/*
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* Copyright (C) 2017 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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#include "ActivationFunctor.h"
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#include "CpuOperationUtils.h"
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#include "OperationResolver.h"
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#include "tensorflow/lite/kernels/internal/optimized/legacy_optimized_ops.h"
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#include "tensorflow/lite/kernels/internal/optimized/optimized_ops.h"
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#include "Tracing.h"
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namespace android {
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namespace nn {
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namespace activation {
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constexpr uint32_t kNumInputs = 1;
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constexpr uint32_t kInputTensor = 0;
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constexpr uint32_t kNumOutputs = 1;
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constexpr uint32_t kOutputTensor = 0;
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namespace {
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template <typename T>
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bool reluFloat(const T* inputData, const Shape& inputShape, T* outputData, const Shape& outputShape,
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float reluMin = 0.f, float reluMax = std::numeric_limits<float>::max()) {
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NNTRACE_COMP("reluX");
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int numElements = getNumberOfElements(inputShape);
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for (int i = 0; i < numElements; i++, inputData++, outputData++) {
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*outputData = static_cast<T>(
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std::min(std::max(reluMin, static_cast<float>(*inputData)), reluMax));
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}
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return true;
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}
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template bool reluFloat<float>(const float* inputData, const Shape& inputShape, float* outputData,
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const Shape& outputShape, float reluMin, float reluMax);
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template bool reluFloat<_Float16>(const _Float16* inputData, const Shape& inputShape,
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_Float16* outputData, const Shape& outputShape, float reluMin,
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float reluMax);
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template <typename T>
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bool relu1Float(const T* inputData, const Shape& inputShape, T* outputData,
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const Shape& outputShape) {
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return reluFloat(inputData, inputShape, outputData, outputShape, -1.f, 1.f);
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}
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template bool relu1Float<float>(const float* inputData, const Shape& inputShape, float* outputData,
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const Shape& outputShape);
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template bool relu1Float<_Float16>(const _Float16* inputData, const Shape& inputShape,
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_Float16* outputData, const Shape& outputShape);
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template <typename T>
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bool relu6Float(const T* inputData, const Shape& inputShape, T* outputData,
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const Shape& outputShape) {
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return reluFloat(inputData, inputShape, outputData, outputShape, 0.f, 6.f);
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}
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template bool relu6Float<float>(const float* inputData, const Shape& inputShape, float* outputData,
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const Shape& outputShape);
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template bool relu6Float<_Float16>(const _Float16* inputData, const Shape& inputShape,
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_Float16* outputData, const Shape& outputShape);
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bool tanhFloat16(const _Float16* inputData, const Shape& inputShape, _Float16* outputData,
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const Shape& outputShape) {
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NNTRACE_COMP("tanhFloat16");
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int numElements = getNumberOfElements(inputShape);
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for (int i = 0; i < numElements; i++, inputData++, outputData++) {
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*outputData = static_cast<_Float16>(std::tanh(static_cast<float>(*inputData)));
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}
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return true;
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}
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bool tanhFloat32(const float* inputData, const Shape& inputShape, float* outputData,
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const Shape& outputShape) {
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NNTRACE_COMP("tanhFloat32");
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int numElements = getNumberOfElements(inputShape);
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for (int i = 0; i < numElements; i++, inputData++, outputData++) {
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*outputData = std::tanh(*inputData);
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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 logisticFloat(const T* inputData, const Shape& inputShape, T* outputData,
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const Shape& outputShape) {
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NNTRACE_COMP("logisticFloat");
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int numElements = getNumberOfElements(inputShape);
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for (int i = 0; i < numElements; i++, inputData++, outputData++) {
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*outputData = static_cast<T>(1.f / (1.f + std::exp(static_cast<float>(-*inputData))));
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}
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return true;
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}
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template bool logisticFloat<float>(const float* inputData, const Shape& inputShape,
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float* outputData, const Shape& outputShape);
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template bool logisticFloat<_Float16>(const _Float16* inputData, const Shape& inputShape,
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_Float16* outputData, const Shape& outputShape);
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#define ANDROID_NN_RELUX_QUANT8(activation) \
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int numElements = getNumberOfElements(inputShape); \
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int32_t output_activation_min = 0; \
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int32_t output_activation_max = 0; \
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\
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CalculateActivationRangeUint8(activation, inputShape, &output_activation_min, \
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&output_activation_max); \
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\
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for (int i = 0; i < numElements; i++, inputData++, outputData++) { \
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*outputData = std::min((uint8_t)output_activation_max, \
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std::max((uint8_t)output_activation_min, *inputData)); \
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}
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bool reluQuant8(const uint8_t* inputData, const Shape& inputShape, uint8_t* outputData,
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const Shape& outputShape) {
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NNTRACE_COMP("reluQuant8");
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ANDROID_NN_RELUX_QUANT8(kActivationRelu)
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return true;
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}
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bool relu1Quant8(const uint8_t* inputData, const Shape& inputShape, uint8_t* outputData,
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const Shape& outputShape) {
