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android / platform / external / tensorflow / 3e761638397 / . / tensorflow / lite / kernels / reduce_test.cc
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/* Copyright 2018 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 <stdint.h>
#include <initializer_list>
#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;
using ::testing::IsEmpty;
class BaseOpModel : public SingleOpModel {
public:
void SetAxis(const std::vector<int>& data) { PopulateTensor(axis_, data); }
template <class T>
void SetInput(std::vector<T> data) {
PopulateTensor(input_, data);
}
template <class T>
std::vector<T> GetOutput() {
return ExtractVector<T>(output_);
}
template <typename T>
std::vector<float> GetDequantizedOutput() {
return Dequantize<T>(ExtractVector<T>(output_), GetScale(output_),
GetZeroPoint(output_));
}
std::vector<int> GetOutputShape() { return GetTensorShape(output_); }
int Input() { return input_; }
protected:
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;
}
protected:
int input_;
int axis_;
int output_;
};
// Model for the tests case where axis is a const tensor.
class MeanOpConstModel : public BaseOpModel {
public:
MeanOpConstModel(TensorData input, TensorData output,
std::initializer_list<int> axis_shape,
std::initializer_list<int> axis, bool keep_dims) {
input_ = AddInput(SymmetricInt16Scaling(input));
axis_ = AddConstInput(TensorType_INT32, axis, axis_shape);
output_ = AddOutput(SymmetricInt16Scaling(output));
SetBuiltinOp(BuiltinOperator_MEAN, BuiltinOptions_ReducerOptions,
CreateReducerOptions(builder_, keep_dims).Union());
BuildInterpreter({GetShape(input_)});
}
};
// Model for the tests case where axis is a dynamic tensor.
class MeanOpDynamicModel : public BaseOpModel {
public:
MeanOpDynamicModel(const TensorData& input, const TensorData& output,
const TensorData& axis, bool keep_dims) {
input_ = AddInput(input);
axis_ = AddInput(axis);
output_ = AddOutput(output);
SetBuiltinOp(BuiltinOperator_MEAN, BuiltinOptions_ReducerOptions,
CreateReducerOptions(builder_, keep_dims).Union());
BuildInterpreter({GetShape(input_)});
}
};
// Model for the tests case where axis is a const tensor.
class SumOpConstModel : public BaseOpModel {
public:
SumOpConstModel(const TensorData& input, const TensorData& output,
std::initializer_list<int> axis_shape,
std::initializer_list<int> axis, bool keep_dims) {
input_ = AddInput(input);
axis_ = AddConstInput(TensorType_INT32, axis, axis_shape);
output_ = AddOutput(output);
SetBuiltinOp(BuiltinOperator_SUM, BuiltinOptions_ReducerOptions,
CreateReducerOptions(builder_, keep_dims).Union());
BuildInterpreter({GetShape(input_)});
}
};
// Model for the tests case where axis is a dynamic tensor.
class SumOpDynamicModel : public BaseOpModel {
public:
SumOpDynamicModel(const TensorData& input, const TensorData& output,
const TensorData& axis, bool keep_dims) {
input_ = AddInput(input);
axis_ = AddInput(axis);
output_ = AddOutput(output);
SetBuiltinOp(BuiltinOperator_SUM, BuiltinOptions_ReducerOptions,
CreateReducerOptions(builder_, keep_dims).Union());
BuildInterpreter({GetShape(input_)});
}
};
// Model for the tests case where axis is a const tensor.
class ProdOpConstModel : public BaseOpModel {
public:
ProdOpConstModel(const TensorData& input, const TensorData& output,
std::initializer_list<int> axis_shape,
std::initializer_list<int> axis, bool keep_dims) {
input_ = AddInput(input);
axis_ = AddConstInput(TensorType_INT32, axis, axis_shape);
output_ = AddOutput(output);
SetBuiltinOp(BuiltinOperator_REDUCE_PROD, BuiltinOptions_ReducerOptions,
CreateReducerOptions(builder_, keep_dims).Union());
BuildInterpreter({GetShape(input_)});
}
};
// Model for the tests case where axis is a dynamic tensor.
class ProdOpDynamicModel : public BaseOpModel {
public:
ProdOpDynamicModel(const TensorData& input, const TensorData& output,
const TensorData& axis, bool keep_dims) {
input_ = AddInput(input);
axis_ = AddInput(axis);
output_ = AddOutput(output);
SetBuiltinOp(BuiltinOperator_REDUCE_PROD, BuiltinOptions_ReducerOptions,
CreateReducerOptions(builder_, keep_dims).Union());
BuildInterpreter({GetShape(input_)});
}
};
// Model for the tests case where axis is a const tensor.
class MaxOpConstModel : public BaseOpModel {
public:
MaxOpConstModel(const TensorData& input, const TensorData& output,
std::initializer_list<int> axis_shape,
std::initializer_list<int> axis, bool keep_dims) {
input_ = AddInput(input);
axis_ = AddConstInput(TensorType_INT32, axis, axis_shape);
output_ = AddOutput(output);
SetBuiltinOp(BuiltinOperator_REDUCE_MAX, BuiltinOptions_ReducerOptions,
CreateReducerOptions(builder_, keep_dims).Union());
BuildInterpreter({GetShape(input_)});
}
};
// Model for the tests case where axis is a dynamic tensor.
class MaxOpDynamicModel : public BaseOpModel {
public:
MaxOpDynamicModel(const TensorData& input, const TensorData& output,
const TensorData& axis, bool keep_dims) {
input_ = AddInput(input);
axis_ = AddInput(axis);
output_ = AddOutput(output);
SetBuiltinOp(BuiltinOperator_REDUCE_MAX, BuiltinOptions_ReducerOptions,
CreateReducerOptions(builder_, keep_dims).Union());
BuildInterpreter({GetShape(input_)});
}
};
// Model for the tests case where axis is a const tensor.
class MinOpConstModel : public BaseOpModel {
public:
MinOpConstModel(const TensorData& input, const TensorData& output,
std::initializer_list<int> axis_shape,
std::initializer_list<int> axis, bool keep_dims) {
input_ = AddInput(input);
axis_ = AddConstInput(TensorType_INT32, axis, axis_shape);
output_ = AddOutput(output);
SetBuiltinOp(BuiltinOperator_REDUCE_MIN, BuiltinOptions_ReducerOptions,
CreateReducerOptions(builder_, keep_dims).Union());
BuildInterpreter({GetShape(input_)});
}
};
// Model for the tests case where axis is a dynamic tensor.
class MinOpDynamicModel : public BaseOpModel {
public:
MinOpDynamicModel(const TensorData& input, const TensorData& output,
const TensorData& axis, bool keep_dims) {
input_ = AddInput(input);
axis_ = AddInput(axis);
output_ = AddOutput(output);
SetBuiltinOp(BuiltinOperator_REDUCE_MIN, BuiltinOptions_ReducerOptions,
CreateReducerOptions(builder_, keep_dims).Union());
BuildInterpreter({GetShape(input_)});
}
};
// Model for the tests case where axis is a const tensor.
class AnyOpConstModel : public BaseOpModel {
public:
AnyOpConstModel(const TensorData& input, const TensorData& output,
std::initializer_list<int> axis_shape,
std::initializer_list<int> axis, bool keep_dims) {
input_ = AddInput(input);
axis_ = AddConstInput(TensorType_INT32, axis, axis_shape);
output_ = AddOutput(output);
SetBuiltinOp(BuiltinOperator_REDUCE_ANY, BuiltinOptions_ReducerOptions,
CreateReducerOptions(builder_, keep_dims).Union());
BuildInterpreter({GetShape(input_)});
}
};
// Model for the tests case where axis is a dynamic tensor.
