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217 lines
9.2 KiB
217 lines
9.2 KiB
4 months ago
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/*
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* Copyright (C) 2020 The Android Open Source Project
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#define LOG_TAG "Operations"
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#include <algorithm>
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#include <vector>
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#include "OperationResolver.h"
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#include "Tracing.h"
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#ifdef NN_INCLUDE_CPU_IMPLEMENTATION
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#include <tensorflow/lite/kernels/internal/optimized/optimized_ops.h>
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#include "CpuOperationUtils.h"
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#endif // NN_INCLUDE_CPU_IMPLEMENTATION
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namespace android {
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namespace nn {
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namespace local_response_norm {
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constexpr char kOperationName[] = "LOCAL_RESPONSE_NORMALIZATION";
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constexpr uint32_t kNumInputs = 6;
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constexpr uint32_t kInputTensor = 0;
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constexpr uint32_t kRadiusScalar = 1;
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constexpr uint32_t kBiasScalar = 2;
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constexpr uint32_t kAlphaScalar = 3;
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constexpr uint32_t kBetaScalar = 4;
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constexpr uint32_t kAxisScalar = 5;
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constexpr uint32_t kNumOutputs = 1;
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constexpr uint32_t kOutputTensor = 0;
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#ifdef NN_INCLUDE_CPU_IMPLEMENTATION
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namespace {
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inline bool localResponseNormFloat32Impl(const float* inputData, const Shape& inputShape,
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int32_t radius, float bias, float alpha, float beta,
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int32_t axis, float* outputData,
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const Shape& outputShape) {
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NNTRACE_TRANS("localResponseNormFloat32");
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const uint32_t outerSize = getNumberOfElements(inputShape, 0, axis);
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const uint32_t axisSize = getSizeOfDimension(inputShape, axis);
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const uint32_t innerSize =
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getNumberOfElements(inputShape, axis + 1, getNumberOfDimensions(inputShape));
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for (uint32_t outer = 0; outer < outerSize; ++outer) {
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const float* inputBase = inputData + outer * axisSize * innerSize;
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float* outputBase = outputData + outer * axisSize * innerSize;
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for (uint32_t inner = 0; inner < innerSize; ++inner, ++inputBase, ++outputBase) {
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for (int32_t i = 0; i < axisSize; i++) {
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const int32_t dBegin = std::max(0, i - radius);
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// Add 1 on dEnd to comply with optimized_ops in TFLite
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const int32_t dEnd = std::min(static_cast<int32_t>(axisSize), i + radius + 1);
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float sum = 0.0f;
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for (int32_t d = dBegin; d < dEnd; d++) {
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float val = inputBase[d * innerSize];
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sum += val * val;
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}
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float multiplier = std::pow(bias + alpha * sum, -beta);
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outputBase[i * innerSize] = inputBase[i * innerSize] * multiplier;
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}
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}
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}
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return true;
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}
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template <typename T>
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bool localResponseNorm(const T* inputData, const Shape& inputShape, int32_t radius, T bias, T alpha,
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T beta, int32_t axis, T* outputData, const Shape& outputShape);
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template <>
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bool localResponseNorm<float>(const float* inputData, const Shape& inputShape, int32_t radius,
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float bias, float alpha, float beta, int32_t axis, float* outputData,
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const Shape& outputShape) {
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int32_t ndim = getNumberOfDimensions(inputShape);
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NN_CHECK(handleNegativeAxis(inputShape, &axis));
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radius = std::min(radius, static_cast<int32_t>(inputShape.dimensions[axis]));
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// TFLite optimized implementation only supports computation along the last axis
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if (axis == ndim - 1) {
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NNTRACE_COMP("optimized_ops::LocalResponseNormalization::float");
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tflite::LocalResponseNormalizationParams param = {
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.range = radius, .bias = bias, .alpha = alpha, .beta = beta};
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tflite::optimized_ops::LocalResponseNormalization(
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param, convertShapeToTflshape(inputShape), inputData,
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convertShapeToTflshape(outputShape), outputData);
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return true;
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} else {
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return localResponseNormFloat32Impl(inputData, inputShape, radius, bias, alpha, beta, axis,
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outputData, outputShape);
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}
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}
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template <>
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bool localResponseNorm<_Float16>(const _Float16* inputData, const Shape& inputShape, int32_t radius,
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_Float16 bias, _Float16 alpha, _Float16 beta, int32_t axis,
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_Float16* outputData, const Shape& outputShape) {
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NNTRACE_TRANS("localResponseNormFloat16");
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std::vector<float> inputDataFloat32(getNumberOfElements(inputShape));
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convertFloat16ToFloat32(inputData, &inputDataFloat32);
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std::vector<float> outputDataFloat32(getNumberOfElements(outputShape));
