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rmothukuru-patch-1
STT-tensorflow/tensorflow/lite/kernels/add_test.cc
Elena Zhelezina 1622cca6dc - Added checks that zero point iz zero for ADD/SUB. 
- POT int16x8: create a new BroadcastSub16POTSlow function to manage the POT scaling.
- General int16x8: the BroadcastAdd4DSlow should be used instead of BroadcastSubSlow as the sign of input2 multiplier is changed in PrepareGeneralSubOp.

Change-Id: Id8042d089af51f402cba72b1db9bb5d948ba5cbc
2020-09-01 14:43:51 +01:00

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/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
#include <stddef.h>
#include <stdint.h>
#include <vector>
#include <gtest/gtest.h>
#include "flatbuffers/flatbuffers.h" // from @flatbuffers
#include "tensorflow/lite/kernels/test_util.h"
#include "tensorflow/lite/schema/schema_generated.h"
namespace tflite {
namespace {
using ::testing::ElementsAreArray;
class BaseAddOpModel : public SingleOpModel {
public:
BaseAddOpModel(const TensorData& input1, const TensorData& input2,
const TensorData& output,
ActivationFunctionType activation_type) {
input1_ = AddInput(input1);
input2_ = AddInput(input2);
output_ = AddOutput(output);
SetBuiltinOp(BuiltinOperator_ADD, BuiltinOptions_AddOptions,
CreateAddOptions(builder_, activation_type).Union());
BuildInterpreter({GetShape(input1_), GetShape(input2_)});
}
int input1() { return input1_; }
int input2() { return input2_; }
protected:
int input1_;
int input2_;
int output_;
};
class FloatAddOpModel : public BaseAddOpModel {
public:
using BaseAddOpModel::BaseAddOpModel;
std::vector<float> GetOutput() { return ExtractVector<float>(output_); }
};
class IntegerAddOpModel : public BaseAddOpModel {
public:
using BaseAddOpModel::BaseAddOpModel;
std::vector<int32_t> GetOutput() { return ExtractVector<int32_t>(output_); }
};
class QuantizedAddOpModel : public BaseAddOpModel {
public:
QuantizedAddOpModel(TensorData input1, TensorData input2, TensorData output,
ActivationFunctionType activation_type)
: BaseAddOpModel(SymmetricInt16Scaling(std::move(input1)),
SymmetricInt16Scaling(std::move(input2)),
SymmetricInt16Scaling(std::move(output)),
activation_type) {}
template <typename integer_dtype>
std::vector<float> GetDequantizedOutput() {
return Dequantize<integer_dtype>(ExtractVector<integer_dtype>(output_),
GetScale(output_), GetZeroPoint(output_));
}
std::vector<float> GetDequantizedOutputInt16() {
return Dequantize<int16_t>(ExtractVector<int16_t>(output_),
GetScale(output_), GetZeroPoint(output_));
}
private:
TensorData SymmetricInt16Scaling(TensorData tensor) {
// Symmetric range and null zero-point is required for INT16 tensors. As
// SingleOpModel::QuantizationParams calculates the scale on an asymmetric
// base [int_type::min, int_type::max], manually calculate the scale on a
// symmetric range [int_type::min+1, int_type::max] to ensure a null
// zero-point.
