/* Copyright 2017 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 <gtest/gtest.h>
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#include "tensorflow/lite/interpreter.h"
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#include "tensorflow/lite/kernels/register.h"
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#include "tensorflow/lite/kernels/test_util.h"
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#include "tensorflow/lite/model.h"
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namespace tflite {
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namespace {
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using ::testing::ElementsAreArray;
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class BaseDivOpModel : public SingleOpModel {
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public:
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BaseDivOpModel(const TensorData& input1, const TensorData& input2,
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const TensorData& output,
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ActivationFunctionType activation_type) {
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input1_ = AddInput(input1);
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input2_ = AddInput(input2);
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output_ = AddOutput(output);
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SetBuiltinOp(BuiltinOperator_DIV, BuiltinOptions_DivOptions,
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CreateDivOptions(builder_, activation_type).Union());
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BuildInterpreter({GetShape(input1_), GetShape(input2_)});
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}
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int input1() { return input1_; }
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int input2() { return input2_; }
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protected:
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int input1_;
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int input2_;
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int output_;
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};
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class FloatDivOpModel : public BaseDivOpModel {
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public:
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using BaseDivOpModel::BaseDivOpModel;
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std::vector<float> GetOutput() { return ExtractVector<float>(output_); }
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};
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class IntegerDivOpModel : public BaseDivOpModel {
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public:
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using BaseDivOpModel::BaseDivOpModel;
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std::vector<int32_t> GetOutput() { return ExtractVector<int32_t>(output_); }
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};
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TEST(FloatDivOpTest, NoActivation) {
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FloatDivOpModel m({TensorType_FLOAT32, {1, 2, 2, 1}},
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{TensorType_FLOAT32, {1, 2, 2, 1}},
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{TensorType_FLOAT32, {}}, ActivationFunctionType_NONE);
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m.PopulateTensor<float>(m.input1(), {-0.2, 0.2, -1.2, 0.8});
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m.PopulateTensor<float>(m.input2(), {0.5, 0.2, -1.5, 0.5});
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m.Invoke();
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EXPECT_THAT(m.GetOutput(),
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ElementsAreArray(ArrayFloatNear({-0.4, 1.0, 0.8, 1.6})));
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}
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TEST(FloatDivOpTest, ActivationRELU_N1_TO_1) {
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FloatDivOpModel m(
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{TensorType_FLOAT32, {1, 2, 2, 1}}, {TensorType_FLOAT32, {1, 2, 2, 1}},
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{TensorType_FLOAT32, {}}, ActivationFunctionType_RELU_N1_TO_1);
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m.PopulateTensor<float>(m.input1(), {-0.2, 0.2, -1.2, 0.8});
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m.PopulateTensor<float>(m.input2(), {0.1, 0.2, -1.5, 0.5});
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m.Invoke();
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EXPECT_THAT(m.GetOutput(),
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ElementsAreArray(ArrayFloatNear({-1.0, 1.0, 0.8, 1.0})));
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}
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TEST(FloatDivOpTest, VariousInputShapes) {
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std::vector<std::vector<int>> test_shapes = {
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{6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}};
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for (int i = 0; i < test_shapes.size(); ++i) {
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FloatDivOpModel m({TensorType_FLOAT32, test_shapes[i]},
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{TensorType_FLOAT32, test_shapes[i]},
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{TensorType_FLOAT32, {}}, ActivationFunctionType_NONE);
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m.PopulateTensor<float>(m.input1(), {-2.0, 0.2, 0.3, 0.8, 1.1, -2.0});
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m.PopulateTensor<float>(m.input2(), {0.1, 0.2, 0.6, 0.5, -1.1, -0.1});
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m.Invoke();
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EXPECT_THAT(
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m.GetOutput(),
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ElementsAreArray(ArrayFloatNear({-20.0, 1.0, 0.5, 1.6, -1.0, 20.0})))
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<< "With shape number " << i;
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}
