def test_decoders(self):
"""Test with decoders (e.g., SkipDecoding)."""
images = [
'root_dir/train/label1/img1.png',
'root_dir/train/label3/img3.png',
'root_dir/train/label3/img1.png',
'root_dir/train/label3/img2.png',
'root_dir/train/label2/img1.png',
'root_dir/train/label2/img2.png',
]
with tfds.testing.MockFs() as fs:
for file in images:
fs.add_file(file)
split_examples, labels = image_folder._get_split_label_images('root_dir')
builder = tfds.ImageFolder(
root_dir='root_dir', dtype=tf.uint8, shape=(128, 128, 1),
)
# Decoded images should be found if passing decoders=None
ds = builder.as_dataset(split="train",
decoders=None)
expected_element_spec = {
'image/filename': tf.TensorSpec(shape=(), dtype=tf.string),
'image': tf.TensorSpec(shape=(128, 128, 1), dtype=tf.uint8),
'label': tf.TensorSpec(shape=(), dtype=tf.int64),
}
self.assertEqual(ds.element_spec, expected_element_spec)
# Encoded images should be found if passing decoders=SkipDecoding()
ds = builder.as_dataset(split="train",
decoders={'image': tfds.decode.SkipDecoding()})
expected_element_spec = {
'image/filename': tf.TensorSpec(shape=(), dtype=tf.string),
'image': tf.TensorSpec(shape=(), dtype=tf.string),
'label': tf.TensorSpec(shape=(), dtype=tf.int64),
}
self.assertEqual(ds.element_spec, expected_element_spec)
# Unused keys should throw ValueError
def ds_fn():
builder.as_dataset(split="train",
decoders={'text': tfds.decode.SkipDecoding()})
self.assertRaises(ValueError, ds_fn)
def test_decoders(self):
"""Test with decoders (e.g., SkipDecoding)."""
images = [
'root_dir/train/label1/img1.png',
'root_dir/train/label3/img3.png',
'root_dir/train/label3/img1.png',
'root_dir/train/label3/img2.png',
'root_dir/train/label2/img1.png',
'root_dir/train/label2/img2.png',
]
with tfds.testing.MockFs() as fs:
for file in images:
fs.add_file(file)
builder = tfds.ImageFolder(root_dir='root_dir', dtype=tf.uint8)
# Decoded images should be found if passing decoders=None
ds = builder.as_dataset(split='train', decoders=None)
self.assertEqual(
ds.element_spec, {
'image/filename': tf.TensorSpec(shape=(), dtype=tf.string),
'image': tf.TensorSpec(shape=(None, None, 3),
dtype=tf.uint8),
'label': tf.TensorSpec(shape=(), dtype=tf.int64),
})
# Encoded images should be found if passing decoders=SkipDecoding()
ds = builder.as_dataset(
split='train', decoders={'image': tfds.decode.SkipDecoding()})
self.assertEqual(
ds.element_spec, {
'image/filename': tf.TensorSpec(shape=(), dtype=tf.string),
'image': tf.TensorSpec(shape=(), dtype=tf.string),
'label': tf.TensorSpec(shape=(), dtype=tf.int64),
})
# Invalid keys should throw ValueError
with self.assertRaisesWithPredicateMatch(ValueError,
'Unrecognized keys'):
builder.as_dataset(
split='train',
decoders={'invalid_key': tfds.decode.SkipDecoding()})
def test_properties(self):
images = [
'root_dir/train/label1/img1.png',
'root_dir/train/label3/img3.png',
'root_dir/train/label3/img1.png',
'root_dir/train/label3/img2.png',
'root_dir/train/label2/img1.png',
'root_dir/train/label2/img2.png',
'root_dir/val/label1/img1.png',
'root_dir/val/label2/img2.png',
'root_dir/test/label1/img1.png',
'root_dir/test/label2/img1.png',
'root_dir/test/label4/img1.PNG',
'root_dir/test/label4/unsuported.txt',
]
with tfds.testing.MockFs() as fs:
for file in images:
fs.add_file(file)
split_examples, labels = image_folder._get_split_label_images(
'root_dir')
builder = tfds.ImageFolder(root_dir='root_dir')
builder_params = tfds.ImageFolder(root_dir='root_dir',
dtype=tf.uint16,
shape=(128, 128, 1))
self.assertEqual(
split_examples, {
'train': [
image_folder._Example(
image_path='root_dir/train/label2/img1.png',
label='label2'),
image_folder._Example(
image_path='root_dir/train/label3/img3.png',
label='label3'),
image_folder._Example(
image_path='root_dir/train/label3/img2.png',
label='label3'),
image_folder._Example(
image_path='root_dir/train/label3/img1.png',
label='label3'),
image_folder._Example(
image_path='root_dir/train/label2/img2.png',
label='label2'),
image_folder._Example(
image_path='root_dir/train/label1/img1.png',
label='label1'),
],
'val': [
image_folder._Example(
image_path='root_dir/val/label2/img2.png',
label='label2'),
image_folder._Example(
image_path='root_dir/val/label1/img1.png',
label='label1'),
],
'test': [
image_folder._Example(
image_path='root_dir/test/label1/img1.png',
label='label1'),
image_folder._Example(
image_path='root_dir/test/label2/img1.png',
label='label2'),
image_folder._Example(
image_path='root_dir/test/label4/img1.PNG',
label='label4'),
],
})
self.assertEqual(builder.info.splits['train'].num_examples, 6)
self.assertEqual(builder.info.splits['val'].num_examples, 2)
self.assertEqual(builder.info.splits['test'].num_examples, 3)
expected_labels = [
'label1',
'label2',
'label3',
'label4',
]
self.assertEqual(expected_labels, labels)
self.assertEqual(builder.info.features['label'].names, expected_labels)
self.assertEqual(builder.info.features['image'].shape, (None, None, 3))
self.assertEqual(builder.info.features['image'].dtype, tf.uint8)
self.assertEqual(builder_params.info.features['image'].shape,
(128, 128, 1))
self.assertEqual(builder_params.info.features['image'].dtype,
tf.uint16)