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# Copyright 2020 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 broadcast_to."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow 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("make_broadcast_to_tests")
def make_broadcast_to_tests(options):
"""Make a set of tests to do broadcast_to."""
# Chose a set of parameters
test_parameters = [{
"input_dtype": [tf.float32, tf.int32],
"input_shape": [[1, 2], [2, 3, 4], [1], [2, 5, 2, 3, 4]],
"output_shape": [[3, 1, 2], [5, 2, 3, 4], [10, 10],
[1, 2, 1, 2, 5, 2, 3, 4]],
}, {
"input_dtype": [tf.float32, tf.int32],
"input_shape": [[3, 2, 3, 4, 5, 6, 7, 8]],
"output_shape": [[3, 2, 3, 4, 5, 6, 7, 8]],
}, {
"input_dtype": [tf.float32, tf.int32],
"input_shape": [[1, 3, 1, 2, 1, 4, 1, 1]],
"output_shape": [[2, 3, 1, 2, 2, 4, 1, 1]],
}, {
"input_dtype": [tf.float32, tf.int32],
"input_shape": [[2, 1, 1, 2, 1, 4, 1, 1]],
"output_shape": [[2, 3, 2, 2, 2, 4, 1, 1]],
}, {
"input_dtype": [tf.float32, tf.int32],
"input_shape": [[3, 4, 1]],
"output_shape": [[3, 4, 0]],
}]
def build_graph(parameters):
"""Build the graph for cond tests."""
input_tensor = tf.compat.v1.placeholder(
dtype=parameters["input_dtype"],
name="input",
shape=parameters["input_shape"])
out = tf.broadcast_to(input_tensor, shape=parameters["output_shape"])
return [input_tensor], [out]
def build_inputs(parameters, sess, inputs, outputs):
input_values = [
create_tensor_data(parameters["input_dtype"], parameters["input_shape"])
]
return input_values, sess.run(
outputs, feed_dict=dict(zip(inputs, input_values)))
make_zip_of_tests(
options,
test_parameters,
build_graph,
build_inputs,
expected_tf_failures=16)