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micro: copy operator DIV kernel from lite

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This is a copy without modification of the kernel and test for
operator DIV from tensorflow/lite/kernels.
Adaptations to micro and addition to the micro build to follow.

This represents PR step 3 of the work to port operator DIV as tracked in Issue #45431
This commit is contained in:
David Davis 2020-12-09 16:28:51 -08:00 committed by ddavis-2015
parent 635e8a0749
commit 8d8343910c
2 changed files with 576 additions and 0 deletions
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266
tensorflow/lite/micro/kernels/div.cc Normal file
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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 "tensorflow/lite/c/builtin_op_data.h"
#include "tensorflow/lite/c/common.h"
#include "tensorflow/lite/kernels/internal/compatibility.h"
#include "tensorflow/lite/kernels/internal/optimized/cpu_check.h"
#include "tensorflow/lite/kernels/internal/optimized/neon_check.h"
#include "tensorflow/lite/kernels/internal/optimized/optimized_ops.h"
#include "tensorflow/lite/kernels/internal/quantization_util.h"
#include "tensorflow/lite/kernels/internal/reference/process_broadcast_shapes.h"
#include "tensorflow/lite/kernels/internal/reference/reference_ops.h"
#include "tensorflow/lite/kernels/internal/tensor.h"
#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
#include "tensorflow/lite/kernels/internal/types.h"
#include "tensorflow/lite/kernels/kernel_util.h"
namespace tflite {
namespace ops {
namespace builtin {
namespace div {
// This file has three implementation of Div.
enum KernelType {
kReference,
kGenericOptimized, // Neon-free
kNeonOptimized,
};
constexpr int kInputTensor1 = 0;
constexpr int kInputTensor2 = 1;
constexpr int kOutputTensor = 0;
struct OpData {
bool requires_broadcast;
// Parameters used in the quantized paths where the output is 8bit
int32 output_activation_min;
int32 output_activation_max;
// Parameters used in all quantized paths
int32_t output_multiplier;
int output_shift;
};
void* Init(TfLiteContext* context, const char* buffer, size_t length) {
auto* data = new OpData;
data->requires_broadcast = false;
return data;
}
void Free(TfLiteContext* context, void* buffer) {
delete reinterpret_cast<OpData*>(buffer);
}
TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
auto* params = reinterpret_cast<TfLiteDivParams*>(node->builtin_data);
OpData* data = reinterpret_cast<OpData*>(node->user_data);
TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
const TfLiteTensor* input1;
TF_LITE_ENSURE_OK(context,
GetInputSafe(context, node, kInputTensor1, &input1));
const TfLiteTensor* input2;
TF_LITE_ENSURE_OK(context,
GetInputSafe(context, node, kInputTensor2, &input2));
TfLiteTensor* output;
TF_LITE_ENSURE_OK(context,
GetOutputSafe(context, node, kOutputTensor, &output));
TF_LITE_ENSURE_TYPES_EQ(context, input1->type, input2->type);
output->type = input2->type;
data->requires_broadcast = !HaveSameShapes(input1, input2);
TfLiteIntArray* output_size = nullptr;
if (data->requires_broadcast) {
TF_LITE_ENSURE_OK(context, CalculateShapeForBroadcast(
context, input1, input2, &output_size));
} else {
output_size = TfLiteIntArrayCopy(input1->dims);
}
if (output->type == kTfLiteUInt8) {
TF_LITE_ENSURE_STATUS(CalculateActivationRangeQuantized(