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NNTRACE_COMP("relu1Quant8");
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ANDROID_NN_RELUX_QUANT8(kActivationRelu1)
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return true;
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}
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bool relu6Quant8(const uint8_t* inputData, const Shape& inputShape, uint8_t* outputData,
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const Shape& outputShape) {
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NNTRACE_COMP("relu6Quant8");
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ANDROID_NN_RELUX_QUANT8(kActivationRelu6)
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return true;
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}
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#undef ANDROID_NN_RELUX_QUANT8
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bool tanhQuant8(const uint8_t* inputData, const Shape& inputShape, uint8_t* outputData,
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const Shape& outputShape) {
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NNTRACE_TRANS("tanhQuant8");
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if (outputShape.offset != 128 || outputShape.scale != 1.f / 128) {
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LOG(ERROR) << "incorrect scale or offset for TANH output";
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return false;
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}
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int numElements = getNumberOfElements(inputShape);
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static constexpr int kInputIntegerBits = 4;
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const double input_real_multiplier =
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inputShape.scale * static_cast<double>(1 << (31 - kInputIntegerBits));
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int32_t input_multiplier = 0;
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int32_t input_left_shift = 0;
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if (!QuantizeMultiplierGreaterThanOne(input_real_multiplier, &input_multiplier,
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&input_left_shift)) {
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return false;
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}
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int32_t input_range_radius = CalculateInputRadius(kInputIntegerBits, input_left_shift);
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NNTRACE_COMP_SWITCH("optimized_ops::Tanh");
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tflite::optimized_ops::Tanh(inputData, convertShapeToTflshape(inputShape), inputShape.offset,
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input_range_radius, input_multiplier, input_left_shift, outputData,
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convertShapeToTflshape(outputShape));
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return true;
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}
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bool logisticQuant8(const uint8_t* inputData, const Shape& inputShape, uint8_t* outputData,
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const Shape& outputShape) {
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NNTRACE_TRANS("logisticQuant8");
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if (outputShape.offset != 0 || outputShape.scale != 1.f / 256) {
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LOG(ERROR) << "incorrect scale / offset for output";
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return false;
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}
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int numElements = getNumberOfElements(inputShape);
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static constexpr int kInputIntegerBits = 4;
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const double input_real_multiplier =
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inputShape.scale * static_cast<double>(1 << (31 - kInputIntegerBits));
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int32_t input_multiplier = 0;
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int32_t input_left_shift = 0;
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if (!QuantizeMultiplierGreaterThanOne(input_real_multiplier, &input_multiplier,
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&input_left_shift)) {
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return false;
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}
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int32_t input_range_radius = CalculateInputRadius(kInputIntegerBits, input_left_shift);
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NNTRACE_COMP_SWITCH("optimized_ops::Logistic");
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tflite::optimized_ops::Logistic(
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inputData, convertShapeToTflshape(inputShape), inputShape.offset, input_range_radius,
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input_multiplier, input_left_shift, outputData, convertShapeToTflshape(outputShape));
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return true;
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}
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} // namespace
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bool validate(OperationType opType, const IOperationValidationContext* context) {
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NN_RET_CHECK_EQ(context->getNumInputs(), kNumInputs);
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NN_RET_CHECK_EQ(context->getNumOutputs(), kNumOutputs);
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auto inputType = context->getInputType(kInputTensor);
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if (inputType == OperandType::TENSOR_FLOAT32) {
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NN_RET_CHECK(validateHalVersion(context, HalVersion::V1_0));
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} else if (inputType == OperandType::TENSOR_FLOAT16) {
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NN_RET_CHECK(validateHalVersion(context, HalVersion::V1_2));
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} else if (inputType == OperandType::TENSOR_QUANT8_ASYMM) {
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if (opType == OperationType::TANH) {
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NN_RET_CHECK(validateHalVersion(context, HalVersion::V1_2));
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} else {
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NN_RET_CHECK(validateHalVersion(context, HalVersion::V1_0));
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}
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} else {
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NN_RET_CHECK_FAIL() << "Unsupported tensor type for operation " << getOperationName(opType);
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}
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return validateInputTypes(context, {inputType}) && validateOutputTypes(context, {inputType});
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}
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bool prepare(OperationType opType, IOperationExecutionContext* context) {
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Shape input = context->getInputShape(kInputTensor);
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NN_RET_CHECK_LE(getNumberOfDimensions(input), 4);
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Shape output = input;
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if (input.type == OperandType::TENSOR_QUANT8_ASYMM) {
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switch (opType) {
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case OperationType::RELU:
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case OperationType::RELU1:
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case OperationType::RELU6:
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break;
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case OperationType::LOGISTIC:
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output.scale = 1.f / 256;
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output.offset = 0;
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break;
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case OperationType::TANH:
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output.scale = 1.f / 128;
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output.offset = 128;
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break;
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default:
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NN_RET_CHECK_FAIL() << "Unsupported operation type";
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}
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}
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return context->setOutputShape(kOutputTensor, output);
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}
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bool executeRelu(IOperationExecutionContext* context) {
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// Bypass execution in the case of zero-sized input.
