def main():
args = get_args()
input_path1 = args.input1
input_path2 = args.input2
db_name = args.db
batch_size = args.batch_size
nb_epochs = args.nb_epochs
netType1 = args.netType1
netType2 = args.netType2
logging.debug("Loading training data...")
image1, age1, image_size = load_data_npz(input_path1)
logging.debug("Loading testing data...")
image2, age2, image_size = load_data_npz(input_path2)
start_decay_epoch = [30, 60]
optMethod = Adam()
stage_num = [3, 3, 3]
lambda_local = 0.25 * (netType1 % 5)
lambda_d = 0.25 * (netType2 % 5)
model = SSR_net(image_size, stage_num, lambda_local, lambda_d)()
save_name = 'ssrnet_%d_%d_%d_%d_%s_%s' % (stage_num[0], stage_num[1],
stage_num[2], image_size,
lambda_local, lambda_d)
model.compile(optimizer=optMethod, loss=["mae"], metrics={'pred_a': 'mae'})
if db_name == "megaage":
weight_file = "./pre-trained/wiki/ssrnet_3_3_3_64_1.0_1.0/ssrnet_3_3_3_64_1.0_1.0.h5"
#weight_file = "./pre-trained/imdb/ssrnet_3_3_3_64_1.0_1.0/ssrnet_3_3_3_64_1.0_1.0.h5"
model.load_weights(weight_file)
logging.debug("Model summary...")
model.count_params()
model.summary()
logging.debug("Saving model...")
mk_dir(db_name + "_models")
mk_dir(db_name + "_models/batch_size_%d/" % (batch_size))
mk_dir(db_name + "_models/batch_size_%d/" % (batch_size) + save_name)
mk_dir(db_name + "_checkpoints")
mk_dir(db_name + "_checkpoints/batch_size_%d/" % (batch_size))
plot_model(model,
to_file=db_name + "_models/batch_size_%d/" % (batch_size) +
save_name + "/" + save_name + ".png")
with open(
os.path.join(
db_name + "_models/batch_size_%d/" % (batch_size) + save_name,
save_name + '.json'), "w") as f:
f.write(model.to_json())
decaylearningrate = TYY_callbacks.DecayLearningRate(start_decay_epoch)
callbacks = [
ModelCheckpoint(db_name + "_checkpoints/batch_size_%d/" %
(batch_size) +
"weights.{epoch:02d}-{val_loss:.2f}.hdf5",
monitor="val_loss",
verbose=1,
save_best_only=True,
mode="auto"), decaylearningrate
]
logging.debug("Running training...")
data_num = len(image1) + len(image2)
indexes1 = np.arange(len(image1))
indexes2 = np.arange(len(image2))
np.random.shuffle(indexes1)
np.random.shuffle(indexes2)
x_train = image1[indexes1]
x_test = image2[indexes2]
y_train_a = age1[indexes1]
y_test_a = age2[indexes2]
train_num = len(image1)
hist = model.fit_generator(generator=data_generator_reg(
X=x_train, Y=y_train_a, batch_size=batch_size),
steps_per_epoch=train_num // batch_size,
validation_data=(x_test, [y_test_a]),
epochs=nb_epochs,
verbose=1,
callbacks=callbacks)
logging.debug("Saving weights...")
model.save_weights(os.path.join(
db_name + "_models/batch_size_%d/" % (batch_size) + save_name,
save_name + '.h5'),
overwrite=True)
pd.DataFrame(hist.history).to_hdf(
os.path.join(
db_name + "_models/batch_size_%d/" % (batch_size) + save_name,
'history_' + save_name + '.h5'), "history")
def main():
args = get_args()
input_path = args.input
db_name = args.db
batch_size = args.batch_size
nb_epochs = args.nb_epochs
validation_split = args.validation_split
netType1 = args.netType1
netType2 = args.netType2
logging.debug("Loading data...")
image, gender, age, image_size = load_data_npz(input_path)
x_data = image
y_data_a = age
start_decay_epoch = [30,60]
optMethod = Adam()
stage_num = [3,3,3]
lambda_local = 0.25*(netType1%5)
lambda_d = 0.25*(netType2%5)
model = SSR_net(image_size,stage_num, lambda_local, lambda_d)()
save_name = 'ssrnet_%d_%d_%d_%d_%s_%s' % (stage_num[0],stage_num[1],stage_num[2], image_size, lambda_local, lambda_d)
model.compile(optimizer=optMethod, loss=["mae"], metrics={'pred_a':'mae'})
if db_name == "wiki":
weight_file = f'imdb_models/{save_name}/{save_name}.h5'
if os.path.isfile(weight_file): # error out if file not exist
print("previous weight loading...")
