def test_image_dataset_from_directory_validation_split(self): if PIL is None: return # Skip test if PIL is not available. directory = self._prepare_directory(num_classes=2, count=10) dataset = image_dataset.image_dataset_from_directory( directory, batch_size=10, image_size=(18, 18), validation_split=0.2, subset='training', seed=1337) batch = next(iter(dataset)) self.assertLen(batch, 2) self.assertEqual(batch[0].shape, (8, 18, 18, 3)) dataset = image_dataset.image_dataset_from_directory( directory, batch_size=10, image_size=(18, 18), validation_split=0.2, subset='validation', seed=1337) batch = next(iter(dataset)) self.assertLen(batch, 2) self.assertEqual(batch[0].shape, (2, 18, 18, 3))
def test_image_dataset_from_directory_binary(self): if PIL is None: return # Skip test if PIL is not available. directory = self._prepare_directory(num_classes=2) dataset = image_dataset.image_dataset_from_directory( directory, batch_size=8, image_size=(18, 18), label_mode='int') batch = next(iter(dataset)) self.assertLen(batch, 2) self.assertEqual(batch[0].shape, (8, 18, 18, 3)) self.assertEqual(batch[0].dtype.name, 'float32') self.assertEqual(batch[1].shape, (8,)) self.assertEqual(batch[1].dtype.name, 'int32') dataset = image_dataset.image_dataset_from_directory( directory, batch_size=8, image_size=(18, 18), label_mode='binary') batch = next(iter(dataset)) self.assertLen(batch, 2) self.assertEqual(batch[0].shape, (8, 18, 18, 3)) self.assertEqual(batch[0].dtype.name, 'float32') self.assertEqual(batch[1].shape, (8, 1)) self.assertEqual(batch[1].dtype.name, 'float32') dataset = image_dataset.image_dataset_from_directory( directory, batch_size=8, image_size=(18, 18), label_mode='categorical') batch = next(iter(dataset)) self.assertLen(batch, 2) self.assertEqual(batch[0].shape, (8, 18, 18, 3)) self.assertEqual(batch[0].dtype.name, 'float32') self.assertEqual(batch[1].shape, (8, 2)) self.assertEqual(batch[1].dtype.name, 'float32')
def test_image_dataset_from_directory_color_modes(self): if PIL is None: return # Skip test if PIL is not available. directory = self._prepare_directory(num_classes=4, color_mode='rgba') dataset = image_dataset.image_dataset_from_directory(directory, batch_size=8, image_size=(18, 18), color_mode='rgba') batch = next(iter(dataset)) self.assertLen(batch, 2) self.assertEqual(batch[0].shape, (8, 18, 18, 4)) self.assertEqual(batch[0].dtype.name, 'float32') directory = self._prepare_directory(num_classes=4, color_mode='grayscale') dataset = image_dataset.image_dataset_from_directory( directory, batch_size=8, image_size=(18, 18), color_mode='grayscale') batch = next(iter(dataset)) self.assertLen(batch, 2) self.assertEqual(batch[0].shape, (8, 18, 18, 1)) self.assertEqual(batch[0].dtype.name, 'float32')
def test_image_dataset_from_directory_multiclass(self): if PIL is None: return # Skip test if PIL is not available. directory = self._prepare_directory(num_classes=4, count=15) dataset = image_dataset.image_dataset_from_directory(directory, batch_size=8, image_size=(18, 18), label_mode=None) batch = next(iter(dataset)) self.assertEqual(batch.shape, (8, 18, 18, 3)) dataset = image_dataset.image_dataset_from_directory(directory, batch_size=8, image_size=(18, 18), label_mode=None) sample_count = 0 iterator = iter(dataset) for batch in dataset: sample_count += next(iterator).shape[0] self.assertEqual(sample_count, 15) dataset = image_dataset.image_dataset_from_directory(directory, batch_size=8, image_size=(18, 18), label_mode='int') batch = next(iter(dataset)) self.assertLen(batch, 2) self.assertEqual(batch[0].shape, (8, 18, 18, 3)) self.assertEqual(batch[0].dtype.name, 'float32') self.assertEqual(batch[1].shape, (8, )) self.assertEqual(batch[1].dtype.name, 'int32') dataset = image_dataset.image_dataset_from_directory( directory, batch_size=8, image_size=(18, 18), label_mode='categorical') batch = next(iter(dataset)) self.assertLen(batch, 2) self.assertEqual(batch[0].shape, (8, 18, 18, 3)) self.assertEqual(batch[0].dtype.name, 'float32') self.assertEqual(batch[1].shape, (8, 4)) self.assertEqual(batch[1].dtype.name, 'float32')
def test_image_dataset_from_directory_standalone(self): # Test retrieving images without labels from a directory and its subdirs. if PIL is None: return # Skip test if PIL is not available. # Save a few extra images in the parent directory. directory = self._prepare_directory(count=7, num_classes=2) for i, img in enumerate(self._get_images(3)): filename = 'image_%s.jpg' % (i, ) img.save(os.path.join(directory, filename)) dataset = image_dataset.image_dataset_from_directory(directory, batch_size=5, image_size=(18, 18), labels=None) batch = next(iter(dataset)) # We return plain images self.assertEqual(batch.shape, (5, 18, 18, 3)) self.assertEqual(batch.dtype.name, 'float32') # Count samples batch_count = 0 sample_count = 0 for batch in dataset: batch_count += 1 sample_count += batch.shape[0] self.assertEqual(batch_count, 2) self.assertEqual(sample_count, 10)
