You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
217 lines
9.2 KiB
217 lines
9.2 KiB
/*
|
|
* Copyright (C) 2020 The Android Open Source Project
|
|
*
|
|
* 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.
|
|
*/
|
|
|
|
#define LOG_TAG "Operations"
|
|
|
|
#include <algorithm>
|
|
#include <vector>
|
|
|
|
#include "OperationResolver.h"
|
|
#include "Tracing.h"
|
|
|
|
#ifdef NN_INCLUDE_CPU_IMPLEMENTATION
|
|
#include <tensorflow/lite/kernels/internal/optimized/optimized_ops.h>
|
|
|
|
#include "CpuOperationUtils.h"
|
|
#endif // NN_INCLUDE_CPU_IMPLEMENTATION
|
|
|
|
namespace android {
|
|
namespace nn {
|
|
namespace local_response_norm {
|
|
|
|
constexpr char kOperationName[] = "LOCAL_RESPONSE_NORMALIZATION";
|
|
|
|
constexpr uint32_t kNumInputs = 6;
|
|
constexpr uint32_t kInputTensor = 0;
|
|
constexpr uint32_t kRadiusScalar = 1;
|
|
constexpr uint32_t kBiasScalar = 2;
|
|
constexpr uint32_t kAlphaScalar = 3;
|
|
constexpr uint32_t kBetaScalar = 4;
|
|
constexpr uint32_t kAxisScalar = 5;
|
|
|
|
constexpr uint32_t kNumOutputs = 1;
|
|
constexpr uint32_t kOutputTensor = 0;
|
|
|
|
#ifdef NN_INCLUDE_CPU_IMPLEMENTATION
|
|
namespace {
|
|
|
|
inline bool localResponseNormFloat32Impl(const float* inputData, const Shape& inputShape,
|
|
int32_t radius, float bias, float alpha, float beta,
|
|
int32_t axis, float* outputData,
|
|
const Shape& outputShape) {
|
|
NNTRACE_TRANS("localResponseNormFloat32");
|
|
const uint32_t outerSize = getNumberOfElements(inputShape, 0, axis);
|
|
const uint32_t axisSize = getSizeOfDimension(inputShape, axis);
|
|
const uint32_t innerSize =
|
|
getNumberOfElements(inputShape, axis + 1, getNumberOfDimensions(inputShape));
|
|
for (uint32_t outer = 0; outer < outerSize; ++outer) {
|
|
const float* inputBase = inputData + outer * axisSize * innerSize;
|
|
float* outputBase = outputData + outer * axisSize * innerSize;
|
|
for (uint32_t inner = 0; inner < innerSize; ++inner, ++inputBase, ++outputBase) {
|
|
for (int32_t i = 0; i < axisSize; i++) {
|
|
const int32_t dBegin = std::max(0, i - radius);
|
|
// Add 1 on dEnd to comply with optimized_ops in TFLite
|
|
const int32_t dEnd = std::min(static_cast<int32_t>(axisSize), i + radius + 1);
|
|
float sum = 0.0f;
|
|
for (int32_t d = dBegin; d < dEnd; d++) {
|
|
float val = inputBase[d * innerSize];
|
|
sum += val * val;
|
|
}
|
|
float multiplier = std::pow(bias + alpha * sum, -beta);
|
|
outputBase[i * innerSize] = inputBase[i * innerSize] * multiplier;
|
|
}
|
|
}
|
|
}
|
|
return true;
|
|
}
|
|
|
|
template <typename T>
|
|
bool localResponseNorm(const T* inputData, const Shape& inputShape, int32_t radius, T bias, T alpha,
|
|
T beta, int32_t axis, T* outputData, const Shape& outputShape);
|
|
|
|
template <>
|
|
bool localResponseNorm<float>(const float* inputData, const Shape& inputShape, int32_t radius,
|
|
float bias, float alpha, float beta, int32_t axis, float* outputData,
|
|
const Shape& outputShape) {
|
|
int32_t ndim = getNumberOfDimensions(inputShape);
|
|
NN_CHECK(handleNegativeAxis(inputShape, &axis));
|
|
radius = std::min(radius, static_cast<int32_t>(inputShape.dimensions[axis]));
|
|
// TFLite optimized implementation only supports computation along the last axis
|
|
if (axis == ndim - 1) {
|
|
NNTRACE_COMP("optimized_ops::LocalResponseNormalization::float");
|
|
tflite::LocalResponseNormalizationParams param = {
|
|
.range = radius, .bias = bias, .alpha = alpha, .beta = beta};
|
|
tflite::optimized_ops::LocalResponseNormalization(
|
|
param, convertShapeToTflshape(inputShape), inputData,
|
|
convertShapeToTflshape(outputShape), outputData);
|
|
return true;
|
|
} else {
|
|
return localResponseNormFloat32Impl(inputData, inputShape, radius, bias, alpha, beta, axis,
|
|
outputData, outputShape);
|
|
}
|
|
}
|
|
|
|
template <>
|
|
bool localResponseNorm<_Float16>(const _Float16* inputData, const Shape& inputShape, int32_t radius,
|
|
_Float16 bias, _Float16 alpha, _Float16 beta, int32_t axis,
|
|
_Float16* outputData, const Shape& outputShape) {
|
|
NNTRACE_TRANS("localResponseNormFloat16");
|
|
std::vector<float> inputDataFloat32(getNumberOfElements(inputShape));
|
|
convertFloat16ToFloat32(inputData, &inputDataFloat32);
|
|
