def test_embedding_lookup_shape(self):
def _evaluate(tensors, feed_dict):
sess = ops.get_default_session()
if sess is None:
with self.test_session() as sess:
return sess.run(tensors, feed_dict=feed_dict)
else:
return sess.run(tensors, feed_dict=feed_dict)
with self.session(use_gpu=test_util.is_gpu_available(),
config=default_config):
default_val = -1
keys = constant_op.constant([0, 1, 2], dtypes.int64)
values = constant_op.constant([[0, 0, 0], [1, 1, 1], [2, 2, 2]],
dtypes.int32)
table = de.get_variable("t140",
dtypes.int64,
dtypes.int32,
dim=3,
initializer=default_val)
self.evaluate(table.upsert(keys, values))
self.assertAllEqual(3, self.evaluate(table.size()))
# shape of ids is fully defined
ids = constant_op.constant([[0, 1], [2, 4]], dtypes.int64)
embeddings = de.embedding_lookup(table, ids)
self.assertAllEqual([2, 2, 3], embeddings.get_shape())
re = self.evaluate(embeddings)
self.assertAllEqual([[[0, 0, 0], [1, 1, 1]], [[2, 2, 2], [-1, -1, -1]]],
re)
# shape of ids is partially defined
ids = gen_array_ops.placeholder(shape=(2, None), dtype=dtypes.int64)
embeddings = de.embedding_lookup(table, ids)
self.assertFalse(embeddings.get_shape().is_fully_defined())
re = _evaluate(
embeddings,
feed_dict={ids: np.asarray([[0, 1], [2, 4]], dtype=np.int64)})
self.assertAllEqual([[[0, 0, 0], [1, 1, 1]], [[2, 2, 2], [-1, -1, -1]]],
re)
# shape of ids is unknown
ids = gen_array_ops.placeholder(dtype=dtypes.int64)
embeddings = de.embedding_lookup(table, ids)
self.assertEqual(embeddings.get_shape(), tensor_shape.unknown_shape())
re = _evaluate(
embeddings,
feed_dict={ids: np.asarray([[0, 1], [2, 4]], dtype=np.int64)})
self.assertAllEqual([[[0, 0, 0], [1, 1, 1]], [[2, 2, 2], [-1, -1, -1]]],
re)
def _prepare_signature_inputs(layers: dict, dtype_layer, model_keys):
signature = {}
for key, value in model_keys.items():
if value in layers.keys():
x = gen_array_ops.placeholder(dtype=dtype_layer,
shape=layers[value],
name=value)
x_tensor_info = build_tensor_info(x)
signature[key] = x_tensor_info
return signature
def _prepare_signature(layers: dict, model_keys):
signature = {}
for key, value in model_keys.items():
if value in layers.keys():
x = gen_array_ops.placeholder(
dtype=type_mapping[layers[value].precision],
shape=layers[value].shape,
name=value)
x_tensor_info = build_tensor_info(x)
signature[key] = x_tensor_info
return signature
def _prepare_signature_outputs(names: list, dtype_layer, shape, model_keys):
"""
Inference Engine Api does not report outputs shapes,
for signature format consistency with TF Serving theres returned a
dummy shape==(1,1,1).
To be fixed in the future.
"""
signature = {}
for key, value in model_keys.items():
if value in names:
x = gen_array_ops.placeholder(dtype=dtype_layer,
shape=shape,
name=value)
x_tensor_info = build_tensor_info(x)
signature[key] = x_tensor_info
return signature
def placeholder(dtype, shape=None, name=None):
if context.executing_eagerly():
raise RuntimeError("tf.placeholder() is not compatible with "
"eager execution.")
return gen_array_ops.placeholder(dtype=dtype, shape=shape, name=name)
def add_op_to_graph(num_ops):
with func_graph.FuncGraph("add").as_default():
a = gen_array_ops.placeholder(dtypes.float32)
b = gen_array_ops.placeholder(dtypes.float32)
for _ in range(num_ops):
gen_math_ops.add(a, b)