if (tensor.type == TensorType_INT16) {
CHECK_EQ(std::abs(tensor.min), tensor.max);
tensor.scale = tensor.max / std::numeric_limits<int16_t>::max();
tensor.zero_point = 0;
tensor.min = 0;
tensor.max = 0;
}
return tensor;
}
};
// for quantized Add, the error shouldn't exceed step
template <typename T>
float GetTolerance(float min, float max) {
float kQuantizedStep =
2.0 * (max - min) /
(std::numeric_limits<T>::max() - std::numeric_limits<T>::min());
return kQuantizedStep;
}
TEST(FloatAddOpModel, NoActivation) {
FloatAddOpModel m({TensorType_FLOAT32, {1, 2, 2, 1}},
{TensorType_FLOAT32, {1, 2, 2, 1}},
{TensorType_FLOAT32, {}}, ActivationFunctionType_NONE);
m.PopulateTensor<float>(m.input1(), {-2.0, 0.2, 0.7, 0.8});
m.PopulateTensor<float>(m.input2(), {0.1, 0.2, 0.3, 0.5});
m.Invoke();
EXPECT_THAT(m.GetOutput(), ElementsAreArray({-1.9, 0.4, 1.0, 1.3}));
}
TEST(FloatAddOpModel, ActivationRELU_N1_TO_1) {
FloatAddOpModel m(
{TensorType_FLOAT32, {1, 2, 2, 1}}, {TensorType_FLOAT32, {1, 2, 2, 1}},
{TensorType_FLOAT32, {}}, ActivationFunctionType_RELU_N1_TO_1);
m.PopulateTensor<float>(m.input1(), {-2.0, 0.2, 0.7, 0.8});
m.PopulateTensor<float>(m.input2(), {0.1, 0.2, 0.3, 0.5});
m.Invoke();
EXPECT_THAT(m.GetOutput(), ElementsAreArray({-1.0, 0.4, 1.0, 1.0}));
}
TEST(FloatAddOpModel, VariousInputShapes) {
std::vector<std::vector<int>> test_shapes = {
{6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}};
for (size_t i = 0; i < test_shapes.size(); ++i) {
FloatAddOpModel m({TensorType_FLOAT32, test_shapes[i]},
{TensorType_FLOAT32, test_shapes[i]},
{TensorType_FLOAT32, {}}, ActivationFunctionType_NONE);
m.PopulateTensor<float>(m.input1(), {-2.0, 0.2, 0.7, 0.8, 1.1, 2.0});
m.PopulateTensor<float>(m.input2(), {0.1, 0.2, 0.3, 0.5, 1.1, 0.1});
m.Invoke();
EXPECT_THAT(m.GetOutput(),
ElementsAreArray({-1.9, 0.4, 1.0, 1.3, 2.2, 2.1}))
<< "With shape number " << i;
}
}
TEST(FloatAddOpModel, WithBroadcast) {
std::vector<std::vector<int>> test_shapes = {
{6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}};
for (size_t i = 0; i < test_shapes.size(); ++i) {
FloatAddOpModel m({TensorType_FLOAT32, test_shapes[i]},
{TensorType_FLOAT32, {}}, // always a scalar
{TensorType_FLOAT32, {}}, ActivationFunctionType_NONE);
m.PopulateTensor<float>(m.input1(), {-2.0, 0.2, 0.7, 0.8, 1.1, 2.0});
m.PopulateTensor<float>(m.input2(), {0.1});
m.Invoke();
EXPECT_THAT(
m.GetOutput(),
ElementsAreArray(ArrayFloatNear({-1.9, 0.3, 0.8, 0.9, 1.2, 2.1})))
<< "With shape number " << i;
}
}
TEST(FloatAddOpModel, WithBroadcastGeneric) {
std::vector<int> test_shape1 = {1, 3, 1};
std::vector<int> test_shape2 = {2, 1, 2};
FloatAddOpModel m({TensorType_FLOAT32, test_shape1},
