils.get_optimizer('Adam', lr_G / 10.0)
opt_C = data_utils.get_optimizer('Adam', lr_D)
opt_Z = data_utils.get_optimizer('Adam', lr_G)
#######################
# Load models
#######################
noise_dim = (noise_dim, )
generator_model = models.generator_google_mnistM(noise_dim, img_source_dim,
img_dest_dim,
deterministic, pureGAN,
wd)
# discriminator_model = models.discriminator_google_mnistM(img_dest_dim, wd)
discriminator_model = models.discriminator_dcgan(img_dest_dim, wd,
n_classes, disc_type)
classificator_model = models.classificator_google_mnistM(
img_dest_dim, n_classes, wd)
DCGAN_model = models.DCGAN_naive(generator_model, discriminator_model,
noise_dim, img_source_dim)
zclass_model = z_coerence(generator_model,
img_source_dim,
bn_mode,
wd,
inject_noise,
n_classes,
noise_dim,
model_name="zClass")
# GenToClassifier_model = models.GenToClassifierModel(generator_model, classificator_model, noise_dim, img_source_dim)
#disc_penalty_model = models.disc_penalty(discriminator_model,noise_dim,img_source_dim,opt_D,model_name="disc_penalty_model")
zclass_model = z_coerence(generator_model,
img_source_dim,
bn_mode,
wd,
inject_noise,
n_classes,
noise_dim,
model_name="zClass")
############################
# Compile models
############################
generator_model.compile(loss='mse', optimizer=opt_G)
models.make_trainable(discriminator_model, False)
models.make_trainable(classificator_model, False)
# models.make_trainable(disc_penalty_model, False)
if model == 'wgan':
DCGAN_model.compile(loss=models.wasserstein, optimizer=opt_G)
models.make_trainable(discriminator_model, True)
# models.make_trainable(disc_penalty_model, True)
discriminator_model.compile(loss=models.wasserstein, optimizer=opt_D)
if model == 'lsgan':
if disc_type == "simple_disc":
DCGAN_model.compile(loss=['mse'], optimizer=opt_G)
models.make_trainable(discriminator_model, True)
discriminator_model.compile(loss=['mse'], optimizer=opt_D)
elif disc_type == "nclass_disc":
DCGAN_model.compile(loss=['mse', 'categorical_crossentropy'],
loss_weights=[1.0, 0.1],
optimizer=opt_G)
models.make_trainable(discriminator_model, True)
discriminator_model.compile(
loss=['mse', 'categorical_crossentropy'],
loss_weights=[1.0, 0.1],
optimizer=opt_D)
# GenToClassifier_model.compile(loss='categorical_crossentropy', optimizer=opt_GC)
models.make_trainable(classificator_model, True)
classificator_model.compile(loss='categorical_crossentropy',
metrics=['accuracy'],
optimizer=opt_C)
zclass_model.compile(loss=['mse'], optimizer=opt_Z)
visualize = True
########
#MAKING TRAIN+TEST numpy array for global testing:
########
Xtarget_dataset = np.concatenate([X_dest_train, X_dest_test], axis=0)
Ytarget_dataset = np.concatenate([Y_dest_train, Y_dest_test], axis=0)
if resume: ########loading previous saved model weights and checking actual performance
data_utils.load_model_weights(generator_model, discriminator_model,
DCGAN_model, name, classificator_model,
zclass_model)
# data_utils.load_model_weights(generator_model, discriminator_model, DCGAN_model, name,classificator_model)
# loss4, acc4 = classificator_model.evaluate(Xtarget_dataset, Ytarget_dataset,batch_size=1024, verbose=0)
# print('\n Classifier Accuracy on full target domain: %.2f%%' % (100 * acc4))
else:
X_gen = data_utils.sample_noise(noise_scale, X_source_train.shape[0],
noise_dim)
zclass_loss = zclass_model.fit([X_gen, X_source_train], [X_gen],
batch_size=256,
epochs=10)
####train zclass regression model only if not resuming:
gen_iterations = 0
max_history_size = int(history_size * batch_size)
img_buffer = ImageHistoryBuffer((0, ) + img_source_dim, max_history_size,
batch_size, n_classes)
#################
# Start training
################
for e in range(nb_epoch):
# Initialize progbar and batch counter
progbar = generic_utils.Progbar(epoch_size)
batch_counter = 1
start = time.time()
while batch_counter < n_batch_per_epoch:
if no_supertrain is None:
if (gen_iterations < 25) and (not resume):
disc_iterations = 100
if gen_iterations % 500 == 0:
disc_iterations = 100
else:
disc_iterations = kwargs["disc_iterations"]
else:
if (gen_iterations < 25) and (not resume):
disc_iterations = 100
else:
disc_iterations = kwargs["disc_iterations"]
###################################
# 1) Train the critic / discriminator
###################################
list_disc_loss_real = deque(10 * [0], 10)
list_disc_loss_gen = deque(10 * [0], 10)
list_gen_loss = deque(10 * [0], 10)
list_zclass_loss = deque(10 * [0], 10)
list_classifier_loss = deque(10 * [0], 10)
list_gp_loss = deque(10 * [0], 10)
for disc_it in range(disc_iterations):
X_dest_batch, Y_dest_batch, idx_dest_batch = next(
data_utils.gen_batch(X_dest_train, Y_dest_train,
batch_size))
X_source_batch, Y_source_batch, idx_source_batch = next(
data_utils.gen_batch(X_source_train, Y_source_train,
batch_size))
##########
# Create a batch to feed the discriminator model
#########
X_disc_real, X_disc_gen = data_utils.get_disc_batch(
X_dest_batch,
generator_model,
batch_counter,
batch_size,
noise_dim,
X_source_batch,
noise_scale=noise_scale)
# Update the discriminator
if model == 'wgan':
current_labels_real = -np.ones(X_disc_real.shape[0])
current_labels_gen = np.ones(X_disc_gen.shape[0])
elif model == 'lsgan':
if disc_type == "simple_disc":
current_labels_real = np.ones(X_disc_real.shape[0])
current_labels_gen = np.zeros(X_disc_gen.shape[0])
elif disc_type == "nclass_disc":
virtual_real_labels = np.zeros(
[X_disc_gen.shape[0], n_classes])
current_labels_real = [
np.ones(X_disc_real.shape[0]), virtual_real_labels
]
current_labels_gen = [
np.zeros(X_disc_gen.shape[0]), Y_source_batch
]
##############
#Train the disc on gen-buffered samples and on current real samples
##############
disc_loss_real = discriminator_model.train_on_batch(
X_disc_real, current_labels_real)
img_buffer.add_to_buffer(X_disc_gen, current_labels_gen,
batch_size)
bufferImages, bufferLabels = img_buffer.get_from_buffer(
batch_size)
disc_loss_gen = discriminator_model.train_on_batch(
bufferImages, bufferLabels)
#if not isinstance(disc_loss_real, collections.Iterable): disc_loss_real = [disc_loss_real]
#if not isinstance(disc_loss_real, collections.Iterable): disc_loss_gen = [disc_loss_gen]
if disc_type == "simple_disc":
list_disc_loss_real.appendleft(disc_loss_real)
list_disc_loss_gen.appendleft(disc_loss_gen)
elif disc_type == "nclass_disc":
list_disc_loss_real.appendleft(disc_loss_real[0])
list_disc_loss_gen.appendleft(disc_loss_gen[0])
#############
####Train the discriminator w.r.t gradient penalty
#############
#gp_loss = disc_penalty_model.train_on_batch([X_disc_real,X_disc_gen],current_labels_real) #dummy labels,not used in the loss function
#list_gp_loss.appendleft(gp_loss)
################
###CLASSIFIER TRAINING OUTSIDE DISC LOOP(wanna train in just 1 time even if disc_iter > 1)
#################
class_loss_gen = classificator_model.train_on_batch(
X_disc_gen, Y_source_batch * 0.7) #LABEL SMOOTHING!!!!
list_classifier_loss.appendleft(class_loss_gen[1])
#######################
# 2) Train the generator
#######################
X_gen = data_utils.sample_noise(noise_scale, batch_size, noise_dim)
X_source_batch2, Y_source_batch2, idx_source_batch2 = next(
data_utils.gen_batch(X_source_train, Y_source_train,
batch_size))
if model == 'wgan':
gen_loss = DCGAN_model.train_on_batch([X_gen, X_source_batch2],
-np.ones(X_gen.shape[0]))
if model == 'lsgan':
if disc_type == "simple_disc":
gen_loss = DCGAN_model.train_on_batch(
[X_gen, X_source_batch2],
np.ones(X_gen.shape[0])) #TRYING SAME BATCH OF DISC
elif disc_type == "nclass_disc":
gen_loss = DCGAN_model.train_on_batch(
[X_gen, X_source_batch2],
[np.ones(X_gen.shape[0]), Y_source_batch2])
gen_loss = gen_loss[0]
list_gen_loss.appendleft(gen_loss)
zclass_loss = zclass_model.train_on_batch([X_gen, X_source_batch2],
[X_gen])
list_zclass_loss.appendleft(zclass_loss)
##############
#Train the generator w.r.t the aux classifier:
#############
# GenToClassifier_model.train_on_batch([X_gen,X_source_batch2],Y_source_batch2)
# I SHOULD TRY TO CLASSIFY EVEN ON DISCRIMINATOR, PUTTING ONE CLASS FOR REAL SAMPLES AND N CLASS FOR FAKE
gen_iterations += 1
batch_counter += 1
progbar.add(batch_size,
values=[("Loss_D_real", np.mean(list_disc_loss_real)),
("Loss_D_gen", np.mean(list_disc_loss_gen)),
("Loss_G", np.mean(list_gen_loss)),
("Loss_Z", np.mean(list_zclass_loss)),
("Loss_Classifier",
np.mean(list_classifier_loss))])
# plot images 1 times per epoch
if batch_counter % (n_batch_per_epoch) == 0:
X_source_batch_plot, Y_source_batch_plot, idx_source_plot = next(
data_utils.gen_batch(X_source_test,
Y_source_test,
batch_size=32))
data_utils.plot_generated_batch(X_dest_test,
X_source_test,
generator_model,
noise_dim,
image_dim_ordering,
idx_source_plot,
batch_size=32)
if gen_iterations % (n_batch_per_epoch * 5) == 0:
if visualize:
BIG_ASS_VISUALIZATION_slerp(X_source_train[1],
generator_model, noise_dim)
# if (e % 20) == 0:
# lr_decay([discriminator_model,DCGAN_model,classificator_model],decay_value=0.95)
print("Dest labels:")
print(Y_dest_test[idx_source_plot].argmax(1))
print("Source labels:")
print(Y_source_batch_plot.argmax(1))
print('\nEpoch %s/%s, Time: %s' %
(e + 1, nb_epoch, time.time() - start))
# Save model weights (by default, every 5 epochs)
data_utils.save_model_weights(generator_model, discriminator_model,
DCGAN_model, e, name,
classificator_model, zclass_model)
#testing accuracy of trained classifier
loss4, acc4 = classificator_model.evaluate(Xtarget_dataset,
Ytarget_dataset,
batch_size=1024,
verbose=0)
print(
'\n Classifier Accuracy and loss on full target domain: %.2f%% / %.5f%%'
% ((100 * acc4), loss4))
def train(**kwargs):
"""
Train model
Load the whole train data in memory for faster operations
args: **kwargs (dict) keyword arguments that specify the model hyperparameters
"""
# Roll out the parameters
batch_size = kwargs["batch_size"]
n_batch_per_epoch = kwargs["n_batch_per_epoch"]
nb_epoch = kwargs["nb_epoch"]
generator = kwargs["generator"]
model_name = kwargs["model_name"]
image_data_format = kwargs["image_data_format"]
img_dim = kwargs["img_dim"]
bn_mode = kwargs["bn_mode"]
label_smoothing = kwargs["label_smoothing"]
label_flipping = kwargs["label_flipping"]
noise_scale = kwargs["noise_scale"]
dset = kwargs["dset"]
use_mbd = kwargs["use_mbd"]
epoch_size = n_batch_per_epoch * batch_size
# Setup environment (logging directory etc)
general_utils.setup_logging(model_name)
# Load and rescale data
if dset == "celebA":
X_real_train = data_utils.load_celebA(img_dim, image_data_format)
if dset == "mnist":
X_real_train, _, _, _ = data_utils.load_mnist(image_data_format)
img_dim = X_real_train.shape[-3:]
noise_dim = (100, )
try:
# Create optimizers
opt_dcgan = Adam(lr=1E-3, beta_1=0.5, beta_2=0.999, epsilon=1e-08)
opt_discriminator = SGD(lr=1E-3, momentum=0.9, nesterov=True)
# Load generator model
generator_model = models.load("generator_%s" % generator,
noise_dim,
img_dim,
bn_mode,
batch_size,
dset=dset,
use_mbd=use_mbd)
# Load discriminator model
discriminator_model = models.load("DCGAN_discriminator",
noise_dim,
img_dim,
bn_mode,
batch_size,
dset=dset,
use_mbd=use_mbd)
generator_model.compile(loss='mse', optimizer=opt_discriminator)
discriminator_model.trainable = False
DCGAN_model = models.DCGAN(generator_model, discriminator_model,
noise_dim, img_dim)
loss = ['binary_crossentropy']
loss_weights = [1]
DCGAN_model.compile(loss=loss,
loss_weights=loss_weights,
optimizer=opt_dcgan)
discriminator_model.trainable = True
discriminator_model.compile(loss='binary_crossentropy',
optimizer=opt_discriminator)
gen_loss = 100
disc_loss = 100
# Start training
print("Start training")
for e in range(nb_epoch):
# Initialize progbar and batch counter
progbar = generic_utils.Progbar(epoch_size)
batch_counter = 1
start = time.time()
for X_real_batch in data_utils.gen_batch(X_real_train, batch_size):
# Create a batch to feed the discriminator model
X_disc, y_disc = data_utils.get_disc_batch(
X_real_batch,
generator_model,
batch_counter,
batch_size,
noise_dim,
noise_scale=noise_scale,
label_smoothing=label_smoothing,
label_flipping=label_flipping)
# Update the discriminator
disc_loss = discriminator_model.train_on_batch(X_disc, y_disc)
# Create a batch to feed the generator model
X_gen, y_gen = data_utils.get_gen_batch(
batch_size, noise_dim, noise_scale=noise_scale)
# Freeze the discriminator
discriminator_model.trainable = False
gen_loss = DCGAN_model.train_on_batch(X_gen, y_gen)
# Unfreeze the discriminator
discriminator_model.trainable = True
batch_counter += 1
progbar.add(batch_size,
values=[("D logloss", disc_loss),
("G logloss", gen_loss)])
# Save images for visualization
if batch_counter % 100 == 0:
data_utils.plot_generated_batch(X_real_batch,
generator_model,
batch_size, noise_dim,
image_data_format)
if batch_counter >= n_batch_per_epoch:
break
print("")
print('Epoch %s/%s, Time: %s' %
(e + 1, nb_epoch, time.time() - start))
if e % 5 == 0:
gen_weights_path = os.path.join(
'../../models/%s/gen_weights_epoch%s.h5' % (model_name, e))
generator_model.save_weights(gen_weights_path, overwrite=True)
disc_weights_path = os.path.join(
'../../models/%s/disc_weights_epoch%s.h5' %
(model_name, e))
discriminator_model.save_weights(disc_weights_path,
overwrite=True)
DCGAN_weights_path = os.path.join(
'../../models/%s/DCGAN_weights_epoch%s.h5' %
(model_name, e))
DCGAN_model.save_weights(DCGAN_weights_path, overwrite=True)
except KeyboardInterrupt:
pass
def train_cs_modis_cgan(
scenes_fn=None,
noise_dim=64,
epoch=1,
model_name="cs_modis_cgan",
num_epochs=1,
batch_size=32,
noise_scale=1.0,
cs_scenes=None,
modis_vars=None,
modis_mask=None,
save_every=5,
lr_disc=0.0001,
lr_gan=0.0002,
):
# Load and rescale data
if cs_scenes is None:
print("Loading data...")
