STT-tensorflow/tensorflow/lite/testing/op_tests/embedding_lookup.py
Nupur Garg 2fb71ff8cf Make generate_examples run in 2.0.
PiperOrigin-RevId: 298616596
Change-Id: Ib0be0a8929e75634924c28165f6fcd998b77add9
2020-03-03 08:59:39 -08:00

61 lines
2.3 KiB
Python

# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Test configs for embedding_lookup."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow.compat.v1 as tf
from tensorflow.lite.testing.zip_test_utils import create_tensor_data
from tensorflow.lite.testing.zip_test_utils import make_zip_of_tests
from tensorflow.lite.testing.zip_test_utils import register_make_test_function
@register_make_test_function()
def make_embedding_lookup_tests(options):
"""Make a set of tests to do gather."""
test_parameters = [
{
"params_dtype": [tf.float32],
"params_shape": [[10], [10, 10]],
"ids_dtype": [tf.int32],
"ids_shape": [[3], [5]],
},
]
def build_graph(parameters):
"""Build the gather op testing graph."""
params = tf.compat.v1.placeholder(
dtype=parameters["params_dtype"],
name="params",
shape=parameters["params_shape"])
ids = tf.compat.v1.placeholder(
dtype=parameters["ids_dtype"],
name="ids",
shape=parameters["ids_shape"])
out = tf.nn.embedding_lookup(params, ids)
return [params, ids], [out]
def build_inputs(parameters, sess, inputs, outputs):
params = create_tensor_data(parameters["params_dtype"],
parameters["params_shape"])
ids = create_tensor_data(parameters["ids_dtype"], parameters["ids_shape"],
0, parameters["params_shape"][0] - 1)
return [params, ids], sess.run(
outputs, feed_dict=dict(zip(inputs, [params, ids])))
make_zip_of_tests(options, test_parameters, build_graph, build_inputs)