wehub-resource-sync
631 lines
24 KiB
C++
631 lines
24 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 <algorithm>
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#include <cmath>
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#include <cstdint>
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#include <initializer_list>
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#include <limits>
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#include <type_traits>
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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 "tensorflow/lite/core/c/common.h"
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#include "tensorflow/lite/kernels/internal/portable_tensor_utils.h"
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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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#include "tensorflow/lite/types/half.h"
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namespace tflite {
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namespace {
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using ::testing::ElementsAreArray;
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class ElementWiseOpBaseModel : public SingleOpModel {
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public:
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int input() const { return input_; }
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int output() const { return 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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class ElementWiseOpIntModel : public ElementWiseOpBaseModel {
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public:
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ElementWiseOpIntModel(BuiltinOperator op,
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std::initializer_list<int> input_shape) {
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input_ = AddInput(TensorType_INT32);
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output_ = AddOutput(TensorType_INT32);
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SetBuiltinOp(op, BuiltinOptions_NONE, 0);
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BuildInterpreter({input_shape});
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}
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};
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class ElementWiseOpFloatModel : public ElementWiseOpBaseModel {
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public:
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ElementWiseOpFloatModel(BuiltinOperator op,
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std::initializer_list<int> input_shape) {
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input_ = AddInput(TensorType_FLOAT32);
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output_ = AddOutput(TensorType_FLOAT32);
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SetBuiltinOp(op, BuiltinOptions_NONE, 0);
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BuildInterpreter({input_shape});
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}
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};
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template <typename T>
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class ElementWiseOpModel : public ElementWiseOpBaseModel {
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public:
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ElementWiseOpModel(BuiltinOperator op,
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std::initializer_list<int> input_shape) {
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input_ = AddInput(GetTensorType<T>());
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output_ = AddOutput(GetTensorType<T>());
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SetBuiltinOp(op, BuiltinOptions_NONE, 0);
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BuildInterpreter({input_shape});
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}
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};
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class ElementWiseOpQuantizedModel : public ElementWiseOpBaseModel {
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public:
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ElementWiseOpQuantizedModel(BuiltinOperator op, TensorData input_tensor_data,
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TensorData output_tensor_data) {
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input_ = AddInput(SymmetricInt16Scaling(input_tensor_data));
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output_ = AddOutput(SymmetricInt16Scaling(output_tensor_data));
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SetBuiltinOp(op, BuiltinOptions_NONE, 0);
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BuildInterpreter({input_tensor_data.shape});
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}
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template <typename T>
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void AsymmetricQuantizeAndPopulate(int index,
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const std::vector<float>& data) {
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std::vector<int8_t> q(data.size());
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float scaling_factor;
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int zero_point;
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tensor_utils::AsymmetricQuantizeFloats(data.data(), data.size(), q.data(),
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&scaling_factor, &zero_point);
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PopulateTensor<T>(index, /*offset=*/0, reinterpret_cast<T*>(q.data()),
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reinterpret_cast<T*>(q.data() + q.size()));
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}
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template <typename T>
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std::vector<float> ExtractDequantVector(int index) {
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auto vec = ExtractVector<T>(index);
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TfLiteTensor* t = interpreter_->tensor(index);
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auto* affine_quantization =
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reinterpret_cast<TfLiteAffineQuantization*>(t->quantization.params);
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float scaling_factor = affine_quantization->scale->data[0];
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int zero_point = affine_quantization->zero_point->data[0];
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std::vector<float> output;
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for (const auto& v : vec) {
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output.push_back((static_cast<T>(v) - zero_point) * scaling_factor);
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}
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return output;
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}
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private:
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TensorData& SymmetricInt16Scaling(TensorData& tensor) {
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// Symmetric range and null zero-point is required for INT16 tensors. As
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// SingleOpModel::QuantizationParams calculates the scale on an asymmetric
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// base [int_type::min, int_type::max], manually calculate the scale on a
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// symmetric range [int_type::min+1, int_type::max] to ensure a null
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// zero-point.
