/* 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 "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 { kDynamic = 0, // Both split_sizes and axis are dynamic kConstAxis = 1, // split_sizes is dynamic and axis is constant kConstSplits = 2, // Both split_sizes and axis are constant }; class SplitVOpModel : public SingleOpModel { public: SplitVOpModel(const TensorData& input, const TensorData& size_splits, int num_splits, int axis, std::initializer_list size_splits_data) { input_ = AddInput(input); if (size_splits_data.size() == 0) { size_splits_ = AddInput(size_splits); } else { size_splits_ = AddConstInput(size_splits, size_splits_data); } if (axis == kAxisIsATensor) { axis_ = AddInput({TensorType_INT32, {1}}); } else { axis_ = AddConstInput(TensorType_INT32, {axis}, {1}); } for (int i = 0; i < num_splits; ++i) { outputs_.push_back(AddOutput(input.type)); } SetBuiltinOp(BuiltinOperator_SPLIT_V, BuiltinOptions_SplitVOptions, CreateSplitVOptions(builder_, num_splits).Union()); if (axis == kAxisIsATensor) { BuildInterpreter( {GetShape(input_), GetShape(size_splits_), GetShape(axis_)}); } else { BuildInterpreter({GetShape(input_), GetShape(size_splits_), {}}); } } template void SetInput(std::initializer_list data) { PopulateTensor(input_, data); } void SetSizeSplits(std::initializer_list data) { PopulateTensor(size_splits_, 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 size_splits_; int axis_; std::vector outputs_; }; template void Check(TestType test_type, int axis, std::initializer_list input_shape, std::initializer_list size_splits_shape, std::vector> output_shapes, const std::initializer_list& input_data, const std::initializer_list& size_splits_data, const std::vector>& output_data) { int num_splits = size_splits_data.size(); switch (test_type) { case TestType::kDynamic: { SplitVOpModel m({GetTensorType(), input_shape}, {TensorType_INT32, size_splits_shape}, num_splits, kAxisIsATensor, {/*size_splits is a tensor*/}); m.SetInput(input_data); m.SetSizeSplits(size_splits_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])); EXPECT_THAT(m.GetOutputShape(i), ElementsAreArray(output_shapes[i])); } } break; case TestType::kConstAxis: { SplitVOpModel m({GetTensorType(), input_shape}, {TensorType_INT32, size_splits_shape}, num_splits, axis, {/*size_splits is a tensor*/}); m.SetInput(input_data); m.SetSizeSplits(size_splits_data); ASSERT_EQ(m.Invoke(), kTfLiteOk); for (int i = 0; i < num_splits; ++i) { EXPECT_THAT(m.GetOutput(i), ElementsAreArray(output_data[i])); EXPECT_THAT(m.GetOutputShape(i), ElementsAreArray(output_shapes[i])); } } break; case TestType::kConstSplits: { SplitVOpModel m({GetTensorType(), input_shape}, {TensorType_INT32, size_splits_shape}, num_splits, axis, size_splits_data); m.SetInput(input_data); ASSERT_EQ(m.Invoke(), kTfLiteOk); for (int i = 0; i < num_splits; ++i) { EXPECT_THAT(m.GetOutputShape(i), ElementsAreArray(output_shapes[i])); if (output_data[i].size() != 0) { EXPECT_THAT(m.GetOutput(i), ElementsAreArray(output_data[i])); } } } break; } } template class SplitVOpTypedTest : public ::testing::Test {}; using DataTypes = ::testing::Types; TYPED_TEST_SUITE(SplitVOpTypedTest, DataTypes); #define TYPED_SPLIT_V_TEST(TestSuiteName, CaseName) \ template \ void Check##TestSuiteName##CaseName(TestType test_type); \ \ TYPED_TEST(TestSuiteName, Dynamic##CaseName) { \ Check##TestSuiteName##CaseName(TestType::kDynamic); \ } \ TYPED_TEST(TestSuiteName, ConstAxis##CaseName) { \ Check##TestSuiteName##CaseName(TestType::kConstAxis); \ } \ TYPED_TEST(TestSuiteName, ConstSplits##CaseName) { \ Check##TestSuiteName##CaseName(TestType::kConstSplits); \ } \ \ template \ void Check##TestSuiteName##CaseName(TestType test_type) TYPED_SPLIT_V_TEST(SplitVOpTypedTest, TwoDimensional) { // Input shape: {4, 3} // size_splits: {1, 1, 2} // axis: 0 // We should have 3 outpus with shapes respectively: // output 1 : {1, 3} // output 2 : {1, 3} // output 3 : {2, 3} Check(test_type, /*axis=*/0, {4, 3}, {3}, {{1, 3}, {1, 3}, {2, 3}}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {1, 1, 2}, {{1, 2, 3}, {4, 5, 6}, {7, 8, 9, 10, 11, 12}}); } TYPED_SPLIT_V_TEST(SplitVOpTypedTest, FourDimensional) { Check(test_type, /*axis=*/0, {2, 2, 2, 2}, {2}, {{1, 2, 2, 2}, {1, 2, 2, 2}}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {1, 1}, { {1, 2, 3, 4, 5, 6, 7, 8}, {9, 10, 11, 12, 13, 14, 15, 16}, }); Check(test_type, /*axis=*/1, {2, 2, 2, 2}, {2}, {{2, 1, 2, 2}, {2, 1, 2, 2}}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {1, -1}, { {1, 2, 3, 4, 9, 10, 11, 12}, {5, 6, 7, 8, 13, 14, 15, 16}, }); Check(test_type, /*axis=*/2, {2, 2, 2, 2}, {2}, {{2, 2, 1, 2}, {2, 2, 1, 2}}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {1, 1}, { {1, 2, 5, 6, 9, 10, 13, 14}, {3, 4, 7, 8, 11, 12, 15, 16}, }); Check(test_type, /*axis=*/3, {2, 2, 2, 2}, {2}, {{2, 2, 2, 1}, {2, 2, 2, 1}}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {1, 1}, { {1, 3, 5, 7, 9, 11, 13, 15}, {2, 4, 6, 8, 10, 12, 14, 16}, }); } TYPED_SPLIT_V_TEST(SplitVOpTypedTest, OneDimensional) { Check(test_type, /*axis=*/0, {8}, {8}, {{1}, {1}, {1}, {1}, {1}, {1}, {1}, {1}}, {1, 2, 3, 4, 5, 6, 7, 8}, {1, 1, 1, 1, 1, 1, 1, 1}, {{1}, {2}, {3}, {4}, {5}, {6}, {7}, {8}}); } TYPED_SPLIT_V_TEST(SplitVOpTypedTest, OneDimensional2) { Check(test_type, /*axis=*/0, {8}, {8}, {{1}, {1}, {1}, {1}, {1}, {1}, {2}, {0}}, {1, 2, 3, 4, 5, 6, 7, 8}, {1, 1, 1, 1, 1, 1, 2, -1}, {{1}, {2}, {3}, {4}, {5}, {6}, {7, 8}, {}}); } TYPED_SPLIT_V_TEST(SplitVOpTypedTest, NegativeAxis) { Check(test_type, /*axis=*/-4, {2, 2, 2, 2}, {2}, {{1, 2, 2, 2}, {1, 2, 2, 2}}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {1, 1}, { {1, 2, 3, 4, 5, 6, 7, 8}, {9, 10, 11, 12, 13, 14, 15, 16}, }); } } // namespace } // namespace tflite