1 /* Copyright 2018 The TensorFlow Authors. All Rights Reserved. 2 3 Licensed under the Apache License, Version 2.0 (the "License"); 4 you may not use this file except in compliance with the License. 5 You may obtain a copy of the License at 6 7 http://www.apache.org/licenses/LICENSE-2.0 8 9 Unless required by applicable law or agreed to in writing, software 10 distributed under the License is distributed on an "AS IS" BASIS, 11 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 See the License for the specific language governing permissions and 13 limitations under the License. 14 ==============================================================================*/ 15 #include <gtest/gtest.h> 16 #include "tensorflow/lite/interpreter.h" 17 #include "tensorflow/lite/kernels/register.h" 18 #include "tensorflow/lite/kernels/test_util.h" 19 #include "tensorflow/lite/model.h" 20 21 namespace tflite { 22 namespace { 23 24 using ::testing::ElementsAreArray; 25 26 class BaseSquaredDifferenceOpModel : public SingleOpModel { 27 public: 28 BaseSquaredDifferenceOpModel(const TensorData& input1, 29 const TensorData& input2, 30 const TensorData& output) { 31 input1_ = AddInput(input1); 32 input2_ = AddInput(input2); 33 output_ = AddOutput(output); 34 SetBuiltinOp(BuiltinOperator_SQUARED_DIFFERENCE, 35 BuiltinOptions_SquaredDifferenceOptions, 36 CreateSquaredDifferenceOptions(builder_).Union()); 37 BuildInterpreter({GetShape(input1_), GetShape(input2_)}); 38 } 39 40 int input1() { return input1_; } 41 int input2() { return input2_; } 42 43 protected: 44 int input1_; 45 int input2_; 46 int output_; 47 }; 48 49 class FloatSquaredDifferenceOpModel : public BaseSquaredDifferenceOpModel { 50 public: 51 using BaseSquaredDifferenceOpModel::BaseSquaredDifferenceOpModel; 52 53 std::vector<float> GetOutput() { return ExtractVector<float>(output_); } 54 }; 55 56 class IntegerSquaredDifferenceOpModel : public BaseSquaredDifferenceOpModel { 57 public: 58 using BaseSquaredDifferenceOpModel::BaseSquaredDifferenceOpModel; 59 60 std::vector<int32_t> GetOutput() { return ExtractVector<int32_t>(output_); } 61 }; 62 63 TEST(FloatSquaredDifferenceOpTest, FloatType_SameShape) { 64 FloatSquaredDifferenceOpModel m({TensorType_FLOAT32, {1, 2, 2, 1}}, 65 {TensorType_FLOAT32, {1, 2, 2, 1}}, 66 {TensorType_FLOAT32, {}}); 67 m.PopulateTensor<float>(m.input1(), {-0.2, 0.2, -1.2, 0.8}); 68 m.PopulateTensor<float>(m.input2(), {0.5, 0.2, -1.5, 0.5}); 69 m.Invoke(); 70 EXPECT_THAT(m.GetOutput(), 71 ElementsAreArray(ArrayFloatNear({0.49, 0.0, 0.09, 0.09}))); 72 } 73 74 TEST(FloatSquaredDifferenceOpTest, FloatType_VariousInputShapes) { 75 std::vector<std::vector<int>> test_shapes = { 76 {6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}}; 77 for (int i = 0; i < test_shapes.size(); ++i) { 78 FloatSquaredDifferenceOpModel m({TensorType_FLOAT32, test_shapes[i]}, 79 {TensorType_FLOAT32, test_shapes[i]}, 80 {TensorType_FLOAT32, {}}); 