class AnyOpDynamicModel : public BaseOpModel {
public:
AnyOpDynamicModel(const TensorData& input, const TensorData& output,
const TensorData& axis, bool keep_dims) {
input_ = AddInput(input);
axis_ = AddInput(axis);
output_ = AddOutput(output);
SetBuiltinOp(BuiltinOperator_REDUCE_ANY, BuiltinOptions_ReducerOptions,
CreateReducerOptions(builder_, keep_dims).Union());
BuildInterpreter({GetShape(input_)});
}
};
// for quantized Add, the error shouldn't exceed step
template <typename integer_type = int8_t>
float GetTolerance(int min, int max) {
if (std::is_same<int16_t, integer_type>::value) {
return (max - min) / 65536.0;
} else {
return (max - min) / 255.0;
}
}
// Tests for reduce_mean
TEST(ConstFloatMeanOpTest, NotKeepDims) {
std::vector<float> data = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0,
9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0,
17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0};
MeanOpConstModel m({TensorType_FLOAT32, {4, 3, 2}}, {TensorType_FLOAT32, {2}},
{4}, {1, 0, -3, -3}, false);
m.SetInput(data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2}));
EXPECT_THAT(m.GetOutput<float>(), ElementsAreArray(ArrayFloatNear({12, 13})));
}
TEST(ConstFloatMeanOpTest, KeepDims) {
std::vector<float> data = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0,
9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0,
17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0};
MeanOpConstModel m({TensorType_FLOAT32, {4, 3, 2}}, {TensorType_FLOAT32, {3}},
{2}, {0, 2}, true);
m.SetInput(data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 3, 1}));
EXPECT_THAT(m.GetOutput<float>(),
ElementsAreArray(ArrayFloatNear({10.5, 12.5, 14.5})));
}
// Uses a set of reduction conditions that trigger the specialized 4D version
// of Mean.
TEST(ConstFloatMeanOpTest, KeepDims4DMean) {
std::vector<float> data = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0,
9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0,
17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0};
MeanOpConstModel m({TensorType_FLOAT32, {2, 2, 3, 2}},
{TensorType_FLOAT32, {3}}, {2}, {1, 2}, true);
m.SetInput(data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2, 1, 1, 2}));
EXPECT_THAT(m.GetOutput<float>(),
ElementsAreArray(ArrayFloatNear({6, 7, 18, 19})));
}
TEST(ConstFloatMeanOpTest, KeepDims4DMeanUInt8) {
float kQuantizedTolerance = GetTolerance(-1.0, 1.0);
std::vector<float> data = {0.1, 0.2, 0.3, 0.4, 0.1, 0.2,
0.3, 0.4, 0.1, 0.2, 0.3, 0.4};
MeanOpConstModel m({TensorType_UINT8, {1, 2, 2, 3}, -1.0, 1.0},
{TensorType_UINT8, {2}, -1.0, 1.0}, {2}, {1, 2}, true);
m.QuantizeAndPopulate<uint8_t>(m.Input(), data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 1, 1, 3}));
EXPECT_THAT(m.GetDequantizedOutput<uint8_t>(),
ElementsAreArray(ArrayFloatNear({0.25098, 0.25098, 0.25098},
kQuantizedTolerance)));
}
TEST(ConstFloatMeanOpTest, KeepDims4DMeanLargeDepthUInt8) {
float kQuantizedTolerance = GetTolerance(-5.0, 5.0);
std::vector<float> data = {
0.1, 0.2, 0.3, 0.4, 0.2, 0.3, 0.4, 0.5, 0.1, 0.1, 0.1, 0.1, 0.4, 0.2, 0.2,
0.2, 0.9, 0.9, 0.9, 0.9, 0.2, 0.3, 0.7, 0.7, 0.1, 0.1, 0.3, 0.3, 0.1, 0.2,
0.3, 0.4, 0.1, 0.2, 0.3, 0.4, 0.1, 0.2, 0.3, 0.4, 0.2, 0.3, 0.4, 0.5, 0.1,
0.1, 0.1, 0.1, 0.4, 0.2, 0.2, 0.2, 0.9, 0.9, 0.9, 0.9, 0.2, 0.3, 0.7, 0.7,
0.1, 0.1, 0.3, 0.3, 0.1, 0.2, 0.3, 0.4, 0.1, 0.2, 0.3, 0.4};
MeanOpConstModel m({TensorType_UINT8, {1, 2, 2, 18}, -1.0, 1.0},
{TensorType_UINT8, {2}, -1.0, 1.0}, {2}, {1, 2}, true);
m.QuantizeAndPopulate<uint8_t>(m.Input(), data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 1, 1, 18}));
EXPECT_THAT(m.GetDequantizedOutput<uint8_t>(),
ElementsAreArray(ArrayFloatNear(
{0.5, 0.55, 0.25, 0.35, 0.45, 0.5, 0.25, 0.3, 0.2, 0.2, 0.1,
0.15, 0.35, 0.3, 0.15, 0.2, 0.6, 0.65},
kQuantizedTolerance)));
}
TEST(ConstFloatMeanOpTest, KeepDims4DMeanQuantized) {
float kQuantizedTolerance = GetTolerance(-5.0, 5.0);
std::vector<float> data = {0.1, 0.2, 0.3, 0.4, 0.1, 0.2,
0.3, 0.4, 0.1, 0.2, 0.3, 0.4};
MeanOpConstModel m({TensorType_UINT8, {1, 2, 3, 2}, 0.0, 1.0},
{TensorType_UINT8, {3}, -5.0, 5.0}, {2}, {1, 2}, true);
m.QuantizeAndPopulate<uint8_t>(m.Input(), data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 1, 1, 2}));
EXPECT_THAT(m.GetDequantizedOutput<uint8_t>(),
ElementsAreArray(
ArrayFloatNear({0.235294, 0.313726}, kQuantizedTolerance)));
}
TEST(ConstFloatMeanOpTest, Scalar) {
std::vector<float> data = {3.27};
MeanOpConstModel m({TensorType_FLOAT32, {}}, {TensorType_FLOAT32, {}}, {},
{0}, true);
m.SetInput(data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), IsEmpty());
EXPECT_THAT(m.GetOutput<float>(), ElementsAreArray(ArrayFloatNear({3.27})));
}
TEST(ConstFloatMeanOpTest, ScalarAxis) {
std::vector<float> data = {4., 2.};
MeanOpConstModel m({TensorType_FLOAT32, {2}}, {TensorType_FLOAT32, {1}}, {},
{0}, true);
m.SetInput(data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1}));
EXPECT_THAT(m.GetOutput<float>(), ElementsAreArray(ArrayFloatNear({3.})));
}
TEST(DynamicFloatMeanOpTest, NotKeepDims) {
std::vector<float> data = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0,
9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0,
17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0};
MeanOpDynamicModel m({TensorType_FLOAT32, {4, 3, 2}},