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if (getNumberOfElements(context->getOutputShape(kOutputTensor)) == 0) return true;
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switch (context->getInputType(kInputTensor)) {
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case OperandType::TENSOR_FLOAT16:
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return reluFloat(context->getInputBuffer<_Float16>(kInputTensor),
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context->getInputShape(kInputTensor),
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context->getOutputBuffer<_Float16>(kOutputTensor),
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context->getOutputShape(kOutputTensor));
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case OperandType::TENSOR_FLOAT32:
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return reluFloat(context->getInputBuffer<float>(kInputTensor),
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context->getInputShape(kInputTensor),
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context->getOutputBuffer<float>(kOutputTensor),
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context->getOutputShape(kOutputTensor));
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case OperandType::TENSOR_QUANT8_ASYMM:
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return reluQuant8(context->getInputBuffer<uint8_t>(kInputTensor),
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context->getInputShape(kInputTensor),
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context->getOutputBuffer<uint8_t>(kOutputTensor),
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context->getOutputShape(kOutputTensor));
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default:
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NN_RET_CHECK_FAIL() << "Unsupported tensor type for operation RELU";
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}
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}
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bool executeRelu1(IOperationExecutionContext* context) {
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// Bypass execution in the case of zero-sized input.
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if (getNumberOfElements(context->getOutputShape(kOutputTensor)) == 0) return true;
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switch (context->getInputType(kInputTensor)) {
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case OperandType::TENSOR_FLOAT16:
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return relu1Float(context->getInputBuffer<_Float16>(kInputTensor),
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context->getInputShape(kInputTensor),
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context->getOutputBuffer<_Float16>(kOutputTensor),
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context->getOutputShape(kOutputTensor));
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case OperandType::TENSOR_FLOAT32:
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return relu1Float(context->getInputBuffer<float>(kInputTensor),
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context->getInputShape(kInputTensor),
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context->getOutputBuffer<float>(kOutputTensor),
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context->getOutputShape(kOutputTensor));
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case OperandType::TENSOR_QUANT8_ASYMM:
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return relu1Quant8(context->getInputBuffer<uint8_t>(kInputTensor),
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context->getInputShape(kInputTensor),
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context->getOutputBuffer<uint8_t>(kOutputTensor),
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context->getOutputShape(kOutputTensor));
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default:
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NN_RET_CHECK_FAIL() << "Unsupported tensor type for operation RELU1";
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}
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}
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bool executeRelu6(IOperationExecutionContext* context) {
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// Bypass execution in the case of zero-sized input.
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if (getNumberOfElements(context->getOutputShape(kOutputTensor)) == 0) return true;
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switch (context->getInputType(kInputTensor)) {
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case OperandType::TENSOR_FLOAT16:
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return relu6Float(context->getInputBuffer<_Float16>(kInputTensor),
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context->getInputShape(kInputTensor),
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context->getOutputBuffer<_Float16>(kOutputTensor),
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context->getOutputShape(kOutputTensor));
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case OperandType::TENSOR_FLOAT32:
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return relu6Float(context->getInputBuffer<float>(kInputTensor),
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context->getInputShape(kInputTensor),
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context->getOutputBuffer<float>(kOutputTensor),
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context->getOutputShape(kOutputTensor));
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case OperandType::TENSOR_QUANT8_ASYMM:
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return relu6Quant8(context->getInputBuffer<uint8_t>(kInputTensor),
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context->getInputShape(kInputTensor),
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context->getOutputBuffer<uint8_t>(kOutputTensor),
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context->getOutputShape(kOutputTensor));
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default:
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NN_RET_CHECK_FAIL() << "Unsupported tensor type for operation RELU6";
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}
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}
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bool executeLogistic(IOperationExecutionContext* context) {
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// Bypass execution in the case of zero-sized input.