81 m.PopulateTensor<float>(m.input1(), {-2.0, 0.2, 0.3, 0.8, 1.1, -2.0}); 82 m.PopulateTensor<float>(m.input2(), {1.0, 0.2, 0.6, 0.4, -1.0, -0.0}); 83 m.Invoke(); 84 EXPECT_THAT( 85 m.GetOutput(), 86 ElementsAreArray(ArrayFloatNear({9.0, 0.0, 0.09, 0.16, 4.41, 4.0}))) 87 << "With shape number " << i; 88 } 89 } 90 91 TEST(FloatSquaredDifferenceOpTest, FloatType_WithBroadcast) { 92 std::vector<std::vector<int>> test_shapes = { 93 {6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}}; 94 for (int i = 0; i < test_shapes.size(); ++i) { 95 FloatSquaredDifferenceOpModel m( 96 {TensorType_FLOAT32, test_shapes[i]}, 97 {TensorType_FLOAT32, {}}, // always a scalar 98 {TensorType_FLOAT32, {}}); 99 m.PopulateTensor<float>(m.input1(), {-0.2, 0.2, 0.5, 0.8, 0.11, 1.1}); 100 m.PopulateTensor<float>(m.input2(), {0.1}); 101 m.Invoke(); 102 EXPECT_THAT( 103 m.GetOutput(), 104 ElementsAreArray(ArrayFloatNear({0.09, 0.01, 0.16, 0.49, 0.0001, 1.0}))) 105 << "With shape number " << i; 106 } 107 } 108 109 TEST(IntegerSquaredDifferenceOpTest, IntegerType_SameShape) { 110 IntegerSquaredDifferenceOpModel m({TensorType_INT32, {1, 2, 2, 1}}, 111 {TensorType_INT32, {1, 2, 2, 1}}, 112 {TensorType_INT32, {}}); 113 m.PopulateTensor<int32_t>(m.input1(), {-2, 2, -15, 8}); 114 m.PopulateTensor<int32_t>(m.input2(), {5, -2, -3, 5}); 115 m.Invoke(); 116 EXPECT_THAT(m.GetOutput(), ElementsAreArray({49, 16, 144, 9})); 117 } 118 119 TEST(IntegerSquaredDifferenceOpTest, IntegerType_VariousInputShapes) { 120 std::vector<std::vector<int>> test_shapes = { 121 {6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}}; 122 for (int i = 0; i < test_shapes.size(); ++i) { 123 IntegerSquaredDifferenceOpModel m({TensorType_INT32, test_shapes[i]}, 124 {TensorType_INT32, test_shapes[i]}, 125 {TensorType_INT32, {}}); 126 m.PopulateTensor<int32_t>(m.input1(), {-20, 2, 3, 8, 11, -20}); 127 m.PopulateTensor<int32_t>(m.input2(), {1, 2, 6, 5, -5, -20}); 128 m.Invoke(); 129 EXPECT_THAT(m.GetOutput(), ElementsAreArray({441, 0, 9, 9, 256, 0})) 130 << "With shape number " << i; 131 } 132 } 133 134 TEST(IntegerSquaredDifferenceOpTest, IntegerType_WithBroadcast) { 135 std::vector<std::vector<int>> test_shapes = { 136 {6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}}; 137 for (int i = 0; i < test_shapes.size(); ++i) { 138 IntegerSquaredDifferenceOpModel m( 139 {TensorType_INT32, test_shapes[i]}, 140 {TensorType_INT32, {}}, // always a scalar 141 {TensorType_INT32, {}}); 142 m.PopulateTensor<int32_t>(m.input1(), {-20, 10, 7, 3, 1, 13}); 143 m.PopulateTensor<int32_t>(m.input2(), {3}); 144 m.Invoke(); 145 EXPECT_THAT(m.GetOutput(), ElementsAreArray({529, 49, 16, 0, 4, 100})) 146 << "With shape number " << i; 147 } 148 } 149 150 } // namespace 151 } // namespace tflite 152 153 int main(int argc, char** argv) { 154 ::tflite::LogToStderr(); 155 ::testing::InitGoogleTest(&argc, argv); 156 return RUN_ALL_TESTS(); 157 } 158