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}
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TEST(FloatDivOpTest, WithBroadcast) {
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std::vector<std::vector<int>> test_shapes = {
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{6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}};
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for (int i = 0; i < test_shapes.size(); ++i) {
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FloatDivOpModel m({TensorType_FLOAT32, test_shapes[i]},
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{TensorType_FLOAT32, {}}, // always a scalar
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{TensorType_FLOAT32, {}}, ActivationFunctionType_NONE);
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m.PopulateTensor<float>(m.input1(), {-0.2, 0.2, 0.07, 0.08, 0.11, -0.123});
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m.PopulateTensor<float>(m.input2(), {0.1});
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m.Invoke();
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EXPECT_THAT(
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m.GetOutput(),
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ElementsAreArray(ArrayFloatNear({-2.0, 2.0, 0.7, 0.8, 1.1, -1.23})))
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<< "With shape number " << i;
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}
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}
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TEST(IntegerDivOpTest, NoActivation) {
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IntegerDivOpModel m({TensorType_INT32, {1, 2, 2, 1}},
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{TensorType_INT32, {1, 2, 2, 1}}, {TensorType_INT32, {}},
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ActivationFunctionType_NONE);
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m.PopulateTensor<int32_t>(m.input1(), {-2, 2, -15, 8});
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m.PopulateTensor<int32_t>(m.input2(), {5, -2, -3, 5});
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m.Invoke();
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EXPECT_THAT(m.GetOutput(), ElementsAreArray({0, -1, 5, 1}));
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}
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TEST(IntegerDivOpTest, ActivationRELU_N1_TO_1) {
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IntegerDivOpModel m({TensorType_INT32, {1, 2, 2, 1}},
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{TensorType_INT32, {1, 2, 2, 1}}, {TensorType_INT32, {}},
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ActivationFunctionType_RELU_N1_TO_1);
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m.PopulateTensor<int32_t>(m.input1(), {-2, 2, -12, 8});
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m.PopulateTensor<int32_t>(m.input2(), {1, 2, -15, 5});
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m.Invoke();
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EXPECT_THAT(m.GetOutput(), ElementsAreArray({-1, 1, 0, 1}));
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}
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TEST(IntegerDivOpTest, VariousInputShapes) {
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std::vector<std::vector<int>> test_shapes = {
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{6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}};
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for (int i = 0; i < test_shapes.size(); ++i) {
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IntegerDivOpModel m({TensorType_INT32, test_shapes[i]},
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{TensorType_INT32, test_shapes[i]},
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{TensorType_INT32, {}}, ActivationFunctionType_NONE);
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m.PopulateTensor<int32_t>(m.input1(), {-20, 2, 3, 8, 11, -20});
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m.PopulateTensor<int32_t>(m.input2(), {1, 2, 6, 5, -11, -1});
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m.Invoke();
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EXPECT_THAT(m.GetOutput(), ElementsAreArray({-20, 1, 0, 1, -1, 20}))
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<< "With shape number " << i;
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}
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}
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TEST(IntegerDivOpTest, WithBroadcast) {
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std::vector<std::vector<int>> test_shapes = {
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{6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}};
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for (int i = 0; i < test_shapes.size(); ++i) {
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IntegerDivOpModel m({TensorType_INT32, test_shapes[i]},
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{TensorType_INT32, {}}, // always a scalar
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{TensorType_INT32, {}}, ActivationFunctionType_NONE);
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m.PopulateTensor<int32_t>(m.input1(), {-20, 21, 7, 8, 11, -123});
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m.PopulateTensor<int32_t>(m.input2(), {3});
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m.Invoke();
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EXPECT_THAT(m.GetOutput(), ElementsAreArray({-6, 7, 2, 2, 3, -41}))
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<< "With shape number " << i;
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}
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}
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} // namespace
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} // namespace tflite
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int main(int argc, char** argv) {
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::tflite::LogToStderr();
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::testing::InitGoogleTest(&argc, argv);
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return RUN_ALL_TESTS();
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}
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