{TensorType_FLOAT32, {2}}, {TensorType_INT32, {4}},
false);
std::vector<int> axis = {1, 0, -3, -3};
m.SetAxis(axis);
m.SetInput(data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2}));
EXPECT_THAT(m.GetOutput<float>(), ElementsAreArray(ArrayFloatNear({12, 13})));
}
TEST(DynamicFloatMeanOpTest, ReduceOnLastDimNotKeepDims) {
std::vector<float> data = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0,
9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0,
17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0};
MeanOpDynamicModel m({TensorType_FLOAT32, {4, 3, 2}},
{TensorType_FLOAT32, {2}}, {TensorType_INT32, {1}},
false);
std::vector<int> axis = {2};
m.SetAxis(axis);
m.SetInput(data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({4, 3}));
EXPECT_THAT(
m.GetOutput<float>(),
ElementsAreArray(ArrayFloatNear({1.5, 3.5, 5.5, 7.5, 9.5, 11.5, 13.5,
15.5, 17.5, 19.5, 21.5, 23.5})));
}
TEST(DynamicFloatMeanOpTest, KeepDims) {
std::vector<float> data = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0,
9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0,
17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0};
MeanOpDynamicModel m({TensorType_FLOAT32, {4, 3, 2}},
{TensorType_FLOAT32, {3}}, {TensorType_INT32, {2}},
true);
std::vector<int> axis = {0, 2};
m.SetAxis(axis);
m.SetInput(data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 3, 1}));
EXPECT_THAT(m.GetOutput<float>(),
ElementsAreArray(ArrayFloatNear({10.5, 12.5, 14.5})));
}
TEST(DynamicFloatMeanOpTest, Scale) {
std::vector<float> data = {9.527};
MeanOpDynamicModel m({TensorType_FLOAT32, {1}}, {TensorType_FLOAT32, {1}},
{TensorType_INT32, {1}}, true);
std::vector<int> axis = {0};
m.SetAxis(axis);
m.SetInput(data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1}));
EXPECT_THAT(m.GetOutput<float>(), ElementsAreArray(ArrayFloatNear({9.527})));
}
TEST(ConstUint8MeanOpTest, NotKeepDims) {
float kQuantizedTolerance = GetTolerance(-1.0, 1.0);
std::vector<float> data = {0.4, 0.2, 0.3, 0.4, 0.5, 0.6};
MeanOpConstModel m({TensorType_UINT8, {1, 3, 2}, -1.0, 1.0},
{TensorType_UINT8, {2}, -1.0, 1.0}, {1}, {1}, false);
m.QuantizeAndPopulate<uint8_t>(m.Input(), data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 2}));
EXPECT_THAT(
m.GetDequantizedOutput<uint8_t>(),
ElementsAreArray(ArrayFloatNear({0.4, 0.4}, kQuantizedTolerance)));
}
TEST(ConstUint8MeanOpTest, KeepDims) {
float kQuantizedTolerance = GetTolerance(-1.0, 1.0);
std::vector<float> data = {0.4, 0.2, 0.3, 0.4, 0.5, 0.6};
MeanOpConstModel m({TensorType_UINT8, {3, 2}, -1.0, 1.0},
{TensorType_UINT8, {3}, -1.0, 1.0}, {1}, {1}, true);
m.QuantizeAndPopulate<uint8_t>(m.Input(), data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({3, 1}));
EXPECT_THAT(
m.GetDequantizedOutput<uint8_t>(),
ElementsAreArray(ArrayFloatNear({0.3, 0.35, 0.55}, kQuantizedTolerance)));
}
template <typename integer_type, TensorType tensor_dtype>
void MeanOpConstModelTest() {
float kQuantizedTolerance = GetTolerance<integer_type>(-255.0, 255.0);
std::vector<float> data = {105.0, 71.0, 233.0, 92.0, 227.0, 11.0, 14.0, 43.0};
MeanOpConstModel m({tensor_dtype, {1, 1, 2, 4}, -255.0, 255.0},
{tensor_dtype, {1, 2, 4}, -255, 255.0}, {1}, {1},
false);
m.QuantizeAndPopulate<integer_type>(m.Input(), data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 2, 4}));
EXPECT_THAT(m.GetDequantizedOutput<integer_type>(),
ElementsAreArray(ArrayFloatNear(data, kQuantizedTolerance)));
}
class ConstMeanOpTestSameScale : public ::testing::Test {};
TEST_F(ConstMeanOpTestSameScale, NonSpecialAxisSameScaleInt8) {
MeanOpConstModelTest<int8_t, TensorType_INT8>();
}
TEST_F(ConstMeanOpTestSameScale, NonSpecialAxisSameScaleInt16) {
MeanOpConstModelTest<int16_t, TensorType_INT16>();
}
template <typename integer_type, TensorType tensor_dtype>
void ConstMeanOpTestNonSameScale() {
float kQuantizedTolerance = GetTolerance<integer_type>(-5.0, 5.0);
std::vector<float> data = {0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8};
MeanOpConstModel m({tensor_dtype, {1, 1, 2, 4}, -1.0, 1.0},
{tensor_dtype, {1, 2}, -5.0, 5.0}, {2}, {1, 3},
false);
m.QuantizeAndPopulate<integer_type>(m.Input(), data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 2}));
EXPECT_THAT(
m.GetDequantizedOutput<integer_type>(),
ElementsAreArray(ArrayFloatNear({0.25, 0.65}, kQuantizedTolerance)));
}
class ConstMeanOpTestNonSameScale : public ::testing::Test {};
TEST_F(ConstMeanOpTestNonSameScale, NonSpecialAxisNonSameScaleInt8) {
MeanOpConstModelTest<int8_t, TensorType_INT8>();
}
TEST_F(ConstMeanOpTestNonSameScale, NonSpecialAxisNonSameScaleInt16) {
MeanOpConstModelTest<int16_t, TensorType_INT16>();
}
template <typename integer_type, TensorType tensor_dtype>
void MeanOpTestQuantizedSameScale() {
float kQuantizedTolerance = GetTolerance<integer_type>(-5.0, 5.0);
std::vector<float> data = {0.1, 0.2, 0.3, 0.4, 0.2, 0.3, 0.4, 0.5, 0.1,
0.1, 0.1, 0.1, 0.4, 0.2, 0.2, 0.2, 0.9, 0.9,
0.9, 0.9, 0.2, 0.3, 0.7, 0.7, 0.1, 0.1, 0.3,
0.3, 0.1, 0.2, 0.3, 0.4, 0.1, 0.2, 0.3, 0.4};
MeanOpConstModel m({tensor_dtype, {1, 2, 2, 9}, -1.0, 1.0},
{tensor_dtype, {2}, -1.0, 1.0}, {2}, {1, 2},
true);
m.QuantizeAndPopulate<integer_type>(m.Input(), data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 1, 1, 9}));
EXPECT_THAT(m.GetDequantizedOutput<integer_type>(),
ElementsAreArray(ArrayFloatNear(