context, params->activation, output, &data->output_activation_min,
&data->output_activation_max));
const double real_multiplier =
input1->params.scale / (input2->params.scale * output->params.scale);
QuantizeMultiplier(real_multiplier, &data->output_multiplier,
&data->output_shift);
}
return context->ResizeTensor(context, output, output_size);
}
template <KernelType kernel_type>
void EvalDiv(TfLiteContext* context, TfLiteNode* node, TfLiteDivParams* params,
const OpData* data, const TfLiteTensor* input1,
const TfLiteTensor* input2, TfLiteTensor* output) {
#define TF_LITE_DIV(type, opname, data_type) \
tflite::ArithmeticParams op_params; \
data_type output_activation_min, output_activation_max; \
CalculateActivationRange(params->activation, &output_activation_min, \
&output_activation_max); \
SetActivationParams(output_activation_min, output_activation_max, \
&op_params); \
type::opname(op_params, GetTensorShape(input1), \
GetTensorData<data_type>(input1), GetTensorShape(input2), \
GetTensorData<data_type>(input2), GetTensorShape(output), \
GetTensorData<data_type>(output))
if (output->type == kTfLiteInt32) {
if (kernel_type == kReference) {
if (data->requires_broadcast) {
TF_LITE_DIV(reference_ops, BroadcastDivSlow, int32_t);
} else {
TF_LITE_DIV(reference_ops, Div, int32_t);
}
} else {
if (data->requires_broadcast) {
TF_LITE_DIV(optimized_ops, BroadcastDivSlow, int32_t);
} else {
TF_LITE_DIV(optimized_ops, Div, int32_t);
}
}
} else if (output->type == kTfLiteFloat32) {
if (kernel_type == kReference) {
if (data->requires_broadcast) {
TF_LITE_DIV(reference_ops, BroadcastDivSlow, float);
} else {
TF_LITE_DIV(reference_ops, Div, float);
}
} else {
if (data->requires_broadcast) {
TF_LITE_DIV(optimized_ops, BroadcastDivSlow, float);
} else {
TF_LITE_DIV(optimized_ops, Div, float);
}
}
}
#undef TF_LITE_DIV
}
template <KernelType kernel_type>
TfLiteStatus EvalQuantized(TfLiteContext* context, TfLiteNode* node,
TfLiteDivParams* params, const OpData* data,
const TfLiteTensor* input1,
const TfLiteTensor* input2, TfLiteTensor* output) {
if (input1->type == kTfLiteUInt8 && input2->type == kTfLiteUInt8 &&
output->type == kTfLiteUInt8) {
tflite::ArithmeticParams op_params;
SetActivationParams(data->output_activation_min,
data->output_activation_max, &op_params);
op_params.input1_offset = -input1->params.zero_point;
op_params.input2_offset = -input2->params.zero_point;
op_params.output_offset = output->params.zero_point;
op_params.output_multiplier = data->output_multiplier;
op_params.output_shift = data->output_shift;
bool need_broadcast = optimized_ops::ProcessBroadcastShapes(
GetTensorShape(input1), GetTensorShape(input2), &op_params);
#define TF_LITE_DIV(type, opname, dtype) \
type::opname(op_params, GetTensorShape(input1), \
GetTensorData<dtype>(input1), GetTensorShape(input2), \
GetTensorData<dtype>(input2), GetTensorShape(output), \
GetTensorData<dtype>(output))
if (kernel_type == kReference) {
if (need_broadcast) {
TF_LITE_DIV(reference_ops, BroadcastDivSlow, uint8_t);
} else {
TF_LITE_DIV(reference_ops, Div, uint8_t);
}
} else {
if (need_broadcast) {
TF_LITE_DIV(optimized_ops, BroadcastDivSlow, uint8_t);
} else {
TF_LITE_DIV(optimized_ops, Div, uint8_t);
}
}
#undef TF_LITE_DIV
} else {
TF_LITE_KERNEL_LOG(
context, "Unsupported combination of input and output types in Div.");
return kTfLiteError;
}
return kTfLiteOk;
}
template <KernelType kernel_type>
TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
auto* params = reinterpret_cast<TfLiteDivParams*>(node->builtin_data);
OpData* data = reinterpret_cast<OpData*>(node->user_data);
const TfLiteTensor* input1;
TF_LITE_ENSURE_OK(context,
GetInputSafe(context, node, kInputTensor1, &input1));
const TfLiteTensor* input2;
TF_LITE_ENSURE_OK(context,
GetInputSafe(context, node, kInputTensor2, &input2));
TfLiteTensor* output;
TF_LITE_ENSURE_OK(context,
GetOutputSafe(context, node, kOutputTensor, &output));
if (output->type == kTfLiteFloat32 || output->type == kTfLiteInt32) {
EvalDiv<kernel_type>(context, node, params, data, input1, input2, output);
} else if (output->type == kTfLiteUInt8) {
TF_LITE_ENSURE_OK(
context, EvalQuantized<kernel_type>(context, node, params, data, input1,
input2, output));
} else {
TF_LITE_KERNEL_LOG(
context,
"Div only supports FLOAT32, INT32 and quantized UINT8 now, got %d.",
output->type);
return kTfLiteError;
}
return kTfLiteOk;
}
} // namespace div
TfLiteRegistration* Register_DIV_REF() {
static TfLiteRegistration r = {div::Init, div::Free, div::Prepare,
div::Eval<div::kReference>};
return &r;
}
TfLiteRegistration* Register_DIV_GENERIC_OPT() {
static TfLiteRegistration r = {div::Init, div::Free, div::Prepare,
div::Eval<div::kGenericOptimized>};
return &r;
}
TfLiteRegistration* Register_DIV_NEON_OPT() {
static TfLiteRegistration r = {div::Init, div::Free, div::Prepare,
div::Eval<div::kNeonOptimized>};
return &r;
}
TfLiteRegistration* Register_DIV() {
#ifdef USE_NEON
return Register_DIV_NEON_OPT();
#else
return Register_DIV_GENERIC_OPT();
#endif
}
} // namespace builtin
} // namespace ops
} // namespace tflite

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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 <gmock/gmock.h>
#include <gtest/gtest.h>
#include <stdint.h>
#include <vector>
#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 BaseDivOpModel : public SingleOpModel {
public:
BaseDivOpModel(const TensorData& input1, const TensorData& input2,
const TensorData& output,
ActivationFunctionType activation_type) {
input1_ = AddInput(input1);
input2_ = AddInput(input2);
output_ = AddOutput(output);
SetBuiltinOp(BuiltinOperator_DIV, BuiltinOptions_DivOptions,
CreateDivOptions(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 FloatDivOpModel : public BaseDivOpModel {
public:
using BaseDivOpModel::BaseDivOpModel;
std::vector<float> GetOutput() { return ExtractVector<float>(output_); }
};
class IntegerDivOpModel : public BaseDivOpModel {
public:
using BaseDivOpModel::BaseDivOpModel;
std::vector<int32_t> GetOutput() { return ExtractVector<int32_t>(output_); }
};
class QuantizedDivOpModel : public BaseDivOpModel {
public:
using BaseDivOpModel::BaseDivOpModel;
template <typename integer_dtype>
std::vector<float> GetDequantizedOutput() {
return Dequantize<integer_dtype>(ExtractVector<integer_dtype>(output_),
GetScale(output_), GetZeroPoint(output_));
}
};
// For quantized Div, the error shouldn't exceed (2*step + step^2).
inline float GetTolerance(int min, int max) {
const float kQuantizedStep = (max - min) / 255.0f;
const float kQuantizedTolerance =
2.0f * kQuantizedStep + kQuantizedStep * kQuantizedStep;
return kQuantizedTolerance;
}
TEST(FloatDivOpTest, NoActivation) {
FloatDivOpModel m({TensorType_FLOAT32, {1, 2, 2, 1}},
{TensorType_FLOAT32, {1, 2, 2, 1}},
