117 lines
4.6 KiB
C++
117 lines
4.6 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 <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::ElementsAre;
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template <typename T>
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class FloorDivModel : public SingleOpModel {
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public:
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FloorDivModel(const TensorData& input1, const TensorData& input2,
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const TensorData& output) {
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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_FLOOR_DIV, BuiltinOptions_FloorDivOptions,
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CreateFloorDivOptions(builder_).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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std::vector<T> GetOutput() { return ExtractVector<T>(output_); }
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std::vector<int> GetOutputShape() { return GetTensorShape(output_); }
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private:
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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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TEST(FloorDivModel, Simple) {
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FloorDivModel<int32_t> model({TensorType_INT32, {1, 2, 2, 1}},
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{TensorType_INT32, {1, 2, 2, 1}},
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{TensorType_INT32, {}});
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model.PopulateTensor<int32_t>(model.input1(), {10, 9, 11, 3});
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model.PopulateTensor<int32_t>(model.input2(), {2, 2, 3, 4});
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model.Invoke();
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(1, 2, 2, 1));
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EXPECT_THAT(model.GetOutput(), ElementsAre(5, 4, 3, 0));
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}
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TEST(FloorDivModel, NegativeValue) {
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FloorDivModel<int32_t> model({TensorType_INT32, {1, 2, 2, 1}},
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{TensorType_INT32, {1, 2, 2, 1}},
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{TensorType_INT32, {}});
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model.PopulateTensor<int32_t>(model.input1(), {10, -9, -11, 7});
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model.PopulateTensor<int32_t>(model.input2(), {2, 2, -3, -4});
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model.Invoke();
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(1, 2, 2, 1));
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EXPECT_THAT(model.GetOutput(), ElementsAre(5, -5, 3, -2));
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}
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TEST(FloorDivModel, BroadcastFloorDiv) {
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FloorDivModel<int32_t> model({TensorType_INT32, {1, 2, 2, 1}},
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{TensorType_INT32, {1}}, {TensorType_INT32, {}});
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model.PopulateTensor<int32_t>(model.input1(), {10, -9, -11, 7});
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model.PopulateTensor<int32_t>(model.input2(), {-3});
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model.Invoke();
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(1, 2, 2, 1));
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EXPECT_THAT(model.GetOutput(), ElementsAre(-4, 3, 3, -3));
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}
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TEST(FloorDivModel, SimpleFloat) {
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FloorDivModel<float> model({TensorType_FLOAT32, {1, 2, 2, 1}},
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{TensorType_FLOAT32, {1, 2, 2, 1}},
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{TensorType_FLOAT32, {}});
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model.PopulateTensor<float>(model.input1(), {10.05, 9.09, 11.9, 3.01});
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model.PopulateTensor<float>(model.input2(), {2.05, 2.03, 3.03, 4.03});
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model.Invoke();
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(1, 2, 2, 1));
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EXPECT_THAT(model.GetOutput(), ElementsAre(4.0, 4.0, 3.0, 0.0));
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}
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TEST(FloorDivModel, NegativeValueFloat) {
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FloorDivModel<float> model({TensorType_FLOAT32, {1, 2, 2, 1}},
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{TensorType_FLOAT32, {1, 2, 2, 1}},
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{TensorType_FLOAT32, {}});
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model.PopulateTensor<float>(model.input1(), {10.03, -9.9, -11.0, 7.0});
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model.PopulateTensor<float>(model.input2(), {2.0, 2.3, -3.0, -4.1});
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model.Invoke();
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(1, 2, 2, 1));
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EXPECT_THAT(model.GetOutput(), ElementsAre(5.0, -5.0, 3.0, -2.0));
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}
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TEST(FloorDivModel, BroadcastFloorDivFloat) {
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FloorDivModel<float> model({TensorType_FLOAT32, {1, 2, 2, 1}},
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{TensorType_FLOAT32, {1}},
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{TensorType_FLOAT32, {}});
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model.PopulateTensor<float>(model.input1(), {10.03, -9.9, -11.0, 7.0});
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model.PopulateTensor<float>(model.input2(), {-3.3});
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model.Invoke();
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(1, 2, 2, 1));
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EXPECT_THAT(model.GetOutput(), ElementsAre(-4.0, 2.0, 3.0, -3.0));
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}
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} // namespace
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} // namespace tflite
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