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/*
* Copyright (C) 2018 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 <cmath>
#include <vector>
#include "CpuOperationUtils.h"
#include "HalInterfaces.h"
#include "OperationResolver.h"
#include "Tracing.h"
namespace android {
namespace nn {
namespace instance_normalization {
constexpr char kOperationName[] = "INSTANCE_NORMALIZATION";
constexpr uint32_t kNumInputs = 5;
constexpr uint32_t kInputTensor = 0;
constexpr uint32_t kGammaScalar = 1;
constexpr uint32_t kBetaScalar = 2;
constexpr uint32_t kEpsilonScalar = 3;
constexpr uint32_t kLayoutScalar = 4;
constexpr uint32_t kNumOutputs = 1;
constexpr uint32_t kOutputTensor = 0;
namespace {
using namespace hal;
template <typename T>
inline bool instanceNormNhwc(const T* inputData, const Shape& inputShape, T gamma, T beta,
T epsilon, T* outputData, const Shape& outputShape) {
NNTRACE_TRANS("InstanceNormalizationNhwc");
uint32_t numBatches = getSizeOfDimension(inputShape, 0);
uint32_t height = getSizeOfDimension(inputShape, 1);
uint32_t width = getSizeOfDimension(inputShape, 2);
uint32_t depth = getSizeOfDimension(inputShape, 3);
for (uint32_t b = 0; b < numBatches; b++) {
for (uint32_t d = 0; d < depth; d++) {
uint32_t indexBase = b * height * width * depth + d;
T mean = 0, sigma = 0;
// Compute the mean of a single layer.
for (uint32_t h = 0; h < height; h++) {
for (uint32_t w = 0; w < width; w++) {
T val = inputData[indexBase + (h * width + w) * depth];
mean += val;
}
}
mean /= static_cast<T>(height * width);
// Compute the standard deviation (sigma) of a single layer.
for (uint32_t h = 0; h < height; h++) {
for (uint32_t w = 0; w < width; w++) {
T val = inputData[indexBase + (h * width + w) * depth] - mean;
sigma += val * val;
}
}
sigma = std::sqrt(static_cast<float>(sigma / static_cast<T>(height * width)) + epsilon);
// Apply instance normalization.
for (uint32_t h = 0; h < height; h++) {
for (uint32_t w = 0; w < width; w++) {
uint32_t ind = indexBase + (h * width + w) * depth;
outputData[ind] = (inputData[ind] - mean) * gamma / sigma + beta;
}
}
}
}
return true;
}
template <typename T>
inline bool instanceNorm(const T* inputData, const Shape& inputShape, T gamma, T beta, T epsilon,
bool useNchw, T* outputData, const Shape& outputShape) {
InputWithLayout<T> input(useNchw);
OutputWithLayout<T> output(useNchw);
NN_RET_CHECK(input.initialize(inputData, inputShape));
NN_RET_CHECK(output.initialize(outputData, outputShape));
NN_RET_CHECK(instanceNormNhwc(input.getNhwcBuffer(), input.getNhwcShape(), gamma, beta, epsilon,
output.getNhwcBuffer(), output.getNhwcShape()));
NN_RET_CHECK(output.commit());
return true;
}
} // namespace
bool validate(const IOperationValidationContext* context) {
NN_RET_CHECK_EQ(context->getNumInputs(), kNumInputs);
NN_RET_CHECK_EQ(context->getNumOutputs(), kNumOutputs);
std::vector<OperandType> inExpectedTypes;
auto inputType = context->getInputType(kInputTensor);
if (inputType == OperandType::TENSOR_FLOAT32) {
inExpectedTypes = {OperandType::TENSOR_FLOAT32, OperandType::FLOAT32, OperandType::FLOAT32,
OperandType::FLOAT32, OperandType::BOOL};
} else if (inputType == OperandType::TENSOR_FLOAT16) {
inExpectedTypes = {OperandType::TENSOR_FLOAT16, OperandType::FLOAT16, OperandType::FLOAT16,
OperandType::FLOAT16, OperandType::BOOL};
} else {
LOG(ERROR) << "Unsupported input tensor type for operation " << kOperationName;
return false;
}
NN_RET_CHECK(validateInputTypes(context, inExpectedTypes));
NN_RET_CHECK(validateOutputTypes(context, {inputType}));
return validateHalVersion(context, HalVersion::V1_2);
}
bool prepare(IOperationExecutionContext* context) {
Shape input = context->getInputShape(kInputTensor);
NN_RET_CHECK_EQ(getNumberOfDimensions(input), 4);
return context->setOutputShape(kOutputTensor, input);
}
bool execute(IOperationExecutionContext* context) {
switch (context->getInputType(kInputTensor)) {
case OperandType::TENSOR_FLOAT16:
return instanceNorm(context->getInputBuffer<_Float16>(kInputTensor),
context->getInputShape(kInputTensor),
context->getInputValue<_Float16>(kGammaScalar),
context->getInputValue<_Float16>(kBetaScalar),
context->getInputValue<_Float16>(kEpsilonScalar),
context->getInputValue<bool>(kLayoutScalar),
context->getOutputBuffer<_Float16>(kOutputTensor),
context->getOutputShape(kOutputTensor));
case OperandType::TENSOR_FLOAT32:
return instanceNorm(context->getInputBuffer<float>(kInputTensor),
context->getInputShape(kInputTensor),
context->getInputValue<float>(kGammaScalar),
context->getInputValue<float>(kBetaScalar),
context->getInputValue<float>(kEpsilonScalar),
context->getInputValue<bool>(kLayoutScalar),
context->getOutputBuffer<float>(kOutputTensor),
context->getOutputShape(kOutputTensor));
default:
NN_RET_CHECK_FAIL() << "Unsupported tensor type for operation " << kOperationName;
}
}
} // namespace instance_normalization
NN_REGISTER_OPERATION(INSTANCE_NORMALIZATION, instance_normalization::kOperationName,
instance_normalization::validate, instance_normalization::prepare,
instance_normalization::execute);
} // namespace nn
} // namespace android
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