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STT-tensorflow/tensorflow/lite/kernels/sub_test.cc
Jared Duke 6be604aaac Reland (Attempt ) PR : [TFLite int16] 16-bit version of ADD/SUB reference kernel operators 
Imported from GitHub PR https://github.com/tensorflow/tensorflow/pull/35985

This PR is one of steps to extend 8-bit quantization to support symmetric 16-bit activations.

Each activation is of type int16 and symmetric around zero. The weight tensor precision remains at 8-bit signed values. The bias is set to int64 precision.

In this PR we introduce implementation and tests for ADD/SUB kernel reference function.
The specification of this operator:

SUB
  Input 0:
    data_type  : int16
    range      : [-32768, 32767]
    granularity: per-tensor, zero_point=0
  Input 1:
    data_type  : int16
    range      : [-32768, 32767]
    granularity: per-tensor, zero_point=0
  Output 0:
    data_type  : int16
    range      : [-32768, 32767]
    granularity: per-tensor, zero_point=0

ADD
  Input 0:
    data_type  : int16
    range      : [-32768, 32767]
    granularity: per-tensor, zero_point=0
  Input 1:
    data_type  : int16
    range      : [-32768, 32767]
    granularity: per-tensor, zero_point=0
  Output 0:
    data_type  : int16
    range      : [-32768, 32767]
    granularity: per-tensor, zero_point=0
Copybara import of the project:

--
b94cb4732a by Elena Zhelezina <elena.zhelezina@arm.com>:

Added 16-bit version of ADD/SUB operators. Broadcasting is included.

--
924d0b72c5 by Elena Zhelezina <elena.zhelezina@arm.com>:

Addressed reviewer comments.

--
dd0d9e8f03 by Elena Zhelezina <elena.zhelezina@arm.com>:

Added versioning to ADD/SUB + some rework of the existing code.

--
abae3fd9a9 by Elena Zhelezina <elena.zhelezina@arm.com>:

Added versioning for ADD/SUB with new option in the schema.fbs
schema_generated.h is edited manually.

--
24f3f5593a by Elena Zhelezina <elena.zhelezina@arm.com>:

Fix for broken build.

--
d252fe175a by Elena Zhelezina <elena.zhelezina@arm.com>:

Fix for the failing internal test for NN delegates.

--
2223a5c380 by Elena Zhelezina <elena.zhelezina@arm.com>:

Fix for asan failures.

Change-Id: I2cf421ddda7f9e802202239136ab062bcd63b4aa

--
3c219a46ce by Elena Zhelezina <elena.zhelezina@arm.com>:

Added broadcast params to addsub structure.

Change-Id: I61d7d4a94087d052a782890799211031f6ed3015

--
9131a38c77 by Elena Zhelezina <elena.zhelezina@arm.com>:

Corrected defaults.

Change-Id: I9ea50c75014cc03ac91fdef0f5b4fe11395f7074
PiperOrigin-RevId: 324865496
2020-08-04 12:31:25 -07:00

