def test_init(self):
with self.assertRaises(ValueError):
#Arrange
bad_dataset_location = dataset_location / 'non_existent_location'
args = get_args(None, bad_dataset_location)
#Act
_ = ImageGenerationParameters(args)
args = get_args(model_name, dataset_location)
_ = ImageGenerationParameters(args)
def get_inputs(cls, num_prediction_steps, num_results=None):
#Arrange
model = load_test_model()
args = get_args(model_name, Path())
input_params = InputParameters(args)
image_generation_params = ImageGenerationParameters(args)
input_data = get_input_data()
#Update input data parameters
num_classes = len(set(input_data[image_generation_params.label_col]))
image_generation_params_update = dict(num_classes=num_classes,
image_cols=['Image'])
update_params(image_generation_params,
**image_generation_params_update)
#Num results
num_results = num_results or image_generation_params.batch_size * num_prediction_steps
#Mocks
prediction_results = np.zeros((num_results, num_classes))
for row_id in range(num_results):
prediction_results[row_id, row_id % num_classes] = 1
model.predict_generator = MagicMock()
model.predict_generator.return_value = prediction_results
return model, input_data, input_params, image_generation_params, prediction_results
def test_init_invalid_args(self):
#Arrange
args = get_args(model_name, Path())
input_params = InputParameters(args)
image_generation_params = ImageGenerationParameters(args)
with self.assertRaises(ValueError):
_ = Prediction(None, input_params, image_generation_params)
def get_params():
args = get_args(image_cols = ['Image'])
input_params = InputParameters(args)
training_params = TrainingParameters(args)
image_generation_params = ImageGenerationParameters(args)
transformation_params = ImageDataTransformation.Parameters(samplewise_mean = True)
return input_params, training_params, image_generation_params, transformation_params
def test_update_params(self):
#Arrange
args = get_args(model_name, dataset_location)
image_generation_params = ImageGenerationParameters(args)
additional_kwargs = dict(num_classes=num_classes)
#Act
update_params(image_generation_params, **additional_kwargs)
#Assert
self.assertEqual(num_classes, image_generation_params.num_classes)
parser.add_argument(
'-l', '--log_to_console',
action = 'store_true', default = False,
help = 'It enables logging to console')
args = parser.parse_args()
return args
if __name__ == "__main__":
#Parse commandline arguments
args = parse_args()
#Required params
input_params = InputParameters(args)
image_generation_params = ImageGenerationParameters(args)
num_prediction_steps = args.num_prediction_steps
dropbox_parameters = args.dropbox_parameters
log_to_console = args.log_to_console
#Initialize logging
logging.initialize(__file__, log_to_console = log_to_console)
logger = logging.get_logger(__name__)
#Log input parameters
logger.info('Running with parameters input_params: %s', input_params)
logger.info('Additional parameters: image_generation_params: %s log_to_console: %s', image_generation_params, log_to_console)
#Predictable randomness
seed = 3
def get_input_and_image_generation_params():
args = get_args()
input_data_params = InputParameters(args)
image_generation_params = ImageGenerationParameters(args)
return input_data_params, image_generation_params