def test_decode_example_shape_key_tensor(self): np_image = np.random.rand(2, 3, 1).astype('f') np_labels = np.array([[[1], [2], [3]], [[4], [5], [6]]]) example = example_pb2.Example(features=feature_pb2.Features(feature={ 'image': self._encode_float_feature(np_image), 'image/shape': self._encode_int64_feature(np.array(np_image.shape)), 'labels': self._encode_int64_feature(np_labels), 'labels/shape': self._encode_int64_feature(np.array(np_labels.shape)), })) serialized_example = example.SerializeToString() with self.test_session(): serialized_example = array_ops.reshape(serialized_example, shape=[]) keys_to_features = { 'image': parsing_ops.VarLenFeature(dtype=dtypes.float32), 'image/shape': parsing_ops.VarLenFeature(dtype=dtypes.int64), 'labels': parsing_ops.VarLenFeature(dtype=dtypes.int64), 'labels/shape': parsing_ops.VarLenFeature(dtype=dtypes.int64), } items_to_handlers = { 'image': tfexample_decoder.Tensor('image', shape_keys='image/shape'), 'labels': tfexample_decoder.Tensor('labels', shape_keys='labels/shape'), } decoder = TFExampleDecoder(keys_to_features, items_to_handlers) [tf_image, tf_labels] = decoder.decode(serialized_example, ['image', 'labels']) self.assertAllEqual(tf_image.eval(), np_image) self.assertAllEqual(tf_labels.eval(), np_labels)
def test_decode_example_with_tensor(self): tensor_shape = (2, 3, 1) np_array = np.random.rand(2, 3, 1) example = example_pb2.Example(features=feature_pb2.Features( feature={ 'image/depth_map': self._encode_float_feature(np_array), })) serialized_example = example.SerializeToString() with self.test_session(): serialized_example = array_ops.reshape(serialized_example, shape=[]) keys_to_features = { 'image/depth_map': parsing_ops.FixedLenFeature( tensor_shape, dtypes.float32, default_value=array_ops.zeros(tensor_shape)) } items_to_handlers = { 'depth': tfexample_decoder.Tensor('image/depth_map') } decoder = TFExampleDecoder(keys_to_features, items_to_handlers) [tf_depth] = decoder.decode(serialized_example, ['depth']) depth = tf_depth.eval() self.assertAllClose(np_array, depth)
def test_decode_example_with_tensor(self): tensor_shape = (2, 3, 1) np_array = np.random.rand(2, 3, 1) example = example_pb2.Example(features=feature_pb2.Features(feature={ 'image/depth_map': self._encode_float_feature(np_array), })) serialized_example = example.SerializeToString() with self.test_session(): serialized_example = array_ops.reshape(serialized_example, shape=[]) keys_to_features = { 'image/depth_map': parsing_ops.FixedLenFeature( tensor_shape, dtypes.float32, default_value=array_ops.zeros(tensor_shape)) } items_to_handlers = {'depth': tfexample_decoder.Tensor('image/depth_map')} decoder = TFExampleDecoder(keys_to_features, items_to_handlers) [tf_depth] = decoder.decode(serialized_example, ['depth']) depth = tf_depth.eval() self.assertAllClose(np_array, depth)
def test_decode_example_with_fix_len_tensor_with_shape(self): np_array = np.array([[1, 2, 3], [4, 5, 6]]) example = example_pb2.Example(features=feature_pb2.Features( feature={ 'labels': self._encode_int64_feature(np_array), })) serialized_example = example.SerializeToString() with self.test_session(): serialized_example = array_ops.reshape(serialized_example, shape=[]) keys_to_features = { 'labels': parsing_ops.FixedLenFeature(np_array.shape, dtype=dtypes.int64), } items_to_handlers = { 'labels': tfexample_decoder.Tensor('labels', shape=np_array.shape), } decoder = TFExampleDecoder(keys_to_features, items_to_handlers) [tf_labels] = decoder.decode(serialized_example, ['labels']) labels = tf_labels.eval() self.assertAllEqual(labels, np_array)
