def test_serving_input_receiver_features_invalid(self):
receiver_tensors = {
"example0": array_ops.placeholder(dtypes.string, name="example0"),
u"example1": array_ops.placeholder(dtypes.string, name="example1"),
}
with self.assertRaisesRegexp(ValueError, "features must be defined"):
export.TensorServingInputReceiver(
features=None, receiver_tensors=receiver_tensors)
with self.assertRaisesRegexp(ValueError, "feature must be a Tensor"):
export.TensorServingInputReceiver(
features={"1": constant_op.constant([1])},
receiver_tensors=receiver_tensors)
def test_tensor_serving_input_receiver_constructor(self):
features = constant_op.constant([0])
receiver_tensors = {
"example0": array_ops.placeholder(dtypes.string, name="example0"),
u"example1": array_ops.placeholder(dtypes.string, name="example1"),
}
r = export.TensorServingInputReceiver(features, receiver_tensors)
self.assertTrue(isinstance(r.features, ops.Tensor))
self.assertTrue(isinstance(r.receiver_tensors, dict))
def test_tensor_serving_input_receiver_sparse(self):
features = sparse_tensor.SparseTensor(
indices=[[0, 0]], values=[1], dense_shape=[1, 1])
receiver_tensors = {
"example0": array_ops.placeholder(dtypes.string, name="example0"),
u"example1": array_ops.placeholder(dtypes.string, name="example1"),
}
r = export.TensorServingInputReceiver(features, receiver_tensors)
self.assertTrue(isinstance(r.features, sparse_tensor.SparseTensor))
self.assertTrue(isinstance(r.receiver_tensors, dict))
def test_serving_input_receiver_receiver_tensors_invalid(self):
features = constant_op.constant([0])
with self.assertRaisesRegexp(
ValueError, "receiver_tensors must be defined"):
export.TensorServingInputReceiver(
features=features,
receiver_tensors=None)
with self.assertRaisesRegexp(
ValueError, "receiver_tensor keys must be strings"):
export.TensorServingInputReceiver(
features=features,
receiver_tensors={
1: array_ops.placeholder(dtypes.string, name="example0")})
with self.assertRaisesRegexp(
ValueError, "receiver_tensor example1 must be a Tensor"):
export.TensorServingInputReceiver(
features=features,
receiver_tensors={"example1": [1]})
