def test_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_pipeline.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_pipeline.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_pipeline.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_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_pipeline.dataset_from_directory(
directory, batch_size=10, image_size=(18, 18),
validation_split=0.2, subset='training')
batch = next(iter(dataset))
self.assertLen(batch, 2)
self.assertEqual(batch[0].shape, (8, 18, 18, 3))
dataset = image_pipeline.dataset_from_directory(
directory, batch_size=10, image_size=(18, 18),
validation_split=0.2, subset='validation')
batch = next(iter(dataset))
self.assertLen(batch, 2)
self.assertEqual(batch[0].shape, (2, 18, 18, 3))
def test_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_pipeline.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_pipeline.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_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_pipeline.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_pipeline.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_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_pipeline.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_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_pipeline.dataset_from_directory(
directory, labels=None)
with self.assertRaisesRegex(ValueError, '`label_mode` argument must be'):
_ = image_pipeline.dataset_from_directory(directory, label_mode='other')
with self.assertRaisesRegex(ValueError, '`color_mode` must be one of'):
_ = image_pipeline.dataset_from_directory(directory, color_mode='other')
with self.assertRaisesRegex(
ValueError, 'only pass `class_names` if the labels are inferred'):
_ = image_pipeline.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 images'):
_ = image_pipeline.dataset_from_directory(directory, labels=[0, 0, 1, 1])
with self.assertRaisesRegex(
ValueError, '`class_names` passed did not match'):
_ = image_pipeline.dataset_from_directory(
directory, class_names=['class_0', 'class_2'])
with self.assertRaisesRegex(ValueError, 'there must exactly 2 classes'):
_ = image_pipeline.dataset_from_directory(directory, label_mode='binary')
with self.assertRaisesRegex(ValueError,
'`validation_split` must be between 0 and 1'):
_ = image_pipeline.dataset_from_directory(directory, validation_split=2)
with self.assertRaisesRegex(ValueError,
'`subset` must be either "training" or'):
_ = image_pipeline.dataset_from_directory(
directory, validation_split=0.2, subset='other')
def test_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_pipeline.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_pipeline.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_pipeline.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_pipeline.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')