def load_dataset(sourcefolder, split_factor): dataset = Crosswalk_dataset.from_sourcefolder(sourcefolder) dataset.read_samples() dataset.load_images() (train_set, test_set) = dataset.split_train_test(split_factor) _print_dataset_details(dataset, train_set, test_set) return (train_set.to_input_response(), test_set.to_input_response())
def test_dataset_split(self): dataset_path = self.get_test_dataset_path() factor = 0.6 set = Crosswalk_dataset.from_sourcefolder(dataset_path) set.read_samples() (train_set, test_set) = set.split_train_test(factor) self.assertEquals(len(train_set.samples_shuffled), 6) self.assertEquals(len(test_set.samples_shuffled), 4)
def testDataset_read_samples(self): dataset_path = self.get_test_dataset_path() set = Crosswalk_dataset.from_sourcefolder(dataset_path) set.read_samples() nb_samples = 10 self.assertEquals(len(set.samples_shuffled), nb_samples) self.assertGreater(len(set.samples_crosswalk.samples), 0) self.assertGreater(len(set.samples_nocrosswalk.samples), 0)
def test_dataset_split_nodouble(self): dataset_path = self.get_test_dataset_path() factor = 0.5 set = Crosswalk_dataset.from_sourcefolder(dataset_path) set.read_samples() (train_set, test_set) = set.split_train_test(factor) for test_sample in test_set.samples_shuffled: for train_sample in train_set.samples_shuffled: self.assertNotEqual(test_sample.filepath, train_sample.filepath)
def test_dataset_to_input_response(self): dataset_path = self.get_test_dataset_path() set = Crosswalk_dataset.from_sourcefolder(dataset_path) set.read_samples() set.load_images() (inputs, responses) = set.to_input_response() should_input_shape = (10, 3, 50, 50) should_response_shape = (10,2) self.assertEquals(inputs.shape, should_input_shape) self.assertEquals(responses.shape, should_response_shape)
def test_dataset_load_images(self): dataset_path = self.get_test_dataset_path() set = Crosswalk_dataset.from_sourcefolder(dataset_path) set.read_samples() set.load_images() self.assertIsNotNone(set.samples_shuffled[0].pil_image)