{TensorType_FLOAT32, test_shape2}, {TensorType_FLOAT32, {}},
ActivationFunctionType_NONE);
m.PopulateTensor<float>(m.input1(), {0.1, 0.2, 0.3});
m.PopulateTensor<float>(m.input2(), {0.1, 0.2, 0.3, 0.4});
m.Invoke();
EXPECT_THAT(m.GetOutput(),
ElementsAreArray(ArrayFloatNear({0.2, 0.3, 0.3, 0.4, 0.4, 0.5,
0.4, 0.5, 0.5, 0.6, 0.6, 0.7})));
}
TEST(FloatAddOpModel, MixedBroadcast) {
const std::vector<int> base_shape = {2, 3, 1, 2};
std::vector<std::vector<int>> test_shapes = {
{1, 1, 3, 2}, {1, 3, 1, 2}, {2, 1, 3, 1}, {2, 3, 1, 1}};
std::vector<std::vector<float>> test_outputs = {
{-0.1f, 2.6f, -0.7f, 2.8f, 0.7f, 3.2f, 1.1f, 0.8f, 0.5f,
1.0f, 1.9f, 1.4f, 1.0f, -0.8f, 0.4f, -0.6f, 1.8f, -0.2f,
1.4f, 3.1f, 0.8f, 3.3f, 2.2f, 3.7f, -1.4f, 0.3f, -2.0f,
0.5f, -0.6f, 0.9f, 0.9f, -1.9f, 0.3f, -1.7f, 1.7f, -1.3f},
{-0.1f, 2.6f, 0.5f, 1.0f, 1.8f, -0.2f, 1.4f, 3.1f, -2.0f, 0.5f, 1.7f,
-1.3f},
{-0.1f, 2.5f, 0.0f, 2.6f, -0.7f, 1.9f, 1.1f, 0.7f, 1.2f,
0.8f, 0.5f, 0.1f, 1.0f, -0.9f, 1.1f, -0.8f, 0.4f, -1.5f,
1.7f, 3.3f, 2.2f, 3.8f, 2.1f, 3.7f, -1.1f, 0.5f, -0.6f,
1.0f, -0.7f, 0.9f, 1.2f, -1.7f, 1.7f, -1.2f, 1.6f, -1.3f},
{-0.1f, 2.5f, 1.2f, 0.8f, 0.4f, -1.5f, 1.7f, 3.3f, -0.6f, 1.0f, 1.6f,
-1.3f}};
for (size_t i = 0; i < test_shapes.size(); ++i) {
FloatAddOpModel model_fixture(
{TensorType_FLOAT32, base_shape}, {TensorType_FLOAT32, test_shapes[i]},
{TensorType_FLOAT32, {}}, ActivationFunctionType_NONE);
model_fixture.PopulateTensor<float>(
model_fixture.input1(), {-0.3f, 2.3f, 0.9f, 0.5f, 0.8f, -1.1f, 1.2f,
2.8f, -1.6f, 0.0f, 0.7f, -2.2f});
model_fixture.PopulateTensor<float>(model_fixture.input2(),
{0.2f, 0.3f, -0.4f, 0.5f, 1.0f, 0.9f});
model_fixture.Invoke();
EXPECT_THAT(model_fixture.GetOutput(),
ElementsAreArray(ArrayFloatNear(test_outputs[i], 0.0001f)))
<< "With shape number " << i;
}
// Re-run with exchanged inputs.
for (size_t i = 0; i < test_shapes.size(); ++i) {
FloatAddOpModel model_fixture(
{TensorType_FLOAT32, test_shapes[i]}, {TensorType_FLOAT32, base_shape},
{TensorType_FLOAT32, {}}, ActivationFunctionType_NONE);
model_fixture.PopulateTensor<float>(model_fixture.input1(),
{0.2f, 0.3f, -0.4f, 0.5f, 1.0f, 0.9f});
model_fixture.PopulateTensor<float>(
model_fixture.input2(), {-0.3f, 2.3f, 0.9f, 0.5f, 0.8f, -1.1f, 1.2f,
2.8f, -1.6f, 0.0f, 0.7f, -2.2f});
model_fixture.Invoke();
EXPECT_THAT(model_fixture.GetOutput(),
ElementsAreArray(ArrayFloatNear(test_outputs[i], 0.0001f)))
<< "With shape number " << i;
}
}
TEST(IntegerAddOpModel, NoActivation) {
IntegerAddOpModel m({TensorType_INT32, {1, 2, 2, 1}},
{TensorType_INT32, {1, 2, 2, 1}}, {TensorType_INT32, {}},
ActivationFunctionType_NONE);
