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# 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 where_v2."""
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_where_v2_tests(options):
"""Make a set of tests to do where_v2."""
test_parameters = [
{
"input_condition_shape": [[1, 2, 3, 4]],
"input_dtype": [tf.float32, tf.int32],
"input_shape_set": [([1, 2, 3, 4], [1, 1, 1, 1]),],
},
{
"input_condition_shape": [[2], [1]],
"input_dtype": [tf.float32, tf.int32],
"input_shape_set": [([2, 1, 2, 1], [2, 1, 2, 1]),],
},
{
"input_condition_shape": [[1, 4, 2]],
"input_dtype": [tf.float32, tf.int32],
"input_shape_set": [([1, 3, 4, 2], [1, 3, 4, 2]),],
},
{
"input_condition_shape": [[1, 2]],
"input_dtype": [tf.float32, tf.int32],
"input_shape_set": [([1, 2, 2], [1, 2, 2]),],
},
{
"input_condition_shape": [[1, 1]],
"input_dtype": [tf.float32, tf.int32],
"input_shape_set": [([1, 1, 2, 2], [1, 1, 2, 2]),],
},
{
"input_condition_shape": [[4]],
"input_dtype": [tf.float32, tf.int32],
"input_shape_set": [([4, 4], [4, 4]),],
},
{
"input_condition_shape": [[2]],
"input_dtype": [tf.float32, tf.int32],
"input_shape_set": [([2, 3], [2, 3]),],
},
{
"input_condition_shape": [[1, 2]],
"input_dtype": [tf.float32, tf.int32],
"input_shape_set": [([1, 2, 2], [1, 2]),],
},
]
def build_graph(parameters):
"""Build the where op testing graph."""
input_condition = tf.compat.v1.placeholder(
dtype=tf.bool,
name="input_condition",
shape=parameters["input_condition_shape"])
input_value1 = tf.compat.v1.placeholder(
dtype=parameters["input_dtype"],
name="input_x",
shape=parameters["input_shape_set"][0])
input_value2 = tf.compat.v1.placeholder(
dtype=parameters["input_dtype"],
name="input_y",
shape=parameters["input_shape_set"][1])
out = tf.where_v2(input_condition, input_value1, input_value2)
return [input_condition, input_value1, input_value2], [out]
def build_inputs(parameters, sess, inputs, outputs):
input_condition = create_tensor_data(tf.bool,
parameters["input_condition_shape"])
input_value1 = create_tensor_data(parameters["input_dtype"],
parameters["input_shape_set"][0])
input_value2 = create_tensor_data(parameters["input_dtype"],
parameters["input_shape_set"][1])
return [input_condition, input_value1, input_value2], sess.run(
outputs,
feed_dict=dict(
zip(inputs, [input_condition, input_value1, input_value2])))
options.use_experimental_converter = True
make_zip_of_tests(
options,
test_parameters,
build_graph,
build_inputs,
expected_tf_failures=2)