117 lines
4.4 KiB
C++
117 lines
4.4 KiB
C++
/* Copyright 2018 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 <gmock/gmock.h>
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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::ElementsAreArray;
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template <typename T, typename I>
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class UniqueOpModel : public SingleOpModel {
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public:
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UniqueOpModel(const TensorData& input, TensorType input_type,
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TensorType index_out_type) {
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input_id_ = AddInput(input);
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output_id_ = AddOutput(input_type);
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output_index_id_ = AddOutput(index_out_type);
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SetBuiltinOp(BuiltinOperator_UNIQUE, BuiltinOptions_UniqueOptions,
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CreateUniqueOptions(builder_, index_out_type).Union());
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BuildInterpreter({GetShape(input_id_)});
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}
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int input_tensor_id() { return input_id_; }
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std::vector<T> GetOutput() { return ExtractVector<T>(output_id_); }
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std::vector<I> GetIndexesOutput() {
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return ExtractVector<I>(output_index_id_);
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}
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protected:
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int input_id_;
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int output_id_;
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int output_index_id_;
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};
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TEST(UniqueOpModelTest, OneElement) {
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UniqueOpModel<float, int32_t> model({TensorType_FLOAT32, {1}},
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TensorType_FLOAT32, TensorType_INT32);
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model.PopulateTensor<float>(model.input_tensor_id(), {5});
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model.Invoke();
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EXPECT_THAT(model.GetOutput(), ElementsAreArray({5}));
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EXPECT_THAT(model.GetIndexesOutput(), ElementsAreArray({0}));
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}
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TEST(UniqueOpModelTest, MultipleElements_AllUnique) {
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UniqueOpModel<float, int32_t> model({TensorType_FLOAT32, {8}},
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TensorType_FLOAT32, TensorType_INT32);
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model.PopulateTensor<float>(model.input_tensor_id(),
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{5, 2, 3, 51, 6, 72, 7, 8});
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model.Invoke();
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EXPECT_THAT(model.GetOutput(), ElementsAreArray({5, 2, 3, 51, 6, 72, 7, 8}));
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EXPECT_THAT(model.GetIndexesOutput(),
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ElementsAreArray({0, 1, 2, 3, 4, 5, 6, 7}));
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}
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TEST(UniqueOpModelTest, MultipleElements_AllDuplicates) {
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UniqueOpModel<float, int32_t> model({TensorType_FLOAT32, {7}},
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TensorType_FLOAT32, TensorType_INT32);
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model.PopulateTensor<float>(model.input_tensor_id(), {5, 5, 5, 5, 5, 5, 5});
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model.Invoke();
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EXPECT_THAT(model.GetOutput(), ElementsAreArray({5}));
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EXPECT_THAT(model.GetIndexesOutput(),
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ElementsAreArray({0, 0, 0, 0, 0, 0, 0}));
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}
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TEST(UniqueOpModelTest, MultipleElements_SomeDuplicates) {
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UniqueOpModel<float, int32_t> model({TensorType_FLOAT32, {7}},
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TensorType_FLOAT32, TensorType_INT32);
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model.PopulateTensor<float>(model.input_tensor_id(), {2, 3, 5, 7, 2, 7, 3});
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model.Invoke();
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EXPECT_THAT(model.GetOutput(), ElementsAreArray({2, 3, 5, 7}));
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EXPECT_THAT(model.GetIndexesOutput(),
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ElementsAreArray({0, 1, 2, 3, 0, 3, 1}));
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}
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TEST(UniqueOpModelTest, MultipleElements_RepeatedDuplicates) {
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UniqueOpModel<float, int32_t> model({TensorType_FLOAT32, {6}},
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TensorType_FLOAT32, TensorType_INT32);
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model.PopulateTensor<float>(model.input_tensor_id(),
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{-1, -1, -2, -2, -3, -3});
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model.Invoke();
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EXPECT_THAT(model.GetOutput(), ElementsAreArray({-1, -2, -3}));
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EXPECT_THAT(model.GetIndexesOutput(), ElementsAreArray({0, 0, 1, 1, 2, 2}));
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}
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TEST(UniqueOpModelTest, MultipleElements_SomeDuplicates_IndexInt64) {
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UniqueOpModel<float, int64_t> model({TensorType_FLOAT32, {7}},
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TensorType_FLOAT32, TensorType_INT64);
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model.PopulateTensor<float>(model.input_tensor_id(), {2, 3, 5, 7, 2, 7, 3});
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model.Invoke();
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EXPECT_THAT(model.GetOutput(), ElementsAreArray({2, 3, 5, 7}));
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EXPECT_THAT(model.GetIndexesOutput(),
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ElementsAreArray({0, 1, 2, 3, 0, 3, 1}));
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}
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} // namespace
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} // namespace tflite
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