(cs_scenes, modis_vars, modis_mask) = \
data_utils.load_cloudsat_scenes(scenes_fn)
num_scenes = cs_scenes.shape[0]
batches_per_epoch = num_scenes // batch_size
scene_size = cs_scenes.shape[1]
modis_var_dim = modis_vars.shape[-1]
print("Creating models...")
(gen, disc, gan, opt_disc,
opt_gan) = create_models(scene_size, modis_var_dim, noise_dim, lr_disc,
lr_gan)
if epoch > 1:
print("Loading weights...")
load_model_state(gen, disc, gan, model_name, epoch - 1)
# Start training
print("Starting training...")
for e in range(epoch, epoch + num_epochs):
# Initialize progbar and batch counter
progbar = generic_utils.Progbar(num_scenes)
batch_counter = 1
start = time.time()
batch_gen = data_utils.gen_batch(cs_scenes, modis_vars, modis_mask,
batch_size)
for (cs_scenes_b, modis_vars_b, modis_mask_b) in batch_gen:
disc_loss = 0
for fake in [False, True]:
# Create a batch to feed the discriminator model
(X_disc, y_disc) = data_utils.get_disc_batch(
cs_scenes_b,
modis_vars_b,
modis_mask_b,
gen,
fake,
batch_size,
noise_dim,
#cont_dim,
noise_scale=noise_scale)
# Train the discriminator
disc_loss += disc.train_on_batch(X_disc, y_disc)
disc_loss /= 2
# Create a batch to feed the generator model
(X_gan, y_gan_disc) = data_utils.get_gan_batch(
batch_size,
noise_dim,
#cont_dim,
noise_scale=noise_scale)
noise = X_gan
#(noise, cont) = X_gan
# Freeze the discriminator while training the generator
disc.trainable = False
gen_loss = gan.train_on_batch([noise, modis_vars_b, modis_mask_b],
[y_gan_disc])
disc.trainable = True
batch_counter += 1
progbar.add(batch_size,
values=[("D loss", disc_loss), ("G loss", gen_loss)])
if batch_counter % 50 == 0:
scene_gen = gen.predict([noise, modis_vars_b, modis_mask_b])
modis_vars_bs = np.zeros_like(modis_vars_b)
modis_mask_bs = np.zeros_like(modis_mask_b)
for i in range(1, batch_size):
modis_vars_bs[i, ...] = modis_vars_b[0, ...]
modis_mask_bs[i, ...] = modis_mask_b[0, ...]
scene_gen_s = gen.predict(
[noise, modis_vars_bs, modis_mask_bs])
np.savez_compressed(dir_path + "/../data/gen_scene.npz",
scene=scene_gen,
real_scene=cs_scenes_b,
scene_single=scene_gen_s,
modis_vars=modis_vars_b,
modis_mask=modis_mask_b)
gc.collect()
print("")
print('Epoch %s/%s, Time: %s' %
(e, epoch + num_epochs - 1, time.time() - start))
if (e % save_every == 0):
print("Saving weights...")
save_model_state(gen, disc, gan, model_name, e)
return (gan, gen, disc)
def train(**kwargs):
"""
Train model
Load the whole train data in memory for faster operations
args: **kwargs (dict) keyword arguments that specify the model hyperparameters
"""
# Roll out the parameters
batch_size = kwargs["batch_size"]
n_batch_per_epoch = kwargs["n_batch_per_epoch"]
nb_epoch = kwargs["nb_epoch"]
model_name = kwargs["model_name"]
generator = kwargs["generator"]
image_data_format = kwargs["image_data_format"]
img_dim = kwargs["img_dim"]
patch_size = kwargs["patch_size"]
bn_mode = kwargs["bn_mode"]
label_smoothing = kwargs["use_label_smoothing"]
label_flipping = kwargs["label_flipping"]
dset = kwargs["dset"]
use_mbd = kwargs["use_mbd"]
epoch_size = n_batch_per_epoch * batch_size
# Setup environment (logging directory etc)
general_utils.setup_logging(model_name)
# Load and rescale data
X_full_train, X_sketch_train, X_full_val, X_sketch_val, target_train, target_val = data_utils.load_data(
dset, image_data_format)
img_dim = X_full_train.shape[-3:]
# Get the number of non overlapping patch and the size of input image to the discriminator
nb_patch, img_dim_disc = data_utils.get_nb_patch(img_dim, patch_size,
image_data_format)
try:
# Create optimizers
opt_dcgan = Adam(lr=1E-3, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
# opt_discriminator = SGD(lr=1E-3, momentum=0.9, nesterov=True)
opt_discriminator = Adam(lr=1E-3,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-08)
# DCGAN_model = models.DCGAN(generator_model,
# discriminator_model,
# img_dim,
# patch_size,
# image_data_format)
##########################################################################
classifier_model = models.Pereira_classifier(img_dim)
#classifier_model = models.MyResNet18(img_dim)
#classifier_model = models.MyDensNet121(img_dim)
#classifier_model = models.MyNASNetMobile(img_dim)
#########################################################################
loss = [keras.losses.categorical_crossentropy]
loss_weights = [1]
classifier_model.compile(loss=loss,
loss_weights=loss_weights,
optimizer=opt_dcgan)
class_loss = 100
disc_loss = 100
max_accval = 0
# Start training
print("Start training")
for e in range(nb_epoch):
# Initialize progbar and batch counter
progbar = generic_utils.Progbar(epoch_size)
batch_counter = 1
start = time.time()
for X_full_batch, X_sketch_batch, Y_target in data_utils.gen_batch(
X_full_train, X_sketch_train, target_train, batch_size):
class_loss = classifier_model.train_on_batch(
X_sketch_batch, Y_target)
# Unfreeze the discriminator
batch_counter += 1
progbar.add(batch_size, values=[("class_loss", class_loss)])
# Save images for visualization
if batch_counter >= n_batch_per_epoch:
X_full_batch, X_sketch_batch, Y_target_val = next(
data_utils.gen_batch(X_full_val, X_sketch_val,
target_val,
int(X_sketch_val.shape[0])))
y_pred = classifier_model.predict(X_sketch_batch)
y_predd = np.argmax(y_pred, axis=1)
y_true = np.argmax(Y_target_val, axis=1)
#print(y_true.shape)
accval = (sum(
(y_predd == y_true)) / y_predd.shape[0] * 100)
if (accval > max_accval):
max_accval = accval
print('valacc=%.2f' % (accval))
print('max_accval=%.2f' % (max_accval))
break
print("")
print('Epoch %s/%s, Time: %s' %
(e + 1, nb_epoch, time.time() - start))
except KeyboardInterrupt:
pass
# Compile discriminator model
discriminator_model.trainable = True
discriminator_model.compile(loss='binary_crossentropy',
optimizer=opt_discriminator)
# Training
print("Start training")
for e in range(nb_epoch):
# Initialize progbar and batch counter
progbar = generic_utils.Progbar(epoch_size)
batch_counter = 1
start = time.time()
for X_full_batch, X_sketch_batch in gen_batch(X_full_train,
X_sketch_train,
batch_size):
# Create a batch to feed the discriminator model
X_disc, y_disc = get_disc_batch(X_full_batch,
X_sketch_batch,
generator_model,
batch_counter,
patch_size,
image_data_format,
label_smoothing=label_smoothing,
label_flipping=label_flipping)
# Update the discriminator
disc_loss = discriminator_model.train_on_batch(X_disc, y_disc)
def train(**kwargs):
"""
Train model
Load the whole train data in memory for faster operations
args: **kwargs (dict) keyword arguments that specify the model hyperparameters
"""
# Roll out the parameters
batch_size = kwargs["batch_size"]
n_batch_per_epoch = kwargs["n_batch_per_epoch"]
nb_epoch = kwargs["nb_epoch"]
generator = kwargs["generator"]
model_name = kwargs["model_name"]
image_data_format = kwargs["image_data_format"]
celebA_img_dim = kwargs["celebA_img_dim"]
cont_dim = (kwargs["cont_dim"], )
cat_dim = (kwargs["cat_dim"], )
noise_dim = (kwargs["noise_dim"], )
label_smoothing = kwargs["label_smoothing"]
label_flipping = kwargs["label_flipping"]
noise_scale = kwargs["noise_scale"]
dset = kwargs["dset"]
use_mbd = kwargs["use_mbd"]
load_from_dir = kwargs["load_from_dir"]
target_size = kwargs["target_size"]
save_weights_every_n_epochs = kwargs["save_weights_every_n_epochs"]
save_only_last_n_weights = kwargs["save_only_last_n_weights"]
visualize_images_every_n_epochs = kwargs["visualize_images_every_n_epochs"]
epoch_size = n_batch_per_epoch * batch_size
# Setup environment (logging directory etc)
general_utils.setup_logging(**kwargs)
# Load and rescale data
if dset == "celebA":
X_real_train = data_utils.load_celebA(celebA_img_dim,
image_data_format)
elif dset == "mnist":
X_real_train, _, _, _ = data_utils.load_mnist(image_data_format)
else:
X_batch_gen = data_utils.data_generator_from_dir(
dset, target_size, batch_size)
X_real_train = next(X_batch_gen)
img_dim = X_real_train.shape[-3:]
try:
# Create optimizers
opt_dcgan = Adam(lr=1E-4, beta_1=0.5, beta_2=0.999, epsilon=1e-08)
opt_discriminator = Adam(lr=1E-4,
beta_1=0.5,
beta_2=0.999,
epsilon=1e-08)
# opt_discriminator = SGD(lr=1E-4, momentum=0.9, nesterov=True)
# Load generator model
generator_model = models.load("generator_%s" % generator,
cat_dim,
cont_dim,
noise_dim,
img_dim,
batch_size,
dset=dset,
use_mbd=use_mbd)
# Load discriminator model
discriminator_model = models.load("DCGAN_discriminator",
cat_dim,
cont_dim,
noise_dim,
img_dim,
batch_size,
dset=dset,
use_mbd=use_mbd)
generator_model.compile(loss='mse', optimizer=opt_discriminator)
discriminator_model.trainable = False
DCGAN_model = models.DCGAN(generator_model, discriminator_model,
cat_dim, cont_dim, noise_dim)
list_losses = [
'binary_crossentropy', 'categorical_crossentropy', gaussian_loss
]
list_weights = [1, 1, 1]
DCGAN_model.compile(loss=list_losses,
loss_weights=list_weights,
optimizer=opt_dcgan)
# Multiple discriminator losses
discriminator_model.trainable = True
discriminator_model.compile(loss=list_losses,
loss_weights=list_weights,
optimizer=opt_discriminator)
gen_loss = 100
disc_loss = 100
if not load_from_dir:
X_batch_gen = data_utils.gen_batch(X_real_train, batch_size)
# Start training
print("Start training")
disc_total_losses = []
disc_log_losses = []
disc_cat_losses = []
disc_cont_losses = []
gen_total_losses = []
gen_log_losses = []
gen_cat_losses = []
gen_cont_losses = []
start = time.time()
for e in range(nb_epoch):
print('--------------------------------------------')
print('[{0:%Y/%m/%d %H:%M:%S}] Epoch {1:d}/{2:d}\n'.format(
datetime.datetime.now(), e + 1, nb_epoch))
# Initialize progbar and batch counter
progbar = generic_utils.Progbar(epoch_size)
batch_counter = 1
disc_total_loss_batch = 0
disc_log_loss_batch = 0
disc_cat_loss_batch = 0
disc_cont_loss_batch = 0
gen_total_loss_batch = 0
gen_log_loss_batch = 0
gen_cat_loss_batch = 0
gen_cont_loss_batch = 0
for batch_counter in range(n_batch_per_epoch):
# Load data
X_real_batch = next(X_batch_gen)
# Create a batch to feed the discriminator model
X_disc, y_disc, y_cat, y_cont = data_utils.get_disc_batch(
X_real_batch,
generator_model,
batch_counter,
batch_size,
cat_dim,
cont_dim,
noise_dim,
noise_scale=noise_scale,
label_smoothing=label_smoothing,
label_flipping=label_flipping)
# Update the discriminator
disc_loss = discriminator_model.train_on_batch(
X_disc, [y_disc, y_cat, y_cont])
# Create a batch to feed the generator model
X_gen, y_gen, y_cat, y_cont, y_cont_target = data_utils.get_gen_batch(
batch_size,
cat_dim,
cont_dim,
noise_dim,
noise_scale=noise_scale)
# Freeze the discriminator
discriminator_model.trainable = False
gen_loss = DCGAN_model.train_on_batch(
[y_cat, y_cont, X_gen], [y_gen, y_cat, y_cont_target])
# Unfreeze the discriminator
discriminator_model.trainable = True
progbar.add(batch_size,
values=[("D tot", disc_loss[0]),
("D log", disc_loss[1]),
("D cat", disc_loss[2]),
("D cont", disc_loss[3]),
("G tot", gen_loss[0]),
("G log", gen_loss[1]),
("G cat", gen_loss[2]),
("G cont", gen_loss[3])])
disc_total_loss_batch += disc_loss[0]
disc_log_loss_batch += disc_loss[1]
disc_cat_loss_batch += disc_loss[2]
disc_cont_loss_batch += disc_loss[3]
gen_total_loss_batch += gen_loss[0]
gen_log_loss_batch += gen_loss[1]
gen_cat_loss_batch += gen_loss[2]
gen_cont_loss_batch += gen_loss[3]
# # Save images for visualization
# if batch_counter % (n_batch_per_epoch / 2) == 0:
# data_utils.plot_generated_batch(X_real_batch, generator_model, e,
# batch_size, cat_dim, cont_dim, noise_dim,
# image_data_format, model_name)
disc_total_losses.append(disc_total_loss_batch / n_batch_per_epoch)
disc_log_losses.append(disc_log_loss_batch / n_batch_per_epoch)
disc_cat_losses.append(disc_cat_loss_batch / n_batch_per_epoch)
disc_cont_losses.append(disc_cont_loss_batch / n_batch_per_epoch)
gen_total_losses.append(gen_total_loss_batch / n_batch_per_epoch)
gen_log_losses.append(gen_log_loss_batch / n_batch_per_epoch)
gen_cat_losses.append(gen_cat_loss_batch / n_batch_per_epoch)
gen_cont_losses.append(gen_cont_loss_batch / n_batch_per_epoch)
# Save images for visualization
if (e + 1) % visualize_images_every_n_epochs == 0:
data_utils.plot_generated_batch(X_real_batch, generator_model,
e, batch_size, cat_dim,
cont_dim, noise_dim,
image_data_format, model_name)
data_utils.plot_losses(disc_total_losses, disc_log_losses,
disc_cat_losses, disc_cont_losses,
gen_total_losses, gen_log_losses,
gen_cat_losses, gen_cont_losses,
model_name)
if (e + 1) % save_weights_every_n_epochs == 0:
print("Saving weights...")