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if (getNumberOfElements(context->getOutputShape(kOutputTensor)) == 0) return true;
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switch (context->getInputType(kInputTensor)) {
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case OperandType::TENSOR_FLOAT16:
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return logisticFloat(context->getInputBuffer<_Float16>(kInputTensor),
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context->getInputShape(kInputTensor),
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context->getOutputBuffer<_Float16>(kOutputTensor),
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context->getOutputShape(kOutputTensor));
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case OperandType::TENSOR_FLOAT32:
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return logisticFloat(context->getInputBuffer<float>(kInputTensor),
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context->getInputShape(kInputTensor),
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context->getOutputBuffer<float>(kOutputTensor),
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context->getOutputShape(kOutputTensor));
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case OperandType::TENSOR_QUANT8_ASYMM:
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return logisticQuant8(context->getInputBuffer<uint8_t>(kInputTensor),
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context->getInputShape(kInputTensor),
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context->getOutputBuffer<uint8_t>(kOutputTensor),
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context->getOutputShape(kOutputTensor));
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default:
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NN_RET_CHECK_FAIL() << "Unsupported tensor type for operation LOGISTIC";
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}
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}
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bool executeTanh(IOperationExecutionContext* context) {
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// Bypass execution in the case of zero-sized input.
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if (getNumberOfElements(context->getOutputShape(kOutputTensor)) == 0) return true;
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switch (context->getInputType(kInputTensor)) {
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case OperandType::TENSOR_FLOAT16:
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return tanhFloat16(context->getInputBuffer<_Float16>(kInputTensor),
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context->getInputShape(kInputTensor),
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context->getOutputBuffer<_Float16>(kOutputTensor),
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context->getOutputShape(kOutputTensor));
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case OperandType::TENSOR_FLOAT32:
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return tanhFloat32(context->getInputBuffer<float>(kInputTensor),
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context->getInputShape(kInputTensor),
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context->getOutputBuffer<float>(kOutputTensor),
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context->getOutputShape(kOutputTensor));
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case OperandType::TENSOR_QUANT8_ASYMM:
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return tanhQuant8(context->getInputBuffer<uint8_t>(kInputTensor),
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context->getInputShape(kInputTensor),
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context->getOutputBuffer<uint8_t>(kOutputTensor),
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context->getOutputShape(kOutputTensor));
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default:
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NN_RET_CHECK_FAIL() << "Unsupported tensor type for operation TANH";
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}
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}
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} // namespace activation
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using std::placeholders::_1;
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NN_REGISTER_OPERATION(RELU, "RELU", std::bind(activation::validate, OperationType::RELU, _1),
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std::bind(activation::prepare, OperationType::RELU, _1),
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activation::executeRelu, .allowZeroSizedInput = true);
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NN_REGISTER_OPERATION(RELU1, "RELU1", std::bind(activation::validate, OperationType::RELU1, _1),
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std::bind(activation::prepare, OperationType::RELU1, _1),
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activation::executeRelu1, .allowZeroSizedInput = true);
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NN_REGISTER_OPERATION(RELU6, "RELU6", std::bind(activation::validate, OperationType::RELU6, _1),
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std::bind(activation::prepare, OperationType::RELU6, _1),
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activation::executeRelu6, .allowZeroSizedInput = true);
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NN_REGISTER_OPERATION(LOGISTIC, "LOGISTIC",
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std::bind(activation::validate, OperationType::LOGISTIC, _1),
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std::bind(activation::prepare, OperationType::LOGISTIC, _1),
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activation::executeLogistic, .allowZeroSizedInput = true);
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NN_REGISTER_OPERATION(TANH, "TANH", std::bind(activation::validate, OperationType::TANH, _1),
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std::bind(activation::prepare, OperationType::TANH, _1),
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activation::executeTanh, .allowZeroSizedInput = true);
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} // namespace nn
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} // namespace android
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