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if (tensor.type == TensorType_INT16) {
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CHECK_EQ(std::abs(tensor.min), tensor.max);
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tensor.scale = tensor.max / std::numeric_limits<int16_t>::max();
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tensor.zero_point = 0;
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tensor.min = 0;
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tensor.max = 0;
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}
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return tensor;
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}
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};
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class ElementWiseOpBoolModel : public ElementWiseOpBaseModel {
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public:
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ElementWiseOpBoolModel(BuiltinOperator op,
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std::initializer_list<int> input_shape) {
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input_ = AddInput(TensorType_BOOL);
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output_ = AddOutput(TensorType_BOOL);
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SetBuiltinOp(op, BuiltinOptions_NONE, 0);
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BuildInterpreter({input_shape});
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}
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};
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// A LUT of 256 values is used in the int8 case. For the int16 case a 513 LUT is
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// used but as the last value is only used for interpolation we only have 512
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// quantized steps.
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template <typename T>
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inline float GetLUTTolerance(float input_min, float input_max, float output_min,
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float output_max) {
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static_assert(
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std::is_same<T, int8_t>::value || std::is_same<T, int16_t>::value,
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"T must be an int8_t or int16_t.");
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const float range_sum = (input_max - input_min) + (output_max - output_min);
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if (std::is_same<T, int8_t>::value) {
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return range_sum / 256.0f;
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} else {
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return range_sum / 512.0f;
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}
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}
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template <typename T>
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float GetQuantizationStep(float min, float max) {
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const float kQuantizedStep = (max - min) / (std::numeric_limits<T>::max() -
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std::numeric_limits<T>::min());
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return kQuantizedStep;
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}
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TEST(ElementWise, Sin) {
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ElementWiseOpFloatModel m(BuiltinOperator_SIN, {1, 1, 4, 1});
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m.PopulateTensor<float>(m.input(), {0, 3.1415926, -3.1415926, 1});
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ASSERT_EQ(m.Invoke(), kTfLiteOk);
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EXPECT_THAT(m.ExtractVector<float>(m.output()),
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ElementsAreArray(ArrayFloatNear({0, 0, 0, 0.84147})));
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EXPECT_THAT(m.GetTensorShape(m.output()), ElementsAreArray({1, 1, 4, 1}));
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}
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TEST(ElementWise, SinHalf) {
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ElementWiseOpModel<half> m(BuiltinOperator_SIN, {1, 1, 4, 1});
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m.PopulateTensor<half>(m.input(), {0, 3.1415926, -3.1415926, 1});
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ASSERT_EQ(m.Invoke(), kTfLiteOk);
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EXPECT_THAT(m.ExtractVector<half>(m.output()),
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ElementsAreArray(ArrayFloatNear(
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{0, 0, 0, 0.84147},
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static_cast<float>(NumericLimits<half>::epsilon()) * 10)));
