/* Copyright 2018 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. ==============================================================================*/ #include #include #include #include #include #include #include "flatbuffers/flatbuffers.h" // from @flatbuffers #include "tensorflow/lite/kernels/test_util.h" #include "tensorflow/lite/schema/schema_generated.h" namespace tflite { namespace { using ::testing::ElementsAreArray; constexpr int kAxisIsATensor = -1000; enum class TestType { kConst = 0, kDynamic = 1, }; class SplitOpModel : public SingleOpModel { public: SplitOpModel(const TensorData& input, int num_splits, int axis = kAxisIsATensor) { if (axis == kAxisIsATensor) { axis_ = AddInput({TensorType_INT32, {1}}); } else { axis_ = AddConstInput(TensorType_INT32, {axis}, {1}); } input_ = AddInput(input); for (int i = 0; i < num_splits; ++i) { outputs_.push_back(AddOutput(input.type)); } SetBuiltinOp(BuiltinOperator_SPLIT, BuiltinOptions_SplitOptions, CreateSplitOptions(builder_, num_splits).Union()); if (axis == kAxisIsATensor) { BuildInterpreter({GetShape(axis_), GetShape(input_)}); } else { BuildInterpreter({{}, GetShape(input_)}); } } template void SetInput(std::initializer_list data) { PopulateTensor(input_, data); } void SetAxis(int axis) { PopulateTensor(axis_, {axis}); } template std::vector GetOutput(int i) { return ExtractVector(outputs_[i]); } std::vector GetOutputShape(int i) { return GetTensorShape(outputs_[i]); } private: int input_; int axis_; std::vector outputs_; }; template void Check(TestType test_type, int axis, int num_splits, std::initializer_list input_shape, std::initializer_list output_shape, const std::initializer_list& input_data, const std::vector>& output_data, const TensorType& type) { auto debug = [&](int i) { std::stringstream ss; ss << "for output tensor " << i << " axis=" << axis << " and num_splits=" << num_splits; return ss.str(); }; if (test_type == TestType::kDynamic) { SplitOpModel m({type, input_shape}, num_splits); m.SetInput(input_data); m.SetAxis(axis); ASSERT_EQ(m.Invoke(), kTfLiteOk); for (int i = 0; i < num_splits; ++i) { EXPECT_THAT(m.GetOutput(i), ElementsAreArray(output_data[i])) << debug(i); EXPECT_THAT(m.GetOutputShape(i), ElementsAreArray(output_shape)) << debug(i); } } else { SplitOpModel const_m({type, input_shape}, num_splits, axis); const_m.SetInput(input_data); ASSERT_EQ(const_m.Invoke(), kTfLiteOk); for (int i = 0; i < num_splits; ++i) { EXPECT_THAT(const_m.GetOutput(i), ElementsAreArray(output_data[i])) << debug(i); EXPECT_THAT(const_m.GetOutputShape(i), ElementsAreArray(output_shape)) << debug(i); } } } template class SplitOpTest : public ::testing::Test { public: static std::vector range_; }; template <> std::vector SplitOpTest::range_{TestType::kConst, TestType::kDynamic}; using DataTypes = ::testing::Types; TYPED_TEST_SUITE(SplitOpTest, DataTypes); TYPED_TEST(SplitOpTest, FourDimensional) { for (TestType test_type : SplitOpTest::range_) { Check(/*axis_as_tensor*/ test_type, /*axis=*/0, /*num_splits=*/2, {2, 2, 2, 2}, {1, 2, 2, 2}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, { {1, 2, 3, 4, 5, 6, 7, 8}, {9, 10, 11, 12, 13, 14, 15, 16}, }, GetTensorType()); Check(/*axis_as_tensor*/ test_type, /*axis=*/1, /*num_splits=*/2, {2, 2, 2, 