440 lines
18 KiB
C++
440 lines
18 KiB
C++
/* 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 <stdint.h>
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#include <limits>
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#include <vector>
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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 BaseSubOpModel : public SingleOpModel {
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public:
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BaseSubOpModel(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_SUB, BuiltinOptions_SubOptions,
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CreateSubOptions(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 FloatSubOpModel : public BaseSubOpModel {
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public:
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using BaseSubOpModel::BaseSubOpModel;
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std::vector<float> GetOutput() { return ExtractVector<float>(output_); }
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};
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class IntegerSubOpModel : public BaseSubOpModel {
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public:
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using BaseSubOpModel::BaseSubOpModel;
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std::vector<int32_t> GetOutput() { return ExtractVector<int32_t>(output_); }
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};
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class QuantizedSubOpModel : public BaseSubOpModel {
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public:
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using BaseSubOpModel::BaseSubOpModel;
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template <typename integer_dtype>
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std::vector<float> GetDequantizedOutput() {
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return Dequantize<integer_dtype>(ExtractVector<integer_dtype>(output_),
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GetScale(output_), GetZeroPoint(output_));
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}
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std::vector<float> GetDequantizedOutputInt16() {
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return Dequantize<int16_t>(ExtractVector<int16_t>(output_),
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GetScale(output_), GetZeroPoint(output_));
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}
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};
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// for quantized Sub, the error shouldn't exceed step
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float GetTolerance(int min, int max) {
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float kQuantizedStep = (max - min) / 255.0;
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return kQuantizedStep;
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}
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float GetToleranceInt16(float min, float max) {
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float kQuantizedStep = (max - min) / std::numeric_limits<int16_t>::max();
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return kQuantizedStep;
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}
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TEST(FloatSubOpModel, NoActivation) {
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FloatSubOpModel 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(), {-2.0, 0.2, 1.7, 0.5});
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m.PopulateTensor<float>(m.input2(), {0.1, 0.2, 0.3, 0.8});
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m.Invoke();
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EXPECT_THAT(m.GetOutput(),
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ElementsAreArray(ArrayFloatNear({-2.1, 0.0, 1.4, -0.3})));
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}
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TEST(FloatSubOpModel, ActivationRELU_N1_TO_1) {
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FloatSubOpModel 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(), {-2.0, 0.2, 1.7, 0.5});
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m.PopulateTensor<float>(m.input2(), {0.1, 0.2, 0.3, 0.8});
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m.Invoke();
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EXPECT_THAT(m.GetOutput(),
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ElementsAreArray(ArrayFloatNear({-1.0, 0.0, 1.0, -0.3})));
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}
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TEST(FloatSubOpModel, 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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FloatSubOpModel 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, 1.7, 0.5, -1.1, 2.0});
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m.PopulateTensor<float>(m.input2(), {0.1, 0.2, 0.3, 0.8, -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({-2.1, 0.0, 1.4, -0.3, 0.0, 1.9})))
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<< "With shape number " << i;
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}
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}
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TEST(FloatSubOpModel, 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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FloatSubOpModel 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(), {-2.0, 0.2, 1.7, 0.5, -1.1, 2.0});
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m.PopulateTensor<float>(m.input2(), {0.5});
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m.Invoke();
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EXPECT_THAT(
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m.GetOutput(),
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ElementsAreArray(ArrayFloatNear({-2.5, -0.3, 1.2, 0.0, -1.6, 1.5})))
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<< "With shape number " << i;
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}
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}
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TEST(FloatSubOpModel, WithBroadcast5D) {
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std::vector<std::vector<int>> test_shapes = {{1, 3, 1, 2, 1}};
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for (int i = 0; i < test_shapes.size(); ++i) {
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FloatSubOpModel 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(), {-2.0, 0.2, 1.7, 0.5, -1.1, 2.0});
