def testMatchingTensorInfoProtoMaps(self):
with tf_v1.Graph().as_default():
sig1 = _make_signature({
"x": tf_v1.placeholder(tf.int32, [2]),
}, {
"x": tf_v1.placeholder(tf.int32, [2]),
})
sig2 = _make_signature(
{
"x": tf_v1.placeholder(tf.int32, [2]),
}, {
"x": tf_v1.sparse_placeholder(tf.int64, [2]),
})
self.assertTrue(
tensor_info.tensor_info_proto_maps_match(
sig1.inputs, sig2.inputs))
self.assertFalse(
tensor_info.tensor_info_proto_maps_match(
sig1.outputs, sig2.outputs))
sig3 = _make_signature({
"x": tf_v1.placeholder(tf.int32, [None]),
}, {
"x": tf_v1.placeholder(tf.int32, [2]),
})
self.assertFalse(
tensor_info.tensor_info_proto_maps_match(
sig1.inputs, sig3.inputs))
self.assertTrue(
tensor_info.tensor_info_proto_maps_match(
sig1.outputs, sig3.outputs))
def testParsingTensorInfoProtoMaps(self):
with tf_v1.Graph().as_default():
sig = _make_signature(
{
"x": tf_v1.placeholder(tf.string, [2]),
}, {
"y": tf_v1.placeholder(tf.int32, [2]),
"z": tf_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 testBuildOutputMap(self):
with tf_v1.Graph().as_default():
x = tf_v1.placeholder(tf.int32, [2])
y = tf_v1.sparse_placeholder(tf.string, [None])
sig = _make_signature({}, {"x": x, "y": y})
def _get_tensor(name):
return tf_v1.get_default_graph().get_tensor_by_name(name)
output_map = tensor_info.build_output_map(sig.outputs, _get_tensor)
self.assertEqual(len(output_map), 2)
self.assertEqual(output_map["x"], x)
self.assertEqual(output_map["y"].indices, y.indices)
self.assertEqual(output_map["y"].values, y.values)
self.assertEqual(output_map["y"].dense_shape, y.dense_shape)
def testBuildInputMap(self):
with tf_v1.Graph().as_default():
x = tf_v1.placeholder(tf.int32, [2])
y = tf_v1.sparse_placeholder(tf.string, [None])
sig = _make_signature({"x": x, "y": y}, {})
input_map = tensor_info.build_input_map(sig.inputs, {
"x": x,
"y": y
})
self.assertEqual(len(input_map), 4)
self.assertEqual(input_map[x.name], x)
self.assertEqual(input_map[y.indices.name], y.indices)
self.assertEqual(input_map[y.values.name], y.values)
self.assertEqual(input_map[y.dense_shape.name], y.dense_shape)
def testConvertTensors(self):
with tf_v1.Graph().as_default():
a = tf_v1.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.assertEqual(output["a"].dtype, a.dtype)
# check sparsity
in1 = tf_v1.sparse_placeholder(tf.int32, [])
with self.assertRaisesRegexp(TypeError, "dense"):
tensor_info.convert_dict_to_compatible_tensor({"a": in1},
targets)
def testRepr(self):
with tf_v1.Graph().as_default():
sig = _make_signature(
{
"x": tf_v1.placeholder(tf.string, [2]),
}, {
"y": tf_v1.placeholder(tf.int32, [2]),
"z": tf_v1.sparse_placeholder(tf.float32, [2, 10]),
})
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>"
)
def eval_function_for_module(spec, tags=None):
"""Context manager that yields a function to directly evaluate a Module.
This creates a separate graph, in which all of the signatures of the module
are instantiated. Then, it creates a session and initializes the module
variables. Finally, it returns a function which can be used to evaluate the
module signatures.
The function returned by eval_function_for_module has the same syntax as
Module.__call__ , except that inputs and outputs are not tensors but actual
values as used with Session.run().
```python
with hub.eval_function_for_module("/tmp/text-embedding") as f:
# The module can be directly evaluated using f without constructing a graph.
embeddings = f(["Hello world!",], signature="mysignature")
```
Args:
spec: A ModuleSpec defining the Module to instantiate or a path where to
load a ModuleSpec from via `load_module_spec`.
tags: A set of strings specifying the graph variant to use.
Yields:
A function whose keyword arguments are fed into the tfhub module and which
returns a dictionary with the value of the output tensors.
Raises:
RuntimeError: explaning the reason why it failed to instantiate the
Module.
ValueError: if the requested graph variant does not exists.
"""
# We create a separate graph and add all the signatures of the module to it.
original_graph = tf_v1.get_default_graph()
with tf.Graph().as_default():
module = Module(spec, tags=tags)
input_tensors_per_signature = {}
output_tensors_per_signature = {}
for signature in module.get_signature_names():
# We scope with the signature name as different signatures will likely
# contain tensors with the same name (e.g. the input and output tensors).
with tf_v1.variable_scope(signature):
input_tensors = {}
for name, tensorinfo in module.get_input_info_dict(
signature).items():
# We need to be care with the shape as it may be fully-known,
# partially-known or even unknown.
shape = tensorinfo.get_shape()
effective_shape = None if shape.dims is None else shape.as_list(
)
if tensorinfo.is_sparse:
input_tensors[name] = tf_v1.sparse_placeholder(
tensorinfo.dtype, shape=effective_shape, name=name)
else:
input_tensors[name] = tf_v1.placeholder(
tensorinfo.dtype, shape=effective_shape, name=name)
input_tensors_per_signature[signature] = input_tensors
output_tensors_per_signature[signature] = module(
input_tensors_per_signature[signature],
signature=signature,
as_dict=True)
# Evaluating the tfhub module requires an active tensorflow session.
with tf_v1.train.SingularMonitoredSession() as sess:
def func(
inputs=None,
_sentinel=None, # pylint: disable=invalid-name
signature=None,
as_dict=None):
"""Function that directly evaluates a signature in the module."""
signature = signature or "default"
input_tensors = input_tensors_per_signature[signature]
dict_inputs = _prepare_dict_inputs(inputs, input_tensors)
# The input arguments are directly fed into the session.
feed_dict = {
input_tensors[key]: value
for key, value in dict_inputs.items()
}
output = output_tensors_per_signature[signature]
output = _prepare_outputs(output, as_dict)
return sess.run(output, feed_dict=feed_dict)
with original_graph.as_default():
# Yield the function since that will keep the session alive until the
# user exits the context.
yield func