def main():
args = get_args()
input_path = args.input
batch_size = args.batch_size
nb_epochs = args.nb_epochs
depth = args.depth
k = args.width
validation_split = args.validation_split
logging.debug("Loading data...")
image, gender, age, _, image_size, _ = load_data(input_path)
X_data = image
y_data_g = np_utils.to_categorical(gender, 2)
y_data_a = np_utils.to_categorical(age, 101)
model = WideResNet(image_size, depth=depth, k=k)()
sgd = SGD(lr=0.1, momentum=0.9, nesterov=True)
model.compile(
optimizer=sgd,
loss=["categorical_crossentropy", "categorical_crossentropy"],
metrics=['accuracy'])
logging.debug("Model summary...")
model.count_params()
model.summary()
logging.debug("Saving model...")
mk_dir("models")
with open(os.path.join("models", "WRN_{}_{}.json".format(depth, k)),
"w") as f:
f.write(model.to_json())
mk_dir("checkpoints")
callbacks = [
LearningRateScheduler(schedule=Schedule(nb_epochs)),
ModelCheckpoint("checkpoints/weights.{epoch:02d}-{val_loss:.2f}.hdf5",
monitor="val_loss",
verbose=1,
save_best_only=True,
mode="auto")
]
logging.debug("Running training...")
hist = model.fit(X_data, [y_data_g, y_data_a],
batch_size=batch_size,
epochs=nb_epochs,
callbacks=callbacks,
validation_split=validation_split)
logging.debug("Saving weights...")
model.save_weights(os.path.join("models", "WRN_{}_{}.h5".format(depth, k)),
overwrite=True)
pd.DataFrame(hist.history).to_hdf(
os.path.join("models", "history_{}_{}.h5".format(depth, k)), "history")
def main():
args = get_args()
input_path = args.input
batch_size = args.batch_size
nb_epochs = args.nb_epochs
depth = args.depth
k = args.width
validation_split = args.validation_split
use_augmentation = args.aug
logging.debug("Loading data...")
image, gender, age, _, image_size, _ = load_data(input_path)
X_data = image
y_data_g = np_utils.to_categorical(gender, 2)
y_data_a = np_utils.to_categorical(age, 101)
model = WideResNet(image_size, depth=depth, k=k)()
sgd = SGD(lr=0.1, momentum=0.9, nesterov=True)
model.compile(
optimizer=sgd,
loss=["categorical_crossentropy", "categorical_crossentropy"],
metrics=['accuracy'])
logging.debug("Model summary...")
model.count_params()
model.summary()
logging.debug("Saving model...")
mk_dir("models")
with open(os.path.join("models", "WRN_{}_{}.json".format(depth, k)),
"w") as f:
f.write(model.to_json())
mk_dir("checkpoints")
callbacks = [
LearningRateScheduler(schedule=Schedule(nb_epochs)),
ModelCheckpoint("checkpoints/weights.{epoch:02d}-{val_loss:.2f}.hdf5",
monitor="val_loss",
verbose=1,
save_best_only=True,
mode="auto")
]
logging.debug("Running training...")
data_num = len(X_data)
indexes = np.arange(data_num)
np.random.shuffle(indexes)
X_data = X_data[indexes]
y_data_g = y_data_g[indexes]
y_data_a = y_data_a[indexes]
train_num = int(data_num * (1 - validation_split))
X_train = X_data[:train_num]
X_test = X_data[train_num:]
y_train_g = y_data_g[:train_num]
y_test_g = y_data_g[train_num:]
y_train_a = y_data_a[:train_num]
y_test_a = y_data_a[train_num:]
if use_augmentation:
datagen = ImageDataGenerator(width_shift_range=0.1,
height_shift_range=0.1,
horizontal_flip=True,
preprocessing_function=get_random_eraser(
v_l=0, v_h=255))
training_generator = MixupGenerator(X_train, [y_train_g, y_train_a],
batch_size=batch_size,
alpha=0.2,
datagen=datagen)()
hist = model.fit_generator(generator=training_generator,
steps_per_epoch=train_num // batch_size,
validation_data=(X_test,
[y_test_g, y_test_a]),
epochs=nb_epochs,
verbose=1,
callbacks=callbacks)
else:
hist = model.fit(X_train, [y_train_g, y_train_a],
batch_size=batch_size,
epochs=nb_epochs,
callbacks=callbacks,
validation_data=(X_test, [y_test_g, y_test_a]))
logging.debug("Saving weights...")