def test_image_dataset_from_directory_crop_to_aspect_ratio(self): if PIL is None: return # Skip test if PIL is not available. directory = self._prepare_directory(num_classes=2, count=5) dataset = image_dataset.image_dataset_from_directory( directory, batch_size=5, image_size=(18, 18), crop_to_aspect_ratio=True) batch = next(iter(dataset)) self.assertLen(batch, 2) self.assertEqual(batch[0].shape, (5, 18, 18, 3))
def test_image_dataset_from_directory_manual_labels(self): if PIL is None: return # Skip test if PIL is not available. directory = self._prepare_directory(num_classes=2, count=2) dataset = image_dataset.image_dataset_from_directory( directory, batch_size=8, image_size=(18, 18), labels=[0, 1], shuffle=False) batch = next(iter(dataset)) self.assertLen(batch, 2) self.assertAllClose(batch[1], [0, 1])
def test_sample_count(self): if PIL is None: return # Skip test if PIL is not available. directory = self._prepare_directory(num_classes=4, count=15) dataset = image_dataset.image_dataset_from_directory( directory, batch_size=8, image_size=(18, 18), label_mode=None) sample_count = 0 for batch in dataset: sample_count += batch.shape[0] self.assertEqual(sample_count, 15)
def test_image_dataset_from_directory_not_batched(self): if PIL is None: return # Skip test if PIL is not available. directory = self._prepare_directory(num_classes=2, count=2) dataset = image_dataset.image_dataset_from_directory(directory, batch_size=None, image_size=(18, 18), label_mode=None, shuffle=False) sample = next(iter(dataset)) self.assertEqual(len(sample.shape), 3)
def test_image_dataset_from_directory_follow_links(self): if PIL is None: return # Skip test if PIL is not available. directory = self._prepare_directory(num_classes=2, count=25, nested_dirs=True) dataset = image_dataset.image_dataset_from_directory( directory, batch_size=8, image_size=(18, 18), label_mode=None, follow_links=True) sample_count = 0 for batch in dataset: sample_count += batch.shape[0] self.assertEqual(sample_count, 25)
def test_static_shape_in_graph(self): if PIL is None: return # Skip test if PIL is not available. directory = self._prepare_directory(num_classes=2) dataset = image_dataset.image_dataset_from_directory( directory, batch_size=8, image_size=(18, 18), label_mode='int') test_case = self @tf.function def symbolic_fn(ds): for x, _ in ds.take(1): test_case.assertListEqual(x.shape.as_list(), [None, 18, 18, 3]) symbolic_fn(dataset)
def test_image_dataset_from_directory_errors(self): if PIL is None: return # Skip test if PIL is not available. directory = self._prepare_directory(num_classes=3, count=5) with self.assertRaisesRegex(ValueError, '`labels` argument should be'): _ = image_dataset.image_dataset_from_directory(directory, labels='other') with self.assertRaisesRegex(ValueError, '`label_mode` argument must be'): _ = image_dataset.image_dataset_from_directory(directory, label_mode='other') with self.assertRaisesRegex(ValueError, '`color_mode` must be one of'): _ = image_dataset.image_dataset_from_directory(directory, color_mode='other') with self.assertRaisesRegex( ValueError, 'only pass `class_names` if the labels are inferred'): _ = image_dataset.image_dataset_from_directory( directory, labels=[0, 0, 1, 1, 1], class_names=['class_0', 'class_1', 'class_2']) with self.assertRaisesRegex( ValueError, 'Expected the lengths of `labels` to match the number of files' ): _ = image_dataset.image_dataset_from_directory(directory, labels=[0, 0, 1, 1]) with self.assertRaisesRegex(ValueError, '`class_names` passed did not match'): _ = image_dataset.image_dataset_from_directory( directory, class_names=['class_0', 'class_2']) with self.assertRaisesRegex(ValueError, 'there must exactly 2 classes'): _ = image_dataset.image_dataset_from_directory(directory, label_mode='binary') with self.assertRaisesRegex( ValueError, '`validation_split` must be between 0 and 1'): _ = image_dataset.image_dataset_from_directory(directory, validation_split=2) with self.assertRaisesRegex(ValueError, '`subset` must be either "training" or'): _ = image_dataset.image_dataset_from_directory( directory, validation_split=0.2, subset='other') with self.assertRaisesRegex(ValueError, '`validation_split` must be set'): _ = image_dataset.image_dataset_from_directory(directory, validation_split=0, subset='training') with self.assertRaisesRegex(ValueError, 'must provide a `seed`'): _ = image_dataset.image_dataset_from_directory( directory, validation_split=0.2, subset='training')
def test_image_dataset_from_directory_no_images(self): directory = self._prepare_directory(num_classes=2, count=0) with self.assertRaisesRegex(ValueError, 'No images found.'): _ = image_dataset.image_dataset_from_directory(directory)