m.PopulateTensor<int32_t>(m.input1(), {-20, 2, 7, 8});
m.PopulateTensor<int32_t>(m.input2(), {1, 2, 3, 5});
m.Invoke();
EXPECT_THAT(m.GetOutput(), ElementsAreArray({-19, 4, 10, 13}));
}
TEST(IntegerAddOpModel, ActivationRELU_N1_TO_1) {
IntegerAddOpModel m({TensorType_INT32, {1, 2, 2, 1}},
{TensorType_INT32, {1, 2, 2, 1}}, {TensorType_INT32, {}},
ActivationFunctionType_RELU_N1_TO_1);
m.PopulateTensor<int32_t>(m.input1(), {-20, 2, 7, 8});
m.PopulateTensor<int32_t>(m.input2(), {1, 2, 3, 5});
m.Invoke();
EXPECT_THAT(m.GetOutput(), ElementsAreArray({-1, 1, 1, 1}));
}
TEST(IntegerAddOpModel, VariousInputShapes) {
std::vector<std::vector<int>> test_shapes = {
{6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}};
for (size_t i = 0; i < test_shapes.size(); ++i) {
IntegerAddOpModel m({TensorType_INT32, test_shapes[i]},
{TensorType_INT32, test_shapes[i]},
{TensorType_INT32, {}}, ActivationFunctionType_NONE);
m.PopulateTensor<int32_t>(m.input1(), {-20, 2, 7, 8, 11, 20});
m.PopulateTensor<int32_t>(m.input2(), {1, 2, 3, 5, 11, 1});
m.Invoke();
EXPECT_THAT(m.GetOutput(), ElementsAreArray({-19, 04, 10, 13, 22, 21}))
<< "With shape number " << i;
}
}
TEST(IntegerAddOpModel, WithBroadcast) {
std::vector<std::vector<int>> test_shapes = {
{6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}};
for (size_t i = 0; i < test_shapes.size(); ++i) {
IntegerAddOpModel m({TensorType_INT32, test_shapes[i]},
{TensorType_INT32, {}}, // always a scalar
{TensorType_INT32, {}}, ActivationFunctionType_NONE);
m.PopulateTensor<int32_t>(m.input1(), {-20, 2, 7, 8, 11, 20});
m.PopulateTensor<int32_t>(m.input2(), {1});
m.Invoke();
EXPECT_THAT(m.GetOutput(),
ElementsAreArray(ArrayFloatNear({-19, 3, 8, 9, 12, 21})))
<< "With shape number " << i;
}
}
TEST(IntegerAddOpModel, Int32MultiDimBroadcast) {
IntegerAddOpModel m({TensorType_INT32, {1, 2}}, {TensorType_INT32, {2, 1}},
{TensorType_INT32, {}}, ActivationFunctionType_NONE);
m.PopulateTensor<int32_t>(m.input1(), {3, 5});
m.PopulateTensor<int32_t>(m.input2(), {1, 4});
m.Invoke();
EXPECT_THAT(m.GetOutput(), ElementsAreArray({4, 6, 7, 9}));
}
TEST(IntegerAddOpModel, Float32MultiDimBroadcast) {
FloatAddOpModel m({TensorType_FLOAT32, {1, 2}}, {TensorType_FLOAT32, {2, 1}},
{TensorType_FLOAT32, {}}, ActivationFunctionType_NONE);
m.PopulateTensor<float>(m.input1(), {3, 5});
m.PopulateTensor<float>(m.input2(), {1, 4});
m.Invoke();
EXPECT_THAT(m.GetOutput(), ElementsAreArray({4, 6, 7, 9}));
}
template <TensorType tensor_type, typename integer_dtype>
void QuantizedTestsNoActivation() {
float kQuantizedTolerance = GetTolerance<integer_dtype>(-1.0, 1.0);
std::vector<std::vector<float>> inputs1 = {