# Delete all but the last n weights
general_utils.purge_weights(save_only_last_n_weights,
model_name)
# Save weights
gen_weights_path = os.path.join(
'../../models/%s/gen_weights_epoch%05d.h5' %
(model_name, e))
generator_model.save_weights(gen_weights_path, overwrite=True)
disc_weights_path = os.path.join(
'../../models/%s/disc_weights_epoch%05d.h5' %
(model_name, e))
discriminator_model.save_weights(disc_weights_path,
overwrite=True)
DCGAN_weights_path = os.path.join(
'../../models/%s/DCGAN_weights_epoch%05d.h5' %
(model_name, e))
DCGAN_model.save_weights(DCGAN_weights_path, overwrite=True)
end = time.time()
print("")
print('Epoch %s/%s, Time: %s' % (e + 1, nb_epoch, end - start))
start = end
except KeyboardInterrupt:
pass
gen_weights_path = '../../models/%s/generator_latest.h5' % (model_name)
print("Saving", gen_weights_path)
generator_model.save(gen_weights_path, overwrite=True)
def train(**kwargs):
"""
Train model
Load the whole train data in memory for faster operations
args: **kwargs (dict) keyword arguments that specify the model hyperparameters
"""
# Roll out the parameters
batch_size = kwargs["batch_size"]
n_batch_per_epoch = kwargs["n_batch_per_epoch"]
nb_epoch = kwargs["nb_epoch"]
model_name = kwargs["model_name"]
generator = kwargs["generator"]
image_dim_ordering = kwargs["image_dim_ordering"]
img_dim = kwargs["img_dim"]
patch_size = kwargs["patch_size"]
bn_mode = kwargs["bn_mode"]
label_smoothing = kwargs["use_label_smoothing"]
label_flipping = kwargs["label_flipping"]
dset = kwargs["dset"]
use_mbd = kwargs["use_mbd"]
epoch_size = n_batch_per_epoch * batch_size
# Setup environment (logging directory etc)
general_utils.setup_logging(model_name)
# Load and rescale data
X_full_train, X_sketch_train, X_full_val, X_sketch_val = data_utils.load_data(dset, image_dim_ordering)
img_dim = X_full_train.shape[-3:]
# Get the number of non overlapping patch and the size of input image to the discriminator
nb_patch, img_dim_disc = data_utils.get_nb_patch(img_dim, patch_size, image_dim_ordering)
try:
# Create optimizers
opt_dcgan = Adam(lr=1E-3, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
# opt_discriminator = SGD(lr=1E-3, momentum=0.9, nesterov=True)
opt_discriminator = Adam(lr=1E-3, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
# Load generator model
generator_model = models.load("generator_unet_%s" % generator,
img_dim,
nb_patch,
bn_mode,
use_mbd,
batch_size)
# Load discriminator model
discriminator_model = models.load("DCGAN_discriminator",
img_dim_disc,
nb_patch,
bn_mode,
use_mbd,
batch_size)
generator_model.compile(loss='mae', optimizer=opt_discriminator)
discriminator_model.trainable = False
DCGAN_model = models.DCGAN(generator_model,
discriminator_model,
img_dim,
patch_size,
image_dim_ordering)
loss = [l1_loss, 'binary_crossentropy']
loss_weights = [1E1, 1]
DCGAN_model.compile(loss=loss, loss_weights=loss_weights, optimizer=opt_dcgan)
discriminator_model.trainable = True
discriminator_model.compile(loss='binary_crossentropy', optimizer=opt_discriminator)
gen_loss = 100
disc_loss = 100
# Start training
print("Start training")
for e in range(nb_epoch):
# Initialize progbar and batch counter
progbar = generic_utils.Progbar(epoch_size)
batch_counter = 1
start = time.time()
for X_full_batch, X_sketch_batch in data_utils.gen_batch(X_full_train, X_sketch_train, batch_size):
# Create a batch to feed the discriminator model
X_disc, y_disc = data_utils.get_disc_batch(X_full_batch,
X_sketch_batch,
generator_model,
batch_counter,
patch_size,
image_dim_ordering,
label_smoothing=label_smoothing,
label_flipping=label_flipping)
# Update the discriminator
disc_loss = discriminator_model.train_on_batch(X_disc, y_disc)
# Create a batch to feed the generator model
X_gen_target, X_gen = next(data_utils.gen_batch(X_full_train, X_sketch_train, batch_size))
y_gen = np.zeros((X_gen.shape[0], 2), dtype=np.uint8)
y_gen[:, 1] = 1
# Freeze the discriminator
discriminator_model.trainable = False
gen_loss = DCGAN_model.train_on_batch(X_gen, [X_gen_target, y_gen])
# Unfreeze the discriminator
discriminator_model.trainable = True
batch_counter += 1
progbar.add(batch_size, values=[("D logloss", disc_loss),
("G tot", gen_loss[0]),
("G L1", gen_loss[1]),
("G logloss", gen_loss[2])])
# Save images for visualization
if batch_counter % (n_batch_per_epoch / 2) == 0:
# Get new images from validation
data_utils.plot_generated_batch(X_full_batch, X_sketch_batch, generator_model,
batch_size, image_dim_ordering, "training")
X_full_batch, X_sketch_batch = next(data_utils.gen_batch(X_full_val, X_sketch_val, batch_size))
data_utils.plot_generated_batch(X_full_batch, X_sketch_batch, generator_model,
batch_size, image_dim_ordering, "validation")
if batch_counter >= n_batch_per_epoch:
break
print("")
print('Epoch %s/%s, Time: %s' % (e + 1, nb_epoch, time.time() - start))
if e % 5 == 0:
gen_weights_path = os.path.join('../../models/%s/gen_weights_epoch%s.h5' % (model_name, e))
generator_model.save_weights(gen_weights_path, overwrite=True)
disc_weights_path = os.path.join('../../models/%s/disc_weights_epoch%s.h5' % (model_name, e))
discriminator_model.save_weights(disc_weights_path, overwrite=True)
DCGAN_weights_path = os.path.join('../../models/%s/DCGAN_weights_epoch%s.h5' % (model_name, e))
DCGAN_model.save_weights(DCGAN_weights_path, overwrite=True)
except KeyboardInterrupt:
pass
def train(**kwargs):
"""
Train model
Load the whole train data in memory for faster operations
args: **kwargs (dict) keyword arguments that specify the model hyperparameters
"""
# Roll out the parameters
batch_size = kwargs["batch_size"]
n_batch_per_epoch = kwargs["n_batch_per_epoch"]
nb_epoch = kwargs["nb_epoch"]
generator = kwargs["generator"]
model_name = kwargs["model_name"]
image_dim_ordering = kwargs["image_dim_ordering"]
img_dim = kwargs["img_dim"]
bn_mode = kwargs["bn_mode"]
label_smoothing = kwargs["label_smoothing"]
label_flipping = kwargs["label_flipping"]
noise_scale = kwargs["noise_scale"]
dset = kwargs["dset"]
use_mbd = kwargs["use_mbd"]
epoch_size = n_batch_per_epoch * batch_size
# Setup environment (logging directory etc)
general_utils.setup_logging(model_name)
# Load and rescale data
if dset == "celebA":
X_real_train = data_utils.load_celebA(img_dim, image_dim_ordering)
if dset == "mnist":
X_real_train, _, _, _ = data_utils.load_mnist(image_dim_ordering)
img_dim = X_real_train.shape[-3:]
noise_dim = (100,)
try:
# Create optimizers
opt_dcgan = Adam(lr=1E-3, beta_1=0.5, beta_2=0.999, epsilon=1e-08)
opt_discriminator = SGD(lr=1E-3, momentum=0.9, nesterov=True)
# Load generator model
generator_model = models.load("generator_%s" % generator,
noise_dim,
img_dim,
bn_mode,
batch_size,
dset=dset,
use_mbd=use_mbd)
# Load discriminator model
discriminator_model = models.load("DCGAN_discriminator",
noise_dim,
img_dim,
bn_mode,
batch_size,
dset=dset,
use_mbd=use_mbd)
generator_model.compile(loss='mse', optimizer=opt_discriminator)
discriminator_model.trainable = False
DCGAN_model = models.DCGAN(generator_model,
discriminator_model,
noise_dim,
img_dim)
loss = ['binary_crossentropy']
loss_weights = [1]
DCGAN_model.compile(loss=loss, loss_weights=loss_weights, optimizer=opt_dcgan)
discriminator_model.trainable = True
discriminator_model.compile(loss='binary_crossentropy', optimizer=opt_discriminator)
gen_loss = 100
disc_loss = 100
# Start training
print("Start training")
for e in range(nb_epoch):
# Initialize progbar and batch counter
progbar = generic_utils.Progbar(epoch_size)
batch_counter = 1
start = time.time()
for X_real_batch in data_utils.gen_batch(X_real_train, batch_size):
# Create a batch to feed the discriminator model
X_disc, y_disc = data_utils.get_disc_batch(X_real_batch,
generator_model,
batch_counter,
batch_size,
noise_dim,
noise_scale=noise_scale,
label_smoothing=label_smoothing,
label_flipping=label_flipping)
# Update the discriminator
disc_loss = discriminator_model.train_on_batch(X_disc, y_disc)
# Create a batch to feed the generator model
X_gen, y_gen = data_utils.get_gen_batch(batch_size, noise_dim, noise_scale=noise_scale)
# Freeze the discriminator
discriminator_model.trainable = False
gen_loss = DCGAN_model.train_on_batch(X_gen, y_gen)
# Unfreeze the discriminator
discriminator_model.trainable = True
batch_counter += 1
progbar.add(batch_size, values=[("D logloss", disc_loss),
("G logloss", gen_loss)])
# Save images for visualization
if batch_counter % 100 == 0:
data_utils.plot_generated_batch(X_real_batch, generator_model,
batch_size, noise_dim, image_dim_ordering)
if batch_counter >= n_batch_per_epoch:
break
print("")
print('Epoch %s/%s, Time: %s' % (e + 1, nb_epoch, time.time() - start))
if e % 5 == 0:
gen_weights_path = os.path.join('../../models/%s/gen_weights_epoch%s.h5' % (model_name, e))
generator_model.save_weights(gen_weights_path, overwrite=True)
disc_weights_path = os.path.join('../../models/%s/disc_weights_epoch%s.h5' % (model_name, e))
discriminator_model.save_weights(disc_weights_path, overwrite=True)
DCGAN_weights_path = os.path.join('../../models/%s/DCGAN_weights_epoch%s.h5' % (model_name, e))
DCGAN_model.save_weights(DCGAN_weights_path, overwrite=True)
except KeyboardInterrupt:
pass
def train(**kwargs):
"""
Train model
Load the whole train data in memory for faster operations
args: **kwargs (dict) keyword arguments that specify the model hyperparameters
"""
# Roll out the parameters
batch_size = kwargs["batch_size"]
n_batch_per_epoch = kwargs["n_batch_per_epoch"]
nb_epoch = kwargs["nb_epoch"]
model_name = kwargs["model_name"]
generator = kwargs["generator"]
image_data_format = kwargs["image_data_format"]
img_dim = kwargs["img_dim"]
patch_size = kwargs["patch_size"]
bn_mode = kwargs["bn_mode"]
label_smoothing = kwargs["use_label_smoothing"]
label_flipping = kwargs["label_flipping"]
dset = kwargs["dset"]
use_mbd = kwargs["use_mbd"]
# right strip '/' to avoid empty '/' dir
save_dir = kwargs["save_dir"].rstrip('/')
# join name with current datetime
save_dir = '_'.join(
[save_dir,
datetime.datetime.now().strftime("%I:%M%p-%B%d-%Y/")])
if not os.path.isdir(save_dir):
os.makedirs(save_dir)
# save the config in save dir
with open('{0}job_config.json'.format(save_dir), 'w') as fp:
json.dump(kwargs, fp, sort_keys=True, indent=4)
epoch_size = n_batch_per_epoch * batch_size
# Setup environment (logging directory etc)
general_utils.setup_logging(model_name)
# Load and rescale data
X_full_train, X_sketch_train, X_full_val, X_sketch_val = data_utils.load_data(
dset, image_data_format)