{0.35, 0.325, 0.2, 0.35, 0.375, 0.325, 0.225, 0.45, 0.425},
kQuantizedTolerance)));
}
class MeanOpTestQuantizedSameScale : public ::testing::Test {};
TEST_F(MeanOpTestQuantizedSameScale, QuantizedSameScaleInt8) {
MeanOpConstModelTest<int8_t, TensorType_INT8>();
}
TEST_F(MeanOpTestQuantizedSameScale, QuantizedSameScaleInt16) {
MeanOpConstModelTest<int16_t, TensorType_INT16>();
}
template <typename integer_type, TensorType tensor_dtype>
void MeanOpTestQuantizedDifferentScale() {
float kQuantizedTolerance = GetTolerance<integer_type>(-5.0, 5.0);
std::vector<float> data = {0.1, 0.2, 0.3, 0.4, 0.2, 0.3, 0.4, 0.5, 0.1,
0.1, 0.1, 0.1, 0.4, 0.2, 0.2, 0.2, 0.9, 0.9,
0.9, 0.9, 0.2, 0.3, 0.7, 0.7, 0.1, 0.1, 0.3,
0.3, 0.1, 0.2, 0.3, 0.4, 0.1, 0.2, 0.3, 0.4};
MeanOpConstModel m({tensor_dtype, {1, 2, 2, 9}, -1.0, 1.0},
{tensor_dtype, {2}, -4.0, 4.0}, {2}, {1, 2},
true);
m.QuantizeAndPopulate<integer_type>(m.Input(), data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 1, 1, 9}));
EXPECT_THAT(m.GetDequantizedOutput<integer_type>(),
ElementsAreArray(ArrayFloatNear(
{0.35, 0.325, 0.2, 0.35, 0.375, 0.325, 0.225, 0.45, 0.425},
kQuantizedTolerance)));
}
class MeanOpTestQuantizedDifferentScale : public ::testing::Test {};
TEST_F(MeanOpTestQuantizedDifferentScale, QuantizedDifferentScaleInt8) {
MeanOpConstModelTest<int8_t, TensorType_INT8>();
}
TEST_F(MeanOpTestQuantizedDifferentScale, QuantizedDifferentScaleInt16) {
MeanOpConstModelTest<int16_t, TensorType_INT16>();
}
TEST(ConstFloatMeanOpTest, KeepDims4DMeanLargeDepthInt8) {
float kQuantizedTolerance = GetTolerance(-5.0, 5.0);
std::vector<float> data = {
0.1, 0.2, 0.3, 0.4, 0.2, 0.3, 0.4, 0.5, 0.1, 0.1, 0.1, 0.1, 0.4, 0.2, 0.2,
0.2, 0.9, 0.9, 0.9, 0.9, 0.2, 0.3, 0.7, 0.7, 0.1, 0.1, 0.3, 0.3, 0.1, 0.2,
0.3, 0.4, 0.1, 0.2, 0.3, 0.4, 0.1, 0.2, 0.3, 0.4, 0.2, 0.3, 0.4, 0.5, 0.1,
0.1, 0.1, 0.1, 0.4, 0.2, 0.2, 0.2, 0.9, 0.9, 0.9, 0.9, 0.2, 0.3, 0.7, 0.7,
0.1, 0.1, 0.3, 0.3, 0.1, 0.2, 0.3, 0.4, 0.1, 0.2, 0.3, 0.4};
MeanOpConstModel m({TensorType_INT8, {1, 2, 2, 18}, -1.0, 1.0},
{TensorType_INT8, {2}, -1.0, 1.0}, {2}, {1, 2}, true);
m.QuantizeAndPopulate<int8_t>(m.Input(), data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 1, 1, 18}));
EXPECT_THAT(m.GetDequantizedOutput<int8_t>(),
ElementsAreArray(ArrayFloatNear(
{0.5, 0.55, 0.25, 0.35, 0.45, 0.5, 0.25, 0.3, 0.2, 0.2, 0.1,
0.15, 0.35, 0.3, 0.15, 0.2, 0.6, 0.65},
kQuantizedTolerance)));
}
TEST(DynamicUint8MeanOpTest, NotKeepDims) {
float kQuantizedTolerance = GetTolerance(-5.0, 2.0);
std::vector<float> data = {1.3, -4.8, -3.6, 0.24};
MeanOpDynamicModel m({TensorType_UINT8, {2, 2}, -5.0, 2.0},
{TensorType_UINT8, {2}, -5.0, 2.0},
{TensorType_INT32, {1}}, false);
std::vector<int> axis = {1};
m.SetAxis(axis);
m.QuantizeAndPopulate<uint8_t>(m.Input(), data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2}));
EXPECT_THAT(
m.GetDequantizedOutput<uint8_t>(),
ElementsAreArray(ArrayFloatNear({-1.75, -1.68}, kQuantizedTolerance)));
}
TEST(DynamicUint8MeanOpTest, KeepDims) {
float kQuantizedTolerance = GetTolerance(-10.0, 12.0);
std::vector<float> data = {11.14, -0.14, 7.423, 0.879};
MeanOpDynamicModel m({TensorType_UINT8, {2, 2}, -10.0, 12.0},
{TensorType_UINT8, {2}, -10.0, 12.0},
{TensorType_INT32, {1}}, true);
std::vector<int> axis = {0};
m.SetAxis(axis);
m.QuantizeAndPopulate<uint8_t>(m.Input(), data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 2}));
EXPECT_THAT(
m.GetDequantizedOutput<uint8_t>(),
ElementsAreArray(ArrayFloatNear({9.2815, 0.3695}, kQuantizedTolerance)));
}
TEST(DynamicUint8MeanOpTest, QuantizedScalar) {
float kQuantizedTolerance = GetTolerance(-10.0, 12.0);
std::vector<float> data = {0.643};
MeanOpDynamicModel m({TensorType_UINT8, {}, 0.0, 1.0},
{TensorType_UINT8, {}, -10.0, 12.0},
{TensorType_INT32, {1}}, true);
std::vector<int> axis = {0};
m.QuantizeAndPopulate<uint8_t>(m.Input(), data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), IsEmpty());
EXPECT_THAT(m.GetDequantizedOutput<uint8_t>(),
ElementsAreArray(ArrayFloatNear({0.643}, kQuantizedTolerance)));
}
TEST(ConstUint8MeanOpTest, QuantizedKeepDims) {
float kQuantizedTolerance = GetTolerance(-5.0, 5.0);
std::vector<float> data = {0.4, 0.2, 0.3, 0.4, 0.5, 0.6};
MeanOpConstModel m({TensorType_UINT8, {3, 2}, 0.0, 1.0},
{TensorType_UINT8, {3}, -5.0, 5.0}, {1}, {1}, true);
m.QuantizeAndPopulate<uint8_t>(m.Input(), data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({3, 1}));
EXPECT_THAT(
m.GetDequantizedOutput<uint8_t>(),
ElementsAreArray(ArrayFloatNear({0.3, 0.35, 0.55}, kQuantizedTolerance)));
}
// Tests for reduce_sum
TEST(ConstFloatSumOpTest, NotKeepDims) {
std::vector<float> data = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0,
9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0,
17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0};
SumOpConstModel m({TensorType_FLOAT32, {4, 3, 2}}, {TensorType_FLOAT32, {2}},
{4}, {1, 0, -3, -3}, false);
m.SetInput(data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2}));
EXPECT_THAT(m.GetOutput<float>(),
ElementsAreArray(ArrayFloatNear({144, 156})));
}
TEST(ConstFloatSumOpTest, KeepDims) {
std::vector<float> data = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0,
9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0,
17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0};
