model_config = configparser.ConfigParser()
model_config.read(args.model)
model = ModelBuilder(model_config)
model.build_graph()
model.compile()
model.summary_txt()
model.print_png()
model.save_graph()
# Train Model
if args.train:
train_config = configparser.ConfigParser()
train_config.read(args.train)
trainer = ModelTrainer(train_config)
trainer.get_hyperparameters()
trainer.get_train_set()
trainer.get_dev_set()
trainer.get_callbacks()
train_history = model.train(trainer.fit_options)
model.save_weights(trainer.outputs_config['weights'])
trainer.write_outputs(train_history)
# Test Model
if args.test:
test_config = configparser.ConfigParser()
test_config.read(args.test)
tester = ModelTester(test_config)
tester.get_weights()
model.load_weights(tester.weights)
tester.get_test_set()
tester.get_header_list()
#parser.add_argument('--weights', type=str, default='./yolo_tf_weights/yolov3.weights.h5')
parser.add_argument('--num_epochs', type=int, default=100)
parser.add_argument('--batch_size', type=int, default=32)
args = parser.parse_args()
anchors = get_anchors(args.anchor_path)
print('Anchors:', anchors)
classes = get_classes(args.class_name_path)
num_classes = len(classes)
print('Number of Classes:', num_classes)
print('Image Shape', args.image_shape)
model = YoloV3(args.image_shape, anchors, num_classes)
model.build_graph()
#model.compile()
# Dataset
# Label: class, x, y, w, h (relative to image)
anchors_mask = [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
trainer = ModelTrainer()
trainer.get_input_options(args.image_shape, args.letterbox_resize, anchors,
num_classes, anchors_mask)
trainer.get_hyperparameters(args.num_epochs, args.batch_size)
trainer.get_train_set(args.train_input, args.train_target)
#dev_set = get_train_set(args.dev_set)
#model.load_weights(args.weights)
# Train Model