img_dim = X_full_train.shape[-3:]
# Get the number of non overlapping patch and the size of input image to the discriminator
nb_patch, img_dim_disc = data_utils.get_nb_patch(img_dim, patch_size,
image_data_format)
try:
# Create optimizers
opt_dcgan = Adam(lr=1E-3, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
# opt_discriminator = SGD(lr=1E-3, momentum=0.9, nesterov=True)
opt_discriminator = Adam(lr=1E-3,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-08)
# Load generator model
generator_model = models.load("generator_unet_%s" % generator, img_dim,
nb_patch, bn_mode, use_mbd, batch_size)
# Load discriminator model
discriminator_model = models.load("DCGAN_discriminator", img_dim_disc,
nb_patch, bn_mode, use_mbd,
batch_size)
generator_model.compile(loss='mae', optimizer=opt_discriminator)
discriminator_model.trainable = False
DCGAN_model = models.DCGAN(generator_model, discriminator_model,
img_dim, patch_size, image_data_format)
loss = [l1_loss, 'binary_crossentropy']
loss_weights = [1E1, 1]
DCGAN_model.compile(loss=loss,
loss_weights=loss_weights,
optimizer=opt_dcgan)
discriminator_model.trainable = True
discriminator_model.compile(loss='binary_crossentropy',
optimizer=opt_discriminator)
gen_loss = 100
disc_loss = 100
# Start training
print("Start training")
for e in range(nb_epoch):
# Initialize progbar and batch counter
progbar = generic_utils.Progbar(epoch_size)
batch_counter = 1
start = time.time()
for X_full_batch, X_sketch_batch in data_utils.gen_batch(
X_full_train, X_sketch_train, batch_size):
# Create a batch to feed the discriminator model
X_disc, y_disc = data_utils.get_disc_batch(
X_full_batch,
X_sketch_batch,
generator_model,
batch_counter,
patch_size,
image_data_format,
label_smoothing=label_smoothing,
label_flipping=label_flipping)
# Update the discriminator
disc_loss = discriminator_model.train_on_batch(X_disc, y_disc)
# Create a batch to feed the generator model
X_gen_target, X_gen = next(
data_utils.gen_batch(X_full_train, X_sketch_train,
batch_size))
y_gen = np.zeros((X_gen.shape[0], 2), dtype=np.uint8)
y_gen[:, 1] = 1
# Freeze the discriminator
discriminator_model.trainable = False
gen_loss = DCGAN_model.train_on_batch(X_gen,
[X_gen_target, y_gen])
# Unfreeze the discriminator
discriminator_model.trainable = True
batch_counter += 1
progbar.add(batch_size,
values=[("D logloss", disc_loss),
("G tot", gen_loss[0]),
("G L1", gen_loss[1]),
("G logloss", gen_loss[2])])
# Save images for visualization
if batch_counter % (n_batch_per_epoch / 2) == 0:
# Get new images from validation
data_utils.plot_generated_batch(
X_full_batch, X_sketch_batch, generator_model,
batch_size, image_data_format,
"{:03}_EPOCH_TRAIN".format(e + 1), save_dir)
X_full_batch, X_sketch_batch = next(
data_utils.gen_batch(X_full_val, X_sketch_val,
batch_size))
data_utils.plot_generated_batch(
X_full_batch, X_sketch_batch, generator_model,
batch_size, image_data_format,
"{:03}_EPOCH_VALID".format(e + 1), save_dir)
if batch_counter >= n_batch_per_epoch:
break
print("")
print('Epoch %s/%s, Time: %s' %
(e + 1, nb_epoch, time.time() - start))
if e % 5 == 0:
pass
# save models
# gen_weights_path = os.path.join('../../models/%s/gen_weights_epoch%s.h5' % (model_name, e))
# generator_model.save_weights(gen_weights_path, overwrite=True)
# disc_weights_path = os.path.join('../../models/%s/disc_weights_epoch%s.h5' % (model_name, e))
# discriminator_model.save_weights(disc_weights_path, overwrite=True)
# DCGAN_weights_path = os.path.join('../../models/%s/DCGAN_weights_epoch%s.h5' % (model_name, e))
# DCGAN_model.save_weights(DCGAN_weights_path, overwrite=True)
except KeyboardInterrupt:
pass
# save models
DCGAN_model.save(save_dir + 'DCGAN.h5')
generator_model.save(save_dir + 'GENERATOR.h5')
discriminator_model.save(save_dir + 'DISCRIMINATOR.h5')
def train(**kwargs):
"""
Train standard DCGAN model
args: **kwargs (dict) keyword arguments that specify the model hyperparameters
"""
# Roll out the parameters
generator = kwargs["generator"]
dset = kwargs["dset"]
img_dim = kwargs["img_dim"]
nb_epoch = kwargs["nb_epoch"]
batch_size = kwargs["batch_size"]
n_batch_per_epoch = kwargs["n_batch_per_epoch"]
bn_mode = kwargs["bn_mode"]
noise_dim = kwargs["noise_dim"]
noise_scale = kwargs["noise_scale"]
lr_D = kwargs["lr_D"]
lr_G = kwargs["lr_G"]
opt_D = kwargs["opt_D"]
opt_G = kwargs["opt_G"]
clamp_lower = kwargs["clamp_lower"]
clamp_upper = kwargs["clamp_upper"]
image_dim_ordering = kwargs["image_dim_ordering"]
epoch_size = n_batch_per_epoch * batch_size
print("\nExperiment parameters:")
for key in kwargs.keys():
print key, kwargs[key]
print("\n")
# Setup environment (logging directory etc)
general_utils.setup_logging("DCGAN")
# Load and normalize data
X_real_train = data_utils.load_image_dataset(dset, img_dim,
image_dim_ordering)
# Get the full real image dimension
img_dim = X_real_train.shape[-3:]
# Create optimizers
opt_G = data_utils.get_optimizer(opt_G, lr_G)
opt_D = data_utils.get_optimizer(opt_D, lr_D)
#######################
# Load models
#######################
noise_dim = (noise_dim, )
if generator == "upsampling":
generator_model = models.generator_upsampling(noise_dim,
img_dim,
bn_mode,
dset=dset)
else:
generator_model = models.generator_deconv(noise_dim,
img_dim,
bn_mode,
batch_size,
dset=dset)
discriminator_model = models.discriminator(img_dim, bn_mode, dset=dset)
DCGAN_model = models.DCGAN(generator_model, discriminator_model, noise_dim,
img_dim)
############################
# Compile models
############################
generator_model.compile(loss='mse', optimizer=opt_G)
discriminator_model.trainable = False
DCGAN_model.compile(loss=models.wasserstein, optimizer=opt_G)
discriminator_model.trainable = True
discriminator_model.compile(loss=models.wasserstein, optimizer=opt_D)
# Global iteration counter for generator updates
gen_iterations = 0
#################
# Start training
################
for e in range(nb_epoch):
# Initialize progbar and batch counter
progbar = generic_utils.Progbar(epoch_size)
batch_counter = 1
start = time.time()
while batch_counter < n_batch_per_epoch:
if gen_iterations < 25 or gen_iterations % 500 == 0:
disc_iterations = 100
else:
disc_iterations = kwargs["disc_iterations"]
###################################
# 1) Train the critic / discriminator
###################################
list_disc_loss_real = []
list_disc_loss_gen = []
for disc_it in range(disc_iterations):
# Clip discriminator weights
for l in discriminator_model.layers:
weights = l.get_weights()
weights = [
np.clip(w, clamp_lower, clamp_upper) for w in weights
]
l.set_weights(weights)
X_real_batch = next(
data_utils.gen_batch(X_real_train, batch_size))
# Create a batch to feed the discriminator model
X_disc_real, X_disc_gen = data_utils.get_disc_batch(
X_real_batch,
generator_model,
batch_counter,
batch_size,
noise_dim,
noise_scale=noise_scale)
# Update the discriminator
disc_loss_real = discriminator_model.train_on_batch(
X_disc_real, -np.ones(X_disc_real.shape[0]))
disc_loss_gen = discriminator_model.train_on_batch(
X_disc_gen, np.ones(X_disc_gen.shape[0]))
list_disc_loss_real.append(disc_loss_real)
list_disc_loss_gen.append(disc_loss_gen)
#######################
# 2) Train the generator
#######################
X_gen = X_gen = data_utils.sample_noise(noise_scale, batch_size,
noise_dim)
# Freeze the discriminator
discriminator_model.trainable = False
gen_loss = DCGAN_model.train_on_batch(X_gen,
-np.ones(X_gen.shape[0]))
# Unfreeze the discriminator
discriminator_model.trainable = True
gen_iterations += 1
batch_counter += 1
progbar.add(batch_size,
values=[("Loss_D", -np.mean(list_disc_loss_real) -
np.mean(list_disc_loss_gen)),
("Loss_D_real", -np.mean(list_disc_loss_real)),
("Loss_D_gen", np.mean(list_disc_loss_gen)),
("Loss_G", -gen_loss)])
# Save images for visualization ~2 times per epoch
if batch_counter % (n_batch_per_epoch / 2) == 0:
data_utils.plot_generated_batch(X_real_batch, generator_model,
batch_size, noise_dim,
image_dim_ordering)
print('\nEpoch %s/%s, Time: %s' %
(e + 1, nb_epoch, time.time() - start))
# Save model weights (by default, every 5 epochs)
data_utils.save_model_weights(generator_model, discriminator_model,
DCGAN_model, e)
def train(**kwargs):
"""
Train model
Load the whole train data in memory for faster operations
args: **kwargs (dict) keyword arguments that specify the model hyperparameters
"""
# Roll out the parameters
patch_size = kwargs["patch_size"]
image_data_format = kwargs["image_data_format"]
generator_type = kwargs["generator_type"]
dset = kwargs["dset"]
batch_size = kwargs["batch_size"]
n_batch_per_epoch = kwargs["n_batch_per_epoch"]
nb_epoch = kwargs["nb_epoch"]
model_name = kwargs["model_name"]
save_weights_every_n_epochs = kwargs["save_weights_every_n_epochs"]
visualize_images_every_n_epochs = kwargs["visualize_images_every_n_epochs"]
use_mbd = kwargs["use_mbd"]
label_smoothing = kwargs["use_label_smoothing"]
label_flipping_prob = kwargs["label_flipping_prob"]
use_l1_weighted_loss = kwargs["use_l1_weighted_loss"]
prev_model = kwargs["prev_model"]
discriminator_optimizer = kwargs["discriminator_optimizer"]
n_run_of_gen_for_1_run_of_disc = kwargs["n_run_of_gen_for_1_run_of_disc"]
MAX_FRAMES_PER_GIF = kwargs["MAX_FRAMES_PER_GIF"]
# batch_size = args.batch_size
# n_batch_per_epoch = args.n_batch_per_epoch
# nb_epoch = args.nb_epoch
# save_weights_every_n_epochs = args.save_weights_every_n_epochs
# generator_type = args.generator_type
# patch_size = args.patch_size
# label_smoothing = False
# label_flipping_prob = False
# dset = args.dset
# use_mbd = False
# Check and make the dataset
# If .h5 file of dset is not present, try making it
if not os.path.exists("../../data/processed/%s_data.h5" % dset):
print("dset %s_data.h5 not present in '../../data/processed'!" % dset)
if not os.path.exists("../../data/%s/" % dset):
print(
"dset folder %s not present in '../../data'!\n\nERROR: Dataset .h5 file not made, and dataset not available in '../../data/'.\n\nQuitting."
% dset)
return
else:
if not os.path.exists(
"../../data/%s/train" % dset) or not os.path.exists(
"../../data/%s/val" % dset) or not os.path.exists(
"../../data/%s/test" % dset):
print(
"'train', 'val' or 'test' folders not present in dset folder '../../data/%s'!\n\nERROR: Dataset must contain 'train', 'val' and 'test' folders.\n\nQuitting."
% dset)
return
else:
print("Making %s dataset" % dset)
subprocess.call([
'python3', '../data/make_dataset.py',
'../../data/%s' % dset, '3'
])
print("Done!")