def test_decode_example_with_sparse_tensor_to_dense(self): np_indices = np.array([1, 2, 5]) np_values = np.array([0.1, 0.2, 0.6]).astype('f') np_shape = np.array([6]) np_dense = np.array([0.0, 0.1, 0.2, 0.0, 0.0, 0.6]).astype('f') example = example_pb2.Example(features=feature_pb2.Features( feature={ 'indices': self._encode_int64_feature(np_indices), 'values': self._encode_float_feature(np_values), })) serialized_example = example.SerializeToString() with self.test_session(): serialized_example = array_ops.reshape(serialized_example, shape=[]) keys_to_features = { 'indices': parsing_ops.VarLenFeature(dtype=dtypes.int64), 'values': parsing_ops.VarLenFeature(dtype=dtypes.float32), } items_to_handlers = { 'labels': tfexample_decoder.SparseTensor(shape=np_shape, densify=True), } decoder = TFExampleDecoder(keys_to_features, items_to_handlers) [tf_labels] = decoder.decode(serialized_example, ['labels']) labels = tf_labels.eval() self.assertAllClose(labels, np_dense)
def test_decode_example_with_string_tensor(self): tensor_shape = (2, 3, 1) np_array = np.array([[['ab'], ['cd'], ['ef']], [['ghi'], ['jkl'], ['mnop']]]) example = example_pb2.Example(features=feature_pb2.Features( feature={ 'labels': self._bytes_feature(np_array), })) serialized_example = example.SerializeToString() with self.test_session(): serialized_example = array_ops.reshape(serialized_example, shape=[]) keys_to_features = { 'labels': parsing_ops.FixedLenFeature(tensor_shape, dtypes.string, default_value=constant_op.constant( '', shape=tensor_shape, dtype=dtypes.string)) } items_to_handlers = {'labels': tfexample_decoder.Tensor('labels')} decoder = TFExampleDecoder(keys_to_features, items_to_handlers) [tf_labels] = decoder.decode(serialized_example, ['labels']) labels = tf_labels.eval() labels = labels.astype(np_array.dtype) self.assertTrue(np.array_equal(np_array, labels))
def test_decode_example_with_sparse_tensor_to_dense(self): np_indices = np.array([1, 2, 5]) np_values = np.array([0.1, 0.2, 0.6]).astype('f') np_shape = np.array([6]) np_dense = np.array([0.0, 0.1, 0.2, 0.0, 0.0, 0.6]).astype('f') example = example_pb2.Example(features=feature_pb2.Features(feature={ 'indices': self._encode_int64_feature(np_indices), 'values': self._encode_float_feature(np_values), })) serialized_example = example.SerializeToString() with self.test_session(): serialized_example = array_ops.reshape(serialized_example, shape=[]) keys_to_features = { 'indices': parsing_ops.VarLenFeature(dtype=dtypes.int64), 'values': parsing_ops.VarLenFeature(dtype=dtypes.float32), } items_to_handlers = { 'labels': tfexample_decoder.SparseTensor(shape=np_shape, densify=True), } decoder = TFExampleDecoder(keys_to_features, items_to_handlers) [tf_labels] = decoder.decode(serialized_example, ['labels']) labels = tf_labels.eval() self.assertAllClose(labels, np_dense)
def test_decode_image_with_item_handler_callback(self): image_shape = (2, 3, 3) for image_encoding in ['jpeg', 'png']: image, serialized_example = self.generate_image( image_format=image_encoding, image_shape=image_shape) with self.test_session(): def conditional_decoding(keys_to_tensors): """See base class.""" image_buffer = keys_to_tensors['image/encoded'] image_format = keys_to_tensors['image/format'] def decode_png(): return image_ops.decode_png(image_buffer, 3) def decode_jpg(): return image_ops.decode_jpeg(image_buffer, 3) image = control_flow_ops.case( {math_ops.equal(image_format, 'png'): decode_png}, default=decode_jpg, exclusive=True) image = array_ops.reshape(image, image_shape) return image keys_to_features = { 'image/encoded': parsing_ops.FixedLenFeature((), dtypes.string, default_value=''), 'image/format': parsing_ops.FixedLenFeature((), dtypes.string, default_value='jpeg') } items_to_handlers = { 'image': tfexample_decoder.ItemHandlerCallback( ['image/encoded', 'image/format'], conditional_decoding) } decoder = TFExampleDecoder(keys_to_features, items_to_handlers) [tf_image] = decoder.decode(serialized_example, ['image']) decoded_image = tf_image.eval() if image_encoding == 'jpeg': # For jenkins: image = image.astype(np.float32) decoded_image = decoded_image.astype(np.float32) self.assertAllClose(image, decoded_image, rtol=.5, atol=1.001) else: self.assertAllClose(image, decoded_image, atol=0)
def test_decode_image_with_item_handler_callback(self): image_shape = (2, 3, 3) for image_encoding in ['jpeg', 'png']: image, serialized_example = self.generate_image( image_format=image_encoding, image_shape=image_shape) with self.test_session(): def conditional_decoding(keys_to_tensors): """See base class.""" image_buffer = keys_to_tensors['image/encoded'] image_format = keys_to_tensors['image/format'] def decode_png(): return image_ops.decode_png(image_buffer, 3) def decode_jpg(): return image_ops.decode_jpeg(image_buffer, 3) image = control_flow_ops.case( {math_ops.equal(image_format, 'png'): decode_png}, default=decode_jpg, exclusive=True) image = array_ops.reshape(image, image_shape) return image keys_to_features = { 'image/encoded': parsing_ops.FixedLenFeature( (), dtypes.string, default_value=''), 'image/format': parsing_ops.FixedLenFeature( (), dtypes.string, default_value='jpeg') } items_to_handlers = { 'image': tfexample_decoder.ItemHandlerCallback( ['image/encoded', 'image/format'], conditional_decoding) } decoder = TFExampleDecoder(keys_to_features, items_to_handlers) [tf_image] = decoder.decode(serialized_example, ['image']) decoded_image = tf_image.eval() if image_encoding == 'jpeg': # For jenkins: image = image.astype(np.float32) decoded_image = decoded_image.astype(np.float32) self.assertAllClose(image, decoded_image, rtol=.5, atol=1.001) else: self.assertAllClose(image, decoded_image, atol=0)
def test_decode_example_with_iInt64_tensor(self): np_array = np.random.randint(1, 10, size=(2, 3, 1)) example = example_pb2.Example(features=feature_pb2.Features(feature={ 'array': self._encode_int64_feature(np_array), })) serialized_example = example.SerializeToString() with self.test_session(): serialized_example = array_ops.reshape(serialized_example, shape=[]) keys_to_features = { 'array': parsing_ops.FixedLenFeature(np_array.shape, dtypes.int64) } items_to_handlers = {'array': tfexample_decoder.Tensor('array'), } decoder = TFExampleDecoder(keys_to_features, items_to_handlers) [tf_array] = decoder.decode(serialized_example, ['array']) self.assertAllEqual(tf_array.eval(), np_array)
def test_decode_example_with_bounding_box(self): num_bboxes = 10 np_ymin = np.random.rand(num_bboxes, 1) np_xmin = np.random.rand(num_bboxes, 1) np_ymax = np.random.rand(num_bboxes, 1) np_xmax = np.random.rand(num_bboxes, 1) np_bboxes = np.hstack([np_ymin, np_xmin, np_ymax, np_xmax]) example = example_pb2.Example(features=feature_pb2.Features( feature={ 'image/object/bbox/ymin': self._encode_float_feature(np_ymin), 'image/object/bbox/xmin': self._encode_float_feature(np_xmin), 'image/object/bbox/ymax': self._encode_float_feature(np_ymax), 'image/object/bbox/xmax': self._encode_float_feature(np_xmax), })) serialized_example = example.SerializeToString() with