class_mode = 'categorical',
subset = subset)
while True:
X = gen.next()
if loss == 'arcface':
yield [X[0], X[1]], X[1]
else:
yield X[0], X[1]
train_generator = mobilefacenet_input_generator(train_datagen, train_path, 'training', LOSS_TYPE)
validate_generator = mobilefacenet_input_generator(train_datagen, train_path, 'validation', LOSS_TYPE)
'''Loading the model & re-defining'''
model = mobile_face_net_train(OLD_NUM_LABELS, loss = OLD_LOSS_TYPE)
print("Reading the pre-trained model... ")
model.load_weights(r'../Models/MobileFaceNet_train.h5')
# model.load_weights("E:\\Python_Coding\\MobileFaceNet\\tl_model_1905270955.hdf5")
print("Reading done. ")
model.summary()
# model.layers
if OLD_NUM_LABELS == NUM_LABELS and not FC_LAYER_CHANGE and OLD_LOSS_TYPE == LOSS_TYPE:
customed_model = model
customed_model.summary()
elif OLD_NUM_LABELS != NUM_LABELS or FC_LAYER_CHANGE or OLD_LOSS_TYPE != LOSS_TYPE:
# Re-define the model
model.layers.pop() # Remove the ArcFace Loss Layer
model.layers.pop() # Remove the Label Input Layer
Created on Mon May 13 14:39:29 2019
@author: TMaysGGS
"""
'''Last updated on 2020.03.29 03:14'''
'''Importing the libraries'''
from tensorflow.python.keras.models import Model
from Model_Structures.MobileFaceNet import mobile_face_net_train, mobile_face_net
NUM_LABELS = 67960
LOSS_TYPE = 'softmax'
'''Loading the training model'''
model = mobile_face_net_train(NUM_LABELS, loss = LOSS_TYPE)
model.load_weights('./Models/MobileFaceNet_train.h5')
model.summary()
pred_model = mobile_face_net()
pred_model.summary()
'''Extracting the weights & transfering to the prediction model'''
temp_weights_list = []
for layer in model.layers:
if 'dropout' in layer.name:
continue
temp_layer = model.get_layer(layer.name)
temp_weights = temp_layer.get_weights()
temp_weights_list.append(temp_weights)
sys.path.append('../')
from Model_Structures.MobileFaceNet import mobile_face_net_train
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
BATCH_SIZE = 128
NUM_LABELS = 153 # 40
m = 34363 # 8922
DATA_SPLIT = 0.01
TOTAL_EPOCHS = 30
LOSS_TYPE = 'arcface'
OPTIMIZER = Adam
LR = 0.001
'''Loading the model'''
model = mobile_face_net_train(
NUM_LABELS, loss=LOSS_TYPE) # change the loss to 'arcface' for fine-tuning
if LOSS_TYPE == 'arcface':
INPUT_NAME_LIST = []
for model_input in model.inputs:
INPUT_NAME_LIST.append(model_input.name[:-2])
'''Importing the TFRecord(s)'''
# Directory where the TFRecords files are
tfrecord_save_dir = r'../Data'
tfrecord_path_list = []
for file_name in os.listdir(tfrecord_save_dir):
if file_name[-9:] == '.tfrecord':
tfrecord_path_list.append(os.path.join(tfrecord_save_dir, file_name))
image_feature_description = {
'height': tf.io.FixedLenFeature([], tf.int64),
def CreateModel(nb_classes):
model = mobile_face_net_train(nb_classes, loss='softmax')
#model.compile(optimizer=Adam(lr=0.001, epsilon=1e-8), loss='categorical_crossentropy', metrics=['accuracy'])
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
return model
# -*- coding: utf-8 -*-
"""
Created on Mon May 13 14:39:29 2019
@author: TMaysGGS
"""
'''Last updated on 12/24/2019 11:16'''
'''Importing the libraries'''
from keras.models import Model
from keras.utils import plot_model
from Model_Structures.MobileFaceNet import mobile_face_net_train
NUM_LABELS = 67960
'''Loading the model & re-defining'''
model = mobile_face_net_train(NUM_LABELS, loss='arcface')