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EXPECT_THAT(m.GetTensorShape(m.output()), ElementsAreArray({1, 1, 4, 1}));
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}
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TEST(ElementWise, Cos) {
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ElementWiseOpFloatModel m(BuiltinOperator_COS, {1, 1, 4, 1});
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m.PopulateTensor<float>(m.input(), {0, 3.1415926, -3.1415926, 1});
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ASSERT_EQ(m.Invoke(), kTfLiteOk);
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EXPECT_THAT(m.ExtractVector<float>(m.output()),
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ElementsAreArray(ArrayFloatNear({1, -1, -1, 0.54030})));
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EXPECT_THAT(m.GetTensorShape(m.output()), ElementsAreArray({1, 1, 4, 1}));
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}
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TEST(ElementWise, CosHalf) {
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ElementWiseOpModel<half> m(BuiltinOperator_COS, {1, 1, 4, 1});
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m.PopulateTensor<half>(m.input(), {0, 3.1415926, -3.1415926, 1});
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ASSERT_EQ(m.Invoke(), kTfLiteOk);
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EXPECT_THAT(m.ExtractVector<half>(m.output()),
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ElementsAreArray(ArrayFloatNear(
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{1, -1, -1, 0.54030},
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static_cast<float>(NumericLimits<half>::epsilon()) * 10)));
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EXPECT_THAT(m.GetTensorShape(m.output()), ElementsAreArray({1, 1, 4, 1}));
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}
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TEST(ElementWise, Log) {
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ElementWiseOpFloatModel m(BuiltinOperator_LOG, {1, 1, 4, 1});
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m.PopulateTensor<float>(m.input(), {1, 3.1415926, 1, 1});
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ASSERT_EQ(m.Invoke(), kTfLiteOk);
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EXPECT_THAT(m.ExtractVector<float>(m.output()),
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ElementsAreArray(ArrayFloatNear({0, 1.14473, 0, 0})));
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EXPECT_THAT(m.GetTensorShape(m.output()), ElementsAreArray({1, 1, 4, 1}));
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}
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TEST(ElementWise, LogInt8) {
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const float input_min = 0.0f;
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const float input_max = 13.2f;
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const float output_min = -2.3026f;
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const float output_max = 2.5802f;
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const float kQuantizedTolerance =
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GetLUTTolerance<int8_t>(input_min, input_max, output_min, output_max);
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ElementWiseOpQuantizedModel m(
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BuiltinOperator_LOG,
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{TensorType_INT8, {1, 2, 2, 2}, input_min, input_max},
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{TensorType_INT8, {}, output_min, output_max});
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m.QuantizeAndPopulate<int8_t>(
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m.input(), {0.1f, 0.5f, 1.0f, 1.15f, 2.3f, 5.01f, 11.0f, 13.2f});
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ASSERT_EQ(m.Invoke(), kTfLiteOk);
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EXPECT_THAT(
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m.ExtractDequantVector<int8_t>(m.output()),
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ElementsAreArray(ArrayFloatNear({-2.3026f, -0.6931f, 0.0f, 0.1398f,
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0.8329f, 1.6114, 2.3979f, 2.5802f},
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kQuantizedTolerance)));
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}
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TEST(ElementWise, LogInt16) {
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const float input_min = -13.2f;
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const float input_max = 13.2f;
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const float output_min = -2.5802f;
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const float output_max = 2.5802f;
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const float kQuantizedTolerance =
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GetLUTTolerance<int16_t>(input_min, input_max, output_min, output_max);
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ElementWiseOpQuantizedModel m(