{0.1, 0.2, 0.3, 0.4}, {-0.8, 0.2, 0.4, 0.7}, {-0.8, 0.2, 0.7, 0.3}};
std::vector<std::vector<float>> inputs2 = {
{0.6, 0.4, 0.3, 0.1}, {0.6, 0.4, 0.5, -0.8}, {0.6, 0.4, -0.8, 0.5}};
std::vector<std::vector<float>> results = {
{0.7, 0.6, 0.6, 0.5}, {-0.2, 0.6, 0.9, -0.1}, {-0.2, 0.6, -0.1, 0.8}};
for (size_t i = 0; i < inputs1.size(); ++i) {
QuantizedAddOpModel m({tensor_type, {1, 2, 2, 1}, -1.0, 1.0},
{tensor_type, {1, 2, 2, 1}, -1.0, 1.0},
{tensor_type, {}, -1.0, 1.0},
ActivationFunctionType_NONE);
m.QuantizeAndPopulate<integer_dtype>(m.input1(), inputs1[i]);
m.QuantizeAndPopulate<integer_dtype>(m.input2(), inputs2[i]);
m.Invoke();
EXPECT_THAT(
m.GetDequantizedOutput<integer_dtype>(),
ElementsAreArray(ArrayFloatNear(results[i], kQuantizedTolerance)))
<< "With test number " << i;
}
}
TEST(QuantizedAddOpModel, QuantizedTestsNoActivationUInt8) {
QuantizedTestsNoActivation<TensorType_UINT8, uint8_t>();
}
TEST(QuantizedAddOpModel, QuantizedTestsNoActivationInt8) {
QuantizedTestsNoActivation<TensorType_INT8, int8_t>();
}
TEST(QuantizedAddOpModel, QuantizedTestsNoActivationInt16) {
float kQuantizedTolerance = GetTolerance<int16_t>(-1.0, 1.0);
std::vector<std::vector<float>> inputs1 = {{0.1, 0.2, 0.3, 0.4, 0.9, 0.7},
{-0.8, 0.2, 0.4, 0.7, 0.1, 0.0},
{-0.8, 0.2, 0.7, 0.3, 0.9, 0.1}};
std::vector<std::vector<float>> inputs2 = {{0.6, 0.4, 0.3, 0.1, -0.1, 0.3},
{0.6, 0.4, 0.5, -0.8, 0.0, -1.0},
{0.6, 0.4, -0.8, 0.5, -0.9, 0.1}};
std::vector<std::vector<float>> results = {{0.7, 0.6, 0.6, 0.5, 0.8, 1.0},
{-0.2, 0.6, 0.9, -0.1, 0.1, -1.0},
{-0.2, 0.6, -0.1, 0.8, 0.0, 0.2}};
for (size_t i = 0; i < inputs1.size(); ++i) {
QuantizedAddOpModel m({TensorType_INT16, {1, 2, 3, 1}, -1.0, 1.0},
{TensorType_INT16, {1, 2, 3, 1}, -1.0, 1.0},
{TensorType_INT16, {}, -1.0, 1.0},
ActivationFunctionType_NONE);
m.QuantizeAndPopulate<int16_t>(m.input1(), inputs1[i]);
m.QuantizeAndPopulate<int16_t>(m.input2(), inputs2[i]);
m.Invoke();
EXPECT_THAT(
m.GetDequantizedOutputInt16(),
ElementsAreArray(ArrayFloatNear(results[i], kQuantizedTolerance)))
<< "With test number " << i;
}
}
template <enum TensorType tensor_type, typename integer_dtype>
void QuantizedTestsActivationRELU_N1_TO_1() {
float kQuantizedTolerance = GetTolerance<integer_dtype>(-1.0, 1.0);
std::vector<std::vector<float>> inputs1 = {{-0.8, 0.2, 0.9, 0.7},
{-0.8, 0.2, 0.7, 0.3}};
std::vector<std::vector<float>> inputs2 = {{0.6, 0.4, 0.9, -0.8},
{0.6, 0.4, -0.8, 0.5}};
std::vector<std::vector<float>> results = {{-0.2, 0.6, 1.0, -0.1},
{-0.2, 0.6, -0.1, 0.8}};
for (size_t i = 0; i < inputs1.size(); ++i) {
QuantizedAddOpModel m({tensor_type, {1, 2, 2, 1}, -1.0, 1.0},
{tensor_type, {1, 2, 2, 1}, -1.0, 1.0},