SumOpConstModel m({TensorType_FLOAT32, {4, 3, 2}}, {TensorType_FLOAT32, {3}},
{2}, {0, 2}, true);
m.SetInput(data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 3, 1}));
EXPECT_THAT(m.GetOutput<float>(),
ElementsAreArray(ArrayFloatNear({84, 100, 116})));
}
TEST(DynamicFloatSumOpTest, NotKeepDims) {
std::vector<float> data = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0,
9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0,
17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0};
SumOpDynamicModel m({TensorType_FLOAT32, {4, 3, 2}},
{TensorType_FLOAT32, {2}}, {TensorType_INT32, {4}},
false);
std::vector<int> axis = {1, 0, -3, -3};
m.SetAxis(axis);
m.SetInput(data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2}));
EXPECT_THAT(m.GetOutput<float>(),
ElementsAreArray(ArrayFloatNear({144, 156})));
}
TEST(ConstFloatSumOpTest, Scalar) {
std::vector<float> data = {17.};
SumOpConstModel m({TensorType_FLOAT32, {}}, {TensorType_FLOAT32, {}}, {}, {0},
false);
m.SetInput(data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), IsEmpty());
EXPECT_THAT(m.GetOutput<float>(), ElementsAreArray(ArrayFloatNear({17.})));
}
TEST(ConstFloatSumOpTest, ScalarAxis) {
std::vector<float> data = {17., 21., 4.};
SumOpConstModel m({TensorType_FLOAT32, {3}}, {TensorType_FLOAT32, {1}}, {},
{0}, true);
m.SetInput(data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1}));
EXPECT_THAT(m.GetOutput<float>(), ElementsAreArray(ArrayFloatNear({42.})));
}
TEST(DynamicFloatSumOpTest, KeepDims) {
std::vector<float> data = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0,
9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0,
17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0};
SumOpDynamicModel m({TensorType_FLOAT32, {4, 3, 2}},
{TensorType_FLOAT32, {3}}, {TensorType_INT32, {2}}, true);
std::vector<int> axis = {0, 2};
m.SetAxis(axis);
m.SetInput(data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 3, 1}));
EXPECT_THAT(m.GetOutput<float>(),
ElementsAreArray(ArrayFloatNear({84, 100, 116})));
}
TEST(DynamicFloatSumOpTest, Scale) {
std::vector<float> data = {9.527};
SumOpDynamicModel m({TensorType_FLOAT32, {1}}, {TensorType_FLOAT32, {1}},
{TensorType_INT32, {1}}, true);
std::vector<int> axis = {0};
m.SetAxis(axis);
m.SetInput(data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1}));
EXPECT_THAT(m.GetOutput<float>(), ElementsAreArray(ArrayFloatNear({9.527})));
}
TEST(ConstUint8SumOpTest, NotKeepDims) {
float kQuantizedTolerance = GetTolerance(-1.0, 1.0);
std::vector<float> data = {0.4, 0.2, 0.3, 0.4, 0.5, 0.6};
SumOpConstModel m({TensorType_UINT8, {1, 3, 2}, -1.0, 1.0},
{TensorType_UINT8, {2}, -1.0, 1.0}, {1}, {1}, false);
m.QuantizeAndPopulate<uint8_t>(m.Input(), data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 2}));
EXPECT_THAT(m.GetDequantizedOutput<uint8_t>(),
ElementsAreArray(
ArrayFloatNear({-0.823529, -0.815686}, kQuantizedTolerance)));
}
TEST(ConstUint8SumOpTest, NotKeepDimsRescaling) {
float kQuantizedTolerance = GetTolerance(0.0, 2.0);
std::vector<float> data = {0.4, 0.2, 0.3, 0.4, 0.5, 0.6};
SumOpConstModel m({TensorType_UINT8, {1, 3, 2}, 0.0, 1.0},
{TensorType_UINT8, {2}, 0.0, 2.0}, {1}, {1}, false);
m.QuantizeAndPopulate<uint8_t>(m.Input(), data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 2}));
EXPECT_THAT(
m.GetDequantizedOutput<uint8_t>(),
ElementsAreArray(ArrayFloatNear({1.2, 1.2}, kQuantizedTolerance)));
}
TEST(ConstUint8SumOpTest, KeepDims) {
float kQuantizedTolerance = GetTolerance(-1.0, 1.0);
std::vector<float> data = {0.4, 0.2, 0.3, 0.4, 0.5, 0.6};
SumOpConstModel m({TensorType_UINT8, {3, 2}, -1.0, 1.0},
{TensorType_UINT8, {3}, -1.0, 1.0}, {1}, {1}, true);
m.QuantizeAndPopulate<uint8_t>(m.Input(), data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({3, 1}));
EXPECT_THAT(m.GetDequantizedOutput<uint8_t>(),
ElementsAreArray(ArrayFloatNear({-0.407843, -0.313726, 0.0941177},
kQuantizedTolerance)));
}
TEST(DynamicUint8SumOpTest, NotKeepDims) {
float kQuantizedTolerance = GetTolerance(-5.0, 2.0);
std::vector<float> data = {1.3, -4.8, -3.6, 0.24};
SumOpDynamicModel m({TensorType_UINT8, {2, 2}, -5.0, 2.0},
{TensorType_UINT8, {2}, -5.0, 2.0},
{TensorType_INT32, {1}}, false);
std::vector<int> axis = {1};
m.SetAxis(axis);
m.QuantizeAndPopulate<uint8_t>(m.Input(), data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2}));
EXPECT_THAT(m.GetDequantizedOutput<uint8_t>(),
ElementsAreArray(
ArrayFloatNear({1.48235, 1.64706}, kQuantizedTolerance)));
}
TEST(DynamicUint8SumOpTest, KeepDims) {
float kQuantizedTolerance = GetTolerance(-10.0, 12.0);
std::vector<float> data = {11.14, -0.14, 7.423, 0.879};
SumOpDynamicModel m({TensorType_UINT8, {2, 2}, -10.0, 12.0},
{TensorType_UINT8, {2}, -10.0, 12.0},
{TensorType_INT32, {1}}, true);
std::vector<int> axis = {0};
m.SetAxis(axis);
m.QuantizeAndPopulate<uint8_t>(m.Input(), data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 2}));
EXPECT_THAT(
m.GetDequantizedOutput<uint8_t>(),
ElementsAreArray(ArrayFloatNear({6.47059, 10.698}, kQuantizedTolerance)));
}
TEST(ConstInt8SumOpTest, Rescale) {
const std::vector<float> data = {0.4, 0.2, 0.3, 0.4, 0.5, 0.3};
SumOpConstModel m({TensorType_INT8, {1, 3, 2}, -1.0, 1.0},
{TensorType_INT8, {2}, -5.0, 5.0}, {1}, {1}, false);
// Expect the sum to be 0.4 + 0.3 + 0.5 = 1.2 and 0.2 + 0.4 + 0.3 = 0.9.