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BuiltinOperator_LOG,
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{TensorType_INT16, {1, 2, 2, 2}, input_min, input_max},
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{TensorType_INT16, {}, output_min, output_max});
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m.QuantizeAndPopulate<int16_t>(
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m.input(), {0.1f, 0.5f, 1.0f, 1.15f, 2.3f, 5.01f, 11.0f, 13.2f});
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ASSERT_EQ(m.Invoke(), kTfLiteOk);
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EXPECT_THAT(
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m.ExtractDequantVector<int16_t>(m.output()),
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ElementsAreArray(ArrayFloatNear({-2.3026f, -0.6931f, 0.0f, 0.1398f,
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0.8329f, 1.6114, 2.3979f, 2.5802f},
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kQuantizedTolerance)));
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}
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TEST(ElementWise, Abs) {
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ElementWiseOpFloatModel m(BuiltinOperator_ABS, {1, 2, 4, 1});
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m.PopulateTensor<float>(m.input(), {
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0.f, -6.2f, 2.f, 4.f, //
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3.f, -2.f, 10.f, 1.f, //
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});
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ASSERT_EQ(m.Invoke(), kTfLiteOk);
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EXPECT_THAT(m.ExtractVector<float>(m.output()),
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Pointwise(FloatingPointEq(), {
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0.f, 6.2f, 2.f, 4.f, //
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3.f, 2.f, 10.f, 1.f, //
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}));
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}
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TEST(ElementWise, AbsInt32) {
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ElementWiseOpIntModel m(BuiltinOperator_ABS, {1, 2, 4, 1});
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m.PopulateTensor<int32_t>(m.input(), {
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0, -6, 2, 4, //
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3, -2, 10, 1, //
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});
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ASSERT_EQ(m.Invoke(), kTfLiteOk);
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EXPECT_THAT(m.ExtractVector<int32_t>(m.output()), ElementsAreArray({
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0, 6, 2, 4, //
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3, 2, 10, 1, //
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}));
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}
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TEST(ElementWise, AbsInt8) {
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const std::vector<float> input_data = {15., 46., 78., -142.,
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-1., -17., -49., 113.};
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std::vector<float> expected_output(input_data.size());
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for (int i = 0; i < expected_output.size(); i++) {
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expected_output[i] = std::abs(input_data[i]);
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}
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const auto minmax = std::minmax_element(input_data.begin(), input_data.end());
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const float abs_max = std::max(std::abs(*minmax.first), *minmax.second);
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const float kInputScale = (*minmax.second - *minmax.first) / 255.0;
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const float kOutputScale = abs_max / 255.0;
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const int input_zero_point = 127 - *minmax.second;
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const int output_zero_point = -128;
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ElementWiseOpQuantizedModel m(
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BuiltinOperator_ABS,
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{TensorType_INT8,
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{1, 8},
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*minmax.first,
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*minmax.second,
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kInputScale,
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input_zero_point,
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true,
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{kInputScale},
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{input_zero_point}},