self.test_session(): serialized_example = array_ops.reshape(serialized_example, shape=[]) keys_to_features = { 'image/object/bbox/ymin': parsing_ops.VarLenFeature(dtypes.float32), 'image/object/bbox/xmin': parsing_ops.VarLenFeature(dtypes.float32), 'image/object/bbox/ymax': parsing_ops.VarLenFeature(dtypes.float32), 'image/object/bbox/xmax': parsing_ops.VarLenFeature(dtypes.float32), } items_to_handlers = { 'object/bbox': tfexample_decoder.BoundingBox(['ymin', 'xmin', 'ymax', 'xmax'], 'image/object/bbox/'), } decoder = TFExampleDecoder(keys_to_features, items_to_handlers) [tf_bboxes] = decoder.decode(serialized_example, ['object/bbox']) bboxes = tf_bboxes.eval() self.assertAllClose(np_bboxes, bboxes)
def test_decode_example_with_var_len_tensor(self): np_array = np.array([[[1], [2], [3]], [[4], [5], [6]]]) example = example_pb2.Example(features=feature_pb2.Features(feature={ 'labels': self._encode_int64_feature(np_array), })) serialized_example = example.SerializeToString() with self.test_session(): serialized_example = array_ops.reshape(serialized_example, shape=[]) keys_to_features = { 'labels': parsing_ops.VarLenFeature(dtype=dtypes.int64), } items_to_handlers = {'labels': tfexample_decoder.Tensor('labels'), } decoder = TFExampleDecoder(keys_to_features, items_to_handlers) [tf_labels] = decoder.decode(serialized_example, ['labels']) labels = tf_labels.eval() self.assertAllEqual(labels, np_array.flatten())
def decode_example(self, serialized_example, item_handler, image_format): """Decodes the given serialized example with the specified item handler. Args: serialized_example: a serialized TF example string. item_handler: the item handler used to decode the image. image_format: the image format being decoded. Returns: the decoded image found in the serialized Example. """ serialized_example = array_ops.reshape(serialized_example, shape=[]) decoder = TFExampleDecoder( keys_to_features={ 'image/encoded': tf.FixedLenFeature((), dtypes.string, default_value=''), 'image/format': tf.FixedLenFeature((), dtypes.string, default_value=image_format), }, items_to_handlers={'image': item_handler}) [tf_image] = decoder.decode(serialized_example, ['image']) return tf_image
def test_decode_example_with_fix_len_tensor_with_shape(self): np_array = np.array([[1, 2, 3], [4, 5, 6]]) example = example_pb2.Example(features=feature_pb2.Features(feature={ 'labels': self._encode_int64_feature(np_array), })) serialized_example = example.SerializeToString() with self.test_session(): serialized_example = array_ops.reshape(serialized_example, shape=[]) keys_to_features = { 'labels': parsing_ops.FixedLenFeature(np_array.shape, dtype=dtypes.int64), } items_to_handlers = { 'labels': tfexample_decoder.Tensor('labels', shape=np_array.shape), } decoder = TFExampleDecoder(keys_to_features, items_to_handlers) [tf_labels] = decoder.decode(serialized_example, ['labels']) labels = tf_labels.eval() self.assertAllEqual(labels, np_array)
def test_decode_example_with_item_handler_callback(self): np.random.seed(0) tensor_shape = (2, 3, 1) np_array = np.random.rand(2, 3, 1) example = example_pb2.Example(features=feature_pb2.Features( feature={ 'image/depth_map': self._encode_float_feature(np_array), })) serialized_example = example.SerializeToString() with self.test_session(): serialized_example = array_ops.reshape(serialized_example, shape=[]) keys_to_features = { 'image/depth_map': parsing_ops.FixedLenFeature( tensor_shape, dtypes.float32, default_value=array_ops.zeros(tensor_shape)) } def handle_depth(keys_to_tensors): depth = list(keys_to_tensors.values())[0] depth += 1 return depth items_to_handlers = { 'depth': tfexample_decoder.ItemHandlerCallback('image/depth_map', handle_depth) } decoder = TFExampleDecoder(keys_to_features, items_to_handlers) [tf_depth] = decoder.decode(serialized_example, ['depth']) depth = tf_depth.eval() self.assertAllClose(np_array, depth - 1)
