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Tflite Micro

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未验证 提交 ca7ed66b 编写于 作者: C cad-audio 提交者： GitHub
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REF_CODE_REFACTOR: activations.cc (#299) 

* REF_CODE_REFACTOR: activations.cc

Refactoring the reference code for activations operators.

BUG=refactoring existing code.

* Remove uint8 support.

* Ran `test_code_style.sh --fix_formatting`

* Fix the build.
Co-authored-by: NAdvait Jain <advaitjain@users.noreply.github.com>
Co-authored-by: NAdvait Jain <advaitjain@google.com>
上级 17546b54
隐藏空白更改
内联 并排
Showing with 246 addition and 314 deletion
tensorflow/lite/micro/kernels/BUILD
浏览文件 @ ca7ed66b
......@@ -112,6 +112,7 @@ cc_library(
name = "micro_ops",
srcs = [
"activations.cc",
"activations_common.cc",
"add.cc",
"add_n.cc",
"arg_min_max.cc",
......@@ -184,6 +185,7 @@ cc_library(
"zeros_like.cc",
],
hdrs = [
"activations.h",
"conv.h",
"depthwise_conv.h",
"ethosu.h",
......
tensorflow/lite/micro/kernels/activations.cc
浏览文件 @ ca7ed66b
/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
/* Copyright 2021 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.
......@@ -13,6 +13,8 @@ See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
#include "tensorflow/lite/micro/kernels/activations.h"
#include "tensorflow/lite/c/builtin_op_data.h"
#include "tensorflow/lite/c/common.h"
#include "tensorflow/lite/kernels/internal/common.h"
......@@ -25,141 +27,21 @@ limitations under the License.
#include "tensorflow/lite/micro/micro_utils.h"
namespace tflite {
namespace ops {
namespace micro {
namespace activations {
namespace {
struct ReluOpData {
ReluParams params;
};
struct Relu6OpData {
int8_t six_int8;
int8_t zero_int8;
uint8_t six_uint8;
uint8_t zero_uint8;
};
} // namespace
constexpr int kInputTensor = 0;
constexpr int kOutputTensor = 0;
template <typename T>
inline void ReluQuantized(const ReluOpData& data,
const RuntimeShape& input_shape,
const RuntimeShape& output_shape, const T* input_data,
T* output_data) {
const int flat_size = MatchingFlatSize(input_shape, output_shape);
for (int i = 0; i < flat_size; ++i) {
const int32_t val = static_cast<int32_t>(input_data[i]);
int32_t clamped =
data.params.output_offset +
MultiplyByQuantizedMultiplier(val - data.params.input_offset,
data.params.output_multiplier,
data.params.output_shift);
clamped = std::max(data.params.quantized_activation_min, clamped);
clamped = std::min(data.params.quantized_activation_max, clamped);
output_data[i] = static_cast<T>(clamped);
}
}
template <typename T>
inline void CalculateReluOpData(const TfLiteTensor* input, TfLiteTensor* output,
ReluOpData* data) {
float act_min = 0.0;
float act_max = std::numeric_limits<float>::infinity();
double real_multiplier =
static_cast<double>(input->params.scale / output->params.scale);
const RuntimeShape input_shape = GetTensorShape(input);
const RuntimeShape output_shape = GetTensorShape(output);
QuantizeMultiplier(real_multiplier, &data->params.output_multiplier,
&data->params.output_shift);
data->params.quantized_activation_min = std::max(
static_cast<int32_t>(std::numeric_limits<T>::min()),
output->params.zero_point +
static_cast<int32_t>(roundf(act_min / output->params.scale)));
data->params.quantized_activation_max =
act_max == std::numeric_limits<float>::infinity()
? static_cast<int32_t>(std::numeric_limits<T>::max())
: std::min(static_cast<int32_t>(std::numeric_limits<T>::max()),
output->params.zero_point +
static_cast<int32_t>(
roundf(act_max / output->params.scale)));
data->params.input_offset = input->params.zero_point;
data->params.output_offset = output->params.zero_point;