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{TensorType_INT8, {1, 8}, 0, abs_max, kOutputScale, output_zero_point});
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m.AsymmetricQuantizeAndPopulate<int8_t>(m.input(), input_data);
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ASSERT_EQ(m.Invoke(), kTfLiteOk);
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EXPECT_THAT(m.ExtractDequantVector<int8_t>(m.output()),
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ElementsAreArray(ArrayFloatNear(expected_output, kInputScale)));
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}
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TEST(ElementWise, AbsSameScaleInt8) {
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const std::vector<float> input_data = {15., 46., 78., -142.,
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-1., -17., -49., 113.};
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std::vector<float> expected_output(input_data.size());
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for (int i = 0; i < expected_output.size(); i++) {
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expected_output[i] = std::abs(input_data[i]);
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}
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const auto minmax = std::minmax_element(input_data.begin(), input_data.end());
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const float abs_max = std::max(std::abs(*minmax.first), *minmax.second);
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const float kInputScale = (*minmax.second - *minmax.first) / 255.0;
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const int input_zero_point = 127 - *minmax.second;
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ElementWiseOpQuantizedModel m(
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BuiltinOperator_ABS,
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{TensorType_INT8,
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{1, 8},
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*minmax.first,
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*minmax.second,
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kInputScale,
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input_zero_point,
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true,
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{kInputScale},
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{input_zero_point}},
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{TensorType_INT8, {1, 8}, 0, abs_max, kInputScale, input_zero_point});
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m.AsymmetricQuantizeAndPopulate<int8_t>(m.input(), input_data);
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ASSERT_EQ(m.Invoke(), kTfLiteOk);
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EXPECT_THAT(m.ExtractDequantVector<int8_t>(m.output()),
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ElementsAreArray(ArrayFloatNear(expected_output, kInputScale)));
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}
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TEST(ElementWise, AbsInt16) {
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const float kQuantizedTolerance = GetQuantizationStep<int16_t>(-150, 150);
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const std::vector<float> input_data = {15., 46., 78., -142.,
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-1., -17., -49., 113.};
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std::vector<float> expected_output(input_data.size());
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for (int i = 0; i < expected_output.size(); i++) {
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expected_output[i] = std::abs(input_data[i]);
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}
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ElementWiseOpQuantizedModel m(BuiltinOperator_ABS,
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{TensorType_INT16, {1, 8}, -142, 142},
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{TensorType_INT16, {1, 8}, -150, 150});
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m.QuantizeAndPopulate<int16_t>(m.input(), input_data);
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ASSERT_EQ(m.Invoke(), kTfLiteOk);
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EXPECT_THAT(
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m.ExtractDequantVector<int16_t>(m.output()),
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ElementsAreArray(ArrayFloatNear(expected_output, kQuantizedTolerance)));
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}
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TEST(ElementWise, Sqrt) {
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ElementWiseOpFloatModel m(BuiltinOperator_SQRT, {1, 1, 4, 1});
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m.PopulateTensor<float>(m.input(), {0, 1, 2, 4});
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ASSERT_EQ(m.Invoke(), kTfLiteOk);
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EXPECT_THAT(m.ExtractVector<float>(m.output()),