model.save_weights(os.path.join("models", "WRN_{}_{}.h5".format(depth, k)),
overwrite=True)
pd.DataFrame(hist.history).to_hdf(
os.path.join("models", "history_{}_{}.h5".format(depth, k)), "history")
def main():
args = get_args()
input_path = args.input
batch_size = args.batch_size
nb_epochs = args.nb_epochs
freeze_layers = args.freeze_layers
depth = args.depth
k = args.width
validation_split = args.validation_split
logging.debug("Loading data...")
image, gender, age, _, image_size, _ = load_data(input_path)
X_data = image
y_data_g = np_utils.to_categorical(gender, 2)
y_data_a = np_utils.to_categorical(age, 101)
#Load weights
weight_file = os.path.join("pretrained_models", "weights.18-4.06.hdf5")
model = WideResNet(image_size, depth=depth, k=k)()
model.load_weights(weight_file)
# set the first 50 layers
# to non-trainable (weights will not be updated)
print(len(model.layers))
if freeze_layers > 0:
for layer in model.layers[:freeze_layers]:
layer.trainable = False
sgd = SGD(lr=0.1, momentum=0.9, nesterov=True)
model.compile(
optimizer=sgd,
loss=["categorical_crossentropy", "categorical_crossentropy"],
metrics=['accuracy'])
logging.debug("Model summary...")
model.count_params()
model.summary()
logging.debug("Saving model...")
mk_dir("models")
with open(os.path.join("models", "WRN_{}_{}.json".format(depth, k)),
"w") as f:
f.write(model.to_json())
mk_dir("checkpoints")
callbacks = [
LearningRateScheduler(schedule=Schedule(nb_epochs)),
ModelCheckpoint("checkpoints/weights.{epoch:02d}-{val_loss:.2f}.hdf5",
monitor="val_loss",
verbose=1,
save_best_only=True,
mode="auto")
]
logging.debug("Running training...")
# print('length of X', len(X_data))
# print('length of y_data_g', y_data_g)
# print('length of y_data_a', len(y_data_a))
hist = model.fit(X_data, [y_data_g, y_data_a],
batch_size=batch_size,
epochs=nb_epochs,
callbacks=callbacks,
validation_split=validation_split)
logging.debug("Saving weights...")
model.save_weights(os.path.join("models", "WRN_{}_{}.h5".format(depth, k)),
overwrite=True)
pd.DataFrame(hist.history).to_hdf(
os.path.join("models", "history_{}_{}.h5".format(depth, k)), "history")
def main():
args = get_args()
input_path = args.input
batch_size = args.batch_size*args.gpus
nb_epochs = args.nb_epochs
depth = args.depth
k = args.width
validation_split = args.validation_split
img_size = args.img_size
val_path = args.val_path
pretrained_fil = args.pretrained_fil
input_shape = [img_size, img_size, 3]
patience = 30
gpu_num = args.gpus
train_db = args.db
logging.debug("Loading data...")
