109 lines
4.6 KiB
C++
109 lines
4.6 KiB
C++
/*
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* Copyright (C) 2019 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 "Quantize.h"
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#include <algorithm>
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#include <cmath>
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#include "IndexedShapeWrapper.h"
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#include "OperationResolver.h"
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#include "OperationsExecutionUtils.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 quantize {
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namespace {
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// The quantization formula also appears in Elementwise.cpp.
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template <typename T>
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bool quantizeToQuant8(const T* inputData, uint8_t* outputData, const Shape& outputShape) {
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NNTRACE_COMP("quantizeToQuant8");
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uint32_t size = getNumberOfElements(outputShape);
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for (uint32_t i = 0; i < size; ++i) {
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outputData[i] = static_cast<uint8_t>(std::max<float>(
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0.0f, std::min<float>(255.0f, outputShape.offset + std::round(inputData[i] /
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outputShape.scale))));
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}
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return true;
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}
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// The quantization formula also appears in Elementwise.cpp.
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template <typename T>
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bool quantizeToQuant8Signed(const T* inputData, int8_t* outputData, const Shape& outputShape) {
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NNTRACE_COMP("quantizeToQuant8Signed");
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uint32_t size = getNumberOfElements(outputShape);
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for (uint32_t i = 0; i < size; ++i) {
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outputData[i] = static_cast<int8_t>(std::max<float>(
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-128.0f,
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std::min<float>(127.0f, outputShape.offset +
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std::round(inputData[i] / outputShape.scale))));
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}
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return true;
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}
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} // namespace
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bool prepare(IOperationExecutionContext* context) {
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const Shape& input = context->getInputShape(kInputTensor);
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Shape output = context->getOutputShape(kOutputTensor);
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output.dimensions = input.dimensions;
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return context->setOutputShape(kOutputTensor, output);
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}
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bool execute(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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const OperandType inputType = context->getInputType(kInputTensor);
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const OperandType outputType = context->getOutputType(kOutputTensor);
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if (inputType == OperandType::TENSOR_FLOAT32) {
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if (outputType == OperandType::TENSOR_QUANT8_ASYMM) {
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return quantizeToQuant8<float>(context->getInputBuffer<float>(kInputTensor),
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context->getOutputBuffer<uint8_t>(kOutputTensor),
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context->getOutputShape(kOutputTensor));
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} else if (outputType == OperandType::TENSOR_QUANT8_ASYMM_SIGNED) {
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return quantizeToQuant8Signed<float>(context->getInputBuffer<float>(kInputTensor),
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context->getOutputBuffer<int8_t>(kOutputTensor),
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context->getOutputShape(kOutputTensor));
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}
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} else if (inputType == OperandType::TENSOR_FLOAT16) {
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if (outputType == OperandType::TENSOR_QUANT8_ASYMM) {
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return quantizeToQuant8<_Float16>(context->getInputBuffer<_Float16>(kInputTensor),
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context->getOutputBuffer<uint8_t>(kOutputTensor),
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context->getOutputShape(kOutputTensor));
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} else if (outputType == OperandType::TENSOR_QUANT8_ASYMM_SIGNED) {
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return quantizeToQuant8Signed<_Float16>(context->getInputBuffer<_Float16>(kInputTensor),
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context->getOutputBuffer<int8_t>(kOutputTensor),
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context->getOutputShape(kOutputTensor));
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}
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}
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NN_RET_CHECK_FAIL() << "Unsupported tensor types combination for QUANTIZE op. (input type: "
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<< inputType << " output type: " << context->getOutputType(kOutputTensor)
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<< ")";
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}
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} // namespace quantize
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NN_REGISTER_OPERATION_DEFAULT_VALIDATION(QUANTIZE, quantize::prepare, quantize::execute,
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.allowZeroSizedInput = true);
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} // namespace nn
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} // namespace android
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