{TensorType_FLOAT32, {}}, ActivationFunctionType_NONE);
m.PopulateTensor<float>(m.input1(), {-0.2, 0.2, -1.2, 0.8});
m.PopulateTensor<float>(m.input2(), {0.5, 0.2, -1.5, 0.5});
m.Invoke();
EXPECT_THAT(m.GetOutput(),
ElementsAreArray(ArrayFloatNear({-0.4, 1.0, 0.8, 1.6})));
}
TEST(FloatDivOpTest, ActivationRELU_N1_TO_1) {
FloatDivOpModel 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(), {-0.2, 0.2, -1.2, 0.8});
m.PopulateTensor<float>(m.input2(), {0.1, 0.2, -1.5, 0.5});
m.Invoke();
EXPECT_THAT(m.GetOutput(),
ElementsAreArray(ArrayFloatNear({-1.0, 1.0, 0.8, 1.0})));
}
TEST(FloatDivOpTest, 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) {
FloatDivOpModel m({TensorType_FLOAT32, test_shapes[i]},
{TensorType_FLOAT32, test_shapes[i]},
{TensorType_FLOAT32, {}}, ActivationFunctionType_NONE);
m.PopulateTensor<float>(m.input1(), {-2.0, 0.2, 0.3, 0.8, 1.1, -2.0});
m.PopulateTensor<float>(m.input2(), {0.1, 0.2, 0.6, 0.5, -1.1, -0.1});
m.Invoke();
EXPECT_THAT(
m.GetOutput(),
ElementsAreArray(ArrayFloatNear({-20.0, 1.0, 0.5, 1.6, -1.0, 20.0})))
<< "With shape number " << i;
}
}
TEST(FloatDivOpTest, WithBroadcast) {
std::vector<std::vector<int>> test_shapes = {
{8}, {2, 4}, {2, 1, 4}, {1, 2, 2, 2}};
for (int i = 0; i < test_shapes.size(); ++i) {
FloatDivOpModel m({TensorType_FLOAT32, test_shapes[i]},
{TensorType_FLOAT32, {}}, // always a scalar
{TensorType_FLOAT32, {}}, ActivationFunctionType_NONE);
m.PopulateTensor<float>(m.input1(),
{-0.2, 0.2, 0.07, 0.08, 0.11, -0.123, -0.32, 0.54});
m.PopulateTensor<float>(m.input2(), {0.1});
m.Invoke();
EXPECT_THAT(m.GetOutput(),
ElementsAreArray(ArrayFloatNear(
{-2.0, 2.0, 0.7, 0.8, 1.1, -1.23, -3.2, 5.4})))
<< "With shape number " << i;
}
}
TEST(FloatDivOpTest, WithBroadcast5D) {
std::vector<std::vector<int>> test_shapes = {{1, 2, 1, 2, 2}};
for (int i = 0; i < test_shapes.size(); ++i) {
FloatDivOpModel m({TensorType_FLOAT32, test_shapes[i]},
{TensorType_FLOAT32, {}}, // always a scalar
{TensorType_FLOAT32, {}}, ActivationFunctionType_NONE);
m.PopulateTensor<float>(m.input1(),
{-0.2, 0.2, 0.07, 0.08, 0.11, -0.123, -0.32, 0.54});
m.PopulateTensor<float>(m.input2(), {0.1});
m.Invoke();
EXPECT_THAT(m.GetOutput(),
ElementsAreArray(ArrayFloatNear(
{-2.0, 2.0, 0.7, 0.8, 1.1, -1.23, -3.2, 5.4})))
<< "With shape number " << i;
}
}
TEST(IntegerDivOpTest, NoActivation) {
IntegerDivOpModel m({TensorType_INT32, {1, 2, 2, 1}},
{TensorType_INT32, {1, 2, 2, 1}}, {TensorType_INT32, {}},
ActivationFunctionType_NONE);
m.PopulateTensor<int32_t>(m.input1(), {-2, 2, -15, 8});
m.PopulateTensor<int32_t>(m.input2(), {5, -2, -3, 5});
m.Invoke();
EXPECT_THAT(m.GetOutput(), ElementsAreArray({0, -1, 5, 1}));
}
TEST(IntegerDivOpTest, ActivationRELU_N1_TO_1) {
IntegerDivOpModel 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(), {-2, 2, -12, 8});
m.PopulateTensor<int32_t>(m.input2(), {1, 2, -15, 5});
m.Invoke();
EXPECT_THAT(m.GetOutput(), ElementsAreArray({-1, 1, 0, 1}));
}
TEST(IntegerDivOpTest, 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) {
IntegerDivOpModel m({TensorType_INT32, test_shapes[i]},
{TensorType_INT32, test_shapes[i]},
{TensorType_INT32, {}}, ActivationFunctionType_NONE);
m.PopulateTensor<int32_t>(m.input1(), {-20, 2, 3, 8, 11, -20});
m.PopulateTensor<int32_t>(m.input2(), {1, 2, 6, 5, -11, -1});
m.Invoke();
EXPECT_THAT(m.GetOutput(), ElementsAreArray({-20, 1, 0, 1, -1, 20}))
<< "With shape number " << i;
}
}