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localResponseNorm<float>(inputDataFloat32.data(), inputShape, radius, bias, alpha, beta, axis,
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outputDataFloat32.data(), outputShape);
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convertFloat32ToFloat16(outputDataFloat32, outputData);
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return true;
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}
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template <typename T>
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bool executeTyped(IOperationExecutionContext* context) {
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int32_t axis = context->getNumInputs() == kNumInputs
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? context->getInputValue<int32_t>(kAxisScalar)
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: -1;
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NN_RET_CHECK(handleNegativeAxis(context->getInputShape(kInputTensor), &axis));
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return localResponseNorm<T>(
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context->getInputBuffer<T>(kInputTensor), context->getInputShape(kInputTensor),
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context->getInputValue<int32_t>(kRadiusScalar), context->getInputValue<T>(kBiasScalar),
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context->getInputValue<T>(kAlphaScalar), context->getInputValue<T>(kBetaScalar), axis,
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context->getOutputBuffer<T>(kOutputTensor), context->getOutputShape(kOutputTensor));
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}
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} // namespace
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#endif // NN_INCLUDE_CPU_IMPLEMENTATION
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Result<Version> validate(const IOperationValidationContext* context) {
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NN_RET_CHECK(context->getNumInputs() == kNumInputs ||
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context->getNumInputs() == kNumInputs - 1);
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NN_RET_CHECK_EQ(context->getNumOutputs(), kNumOutputs);
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const OperandType inputType = context->getInputType(kInputTensor);
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std::vector<OperandType> inExpectedTypes;
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std::vector<OperandType> outExpectedTypes;
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auto minSupportedVersion = Version::ANDROID_OC_MR1;
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if (inputType == OperandType::TENSOR_FLOAT32) {
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minSupportedVersion = Version::ANDROID_OC_MR1;
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inExpectedTypes = {
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OperandType::TENSOR_FLOAT32, OperandType::INT32, OperandType::FLOAT32,
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OperandType::FLOAT32, OperandType::FLOAT32,
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};
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outExpectedTypes = {OperandType::TENSOR_FLOAT32};
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} else if (inputType == OperandType::TENSOR_FLOAT16) {
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minSupportedVersion = Version::ANDROID_Q;
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inExpectedTypes = {
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OperandType::TENSOR_FLOAT16, OperandType::INT32, OperandType::FLOAT16,
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OperandType::FLOAT16, OperandType::FLOAT16,
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};
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outExpectedTypes = {OperandType::TENSOR_FLOAT16};
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} else {
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NN_RET_CHECK_FAIL() << "Unsupported tensor type for operation " << kOperationName;
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}
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if (context->getNumInputs() == kNumInputs) {
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inExpectedTypes.push_back(OperandType::INT32);
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minSupportedVersion = Version::ANDROID_Q;
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} else if (context->getInputShape(kInputTensor).dimensions.size() != 4) {
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minSupportedVersion = Version::ANDROID_Q;
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}
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const Shape& input = context->getInputShape(kInputTensor);
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if (hasKnownRank(input)) {
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NN_RET_CHECK_LE(getNumberOfDimensions(input), 4);
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}
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NN_RET_CHECK(validateInputTypes(context, inExpectedTypes));
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NN_RET_CHECK(validateOutputTypes(context, {inputType}));
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return minSupportedVersion;
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}
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#ifdef NN_INCLUDE_CPU_IMPLEMENTATION
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bool prepare(IOperationExecutionContext* context) {
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const Shape& input = context->getInputShape(kInputTensor);
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int32_t numDimensions = getNumberOfDimensions(input);
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int32_t axis = context->getNumInputs() == kNumInputs
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? context->getInputValue<int32_t>(kAxisScalar)
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: -1;
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NN_RET_CHECK_LE(numDimensions, 4);
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NN_RET_CHECK_GE(axis, -numDimensions);
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NN_RET_CHECK_LT(axis, numDimensions);
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const int32_t radius = context->getInputValue<int32_t>(kRadiusScalar);
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NN_RET_CHECK_GE(radius, 0);
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return context->setOutputShape(kOutputTensor, input);
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}
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bool execute(IOperationExecutionContext* context) {
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switch (context->getInputType(kInputTensor)) {
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case OperandType::TENSOR_FLOAT32:
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return executeTyped<float>(context);
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case OperandType::TENSOR_FLOAT16:
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return executeTyped<_Float16>(context);
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default:
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NN_RET_CHECK_FAIL() << "Unsupported tensor type for operation " << kOperationName;
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}
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}
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#endif // NN_INCLUDE_CPU_IMPLEMENTATION
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} // namespace local_response_norm
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NN_REGISTER_OPERATION(LOCAL_RESPONSE_NORMALIZATION, local_response_norm::kOperationName,
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local_response_norm::validate, local_response_norm::prepare,
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local_response_norm::execute);
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} // namespace nn
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} // namespace android
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