}
inline void ReluFloat(const RuntimeShape& input_shape, const float* input_data,
const RuntimeShape& output_shape, float* output_data) {
const int flat_size = MatchingFlatSize(input_shape, output_shape);
for (int i = 0; i < flat_size; ++i) {
const float val = input_data[i];
const float lower = 0.0f;
const float clamped = val < lower ? lower : val;
output_data[i] = clamped;
}
}
inline void Relu6Float(const RuntimeShape& input_shape, const float* input_data,
const RuntimeShape& output_shape, float* output_data) {
const int flat_size = MatchingFlatSize(input_shape, output_shape);
for (int i = 0; i < flat_size; ++i) {
const float val = input_data[i];
const float upper = 6.0f;
const float lower = 0.0f;
const float clamped = val > upper ? upper : val < lower ? lower : val;
output_data[i] = clamped;
}
}
template <typename Q>
inline void Relu6Quantized(Q lower, Q upper, const RuntimeShape& input_shape,
const Q* input_data,
const RuntimeShape& output_shape, Q* output_data) {
const int flat_size = MatchingFlatSize(input_shape, output_shape);
for (int i = 0; i < flat_size; ++i) {
const Q val = input_data[i];
const Q clamped = val > upper ? upper : val < lower ? lower : val;
output_data[i] = clamped;
}
}
void* ReluInit(TfLiteContext* context, const char* buffer, size_t length) {
TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
return context->AllocatePersistentBuffer(context, sizeof(ReluOpData));
}
TfLiteStatus ReluPrepare(TfLiteContext* context, TfLiteNode* node) {
TFLITE_DCHECK(node->user_data != nullptr);
ReluOpData* data = static_cast<ReluOpData*>(node->user_data);
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
TF_LITE_ENSURE(context, input != nullptr);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
TF_LITE_ENSURE(context, output != nullptr);
if (input->type == kTfLiteInt8) {
CalculateReluOpData<int8_t>(input, output, data);
} else if (input->type == kTfLiteUInt8) {
CalculateReluOpData<uint8_t>(input, output, data);
}
return kTfLiteOk;
}
TfLiteStatus ReluEval(TfLiteContext* context, TfLiteNode* node) {
TFLITE_DCHECK(node->user_data != nullptr);
const ReluOpData& data = *(static_cast<const ReluOpData*>(node->user_data));
const TfLiteEvalTensor* input =
tflite::micro::GetEvalInput(context, node, kInputTensor);
tflite::micro::GetEvalInput(context, node, kActivationsInputTensor);
TfLiteEvalTensor* output =
tflite::micro::GetEvalOutput(context, node, kOutputTensor);
tflite::micro::GetEvalOutput(context, node, kActivationsOutputTensor);
switch (input->type) {
case kTfLiteFloat32: {
......@@ -171,19 +53,12 @@ TfLiteStatus ReluEval(TfLiteContext* context, TfLiteNode* node) {
return kTfLiteOk;
}
case kTfLiteInt8: {
ReluQuantized<int8_t>(data, tflite::micro::GetTensorShape(input),
tflite::ReluQuantized(data, tflite::micro::GetTensorShape(input),
tflite::micro::GetTensorShape(output),
tflite::micro::GetTensorData<int8_t>(input),
tflite::micro::GetTensorData<int8_t>(output));
return kTfLiteOk;
}
case kTfLiteUInt8: {
ReluQuantized<uint8_t>(data, tflite::micro::GetTensorShape(input),
tflite::micro::GetTensorShape(output),
tflite::micro::GetTensorData<uint8_t>(input),
tflite::micro::GetTensorData<uint8_t>(output));
return kTfLiteOk;
}
default: {
TF_LITE_KERNEL_LOG(context, "Only float32 is supported currently, got %s",
TfLiteTypeGetName(input->type));
......@@ -197,34 +72,14 @@ void* Relu6Init(TfLiteContext* context, const char* buffer, size_t length) {
return context->AllocatePersistentBuffer(context, sizeof(Relu6OpData));
}
TfLiteStatus Relu6Prepare(TfLiteContext* context, TfLiteNode* node) {
TFLITE_DCHECK(node->user_data != nullptr);
Relu6OpData* data = static_cast<Relu6OpData*>(node->user_data);