2}, {2, 1, 2, 2}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, { {1, 2, 3, 4, 9, 10, 11, 12}, {5, 6, 7, 8, 13, 14, 15, 16}, }, GetTensorType()); Check(/*axis_as_tensor*/ test_type, /*axis=*/2, /*num_splits=*/2, {2, 2, 2, 2}, {2, 2, 1, 2}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, { {1, 2, 5, 6, 9, 10, 13, 14}, {3, 4, 7, 8, 11, 12, 15, 16}, }, GetTensorType()); Check(/*axis_as_tensor*/ test_type, /*axis=*/3, /*num_splits=*/2, {2, 2, 2, 2}, {2, 2, 2, 1}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, { {1, 3, 5, 7, 9, 11, 13, 15}, {2, 4, 6, 8, 10, 12, 14, 16}, }, GetTensorType()); } } TYPED_TEST(SplitOpTest, FourDimensionalInt8) { for (TestType test_type : SplitOpTest::range_) { Check(/*axis_as_tensor*/ test_type, /*axis=*/0, /*num_splits=*/2, {2, 2, 2, 2}, {1, 2, 2, 2}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, { {1, 2, 3, 4, 5, 6, 7, 8}, {9, 10, 11, 12, 13, 14, 15, 16}, }, GetTensorType()); Check(/*axis_as_tensor*/ test_type, /*axis=*/1, /*num_splits=*/2, {2, 2, 2, 2}, {2, 1, 2, 2}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, { {1, 2, 3, 4, 9, 10, 11, 12}, {5, 6, 7, 8, 13, 14, 15, 16}, }, GetTensorType()); Check(/*axis_as_tensor*/ test_type, /*axis=*/2, /*num_splits=*/2, {2, 2, 2, 2}, {2, 2, 1, 2}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, { {1, 2, 5, 6, 9, 10, 13, 14}, {3, 4, 7, 8, 11, 12, 15, 16}, }, GetTensorType()); Check(/*axis_as_tensor*/ test_type, /*axis=*/3, /*num_splits=*/2, {2, 2, 2, 2}, {2, 2, 2, 1}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, { {1, 3, 5, 7, 9, 11, 13, 15}, {2, 4, 6, 8, 10, 12, 14, 16}, }, GetTensorType()); } } TYPED_TEST(SplitOpTest, FourDimensionalInt32) { for (TestType test_type : SplitOpTest::range_) { Check(/*axis_as_tensor*/ test_type, /*axis=*/0, /*num_splits=*/2, {2, 2, 2, 2}, {1, 2, 2, 2}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, { {1, 2, 3, 4, 5, 6, 7, 8}, {9, 10, 11, 12, 13, 14, 15, 16}, }, GetTensorType()); Check(/*axis_as_tensor*/ test_type, /*axis=*/1, /*num_splits=*/2, {2, 2, 2, 2}, {2, 1, 2, 2}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, { {1, 2, 3, 4, 9, 10, 11, 12}, {5, 6, 7, 8, 13, 14, 15, 16}, }, GetTensorType()); Check(/*axis_as_tensor*/ test_type, /*axis=*/2, /*num_splits=*/2, {2, 2, 2, 2}, {2, 2, 1, 2}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, { {1, 2, 5, 6, 9, 10, 13, 14}, {3, 4, 7, 8, 11, 12, 15, 16}, }, GetTensorType()); Check(/*axis_as_tensor*/ test_type, /*axis=*/3, /*num_splits=*/2, {2, 2, 2, 2}, {2, 2, 2, 1}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, { {1, 3, 5, 7, 9, 11, 13, 15}, {2, 4, 6, 8, 10, 12, 14, 16}, }, GetTensorType()); } } TYPED_TEST(SplitOpTest, OneDimensional) { for (TestType test_type : SplitOpTest::range_) { Check( /*axis_as_tensor*/ test_type, /*axis=*/0, /*num_splits=*/8, {8}, {1}, {1, 2, 3, 4, 5, 6, 7, 8}, {{1}, {2}, {3}, {4}, {5}, {6}, {7}, {8}}, GetTensorType()); } } TYPED_TEST(SplitOpTest, NegativeAxis) { for (TestType test_type : SplitOpTest::range_) { Check(/*axis_as_tensor*/ test_type, /*axis=*/-4, /*num_splits=*/2, {2, 2, 2, 2}, {1, 2, 2, 2}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, { {1, 2, 3, 4, 5, 6, 7, 8}, {9, 10, 11, 12, 13, 14, 15, 16}, }, GetTensorType()); } } } // namespace } // namespace tflite