def testParsingTensorInfoProtoMaps(self):
with tf.compat.v1.Graph().as_default():
sig = _make_signature(
{
"x": tf.compat.v1.placeholder(tf.string, [2]),
}, {
"y": tf.compat.v1.placeholder(tf.int32, [2]),
"z": tf.compat.v1.sparse_placeholder(tf.float32, [2, 10]),
})
inputs = tensor_info.parse_tensor_info_map(sig.inputs)
self.assertEqual(set(inputs.keys()), set(["x"]))
self.assertEqual(inputs["x"].get_shape(), [2])
self.assertEqual(inputs["x"].dtype, tf.string)
self.assertFalse(inputs["x"].is_sparse)
outputs = tensor_info.parse_tensor_info_map(sig.outputs)
self.assertEqual(set(outputs.keys()), set(["y", "z"]))
self.assertEqual(outputs["y"].get_shape(), [2])
self.assertEqual(outputs["y"].dtype, tf.int32)
self.assertFalse(outputs["y"].is_sparse)
self.assertEqual(outputs["z"].get_shape(), [2, 10])
self.assertEqual(outputs["z"].dtype, tf.float32)
self.assertTrue(outputs["z"].is_sparse)
def testRepr(self):
with tf.compat.v1.Graph().as_default():
sig = _make_signature(
{
"x": tf.compat.v1.placeholder(tf.string, [2]),
}, {
"y": tf.compat.v1.placeholder(tf.int32, [2]),
"z": tf.compat.v1.sparse_placeholder(tf.float32, [2, 10]),
"r": tf.compat.v1.ragged.placeholder(tf.float32, 1, ()),
})
outputs = tensor_info.parse_tensor_info_map(sig.outputs)
self.assertEqual(
repr(outputs["y"]),
"<hub.ParsedTensorInfo shape=(2,) dtype=int32 is_sparse=False>"
)
self.assertEqual(
repr(outputs["z"]),
"<hub.ParsedTensorInfo shape=(2, 10) dtype=float32 is_sparse=True>"
)
self.assertRegex(
repr(outputs["r"]),
r"<hub.ParsedTensorInfo type_spec=RaggedTensorSpec\(TensorShape\(\["
r"(None, None|Dimension\(None\), Dimension\(None\))"
r"\]\), tf.float32, 1, tf.int64\)>")
def testParsingTensorInfoProtoMaps(self):
with tf.compat.v1.Graph().as_default():
sig = _make_signature(
{
"x": tf.compat.v1.placeholder(tf.string, [2]),
}, {
"y": tf.compat.v1.placeholder(tf.int32, [2]),
"z": tf.compat.v1.sparse_placeholder(tf.float32, [2, 10]),
"r": tf.compat.v1.ragged.placeholder(tf.float32, 1, ()),
})
inputs = tensor_info.parse_tensor_info_map(sig.inputs)
self.assertEqual(set(inputs.keys()), set(["x"]))
self.assertEqual(inputs["x"].get_shape(), [2])
self.assertEqual(inputs["x"].dtype, tf.string)
self.assertFalse(inputs["x"].is_sparse)
self.assertFalse(inputs["x"].is_composite)
self.assertEqual(inputs["x"].type_spec,
tf.TensorSpec([2], tf.string))
self.assertTrue(inputs["x"].is_supported_type)
outputs = tensor_info.parse_tensor_info_map(sig.outputs)
self.assertEqual(set(outputs.keys()), set(["y", "z", "r"]))
self.assertEqual(outputs["y"].get_shape(), [2])
self.assertEqual(outputs["y"].dtype, tf.int32)
self.assertFalse(outputs["y"].is_sparse)
self.assertFalse(outputs["y"].is_composite)
self.assertEqual(outputs["y"].type_spec,
tf.TensorSpec([2], tf.int32))
self.assertTrue(outputs["y"].is_supported_type)
self.assertEqual(outputs["z"].get_shape(), [2, 10])
self.assertEqual(outputs["z"].dtype, tf.float32)
self.assertTrue(outputs["z"].is_sparse)
self.assertTrue(outputs["z"].is_composite)
self.assertEqual(outputs["z"].type_spec,
tf.SparseTensorSpec([2, 10], tf.float32))
self.assertTrue(outputs["z"].is_supported_type)
self.assertEqual(outputs["r"].get_shape().as_list(), [None, None])
self.assertEqual(outputs["r"].dtype, tf.float32)
self.assertFalse(outputs["r"].is_sparse)
self.assertTrue(outputs["r"].is_composite)
self.assertEqual(outputs["r"].type_spec,
tf.RaggedTensorSpec([None, None], tf.float32))
self.assertTrue(outputs["r"].is_supported_type)
def testParsingTensorInfoProtoMaps(self):
sig = _make_signature({
"x": tf.placeholder(tf.string, [2]),
}, {
"y": tf.placeholder(tf.int32, [2]),
"z": tf.sparse_placeholder(tf.float32, [2, 10]),
})
inputs = tensor_info.parse_tensor_info_map(sig.inputs)
self.assertEquals(set(inputs.keys()), set(["x"]))
self.assertEquals(inputs["x"].get_shape(), [2])
self.assertEquals(inputs["x"].dtype, tf.string)
self.assertFalse(inputs["x"].is_sparse)
outputs = tensor_info.parse_tensor_info_map(sig.outputs)
self.assertEquals(set(outputs.keys()), set(["y", "z"]))
self.assertEquals(outputs["y"].get_shape(), [2])
self.assertEquals(outputs["y"].dtype, tf.int32)
self.assertFalse(outputs["y"].is_sparse)
self.assertEquals(outputs["z"].get_shape(), [2, 10])
self.assertEquals(outputs["z"].dtype, tf.float32)
self.assertTrue(outputs["z"].is_sparse)
def testConvertTensors(self):
a = tf.placeholder(tf.int32, [None])
protomap = _make_signature({"a": a}, {}).inputs
targets = tensor_info.parse_tensor_info_map(protomap)
# convert constant
in0 = [1, 2, 3]
output = tensor_info.convert_dict_to_compatible_tensor({"a": in0}, targets)
self.assertEquals(output["a"].dtype, a.dtype)
# check sparsity
in1 = tf.sparse_placeholder(tf.int32, [])
with self.assertRaisesRegexp(TypeError, "dense"):
tensor_info.convert_dict_to_compatible_tensor({"a": in1}, targets)
def testRepr(self):
sig = _make_signature({
"x": tf.placeholder(tf.string, [2]),
}, {
"y": tf.placeholder(tf.int32, [2]),
"z": tf.sparse_placeholder(tf.float32, [2, 10]),
})
outputs = tensor_info.parse_tensor_info_map(sig.outputs)
self.assertEquals(
repr(outputs["y"]),
"<hub.ParsedTensorInfo shape=(2,) dtype=int32 is_sparse=False>")
self.assertEquals(
repr(outputs["z"]),
"<hub.ParsedTensorInfo shape=(2, 10) dtype=float32 is_sparse=True>")
def get_output_info_dict(self, signature=None, tags=None):
signature_def = self._get_signature_def(signature, tags)
return tensor_info.parse_tensor_info_map(signature_def.outputs)
def get_output_info_dict(self, signature=None, tags=None): signature_def = self._get_signature_def(signature, tags) return tensor_info.parse_tensor_info_map(signature_def.outputs)