epoch_size = n_batch_per_epoch * batch_size
init_epoch = 0
if prev_model:
print('\n\nLoading prev_model from', prev_model, '...\n\n')
prev_model_latest_gen = sorted(
glob.glob(os.path.join('../../models/', prev_model,
'*gen*.h5')))[-1]
prev_model_latest_disc = sorted(
glob.glob(os.path.join('../../models/', prev_model,
'*disc*.h5')))[-1]
prev_model_latest_DCGAN = sorted(
glob.glob(os.path.join('../../models/', prev_model,
'*DCGAN*.h5')))[-1]
# Find prev model name, epoch
model_name = prev_model_latest_DCGAN.split('models')[-1].split('/')[1]
init_epoch = int(prev_model_latest_DCGAN.split('epoch')[1][:5]) + 1
# Setup environment (logging directory etc), if no prev_model is mentioned
general_utils.setup_logging(model_name)
# img_dim = X_full_train.shape[-3:]
img_dim = (256, 256, 3)
# Get the number of non overlapping patch and the size of input image to the discriminator
nb_patch, img_dim_disc = data_utils.get_nb_patch(img_dim, patch_size,
image_data_format)
try:
# Create optimizers
opt_dcgan = Adam(lr=1E-3, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
if discriminator_optimizer == 'sgd':
opt_discriminator = SGD(lr=1E-3, momentum=0.9, nesterov=True)
elif discriminator_optimizer == 'adam':
opt_discriminator = Adam(lr=1E-3,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-08)
# Load generator model
generator_model = models.load("generator_unet_%s" % generator_type,
img_dim, nb_patch, use_mbd, batch_size,
model_name)
generator_model.compile(loss='mae', optimizer=opt_discriminator)
# Load discriminator model
discriminator_model = models.load("DCGAN_discriminator", img_dim_disc,
nb_patch, use_mbd, batch_size,
model_name)
discriminator_model.trainable = False
DCGAN_model = models.DCGAN(generator_model, discriminator_model,
img_dim, patch_size, image_data_format)
if use_l1_weighted_loss:
loss = [l1_weighted_loss, 'binary_crossentropy']
else:
loss = [l1_loss, 'binary_crossentropy']
loss_weights = [1E1, 1]
DCGAN_model.compile(loss=loss,
loss_weights=loss_weights,
optimizer=opt_dcgan)
discriminator_model.trainable = True
discriminator_model.compile(loss='binary_crossentropy',
optimizer=opt_discriminator)
# Load prev_model
if prev_model:
generator_model.load_weights(prev_model_latest_gen)
discriminator_model.load_weights(prev_model_latest_disc)
DCGAN_model.load_weights(prev_model_latest_DCGAN)
# Load and rescale data
print('\n\nLoading data...\n\n')
X_full_train, X_sketch_train, X_full_val, X_sketch_val = data_utils.load_data(
dset, image_data_format)
check_this_process_memory()
print('X_full_train: %.4f' % (X_full_train.nbytes / 2**30), "GB")
print('X_sketch_train: %.4f' % (X_sketch_train.nbytes / 2**30), "GB")
print('X_full_val: %.4f' % (X_full_val.nbytes / 2**30), "GB")
print('X_sketch_val: %.4f' % (X_sketch_val.nbytes / 2**30), "GB")
# Losses
disc_losses = []
gen_total_losses = []
gen_L1_losses = []
gen_log_losses = []
# Start training
print("\n\nStarting training\n\n")
for e in range(nb_epoch):
# Initialize progbar and batch counter
# progbar = generic_utils.Progbar(epoch_size)
batch_counter = 0
gen_total_loss_epoch = 0
gen_L1_loss_epoch = 0
gen_log_loss_epoch = 0
start = time.time()
for X_full_batch, X_sketch_batch in data_utils.gen_batch(
X_full_train, X_sketch_train, batch_size):
# Create a batch to feed the discriminator model
X_disc, y_disc = data_utils.get_disc_batch(
X_full_batch,
X_sketch_batch,
generator_model,
batch_counter,
patch_size,
image_data_format,
label_smoothing=label_smoothing,
label_flipping_prob=label_flipping_prob)
# Update the discriminator
disc_loss = discriminator_model.train_on_batch(X_disc, y_disc)
# Create a batch to feed the generator model
X_gen_target, X_gen = next(
data_utils.gen_batch(X_full_train, X_sketch_train,
batch_size))
y_gen = np.zeros((X_gen.shape[0], 2), dtype=np.uint8)
y_gen[:, 1] = 1
# Freeze the discriminator
discriminator_model.trainable = False
# Train generator
for _ in range(n_run_of_gen_for_1_run_of_disc - 1):
gen_loss = DCGAN_model.train_on_batch(
X_gen, [X_gen_target, y_gen])
gen_total_loss_epoch += gen_loss[
0] / n_run_of_gen_for_1_run_of_disc
gen_L1_loss_epoch += gen_loss[
1] / n_run_of_gen_for_1_run_of_disc
gen_log_loss_epoch += gen_loss[
2] / n_run_of_gen_for_1_run_of_disc
X_gen_target, X_gen = next(
data_utils.gen_batch(X_full_train, X_sketch_train,
batch_size))
gen_loss = DCGAN_model.train_on_batch(X_gen,
[X_gen_target, y_gen])
# Add losses
gen_total_loss_epoch += gen_loss[
0] / n_run_of_gen_for_1_run_of_disc
gen_L1_loss_epoch += gen_loss[
1] / n_run_of_gen_for_1_run_of_disc
gen_log_loss_epoch += gen_loss[
2] / n_run_of_gen_for_1_run_of_disc
# Unfreeze the discriminator
discriminator_model.trainable = True
# Progress
# progbar.add(batch_size, values=[("D logloss", disc_loss),
# ("G tot", gen_loss[0]),
# ("G L1", gen_loss[1]),
# ("G logloss", gen_loss[2])])
print("Epoch", str(init_epoch + e + 1), "batch",
str(batch_counter + 1), "D_logloss", disc_loss, "G_tot",
gen_loss[0], "G_L1", gen_loss[1], "G_log", gen_loss[2])
batch_counter += 1
if batch_counter >= n_batch_per_epoch:
break
gen_total_loss = gen_total_loss_epoch / n_batch_per_epoch
gen_L1_loss = gen_L1_loss_epoch / n_batch_per_epoch
gen_log_loss = gen_log_loss_epoch / n_batch_per_epoch
disc_losses.append(disc_loss)
gen_total_losses.append(gen_total_loss)
gen_L1_losses.append(gen_L1_loss)
gen_log_losses.append(gen_log_loss)
check_this_process_memory()
print('Epoch %s/%s, Time: %.4f' % (init_epoch + e + 1, init_epoch +
nb_epoch, time.time() - start))
# Save images for visualization
if (e + 1) % visualize_images_every_n_epochs == 0:
data_utils.plot_generated_batch(X_full_batch, X_sketch_batch,
generator_model, batch_size,
image_data_format, model_name,
"training", init_epoch + e + 1,
MAX_FRAMES_PER_GIF)
# Get new images from validation
X_full_batch, X_sketch_batch = next(
data_utils.gen_batch(X_full_val, X_sketch_val, batch_size))
data_utils.plot_generated_batch(X_full_batch, X_sketch_batch,
generator_model, batch_size,
image_data_format, model_name,
"validation",
init_epoch + e + 1,
MAX_FRAMES_PER_GIF)
# Plot losses
data_utils.plot_losses(disc_losses, gen_total_losses,
gen_L1_losses, gen_log_losses,
model_name, init_epoch)
# Save weights
if (e + 1) % save_weights_every_n_epochs == 0:
gen_weights_path = os.path.join(
'../../models/%s/gen_weights_epoch%05d_discLoss%.04f_genTotL%.04f_genL1L%.04f_genLogL%.04f.h5'
% (model_name, init_epoch + e, disc_losses[-1],
gen_total_losses[-1], gen_L1_losses[-1],
gen_log_losses[-1]))
generator_model.save_weights(gen_weights_path, overwrite=True)
disc_weights_path = os.path.join(
'../../models/%s/disc_weights_epoch%05d_discLoss%.04f_genTotL%.04f_genL1L%.04f_genLogL%.04f.h5'
% (model_name, init_epoch + e, disc_losses[-1],
gen_total_losses[-1], gen_L1_losses[-1],
gen_log_losses[-1]))
discriminator_model.save_weights(disc_weights_path,
overwrite=True)
DCGAN_weights_path = os.path.join(
'../../models/%s/DCGAN_weights_epoch%05d_discLoss%.04f_genTotL%.04f_genL1L%.04f_genLogL%.04f.h5'
% (model_name, init_epoch + e, disc_losses[-1],
gen_total_losses[-1], gen_L1_losses[-1],
gen_log_losses[-1]))
DCGAN_model.save_weights(DCGAN_weights_path, overwrite=True)
except KeyboardInterrupt:
pass
def train_toy(**kwargs):
"""
Train model
args: **kwargs (dict) keyword arguments that specify the model hyperparameters
"""
# Roll out the parameters
batch_size = kwargs["batch_size"]
n_batch_per_epoch = kwargs["n_batch_per_epoch"]
nb_epoch = kwargs["nb_epoch"]
noise_dim = kwargs["noise_dim"]
noise_scale = kwargs["noise_scale"]
lr_D = kwargs["lr_D"]
lr_G = kwargs["lr_G"]
opt_D = kwargs["opt_D"]
opt_G = kwargs["opt_G"]
clamp_lower = kwargs["clamp_lower"]
clamp_upper = kwargs["clamp_upper"]
epoch_size = n_batch_per_epoch * batch_size
print("\nExperiment parameters:")
for key in kwargs.keys():
print key, kwargs[key]
print("\n")
# Setup environment (logging directory etc)
general_utils.setup_logging("toy_MLP")
# Load and rescale data
X_real_train = data_utils.load_toy()
# Create optimizers
opt_G = data_utils.get_optimizer(opt_G, lr_G)
opt_D = data_utils.get_optimizer(opt_D, lr_D)
#######################
# Load models
#######################
noise_dim = (noise_dim, )
generator_model = models.generator_toy(noise_dim)
discriminator_model = models.discriminator_toy()
GAN_model = models.GAN_toy(generator_model, discriminator_model, noise_dim)
############################
# Compile models
############################
generator_model.compile(loss='mse', optimizer=opt_G)
discriminator_model.trainable = False
GAN_model.compile(loss=models.wasserstein, optimizer=opt_G)
discriminator_model.trainable = True
discriminator_model.compile(loss=models.wasserstein, optimizer=opt_D)
# Global iteration counter for generator updates
gen_iterations = 0
#################
# Start training
#################
for e in range(nb_epoch):
# Initialize progbar and batch counter
progbar = generic_utils.Progbar(epoch_size)
batch_counter = 1
start = time.time()
while batch_counter < n_batch_per_epoch:
disc_iterations = kwargs["disc_iterations"]
###################################
# 1) Train the critic / discriminator
###################################
list_disc_loss_real = []
list_disc_loss_gen = []
for disc_it in range(disc_iterations):
# Clip discriminator weights
for l in discriminator_model.layers:
weights = l.get_weights()
weights = [
np.clip(w, clamp_lower, clamp_upper) for w in weights
]
l.set_weights(weights)
X_real_batch = next(
data_utils.gen_batch(X_real_train, batch_size))
# Create a batch to feed the discriminator model
X_disc_real, X_disc_gen = data_utils.get_disc_batch(
X_real_batch,
generator_model,
batch_counter,
batch_size,
noise_dim,
noise_scale=noise_scale)
# Update the discriminator
disc_loss_real = discriminator_model.train_on_batch(
X_disc_real, -np.ones(X_disc_real.shape[0]))
disc_loss_gen = discriminator_model.train_on_batch(
X_disc_gen, np.ones(X_disc_gen.shape[0]))
list_disc_loss_real.append(disc_loss_real)
list_disc_loss_gen.append(disc_loss_gen)
#######################
# 2) Train the generator
#######################
X_gen = data_utils.sample_noise(noise_scale, batch_size, noise_dim)
# Freeze the discriminator
discriminator_model.trainable = False
gen_loss = GAN_model.train_on_batch(X_gen,
-np.ones(X_gen.shape[0]))
# Unfreeze the discriminator
discriminator_model.trainable = True
batch_counter += 1
progbar.add(batch_size,
values=[("Loss_D", -np.mean(list_disc_loss_real) -
np.mean(list_disc_loss_gen)),
("Loss_D_real", -np.mean(list_disc_loss_real)),
("Loss_D_gen", np.mean(list_disc_loss_gen)),
("Loss_G", -gen_loss)])
# # Save images for visualization
if gen_iterations % 50 == 0:
data_utils.plot_generated_toy_batch(X_real_train,
generator_model,
discriminator_model,
noise_dim, gen_iterations)
gen_iterations += 1
print('\nEpoch %s/%s, Time: %s' %
(e + 1, nb_epoch, time.time() - start))
def trainClassAux(**kwargs):
"""
Train standard DCGAN model
args: **kwargs (dict) keyword arguments that specify the model hyperparameters
"""
# Roll out the parameters
generator = kwargs["generator"]
discriminator = kwargs["discriminator"]
dset = kwargs["dset"]
img_dim = kwargs["img_dim"]
nb_epoch = kwargs["nb_epoch"]
batch_size = kwargs["batch_size"]
n_batch_per_epoch = kwargs["n_batch_per_epoch"]
bn_mode = kwargs["bn_mode"]
noise_dim = kwargs["noise_dim"]
noise_scale = kwargs["noise_scale"]
lr_D = kwargs["lr_D"]
lr_G = kwargs["lr_G"]
opt_D = kwargs["opt_D"]
opt_G = kwargs["opt_G"]
clamp_lower = kwargs["clamp_lower"]
clamp_upper = kwargs["clamp_upper"]
image_dim_ordering = kwargs["image_dim_ordering"]
epoch_size = n_batch_per_epoch * batch_size
deterministic = kwargs["deterministic"]
inject_noise = kwargs["inject_noise"]
model = kwargs["model"]
no_supertrain = kwargs["no_supertrain"]
noClass = kwargs["noClass"]
resume = kwargs["resume"]
name = kwargs["name"]
wd = kwargs["wd"]
C_weight = kwargs["C_weight"]
monsterClass = kwargs["monsterClass"]
pretrained = kwargs["pretrained"]
print("\nExperiment parameters:")
for key in kwargs.keys():
print key, kwargs[key]
print("\n")
# Setup environment (logging directory etc)
general_utils.setup_logging("DCGAN")
# Load and normalize data
if dset == "mnistM":
X_source_train,Y_source_train, X_source_test, Y_source_test, n_classes1 = data_utils.load_image_dataset(img_dim, image_dim_ordering,dset='mnist')
X_dest_train,Y_dest_train, X_dest_test, Y_dest_test,n_classes2 = data_utils.load_image_dataset(img_dim, image_dim_ordering,dset='mnistM')
elif dset == "washington_vandal50k":
X_source_train = data_utils.load_image_dataset(img_dim, image_dim_ordering,dset='washington')
X_dest_train = data_utils.load_image_dataset(img_dim, image_dim_ordering,dset='vandal50k')
elif dset == "washington_vandal12classes":
X_source_train = data_utils.load_image_dataset(img_dim, image_dim_ordering,dset='washington12classes')
X_dest_train = data_utils.load_image_dataset(img_dim, image_dim_ordering,dset='vandal12classes')
elif dset == "washington_vandal12classesNoBackground":
X_source_train,Y_source_train,n_classes1 = data_utils.load_image_dataset(img_dim, image_dim_ordering,dset='washington12classes')
X_dest_train,Y_dest_train,n_classes2 = data_utils.load_image_dataset(img_dim, image_dim_ordering,dset='vandal12classesNoBackground')
elif dset == "Wash_Vand_12class_LMDB":
X_source_train,Y_source_train,n_classes1 = data_utils.load_image_dataset(img_dim, image_dim_ordering,dset='Wash_12class_LMDB')
X_dest_train,Y_dest_train,n_classes2 = data_utils.load_image_dataset(img_dim, image_dim_ordering,dset='Vand_12class_LMDB')
elif dset == "Vand_Vand_12class_LMDB":
X_source_train,Y_source_train,X_source_test, Y_source_test,n_classes1 = data_utils.load_image_dataset(img_dim, image_dim_ordering,dset='Vand_12class_LMDB_Background')
X_dest_train,Y_dest_train, X_dest_test, Y_dest_test, n_classes2 = data_utils.load_image_dataset(img_dim, image_dim_ordering,dset='Vand_12class_LMDB')
elif dset == "Wash_Color_LMDB":
X_source_train,Y_source_train,X_source_test, Y_source_test,n_classes1 = data_utils.load_image_dataset(img_dim, image_dim_ordering,dset='Wash_Color_LMDB')
X_dest_train,Y_dest_train, X_dest_test, Y_dest_test, n_classes2 = data_utils.load_image_dataset(img_dim, image_dim_ordering,dset='Wash_Color_LMDB')
else:
print "dataset not supported"
if n_classes1 != n_classes2: #sanity check
print "number of classes mismatch between source and dest domains"
n_classes = n_classes1 #
img_source_dim = X_source_train.shape[-3:] # is it backend agnostic?