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
TF_LITE_ENSURE(context, input != nullptr);
if (input->type == kTfLiteInt8) {
data->six_int8 = FloatToQuantizedType<int8_t>(6.0f, input->params.scale,
input->params.zero_point);
data->zero_int8 = input->params.zero_point;
} else if (input->type == kTfLiteUInt8) {
data->six_uint8 = FloatToQuantizedType<uint8_t>(6.0f, input->params.scale,
input->params.zero_point);
data->zero_uint8 = input->params.zero_point;
}
return kTfLiteOk;
}
TfLiteStatus Relu6Eval(TfLiteContext* context, TfLiteNode* node) {
TFLITE_DCHECK(node->user_data != nullptr);
const Relu6OpData& data = *(static_cast<const Relu6OpData*>(node->user_data));
const TfLiteEvalTensor* input =
tflite::micro::GetEvalInput(context, node, kInputTensor);
tflite::micro::GetEvalInput(context, node, kActivationsInputTensor);
TfLiteEvalTensor* output =
tflite::micro::GetEvalOutput(context, node, kOutputTensor);
tflite::micro::GetEvalOutput(context, node, kActivationsOutputTensor);
switch (input->type) {
case kTfLiteFloat32: {
......@@ -236,19 +91,11 @@ TfLiteStatus Relu6Eval(TfLiteContext* context, TfLiteNode* node) {
return kTfLiteOk;
}
case kTfLiteInt8: {
Relu6Quantized<int8_t>(data.zero_int8, data.six_int8,
tflite::micro::GetTensorShape(input),
tflite::micro::GetTensorData<int8_t>(input),
tflite::micro::GetTensorShape(output),
tflite::micro::GetTensorData<int8_t>(output));
return kTfLiteOk;
}
case kTfLiteUInt8: {
Relu6Quantized<uint8_t>(data.zero_uint8, data.six_uint8,
tflite::micro::GetTensorShape(input),
tflite::micro::GetTensorData<uint8_t>(input),
tflite::micro::GetTensorShape(output),
tflite::micro::GetTensorData<uint8_t>(output));
Relu6Quantized(data.zero_int8, data.six_int8,
tflite::micro::GetTensorShape(input),
tflite::micro::GetTensorData<int8_t>(input),
tflite::micro::GetTensorShape(output),
tflite::micro::GetTensorData<int8_t>(output));
return kTfLiteOk;
}
default: {
......@@ -259,13 +106,13 @@ TfLiteStatus Relu6Eval(TfLiteContext* context, TfLiteNode* node) {
}
}
} // namespace activations
} // namespace
TfLiteRegistration Register_RELU() {
return {/*init=*/activations::ReluInit,
return {/*init=*/ReluInit,
/*free=*/nullptr,
/*prepare=*/activations::ReluPrepare,
/*invoke=*/activations::ReluEval,
/*prepare=*/ReluPrepare,
/*invoke=*/ReluEval,
/*profiling_string=*/nullptr,
/*builtin_code=*/0,
/*custom_name=*/nullptr,
......@@ -273,16 +120,14 @@ TfLiteRegistration Register_RELU() {
}
TfLiteRegistration Register_RELU6() {
return {/*init=*/activations::Relu6Init,
return {/*init=*/Relu6Init,
/*free=*/nullptr,
/*prepare=*/activations::Relu6Prepare,
/*invoke=*/activations::Relu6Eval,
/*prepare=*/Relu6Prepare,
/*invoke=*/Relu6Eval,
/*profiling_string=*/nullptr,
/*builtin_code=*/0,
/*custom_name=*/nullptr,
/*version=*/0};
}
} // namespace micro
} // namespace ops
} // namespace tflite
tensorflow/lite/micro/kernels/activations.h 0 → 100644
浏览文件 @ ca7ed66b
/* Copyright 2021 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_MICRO_KERNELS_ACTIVATIONS_H_
#define TENSORFLOW_LITE_MICRO_KERNELS_ACTIVATIONS_H_
#include <cstdint>
#include "tensorflow/lite/c/builtin_op_data.h"
#include "tensorflow/lite/c/common.h"
#include "tensorflow/lite/kernels/internal/types.h"
namespace tflite {
extern const int kActivationsInputTensor;
extern const int kActivationsOutputTensor;
struct ReluOpData {
ReluParams params;
};
struct Relu6OpData {
int8_t six_int8;
int8_t zero_int8;
};
void ReluQuantized(const ReluOpData& data, const RuntimeShape& input_shape,
const RuntimeShape& output_shape, const int8_t* input_data,
int8_t* output_data);
template <typename T>
void CalculateReluOpData(const TfLiteTensor* input, TfLiteTensor* output,