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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 <stdint.h>
#include <limits>
#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 BaseSubOpModel : public SingleOpModel {
public:
BaseSubOpModel(const TensorData& input1, const TensorData& input2,
const TensorData& output,
ActivationFunctionType activation_type) {
input1_ = AddInput(input1);
input2_ = AddInput(input2);
output_ = AddOutput(output);
SetBuiltinOp(BuiltinOperator_SUB, BuiltinOptions_SubOptions,
CreateSubOptions(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 FloatSubOpModel : public BaseSubOpModel {
public:
using BaseSubOpModel::BaseSubOpModel;
std::vector<float> GetOutput() { return ExtractVector<float>(output_); }
};
class IntegerSubOpModel : public BaseSubOpModel {
public:
using BaseSubOpModel::BaseSubOpModel;
std::vector<int32_t> GetOutput() { return ExtractVector<int32_t>(output_); }
};
class Int64SubOpModel : public BaseSubOpModel {
public:
using BaseSubOpModel::BaseSubOpModel;
std::vector<int64_t> GetOutput() { return ExtractVector<int64_t>(output_); }
};
class QuantizedSubOpModel : public BaseSubOpModel {
public:
using BaseSubOpModel::BaseSubOpModel;
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_));
}
};
// for quantized Sub, the error shouldn't exceed step
float GetTolerance(int min, int max) {
float kQuantizedStep = (max - min) / 255.0;
return kQuantizedStep;
}
float GetToleranceInt16(float min, float max) {
float kQuantizedStep = (max - min) / std::numeric_limits<int16_t>::max();
return kQuantizedStep;
}
TEST(FloatSubOpModel, NoActivation) {
FloatSubOpModel 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, 1.7, 0.5});
m.PopulateTensor<float>(m.input2(), {0.1, 0.2, 0.3, 0.8});
m.Invoke();
EXPECT_THAT(m.GetOutput(),
ElementsAreArray(ArrayFloatNear({-2.1, 0.0, 1.4, -0.3})));
}
TEST(FloatSubOpModel, ActivationRELU_N1_TO_1) {
FloatSubOpModel 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, 1.7, 0.5});
m.PopulateTensor<float>(m.input2(), {0.1, 0.2, 0.3, 0.8});
m.Invoke();
EXPECT_THAT(m.GetOutput(),
ElementsAreArray(ArrayFloatNear({-1.0, 0.0, 1.0, -0.3})));
}
TEST(FloatSubOpModel, VariousInputShapes) {
std::vector<std::vector<int>> test_shapes = {
{6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}};
for (int i = 0; i < test_shapes.size(); ++i) {
FloatSubOpModel m({TensorType_FLOAT32, test_shapes[i]},
{TensorType_FLOAT32, test_shapes[i]},
{TensorType_FLOAT32, {}}, ActivationFunctionType_NONE);
m.PopulateTensor<float>(m.input1(), {-2.0, 0.2, 1.7, 0.5, -1.1, 2.0});
m.PopulateTensor<float>(m.input2(), {0.1, 0.2, 0.3, 0.8, -1.1, 0.1});
m.Invoke();
EXPECT_THAT(
m.GetOutput(),
ElementsAreArray(ArrayFloatNear({-2.1, 0.0, 1.4, -0.3, 0.0, 1.9})))
<< "With shape number " << i;
}
}
TEST(FloatSubOpModel, WithBroadcast) {
std::vector<std::vector<int>> test_shapes = {
{6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}};
for (int i = 0; i < test_shapes.size(); ++i) {
FloatSubOpModel m({TensorType_FLOAT32, test_shapes[i]},
{TensorType_FLOAT32, {}}, // always a scalar
{TensorType_FLOAT32, {}}, ActivationFunctionType_NONE);
m.PopulateTensor<float>(m.input1(), {-2.0, 0.2, 1.7, 0.5, -1.1, 2.0});
m.PopulateTensor<float>(m.input2(), {0.5});
m.Invoke();
EXPECT_THAT(
m.GetOutput(),
ElementsAreArray(ArrayFloatNear({-2.5, -0.3, 1.2, 0.0, -1.6, 1.5})))
<< "With shape number " << i;
}
}
TEST(FloatSubOpModel, WithBroadcast5D) {
std::vector<std::vector<int>> test_shapes = {{1, 3, 1, 2, 1}};
for (int i = 0; i < test_shapes.size(); ++i) {
FloatSubOpModel m({TensorType_FLOAT32, test_shapes[i]},
{TensorType_FLOAT32, {}}, // always a scalar
{TensorType_FLOAT32, {}}, ActivationFunctionType_NONE);
m.PopulateTensor<float>(m.input1(), {-2.0, 0.2, 1.7, 0.5, -1.1, 2.0});
m.PopulateTensor<float>(m.input2(), {0.5});
m.Invoke();
EXPECT_THAT(
m.GetOutput(),
ElementsAreArray(ArrayFloatNear({-2.5, -0.3, 1.2, 0.0, -1.6, 1.5})))
<< "With shape number " << i;