img_dest_dim = X_dest_train.shape[-3:]
X_source_train.flags.writeable = False
X_source_test.flags.writeable = False
X_dest_train.flags.writeable = False
X_dest_test.flags.writeable = False
# Create optimizers
opt_G = data_utils.get_optimizer(opt_G, lr_G)
opt_G_C = data_utils.get_optimizer(opt_G, lr_G*C_weight)
opt_D = data_utils.get_optimizer(opt_D, lr_D)
opt_C = data_utils.get_optimizer('SGD', 0.01)
#######################
# Load models
#######################
noise_dim = (noise_dim,)
if generator == "upsampling":
generator_model = models.generator_upsampling_mnistM(noise_dim, img_source_dim,img_dest_dim, bn_mode,deterministic,inject_noise,wd, dset=dset)
else:
generator_model = models.generator_deconv(noise_dim, img_dest_dim, bn_mode, batch_size, dset=dset)
discriminator_model = models.discriminator(img_dest_dim, bn_mode,model,wd,monsterClass,inject_noise,n_classes)
DCGAN_model = models.DCGAN_naive(generator_model, discriminator_model, noise_dim, img_source_dim)
classifier = models.resnet(img_dest_dim,n_classes,pretrained,wd=0.0001) #it is img_dest_dim because it is actually the generated image dim,that is equal to dest_dim
GenToClassifierModel = models.GenToClassifierModel(generator_model, classifier, noise_dim, img_source_dim)
############################
# Load weights
############################
if resume:
data_utils.load_model_weights(generator_model, discriminator_model, DCGAN_model, name)
#if pretrained:
# model_path = "../../models/DCGAN"
# class_weights_path = os.path.join(model_path, 'NoBackground_100epochs.h5')
# classifier.load_weights(class_weights_path)
############################
# Compile models
############################
generator_model.compile(loss='mse', optimizer=opt_G)
if model == 'wgan':
discriminator_model.compile(loss=models.wasserstein, optimizer=opt_D)
models.make_trainable(discriminator_model, False)
DCGAN_model.compile(loss=models.wasserstein, optimizer=opt_G)
if model == 'lsgan':
discriminator_model.compile(loss='mse', optimizer=opt_D)
models.make_trainable(discriminator_model, False)
DCGAN_model.compile(loss='mse', optimizer=opt_G)
classifier.compile(loss='categorical_crossentropy', optimizer=opt_C,metrics=['accuracy']) # it is actually never using optimizer
models.make_trainable(classifier, False)
GenToClassifierModel.compile(loss='categorical_crossentropy', optimizer=opt_G,metrics=['accuracy'])
#######################
# Train classifier
#######################
# if not pretrained:
# #print ("Testing accuracy on target domain test set before training:")
# loss1,acc1 =classifier.evaluate(X_dest_test, Y_dest_test,batch_size=256, verbose=0)
# print('\n Classifier Accuracy on target domain test set before training: %.2f%%' % (100 * acc1))
# classifier.fit(X_dest_train, Y_dest_train, validation_split=0.1, batch_size=512, nb_epoch=10, verbose=1)
# print ("\n Testing accuracy on target domain test set AFTER training:")
# else:
# print ("Loaded pretrained classifier, computing accuracy on target domain test set:")
# loss2,acc2 = classifier.evaluate(X_dest_test, Y_dest_test,batch_size=512, verbose=0)
# print('\n Classifier Accuracy on target domain test set after training: %.2f%%' % (100 * acc2))
#print ("Testing accuracy on source domain test set:")
# loss3, acc3 = classifier.evaluate(X_source_test, Y_source_test,batch_size=512, verbose=0)
# print('\n Classifier Accuracy on source domain test set: %.2f%%' % (100 * acc3))
# evaluating_GENned(noise_scale, noise_dim, X_source_test, Y_source_test, classifier, generator_model)
# model_path = "../../models/DCGAN"
# class_weights_path = os.path.join(model_path, 'VandToVand_5epochs.h5')
# classifier.save_weights(class_weights_path, overwrite=True)
#################
# GAN training
################
gen_iterations = 0
for e in range(nb_epoch):
# Initialize progbar and batch counter
progbar = generic_utils.Progbar(epoch_size)
batch_counter = 1
start = time.time()
while batch_counter < n_batch_per_epoch:
if no_supertrain is None:
if (gen_iterations < 25) and (not resume):
disc_iterations = 100
if gen_iterations % 500 == 0:
disc_iterations = 100
else:
disc_iterations = kwargs["disc_iterations"]
else:
if (gen_iterations <25) and (not resume):
disc_iterations = 100
else:
disc_iterations = kwargs["disc_iterations"]
###################################
# 1) Train the critic / discriminator
###################################
list_disc_loss_real = []
list_disc_loss_gen = []
list_gen_loss = []
list_class_loss_real = []
for disc_it in range(disc_iterations):
# Clip discriminator weights
# for l in discriminator_model.layers:
# weights = l.get_weights()
# weights = [np.clip(w, clamp_lower, clamp_upper) for w in weights]
# l.set_weights(weights)
X_dest_batch, Y_dest_batch,idx_dest_batch = next(data_utils.gen_batch(X_dest_train, Y_dest_train, batch_size))
X_source_batch, Y_source_batch,idx_source_batch = next(data_utils.gen_batch(X_source_train, Y_source_train, batch_size))
# Create a batch to feed the discriminator model
X_disc_real, X_disc_gen = data_utils.get_disc_batch(X_dest_batch,
generator_model,
batch_counter,
batch_size,
noise_dim,
X_source_batch,
noise_scale=noise_scale)
if model == 'wgan':
# Update the discriminator
disc_loss_real = discriminator_model.train_on_batch(X_disc_real, -np.ones(X_disc_real.shape[0]))
disc_loss_gen = discriminator_model.train_on_batch(X_disc_gen, np.ones(X_disc_gen.shape[0]))
if model == 'lsgan':
disc_loss_real = discriminator_model.train_on_batch(X_disc_real, np.ones(X_disc_real.shape[0]))
disc_loss_gen = discriminator_model.train_on_batch(X_disc_gen, np.zeros(X_disc_gen.shape[0]))
list_disc_loss_real.append(disc_loss_real)
list_disc_loss_gen.append(disc_loss_gen)
#######################
# 2) Train the generator with GAN loss
#######################
X_gen = data_utils.sample_noise(noise_scale, batch_size, noise_dim)
source_images = X_source_train[np.random.randint(0,X_source_train.shape[0],size=batch_size),:,:,:]
X_source_batch2, Y_source_batch2,idx_source_batch2 = next(data_utils.gen_batch(X_source_train, Y_source_train, batch_size))
# Freeze the discriminator
# discriminator_model.trainable = False
if model == 'wgan':
gen_loss = DCGAN_model.train_on_batch([X_gen,X_source_batch2], -np.ones(X_gen.shape[0]))
if model == 'lsgan':
gen_loss = DCGAN_model.train_on_batch([X_gen,X_source_batch2], np.ones(X_gen.shape[0]))
list_gen_loss.append(gen_loss)
#######################
# 3) Train the generator with Classifier loss
#######################
w1 = classifier.get_weights() #FOR DEBUG
if not noClass:
new_gen_loss = GenToClassifierModel.train_on_batch([X_gen,X_source_batch2], Y_source_batch2)
list_class_loss_real.append(new_gen_loss)
else:
list_class_loss_real.append(0.0)
w2 = classifier.get_weights() #FOR DEBUG
for a,b in zip(w1, w2):
if np.all(a == b):
print "no bug in GEN model update"
else:
print "BUG IN GEN MODEL UPDATE"
gen_iterations += 1
batch_counter += 1
progbar.add(batch_size, values=[("Loss_D", 0.5*np.mean(list_disc_loss_real) + 0.5*np.mean(list_disc_loss_gen)),
("Loss_D_real", np.mean(list_disc_loss_real)),
("Loss_D_gen", np.mean(list_disc_loss_gen)),
("Loss_G", np.mean(list_gen_loss)),
("Loss_classifier", np.mean(list_class_loss_real))])
# plot images 1 times per epoch
if batch_counter % (n_batch_per_epoch) == 0:
# train_WGAN.plot_images(X_dest_batch)
X_dest_batch_plot,Y_dest_batch_plot,idx_dest_plot = next(data_utils.gen_batch(X_dest_train,Y_dest_train, batch_size=32))
X_source_batch_plot,Y_source_batch_plot,idx_source_plot = next(data_utils.gen_batch(X_source_train,Y_source_train, batch_size=32))
data_utils.plot_generated_batch(X_dest_train,X_source_train, generator_model,
noise_dim, image_dim_ordering,idx_source_plot,batch_size=32)
print ("Dest labels:")
print (Y_dest_train[idx_source_plot].argmax(1))
print ("Source labels:")
print (Y_source_batch_plot.argmax(1))
print('\nEpoch %s/%s, Time: %s' % (e + 1, nb_epoch, time.time() - start))
# Save model weights (by default, every 5 epochs)
data_utils.save_model_weights(generator_model, discriminator_model, DCGAN_model, e, name)
evaluating_GENned(noise_scale,noise_dim,X_source_test,Y_source_test,classifier,generator_model)
loss3, acc3 = classifier.evaluate(X_source_test, Y_source_test,batch_size=512, verbose=0)
print('\n Classifier Accuracy on source domain test set: %.2f%%' % (100 * acc3))
save_interval = 2
#num_epoch = 200
#tc = int(num_epoch * 0.18)
#td = int(num_epoch * 0.02)
num_epoch = 13
tc = 2
td = 1
print('num_epoch=', num_epoch, 'tc=', tc, 'td=', td)
valids = np.ones((BATCH_SIZE, 1))
fakes = np.zeros((BATCH_SIZE, 1))
for epoch in range(num_epoch):
epoch_timer = ElapsedTimer('1 epoch training time')
#--------------------------------------------------------------------------------------
for batch in gen_batch(data_arr, BATCH_SIZE, IMG_SIZE, LD_CROP_SIZE,
MIN_LEN, MAX_LEN, mean_pixel_value):
origins, complnet_inputs, masked_origins, maskeds, ld_crop_yxhws = batch
#batch_timer = ElapsedTimer('1 batch training time')
#--------------------------------------------------------------------------------------
if epoch < tc:
mse_loss = compl_model.train_on_batch(
[masked_origins, complnet_inputs, maskeds], origins)
else:
completed = compl_model.predict(
[masked_origins, complnet_inputs, maskeds])
d_loss_real = discrim_model.train_on_batch(
[origins, ld_crop_yxhws], valids)
d_loss_fake = discrim_model.train_on_batch(
[completed, ld_crop_yxhws], fakes)
bce_d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)
# loss = [perceptual_loss, 'binary_crossentropy']
loss_weights = [1E1, 1]
DCGAN_model.compile(loss=loss, loss_weights=loss_weights, optimizer=G_opt)
discriminator_model.trainable = True
discriminator_model.compile(loss='binary_crossentropy', optimizer=D_opt)
# Start training
print("Start training")
for e in range(1, nb_epoch + 1):
# Initialize progbar and batch counter
progbar = generic_utils.Progbar(epoch_size)
print('Epoch %s/%s' % (e, nb_epoch))
for b in range(1, n_batch_per_epoch + 1):
X_HR_batch, X_LR_batch = gen_batch(train_list, batch_size)
# Create a batch to feed the discriminator model
X_disc, y_disc = get_disc_batch(X_HR_batch, X_LR_batch,
generator_model, b)
# Update the discriminator
disc_loss = discriminator_model.train_on_batch(X_disc, y_disc)
# Create a batch to feed the generator model
X_gen_target, X_gen = gen_batch(train_list, batch_size)
y_gen = np.zeros((X_gen.shape[0], 1), dtype=np.uint8)
y_gen[:, 0] = 1
# Freeze the discriminator
discriminator_model.trainable = False
gen_loss = DCGAN_model.train_on_batch(X_gen, [X_gen_target, y_gen])
def train(cat_dim,
noise_dim,
batch_size,
n_batch_per_epoch,
nb_epoch,
dset="mnist"):
"""
Train model
Load the whole train data in memory for faster operations
args: **kwargs (dict) keyword arguments that specify the model hyperparameters
"""
general_utils.setup_logging("IG")
# Load and rescale data
if dset == "mnist":
print("loading mnist data")
X_real_train, Y_real_train, X_real_test, Y_real_test = data_utils.load_mnist(
)
# pick 1000 sample for testing
# X_real_test = X_real_test[-1000:]
# Y_real_test = Y_real_test[-1000:]
img_dim = X_real_train.shape[-3:]
epoch_size = n_batch_per_epoch * batch_size
try:
# Create optimizers
opt_dcgan = Adam(lr=1E-3, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
opt_discriminator = Adam(lr=2E-4,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-08)
# opt_discriminator = SGD(lr=1E-4, momentum=0.9, nesterov=True)
# Load generator model
generator_model = models.load("generator_deconv",
cat_dim,
noise_dim,
img_dim,
batch_size,
dset=dset)
# Load discriminator model
discriminator_model = models.load("DCGAN_discriminator",
cat_dim,
noise_dim,
img_dim,
batch_size,
dset=dset)
generator_model.compile(loss='mse', optimizer=opt_discriminator)
# stop the discriminator to learn while in generator is learning
discriminator_model.trainable = False
DCGAN_model = models.DCGAN(generator_model, discriminator_model,
cat_dim, noise_dim)
list_losses = ['binary_crossentropy', 'categorical_crossentropy']
list_weights = [1, 1]
DCGAN_model.compile(loss=list_losses,
loss_weights=list_weights,
optimizer=opt_dcgan)
# Multiple discriminator losses
# allow the discriminator to learn again
discriminator_model.trainable = True
discriminator_model.compile(loss=list_losses,
loss_weights=list_weights,
optimizer=opt_discriminator)
# Start training
print("Start training")
for e in range(nb_epoch + 1):
# Initialize progbar and batch counter
# progbar = generic_utils.Progbar(epoch_size)
batch_counter = 1
start = time.time()
print("Epoch: {}".format(e))
for X_real_batch, Y_real_batch in zip(
data_utils.gen_batch(X_real_train, batch_size),
data_utils.gen_batch(Y_real_train, batch_size)):
# Create a batch to feed the discriminator model
X_disc_fake, y_disc_fake, noise_sample = data_utils.get_disc_batch(
X_real_batch,
Y_real_batch,
generator_model,
batch_size,
cat_dim,
noise_dim,
type="fake")
X_disc_real, y_disc_real = data_utils.get_disc_batch(