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m.PopulateTensor<float>(m.input2(), {0.5});
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m.Invoke();
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EXPECT_THAT(
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m.GetOutput(),
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ElementsAreArray(ArrayFloatNear({-2.5, -0.3, 1.2, 0.0, -1.6, 1.5})))
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<< "With shape number " << i;
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}
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}
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TEST(IntegerSubOpModel, NoActivation) {
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IntegerSubOpModel 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(), {-20, 2, 7, 8});
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m.PopulateTensor<int32_t>(m.input2(), {1, 2, 3, 5});
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m.Invoke();
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EXPECT_THAT(m.GetOutput(), ElementsAreArray({-21, 0, 4, 3}));
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}
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TEST(IntegerSubOpModel, ActivationRELU_N1_TO_1) {
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IntegerSubOpModel 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(), {-20, 2, 7, 8});
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m.PopulateTensor<int32_t>(m.input2(), {1, 2, 3, 5});
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m.Invoke();
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EXPECT_THAT(m.GetOutput(), ElementsAreArray({-1, 0, 1, 1}));
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}
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TEST(IntegerSubOpModel, 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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IntegerSubOpModel 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, 7, 8, 11, 20});
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m.PopulateTensor<int32_t>(m.input2(), {1, 2, 3, 5, 11, 1});
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m.Invoke();
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EXPECT_THAT(m.GetOutput(), ElementsAreArray({-21, 0, 4, 3, 0, 19}))
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<< "With shape number " << i;
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}
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}
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TEST(IntegerSubOpModel, 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}, {1, 3, 1, 2, 1}};
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for (int i = 0; i < test_shapes.size(); ++i) {
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IntegerSubOpModel 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, 2, 7, 8, 11, 20});
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m.PopulateTensor<int32_t>(m.input2(), {1});
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m.Invoke();
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EXPECT_THAT(m.GetOutput(),
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ElementsAreArray(ArrayFloatNear({-21, 1, 6, 7, 10, 19})))
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<< "With shape number " << i;
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}
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}
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template <TensorType tensor_type, typename integer_dtype>
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void QuantizedTestsNoActivation() {
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float kQuantizedTolerance = GetTolerance(-1.0, 1.0);
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std::vector<std::vector<float>> inputs1 = {
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{0.1, 0.2, 0.3, 0.4}, {-0.2, 0.2, 0.4, 0.7}, {-0.01, 0.2, 0.7, 0.3}};
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std::vector<std::vector<float>> inputs2 = {
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{0.6, 0.4, 0.3, 0.1}, {0.6, 0.4, 0.5, -0.2}, {0.6, 0.4, -0.18, 0.5}};
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std::vector<std::vector<float>> results = {{-0.5, -0.2, 0.0, 0.3},
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{-0.8, -0.2, -0.1, 0.9},
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{-0.61, -0.2, 0.88, -0.2}};
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for (int i = 0; i < inputs1.size(); ++i) {
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QuantizedSubOpModel m({tensor_type, {1, 2, 2, 1}, -1.0, 1.0},
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{tensor_type, {1, 2, 2, 1}, -1.0, 1.0},
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{tensor_type, {}, -1.0, 1.0},
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ActivationFunctionType_NONE);
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m.QuantizeAndPopulate<integer_dtype>(m.input1(), inputs1[i]);
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m.QuantizeAndPopulate<integer_dtype>(m.input2(), inputs2[i]);
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m.Invoke();
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EXPECT_THAT(
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m.GetDequantizedOutput<integer_dtype>(),
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ElementsAreArray(ArrayFloatNear(results[i], kQuantizedTolerance)))
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<< "With test number " << i;
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}
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}
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TEST(QuantizedSubOpModel, QuantizedTestsNoActivationUInt8) {
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QuantizedTestsNoActivation<TensorType_UINT8, uint8_t>();
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}
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TEST(QuantizedSubOpModel, QuantizedTestsNoActivationInt8) {
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QuantizedTestsNoActivation<TensorType_INT8, int8_t>();
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}
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template <TensorType tensor_type, typename integer_dtype>
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void QuantizedTestsActivationRELU_N1_TO_1() {
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float kQuantizedTolerance = GetTolerance(-1.0, 1.0);
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std::vector<std::vector<float>> inputs1 = {{-0.8, 0.2, 0.9, 0.7},
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{-0.8, 0.2, 0.7, 0.5}};
