from data_utils import load_data, facades_generator
import cv2
x_batch, y_batch = next(facades_generator(256, 10))
x_train, y_train = load_data("channels_last")
print x_train.shape
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 = data_utils.load_data(dset, image_data_format)
img_dim = (256, 256, 3) # Manual entry
# 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
best_loss = [100] * 3
# 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.facades_generator(
img_dim, batch_size=batch_size):
X_gen, X_gen_target = next(
data_utils.facades_generator(img_dim,
batch_size=batch_size))
generator_model.train_on_batch(X_gen, X_gen_target)
# 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) # X_disc, y_disc
# Create a batch to feed the generator model
X_gen, X_gen_target = next(
data_utils.facades_generator(img_dim,
batch_size=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
figure_name = "training_" + str(e)
data_utils.plot_generated_batch(
X_full_batch, X_sketch_batch, generator_model,
batch_size, image_data_format, figure_name)
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)
Best_gen_L1_weights_path = os.path.join(
'../../models/%s/best_gen_L1_weights_epoch.h5' %
(model_name))
if (gen_loss[1] <= best_loss[1]):
generator_model.save_weights(Best_gen_L1_weights_path,
overwrite=True)
best_loss[1] = gen_loss[1]
Best_gen_Totweights_path = os.path.join(
'../../models/%s/best_gen_Totweights_epoch.h5' %
(model_name))
if (gen_loss[0] <= best_loss[0]):
generator_model.save_weights(Best_gen_Totweights_path,
overwrite=True)
best_loss[0] = gen_loss[0]
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"]
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 = (256,256,3) # Manual entry
# 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
best_loss=[100]*3
# 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.facades_generator(img_dim,batch_size=batch_size):
X_gen, X_gen_target = next(data_utils.facades_generator(img_dim,batch_size=batch_size))
generator_model.train_on_batch(X_gen, X_gen_target)
# 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) # X_disc, y_disc
# Create a batch to feed the generator model
X_gen, X_gen_target = next(data_utils.facades_generator(img_dim,batch_size=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
figure_name = "training_"+str(e)
data_utils.plot_generated_batch(X_full_batch, X_sketch_batch, generator_model,
batch_size, image_data_format, figure_name)
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)
Best_gen_L1_weights_path = os.path.join('../../models/%s/best_gen_L1_weights_epoch.h5' % (model_name))
if(gen_loss[1]<=best_loss[1]):
generator_model.save_weights(Best_gen_L1_weights_path, overwrite=True)
best_loss[1]=gen_loss[1]
Best_gen_Totweights_path = os.path.join('../../models/%s/best_gen_Totweights_epoch.h5' % (model_name))
if(gen_loss[0]<=best_loss[0]):
generator_model.save_weights(Best_gen_Totweights_path, overwrite=True)
best_loss[0]=gen_loss[0]
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"]
lastLayerActivation=kwargs["lastLayerActivation"]
PercentageOfTrianable=kwargs["PercentageOfTrianable"]
SpecificPathStr=kwargs["SpecificPathStr"]
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 = (256,256,3) # Manual entry
# 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)
"""
generator_model=CreatErrorMapModel(input_shape=img_dim,lastLayerActivation=lastLayerActivation, PercentageOfTrianable=PercentageOfTrianable)
# 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)
#-------------------------------------------------------------------------------
logpath=os.path.join('../../log','DepthMapWith'+lastLayerActivation+str(PercentageOfTrianable)+'UnTr'+SpecificPathStr)
modelPath=os.path.join('../../models','DepthMapwith'+lastLayerActivation+str(PercentageOfTrianable)+'Untr'+SpecificPathStr)
os.makedirs(logpath, exist_ok=True)
os.makedirs(modelPath, exist_ok=True)os.makedirs(modelPath, exist_ok=True)
#-----------------------PreTraining Depth Map-------------------------------------
nb_train_samples = 2000
nb_validation_samples =
epochs = 20
