110 lines
3.9 KiB
C++
110 lines
3.9 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 <initializer_list>
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#include <vector>
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#include <gmock/gmock.h>
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#include <gtest/gtest.h>
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#include "flatbuffers/flatbuffers.h" // from @flatbuffers
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#include "tensorflow/lite/kernels/test_util.h"
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#include "tensorflow/lite/schema/schema_generated.h"
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namespace tflite {
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namespace {
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using ::testing::ElementsAreArray;
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class FakeQuantOpModel : public SingleOpModel {
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public:
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FakeQuantOpModel(const TensorData& input, const TensorType& output, float min,
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float max, int num_bits) {
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input_ = AddInput(input);
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output_ = AddOutput(output);
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SetBuiltinOp(BuiltinOperator_FAKE_QUANT, BuiltinOptions_FakeQuantOptions,
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CreateFakeQuantOptions(builder_, min, max, num_bits).Union());
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BuildInterpreter({GetShape(input_)});
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}
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template <class T>
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void SetInput(std::initializer_list<T> data) {
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PopulateTensor(input_, data);
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}
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template <class T>
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std::vector<T> GetOutput() {
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return ExtractVector<T>(output_);
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}
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std::vector<int> GetOutputShape() { return GetTensorShape(output_); }
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protected:
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int input_;
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int output_;
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};
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TEST(FakeQuantOpTest, FloatPositiveRange8Test) {
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std::initializer_list<float> data = {0.0, 1.0, 0.25,
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0.50, 0.4444444, 0.00001};
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FakeQuantOpModel m({TensorType_FLOAT32, {3, 1, 2}}, TensorType_FLOAT32, 0.0f,
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1.0f, 8);
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m.SetInput<float>(data);
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m.Invoke();
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EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({3, 1, 2}));
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EXPECT_THAT(
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m.GetOutput<float>(),
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ElementsAreArray(ArrayFloatNear({0, 1, 0.25098, 0.498039, 0.443137, 0})));
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}
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TEST(FakeQuantOpTest, FloatNegativeRange8Test) {
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std::initializer_list<float> data = {0.0, -0.9, 0.25,
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0.50, 0.4444444, -0.00001};
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FakeQuantOpModel m({TensorType_FLOAT32, {3, 1, 2}}, TensorType_FLOAT32, -0.9f,
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0.9f, 8);
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m.SetInput<float>(data);
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m.Invoke();
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EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({3, 1, 2}));
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EXPECT_THAT(m.GetOutput<float>(),
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ElementsAreArray(ArrayFloatNear(
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{0, -0.896471, 0.247059, 0.501176, 0.444706, 0})));
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}
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TEST(FakeQuantOpTest, FloatPositiveRange16Test) {
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std::initializer_list<float> data = {0.0, 1.0, 0.25,
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0.50, 0.4444444, 0.00001};
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FakeQuantOpModel m({TensorType_FLOAT32, {3, 1, 2}}, TensorType_FLOAT32, 0.0f,
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1.0f, 16);
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m.SetInput<float>(data);
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m.Invoke();
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EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({3, 1, 2}));
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EXPECT_THAT(m.GetOutput<float>(),
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ElementsAreArray(ArrayFloatNear(
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{0, 1, 0.250004, 0.500008, 0.44445, 1.5259e-05})));
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}
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TEST(FakeQuantOpTest, FloatNegativeRange16Test) {
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std::initializer_list<float> data = {0.0, -0.9, 0.25,
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0.50, 0.4444444, -0.00001};
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FakeQuantOpModel m({TensorType_FLOAT32, {3, 1, 2}}, TensorType_FLOAT32, -0.9f,
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0.9f, 16);
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m.SetInput<float>(data);
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m.Invoke();
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EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({3, 1, 2}));
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EXPECT_THAT(m.GetOutput<float>(),
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ElementsAreArray(ArrayFloatNear(
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{0, -0.900014, 0.249998, 0.499995, 0.444431, 0})));
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}
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} // namespace
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} // namespace tflite
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