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std::vector<std::vector<float>> inputs2 = {{0.6, 0.4, 0.9, -0.8},
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{0.6, 0.4, -0.8, 0.3}};
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std::vector<std::vector<float>> results = {{-1.0, -0.2, 0.0, 1.0},
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{-1.0, -0.2, 1.0, 0.2}};
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for (int i = 0; i < inputs1.size(); ++i) {
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QuantizedSubOpModel m({tensor_type, {1, 2, 2, 1}, -1.0, 1.0},
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{tensor_type, {1, 2, 2, 1}, -1.0, 1.0},
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{tensor_type, {}, -1.0, 1.0},
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ActivationFunctionType_RELU_N1_TO_1);
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m.QuantizeAndPopulate<integer_dtype>(m.input1(), inputs1[i]);
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m.QuantizeAndPopulate<integer_dtype>(m.input2(), inputs2[i]);
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m.Invoke();
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EXPECT_THAT(
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m.GetDequantizedOutput<integer_dtype>(),
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ElementsAreArray(ArrayFloatNear(results[i], kQuantizedTolerance)))
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<< "With test number " << i;
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}
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}
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TEST(QuantizedSubOpModel, QuantizedTestsActivationRELUN1TO1UInt8) {
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QuantizedTestsActivationRELU_N1_TO_1<TensorType_UINT8, uint8_t>();
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}
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TEST(QuantizedSubOpModel, QuantizedTestsActivationRELUN1TO1Int8) {
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QuantizedTestsActivationRELU_N1_TO_1<TensorType_INT8, int8_t>();
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}
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template <TensorType tensor_type, typename integer_dtype>
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void QuantizedVariousInputShapes() {
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float kQuantizedTolerance = GetTolerance(-3.0, 3.0);
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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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QuantizedSubOpModel m({tensor_type, test_shapes[i], -3.0, 3.0},
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{tensor_type, test_shapes[i], -3.0, 3.0},
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{tensor_type, {}, -3.0, 3.0},
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ActivationFunctionType_NONE);
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m.QuantizeAndPopulate<integer_dtype>(m.input1(),
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{-2.0, 0.2, 0.7, 0.8, 1.1, 2.0});
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m.QuantizeAndPopulate<integer_dtype>(m.input2(),
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{0.1, 0.3, 0.3, 0.5, 1.1, 0.1});
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m.Invoke();
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EXPECT_THAT(m.GetDequantizedOutput<integer_dtype>(),
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ElementsAreArray(ArrayFloatNear(
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{-2.1, -0.1, 0.4, 0.3, 0.0, 1.9}, kQuantizedTolerance)))
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<< "With shape number " << i;
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}
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}
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TEST(QuantizedSubOpModel, QuantizedVariousInputShapesUInt8) {
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QuantizedVariousInputShapes<TensorType_UINT8, uint8_t>();
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}
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TEST(QuantizedSubOpModel, QuantizedVariousInputShapesInt8) {
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QuantizedVariousInputShapes<TensorType_INT8, int8_t>();
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}
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template <TensorType tensor_type, typename integer_dtype>
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void QuantizedWithBroadcast() {
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float kQuantizedTolerance = GetTolerance(-3.0, 3.0);
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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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QuantizedSubOpModel m(
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{tensor_type, test_shapes[i], -3.0, 3.0}, {tensor_type, {}, -3.0, 3.0},
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{tensor_type, {}, -3.0, 3.0}, ActivationFunctionType_NONE);
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m.QuantizeAndPopulate<integer_dtype>(m.input1(),
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{-2.0, 0.2, 0.7, 0.8, 1.1, 2.0});
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m.QuantizeAndPopulate<integer_dtype>(m.input2(), {0.7});
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m.Invoke();
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EXPECT_THAT(m.GetDequantizedOutput<integer_dtype>(),
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ElementsAreArray(ArrayFloatNear(
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{-2.7, -0.5, 0.0, 0.1, 0.4, 1.3}, kQuantizedTolerance)))
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<< "With shape number " << i;
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}
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}
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TEST(QuantizedSubOpModel, QuantizedWithBroadcastUInt8) {
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QuantizedWithBroadcast<TensorType_UINT8, uint8_t>();
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}
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TEST(QuantizedSubOpModel, QuantizedWithBroadcastInt8) {
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QuantizedWithBroadcast<TensorType_INT8, int8_t>();
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}
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TEST(QuantizedSubOpModel, QuantizedTestsNoActivationInt16) {