const std::vector<float> expected_sum = {1.2, 0.9};
const float kQuantizedTolerance = GetTolerance(-5.0, 5.0);
m.QuantizeAndPopulate<int8_t>(m.Input(), data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 2}));
EXPECT_THAT(
m.GetDequantizedOutput<int8_t>(),
ElementsAreArray(ArrayFloatNear(expected_sum, kQuantizedTolerance)));
}
// Tests for reduce_prod
TEST(ConstFloatProdOpTest, NotKeepDims) {
std::vector<float> data = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0,
9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0,
17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0};
ProdOpConstModel m({TensorType_FLOAT32, {4, 3, 2}}, {TensorType_FLOAT32, {2}},
{4}, {1, 0, -3, -3}, false);
m.SetInput(data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2}));
EXPECT_THAT(
m.GetOutput<float>(),
ElementsAreArray(ArrayFloatNear({3.162341376e+11, 1.9619905536e+12})));
}
TEST(ConstFloatProdOpTest, KeepDims) {
std::vector<float> data = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0,
9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0,
17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0};
ProdOpConstModel m({TensorType_FLOAT32, {4, 3, 2}}, {TensorType_FLOAT32, {3}},
{2}, {0, 2}, true);
m.SetInput(data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 3, 1}));
EXPECT_THAT(m.GetOutput<float>(),
ElementsAreArray(
ArrayFloatNear({7.74592e+06, 1.197504e+08, 6.6889152e+08})));
}
TEST(DynamicFloatProdOpTest, NotKeepDims) {
std::vector<float> data = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0,
9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0,
17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0};
ProdOpDynamicModel m({TensorType_FLOAT32, {4, 3, 2}},
{TensorType_FLOAT32, {2}}, {TensorType_INT32, {4}},
false);
std::vector<int> axis = {1, 0, -3, -3};
m.SetAxis(axis);
m.SetInput(data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2}));
EXPECT_THAT(
m.GetOutput<float>(),
ElementsAreArray(ArrayFloatNear({3.16234143225e+11, 1.9619905536e+12})));
}
TEST(DynamicFloatProdOpTest, KeepDims) {
std::vector<float> data = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0,
9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0,
17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0};
ProdOpDynamicModel m({TensorType_FLOAT32, {4, 3, 2}},
{TensorType_FLOAT32, {3}}, {TensorType_INT32, {2}},
true);
std::vector<int> axis = {0, 2};
m.SetAxis(axis);
m.SetInput(data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 3, 1}));
EXPECT_THAT(m.GetOutput<float>(),
ElementsAreArray(
ArrayFloatNear({7.74592e+06, 1.197504e+08, 6.6889152e+08})));
}
TEST(DynamicFloatProdOpTest, Scale) {
std::vector<float> data = {9.527};
ProdOpDynamicModel m({TensorType_FLOAT32, {1}}, {TensorType_FLOAT32, {1}},
{TensorType_INT32, {1}}, true);
std::vector<int> axis = {0};
m.SetAxis(axis);
m.SetInput(data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1}));
EXPECT_THAT(m.GetOutput<float>(), ElementsAreArray(ArrayFloatNear({9.527})));
}
// Tests for reduce_max
TEST(ConstFloatMaxOpTest, NotKeepDims) {
std::vector<float> data = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0,
9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0,
17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0};
MaxOpConstModel m({TensorType_FLOAT32, {4, 3, 2}}, {TensorType_FLOAT32, {2}},
{4}, {1, 0, -3, -3}, false);
m.SetInput(data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2}));
EXPECT_THAT(m.GetOutput<float>(), ElementsAreArray(ArrayFloatNear({23, 24})));
}
TEST(ConstFloatMaxOpTest, KeepDims) {
std::vector<float> data = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0,
9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0,
17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0};
MaxOpConstModel m({TensorType_FLOAT32, {4, 3, 2}}, {TensorType_FLOAT32, {3}},
{2}, {0, 2}, true);
m.SetInput(data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 3, 1}));
EXPECT_THAT(m.GetOutput<float>(),
ElementsAreArray(ArrayFloatNear({20, 22, 24})));
}
TEST(DynamicFloatMaxOpTest, NotKeepDims) {
std::vector<float> data = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0,
9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0,
17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0};
MaxOpDynamicModel m({TensorType_FLOAT32, {4, 3, 2}},
{TensorType_FLOAT32, {2}}, {TensorType_INT32, {4}},
false);
std::vector<int> axis = {1, 0, -3, -3};
m.SetAxis(axis);
m.SetInput(data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2}));
EXPECT_THAT(m.GetOutput<float>(), ElementsAreArray(ArrayFloatNear({23, 24})));
}
TEST(DynamicFloatMaxOpTest, KeepDims) {
std::vector<float> data = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0,
9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0,
17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0};
MaxOpDynamicModel m({TensorType_FLOAT32, {4, 3, 2}},
{TensorType_FLOAT32, {3}}, {TensorType_INT32, {2}}, true);
std::vector<int> axis = {0, 2};
m.SetAxis(axis);
m.SetInput(data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 3, 1}));
EXPECT_THAT(m.GetOutput<float>(),
ElementsAreArray(ArrayFloatNear({20, 22, 24})));
}
TEST(DynamicFloatMaxOpTest, Scale) {
std::vector<float> data = {9.527};
MaxOpDynamicModel m({TensorType_FLOAT32, {1}}, {TensorType_FLOAT32, {1}},
{TensorType_INT32, {1}}, true);
std::vector<int> axis = {0};
m.SetAxis(axis);
m.SetInput(data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1}));
EXPECT_THAT(m.GetOutput<float>(), ElementsAreArray(ArrayFloatNear({9.527})));
}
TEST(ConstUint8MaxOpTest, NotKeepDims) {
float kQuantizedTolerance = GetTolerance(-1.0, 1.0);
std::vector<float> data = {0.4, 0.2, 0.3, 0.4, 0.5, 0.6};
MaxOpConstModel m({TensorType_UINT8, {1, 3, 2}, -1.0, 1.0},
{TensorType_UINT8, {2}, -1.0, 1.0}, {1}, {1}, false);
m.QuantizeAndPopulate<uint8_t>(m.Input(), data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 2}));
EXPECT_THAT(m.GetDequantizedOutput<uint8_t>(),
ElementsAreArray(
ArrayFloatNear({0.501961, 0.603922}, kQuantizedTolerance)));
}
TEST(ConstInt8MaxOpTest, NotKeepDims) {
float kQuantizedTolerance = GetTolerance(-1.0, 1.0);
std::vector<float> data = {0.4, 0.2, 0.3, 0.4, 0.5, 0.6};
MaxOpConstModel m({TensorType_INT8, {1, 3, 2}, -1.0, 1.0},
{TensorType_INT8, {2}, -1.0, 1.0}, {1}, {1}, false);
m.QuantizeAndPopulate<int8_t>(m.Input(), data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 2}));
EXPECT_THAT(m.GetDequantizedOutput<int8_t>(),
ElementsAreArray(
ArrayFloatNear({0.501961, 0.603922}, kQuantizedTolerance)));
}
TEST(ConstUint8MaxOpTest, KeepDims) {
float kQuantizedTolerance = GetTolerance(-1.0, 1.0);
std::vector<float> data = {0.4, 0.2, 0.3, 0.4, 0.5, 0.6};
MaxOpConstModel m({TensorType_UINT8, {3, 2}, -1.0, 1.0},
{TensorType_UINT8, {3}, -1.0, 1.0}, {1}, {1}, true);
m.QuantizeAndPopulate<uint8_t>(m.Input(), data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({3, 1}));