ReluOpData* data);
void ReluFloat(const RuntimeShape& input_shape, const float* input_data,
const RuntimeShape& output_shape, float* output_data);
void Relu6Float(const RuntimeShape& input_shape, const float* input_data,
const RuntimeShape& output_shape, float* output_data);
void Relu6Quantized(int8_t lower, int8_t upper, const RuntimeShape& input_shape,
const int8_t* input_data, const RuntimeShape& output_shape,
int8_t* output_data);
TfLiteStatus ReluPrepare(TfLiteContext* context, TfLiteNode* node);
TfLiteStatus Relu6Prepare(TfLiteContext* context, TfLiteNode* node);
} // namespace tflite
#endif // TENSORFLOW_LITE_MICRO_KERNELS_ACTIVATIONS_H_
tensorflow/lite/micro/kernels/activations_common.cc 0 → 100644
浏览文件 @ ca7ed66b
/* Copyright 2021 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 <algorithm>
#include <cstdint>
#include "tensorflow/lite/c/builtin_op_data.h"
#include "tensorflow/lite/c/common.h"
#include "tensorflow/lite/kernels/internal/common.h"
#include "tensorflow/lite/kernels/internal/quantization_util.h"
#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
#include "tensorflow/lite/kernels/internal/types.h"
#include "tensorflow/lite/kernels/kernel_util.h"
#include "tensorflow/lite/kernels/op_macros.h"
#include "tensorflow/lite/micro/kernels/activations.h"
#include "tensorflow/lite/micro/kernels/kernel_util.h"
#include "tensorflow/lite/micro/micro_utils.h"
namespace tflite {
const int kActivationsInputTensor = 0;
const int kActivationsOutputTensor = 0;
void ReluQuantized(const ReluOpData& data, const RuntimeShape& input_shape,
const RuntimeShape& output_shape, const int8_t* input_data,
int8_t* output_data) {
const int flat_size = MatchingFlatSize(input_shape, output_shape);
for (int i = 0; i < flat_size; ++i) {
const int32_t val = static_cast<int32_t>(input_data[i]);
int32_t clamped =
data.params.output_offset +
MultiplyByQuantizedMultiplier(val - data.params.input_offset,
data.params.output_multiplier,
data.params.output_shift);
clamped = std::max(data.params.quantized_activation_min, clamped);
clamped = std::min(data.params.quantized_activation_max, clamped);
output_data[i] = static_cast<int8_t>(clamped);
}
}
template <typename T>
void CalculateReluOpData(const TfLiteTensor* input, TfLiteTensor* output,
ReluOpData* data) {
float act_min = 0.0;
float act_max = std::numeric_limits<float>::infinity();
double real_multiplier =
static_cast<double>(input->params.scale / output->params.scale);
const RuntimeShape input_shape = GetTensorShape(input);
const RuntimeShape output_shape = GetTensorShape(output);
QuantizeMultiplier(real_multiplier, &data->params.output_multiplier,
&data->params.output_shift);
data->params.quantized_activation_min = std::max(
static_cast<int32_t>(std::numeric_limits<T>::min()),
output->params.zero_point +
static_cast<int32_t>(roundf(act_min / output->params.scale)));
data->params.quantized_activation_max =
act_max == std::numeric_limits<float>::infinity()
? static_cast<int32_t>(std::numeric_limits<T>::max())
: std::min(static_cast<int32_t>(std::numeric_limits<T>::max()),
output->params.zero_point +
static_cast<int32_t>(
roundf(act_max / output->params.scale)));
data->params.input_offset = input->params.zero_point;
data->params.output_offset = output->params.zero_point;
}
void ReluFloat(const RuntimeShape& input_shape, const float* input_data,
const RuntimeShape& output_shape, float* output_data) {
const int flat_size = MatchingFlatSize(input_shape, output_shape);
for (int i = 0; i < flat_size; ++i) {
const float val = input_data[i];
const float lower = 0.0f;
const float clamped = val < lower ? lower : val;
output_data[i] = clamped;
}
}