'''
image, gender, age, _, image_size, _ = load_data(input_path)
X_data = image
y_data_g = np_utils.to_categorical(gender, 2)
y_data_a = np_utils.to_categorical(age, 101)
'''
batchdataload = BatchLoader(input_path,batch_size,img_size,train_db)
valdataload = BatchLoader(val_path,batch_size,img_size)
model = WideResNet(img_size, depth=depth, k=k)()
#model = mini_XCEPTION(input_shape,101)
with open(os.path.join("ag_models", "WRN_{}_{}.json".format(depth, k)), "w") as f:
f.write(model.to_json())
if pretrained_fil :
model.load_weights(pretrained_fil)
#sgd = SGD(lr=0.001, momentum=0.7, nesterov=True)
adam = Adam(lr=0.0001,beta_1=0.9,beta_2=0.999,epsilon=1e-5)
#model.compile(optimizer=sgd, loss=["categorical_crossentropy", "categorical_crossentropy"],
# metrics=['accuracy'])
#if gpu_num >1:
#model = multi_gpu_model(model,gpu_num)
model.compile(optimizer=adam, loss=["categorical_crossentropy"],
metrics=['accuracy'])
logging.debug("Model summary...")
#model.count_params()
model.summary()
logging.debug("Saving model...")
if not os.path.exists("./ag_models"):
mk_dir("ag_models")
reduce_lr = ReduceLROnPlateau(monitor="val_loss",factor=0.1,patience=patience*2,verbose=1,min_lr=0.0000001)
early_stop = EarlyStopping('val_loss', patience=patience)
modelcheckpoint = ModelCheckpoint("ag_models/weights.{epoch:02d}-{val_loss:.2f}.hdf5",\
monitor="val_loss",verbose=1,save_best_only=True,mode="auto",period=1000)
#mk_dir("checkpoints")
#reduce_lr = LearningRateScheduler(schedule=reduce_lr)
#callbacks = [modelcheckpoint,early_stop,reduce_lr]
callbacks = [modelcheckpoint,reduce_lr]
logging.debug("Running training...")
#whole training
error_min = 0
if whole_data :
hist = model.fit_generator(data_geneter(batchdataload), steps_per_epoch=batchdataload.batch_num,
epochs=nb_epochs, verbose=1,
callbacks=callbacks,
validation_data=data_geneter(valdataload),
nb_val_samples=valdataload.batch_num,
nb_worker=1)
logging.debug("Saving weights...")
model.save_weights(os.path.join("ag_models", "WRN_{}_{}.h5".format(depth, k)),overwrite=True)
#pd.DataFrame(hist.history).to_hdf(os.path.join("ag_models", "history_{}_{}.h5".format(depth, k)), "history")
else:
epoch_step = 0
while epoch_step < nb_epochs:
step = 0
while step < batchdataload.batch_num:
#X_data, y_data_g, y_data_a = batch_geneter(batchdataload)
X_data, y_data_g = batch_geneter(batchdataload)
#hist = model.fit(X_data, [y_data_g, y_data_a], batch_size=batch_size, epochs=1, verbose=2)
hist = model.fit(X_data, y_data_g, batch_size=batch_size, epochs=1, verbose=2)
step+=1
if step % 100 ==0:
#val_data,val_g,val_a = batch_geneter(valdataload)
val_data,val_g = batch_geneter(valdataload)
#error_t = model.evaluate(val_data,[val_g,val_a],batch_size=batch_size,verbose=1)
error_t = model.evaluate(val_data,val_g,batch_size=batch_size,verbose=1)
print ("****** Epoch {} Step {}: ***********".format(str(epoch_step),str(step)) )
print (" loss: {}".format(error_t))
if epoch_step % 5 ==0:
#logging.debug("Saving weights...")
#val_data,val_g,val_a = batch_geneter(valdataload)
#error_t = model.evaluate(val_data,[val_g,val_a],batch_size=batch_size,verbose=1)
if error_t[4] >error_min:
logging.debug("Saving weights...")
model.save_weights(os.path.join("ag_models", "WRN_{}_{}_epoch{}_step{}.h5".format(depth, k,epoch_step,step)))
error_min = error_t[4]
epoch_step+=1
if epoch_step % 5 ==0:
logging.debug("Saving weights...")