model.load_weights('./Models/MobileFaceNet_train.h5')
# model.load_weights("E:\\Python_Coding\\MobileFaceNet\\model.hdf5")
model.summary()
model.layers
# Re-define the model
model.layers.pop() # Remove the ArcFace Loss Layer
model.layers.pop() # Remove the Label Input Layer
model.summary()
model.layers[-1].outbound_nodes = []
model.outputs = [model.layers[-1].output] # Reset the output
output = model.get_layer(model.layers[-1].name).output
model.input
# The model used for prediction
yield [X[0], X[1]], X[1]
else:
yield X[0], X[1]
train_generator = mobilefacenet_input_generator(train_datagen, train_path,
'training', 'softmax')
validate_generator = mobilefacenet_input_generator(train_datagen, train_path,
'validation', 'softmax')
'''Training the Model'''
# Train on multiple GPUs
# from tensorflow.keras.utils import multi_gpu_model
# model = multi_gpu_model(model, gpus = 2)
model = mobile_face_net_train(
NUM_LABELS, loss='softmax') # change the loss to 'arcface' for fine-tuning
model.summary()
model.layers
model.compile(optimizer=Adam(lr=0.001, epsilon=1e-8),
loss='categorical_crossentropy',
metrics=['accuracy'])
# Save the model after every epoch
check_pointer = ModelCheckpoint(filepath='../Models/MobileFaceNet_train.h5',
verbose=1,
save_best_only=True)
# Interrupt the training when the validation loss is not decreasing
early_stopping = EarlyStopping(monitor='val_loss', patience=10000)
yield [X[0], X[1]], X[1]
else:
yield X[0], X[1]
train_generator = mobilefacenet_input_generator(train_datagen, train_path,
'training', 'softmax')
validate_generator = mobilefacenet_input_generator(train_datagen, train_path,
'validation', 'softmax')
'''Training the Model'''
# Train on multiple GPUs
# from keras.utils import multi_gpu_model
# model = multi_gpu_model(model, gpus = 2)
model = mobile_face_net_train(NUM_LABELS, loss='softmax')
model.summary()
model.layers
model.compile(optimizer=Adam(lr=0.001, epsilon=1e-8),
loss='categorical_crossentropy',
metrics=['accuracy'])
# Save the model after every epoch
from keras.callbacks import ModelCheckpoint
check_pointer = ModelCheckpoint(filepath='../Models/MobileFaceNet_train.h5',
verbose=1,
save_best_only=True)
# Interrupt the training when the validation loss is not decreasing
from keras.callbacks import EarlyStopping
class_mode = 'categorical',
subset = subset)
while True:
X = gen.next()
if loss == 'arcface':
yield [X[0], X[1]], X[1]
else:
yield X[0], X[1]
train_generator = mobilefacenet_input_generator(train_datagen, train_path, 'training', LOSS_TYPE)
validate_generator = mobilefacenet_input_generator(train_datagen, train_path, 'validation', LOSS_TYPE)
'''Loading the model & re-defining'''
model = mobile_face_net_train(OLD_NUM_LABELS, loss = OLD_LOSS_TYPE)
print("Reading the pre-trained model... ")
model.load_weights(r'../Models/MobileFaceNet_train.h5')
print("Reading done. ")
model.summary()
# plot_model(model, to_file = r'../Models/training_model.png', show_shapes = True, show_layer_names = True)
# model.layers
# SoftMax-ArcFace, FC layer change/not chage
if OLD_LOSS_TYPE == 'softmax' and LOSS_TYPE == 'arcface':
customed_model = mobile_face_net_train(NUM_LABELS, loss = LOSS_TYPE)
temp_weights_list = []
for layer in model.layers:
temp_layer = model.get_layer(layer.name)
temp_weights = temp_layer.get_weights()
temp_weights_list.append(temp_weights)