X_real_batch,
Y_real_batch,
generator_model,
batch_size,
cat_dim,
noise_dim,
type="real")
# Update the discriminator
disc_loss_fake = discriminator_model.train_on_batch(
X_disc_fake, [y_disc_fake, Y_real_batch])
disc_loss_real = discriminator_model.train_on_batch(
X_disc_real, [y_disc_real, Y_real_batch])
disc_loss = disc_loss_fake + disc_loss_real
# Create a batch to feed the generator model
# X_noise, y_gen = data_utils.get_gen_batch(batch_size, cat_dim, noise_dim)
# Freeze the discriminator
discriminator_model.trainable = False
gen_loss = DCGAN_model.train_on_batch(
[Y_real_batch, noise_sample], [y_disc_real, Y_real_batch])
# Unfreeze the discriminator
discriminator_model.trainable = True
# training validation
p_real_batch, p_Y_batch = discriminator_model.predict(
X_real_batch, batch_size=batch_size)
acc_train = data_utils.accuracy(p_Y_batch, Y_real_batch)
batch_counter += 1
# progbar.add(batch_size, values=[("D tot", disc_loss[0]),
# ("D cat", disc_loss[2]),
# ("G tot", gen_loss[0]),
# ("G cat", gen_loss[2]),
# ("P Real:", p_real_batch),
# ("Q acc", acc_train)])
# Save images for visualization
if batch_counter % (n_batch_per_epoch /
2) == 0 and e % 10 == 0:
data_utils.plot_generated_batch(X_real_batch,
generator_model,
batch_size, cat_dim,
noise_dim, e)
if batch_counter >= n_batch_per_epoch:
break
print("")
print('Epoch %s/%s, Time: %s' %
(e + 1, nb_epoch, time.time() - start))
_, p_Y_test = discriminator_model.predict(
X_real_test, batch_size=X_real_test.shape[0])
acc_test = data_utils.accuracy(p_Y_test, Y_real_test)
print("Epoch: {} Accuracy: {}".format(e + 1, acc_test))
if e % 1000 == 0:
gen_weights_path = os.path.join(
'../../models/IG/gen_weights.h5')
generator_model.save_weights(gen_weights_path, overwrite=True)
disc_weights_path = os.path.join(
'../../models/IG/disc_weights.h5')
discriminator_model.save_weights(disc_weights_path,
overwrite=True)
DCGAN_weights_path = os.path.join(
'../../models/IG/DCGAN_weights.h5')
DCGAN_model.save_weights(DCGAN_weights_path, overwrite=True)
except KeyboardInterrupt:
pass
def trainDECO(**kwargs):
"""
Train standard DCGAN model
args: **kwargs (dict) keyword arguments that specify the model hyperparameters
"""
# Roll out the parameters
generator = kwargs["generator"]
discriminator = kwargs["discriminator"]
dset = kwargs["dset"]
img_dim = kwargs["img_dim"]
nb_epoch = kwargs["nb_epoch"]
batch_size = kwargs["batch_size"]
n_batch_per_epoch = kwargs["n_batch_per_epoch"]
bn_mode = kwargs["bn_mode"]
noise_dim = kwargs["noise_dim"]
noise_scale = kwargs["noise_scale"]
lr_D = kwargs["lr_D"]
lr_G = kwargs["lr_G"]
opt_D = kwargs["opt_D"]
opt_G = kwargs["opt_G"]
clamp_lower = kwargs["clamp_lower"]
clamp_upper = kwargs["clamp_upper"]
image_dim_ordering = kwargs["image_dim_ordering"]
epoch_size = n_batch_per_epoch * batch_size
deterministic = kwargs["deterministic"]
inject_noise = kwargs["inject_noise"]
model = kwargs["model"]
no_supertrain = kwargs["no_supertrain"]
noClass = kwargs["noClass"]
resume = kwargs["resume"]
name = kwargs["name"]
wd = kwargs["wd"]
monsterClass = kwargs["monsterClass"]
pretrained = kwargs["pretrained"]
print("\nExperiment parameters:")
for key in kwargs.keys():
print key, kwargs[key]
print("\n")
# Setup environment (logging directory etc)
general_utils.setup_logging("DCGAN")
# Load and normalize data
if dset == "mnistM":
X_source_train,Y_source_train, _, _, n_classes1 = data_utils.load_image_dataset(img_dim, image_dim_ordering,dset='mnist')
X_dest_train,Y_dest_train, _, _,n_classes2 = data_utils.load_image_dataset(img_dim, image_dim_ordering,dset='mnistM')
elif dset == "washington_vandal50k":
X_source_train = data_utils.load_image_dataset(img_dim, image_dim_ordering,dset='washington')
X_dest_train = data_utils.load_image_dataset(img_dim, image_dim_ordering,dset='vandal50k')
elif dset == "washington_vandal12classes":
X_source_train = data_utils.load_image_dataset(img_dim, image_dim_ordering,dset='washington12classes')
X_dest_train = data_utils.load_image_dataset(img_dim, image_dim_ordering,dset='vandal12classes')
elif dset == "washington_vandal12classesNoBackground":
X_source_train,Y_source_train,n_classes1 = data_utils.load_image_dataset(img_dim, image_dim_ordering,dset='washington12classes')
X_dest_train,Y_dest_train,n_classes2 = data_utils.load_image_dataset(img_dim, image_dim_ordering,dset='vandal12classesNoBackground')
elif dset == "Wash_Vand_12class_LMDB":
X_source_train,Y_source_train,n_classes1 = data_utils.load_image_dataset(img_dim, image_dim_ordering,dset='Wash_12class_LMDB')
X_dest_train,Y_dest_train,n_classes2 = data_utils.load_image_dataset(img_dim, image_dim_ordering,dset='Vand_12class_LMDB')
elif dset == "Vand_Vand_12class_LMDB":
X_source_train,Y_source_train,X_source_test, Y_source_test,n_classes1 = data_utils.load_image_dataset(img_dim, image_dim_ordering,dset='Vand_12class_LMDB_Background')
X_dest_train,Y_dest_train, X_dest_test, Y_dest_test, n_classes2 = data_utils.load_image_dataset(img_dim, image_dim_ordering,dset='Vand_12class_LMDB')
elif dset == "Wash_Color_LMDB":
X_source_train,Y_source_train,X_source_test, Y_source_test,n_classes1 = data_utils.load_image_dataset(img_dim, image_dim_ordering,dset='Wash_Color_LMDB')
X_dest_train,Y_dest_train, X_dest_test, Y_dest_test, n_classes2 = data_utils.load_image_dataset(img_dim, image_dim_ordering,dset='Wash_Color_LMDB')
else:
print "dataset not supported"
if n_classes1 != n_classes2: #sanity check
print "number of classes mismatch between source and dest domains"
n_classes = n_classes1
# Get the full real image dimension
img_source_dim = X_source_train.shape[-3:] # is it backend agnostic?
img_dest_dim = X_dest_train.shape[-3:]
# Create optimizers
opt_G = data_utils.get_optimizer(opt_G, lr_G)
opt_D = data_utils.get_optimizer(opt_D, lr_D)
opt_C = data_utils.get_optimizer('SGD', 0.01)
#######################
# Load models
#######################
noise_dim = (noise_dim,)
generator_model = models.generator_upsampling_mnistM(noise_dim, img_source_dim,img_dest_dim, bn_mode,deterministic,inject_noise,wd, dset=dset)
classifier = models.resnet(img_dest_dim,n_classes,wd=0.0001) #it is img_dest_dim because it is actually the generated image dim,that is equal to dest_dim
GenToClassifierModel = models.GenToClassifierModel(generator_model, classifier, noise_dim, img_source_dim)
#################
# Load weight
################
if pretrained:
model_path = "../../models/DCGAN"
class_weights_path = os.path.join(model_path, 'NoBackground_100epochs.h5')
classifier.load_weights(class_weights_path)
# if resume: ########loading previous saved model weights
# data_utils.load_model_weights(generator_model, discriminator_model, DCGAN_model, name)
#######################
# Compile models
#######################
generator_model.compile(loss='mse', optimizer=opt_G)
# classifier.trainable = True # I wanna freeze the classifier without any training updates
classifier.compile(loss='categorical_crossentropy', optimizer=opt_C,metrics=['accuracy']) # it is actually never using optimizer
models.make_trainable(classifier, False)
GenToClassifierModel.compile(loss='categorical_crossentropy', optimizer=opt_G,metrics=['accuracy'])
#######################
# Train classifier
#######################
if not pretrained:
loss1,acc1 =classifier.evaluate(X_dest_test, Y_dest_test,batch_size=256, verbose=0)
print('\n Classifier Accuracy on target domain test set before training: %.00f%%' % (100.0 * acc1))
classifier.fit(X_dest_train, Y_dest_train, validation_split=0.1, batch_size=512, nb_epoch=20, verbose=1)
else:
print ("Loaded pretrained classifier, computing accuracy on target domain test set:")
loss2,acc2 = classifier.evaluate(X_dest_test, Y_dest_test,batch_size=512, verbose=0)
print('\n Classifier Accuracy on target domain test set after training: %.00f%%' % (100.0 * acc2))
loss3, acc3 = classifier.evaluate(X_source_test, Y_source_test,batch_size=512, verbose=0)
print('\n Classifier Accuracy on source domain test set: %.00f%%' % (100.0 * acc3))
evaluating_GENned(noise_scale,noise_dim,X_source_test,Y_source_test,classifier,generator_model)
# model_path = "../../models/DCGAN"
# class_weights_path = os.path.join(model_path, 'NoBackground_100epochs.h5')
# classifier.save_weights(class_weights_path, overwrite=True)
# models.make_trainable(classifier, False)
#classifier.trainable = False # I wanna freeze the classifier without any more training updates
# classifier.compile(loss='categorical_crossentropy', optimizer=opt_C,metrics=['accuracy'])
#################
# DECO training
################
gen_iterations = 0
for e in range(nb_epoch):
# Initialize progbar and batch counter
progbar = generic_utils.Progbar(epoch_size)
batch_counter = 1
start = time.time()
###################################
# 1) Train the critic / discriminator
###################################
list_class_loss_real = []
X_dest_batch, Y_dest_batch,idx_dest_batch = next(data_utils.gen_batch(X_dest_train, Y_dest_train, batch_size))
X_source_batch, Y_source_batch,idx_source_batch = next(data_utils.gen_batch(X_source_train, Y_source_train, batch_size))
#######################
# 2) Train the generator
#######################
X_gen = data_utils.sample_noise(noise_scale, batch_size*n_batch_per_epoch, noise_dim)
X_source_batch2, Y_source_batch2,idx_source_batch2 = next(data_utils.gen_batch(X_source_train, Y_source_train, batch_size*n_batch_per_epoch))
GenToClassifierModel.fit([X_gen, X_source_batch2], Y_source_batch2, batch_size=256, nb_epoch=1, verbose=1)
gen_iterations += 1
batch_counter += 1
# plot images 1 times per epoch
X_dest_batch_plot,Y_dest_batch_plot,idx_dest_plot = next(data_utils.gen_batch(X_dest_train,Y_dest_train, batch_size=32))
X_source_batch_plot,Y_source_batch_plot,idx_source_plot = next(data_utils.gen_batch(X_source_train,Y_source_train, batch_size=32))
data_utils.plot_generated_batch(X_dest_train,X_source_train, generator_model,
noise_dim, image_dim_ordering,idx_source_plot,batch_size=32)
print ("Dest labels:")
print (Y_dest_train[idx_source_plot].argmax(1))
print ("Source labels:")
print (Y_source_batch_plot.argmax(1))
print('\nEpoch %s/%s, Time: %s' % (e + 1, nb_epoch, time.time() - start))
# Save model weights (by default, every 5 epochs)
#data_utils.save_model_weights(generator_model, discriminator_model, DCGAN_model, e, name)
evaluating_GENned(noise_scale,noise_dim,X_source_test,Y_source_test,classifier,generator_model)
loss3, acc3 = classifier.evaluate(X_source_test, Y_source_test,batch_size=512, verbose=0)
print('\n Classifier Accuracy on source domain test set: %.00f%%' % (100.0 * acc3))
def train(**kwargs):
"""
Train standard DCGAN model
args: **kwargs (dict) keyword arguments that specify the model hyperparameters
"""
# Roll out the parameters
generator = kwargs["generator"]
dset = kwargs["dset"]
img_dim = kwargs["img_dim"]
nb_epoch = kwargs["nb_epoch"]
batch_size = kwargs["batch_size"]
n_batch_per_epoch = kwargs["n_batch_per_epoch"]
bn_mode = kwargs["bn_mode"]
noise_dim = kwargs["noise_dim"]
noise_scale = kwargs["noise_scale"]
lr_D = kwargs["lr_D"]
lr_G = kwargs["lr_G"]
opt_D = kwargs["opt_D"]
opt_G = kwargs["opt_G"]
clamp_lower = kwargs["clamp_lower"]
clamp_upper = kwargs["clamp_upper"]
image_dim_ordering = kwargs["image_dim_ordering"]
epoch_size = n_batch_per_epoch * batch_size
print("\nExperiment parameters:")
for key in kwargs.keys():
print key, kwargs[key]
print("\n")
# Setup environment (logging directory etc)
general_utils.setup_logging("DCGAN")
# Load and normalize data
X_real_train = data_utils.load_image_dataset(dset, img_dim, image_dim_ordering)
# Get the full real image dimension
img_dim = X_real_train.shape[-3:]
# Create optimizers
opt_G = data_utils.get_optimizer(opt_G, lr_G)
opt_D = data_utils.get_optimizer(opt_D, lr_D)
#######################
# Load models
#######################
noise_dim = (noise_dim,)
if generator == "upsampling":
generator_model = models.generator_upsampling(noise_dim, img_dim, bn_mode, dset=dset)
else:
generator_model = models.generator_deconv(noise_dim, img_dim, bn_mode, batch_size, dset=dset)
discriminator_model = models.discriminator(img_dim, bn_mode)
DCGAN_model = models.DCGAN(generator_model, discriminator_model, noise_dim, img_dim)
############################
# Compile models
############################
generator_model.compile(loss='mse', optimizer=opt_G)
discriminator_model.trainable = False
DCGAN_model.compile(loss=models.wasserstein, optimizer=opt_G)
discriminator_model.trainable = True
discriminator_model.compile(loss=models.wasserstein, optimizer=opt_D)
# Global iteration counter for generator updates
gen_iterations = 0
#################
# Start training
################
for e in range(nb_epoch):
# Initialize progbar and batch counter
progbar = generic_utils.Progbar(epoch_size)
batch_counter = 1
start = time.time()
while batch_counter < n_batch_per_epoch:
if gen_iterations < 25 or gen_iterations % 500 == 0:
disc_iterations = 100