#val_data,val_g,val_a = batch_geneter(valdataload)
val_data,val_g = batch_geneter(valdataload)
#error_t = model.evaluate(val_data,[val_g,val_a],batch_size=batch_size,verbose=1)
error_t = model.evaluate(val_data,val_g,batch_size=batch_size,verbose=1)
if error_t[1] >error_min:
model.save_weights(os.path.join("ag_models", "WRN_{}_{}_epoch{}.h5".format(depth, k,epoch_step)))
error_min = error_t[4]
error_min =0
def main():
args = get_args()
input_path = args.input
batch_size = args.batch_size
nb_epochs = args.nb_epochs
depth = args.depth
k = args.width
validation_split = args.validation_split
use_augmentation = args.aug
logging.debug("Loading data...")
image, gender, age, _, image_size, _ = load_data(input_path)
X_data = image
y_data_g = np_utils.to_categorical(gender, 2)
y_data_a = np_utils.to_categorical(age, 101)
model = WideResNet(image_size, depth=depth, k=k)()
sgd = SGD(lr=0.1, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd, loss=["categorical_crossentropy", "categorical_crossentropy"],
metrics=['accuracy'])
logging.debug("Model summary...")
model.count_params()
model.summary()
logging.debug("Saving model...")
mk_dir("models")
with open(os.path.join("models", "WRN_{}_{}.json".format(depth, k)), "w") as f:
f.write(model.to_json())
mk_dir("checkpoints")
callbacks = [LearningRateScheduler(schedule=Schedule(nb_epochs)),
ModelCheckpoint("checkpoints/weights.{epoch:02d}-{val_loss:.2f}.hdf5",
monitor="val_loss",
verbose=1,
save_best_only=True,
mode="auto")
]
logging.debug("Running training...")
data_num = len(X_data)
indexes = np.arange(data_num)
np.random.shuffle(indexes)
X_data = X_data[indexes]
y_data_g = y_data_g[indexes]
y_data_a = y_data_a[indexes]
train_num = int(data_num * (1 - validation_split))
X_train = X_data[:train_num]
X_test = X_data[train_num:]
y_train_g = y_data_g[:train_num]
y_test_g = y_data_g[train_num:]
y_train_a = y_data_a[:train_num]
y_test_a = y_data_a[train_num:]
if use_augmentation:
datagen = ImageDataGenerator(
width_shift_range=0.1,
height_shift_range=0.1,
horizontal_flip=True,
preprocessing_function=get_random_eraser(v_l=0, v_h=255))
training_generator = MixupGenerator(X_train, [y_train_g, y_train_a], batch_size=batch_size, alpha=0.2,
datagen=datagen)()
hist = model.fit_generator(generator=training_generator,
steps_per_epoch=train_num // batch_size,
validation_data=(X_test, [y_test_g, y_test_a]),
epochs=nb_epochs, verbose=1,
callbacks=callbacks)
else:
hist = model.fit(X_train, [y_train_g, y_train_a], batch_size=batch_size, epochs=nb_epochs, callbacks=callbacks,
validation_data=(X_test, [y_test_g, y_test_a]))
logging.debug("Saving weights...")
model.save_weights(os.path.join("models", "WRN_{}_{}.h5".format(depth, k)), overwrite=True)
pd.DataFrame(hist.history).to_hdf(os.path.join("models", "history_{}_{}.h5".format(depth, k)), "history")
def main():
args = get_args()
input_path = args.input
batch_size = args.batch_size
nb_epochs = args.nb_epochs
max_age = args.max_age + 1
depth = args.depth
k = args.width
transfer_learning = args.transfer_learning
validation_split = args.validation_split
use_augmentation = args.aug
initial_weights = '/home/paula/THINKSMARTER_/Model/demographics-model-prediction/pretrained_models/weights.18-4.06.hdf5'
# weight_file = '/home/paula/THINKSMARTER_/Model/age-gender-estimation-adapted/checkpoints/weights.09-4.32.hdf5'
_weight_decay = 0.0005
_use_bias = False
_weight_init = "he_normal"
logging.debug("Loading data...")