{tensor_type, {}, -1.0, 1.0},
ActivationFunctionType_RELU_N1_TO_1);
m.QuantizeAndPopulate<integer_dtype>(m.input1(), inputs1[i]);
m.QuantizeAndPopulate<integer_dtype>(m.input2(), inputs2[i]);
m.Invoke();
EXPECT_THAT(
m.GetDequantizedOutput<integer_dtype>(),
ElementsAreArray(ArrayFloatNear(results[i], kQuantizedTolerance)))
<< "With test number " << i;
}
}
TEST(QuantizedAddOpModel, QuantizedTestsActivationRELU_N1_TO_1UInt8) {
QuantizedTestsActivationRELU_N1_TO_1<TensorType_UINT8, uint8_t>();
}
TEST(QuantizedAddOpModel, QuantizedTestsActivationRELU_N1_TO_1Int8) {
QuantizedTestsActivationRELU_N1_TO_1<TensorType_INT8, int8_t>();
}
template <enum TensorType tensor_type, typename integer_dtype>
void QuantizedVariousInputShapes() {
float kQuantizedTolerance = GetTolerance<integer_dtype>(-3.0, 3.0);
std::vector<std::vector<int>> test_shapes = {
{6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}};
for (size_t i = 0; i < test_shapes.size(); ++i) {
QuantizedAddOpModel m({tensor_type, test_shapes[i], -3.0, 3.0},
{tensor_type, test_shapes[i], -3.0, 3.0},
{tensor_type, {}, -3.0, 3.0},
ActivationFunctionType_NONE);
m.QuantizeAndPopulate<integer_dtype>(m.input1(),
{-2.0, 0.2, 0.7, 0.8, 1.1, 2.0});
m.QuantizeAndPopulate<integer_dtype>(m.input2(),
{0.1, 0.3, 0.3, 0.5, 1.1, 0.1});
m.Invoke();
EXPECT_THAT(m.GetDequantizedOutput<integer_dtype>(),
ElementsAreArray(ArrayFloatNear({-1.9, 0.5, 1.0, 1.3, 2.2, 2.1},
kQuantizedTolerance)))
<< "With shape number " << i;
}
}
TEST(QuantizedAddOpModel, QuantizedVariousInputShapesUInt8) {
QuantizedVariousInputShapes<TensorType_UINT8, uint8_t>();
}
TEST(QuantizedAddOpModel, QuantizedVariousInputShapesInt8) {
QuantizedVariousInputShapes<TensorType_INT8, int8_t>();
}
template <enum TensorType tensor_type, typename integer_dtype>
void QuantizedWithScalarBroadcast() {
float kQuantizedTolerance = GetTolerance<integer_dtype>(-3.f, 3.f);
std::vector<std::vector<int>> test_shapes = {
{6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}};
for (size_t i = 0; i < test_shapes.size(); ++i) {
QuantizedAddOpModel model_fixture(
{tensor_type, test_shapes[i], -3.f, 3.f}, {tensor_type, {}, -3.f, 3.f},
{tensor_type, {}, -3.f, 3.f}, ActivationFunctionType_NONE);
model_fixture.QuantizeAndPopulate<integer_dtype>(
model_fixture.input1(), {-2.0f, 0.2f, 0.7f, 0.8f, 1.1f, 2.0f});
model_fixture.QuantizeAndPopulate<integer_dtype>(model_fixture.input2(),
{0.1f});
model_fixture.Invoke();
EXPECT_THAT(
model_fixture.GetDequantizedOutput<integer_dtype>(),
ElementsAreArray(ArrayFloatNear({-1.9f, 0.3f, 0.8f, 0.9f, 1.2f, 2.1f},
kQuantizedTolerance)))
<< "With shape number " << i;
}
// Re-run with exchanged inputs.