void Relu6Float(const RuntimeShape& input_shape, const float* input_data,
const RuntimeShape& output_shape, float* output_data) {
const int flat_size = MatchingFlatSize(input_shape, output_shape);
for (int i = 0; i < flat_size; ++i) {
const float val = input_data[i];
const float upper = 6.0f;
const float lower = 0.0f;
const float clamped = val > upper ? upper : val < lower ? lower : val;
output_data[i] = clamped;
}
}
void Relu6Quantized(int8_t lower, int8_t upper, const RuntimeShape& input_shape,
const int8_t* input_data, const RuntimeShape& output_shape,
int8_t* output_data) {
const int flat_size = MatchingFlatSize(input_shape, output_shape);
for (int i = 0; i < flat_size; ++i) {
const int8_t val = input_data[i];
const int8_t clamped = val > upper ? upper : val < lower ? lower : val;
output_data[i] = clamped;
}
}
TfLiteStatus ReluPrepare(TfLiteContext* context, TfLiteNode* node) {
TFLITE_DCHECK(node->user_data != nullptr);
ReluOpData* data = static_cast<ReluOpData*>(node->user_data);
const TfLiteTensor* input = GetInput(context, node, kActivationsInputTensor);
TF_LITE_ENSURE(context, input != nullptr);
TfLiteTensor* output = GetOutput(context, node, kActivationsOutputTensor);
TF_LITE_ENSURE(context, output != nullptr);
if (input->type == kTfLiteInt8) {
CalculateReluOpData<int8_t>(input, output, data);
}
return kTfLiteOk;
}
TfLiteStatus Relu6Prepare(TfLiteContext* context, TfLiteNode* node) {
TFLITE_DCHECK(node->user_data != nullptr);
Relu6OpData* data = static_cast<Relu6OpData*>(node->user_data);
const TfLiteTensor* input = GetInput(context, node, kActivationsInputTensor);
TF_LITE_ENSURE(context, input != nullptr);
if (input->type == kTfLiteInt8) {
data->six_int8 = FloatToQuantizedType<int8_t>(6.0f, input->params.scale,
input->params.zero_point);
data->zero_int8 = input->params.zero_point;
}
return kTfLiteOk;
}
} // namespace tflite
tensorflow/lite/micro/kernels/activations_test.cc
浏览文件 @ ca7ed66b
/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
/* Copyright 2021 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.
......@@ -44,7 +44,7 @@ void TestReluFloat(int* input_dims_data, const float* input_data,
int outputs_array_data[] = {1, 1};
TfLiteIntArray* outputs_array = IntArrayFromInts(outputs_array_data);
const TfLiteRegistration registration = ops::micro::Register_RELU();
const TfLiteRegistration registration = Register_RELU();
micro::KernelRunner runner(registration, tensors, tensors_size, inputs_array,
outputs_array,
/*builtin_data=*/nullptr);
......@@ -77,7 +77,7 @@ void TestRelu6Float(int* input_dims_data, const float* input_data,
int outputs_array_data[] = {1, 1};
TfLiteIntArray* outputs_array = IntArrayFromInts(outputs_array_data);
const TfLiteRegistration registration = ops::micro::Register_RELU6();
const TfLiteRegistration registration = Register_RELU6();
micro::KernelRunner runner(registration, tensors, tensors_size, inputs_array,
outputs_array,
/*builtin_data=*/nullptr);
......@@ -90,88 +90,6 @@ void TestRelu6Float(int* input_dims_data, const float* input_data,
}
}
void TestReluUint8(int* input_dims_data, const float* input_data,
uint8_t* input_data_quantized, const float input_scale,
const int input_zero_point, const float* golden,
uint8_t* golden_quantized, int* output_dims_data,
const float output_scale, const int output_zero_point,
uint8_t* output_data) {
TfLiteIntArray* input_dims = IntArrayFromInts(input_dims_data);
TfLiteIntArray* output_dims = IntArrayFromInts(output_dims_data);
const int output_elements_count = ElementCount(*output_dims);
constexpr int inputs_size = 1;
constexpr int outputs_size = 1;
constexpr int tensors_size = inputs_size + outputs_size;
TfLiteTensor tensors[tensors_size] = {
