logging.basicConfig(level=logging.DEBUG)
if IS_TRAINING:
logger.info("Preprocess...")
preprocessor = ImagePreprocessor(preprocessor_config)
logger.info("Making dataset...")
data_loader_config = ImageSetDataLoaderConfig(
image_dict=preprocessor.get_image_dict(),
image_shape=INPUT_SHAPE_REVERSE,
shuffle_buffer_size=100000,
batch_size=100
)
data_loader = ImageSetDataLoader(data_loader_config)
model = CNNModel(model_config)
trainer = UniversalTrainer(model.get_model(), data_loader.get_dataset(), trainer_config)
logger.info("Training...")
trainer.train()
trainer.save("log/test.h5")
else:
model = CNNModel(model_config)
trainer = UniversalTrainer(model.get_model(), None, trainer_config)
trainer.load("log/test.h5")
valid_image_list = os.listdir(VALIDATION_IMAGE_ROOT)
logger.debug("Validation image list: %s", valid_image_list)
for image_file_name in valid_image_list:
image_file = os.path.join(VALIDATION_IMAGE_ROOT, image_file_name)
image = cv2.imread(image_file, flags=cv2.IMREAD_COLOR)
image_shape=INPUT_SHAPE_REVERSE,
shuffle_buffer_size=100000,
batch_size=100)
data_loader_province_config = ImageSetDataLoaderConfig(
image_dict=preprocessor_province.get_image_dict(),
image_shape=INPUT_SHAPE_REVERSE,
shuffle_buffer_size=100000,
batch_size=100)
data_loader_char = ImageSetDataLoader(data_loader_char_config)
data_loader_province = ImageSetDataLoader(data_loader_province_config)
model_char = CNNModel(model_char_config)
model_province = CNNModel(model_province_config)
trainer_char = UniversalTrainer(model_char.get_model(),
data_loader_char.get_dataset(),
trainer_config)
trainer_province = UniversalTrainer(model_province.get_model(),
data_loader_province.get_dataset(),
trainer_config)
logger.info("Training...")
trainer_char.train()
trainer_province.train()
trainer_char.save("log/test_split_char.h5")
trainer_province.save("log/test_split_province.h5")
else:
model_char = CNNModel(model_char_config)
trainer_char = UniversalTrainer(model_char.get_model(), None,
trainer_config)