image, gender, age, _, image_size, _ = load_data(input_path)
X_data = image
y_data_g = np_utils.to_categorical(gender, 2)
y_data_a = np_utils.to_categorical(age, max_age)
if transfer_learning:
model = WideResNet(image_size, depth=depth, k=k, units_age=101)()
model.load_weights(initial_weights)
inputs = model.input
flatten = model.layers[-3].output # capa flatten
dense1 = Dense(units=2,
kernel_initializer=_weight_init,
use_bias=_use_bias,
kernel_regularizer=l2(_weight_decay),
activation="softmax")(flatten)
dense2 = Dense(units=117,
kernel_initializer=_weight_init,
use_bias=_use_bias,
kernel_regularizer=l2(_weight_decay),
activation="softmax")(flatten)
model = Model(inputs=inputs, outputs=[dense1, dense2])
# ---------------------------------
# IDEA: fine tuning (nomes entreno les dos ultimes capes)
# for layer in model.layers[:-2]:
# layer.trainable = False
else:
model = WideResNet(image_size, depth=depth, k=k, units_age=max_age)()
sgd = SGD(lr=0.1, momentum=0.9, nesterov=True)
model.compile(
optimizer=sgd,
loss=["categorical_crossentropy", "categorical_crossentropy"],
metrics=['accuracy'])
logging.debug("Model summary...")
model.count_params()
model.summary()
if args.plot_model:
plot_model(model,
to_file='experiments_pictures/model_plot.png',
show_shapes=True,
show_layer_names=True)
logging.debug("Saving model...")
mk_dir("models")
with open(os.path.join("models", "WRN_{}_{}.json".format(depth, k)),
"w") as f:
f.write(model.to_json())
mk_dir("checkpoints")
# tensorBoard = TensorBoard(log_dir='events', histogram_freq=0, batch_size=32, write_graph=True, write_grads=False, write_images=True, embeddings_freq=0, embeddings_layer_names=None, embeddings_metadata=None, embeddings_data=None)
callbacks = [
LearningRateScheduler(schedule=Schedule(nb_epochs)),
ModelCheckpoint("checkpoints/weights.{epoch:02d}-{val_loss:.2f}.hdf5",
monitor="val_loss",
verbose=1,
save_best_only=True,
mode="auto")
]
logging.debug("Running training...")
data_num = len(X_data)
indexes = np.arange(data_num)
np.random.shuffle(indexes)
X_data = X_data[indexes]
y_data_g = y_data_g[indexes]
y_data_a = y_data_a[indexes]
train_num = int(data_num * (1 - validation_split))
X_train = X_data[:train_num]
X_test = X_data[train_num:]
y_train_g = y_data_g[:train_num]
y_test_g = y_data_g[train_num:]
y_train_a = y_data_a[:train_num]
y_test_a = y_data_a[train_num:]
if use_augmentation:
datagen = ImageDataGenerator(width_shift_range=0.1,
height_shift_range=0.1,
horizontal_flip=True,
preprocessing_function=get_random_eraser(
v_l=0, v_h=255))
training_generator = MixupGenerator(X_train, [y_train_g, y_train_a],
batch_size=batch_size,
alpha=0.2,
datagen=datagen)()
hist = model.fit_generator(generator=training_generator,
steps_per_epoch=train_num // batch_size,
validation_data=(X_test,
[y_test_g, y_test_a]),
epochs=nb_epochs,
verbose=1,
callbacks=callbacks)
else:
hist = model.fit(X_train, [y_train_g, y_train_a],
batch_size=batch_size,
epochs=nb_epochs,
callbacks=callbacks,
validation_data=(X_test, [y_test_g, y_test_a]))
logging.debug("Saving weights...")
model.save_weights(os.path.join("models", "WRN_{}_{}.h5".format(depth, k)),
overwrite=True)
pd.DataFrame(hist.history).to_hdf(
os.path.join("models", "history_{}_{}.h5".format(depth, k)), "history")
with open('history_tmp.txt', 'w') as f:
for key in hist.history:
print(key, file=f)
f.write('\n')
json.dump(hist.history, f)