CreateQuantizedTensor(input_data, input_data_quantized, input_dims,
input_scale, input_zero_point),
CreateQuantizedTensor(output_data, output_dims, output_scale,
output_zero_point),
};
int inputs_array_data[] = {1, 0};
TfLiteIntArray* inputs_array = IntArrayFromInts(inputs_array_data);
int outputs_array_data[] = {1, 1};
TfLiteIntArray* outputs_array = IntArrayFromInts(outputs_array_data);
const TfLiteRegistration registration = ops::micro::Register_RELU();
micro::KernelRunner runner(registration, tensors, tensors_size, inputs_array,
outputs_array,
/*builtin_data=*/nullptr);
TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, runner.InitAndPrepare());
TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, runner.Invoke());
Quantize(golden, golden_quantized, output_elements_count, output_scale,
output_zero_point);
for (int i = 0; i < output_elements_count; ++i) {
TF_LITE_MICRO_EXPECT_EQ(golden_quantized[i], output_data[i]);
}
}
void TestRelu6Uint8(int* input_dims_data, const float* input_data,
uint8_t* input_data_quantized, const float input_scale,
const int input_zero_point, const float* golden,
uint8_t* golden_quantized, int* output_dims_data,
const float output_scale, const int output_zero_point,
uint8_t* output_data) {
TfLiteIntArray* input_dims = IntArrayFromInts(input_dims_data);
TfLiteIntArray* output_dims = IntArrayFromInts(output_dims_data);
const int output_elements_count = ElementCount(*output_dims);
constexpr int inputs_size = 1;
constexpr int outputs_size = 1;
constexpr int tensors_size = inputs_size + outputs_size;
TfLiteTensor tensors[tensors_size] = {
CreateQuantizedTensor(input_data, input_data_quantized, input_dims,
input_scale, input_zero_point),
CreateQuantizedTensor(output_data, output_dims, output_scale,
output_zero_point),
};
int inputs_array_data[] = {1, 0};
TfLiteIntArray* inputs_array = IntArrayFromInts(inputs_array_data);
int outputs_array_data[] = {1, 1};
TfLiteIntArray* outputs_array = IntArrayFromInts(outputs_array_data);
const TfLiteRegistration registration = ops::micro::Register_RELU6();
micro::KernelRunner runner(registration, tensors, tensors_size, inputs_array,
outputs_array,
/*builtin_data=*/nullptr);
TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, runner.InitAndPrepare());
TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, runner.Invoke());
Quantize(golden, golden_quantized, output_elements_count, output_scale,
output_zero_point);
for (int i = 0; i < output_elements_count; ++i) {
TF_LITE_MICRO_EXPECT_EQ(golden_quantized[i], output_data[i]);
}
}
void TestReluInt8(int* input_dims_data, const float* input_data,
int8_t* input_data_quantized, const float input_scale,
const int input_zero_point, const float* golden,
......@@ -196,7 +114,7 @@ void TestReluInt8(int* input_dims_data, const float* input_data,
int outputs_array_data[] = {1, 1};
TfLiteIntArray* outputs_array = IntArrayFromInts(outputs_array_data);
const TfLiteRegistration registration = ops::micro::Register_RELU();
const TfLiteRegistration registration = Register_RELU();
micro::KernelRunner runner(registration, tensors, tensors_size, inputs_array,
outputs_array,
/*builtin_data=*/nullptr);
......@@ -236,7 +154,7 @@ void TestRelu6Int8(int* input_dims_data, const float* input_data,
int outputs_array_data[] = {1, 1};
TfLiteIntArray* outputs_array = IntArrayFromInts(outputs_array_data);
const TfLiteRegistration registration = ops::micro::Register_RELU6();
const TfLiteRegistration registration = Register_RELU6();
micro::KernelRunner runner(registration, tensors, tensors_size, inputs_array,
outputs_array,
/*builtin_data=*/nullptr);
......@@ -286,50 +204,6 @@ TF_LITE_MICRO_TEST(SimpleRelu6TestFloat) {