}
}
TEST(IntegerSubOpModel, NoActivation) {
IntegerSubOpModel 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({-21, 0, 4, 3}));
}
TEST(IntegerSubOpModel, ActivationRELU_N1_TO_1) {
IntegerSubOpModel 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, 0, 1, 1}));
}
TEST(IntegerSubOpModel, VariousInputShapes) {
std::vector<std::vector<int>> test_shapes = {
{6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}};
for (int i = 0; i < test_shapes.size(); ++i) {
IntegerSubOpModel 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({-21, 0, 4, 3, 0, 19}))
<< "With shape number " << i;
}
}
TEST(IntegerSubOpModel, WithBroadcast) {
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) {
IntegerSubOpModel 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({-21, 1, 6, 7, 10, 19})))
<< "With shape number " << i;
}
}
TEST(Int64SubOpModel, NoActivation) {
Int64SubOpModel m({TensorType_INT64, {1, 2, 2, 1}},
{TensorType_INT64, {1, 2, 2, 1}}, {TensorType_INT64, {}},
ActivationFunctionType_NONE);
m.PopulateTensor<int64_t>(m.input1(), {-20, 2, 7, 8});
m.PopulateTensor<int64_t>(m.input2(), {1, 2, 3, 5});
m.Invoke();
EXPECT_THAT(m.GetOutput(), ElementsAreArray({-21, 0, 4, 3}));
}
TEST(Int64SubOpModel, ActivationRELU_N1_TO_1) {
Int64SubOpModel m({TensorType_INT64, {1, 2, 2, 1}},
{TensorType_INT64, {1, 2, 2, 1}}, {TensorType_INT64, {}},
ActivationFunctionType_RELU_N1_TO_1);
m.PopulateTensor<int64_t>(m.input1(), {-20, 2, 7, 8});
m.PopulateTensor<int64_t>(m.input2(), {1, 2, 3, 5});
m.Invoke();
EXPECT_THAT(m.GetOutput(), ElementsAreArray({-1, 0, 1, 1}));
}
TEST(Int64SubOpModel, VariousInputShapes) {
std::vector<std::vector<int>> test_shapes = {
{6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}};
for (int i = 0; i < test_shapes.size(); ++i) {
Int64SubOpModel m({TensorType_INT64, test_shapes[i]},
{TensorType_INT64, test_shapes[i]},
{TensorType_INT64, {}}, ActivationFunctionType_NONE);
m.PopulateTensor<int64_t>(m.input1(), {-20, 2, 7, 8, 11, 20});
m.PopulateTensor<int64_t>(m.input2(), {1, 2, 3, 5, 11, 1});
m.Invoke();
EXPECT_THAT(m.GetOutput(), ElementsAreArray({-21, 0, 4, 3, 0, 19}))
<< "With shape number " << i;
}
}
TEST(Int64SubOpModel, WithBroadcast) {
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) {
Int64SubOpModel m({TensorType_INT64, test_shapes[i]},
{TensorType_INT64, {}}, // always a scalar
{TensorType_INT64, {}}, ActivationFunctionType_NONE);
m.PopulateTensor<int64_t>(m.input1(), {-20, 2, 7, 8, 11, 20});
m.PopulateTensor<int64_t>(m.input2(), {1});
m.Invoke();
EXPECT_THAT(m.GetOutput(),
ElementsAreArray(ArrayFloatNear({-21, 1, 6, 7, 10, 19})))
<< "With shape number " << i;
}
}
template <TensorType tensor_type, typename integer_dtype>
void QuantizedTestsNoActivation() {
float kQuantizedTolerance = GetTolerance(-1.0, 1.0);
std::vector<std::vector<float>> inputs1 = {
{0.1, 0.2, 0.3, 0.4}, {-0.2, 0.2, 0.4, 0.7}, {-0.01, 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, -0.2}, {0.6, 0.4, -0.18, 0.5}};
std::vector<std::vector<float>> results = {{-0.5, -0.2, 0.0, 0.3},
{-0.8, -0.2, -0.1, 0.9},
{-0.61, -0.2, 0.88, -0.2}};
for (int i = 0; i < inputs1.size(); ++i) {
QuantizedSubOpModel 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(QuantizedSubOpModel, QuantizedTestsNoActivationUInt8) {
QuantizedTestsNoActivation<TensorType_UINT8, uint8_t>();
}
TEST(QuantizedSubOpModel, QuantizedTestsNoActivationInt8) {
QuantizedTestsNoActivation<TensorType_INT8, int8_t>();
}
TEST(QuantizedSubOpModel, QuantizedTestsNoActivationGenericInt16) {
QuantizedTestsNoActivation<TensorType_INT16, int16_t>();
}
template <TensorType tensor_type, typename integer_dtype>
void QuantizedTestsActivationRELU_N1_TO_1() {
float kQuantizedTolerance = GetTolerance(-1.0, 1.0);
std::vector<std::vector<float>> inputs1 = {{-0.8, 0.2, 0.9, 0.7},
{-0.8, 0.2, 0.7, 0.5}};
std::vector<std::vector<float>> inputs2 = {{0.6, 0.4, 0.9, -0.8},
{0.6, 0.4, -0.8, 0.3}};
std::vector<std::vector<float>> results = {{-1.0, -0.2, 0.0, 1.0},