EXPECT_THAT(m.GetDequantizedOutput<uint8_t>(),
ElementsAreArray(
ArrayFloatNear({0.4, 0.4, 0.603922}, kQuantizedTolerance)));
}
TEST(ConstInt8MaxOpTest, KeepDims) {
float kQuantizedTolerance = GetTolerance(-1.0, 1.0);
std::vector<float> data = {0.4, 0.2, 0.3, 0.4, 0.5, 0.6};
MaxOpConstModel m({TensorType_INT8, {3, 2}, -1.0, 1.0},
{TensorType_INT8, {3}, -1.0, 1.0}, {1}, {1}, true);
m.QuantizeAndPopulate<int8_t>(m.Input(), data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({3, 1}));
EXPECT_THAT(m.GetDequantizedOutput<int8_t>(),
ElementsAreArray(
ArrayFloatNear({0.4, 0.4, 0.603922}, kQuantizedTolerance)));
}
TEST(DynamicUint8MaxOpTest, NotKeepDims) {
float kQuantizedTolerance = GetTolerance(-5.0, 2.0);
std::vector<float> data = {1.3, -4.8, -3.6, 0.24};
MaxOpDynamicModel m({TensorType_UINT8, {2, 2}, -5.0, 2.0},
{TensorType_UINT8, {2}, -5.0, 2.0},
{TensorType_INT32, {1}}, false);
std::vector<int> axis = {1};
m.SetAxis(axis);
m.QuantizeAndPopulate<uint8_t>(m.Input(), data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2}));
EXPECT_THAT(m.GetDequantizedOutput<uint8_t>(),
ElementsAreArray(
ArrayFloatNear({1.2902, 0.247059}, kQuantizedTolerance)));
}
TEST(DynamicInt8MaxOpTest, NotKeepDims) {
float kQuantizedTolerance = GetTolerance(-5.0, 2.0);
std::vector<float> data = {1.3, -4.8, -3.6, 0.24};
MaxOpDynamicModel m({TensorType_INT8, {2, 2}, -5.0, 2.0},
{TensorType_INT8, {2}, -5.0, 2.0},
{TensorType_INT32, {1}}, false);
std::vector<int> axis = {1};
m.SetAxis(axis);
m.QuantizeAndPopulate<int8_t>(m.Input(), data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2}));
EXPECT_THAT(m.GetDequantizedOutput<int8_t>(),
ElementsAreArray(
ArrayFloatNear({1.2902, 0.247059}, kQuantizedTolerance)));
}
TEST(DynamicUint8MaxOpTest, KeepDims) {
float kQuantizedTolerance = GetTolerance(-10.0, 12.0);
std::vector<float> data = {11.14, -0.14, 7.423, 0.879};
MaxOpDynamicModel m({TensorType_UINT8, {2, 2}, -10.0, 12.0},
{TensorType_UINT8, {2}, -10.0, 12.0},
{TensorType_INT32, {1}}, true);
std::vector<int> axis = {0};
m.SetAxis(axis);
m.QuantizeAndPopulate<uint8_t>(m.Input(), data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 2}));
EXPECT_THAT(m.GetDequantizedOutput<uint8_t>(),
ElementsAreArray(
ArrayFloatNear({11.1294, 0.862745}, kQuantizedTolerance)));
}
TEST(DynamicInt8MaxOpTest, KeepDims) {
float kQuantizedTolerance = GetTolerance(-10.0, 12.0);
std::vector<float> data = {11.14, -0.14, 7.423, 0.879};
MaxOpDynamicModel m({TensorType_INT8, {2, 2}, -10.0, 12.0},
{TensorType_INT8, {2}, -10.0, 12.0},
{TensorType_INT32, {1}}, true);
std::vector<int> axis = {0};
m.SetAxis(axis);
m.QuantizeAndPopulate<int8_t>(m.Input(), data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 2}));
EXPECT_THAT(m.GetDequantizedOutput<int8_t>(),
ElementsAreArray(
ArrayFloatNear({11.1294, 0.862745}, kQuantizedTolerance)));
}
TEST(DynamicUint8MaxOpTest, Scalar) {
float kQuantizedTolerance = GetTolerance(-10.0, 12.0);
std::vector<float> data = {11.14};
MaxOpDynamicModel m({TensorType_UINT8, {}, -10.0, 12.0},
{TensorType_UINT8, {}, -10.0, 12.0},
{TensorType_INT32, {1}}, true);
std::vector<int> axis = {0};
m.QuantizeAndPopulate<uint8_t>(m.Input(), data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), IsEmpty());
EXPECT_THAT(m.GetDequantizedOutput<uint8_t>(),
ElementsAreArray(ArrayFloatNear({11.1294}, kQuantizedTolerance)));
}
TEST(DynamicInt8MaxOpTest, Scalar) {
float kQuantizedTolerance = GetTolerance(-10.0, 12.0);
std::vector<float> data = {11.14};
MaxOpDynamicModel m({TensorType_INT8, {}, -10.0, 12.0},
{TensorType_INT8, {}, -10.0, 12.0},
{TensorType_INT32, {1}}, true);
std::vector<int> axis = {0};
m.QuantizeAndPopulate<int8_t>(m.Input(), data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), IsEmpty());
EXPECT_THAT(m.GetDequantizedOutput<int8_t>(),
ElementsAreArray(ArrayFloatNear({11.1294}, kQuantizedTolerance)));
}
// Tests for reduce_min
TEST(ConstFloatMinOpTest, NotKeepDims) {
std::vector<float> data = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0,
9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0,
17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0};
MinOpConstModel m({TensorType_FLOAT32, {4, 3, 2}}, {TensorType_FLOAT32, {2}},
{4}, {1, 0, -3, -3}, false);
m.SetInput(data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2}));
EXPECT_THAT(m.GetOutput<float>(), ElementsAreArray(ArrayFloatNear({1, 2})));
}
TEST(ConstFloatMinOpTest, KeepDims) {
std::vector<float> data = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0,
9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0,
17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0};
MinOpConstModel m({TensorType_FLOAT32, {4, 3, 2}}, {TensorType_FLOAT32, {3}},
{2}, {0, 2}, true);
m.SetInput(data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 3, 1}));
EXPECT_THAT(m.GetOutput<float>(),
ElementsAreArray(ArrayFloatNear({1, 3, 5})));
}
TEST(DynamicFloatMinOpTest, NotKeepDims) {
std::vector<float> data = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0,
9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0,
17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0};
MinOpDynamicModel m({TensorType_FLOAT32, {4, 3, 2}},
{TensorType_FLOAT32, {2}}, {TensorType_INT32, {4}},
false);
std::vector<int> axis = {1, 0, -3, -3};
m.SetAxis(axis);
m.SetInput(data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2}));
EXPECT_THAT(m.GetOutput<float>(), ElementsAreArray(ArrayFloatNear({1, 2})));
}
TEST(DynamicFloatMinOpTest, KeepDims) {
std::vector<float> data = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0,
9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0,
17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0};
MinOpDynamicModel m({TensorType_FLOAT32, {4, 3, 2}},
{TensorType_FLOAT32, {3}}, {TensorType_INT32, {2}}, true);
std::vector<int> axis = {0, 2};
m.SetAxis(axis);
m.SetInput(data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 3, 1}));
EXPECT_THAT(m.GetOutput<float>(),
ElementsAreArray(ArrayFloatNear({1, 3, 5})));
}
TEST(DynamicFloatMinOpTest, Scalar) {
std::vector<float> data = {9.527};
MinOpDynamicModel m({TensorType_FLOAT32, {1}}, {TensorType_FLOAT32, {1}},
{TensorType_INT32, {1}}, true);
std::vector<int> axis = {0};
m.SetAxis(axis);
m.SetInput(data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1}));
EXPECT_THAT(m.GetOutput<float>(), ElementsAreArray(ArrayFloatNear({9.527})));
}
TEST(ConstUint8MinOpTest, NotKeepDims) {
float kQuantizedTolerance = GetTolerance(-1.0, 1.0);
std::vector<float> data = {0.4, 0.2, 0.3, 0.4, 0.5, 0.6};
MinOpConstModel m({TensorType_UINT8, {1, 3, 2}, -1.0, 1.0},
{TensorType_UINT8, {2}, -1.0, 1.0}, {1}, {1}, false);
m.QuantizeAndPopulate<uint8_t>(m.Input(), data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 2}));
EXPECT_THAT(
m.GetDequantizedOutput<uint8_t>(),
ElementsAreArray(ArrayFloatNear({0.294117, 0.2}, kQuantizedTolerance)));