output_data);
}
TF_LITE_MICRO_TEST(SimpleReluTestUint8) {
const int elements_count = 10;
int input_shape[] = {2, 1, 5};
const float input_data[] = {1, 2, 3, 4, 5, -1, -2, -3, -4, -5};
uint8_t input_quantized[elements_count];
int output_shape[] = {2, 1, 5};
const float golden[] = {1, 2, 3, 4, 5, 0, 0, 0, 0, 0};
uint8_t golden_quantized[elements_count];
uint8_t output_data[elements_count];
const float input_scale = 0.5f;
const int input_zero_point = 127;
const float output_scale = 0.5f;
const int output_zero_point = 127;
tflite::testing::TestReluUint8(input_shape, input_data, input_quantized,
input_scale, input_zero_point, golden,
golden_quantized, output_shape, output_scale,
output_zero_point, output_data);
}
TF_LITE_MICRO_TEST(SimpleRelu6TestUint8) {
const int elements_count = 10;
int input_shape[] = {2, 1, 5};
const float input_data[] = {4, 5, 6, 7, 8, -1, -2, -3, -4, -5};
uint8_t input_quantized[elements_count];
int output_shape[] = {2, 1, 5};
const float golden[] = {4, 5, 6, 6, 6, 0, 0, 0, 0, 0};
uint8_t golden_quantized[elements_count];
uint8_t output_data[elements_count];
const float input_scale = 0.5f;
const int input_zero_point = 127;
const float output_scale = 0.5f;
const int output_zero_point = 127;
tflite::testing::TestRelu6Uint8(input_shape, input_data, input_quantized,
input_scale, input_zero_point, golden,
golden_quantized, output_shape, output_scale,
output_zero_point, output_data);
}
TF_LITE_MICRO_TEST(SimpleReluTestInt8) {
const int elements_count = 10;
......
tensorflow/lite/micro/kernels/micro_ops.h
浏览文件 @ ca7ed66b
/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
/* Copyright 2021 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.
......@@ -53,6 +53,8 @@ TfLiteRegistration Register_LEAKY_RELU();
TfLiteRegistration Register_LOG_SOFTMAX();
TfLiteRegistration Register_MAX_POOL_2D();
TfLiteRegistration Register_QUANTIZE();
TfLiteRegistration Register_RELU();
TfLiteRegistration Register_RELU6();
TfLiteRegistration Register_RESIZE_BILINEAR();
TfLiteRegistration Register_SHAPE();
TfLiteRegistration Register_SPACE_TO_BATCH_ND();
......@@ -99,8 +101,6 @@ TfLiteRegistration Register_PAD();
TfLiteRegistration Register_PADV2();
TfLiteRegistration Register_PRELU();
TfLiteRegistration Register_REDUCE_MAX();
TfLiteRegistration Register_RELU();
TfLiteRegistration Register_RELU6();
TfLiteRegistration Register_RESHAPE();
TfLiteRegistration Register_RESIZE_NEAREST_NEIGHBOR();
TfLiteRegistration Register_ROUND();
......
tensorflow/lite/micro/micro_mutable_op_resolver.h
浏览文件 @ ca7ed66b
/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
/* Copyright 2021 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.
......@@ -408,13 +408,12 @@ class MicroMutableOpResolver : public MicroOpResolver {
}
TfLiteStatus AddRelu() {
return AddBuiltin(BuiltinOperator_RELU, tflite::ops::micro::Register_RELU(),
ParseRelu);
return AddBuiltin(BuiltinOperator_RELU, tflite::Register_RELU(), ParseRelu);
}
TfLiteStatus AddRelu6() {
return AddBuiltin(BuiltinOperator_RELU6,
tflite::ops::micro::Register_RELU6(), ParseRelu6);
return AddBuiltin(BuiltinOperator_RELU6, tflite::Register_RELU6(),
ParseRelu6);
}
TfLiteStatus AddReshape() {
......
tensorflow/lite/micro/tools/make/Makefile
浏览文件 @ ca7ed66b
......@@ -348,6 +348,7 @@ tensorflow/lite/micro/memory_planner/linear_memory_planner_test.cc
MICROLITE_CC_KERNEL_SRCS := \
tensorflow/lite/micro/kernels/activations.cc \
tensorflow/lite/micro/kernels/activations_common.cc \
tensorflow/lite/micro/kernels/add.cc \
tensorflow/lite/micro/kernels/add_n.cc \
tensorflow/lite/micro/kernels/arg_min_max.cc \
......
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