wehub-resource-sync
264 lines
10 KiB
C++
264 lines
10 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 <initializer_list>
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#include <sstream>
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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 "flatbuffers/flatbuffers.h" // from @flatbuffers
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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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constexpr int kAxisIsATensor = -1000;
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enum class TestType {
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kConst = 0,
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kDynamic = 1,
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};
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class SplitOpModel : public SingleOpModel {
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public:
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SplitOpModel(const TensorData& input, int num_splits,
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int axis = kAxisIsATensor) {
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if (axis == kAxisIsATensor) {
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axis_ = AddInput({TensorType_INT32, {1}});
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} else {
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axis_ = AddConstInput(TensorType_INT32, {axis}, {1});
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}
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input_ = AddInput(input);
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for (int i = 0; i < num_splits; ++i) {
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outputs_.push_back(AddOutput(input.type));
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}
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SetBuiltinOp(BuiltinOperator_SPLIT, BuiltinOptions_SplitOptions,
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CreateSplitOptions(builder_, num_splits).Union());
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if (axis == kAxisIsATensor) {
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BuildInterpreter({GetShape(axis_), GetShape(input_)});
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} else {
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BuildInterpreter({{}, GetShape(input_)});
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}
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}
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template <typename T>
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void SetInput(std::initializer_list<T> data) {
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PopulateTensor(input_, data);
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}
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void SetAxis(int axis) { PopulateTensor(axis_, {axis}); }
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template <typename T>
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std::vector<T> GetOutput(int i) {
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return ExtractVector<T>(outputs_[i]);
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}
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std::vector<int> GetOutputShape(int i) { return GetTensorShape(outputs_[i]); }
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private:
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int input_;
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int axis_;
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std::vector<int> outputs_;
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};
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template <typename T>
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void Check(TestType test_type, int axis, int num_splits,
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std::initializer_list<int> input_shape,
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std::initializer_list<int> output_shape,
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const std::initializer_list<T>& input_data,
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const std::vector<std::initializer_list<T>>& output_data,
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const TensorType& type) {
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auto debug = [&](int i) {
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std::stringstream ss;
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ss << "for output tensor " << i << " axis=" << axis
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<< " and num_splits=" << num_splits;
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return ss.str();
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};
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if (test_type == TestType::kDynamic) {
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SplitOpModel m({type, input_shape}, num_splits);
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m.SetInput(input_data);
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m.SetAxis(axis);
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ASSERT_EQ(m.Invoke(), kTfLiteOk);
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for (int i = 0; i < num_splits; ++i) {
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EXPECT_THAT(m.GetOutput<T>(i), ElementsAreArray(output_data[i]))
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<< debug(i);
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EXPECT_THAT(m.GetOutputShape(i), ElementsAreArray(output_shape))
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<< debug(i);
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}
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} else {
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SplitOpModel const_m({type, input_shape}, num_splits, axis);
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const_m.SetInput(input_data);
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ASSERT_EQ(const_m.Invoke(), kTfLiteOk);
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for (int i = 0; i < num_splits; ++i) {
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EXPECT_THAT(const_m.GetOutput<T>(i), ElementsAreArray(output_data[i]))
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<< debug(i);
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EXPECT_THAT(const_m.GetOutputShape(i), ElementsAreArray(output_shape))
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<< debug(i);
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}
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}
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}
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template <typename T>
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class SplitOpTest : public ::testing::Test {
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public:
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static std::vector<TestType> range_;
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};
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template <>
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std::vector<TestType> SplitOpTest<TestType>::range_{TestType::kConst,
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TestType::kDynamic};
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using DataTypes = ::testing::Types<float, int8_t, int16_t>;
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TYPED_TEST_SUITE(SplitOpTest, DataTypes);
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TYPED_TEST(SplitOpTest, FourDimensional) {
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for (TestType test_type : SplitOpTest<TestType>::range_) {
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Check<TypeParam>(/*axis_as_tensor*/ test_type,
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/*axis=*/0, /*num_splits=*/2, {2, 2, 2, 2}, {1, 2, 2, 2},
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{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16},
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{
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{1, 2, 3, 4, 5, 6, 7, 8},
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{9, 10, 11, 12, 13, 14, 15, 16},
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},
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GetTensorType<TypeParam>());
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Check<TypeParam>(/*axis_as_tensor*/ test_type,
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/*axis=*/1, /*num_splits=*/2, {2, 2, 2, 2}, {2, 1, 2, 2},
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{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16},
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{
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{1, 2, 3, 4, 9, 10, 11, 12},
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{5, 6, 7, 8, 13, 14, 15, 16},
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},
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GetTensorType<TypeParam>());
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Check<TypeParam>(/*axis_as_tensor*/ test_type,