}
TEST(ConstInt8MinOpTest, NotKeepDims) {
float kQuantizedTolerance = GetTolerance(-1.0, 1.0);
std::vector<float> data = {0.4, 0.2, 0.3, 0.4, 0.5, 0.6};
MinOpConstModel m({TensorType_INT8, {1, 3, 2}, -1.0, 1.0},
{TensorType_INT8, {2}, -1.0, 1.0}, {1}, {1}, false);
m.QuantizeAndPopulate<int8_t>(m.Input(), data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 2}));
EXPECT_THAT(
m.GetDequantizedOutput<int8_t>(),
ElementsAreArray(ArrayFloatNear({0.294117, 0.2}, kQuantizedTolerance)));
}
TEST(ConstUint8MinOpTest, KeepDims) {
float kQuantizedTolerance = GetTolerance(-1.0, 1.0);
std::vector<float> data = {0.4, 0.2, 0.3, 0.4, 0.5, 0.6};
MinOpConstModel m({TensorType_UINT8, {3, 2}, -1.0, 1.0},
{TensorType_UINT8, {3}, -1.0, 1.0}, {1}, {1}, true);
m.QuantizeAndPopulate<uint8_t>(m.Input(), data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({3, 1}));
EXPECT_THAT(
m.GetDequantizedOutput<uint8_t>(),
ElementsAreArray(ArrayFloatNear({0.2, 0.3, 0.5}, kQuantizedTolerance)));
}
TEST(ConstInt8MinOpTest, KeepDims) {
float kQuantizedTolerance = GetTolerance(-1.0, 1.0);
std::vector<float> data = {0.4, 0.2, 0.3, 0.4, 0.5, 0.6};
MinOpConstModel m({TensorType_INT8, {3, 2}, -1.0, 1.0},
{TensorType_INT8, {3}, -1.0, 1.0}, {1}, {1}, true);
m.QuantizeAndPopulate<int8_t>(m.Input(), data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({3, 1}));
EXPECT_THAT(
m.GetDequantizedOutput<int8_t>(),
ElementsAreArray(ArrayFloatNear({0.2, 0.3, 0.5}, kQuantizedTolerance)));
}
TEST(DynamicUint8MinOpTest, NotKeepDims) {
float kQuantizedTolerance = GetTolerance(-5.0, 2.0);
std::vector<float> data = {1.3, -4.8, -3.6, 0.24};
MinOpDynamicModel m({TensorType_UINT8, {2, 2}, -5.0, 2.0},
{TensorType_UINT8, {2}, -5.0, 2.0},
{TensorType_INT32, {1}}, false);
std::vector<int> axis = {1};
m.SetAxis(axis);
m.QuantizeAndPopulate<uint8_t>(m.Input(), data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2}));
EXPECT_THAT(
m.GetDequantizedOutput<uint8_t>(),
ElementsAreArray(ArrayFloatNear({-4.807843, -3.6}, kQuantizedTolerance)));
}
TEST(DynamicInt8MinOpTest, NotKeepDims) {
float kQuantizedTolerance = GetTolerance(-5.0, 2.0);
std::vector<float> data = {1.3, -4.8, -3.6, 0.24};
MinOpDynamicModel m({TensorType_INT8, {2, 2}, -5.0, 2.0},
{TensorType_INT8, {2}, -5.0, 2.0},
{TensorType_INT32, {1}}, false);
std::vector<int> axis = {1};
m.SetAxis(axis);
m.QuantizeAndPopulate<int8_t>(m.Input(), data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2}));
EXPECT_THAT(
m.GetDequantizedOutput<int8_t>(),
ElementsAreArray(ArrayFloatNear({-4.807843, -3.6}, kQuantizedTolerance)));
}
TEST(DynamicUint8MinOpTest, KeepDims) {
float kQuantizedTolerance = GetTolerance(-10.0, 12.0);
std::vector<float> data = {11.14, -0.14, 7.423, 0.879};
MinOpDynamicModel m({TensorType_UINT8, {2, 2}, -10.0, 12.0},
{TensorType_UINT8, {2}, -10.0, 12.0},
{TensorType_INT32, {1}}, true);
std::vector<int> axis = {0};
m.SetAxis(axis);
m.QuantizeAndPopulate<uint8_t>(m.Input(), data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 2}));
EXPECT_THAT(m.GetDequantizedOutput<uint8_t>(),
ElementsAreArray(
ArrayFloatNear({7.427451, -0.164706}, kQuantizedTolerance)));
}
TEST(DynamicInt8MinOpTest, KeepDims) {
float kQuantizedTolerance = GetTolerance(-10.0, 12.0);
std::vector<float> data = {11.14, -0.14, 7.423, 0.879};
MinOpDynamicModel m({TensorType_INT8, {2, 2}, -10.0, 12.0},
{TensorType_INT8, {2}, -10.0, 12.0},
{TensorType_INT32, {1}}, true);
std::vector<int> axis = {0};
m.SetAxis(axis);
m.QuantizeAndPopulate<int8_t>(m.Input(), data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 2}));
EXPECT_THAT(m.GetDequantizedOutput<int8_t>(),
ElementsAreArray(
ArrayFloatNear({7.427451, -0.164706}, kQuantizedTolerance)));
}
TEST(DynamicUint8MinOpTest, Scalar) {
float kQuantizedTolerance = GetTolerance(-10.0, 12.0);
std::vector<float> data = {11.14};
MinOpDynamicModel m({TensorType_UINT8, {}, -10.0, 12.0},
{TensorType_UINT8, {}, -10.0, 12.0},
{TensorType_INT32, {1}}, true);
std::vector<int> axis = {0};
m.QuantizeAndPopulate<uint8_t>(m.Input(), data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), IsEmpty());
EXPECT_THAT(m.GetDequantizedOutput<uint8_t>(),
ElementsAreArray(ArrayFloatNear({11.1294}, kQuantizedTolerance)));
}
TEST(DynamicInt8MinOpTest, Scalar) {
float kQuantizedTolerance = GetTolerance(-10.0, 12.0);
std::vector<float> data = {11.14};
MinOpDynamicModel m({TensorType_INT8, {}, -10.0, 12.0},
{TensorType_INT8, {}, -10.0, 12.0},
{TensorType_INT32, {1}}, true);
std::vector<int> axis = {0};
m.QuantizeAndPopulate<int8_t>(m.Input(), data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), IsEmpty());
EXPECT_THAT(m.GetDequantizedOutput<int8_t>(),
ElementsAreArray(ArrayFloatNear({11.1294}, kQuantizedTolerance)));
}
// Tests for reduce_any
TEST(ConstAnyOpTest, NotKeepDims) {
std::vector<bool> data = {false, false, false, false, false, false,
false, true, false, false, false, true};
AnyOpConstModel m({TensorType_BOOL, {2, 3, 2}}, {TensorType_BOOL, {2}}, {4},
{1, 0, -3, -3}, false);
m.SetInput(data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2}));
EXPECT_THAT(m.GetOutput<bool>(), ElementsAreArray({false, true}));
}
TEST(ConstAnyOpTest, KeepDims) {
std::vector<bool> data = {false, false, false, false, false, false,
false, true, false, false, false, true};
AnyOpConstModel m({TensorType_BOOL, {2, 3, 2}}, {TensorType_BOOL, {3}}, {2},
{0, 2}, true);
m.SetInput(data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 3, 1}));
EXPECT_THAT(m.GetOutput<bool>(), ElementsAreArray({true, false, true}));
}
TEST(DynamicAnyOpTest, NotKeepDims) {
std::vector<bool> data = {false, false, false, false, false, false,
false, true, false, false, false, true};
AnyOpDynamicModel m({TensorType_BOOL, {2, 3, 2}}, {TensorType_BOOL, {2}},
{TensorType_INT32, {4}}, false);
std::vector<int> axis = {1, 0, -3, -3};
m.SetAxis(axis);
m.SetInput(data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2}));
EXPECT_THAT(m.GetOutput<bool>(), ElementsAreArray({false, true}));
}
TEST(DynamicAnyOpTest, KeepDims) {
std::vector<bool> data = {false, false, false, false, false, false,
false, true, false, false, false, true};
AnyOpDynamicModel m({TensorType_BOOL, {2, 3, 2}}, {TensorType_BOOL, {3}},
{TensorType_INT32, {2}}, true);
std::vector<int> axis = {0, 2};
m.SetAxis(axis);
m.SetInput(data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 3, 1}));
EXPECT_THAT(m.GetOutput<bool>(), ElementsAreArray({true, false, true}));
}
TEST(DynamicAnyOpTest, Scalar) {
std::vector<bool> data = {false};
AnyOpDynamicModel m({TensorType_BOOL, {1}}, {TensorType_BOOL, {1}},
{TensorType_INT32, {1}}, true);
std::vector<int> axis = {0};
m.SetAxis(axis);
m.SetInput(data);
m.Invoke();
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1}));
EXPECT_THAT(m.GetOutput<bool>(), ElementsAreArray({false}));
}
} // namespace
} // namespace tflite