wehub-resource-sync
98 lines
3.3 KiB
C++
98 lines
3.3 KiB
C++
/* Copyright 2021 The TensorFlow Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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#include <stdint.h>
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#include <vector>
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#include <gtest/gtest.h>
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#include "tensorflow/lite/kernels/test_util.h"
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#include "tensorflow/lite/schema/schema_generated.h"
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namespace tflite {
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namespace {
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using ::testing::ElementsAre;
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using ::testing::ElementsAreArray;
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template <typename T>
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class BucketizeOpModel : public SingleOpModel {
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public:
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BucketizeOpModel(const TensorData& input,
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const std::vector<float>& boundaries) {
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input_ = AddInput(input);
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boundaries_ = boundaries;
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output_ = AddOutput({TensorType_INT32, input.shape});
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SetBuiltinOp(BuiltinOperator_BUCKETIZE, BuiltinOptions_BucketizeOptions,
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CreateBucketizeOptions(
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builder_, builder_.CreateVector<float>(boundaries_))
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.Union());
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BuildInterpreter({GetShape(input_)});
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}
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int input() { return input_; }
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const std::vector<float>& boundaries() { return boundaries_; }
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std::vector<int> GetOutput() { return ExtractVector<int>(output_); }
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std::vector<int> GetOutputShape() { return GetTensorShape(output_); }
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private:
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int input_;
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std::vector<float> boundaries_;
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int output_;
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};
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TEST(BucketizeOpTest, Float) {
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// Buckets are: (-inf, 0.), [0., 10.), [10., 100.), [100., +inf).
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BucketizeOpModel<float> model(
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/*input=*/{/*type=*/TensorType_FLOAT32, /*shape=*/{3, 2}},
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/*boundaries=*/{0.0f, 10.0f, 100.0f});
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// input: [[-5, 10000], [150, 10], [5, 100]]
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model.PopulateTensor<float>(model.input(),
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{-5.0f, 10000.0f, 150.0f, 10.0f, 5.0f, 100.0f});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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// output: [[0, 3], [3, 2], [1, 3]]
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(3, 2));
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EXPECT_THAT(model.GetOutput(), ElementsAreArray({0, 3, 3, 2, 1, 3}));
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}
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TEST(BucketizeOpTest, Int32) {
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// Buckets are: (-inf, 0.), [0., 10.), [10., 100.), [100., +inf).
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BucketizeOpModel<int32_t> model(
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/*input=*/{/*type=*/TensorType_INT32, /*shape=*/{3, 2}},
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/*boundaries=*/{0, 10, 100});
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// input: [[-5, 10000], [150, 10], [5, 100]]
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model.PopulateTensor<int32_t>(model.input(), {-5, 10000, 150, 10, 5, 100});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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// output: [[0, 3], [3, 2], [1, 3]]
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(3, 2));
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EXPECT_THAT(model.GetOutput(), ElementsAreArray({0, 3, 3, 2, 1, 3}));
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}
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#if GTEST_HAS_DEATH_TEST
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TEST(BucketizeOpTest, UnsortedBuckets) {
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EXPECT_DEATH(BucketizeOpModel<float>(
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/*input=*/{/*type=*/TensorType_INT32, /*shape=*/{3, 2}},
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/*boundaries=*/{0, 10, -10}),
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"Expected sorted boundaries");
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}
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#endif
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} // namespace
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} // namespace tflite
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