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ElementsAreArray(ArrayFloatNear({0, 1, 1.41421, 2})));
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EXPECT_THAT(m.GetTensorShape(m.output()), ElementsAreArray({1, 1, 4, 1}));
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}
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TEST(ElementWise, SqrtInt8) {
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const std::vector<float> input_data = {0, 1, 2, 9, 16, 25, 1.44, 0.5};
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std::vector<float> expected_output(input_data.size());
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for (int i = 0; i < expected_output.size(); i++) {
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expected_output[i] = std::sqrt(input_data[i]);
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}
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const std::vector<int> shape = {1, 8};
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float kInputScale = 25.0 / 255.0;
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float kOutputScale = 5.0 / 255.0;
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int32_t zero_point = -128;
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ElementWiseOpQuantizedModel m(
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BuiltinOperator_SQRT,
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/*input_tensor_data=*/
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{/*type=*/TensorType_INT8,
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/*shape=*/shape,
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/*min=*/0,
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/*max=*/25.0,
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/*scale=*/kInputScale,
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/*zero_point=*/zero_point,
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/*per_channel_quantization=*/true,
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/*per_channel_quantization_scales=*/{kInputScale},
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/*per_channel_quantization_offsets=*/{zero_point}},
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/*output_tensor_data=*/
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{/*type=*/TensorType_INT8,
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/*shape=*/shape,
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/*min=*/0,
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/*max=*/5.0,
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/*scale=*/kOutputScale,
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/*zero_point=*/zero_point,
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/*per_channel_quantization=*/true,
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/*per_channel_quantization_scales=*/{kOutputScale},
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/*per_channel_quantization_offsets=*/{zero_point}});
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m.QuantizeAndPopulate<int8_t>(m.input(), input_data);
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ASSERT_EQ(m.Invoke(), kTfLiteOk);
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EXPECT_THAT(m.ExtractDequantVector<int8_t>(m.output()),
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ElementsAreArray(ArrayFloatNear(expected_output, kInputScale)));
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}
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TEST(ElementWise, SqrtNegativeInt8) {
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const std::vector<float> input_data = {-1.0};
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float kInputScale = 1.0 / 255.0;
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float kOutputScale = 1.0 / 255.0;
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int32_t zero_point = 0;
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ElementWiseOpQuantizedModel m(BuiltinOperator_SQRT,
|
|
{TensorType_INT8,
|
|
{1, 1},
|
|
0,
|
|
1.0,
|
|
kInputScale,
|
|
zero_point,
|
|
true,
|
|
{kInputScale},
|
|
{zero_point}},
|
|
{TensorType_INT8,
|
|
{1, 1},
|
|
0,
|
|
1.0,
|
|
kOutputScale,
|
|
zero_point,
|
|
true,
|
|
{kOutputScale},
|
|
{zero_point}});
|
|
m.QuantizeAndPopulate<int8_t>(m.input(), input_data);
|
|
EXPECT_EQ(m.Invoke(), kTfLiteError);
|
|
}
|
|
|
|
TEST(ElementWise, SqrtInt16) {
|
|
const std::vector<float> input_data = {0, 1, 2, 9, 16, 25, 1.44, 0.5};
|
|
std::vector<float> expected_output(input_data.size());
|
|
for (int i = 0; i < expected_output.size(); i++) {
|
|
expected_output[i] = std::sqrt(input_data[i]);
|
|
}
|
|
|
|
const float kQuantizedTolerance = GetQuantizationStep<int16_t>(-25, 25);
|
|
|
|
ElementWiseOpQuantizedModel m(BuiltinOperator_SQRT,
|
|
{TensorType_INT16, {1, 8}, -25, 25},
|
|
{TensorType_INT16, {1, 8}, -5, 5});
|
|
m.QuantizeAndPopulate<int16_t>(m.input(), input_data);
|
|
ASSERT_EQ(m.Invoke(), kTfLiteOk);
|
|
EXPECT_THAT(
|
|
m.ExtractDequantVector<int16_t>(m.output()),
|
|
ElementsAreArray(ArrayFloatNear(expected_output, kQuantizedTolerance)));
|
|
}
|
|
|
|
TEST(ElementWise, Rsqrt) {
|
|