def test_decode_example_with_string_tensor(self): tensor_shape = (2, 3, 1) np_array = np.array([[['ab'], ['cd'], ['ef']], [['ghi'], ['jkl'], ['mnop']]]) example = example_pb2.Example(features=feature_pb2.Features(feature={ 'labels': self._bytes_feature(np_array), })) serialized_example = example.SerializeToString() with self.test_session(): serialized_example = array_ops.reshape(serialized_example, shape=[]) keys_to_features = { 'labels': parsing_ops.FixedLenFeature( tensor_shape, dtypes.string, default_value=constant_op.constant('', shape=tensor_shape, dtype=dtypes.string)) } items_to_handlers = {'labels': tfexample_decoder.Tensor('labels')} decoder = TFExampleDecoder(keys_to_features, items_to_handlers) [tf_labels] = decoder.decode(serialized_example, ['labels']) labels = tf_labels.eval() labels = labels.astype(np_array.dtype) self.assertTrue(np.array_equal(np_array, labels))
def test_decode_example_with_bounding_box(self): num_bboxes = 10 np_ymin = np.random.rand(num_bboxes, 1) np_xmin = np.random.rand(num_bboxes, 1) np_ymax = np.random.rand(num_bboxes, 1) np_xmax = np.random.rand(num_bboxes, 1) np_bboxes = np.hstack([np_ymin, np_xmin, np_ymax, np_xmax]) example = example_pb2.Example(features=feature_pb2.Features(feature={ 'image/object/bbox/ymin': self._encode_float_feature(np_ymin), 'image/object/bbox/xmin': self._encode_float_feature(np_xmin), 'image/object/bbox/ymax': self._encode_float_feature(np_ymax), 'image/object/bbox/xmax': self._encode_float_feature(np_xmax), })) serialized_example = example.SerializeToString() with self.test_session(): serialized_example = array_ops.reshape(serialized_example, shape=[]) keys_to_features = { 'image/object/bbox/ymin': parsing_ops.VarLenFeature(dtypes.float32), 'image/object/bbox/xmin': parsing_ops.VarLenFeature(dtypes.float32), 'image/object/bbox/ymax': parsing_ops.VarLenFeature(dtypes.float32), 'image/object/bbox/xmax': parsing_ops.VarLenFeature(dtypes.float32), } items_to_handlers = { 'object/bbox': tfexample_decoder.BoundingBox(['ymin', 'xmin', 'ymax', 'xmax'], 'image/object/bbox/'), } decoder = TFExampleDecoder(keys_to_features, items_to_handlers) [tf_bboxes] = decoder.decode(serialized_example, ['object/bbox']) bboxes = tf_bboxes.eval() self.assertAllClose(np_bboxes, bboxes)
def test_decode_example_with_item_handler_callback(self): np.random.seed(0) tensor_shape = (2, 3, 1) np_array = np.random.rand(2, 3, 1) example = example_pb2.Example(features=feature_pb2.Features(feature={ 'image/depth_map': self._encode_float_feature(np_array), })) serialized_example = example.SerializeToString() with self.test_session(): serialized_example = array_ops.reshape(serialized_example, shape=[]) keys_to_features = { 'image/depth_map': parsing_ops.FixedLenFeature( tensor_shape, dtypes.float32, default_value=array_ops.zeros(tensor_shape)) } def handle_depth(keys_to_tensors): depth = list(keys_to_tensors.values())[0] depth += 1 return depth items_to_handlers = { 'depth': tfexample_decoder.ItemHandlerCallback('image/depth_map', handle_depth) } decoder = TFExampleDecoder(keys_to_features, items_to_handlers) [tf_depth] = decoder.decode(serialized_example, ['depth']) depth = tf_depth.eval() self.assertAllClose(np_array, depth - 1)