else:
disc_iterations = kwargs["disc_iterations"]
###################################
# 1) Train the critic / discriminator
###################################
list_disc_loss_real = []
list_disc_loss_gen = []
for disc_it in range(disc_iterations):
# Clip discriminator weights
for l in discriminator_model.layers:
weights = l.get_weights()
weights = [np.clip(w, clamp_lower, clamp_upper) for w in weights]
l.set_weights(weights)
X_real_batch = next(data_utils.gen_batch(X_real_train, batch_size))
# Create a batch to feed the discriminator model
X_disc_real, X_disc_gen = data_utils.get_disc_batch(X_real_batch,
generator_model,
batch_counter,
batch_size,
noise_dim,
noise_scale=noise_scale)
# Update the discriminator
disc_loss_real = discriminator_model.train_on_batch(X_disc_real, -np.ones(X_disc_real.shape[0]))
disc_loss_gen = discriminator_model.train_on_batch(X_disc_gen, np.ones(X_disc_gen.shape[0]))
list_disc_loss_real.append(disc_loss_real)
list_disc_loss_gen.append(disc_loss_gen)
#######################
# 2) Train the generator
#######################
X_gen = data_utils.sample_noise(noise_scale, batch_size, noise_dim)
# Freeze the discriminator
discriminator_model.trainable = False
gen_loss = DCGAN_model.train_on_batch(X_gen, -np.ones(X_gen.shape[0]))
# Unfreeze the discriminator
discriminator_model.trainable = True
gen_iterations += 1
batch_counter += 1
progbar.add(batch_size, values=[("Loss_D", -np.mean(list_disc_loss_real) - np.mean(list_disc_loss_gen)),
("Loss_D_real", -np.mean(list_disc_loss_real)),
("Loss_D_gen", np.mean(list_disc_loss_gen)),
("Loss_G", -gen_loss)])
# Save images for visualization ~2 times per epoch
if batch_counter % (n_batch_per_epoch / 2) == 0:
data_utils.plot_generated_batch(X_real_batch, generator_model,
batch_size, noise_dim, image_dim_ordering)
print('\nEpoch %s/%s, Time: %s' % (e + 1, nb_epoch, time.time() - start))
# Save model weights (by default, every 5 epochs)
data_utils.save_model_weights(generator_model, discriminator_model, DCGAN_model, e)
def get_batch(A_data, A_labels, B_data, B_labels, batch_size):
A_data_batch, A_labels_batch, _ = next(data_utils.gen_batch(A_data, A_labels, batch_size))
B_data_batch, B_labels_batch, _ = next(data_utils.gen_batch(B_data, B_labels, batch_size))
return A_data_batch, A_labels_batch, B_data_batch, B_labels_batch
def train(**kwargs):
"""
Train model
Load the whole train data in memory for faster operations
args: **kwargs (dict) keyword arguments that specify the model hyperparameters
"""
# Roll out the parameters
batch_size = kwargs["batch_size"]
n_batch_per_epoch = kwargs["n_batch_per_epoch"]
nb_epoch = kwargs["nb_epoch"]
model_name = kwargs["model_name"]
generator = kwargs["generator"]
image_data_format = kwargs["image_data_format"]
img_dim = kwargs["img_dim"]
patch_size = kwargs["patch_size"]
bn_mode = kwargs["bn_mode"]
label_smoothing = kwargs["use_label_smoothing"]
label_flipping = kwargs["label_flipping"]
dset = kwargs["dset"]
use_mbd = kwargs["use_mbd"]
pretrained_model_path = kwargs["pretrained_model_path"]
epoch_size = n_batch_per_epoch * batch_size
# Setup environment (logging directory etc)
general_utils.setup_logging(model_name)
# Load and rescale data
X_full_train, X_sketch_train, X_full_val, X_sketch_val = data_utils.load_data(dset, image_data_format)
img_dim = X_full_train.shape[-3:]
# Get the number of non overlapping patch and the size of input image to the discriminator
nb_patch, img_dim_disc = data_utils.get_nb_patch(img_dim, patch_size, image_data_format)
try:
# Create optimizers
opt_dcgan = Adam(lr=1E-3, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
# opt_discriminator = SGD(lr=1E-3, momentum=0.9, nesterov=True)
opt_discriminator = Adam(lr=1E-3, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
load_pretrained = False
if pretrained_model_path:
load_pretrained = True
# Load generator model
generator_model = models.load("generator_unet_%s" % generator,
img_dim,
nb_patch,
bn_mode,
use_mbd,
batch_size,
load_pretrained)
# Load discriminator model
discriminator_model = models.load("DCGAN_discriminator",
img_dim_disc,
nb_patch,
bn_mode,
use_mbd,
batch_size,
load_pretrained)
generator_model.compile(loss='mae', optimizer=opt_discriminator)
discriminator_model.trainable = False
DCGAN_model = models.DCGAN(generator_model,
discriminator_model,
img_dim,
patch_size,
image_data_format)
loss = [l1_loss, 'binary_crossentropy']
loss_weights = [1E1, 1]
DCGAN_model.compile(loss=loss, loss_weights=loss_weights, optimizer=opt_dcgan)
discriminator_model.trainable = True
discriminator_model.compile(loss='binary_crossentropy', optimizer=opt_discriminator)
gen_loss = 100
disc_loss = 100
# Start training
print("Start training")
for e in range(nb_epoch):
# Initialize progbar and batch counter
progbar = generic_utils.Progbar(epoch_size)
batch_counter = 1
start = time.time()
for X_full_batch, X_sketch_batch in data_utils.gen_batch(X_full_train, X_sketch_train, batch_size):
# Create a batch to feed the discriminator model
X_disc, y_disc = data_utils.get_disc_batch(X_full_batch,
X_sketch_batch,
generator_model,
batch_counter,
patch_size,
image_data_format,
label_smoothing=label_smoothing,
label_flipping=label_flipping)
# Update the discriminator
disc_loss = discriminator_model.train_on_batch(X_disc, y_disc)
# Create a batch to feed the generator model
X_gen_target, X_gen = next(data_utils.gen_batch(X_full_train, X_sketch_train, batch_size))
y_gen = np.zeros((X_gen.shape[0], 2), dtype=np.uint8)
y_gen[:, 1] = 1
# Freeze the discriminator
discriminator_model.trainable = False
gen_loss = DCGAN_model.train_on_batch(X_gen, [X_gen_target, y_gen])
# Unfreeze the discriminator
discriminator_model.trainable = True
batch_counter += 1
progbar.add(batch_size, values=[("D logloss", disc_loss),
("G tot", gen_loss[0]),
("G L1", gen_loss[1]),
("G logloss", gen_loss[2])])
# Save images for visualization
if batch_counter % (n_batch_per_epoch / 2) == 0:
# Get new images from validation
data_utils.plot_generated_batch(X_full_batch, X_sketch_batch, generator_model,
batch_size, image_data_format, "training")
X_full_batch, X_sketch_batch = next(data_utils.gen_batch(X_full_val, X_sketch_val, batch_size))
data_utils.plot_generated_batch(X_full_batch, X_sketch_batch, generator_model,
batch_size, image_data_format, "validation")
if batch_counter >= n_batch_per_epoch:
break
print("")
print('Epoch %s/%s, Time: %s' % (e + 1, nb_epoch, time.time() - start))
if e % 5 == 0:
gen_weights_path = os.path.join('../../models/%s/gen_weights_epoch%s.h5' % (model_name, e))
generator_model.save_weights(gen_weights_path, overwrite=True)
disc_weights_path = os.path.join('../../models/%s/disc_weights_epoch%s.h5' % (model_name, e))
discriminator_model.save_weights(disc_weights_path, overwrite=True)
DCGAN_weights_path = os.path.join('../../models/%s/DCGAN_weights_epoch%s.h5' % (model_name, e))
DCGAN_model.save_weights(DCGAN_weights_path, overwrite=True)
except KeyboardInterrupt:
pass
def train(**kwargs):
"""
Train standard DCGAN model
args: **kwargs (dict) keyword arguments that specify the model hyperparameters
"""
# Roll out the parameters
generator = kwargs["generator"]
dset = kwargs["dset"]
img_dim = kwargs["img_dim"]
nb_epoch = kwargs["nb_epoch"]
batch_size = kwargs["batch_size"]
n_batch_per_epoch = kwargs["n_batch_per_epoch"]
noise_dim = kwargs["noise_dim"]
noise_scale = kwargs["noise_scale"]
lr_D = kwargs["lr_D"]
lr_G = kwargs["lr_G"]
opt_D = kwargs["opt_D"]
opt_G = kwargs["opt_G"]
clamp_lower = kwargs["clamp_lower"]
clamp_upper = kwargs["clamp_upper"]
image_data_format = kwargs["image_data_format"]
save_weights_every_n_epochs = kwargs["save_weights_every_n_epochs"]
save_only_last_n_weights = kwargs["save_only_last_n_weights"]
visualize_images_every_n_epochs = kwargs["visualize_images_every_n_epochs"]
model_name = kwargs["model_name"]
epoch_size = n_batch_per_epoch * batch_size
print("\nExperiment parameters:")
for key in kwargs.keys():
print(key, kwargs[key])
print("\n")
# Setup environment (logging directory etc)
general_utils.setup_logging(**kwargs)
# Load and normalize data
X_real_train, X_batch_gen = data_utils.load_image_dataset(
dset, img_dim, image_data_format, batch_size)
# Get the full real image dimension
img_dim = X_real_train.shape[-3:]
# Create optimizers
opt_G = data_utils.get_optimizer(opt_G, lr_G)
opt_D = data_utils.get_optimizer(opt_D, lr_D)
#######################
# Load models
#######################
noise_dim = (noise_dim, )
if generator == "upsampling":
generator_model = models.generator_upsampling(noise_dim,
img_dim,
dset=dset)
else:
generator_model = models.generator_deconv(noise_dim,
img_dim,
batch_size,
dset=dset)
discriminator_model = models.discriminator(img_dim)
DCGAN_model = models.DCGAN(generator_model, discriminator_model, noise_dim,
img_dim)
############################
# Compile models
############################
generator_model.compile(loss='mse', optimizer=opt_G)
discriminator_model.trainable = False
DCGAN_model.compile(loss=models.wasserstein, optimizer=opt_G)
discriminator_model.trainable = True
discriminator_model.compile(loss=models.wasserstein, optimizer=opt_D)
# Global iteration counter for generator updates
gen_iterations = 0
disc_losses = []
disc_losses_real = []
disc_losses_gen = []
gen_losses = []
#################
# Start training
################
try:
for e in range(nb_epoch):
print('--------------------------------------------')
print('[{0:%Y/%m/%d %H:%M:%S}] Epoch {1:d}/{2:d}\n'.format(
datetime.datetime.now(), e + 1, nb_epoch))
# Initialize progbar and batch counter
progbar = generic_utils.Progbar(epoch_size)
batch_counter = 0
start = time.time()
disc_loss_batch = 0
disc_loss_real_batch = 0
disc_loss_gen_batch = 0
gen_loss_batch = 0
for batch_counter in range(n_batch_per_epoch):
if gen_iterations < 25 or gen_iterations % 500 == 0:
disc_iterations = 100
else:
disc_iterations = kwargs["disc_iterations"]
###################################
# 1) Train the critic / discriminator
###################################
list_disc_loss_real = []
list_disc_loss_gen = []
for disc_it in range(disc_iterations):
# Clip discriminator weights
for l in discriminator_model.layers:
weights = l.get_weights()
weights = [
np.clip(w, clamp_lower, clamp_upper)
for w in weights
]
l.set_weights(weights)
X_real_batch = next(
data_utils.gen_batch(X_real_train, X_batch_gen,
batch_size))
# Create a batch to feed the discriminator model
X_disc_real, X_disc_gen = data_utils.get_disc_batch(
X_real_batch,
generator_model,
batch_counter,
batch_size,
noise_dim,
noise_scale=noise_scale)
# Update the discriminator
disc_loss_real = discriminator_model.train_on_batch(
X_disc_real, -np.ones(X_disc_real.shape[0]))
disc_loss_gen = discriminator_model.train_on_batch(
X_disc_gen, np.ones(X_disc_gen.shape[0]))
list_disc_loss_real.append(disc_loss_real)
list_disc_loss_gen.append(disc_loss_gen)
#######################
# 2) Train the generator
#######################
X_gen = data_utils.sample_noise(noise_scale, batch_size,
noise_dim)
# Freeze the discriminator
discriminator_model.trainable = False
gen_loss = DCGAN_model.train_on_batch(X_gen,
-np.ones(X_gen.shape[0]))
# Unfreeze the discriminator
discriminator_model.trainable = True
gen_iterations += 1
disc_loss_batch += -np.mean(list_disc_loss_real) - np.mean(
list_disc_loss_gen)
disc_loss_real_batch += -np.mean(list_disc_loss_real)
disc_loss_gen_batch += np.mean(list_disc_loss_gen)
gen_loss_batch += -gen_loss
progbar.add(batch_size,
values=[
("Loss_D", -np.mean(list_disc_loss_real) -
np.mean(list_disc_loss_gen)),
("Loss_D_real", -np.mean(list_disc_loss_real)),
("Loss_D_gen", np.mean(list_disc_loss_gen)),
("Loss_G", -gen_loss)
])
# # Save images for visualization ~2 times per epoch
# if batch_counter % (n_batch_per_epoch / 2) == 0:
# data_utils.plot_generated_batch(X_real_batch, generator_model,
# batch_size, noise_dim, image_data_format)
disc_losses.append(disc_loss_batch / n_batch_per_epoch)
disc_losses_real.append(disc_loss_real_batch / n_batch_per_epoch)
disc_losses_gen.append(disc_loss_gen_batch / n_batch_per_epoch)
gen_losses.append(gen_loss_batch / n_batch_per_epoch)
# Save images for visualization
if (e + 1) % visualize_images_every_n_epochs == 0:
data_utils.plot_generated_batch(X_real_batch, generator_model,
e, batch_size, noise_dim,
image_data_format, model_name)
data_utils.plot_losses(disc_losses, disc_losses_real,
disc_losses_gen, gen_losses, model_name)
# Save model weights (by default, every 5 epochs)
data_utils.save_model_weights(generator_model, discriminator_model,
DCGAN_model, e,
save_weights_every_n_epochs,
save_only_last_n_weights, model_name)
end = time.time()
print('\nEpoch %s/%s END, Time: %s' %
(e + 1, nb_epoch, end - start))
start = end
except KeyboardInterrupt:
pass
gen_weights_path = '../../models/%s/generator_latest.h5' % (model_name)
print("Saving", gen_weights_path)
generator_model.save(gen_weights_path, overwrite=True)