for (size_t i = 0; i < test_shapes.size(); ++i) {
QuantizedAddOpModel model_fixture(
{tensor_type, {}, -3.f, 3.f}, {tensor_type, test_shapes[i], -3.f, 3.f},
{tensor_type, {}, -3.f, 3.f}, ActivationFunctionType_NONE);
model_fixture.QuantizeAndPopulate<integer_dtype>(model_fixture.input1(),
{0.1f});
model_fixture.QuantizeAndPopulate<integer_dtype>(
model_fixture.input2(), {-2.0f, 0.2f, 0.7f, 0.8f, 1.1f, 2.0f});
model_fixture.Invoke();
EXPECT_THAT(
model_fixture.GetDequantizedOutput<integer_dtype>(),
ElementsAreArray(ArrayFloatNear({-1.9f, 0.3f, 0.8f, 0.9f, 1.2f, 2.1f},
kQuantizedTolerance)))
<< "With shape number " << i;
}
}
TEST(QuantizedAddOpModel, QuantizedWithScalarBroadcastUInt8) {
QuantizedWithScalarBroadcast<TensorType_UINT8, uint8_t>();
}
TEST(QuantizedAddOpModel, QuantizedWithScalarBroadcastInt8) {
QuantizedWithScalarBroadcast<TensorType_INT8, int8_t>();
}
TEST(QuantizedAddOpModel, QuantizedWithScalarBroadcastInt16) {
QuantizedWithScalarBroadcast<TensorType_INT16, int16_t>();
}
template <enum TensorType tensor_type, typename integer_dtype>
void QuantizedWithMixedBroadcast() {
float kQuantizedTolerance = GetTolerance<integer_dtype>(-3.f, 3.f);
const std::vector<int> base_shape = {2, 3, 1, 2};
std::vector<std::vector<int>> test_shapes = {
{1, 1, 3, 2}, {1, 3, 1, 2}, {2, 1, 3, 1}, {2, 3, 1, 1}};
std::vector<std::vector<float>> test_outputs = {
{-0.1f, 2.6f, -0.7f, 2.8f, 0.7f, 3.0f, 1.1f, 0.8f, 0.5f,
1.0f, 1.9f, 1.4f, 1.0f, -0.8f, 0.4f, -0.6f, 1.8f, -0.2f,
1.4f, 3.0f, 0.8f, 3.0f, 2.2f, 3.0f, -1.4f, 0.3f, -2.0f,
0.5f, -0.6f, 0.9f, 0.9f, -1.9f, 0.3f, -1.7f, 1.7f, -1.3f},
{-0.1f, 2.6f, 0.5f, 1.0f, 1.8f, -0.2f, 1.4f, 3.0f, -2.0f, 0.5f, 1.7f,
-1.3f},
{-0.1f, 2.5f, 0.0f, 2.6f, -0.7f, 1.9f, 1.1f, 0.7f, 1.2f,
0.8f, 0.5f, 0.1f, 1.0f, -0.9f, 1.1f, -0.8f, 0.4f, -1.5f,
1.7f, 3.0f, 2.2f, 3.0f, 2.1f, 3.0f, -1.1f, 0.5f, -0.6f,
1.0f, -0.7f, 0.9f, 1.2f, -1.7f, 1.7f, -1.2f, 1.6f, -1.3f},
{-0.1f, 2.5f, 1.2f, 0.8f, 0.4f, -1.5f, 1.7f, 3.0f, -0.6f, 1.0f, 1.6f,
-1.3f}};
for (size_t i = 0; i < test_shapes.size(); ++i) {
QuantizedAddOpModel model_fixture({tensor_type, base_shape, -3.f, 3.f},
{tensor_type, test_shapes[i], -3.f, 3.f},
{tensor_type, {}, -3.f, 3.f},
ActivationFunctionType_NONE);
model_fixture.QuantizeAndPopulate<integer_dtype>(
model_fixture.input1(), {-0.3f, 2.3f, 0.9f, 0.5f, 0.8f, -1.1f, 1.2f,
2.8f, -1.6f, 0.0f, 0.7f, -2.2f});
model_fixture.QuantizeAndPopulate<integer_dtype>(
model_fixture.input2(), {0.2f, 0.3f, -0.4f, 0.5f, 1.0f, 0.9f});
model_fixture.Invoke();
EXPECT_THAT(
model_fixture.GetDequantizedOutput<integer_dtype>(),
ElementsAreArray(ArrayFloatNear(test_outputs[i], kQuantizedTolerance)))
<< "With shape number " << i;
}
// Re-run with exchanged inputs.