std::vector<float> outputDataFloat32(getNumberOfElements(outputShape));
|
|
|
|
localResponseNorm<float>(inputDataFloat32.data(), inputShape, radius, bias, alpha, beta, axis,
|
|
outputDataFloat32.data(), outputShape);
|
|
convertFloat32ToFloat16(outputDataFloat32, outputData);
|
|
|
|
return true;
|
|
}
|
|
|
|
template <typename T>
|
|
bool executeTyped(IOperationExecutionContext* context) {
|
|
int32_t axis = context->getNumInputs() == kNumInputs
|
|
? context->getInputValue<int32_t>(kAxisScalar)
|
|
: -1;
|
|
NN_RET_CHECK(handleNegativeAxis(context->getInputShape(kInputTensor), &axis));
|
|
return localResponseNorm<T>(
|
|
context->getInputBuffer<T>(kInputTensor), context->getInputShape(kInputTensor),
|
|
context->getInputValue<int32_t>(kRadiusScalar), context->getInputValue<T>(kBiasScalar),
|
|
context->getInputValue<T>(kAlphaScalar), context->getInputValue<T>(kBetaScalar), axis,
|
|
context->getOutputBuffer<T>(kOutputTensor), context->getOutputShape(kOutputTensor));
|
|
}
|
|
|
|
} // namespace
|
|
#endif // NN_INCLUDE_CPU_IMPLEMENTATION
|
|
|
|
Result<Version> validate(const IOperationValidationContext* context) {
|
|
NN_RET_CHECK(context->getNumInputs() == kNumInputs ||
|
|
context->getNumInputs() == kNumInputs - 1);
|
|
NN_RET_CHECK_EQ(context->getNumOutputs(), kNumOutputs);
|
|
|
|
const OperandType inputType = context->getInputType(kInputTensor);
|
|
std::vector<OperandType> inExpectedTypes;
|
|
std::vector<OperandType> outExpectedTypes;
|
|
auto minSupportedVersion = Version::ANDROID_OC_MR1;
|
|
if (inputType == OperandType::TENSOR_FLOAT32) {
|
|
minSupportedVersion = Version::ANDROID_OC_MR1;
|
|
inExpectedTypes = {
|
|
OperandType::TENSOR_FLOAT32, OperandType::INT32, OperandType::FLOAT32,
|
|
OperandType::FLOAT32, OperandType::FLOAT32,
|
|
};
|
|
outExpectedTypes = {OperandType::TENSOR_FLOAT32};
|
|
} else if (inputType == OperandType::TENSOR_FLOAT16) {
|
|
minSupportedVersion = Version::ANDROID_Q;
|
|
inExpectedTypes = {
|
|
OperandType::TENSOR_FLOAT16, OperandType::INT32, OperandType::FLOAT16,
|
|
OperandType::FLOAT16, OperandType::FLOAT16,
|
|
};
|
|
outExpectedTypes = {OperandType::TENSOR_FLOAT16};
|
|
} else {
|
|
NN_RET_CHECK_FAIL() << "Unsupported tensor type for operation " << kOperationName;
|
|
}
|
|
|
|
if (context->getNumInputs() == kNumInputs) {
|
|
inExpectedTypes.push_back(OperandType::INT32);
|
|
minSupportedVersion = Version::ANDROID_Q;
|
|
} else if (context->getInputShape(kInputTensor).dimensions.size() != 4) {
|
|
minSupportedVersion = Version::ANDROID_Q;
|
|
}
|
|
|
|
const Shape& input = context->getInputShape(kInputTensor);
|
|
if (hasKnownRank(input)) {
|
|
NN_RET_CHECK_LE(getNumberOfDimensions(input), 4);
|
|
}
|
|
NN_RET_CHECK(validateInputTypes(context, inExpectedTypes));
|
|
NN_RET_CHECK(validateOutputTypes(context, {inputType}));
|
|
return minSupportedVersion;
|
|
}
|
|
|
|
#ifdef NN_INCLUDE_CPU_IMPLEMENTATION
|
|
bool prepare(IOperationExecutionContext* context) {
|
|
const Shape& input = context->getInputShape(kInputTensor);
|
|
int32_t numDimensions = getNumberOfDimensions(input);
|
|
int32_t axis = context->getNumInputs() == kNumInputs
|
|
? context->getInputValue<int32_t>(kAxisScalar)
|
|
: -1;
|
|
NN_RET_CHECK_LE(numDimensions, 4);
|
|
NN_RET_CHECK_GE(axis, -numDimensions);
|
|
NN_RET_CHECK_LT(axis, numDimensions);
|
|
const int32_t radius = context->getInputValue<int32_t>(kRadiusScalar);
|
|
NN_RET_CHECK_GE(radius, 0);
|
|
return context->setOutputShape(kOutputTensor, input);
|
|
}
|
|
|
|
bool execute(IOperationExecutionContext* context) {
|
|
switch (context->getInputType(kInputTensor)) {
|
|
case OperandType::TENSOR_FLOAT32:
|
|
return executeTyped<float>(context);
|
|
case OperandType::TENSOR_FLOAT16:
|
|
return executeTyped<_Float16>(context);
|
|
default:
|
|
NN_RET_CHECK_FAIL() << "Unsupported tensor type for operation " << kOperationName;
|
|
}
|
|
}
|
|
#endif // NN_INCLUDE_CPU_IMPLEMENTATION
|
|
|
|
} // namespace local_response_norm
|
|
|
|
NN_REGISTER_OPERATION(LOCAL_RESPONSE_NORMALIZATION, local_response_norm::kOperationName,
|
|
local_response_norm::validate, local_response_norm::prepare,
|
|
local_response_norm::execute);
|
|
|
|
} // namespace nn
|
|
} // namespace android
|