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const float kMin = -1.f;
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const float kMax =
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static_cast<float>(std::numeric_limits<int16_t>::max() - 1) /
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std::numeric_limits<int16_t>::max();
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float kQuantizedTolerance = GetToleranceInt16(kMin, kMax);
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std::vector<std::vector<float>> inputs1 = {
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{0.7, 0.6, 0.6, 0.5}, {-0.2, 0.6, 0.9, -0.1}, {-0.2, 0.6, -0.3, 0.8}};
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std::vector<std::vector<float>> inputs2 = {
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{0.6, 0.4, 0.3, 0.1}, {0.6, 0.4, 0.5, -0.8}, {0.6, 0.4, 0.8, 0.5}};
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std::vector<std::vector<float>> results = {
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{0.1, 0.2, 0.3, 0.4}, {-0.8, 0.2, 0.4, 0.7}, {-0.8, 0.2, -1.0, 0.3}};
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for (int i = 0; i < inputs1.size(); ++i) {
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QuantizedSubOpModel m({TensorType_INT16, {1, 2, 2, 1}, kMin, kMax},
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{TensorType_INT16, {1, 2, 2, 1}, kMin, kMax},
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{TensorType_INT16, {}, kMin, kMax},
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ActivationFunctionType_NONE);
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m.QuantizeAndPopulate<int16_t>(m.input1(), inputs1[i]);
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m.QuantizeAndPopulate<int16_t>(m.input2(), inputs2[i]);
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m.Invoke();
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EXPECT_THAT(
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m.GetDequantizedOutputInt16(),
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ElementsAreArray(ArrayFloatNear(results[i], kQuantizedTolerance)))
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<< "With test number " << i;
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}
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}
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TEST(QuantizedSubOpModel, QuantizedTestsReluActivationInt16) {
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const float kMin = -2.f;
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const float kMax = 2.0 * (std::numeric_limits<int16_t>::max() - 1) /
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std::numeric_limits<int16_t>::max();
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float kQuantizedTolerance = GetToleranceInt16(kMin, kMax);
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std::vector<std::vector<float>> inputs1 = {{-0.8, 0.2, 0.9, 0.7},
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{-0.8, 0.2, 0.7, 0.5}};
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std::vector<std::vector<float>> inputs2 = {{0.6, 0.4, 0.9, -0.8},
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{0.6, 0.4, -0.8, 0.3}};
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std::vector<std::vector<float>> results = {{-1.0, -0.2, 0.0, 1.0},
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{-1.0, -0.2, 1.0, 0.2}};
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for (int i = 0; i < inputs1.size(); ++i) {
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QuantizedSubOpModel m({TensorType_INT16, {1, 2, 2, 1}, kMin, kMax},
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{TensorType_INT16, {1, 2, 2, 1}, kMin, kMax},
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{TensorType_INT16, {}, kMin, kMax},
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ActivationFunctionType_RELU_N1_TO_1);
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m.QuantizeAndPopulate<int16_t>(m.input1(), inputs1[i]);
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m.QuantizeAndPopulate<int16_t>(m.input2(), inputs2[i]);
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m.Invoke();
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EXPECT_THAT(
|
|
m.GetDequantizedOutputInt16(),
|
|
ElementsAreArray(ArrayFloatNear(results[i], kQuantizedTolerance)))
|
|
<< "With test number " << i;
|
|
}
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|
}
|
|
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TEST(QuantizedSubOpModel, QuantizedTestsNoActivationBroadcastInt16) {
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const float kMin = -1.f;
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|
const float kMax =
|
|
static_cast<float>(std::numeric_limits<int16_t>::max() - 1) /
|
|
std::numeric_limits<int16_t>::max();
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|
float kQuantizedTolerance = GetToleranceInt16(kMin, kMax);
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|
std::vector<std::vector<int>> test_shapes = {
|
|
{6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}, {1, 3, 1, 2, 1}};
|
|
for (int i = 0; i < test_shapes.size(); ++i) {
|
|
QuantizedSubOpModel m({TensorType_INT16, test_shapes[i], kMin, kMax},
|
|
{TensorType_INT16, {}, kMin, kMax},
|
|
{TensorType_INT16, {}, kMin, kMax},
|
|
ActivationFunctionType_NONE);
|
|
m.QuantizeAndPopulate<int16_t>(m.input1(),
|
|
{-0.9, -0.7, -0.3, 0.0, 0.3, 0.5});
|
|
m.QuantizeAndPopulate<int16_t>(m.input2(), {0.2});
|
|
m.Invoke();
|
|
EXPECT_THAT(m.GetDequantizedOutputInt16(),
|
|
ElementsAreArray(ArrayFloatNear(
|
|
{-1.0, -0.9, -0.5, -0.2, 0.1, 0.3}, kQuantizedTolerance)))
|
|
<< "With shape number " << i;
|
|
}
|
|
}
|
|
|
|
TEST(QuantizedSubOpModel, QuantizedTestsReluActivationBroadcastInt16) {
|
|
const float kMin = -2.f;
|
|
const float kMax = 2.0 * (std::numeric_limits<int16_t>::max() - 1) /
|
|
std::numeric_limits<int16_t>::max();
|
|
float kQuantizedTolerance = GetToleranceInt16(kMin, kMax);
|
|
std::vector<std::vector<int>> test_shapes = {
|
|
{6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}, {1, 3, 1, 2, 1}};
|
|
for (int i = 0; i < test_shapes.size(); ++i) {
|
|
QuantizedSubOpModel m({TensorType_INT16, test_shapes[i], kMin, kMax},
|
|
{TensorType_INT16, {}, kMin, kMax},
|
|
{TensorType_INT16, {}, kMin, kMax},
|
|
ActivationFunctionType_RELU_N1_TO_1);
|
|
m.QuantizeAndPopulate<int16_t>(m.input1(),
|
|
{-0.9, -0.7, -0.3, 0.0, 0.3, 0.5});
|
|
m.QuantizeAndPopulate<int16_t>(m.input2(), {0.2});
|
|
m.Invoke();
|
|
EXPECT_THAT(m.GetDequantizedOutputInt16(),
|
|
ElementsAreArray(ArrayFloatNear(
|
|
{-1.0, -0.9, -0.5, -0.2, 0.1, 0.3}, kQuantizedTolerance)))
|
|
<< "With shape number " << i;
|
|
}
|
|
}
|
|
|
|
} // namespace
|
|
} // namespace tflite
|