def test_varlen_images_feature_spec_raises(self, batch_size):
file_pattern = os.path.join(self.create_tempdir().full_path,
'test.tfrecord')
image_width = 640
image_height = 512
padded_varlen_size = 3
maxval = 255 # Maximum value for byte-encoded image.
image_np = np.random.uniform(
size=(image_height, image_width), high=maxval).astype(np.int32)
image_with_invalid_size = np.ones((1024, 1280)) * 255
png_encoded_image = image.numpy_to_image_string(image_np, 'png')
png_encoded_image_with_invalid_size = image.numpy_to_image_string(
image_with_invalid_size, 'png')
test_data = [[png_encoded_image_with_invalid_size],
[png_encoded_image, png_encoded_image_with_invalid_size]]
self._write_test_varlen_images_examples(test_data, file_pattern)
feature_spec = tensorspec_utils.TensorSpecStruct()
feature_spec.varlen_images = tensorspec_utils.ExtendedTensorSpec(
shape=(padded_varlen_size, image_height, image_width, 1),
dtype=tf.uint8,
name='varlen_images',
data_format='png',
varlen_default_value=0)
dataset = tfdata.parallel_read(file_patterns=file_pattern)
dataset = dataset.batch(batch_size, drop_remainder=True)
dataset = tfdata.serialized_to_parsed(dataset, feature_spec, None)
features = dataset.make_one_shot_iterator().get_next()
# Check tensor shapes.
self.assertAllEqual(
[None, padded_varlen_size, image_height, image_width, 1],
features.varlen_images.get_shape().as_list())
with self.session() as session:
with self.assertRaises(tf.errors.InvalidArgumentError):
session.run(features)
def test_images_decoding(self, np_data_type, tf_data_type):
file_pattern = os.path.join(self.create_tempdir().full_path,
'test.tfrecord')
image_width = 640
image_height = 512
maxval = np.iinfo(np_data_type).max # Maximum value for byte-encoded image.
image_np = np.random.uniform(
size=(image_height, image_width), high=maxval).astype(np.int32)
png_encoded_image = image.numpy_to_image_string(image_np, 'png',
np_data_type)
test_data = [[png_encoded_image]]
self._write_test_images_examples(test_data, file_pattern)
feature_spec = tensorspec_utils.TensorSpecStruct()
feature_spec.images = tensorspec_utils.ExtendedTensorSpec(
shape=(image_height, image_width, 1),
dtype=tf_data_type,
name='image/encoded',
data_format='png')
dataset = tfdata.parallel_read(file_patterns=file_pattern)
dataset = dataset.batch(1, drop_remainder=True)
if np_data_type == np.uint32:
with self.assertRaises(tf.errors.InvalidArgumentError):
dataset = tfdata.serialized_to_parsed(dataset, feature_spec, None)
else:
dataset = tfdata.serialized_to_parsed(dataset, feature_spec, None)
features = dataset.make_one_shot_iterator().get_next()
# Check tensor shapes.
self.assertAllEqual(
[1, image_height, image_width, 1],
features.images.get_shape().as_list())
with self.session() as session:
np_features = session.run(features)
self.assertEqual(np_features['images'].dtype, np_data_type)
def _write_test_sequence_examples(self, sequence_length, tfrecord_path):
example = tf.train.SequenceExample()
context_key = 'context_feature'
seq_key = 'sequence_feature'
image_seq_key = 'image_sequence_feature'
example.context.feature[context_key].int64_list.value.append(10)
for i in range(sequence_length):
f = example.feature_lists.feature_list[seq_key].feature.add()
f.float_list.value.extend([3, 1])
f = example.feature_lists.feature_list[image_seq_key].feature.add()
img = TEST_IMAGE * i
f.bytes_list.value.append(image.numpy_to_image_string(img, 'jpeg'))
with tf.python_io.TFRecordWriter(tfrecord_path) as writer:
writer.write(example.SerializeToString())
def test_images_decoding_raises(self):
file_pattern = os.path.join(self.create_tempdir().full_path,
'test.tfrecord')
image_width = 640
image_height = 512
maxval = np.iinfo(np.uint32).max # Maximum value for byte-encoded image.
image_np = np.random.uniform(
size=(image_height, image_width), high=maxval).astype(np.int32)
png_encoded_image = image.numpy_to_image_string(image_np, 'png',
np.uint32)
test_data = [[png_encoded_image]]
self._write_test_images_examples(test_data, file_pattern)
feature_spec = tensorspec_utils.TensorSpecStruct()
feature_spec.images = tensorspec_utils.ExtendedTensorSpec(
shape=(image_height, image_width, 1),
dtype=tf.uint32,
name='image/encoded',
data_format='png')
dataset = tfdata.parallel_read(file_patterns=file_pattern)
dataset = dataset.batch(1, drop_remainder=True)
with self.assertRaises(ValueError):
tfdata.serialized_to_parsed(dataset, feature_spec, None)