for (size_t i = 0; i < test_shapes.size(); ++i) {
QuantizedAddOpModel model_fixture({tensor_type, test_shapes[i], -3.f, 3.f},
{tensor_type, base_shape, -3.f, 3.f},
{tensor_type, {}, -3.f, 3.f},
ActivationFunctionType_NONE);
model_fixture.QuantizeAndPopulate<integer_dtype>(
model_fixture.input1(), {0.2f, 0.3f, -0.4f, 0.5f, 1.0f, 0.9f});
model_fixture.QuantizeAndPopulate<integer_dtype>(
model_fixture.input2(), {-0.3f, 2.3f, 0.9f, 0.5f, 0.8f, -1.1f, 1.2f,
2.8f, -1.6f, 0.0f, 0.7f, -2.2f});
model_fixture.Invoke();
EXPECT_THAT(
model_fixture.GetDequantizedOutput<integer_dtype>(),
ElementsAreArray(ArrayFloatNear(test_outputs[i], kQuantizedTolerance)))
<< "With shape number " << i;
}
}
TEST(QuantizedAddOpModel, QuantizedWithMixedBroadcastUInt8) {
QuantizedWithMixedBroadcast<TensorType_UINT8, uint8_t>();
}
TEST(QuantizedAddOpModel, QuantizedWithMixedBroadcastInt8) {
QuantizedWithMixedBroadcast<TensorType_INT8, int8_t>();
}
TEST(QuantizedAddOpModel, QuantizedWithMixedBroadcastInt16) {
QuantizedWithMixedBroadcast<TensorType_INT16, int16_t>();
}
template <enum TensorType tensor_type, typename integer_dtype>
void QuantizedWithGenericBroadcast() {
float kQuantizedTolerance = GetTolerance<integer_dtype>(-3.0, 3.0);
std::vector<int> test_shape1 = {1, 3, 1};
std::vector<int> test_shape2 = {2, 1, 2};
QuantizedAddOpModel m({tensor_type, test_shape1, -3.0, 3.0},
{tensor_type, test_shape2, -3.0, 3.0},
{tensor_type, {}, -3.0, 3.0},
ActivationFunctionType_NONE);
m.QuantizeAndPopulate<integer_dtype>(m.input1(), {0.1, 0.2, 0.3});
m.QuantizeAndPopulate<integer_dtype>(m.input2(), {0.1, -0.2, 0.3, -0.4});
m.Invoke();
EXPECT_THAT(m.GetDequantizedOutput<integer_dtype>(),
ElementsAreArray(ArrayFloatNear({0.2, -0.1, 0.3, 0., 0.4, 0.1,
0.4, -0.3, 0.5, -0.2, 0.6, -0.1},
kQuantizedTolerance)));
}
TEST(QuantizedAddOpModel, QuantizedWithGenericBroadcastUInt8) {
QuantizedWithGenericBroadcast<TensorType_UINT8, uint8_t>();
}
TEST(QuantizedAddOpModel, QuantizedWithGenericdBroadcastInt8) {
QuantizedWithGenericBroadcast<TensorType_INT8, int8_t>();
}
TEST(QuantizedAddOpModel, QuantizedWithGenericdBroadcastInt16) {
QuantizedWithGenericBroadcast<TensorType_INT16, int16_t>();
}
} // namespace
} // namespace tflite
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