118 lines
4.1 KiB
C++
118 lines
4.1 KiB
C++
/*
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* Copyright (C) 2018 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 "TopK_V2.h"
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#include <algorithm>
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#include <utility>
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#include <vector>
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#include "OperationResolver.h"
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#include "OperationsExecutionUtils.h"
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namespace android {
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namespace nn {
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namespace topk_v2 {
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namespace {
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template <typename T>
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bool evalGeneric(const T* inputData, const Shape& inputShape, const int32_t k, T* valuesData,
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int32_t* indicesData) {
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const int rowSize = inputShape.dimensions.back();
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const int totalSize = getNumberOfElements(inputShape);
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std::vector<std::pair<T, int32_t>> values(rowSize);
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T* curOutputValue = valuesData;
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int32_t* curOutputIndex = indicesData;
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for (int rowBegin = 0; rowBegin < totalSize; rowBegin += rowSize) {
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for (int i = 0; i < rowSize; ++i) {
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values[i] = std::make_pair(inputData[rowBegin + i], i);
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}
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std::nth_element(values.begin(), values.begin() + (rowSize - k), values.end());
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std::sort(values.begin() + (rowSize - k), values.end());
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std::reverse(values.begin(), values.end());
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for (int i = 0; i < k; ++i) {
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*curOutputValue = values[i].first;
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*curOutputIndex = values[i].second;
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curOutputValue++;
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curOutputIndex++;
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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 executeTyped(IOperationExecutionContext* context) {
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return evalGeneric(context->getInputBuffer<T>(kInputTensor),
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context->getInputShape(kInputTensor),
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context->getInputValue<int32_t>(kTopKScalar),
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context->getOutputBuffer<T>(kOutputValuesTensor),
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context->getOutputBuffer<int32_t>(kOutputIndicesTensor));
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}
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} // namespace
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bool prepare(IOperationExecutionContext* context) {
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const Shape inputShape = context->getInputShape(kInputTensor);
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const int32_t k = context->getInputValue<int32_t>(kTopKScalar);
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NN_RET_CHECK_GT(k, 0);
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NN_RET_CHECK_LE(static_cast<uint32_t>(k), inputShape.dimensions.back());
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// Copy input shape to ensure that quantization parameters for the output
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// values are the same as for the input tensor.
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Shape outputValuesShape = inputShape;
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outputValuesShape.dimensions.back() = k;
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Shape outputIndicesShape;
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outputIndicesShape.type = OperandType::TENSOR_INT32;
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outputIndicesShape.dimensions = inputShape.dimensions;
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outputIndicesShape.dimensions.back() = k;
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return context->setOutputShape(kOutputValuesTensor, outputValuesShape) &&
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context->setOutputShape(kOutputIndicesTensor, outputIndicesShape);
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}
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bool execute(IOperationExecutionContext* context) {
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const Shape inputShape = context->getInputShape(kInputTensor);
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switch (inputShape.type) {
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case OperandType::TENSOR_FLOAT16: {
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return executeTyped<_Float16>(context);
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} break;
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case OperandType::TENSOR_FLOAT32: {
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return executeTyped<float>(context);
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} break;
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case OperandType::TENSOR_INT32: {
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return executeTyped<int32_t>(context);
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} break;
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case OperandType::TENSOR_QUANT8_ASYMM: {
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return executeTyped<uint8_t>(context);
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} break;
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case OperandType::TENSOR_QUANT8_ASYMM_SIGNED: {
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return executeTyped<int8_t>(context);
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} break;
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default: {
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LOG(ERROR) << "Unsupported data type: " << inputShape.type;
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return false;
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}
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}
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}
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} // namespace topk_v2
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NN_REGISTER_OPERATION_DEFAULT_VALIDATION(TOPK_V2, topk_v2::prepare, topk_v2::execute);
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} // namespace nn
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} // namespace android
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