def train_fit(self):
tf_record_util = TFRecordUtility(self.output_len)
'''prepare callbacks'''
callbacks_list = self._prepare_callback()
''' define optimizers'''
optimizer = self._get_optimizer()
'''create train, validation, test data iterator'''
train_images, train_landmarks = tf_record_util.create_training_tensor_points(
tfrecord_filename=self.tf_train_path, batch_size=self.BATCH_SIZE)
validation_images, validation_landmarks = \
tf_record_util.create_training_tensor_points(tfrecord_filename=self.tf_eval_path,
batch_size=self.BATCH_SIZE)
'''creating model'''
cnn = CNNModel()
# model = tf.keras.models.load_model(self.weight)
model = cnn.get_model(train_images=train_images,
arch=self.arch,
num_output_layers=self.num_output_layers,
output_len=self.output_len,
input_tensor=train_images,
inp_shape=None)
if self.weight is not None:
model.load_weights(self.weight)
'''compiling model'''
model.compile(
loss=self._generate_loss(),
optimizer=optimizer,
metrics=['mse', 'mae'],
target_tensors=self._generate_target_tensors(train_landmarks),
loss_weights=self._generate_loss_weights())
'''train Model '''
print('< ========== Start Training ============= >')
history = model.fit(train_images,
train_landmarks,
epochs=self.EPOCHS,
steps_per_epoch=self.STEPS_PER_EPOCH,
validation_data=(validation_images,
validation_landmarks),
validation_steps=self.STEPS_PER_VALIDATION_EPOCH,
verbose=1,
callbacks=callbacks_list)
def train(self, teachers_arch, teachers_weight_files, teachers_weight_loss,
teachers_tf_train_paths, student_weight_file):
"""
:param teachers_arch: an array containing architecture of teacher networks
:param teachers_weight_files: an array containing teachers h5 files
:param teachers_weight_loss: an array containing weight of teachers model in loss function
:param teachers_tf_train_paths: an array containing path of train tf records
:param student_weight_file : student h5 weight path
:return: null
"""
tf_record_util = TFRecordUtility(self.output_len)
c_loss = Custom_losses()
'''-------------------------------------'''
''' preparing student models '''
'''-------------------------------------'''
teacher_models = []
cnn = CNNModel()
for i in range(len(teachers_arch)):
student_train_images, student_train_landmarks = tf_record_util.create_training_tensor_points(
tfrecord_filename=teachers_tf_train_paths[i],
batch_size=self.BATCH_SIZE)
model = cnn.get_model(train_images=student_train_images,
arch=teachers_arch[i],
num_output_layers=1,
output_len=self.output_len,
input_tensor=None)
model.load_weights(teachers_weight_files[i])
teacher_models.append(model)
'''---------------------------------'''
''' creating student model '''
'''---------------------------------'''
'''retrieve tf data'''
train_images, train_landmarks = tf_record_util.create_training_tensor_points(
tfrecord_filename=self.tf_train_path, batch_size=self.BATCH_SIZE)
validation_images, validation_landmarks = tf_record_util.create_training_tensor_points(
tfrecord_filename=self.tf_eval_path, batch_size=self.BATCH_SIZE)
'''create model'''
student_model = cnn.get_model(train_images=train_images,
arch=self.arch,
num_output_layers=1,
output_len=self.output_len,
input_tensor=train_images,
inp_shape=None)
if student_weight_file is not None:
student_model.load_weights(student_weight_file)
'''prepare callbacks'''
callbacks_list = self._prepare_callback()
''' define optimizers'''
optimizer = Adam(lr=1e-3,
beta_1=0.9,
beta_2=0.999,
decay=1e-5,
amsgrad=False)
'''create loss'''
# file = open("map_aug" + self.dataset_name, 'rb')
file = open("map_orig" + self.dataset_name, 'rb')
landmark_img_map = pickle.load(file)
file.close()
# loss_func = c_loss.custom_teacher_student_loss_cos(img_path=self.img_path, lnd_img_map=landmark_img_map,
# teacher_models=teacher_models,
# teachers_weight_loss=teachers_weight_loss,
# bath_size=self.BATCH_SIZE,
# num_points=self.output_len, cos_weight=cos_weight)
loss_func = c_loss.custom_teacher_student_loss(
img_path=self.img_path,
lnd_img_map=landmark_img_map,
teacher_models=teacher_models,
teachers_weight_loss=teachers_weight_loss,
bath_size=self.BATCH_SIZE,
num_points=self.output_len,
ds_name=self.dataset_name,
loss_type=0)
'''compiling model'''
student_model.compile(loss=loss_func,
optimizer=optimizer,
metrics=['mse', 'mae'],
target_tensors=train_landmarks)
print('< ========== Start Training Student============= >')
history = student_model.fit(
train_images,
train_landmarks,
epochs=self.EPOCHS,
steps_per_epoch=self.STEPS_PER_EPOCH,
validation_data=(validation_images, validation_landmarks),
validation_steps=self.STEPS_PER_VALIDATION_EPOCH,
verbose=1,
callbacks=callbacks_list)
