tensorflow/lite/testing/op_tests/topk.py
# 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 topk."""
import numpy as np
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()
def make_topk_tests(options):
"""Make a set of tests to do topk."""
test_parameters = [{
"input_dtype": [tf.float32, tf.int32, tf.int16],
"input_k_dtype": [tf.int32, tf.int16],
"input_shape": [[10], [5, 20]],
"input_k": [None, 1, 3],
"output_index_dtype": [tf.int32, tf.int16],
}]
def build_graph(parameters):
"""Build the topk op testing graph."""
input_value = tf.compat.v1.placeholder(
dtype=parameters["input_dtype"],
name="input",
shape=parameters["input_shape"],
)
if parameters["input_k"] is not None:
k = tf.compat.v1.placeholder(
dtype=parameters["input_k_dtype"], name="input_k", shape=[]
)
inputs = [input_value, k]
else:
k = tf.constant(3, name="k", dtype=parameters["input_k_dtype"])
inputs = [input_value]
out = tf.nn.top_k(
input_value, k, index_type=parameters["output_index_dtype"]
)
return inputs, [out[1]]
def build_inputs(parameters, sess, inputs, outputs):
input_value = create_tensor_data(
parameters["input_dtype"], parameters["input_shape"]
)
if parameters["input_k"] is not None:
k = np.array(
parameters["input_k"],
dtype=parameters["input_k_dtype"].as_numpy_dtype,
)
return [input_value, k], sess.run(
outputs, feed_dict=dict(zip(inputs, [input_value, k]))
)
else:
return [input_value], sess.run(
outputs, feed_dict=dict(zip(inputs, [input_value]))
)
# TF currently does not support infering int16 scalar from tensor,
# i.e. input_k = None x input_k_dtype = int16 cases.
make_zip_of_tests(
options,
test_parameters,
build_graph,
build_inputs,
)