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/*axis=*/2, /*num_splits=*/2, {2, 2, 2, 2}, {2, 2, 1, 2},
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{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16},
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{
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{1, 2, 5, 6, 9, 10, 13, 14},
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{3, 4, 7, 8, 11, 12, 15, 16},
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},
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GetTensorType<TypeParam>());
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Check<TypeParam>(/*axis_as_tensor*/ test_type,
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/*axis=*/3, /*num_splits=*/2, {2, 2, 2, 2}, {2, 2, 2, 1},
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{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16},
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{
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{1, 3, 5, 7, 9, 11, 13, 15},
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{2, 4, 6, 8, 10, 12, 14, 16},
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},
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GetTensorType<TypeParam>());
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}
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}
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TYPED_TEST(SplitOpTest, FourDimensionalInt8) {
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for (TestType test_type : SplitOpTest<TestType>::range_) {
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Check<TypeParam>(/*axis_as_tensor*/ test_type,
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/*axis=*/0, /*num_splits=*/2, {2, 2, 2, 2}, {1, 2, 2, 2},
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{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16},
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{
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{1, 2, 3, 4, 5, 6, 7, 8},
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{9, 10, 11, 12, 13, 14, 15, 16},
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},
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GetTensorType<TypeParam>());
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Check<TypeParam>(/*axis_as_tensor*/ test_type,
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/*axis=*/1, /*num_splits=*/2, {2, 2, 2, 2}, {2, 1, 2, 2},
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{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16},
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{
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{1, 2, 3, 4, 9, 10, 11, 12},
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{5, 6, 7, 8, 13, 14, 15, 16},
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},
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GetTensorType<TypeParam>());
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Check<TypeParam>(/*axis_as_tensor*/ test_type,
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/*axis=*/2, /*num_splits=*/2, {2, 2, 2, 2}, {2, 2, 1, 2},
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{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16},
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{
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{1, 2, 5, 6, 9, 10, 13, 14},
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{3, 4, 7, 8, 11, 12, 15, 16},
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},
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GetTensorType<TypeParam>());
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Check<TypeParam>(/*axis_as_tensor*/ test_type,
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/*axis=*/3, /*num_splits=*/2, {2, 2, 2, 2}, {2, 2, 2, 1},
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{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16},
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{
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{1, 3, 5, 7, 9, 11, 13, 15},
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{2, 4, 6, 8, 10, 12, 14, 16},
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},
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GetTensorType<TypeParam>());
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}
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}
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TYPED_TEST(SplitOpTest, FourDimensionalInt32) {
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for (TestType test_type : SplitOpTest<TestType>::range_) {
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Check<TypeParam>(/*axis_as_tensor*/ test_type,
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/*axis=*/0, /*num_splits=*/2, {2, 2, 2, 2}, {1, 2, 2, 2},
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{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16},
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{
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{1, 2, 3, 4, 5, 6, 7, 8},
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{9, 10, 11, 12, 13, 14, 15, 16},
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},
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GetTensorType<TypeParam>());
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Check<TypeParam>(/*axis_as_tensor*/ test_type,
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/*axis=*/1, /*num_splits=*/2, {2, 2, 2, 2}, {2, 1, 2, 2},
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{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16},
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{
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{1, 2, 3, 4, 9, 10, 11, 12},
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{5, 6, 7, 8, 13, 14, 15, 16},
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},
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GetTensorType<TypeParam>());
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Check<TypeParam>(/*axis_as_tensor*/ test_type,
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/*axis=*/2, /*num_splits=*/2, {2, 2, 2, 2}, {2, 2, 1, 2},
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{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16},
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{
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{1, 2, 5, 6, 9, 10, 13, 14},
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{3, 4, 7, 8, 11, 12, 15, 16},
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},
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GetTensorType<TypeParam>());
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Check<TypeParam>(/*axis_as_tensor*/ test_type,
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/*axis=*/3, /*num_splits=*/2, {2, 2, 2, 2}, {2, 2, 2, 1},
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{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16},
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{
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{1, 3, 5, 7, 9, 11, 13, 15},
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{2, 4, 6, 8, 10, 12, 14, 16},
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},
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GetTensorType<TypeParam>());
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}
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}
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TYPED_TEST(SplitOpTest, OneDimensional) {
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for (TestType test_type : SplitOpTest<TestType>::range_) {
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Check<TypeParam>(
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/*axis_as_tensor*/ test_type,
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/*axis=*/0, /*num_splits=*/8, {8}, {1}, {1, 2, 3, 4, 5, 6, 7, 8},
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{{1}, {2}, {3}, {4}, {5}, {6}, {7}, {8}}, GetTensorType<TypeParam>());
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}
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}
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TYPED_TEST(SplitOpTest, NegativeAxis) {
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for (TestType test_type : SplitOpTest<TestType>::range_) {
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Check<TypeParam>(/*axis_as_tensor*/ test_type,
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/*axis=*/-4, /*num_splits=*/2, {2, 2, 2, 2}, {1, 2, 2, 2},
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{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16},
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{
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{1, 2, 3, 4, 5, 6, 7, 8},
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{9, 10, 11, 12, 13, 14, 15, 16},
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},
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GetTensorType<TypeParam>());
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}
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}
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} // namespace
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} // namespace tflite
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