TEST(IntegerDivOpTest, WithBroadcast) {
std::vector<std::vector<int>> test_shapes = {
{8}, {2, 4}, {2, 1, 4}, {1, 4, 1, 2}, {1, 2, 1, 2, 2}};
for (int i = 0; i < test_shapes.size(); ++i) {
IntegerDivOpModel m({TensorType_INT32, test_shapes[i]},
{TensorType_INT32, {}}, // always a scalar
{TensorType_INT32, {}}, ActivationFunctionType_NONE);
m.PopulateTensor<int32_t>(m.input1(), {-20, 21, 7, 8, 11, -123, -42, -48});
m.PopulateTensor<int32_t>(m.input2(), {3});
m.Invoke();
EXPECT_THAT(m.GetOutput(),
ElementsAreArray({-6, 7, 2, 2, 3, -41, -14, -16}))
<< "With shape number " << i;
}
}
template <TensorType tensor_type, typename integer_dtype>
void QuantizedNoActivation() {
const float kQuantizedTolerance = GetTolerance(-1.0, 1.0);
QuantizedDivOpModel 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(), {-0.8, -0.2, 0.3, 0.7});
m.QuantizeAndPopulate<integer_dtype>(m.input2(), {-0.8, 0.4, 0.8, 1.0});
m.Invoke();
EXPECT_THAT(m.GetDequantizedOutput<integer_dtype>(),
ElementsAreArray(ArrayFloatNear({1.0, -0.5, 0.375, 0.7},
kQuantizedTolerance)));
}
TEST(QuantizedDivOpTest, QuantizedNoActivationUInt8) {
QuantizedNoActivation<TensorType_UINT8, uint8_t>();
}
template <TensorType tensor_type, typename integer_dtype>
void QuantizedActivationRELU_N1_TO_1() {
const float kQuantizedTolerance = GetTolerance(-1.0, 1.0);
const std::vector<std::vector<float>> inputs1 = {{-0.8, 0.2, 0.9, 0.7},
{-0.5, 0.2, 0.6, 0.3}};
const std::vector<std::vector<float>> inputs2 = {{0.6, 0.4, 0.9, -0.8},
{0.6, 0.5, -0.8, 0.5}};
const std::vector<std::vector<float>> results = {{-1.0, 0.5, 1.0, -0.875},
{-0.833, 0.4, -0.75, 0.6}};
for (int i = 0; i < inputs1.size(); ++i) {
QuantizedDivOpModel 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(QuantizedDivOpTest, QuantizedActivationRELU_N1_TO_1UInt8) {
QuantizedActivationRELU_N1_TO_1<TensorType_UINT8, uint8_t>();
}
template <TensorType tensor_type, typename integer_dtype>
void QuantizedVariousInputShapes() {
const float kQuantizedTolerance = GetTolerance(-3.0, 3.0);
const 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) {
QuantizedDivOpModel 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, 1.7, 0.9, 0.4, 2.0});
m.QuantizeAndPopulate<integer_dtype>(m.input2(),
{1.3, 0.3, 1.1, 0.4, -1.1, 1.9});
m.Invoke();
EXPECT_THAT(
m.GetDequantizedOutput<integer_dtype>(),
ElementsAreArray(ArrayFloatNear(
{-1.538, 0.667, 1.545, 2.25, -0.364, 1.053}, kQuantizedTolerance)))
<< "With shape number " << i;
}
}
TEST(QuantizedDivOpTest, QuantizedVariousInputShapesUInt8) {
QuantizedVariousInputShapes<TensorType_UINT8, uint8_t>();
}
template <TensorType tensor_type, typename integer_dtype>
void QuantizedWithBroadcast() {
const float kQuantizedTolerance = GetTolerance(-3.0, 3.0);
const std::vector<std::vector<int>> test_shapes = {
{8}, {2, 4}, {2, 1, 4}, {1, 4, 1, 2}, {1, 2, 1, 2, 2}};
for (int i = 0; i < test_shapes.size(); ++i) {
QuantizedDivOpModel 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, -0.5, 1.1, -1.3, 1.2});
m.QuantizeAndPopulate<integer_dtype>(m.input2(), {0.7});
m.Invoke();
EXPECT_THAT(m.GetDequantizedOutput<integer_dtype>(),
ElementsAreArray(ArrayFloatNear(
{-2.857, 0.286, 1.0, 1.143, -0.714, 1.571, -1.857, 1.714},
kQuantizedTolerance)))
<< "With shape number " << i;
}
}
TEST(QuantizedDivOpTest, QuantizedWithBroadcastUInt8) {
QuantizedWithBroadcast<TensorType_UINT8, uint8_t>();
}
} // namespace
} // namespace tflite