def train(dir_name="lane_images"):
print("Loading images...")
images, labels = image_load.read_training(
format_size=model.standard_format, dir_name=dir_name, grayscale=False)
X = image_load.format_X(images, model.standard_format)
Y = image_load.format_Y(labels)
print("Training model...")
cnn = model.SmallModel(input_shape=model.standard_format + (3, ))
cnn.fit(X, Y)
cnn.summary()
print("Saving...")
save_model(cnn)
def main():
parser = get_parser()
args = parser.parse_args()
setup(args)
dataset = data.Dataset(args)
test_acc = []
tf.reset_default_graph()
if args.model_type == "student":
teacher_model = None
if args.load_teacher_from_checkpoint:
teacher_model = model.BigModel(args, "teacher")
teacher_model.start_session()
teacher_model.load_model_from_file(args.load_teacher_checkpoint_dir)
print("Verify Teacher State before Training Student")
teacher_model.run_inference(dataset)
student_model = model.SmallModel(args, "student")
student_model.start_session()
student_model.train(dataset, teacher_model)
acc = student_model.inference(dataset,10,0.5,args.checkpoint_dir)
print("student model acc with variation", acc)
# Testing student model on the best model based on validation set
student_model.start_session()
student_model.load_model_from_file(args.checkpoint_dir)
student_model.run_inference(dataset)
if args.load_teacher_from_checkpoint:
print("Verify Teacher State After Training student Model")
teacher_model.run_inference(dataset)
teacher_model.close_session()
student_model.close_session()
else:
teacher_model = model.BigModel(args, "teacher")
teacher_model.start_session()
#Uncomment the below line if training the teacher model
#teacher_model.train(dataset)
# Testing teacher model on the best model based on validation set
teacher_model.load_model_from_file(args.checkpoint_dir)
teacher_model.run_inference(dataset)
teacher_model.inference(dataset,10,0.5,args.checkpoint_dir)
def main():
paser = get_parser()
args = paser.parse_args()
setup(args)
dataset = data.Dataset(args)
tf.reset_default_graph()
if args.model_type == 'student':
teacher_model = None
#if args.load_teacher_from_checkpoint:
#teacher_model = model.BigModel(args, 'teacher')
#teacher_model.start_session()
#teacher_model.load_model_from_file(args.load_teacher_checkpoint_dir)
#teacher_model.run_inference(dataset)
student_model = model.SmallModel(args, 'student')
student_model.start_session()
# student_model.train(dataset,teacher_model)
student_model.train(dataset, 'teacher')
student_model.load_model_from_file(args.checkpoint_dir)
student_model.run_inference(dataset)
# if args.load_teacher_from_checkpoint:
# print("Verify Teacher State After Training student Model")
# teacher_model.run_inference(dataset)
# teacher_model.close_session()
student_model.close_session()
else:
print('run teacher')
teacher_model = model.BigModel(args, "teacher")
teacher_model.start_session()
teacher_model.train(dataset)
teacher_model.save_as_npy(dataset, args.batch_size)
# Testing teacher model on the best model based on validation set
teacher_model.load_model_from_file(args.checkpoint_dir)
teacher_model.run_inference(dataset)
teacher_model.close_session()
def main():
parser = get_parser()
args = parser.parse_args()
setup(args)
# read train data
train_set = utils.get_dataset(args.train_data_dir)
nrof_classes = len(train_set)
# read validation data
print('unit test directory: %s' % args.unit_test_dir)
unit_test_paths, unit_actual_issame = utils.get_val_paths(
os.path.expanduser(args.unit_test_dir))
nrof_test_img = len(unit_test_paths)
unit_issame_label = np.zeros(nrof_test_img)
for i in range(len(unit_actual_issame)):
unit_issame_label[2 * i] = unit_actual_issame[i]
unit_issame_label[2 * i + 1] = unit_actual_issame[i]
unit_issame_label = np.asarray(unit_issame_label, dtype=np.int32)
# Get a list of image paths and their labels
image_list, label_list = utils.get_image_paths_and_labels(train_set)
assert len(image_list) > 0, 'The dataset should not be empty'
print('Total number of train classes: %d' % nrof_classes)
print('Total number of train examples: %d' % len(image_list))
print("number of validation examples: %d" % nrof_test_img)
#ipdb.set_trace()
train_dataset = data_loader.DataLoader(image_list, label_list, [160, 160],
nrof_classes)
validation_dataset = data_loader.DataLoader(unit_test_paths,
unit_issame_label, [160, 160])
tf.reset_default_graph()
if args.model_type == "student":
teacher_model = None
if args.load_teacher_from_checkpoint:
teacher_model = model.BigModel(args, "teacher", nrof_classes,
nrof_test_img)
teacher_model.start_session()
teacher_model.load_model_from_file(
args.load_teacher_checkpoint_dir)
print("Verify Teacher State before Training Student")
teacher_model.run_inference(validation_dataset, unit_actual_issame)
student_model = model.SmallModel(args, "student", nrof_classes,
nrof_test_img)
student_model.start_session()
student_model.train(train_dataset, validation_dataset,
unit_actual_issame, teacher_model)
# Testing student model on the best model based on validation set
student_model.load_model_from_file(args.checkpoint_dir)
student_model.run_inference(validation_dataset, unit_actual_issame)
if args.load_teacher_from_checkpoint:
print("Verify Teacher State After Training student Model")
teacher_model.run_inference(validation_dataset, unit_actual_issame)
teacher_model.close_session()
student_model.close_session()
else:
teacher_model = model.BigModel(args, "teacher", nrof_classes,
nrof_test_img)
teacher_model.start_session()
teacher_model.train(train_dataset, validation_dataset,
unit_actual_issame)
# Testing teacher model on the best model based on validation set
teacher_model.load_model_from_file(args.checkpoint_dir)
teacher_model.run_inference(validation_dataset, unit_actual_issame)
teacher_model.close_session()