history=whole_model.fit_generator(data_utils.facades_generator(img_dim,batch_size=batch_size), samples_per_epoch=nb_train_samples,epochs=epochs,validation_data=data_utils.facades_generator(img_dim,batch_size=batch_size),nb_val_samples=nb_validation_ samples, callbacks=[
keras.callbacks.ModelCheckpoint(os.path.join(modelPath,'DepthMap_weightsBestLoss.h5'), monitor='val_loss', verbose=1, save_best_only=True),
keras.callbacks.ModelCheckpoint(os.path.join(modelPath,'DepthMap_weightsBestAcc.h5'), monitor='acc', verbose=1, save_best_only=True),
keras.callbacks.ReduceLROnPlateau(monitor='loss', factor=0.1, patience=2, verbose=1, mode='auto', epsilon=0.0001, cooldown=0, min_lr=0),
keras.callbacks.TensorBoard(log_dir=logpath, histogram_freq=0, batch_size=batchSize, write_graph=True, write_grads=False, write_images=True, embeddin gs_freq=0, embeddings_layer_names=None, embeddings_metadata=None)],)
#------------------------------------------------------------------------------------
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.facades_generator(img_dim,batch_size=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) # X_disc, y_disc
# Create a batch to feed the generator model
X_gen_target, X_gen = next(data_utils.facades_generator(img_dim,batch_size=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
figure_name = "training_"+str(e)
data_utils.plot_generated_batch(X_full_batch, X_sketch_batch, generator_model,
batch_size, image_data_format, figure_name)
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)
def trainDepthMap(**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"]
nb_train_samples = kwargs["nb_train_samples"]
nb_validation_samples = kwargs["nb_validation_samples"]
epochs = kwargs["nb_epoch"]
model_name = kwargs["model_name"]
lastLayerActivation = kwargs["lastLayerActivation"]
PercentageOfTrianable = kwargs["PercentageOfTrianable"]
SpecificPathStr = kwargs["SpecificPathStr"]
lossFunction = kwargs["lossFunction"]
if (kwargs["bnAtTheend"] != "True"):
bnAtTheend = False
else:
bnAtTheend = True
# 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 = (256, 256, 3) # Manual entry
try:
print("Ok before directory this point")
generator_model = CreatErrorMapModel(
input_shape=img_dim,
lastLayerActivation=lastLayerActivation,
PercentageOfTrianable=PercentageOfTrianable,
bnAtTheend=bnAtTheend,
lossFunction=lossFunction)
print("Ok before directory this point")
#-------------------------------------------------------------------------------
logpath = os.path.join(
'../../log', 'DepthMapWith' + lastLayerActivation +
str(PercentageOfTrianable) + 'UnTr' + SpecificPathStr)
modelPath = os.path.join(
'../../models', 'DepthMapwith' + lastLayerActivation +
str(PercentageOfTrianable) + 'Untr' + SpecificPathStr)
shutil.rmtree(logpath, ignore_errors=True)
shutil.rmtree(modelPath, ignore_errors=True)
os.makedirs(logpath, exist_ok=True)
os.makedirs(modelPath, exist_ok=True)
print("Ok until this point")
#-----------------------PreTraining Depth Map-------------------------------------
batchSize = batch_size
history = generator_model.fit_generator(
data_utils.facades_generator(img_dim, batch_size=batch_size),
samples_per_epoch=nb_train_samples,
epochs=epochs,
verbose=1,
validation_data=data_utils.facades_generator(
img_dim, batch_size=batch_size),
nb_val_samples=nb_validation_samples,
callbacks=[
keras.callbacks.ModelCheckpoint(os.path.join(
modelPath, 'DepthMap_weightsBestLoss.h5'),
monitor='val_loss',
verbose=1,
save_best_only=True),
keras.callbacks.ModelCheckpoint(os.path.join(
modelPath, 'DepthMap_weightsBestAcc.h5'),
monitor='acc',
verbose=1,
save_best_only=True),
keras.callbacks.ReduceLROnPlateau(monitor='loss',
factor=0.1,
patience=2,
verbose=1,
mode='auto',
epsilon=0.0001,
cooldown=0,
min_lr=0),
keras.callbacks.TensorBoard(log_dir=logpath,
histogram_freq=0,
batch_size=batchSize,
write_graph=True,
write_grads=False,
write_images=False,
embeddings_freq=0,
embeddings_layer_names=None,
embeddings_metadata=None)
],
)
ErrorMap_weights_path = os.path.join(modelPath, 'DepthMap_weights.h5')
generator_model.save_weights(ErrorMap_weights_path, overwrite=True)
plt.plot(history.history['loss'])
plt.savefig(logpath + "/history.png", bbox_inches='tight')
#------------------------------------------------------------------------------------
except KeyboardInterrupt:
pass