{-1.0, -0.2, 1.0, 0.2}};
for (int i = 0; i < inputs1.size(); ++i) {
QuantizedSubOpModel 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(QuantizedSubOpModel, QuantizedTestsActivationRELUN1TO1UInt8) {
QuantizedTestsActivationRELU_N1_TO_1<TensorType_UINT8, uint8_t>();
}
TEST(QuantizedSubOpModel, QuantizedTestsActivationRELUN1TO1Int8) {
QuantizedTestsActivationRELU_N1_TO_1<TensorType_INT8, int8_t>();
}
template <TensorType tensor_type, typename integer_dtype>
void QuantizedVariousInputShapes() {
float kQuantizedTolerance = GetTolerance(-3.0, 3.0);
std::vector<std::vector<int>> test_shapes = {
{6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}};
for (int i = 0; i < test_shapes.size(); ++i) {
QuantizedSubOpModel 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(
{-2.1, -0.1, 0.4, 0.3, 0.0, 1.9}, kQuantizedTolerance)))
<< "With shape number " << i;
}
}
TEST(QuantizedSubOpModel, QuantizedVariousInputShapesUInt8) {
QuantizedVariousInputShapes<TensorType_UINT8, uint8_t>();
}
TEST(QuantizedSubOpModel, QuantizedVariousInputShapesInt8) {
QuantizedVariousInputShapes<TensorType_INT8, int8_t>();
}
TEST(QuantizedSubOpModel, QuantizedVariousInputShapesInt16) {
QuantizedVariousInputShapes<TensorType_INT16, int16_t>();
}
template <TensorType tensor_type, typename integer_dtype>
void QuantizedWithBroadcast() {
float kQuantizedTolerance = GetTolerance(-3.0, 3.0);
std::vector<std::vector<int>> test_shapes = {
{6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}};
for (int i = 0; i < test_shapes.size(); ++i) {
QuantizedSubOpModel m(
{tensor_type, test_shapes[i], -3.0, 3.0}, {tensor_type, {}, -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(), {0.7});
m.Invoke();
EXPECT_THAT(m.GetDequantizedOutput<integer_dtype>(),
ElementsAreArray(ArrayFloatNear(
{-2.7, -0.5, 0.0, 0.1, 0.4, 1.3}, kQuantizedTolerance)))
<< "With shape number " << i;
}
}
TEST(QuantizedSubOpModel, QuantizedWithBroadcastUInt8) {
QuantizedWithBroadcast<TensorType_UINT8, uint8_t>();
}
TEST(QuantizedSubOpModel, QuantizedWithBroadcastInt8) {
QuantizedWithBroadcast<TensorType_INT8, int8_t>();
}
TEST(QuantizedSubOpModel, QuantizedWithBroadcastInt16) {
QuantizedWithBroadcast<TensorType_INT16, int16_t>();
}
TEST(QuantizedSubOpModel, QuantizedTestsNoActivationInt16) {
const float kMin = -1.f;
const float kMax =
static_cast<float>(std::numeric_limits<int16_t>::max() - 1) /
std::numeric_limits<int16_t>::max();
float kQuantizedTolerance = GetToleranceInt16(kMin, kMax);
std::vector<std::vector<float>> inputs1 = {
{0.7, 0.6, 0.6, 0.5}, {-0.2, 0.6, 0.9, -0.1}, {-0.2, 0.6, -0.3, 0.8}};
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 = {
{0.1, 0.2, 0.3, 0.4}, {-0.8, 0.2, 0.4, 0.7}, {-0.8, 0.2, -1.0, 0.3}};
for (int i = 0; i < inputs1.size(); ++i) {
QuantizedSubOpModel m({TensorType_INT16, {1, 2, 2, 1}, kMin, kMax},
{TensorType_INT16, {1, 2, 2, 1}, kMin, kMax},
{TensorType_INT16, {}, kMin, kMax},
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;
}
}
TEST(QuantizedSubOpModel, QuantizedTestsReluActivationInt16) {
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<float>> inputs1 = {{-0.8, 0.2, 0.9, 0.7},
{-0.8, 0.2, 0.7, 0.5}};
std::vector<std::vector<float>> inputs2 = {{0.6, 0.4, 0.9, -0.8},
{0.6, 0.4, -0.8, 0.3}};
std::vector<std::vector<float>> results = {{-1.0, -0.2, 0.0, 1.0},
{-1.0, -0.2, 1.0, 0.2}};
for (int i = 0; i < inputs1.size(); ++i) {
QuantizedSubOpModel m({TensorType_INT16, {1, 2, 2, 1}, kMin, kMax},
{TensorType_INT16, {1, 2, 2, 1}, kMin, kMax},
{TensorType_INT16, {}, kMin, kMax},
ActivationFunctionType_RELU_N1_TO_1);
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;
}
}
TEST(QuantizedSubOpModel, QuantizedTestsNoActivationBroadcastInt16) {
const float kMin = -1.f;
const float kMax =
static_cast<float>(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_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
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