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-rw-r--r--runtimes/nn/common/operations/Activation.cpp211
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diff --git a/runtimes/nn/common/operations/Activation.cpp b/runtimes/nn/common/operations/Activation.cpp
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--- a/runtimes/nn/common/operations/Activation.cpp
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-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- * Copyright (C) 2017 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.
- */
-
-#include "Operations.h"
-#include "OperationsUtils.h"
-
-#include "internal/optimized/optimized_ops.h"
-
-// TODO-NNRT: There was no inlcude "ActivationFunctor.h" in Android NN.
-// This may be included from some other header files.
-#include "ActivationFunctor.h"
-
-namespace nnfw {
-namespace rt {
-
-bool reluFloat32(const float* inputData, const Shape& inputShape,
- float* outputData, const Shape& outputShape) {
- int numElements = getNumberOfElements(inputShape);
- for (int i=0; i<numElements; i++, inputData++, outputData++) {
- *outputData = std::max(0.f, *inputData);
- }
- return true;
-}
-
-bool relu1Float32(const float* inputData, const Shape& inputShape,
- float* outputData, const Shape& outputShape) {
- int numElements = getNumberOfElements(inputShape);
- for (int i=0; i<numElements; i++, inputData++, outputData++) {
- *outputData = std::min(std::max(-1.f, *inputData), 1.f);
- }
- return true;
-}
-
-bool relu6Float32(const float* inputData, const Shape& inputShape,
- float* outputData, const Shape& outputShape) {
- int numElements = getNumberOfElements(inputShape);
- for (int i=0; i<numElements; i++, inputData++, outputData++) {
- *outputData = std::min(std::max(0.f, *inputData), 6.f);
- }
- return true;
-}
-
-bool tanhFloat32(const float* inputData, const Shape& inputShape,
- float* outputData, const Shape& outputShape) {
- int numElements = getNumberOfElements(inputShape);
- for (int i=0; i<numElements; i++, inputData++, outputData++) {
- *outputData = std::tanh(*inputData);
- }
- return true;
-}
-
-bool logisticFloat32(const float* inputData, const Shape& inputShape,
- float* outputData, const Shape& outputShape) {
- int numElements = getNumberOfElements(inputShape);
- for (int i=0; i<numElements; i++, inputData++, outputData++) {
- *outputData = 1.f / (1.f + std::exp(-*inputData));
- }
- return true;
-}
-
-bool softmaxFloat32(const float* inputData, const Shape& inputShape,
- const float beta,
- float* outputData, const Shape& outputShape) {
- Dims<4> dim;
- if (getNumberOfDimensions(inputShape) == 2) {
- uint32_t batch_size = getSizeOfDimension(inputShape, 0);
- uint32_t input_size = getNumberOfElements(inputShape) / batch_size;
-
- Shape shapeIn4D;
- shapeIn4D.dimensions = {batch_size, 1, 1, input_size};
- dim = convertShapeToDims(shapeIn4D);
- } else if (getNumberOfDimensions(inputShape) == 4) {
- dim = convertShapeToDims(inputShape);
- } else {
- LOG(ERROR) << "only 2D and 4D tensors supported";
- return false;
- }
-
- optimized_ops::Softmax(inputData, dim, beta,
- outputData, dim);
- return true;
-}
-
-#define ANDROID_NN_RELUX_QUANT8(activation) \
- int numElements = getNumberOfElements(inputShape); \
- int32_t output_activation_min = 0; \
- int32_t output_activation_max = 0; \
- \
- CalculateActivationRangeUint8(activation, inputShape, \
- &output_activation_min, \
- &output_activation_max); \
- \
- for (int i=0; i<numElements; i++, inputData++, outputData++) { \
- *outputData = std::min((uint8_t)output_activation_max, \
- std::max((uint8_t)output_activation_min, *inputData)); \
- }
-
-
-bool reluQuant8(const uint8_t* inputData, const Shape& inputShape,
- uint8_t* outputData, const Shape& outputShape) {
- ANDROID_NN_RELUX_QUANT8(kActivationRelu)
- return true;
-}
-
-bool relu1Quant8(const uint8_t* inputData, const Shape& inputShape,
- uint8_t* outputData, const Shape& outputShape) {
- ANDROID_NN_RELUX_QUANT8(kActivationRelu1)
- return true;
-}
-
-bool relu6Quant8(const uint8_t* inputData, const Shape& inputShape,
- uint8_t* outputData, const Shape& outputShape) {
- ANDROID_NN_RELUX_QUANT8(kActivationRelu6)
- return true;
-}
-
-#undef ANDROID_NN_RELUX_QUANT8
-
-bool logisticQuant8(const uint8_t* inputData, const Shape& inputShape,
- uint8_t* outputData, const Shape& outputShape) {
- if (outputShape.offset != 0 || outputShape.scale != 1.f / 256) {
- LOG(ERROR) << "incorrect scale / offset for output";
- return false;
- }
-
- static constexpr int kInputIntegerBits = 4;
-
- const double input_real_multiplier =
- inputShape.scale *
- static_cast<double>(1 << (31 - kInputIntegerBits));
-
- int32_t input_multiplier = 0;
- int32_t input_left_shift = 0;
- if (!QuantizeMultiplierGreaterThanOne(input_real_multiplier,
- &input_multiplier,
- &input_left_shift)) {
- return false;
- }
- int32_t input_range_radius =
- CalculateInputRadius(kInputIntegerBits, input_left_shift);
-
- optimized_ops::Logistic(
- inputData, convertShapeToDims(inputShape),
- inputShape.offset, input_range_radius,
- input_multiplier, input_left_shift,
- outputData, convertShapeToDims(outputShape));
-
- return true;
-}
-
-bool softmaxQuant8(const uint8_t* inputData, const Shape& inputShape,
- const float beta,
- uint8_t* outputData, const Shape& outputShape) {
- Dims<4> dim;
- if (getNumberOfDimensions(inputShape) == 2) {
- uint32_t batch_size = getSizeOfDimension(inputShape, 0);
- uint32_t input_size = getNumberOfElements(inputShape) / batch_size;
-
- Shape shapeIn4D;
- shapeIn4D.dimensions = {batch_size, 1, 1, input_size};
- dim = convertShapeToDims(shapeIn4D);
- } else if (getNumberOfDimensions(inputShape) == 4) {
- dim = convertShapeToDims(inputShape);
- } else {
- LOG(ERROR) << "only 2D and 4D tensors supported";
- return false;
- }
-
- if (outputShape.offset != 0 || outputShape.scale != 1.f / 256) {
- LOG(ERROR) << "incorrect scale / offset for output";
- return false;
- }
-
- static const int32_t kScaledDiffIntegerBits = 5;
- const double input_beta_real_multiplier = std::min(
- 1.0 * beta * inputShape.scale * (1 << (31 - kScaledDiffIntegerBits)),
- (1ll << 31) - 1.0);
-
- int32_t input_multiplier = 0;
- int32_t input_left_shift = 0;
- if (!QuantizeMultiplierGreaterThanOne(input_beta_real_multiplier,
- &input_multiplier,
- &input_left_shift)) {
- return false;
- }
- float diff_min = -1.0f * CalculateInputRadius(kScaledDiffIntegerBits,
- input_left_shift);
-
- optimized_ops::Softmax(inputData, dim, input_multiplier,
- input_left_shift, diff_min,
- outputData, dim);
- return true;
-}
-
-
-} // namespace rt
-} // namespace nnfw
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