model.load_weights(weight_file)
elif db_name == "morph":
weight_file = f'wiki_models/{save_name}/{save_name}.h5'
if os.path.isfile(weight_file):
print("previous weight loading...")
model.load_weights(weight_file)
logging.debug("Model summary...")
model.count_params()
model.summary()
logging.debug("Saving model...")
mk_dir(db_name+"_models")
mk_dir(db_name+"_models/"+save_name)
mk_dir(db_name+"_checkpoints")
plot_model(model, to_file=db_name+"_age_models/"+save_name+"/"+save_name+".png")
with open(os.path.join(db_name+"_age_models/"+save_name, save_name+'.json'), "w") as f:
f.write(model.to_json())
decaylearningrate = TYY_callbacks.DecayLearningRate(start_decay_epoch)
callbacks = [ModelCheckpoint(db_name+"_checkpoints/weights.{epoch:02d}-{val_loss:.2f}.hdf5",
monitor="val_loss",
verbose=1,
save_best_only=True,
mode="auto"), decaylearningrate
]
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_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_a = y_data_a[:train_num]
y_test_a = y_data_a[train_num:]
hist = model.fit_generator(generator=data_generator_reg(X=x_train, Y=y_train_a, batch_size=batch_size),
steps_per_epoch=train_num // batch_size,
validation_data=(x_test, [y_test_a]),
epochs=nb_epochs, verbose=1,
callbacks=callbacks)
logging.debug("Saving weights...")
model.save_weights(os.path.join(db_name+"_models/"+save_name, save_name+'.h5'), overwrite=True)
pd.DataFrame(hist.history).to_hdf(os.path.join(db_name+"_models/"+save_name, 'history_'+save_name+'.h5'), "history")
def main():
#dynamicaly allocate GPU memory
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
K.tensorflow_backend.set_session(sess)
print('[LOAD DATA]')
images, ageLabel, genderLabel = prepData(64)
n_fold = 1
img_size = 64
stage_num = [3,3,3]
lambda_local = 1
lambda_d = 1
imdb_model = SSR_net(img_size,stage_num, lambda_local, lambda_d)()
imdb_model.compile(optimizer='adam', loss="mae", metrics=["mae"])
imdb_model.load_weights("imdb_age_ssrnet_3_3_3_64_1.0_1.0.h5")
imdb_model_gender = SSR_net_general(img_size,stage_num, lambda_local, lambda_d)()
imdb_model_gender.compile(optimizer='adam', loss="mae", metrics=["binary_accuracy"])
imdb_model_gender.load_weights("imdb_gender_ssrnet_3_3_3_64_1.0_1.0.h5")
wiki_model = SSR_net(img_size,stage_num, lambda_local, lambda_d)()
wiki_model.compile(optimizer='adam', loss="mae", metrics=["mae"])
wiki_model.load_weights("wiki_age_ssrnet_3_3_3_64_1.0_1.0.h5")
wiki_model_gender = SSR_net_general(img_size,stage_num, lambda_local, lambda_d)()
wiki_model_gender.compile(optimizer='adam', loss="mae", metrics=["binary_accuracy"])
wiki_model_gender.load_weights("wiki_gender_ssrnet_3_3_3_64_1.0_1.0.h5")
morph_model = SSR_net(img_size,stage_num, lambda_local, lambda_d)()
morph_model.compile(optimizer='adam', loss="mae", metrics=["mae"])
morph_model.load_weights("morph_age_ssrnet_3_3_3_64_1.0_1.0.h5")
morph_model_gender = SSR_net_general(img_size,stage_num, lambda_local, lambda_d)()
morph_model_gender.compile(optimizer='adam', loss="mae", metrics=["binary_accuracy"])
morph_model_gender.load_weights("morph_gender_ssrnet_3_3_3_64_1.0_1.0.h5")
print('[K-FOLD] Started...')
kf = KFold(n_splits=10, shuffle=True, random_state=1)
kf_split = kf.split(ageLabel)
for _, test_idx in kf_split:
print('[K-FOLD] Fold {}'.format(n_fold))
testImages = images[test_idx]
testAge = ageLabel[test_idx]
testGender = genderLabel[test_idx]
scores = evaluate(imdb_model, testImages, testAge)
print('imdb Age score:', scores)
scores = evaluate(wiki_model, testImages, testAge)
print('wiki Age score:', scores)
scores = evaluate(morph_model, testImages, testAge)
print('morph Age score:', scores)
scores = evaluate(imdb_model_gender, testImages, testGender)
print('imdb Gender score:', scores)
scores = evaluate(wiki_model_gender, testImages, testGender)
print('wiki Gender score:', scores)
scores = evaluate(morph_model_gender, testImages, testGender)
print('morph Gender score:', scores)
n_fold += 1
del testImages, testAge, testGender, scores