ElementWiseOpFloatModel m(BuiltinOperator_RSQRT, {1, 1, 4, 1});
|
|
m.PopulateTensor<float>(m.input(), {1, 2, 4, 9});
|
|
ASSERT_EQ(m.Invoke(), kTfLiteOk);
|
|
EXPECT_THAT(m.ExtractVector<float>(m.output()),
|
|
ElementsAreArray(ArrayFloatNear({1, 0.7071, 0.5, 0.33333})));
|
|
EXPECT_THAT(m.GetTensorShape(m.output()), ElementsAreArray({1, 1, 4, 1}));
|
|
}
|
|
|
|
TEST(ElementWise, RsqrtInt8) {
|
|
const std::vector<float> input_data = {15., 46., 78., 142.,
|
|
1., 17., 49., 113.};
|
|
std::vector<float> expected_output(input_data.size());
|
|
for (int i = 0; i < expected_output.size(); i++) {
|
|
expected_output[i] = 1.f / std::sqrt(input_data[i]);
|
|
}
|
|
float kInputScale = 142.0 / 255.0;
|
|
float kOutputScale = 1.0 / 255.0;
|
|
int32_t zero_point = -128;
|
|
ElementWiseOpQuantizedModel m(BuiltinOperator_RSQRT,
|
|
{TensorType_INT8,
|
|
{1, 8},
|
|
0,
|
|
142.0,
|
|
kInputScale,
|
|
zero_point,
|
|
true,
|
|
{kInputScale},
|
|
{zero_point}},
|
|
{TensorType_INT8,
|
|
{1, 8},
|
|
0,
|
|
1.0,
|
|
kOutputScale,
|
|
zero_point,
|
|
true,
|
|
{kOutputScale},
|
|
{zero_point}});
|
|
m.QuantizeAndPopulate<int8_t>(m.input(), input_data);
|
|
ASSERT_EQ(m.Invoke(), kTfLiteOk);
|
|
EXPECT_THAT(m.ExtractDequantVector<int8_t>(m.output()),
|
|
ElementsAreArray(ArrayFloatNear(expected_output, kInputScale)));
|
|
}
|
|
|
|
TEST(ElementWise, RsqrtCloseTo0Int8) {
|
|
const std::vector<float> input_data = {15., 46., 78., 142.,
|
|
0.1, 1., 49., 113.};
|
|
std::vector<float> expected_output(input_data.size());
|
|
for (int i = 0; i < expected_output.size(); i++) {
|
|
expected_output[i] = 1.f / std::sqrt(input_data[i]);
|
|
}
|
|
float kInputScale = 142.0 / 255.0;
|
|
float kOutputScale = 3.16 / 255.0;
|
|
int32_t zero_point = -128;
|
|
ElementWiseOpQuantizedModel m(BuiltinOperator_RSQRT,
|
|
{TensorType_INT8,
|
|
{1, 8},
|
|
0,
|
|
142.0,
|
|
kInputScale,
|
|
zero_point,
|
|
true,
|
|
{kInputScale},
|
|
{zero_point}},
|
|
{TensorType_INT8,
|
|
{1, 8},
|
|
0,
|
|
3.16,
|
|
kOutputScale,
|
|
zero_point,
|
|
true,
|
|
{kOutputScale},
|
|
{zero_point}});
|
|
m.QuantizeAndPopulate<int8_t>(m.input(), input_data);
|
|
ASSERT_EQ(m.Invoke(), kTfLiteOk);
|
|
EXPECT_THAT(m.ExtractDequantVector<int8_t>(m.output()),
|
|
ElementsAreArray(ArrayFloatNear(expected_output, kInputScale)));
|
|
}
|
|
|
|
TEST(ElementWise, RsqrtNegativeInt8) {
|
|
const std::vector<float> input_data = {15., 46., 78., 142.,
|
|
1., 17., -49., 113.};
|
|
float kInputScale = 142.0 / 127.0;
|
|
float kOutputScale = 1.0 / 255.0;
|
|
int32_t input_zero_point = 0;
|
|
int32_t output_zero_point = -128;
|
|
ElementWiseOpQuantizedModel m(BuiltinOperator_RSQRT,
|
|
{TensorType_INT8,
|
|
{1, 8},
|
|
0,
|
|
142.0,
|
|
kInputScale,
|
|
input_zero_point,
|
|
true,
|
|
{kInputScale},
|
|
{input_zero_point}},
|
|
{TensorType_INT8,
|
|
{1, 8},
|
|
0,
|
|
1.0,
|
|
kOutputScale,
|
|
output_zero_point,
|
|
true,
|
|
{kOutputScale},
|
|
{output_zero_point}});
|
|
m.QuantizeAndPopulate<int8_t>(m.input(), input_data);
|
|
EXPECT_THAT(m.Invoke(), kTfLiteError);
|
|
}
|
|
|
|
TEST(ElementWise, RsqrtInt16) {
|
|
const std::vector<float> input_data = {1., 0.1, 4., 9.};
|
|
std::vector<float> expected_output(input_data.size());
|
|
for (int i = 0; i < expected_output.size(); i++) {
|
|
expected_output[i] = 1.f / std::sqrt(input_data[i]);
|
|
}
|
|
|
|
const float input_min = -10.;
|
|
const float input_max = 10.;
|
|
|
|
const float output_min = -4.;
|
|
const float output_max = 4.;
|
|
|
|
const float kQuantizedTolerance =
|
|
GetLUTTolerance<int16_t>(input_min, input_max, output_min, output_max);
|
|
|
|
ElementWiseOpQuantizedModel m(
|
|
BuiltinOperator_RSQRT,
|
|
{TensorType_INT16, {1, 1, 4, 1}, input_min, input_max},
|
|
{TensorType_INT16, {1, 1, 4, 1}, output_min, output_max});
|
|
m.QuantizeAndPopulate<int16_t>(m.input(), input_data);
|
|
ASSERT_EQ(m.Invoke(), kTfLiteOk);
|
|
EXPECT_THAT(
|
|
m.ExtractDequantVector<int16_t>(m.output()),
|
|
ElementsAreArray(ArrayFloatNear(expected_output, kQuantizedTolerance)));
|
|
}
|
|
|
|
TEST(ElementWise, RsqrtNegativeInt16) {
|
|
ElementWiseOpQuantizedModel m(BuiltinOperator_RSQRT,
|
|
{TensorType_INT16, {1, 1, 4, 1}, -10, 10},
|
|
{TensorType_INT16, {1, 1, 4, 1}, -10, 10});
|
|
m.QuantizeAndPopulate<int16_t>(m.input(), {-1, 0, -4, -9});
|
|
ASSERT_EQ(m.Invoke(), kTfLiteError);
|
|
}
|
|
|
|
TEST(ElementWise, Square) {
|
|
ElementWiseOpFloatModel m(BuiltinOperator_SQUARE, {1, 1, 4, 1});
|
|
m.PopulateTensor<float>(m.input(), {1, 2, 0.5, -3.0});
|
|
ASSERT_EQ(m.Invoke(), kTfLiteOk);
|
|
EXPECT_THAT(m.ExtractVector<float>(m.output()),
|
|
ElementsAreArray(ArrayFloatNear({1, 4.0, 0.25, 9.0})));
|
|
EXPECT_THAT(m.GetTensorShape(m.output()), ElementsAreArray({1, 1, 4, 1}));
|
|
}
|
|
|
|
TEST(ElementWise, LogicalNot) {
|
|
ElementWiseOpBoolModel m(BuiltinOperator_LOGICAL_NOT, {1, 1, 4, 1});
|
|
m.PopulateTensor<bool>(m.input(), {true, false, true, false});
|
|
ASSERT_EQ(m.Invoke(), kTfLiteOk);
|
|
EXPECT_THAT(m.ExtractVector<bool>(m.output()),
|
|
ElementsAreArray({false, true, false, true}));
|
|
EXPECT_THAT(m.GetTensorShape(m.output()), ElementsAreArray({1, 1, 4, 1}));
|
|
}
|
|
|
|
} // namespace
|
|
} // namespace tflite
|