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340 lines
17 KiB
340 lines
17 KiB
/*
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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 <algorithm>
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#include <cfloat>
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#include <cmath>
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#include <vector>
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#include "OperationResolver.h"
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#include "OperationsUtils.h"
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#include "Tracing.h"
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#ifdef NN_INCLUDE_CPU_IMPLEMENTATION
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#include "CpuOperationUtils.h"
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#endif // NN_INCLUDE_CPU_IMPLEMENTATION
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namespace android {
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namespace nn {
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namespace roi_pooling {
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constexpr char kOperationName[] = "ROI_POOLING";
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constexpr uint32_t kNumInputs = 8;
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constexpr uint32_t kInputTensor = 0;
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constexpr uint32_t kRoiTensor = 1;
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constexpr uint32_t kBatchSplitTensor = 2;
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constexpr uint32_t kOutputHeightScalar = 3;
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constexpr uint32_t kOutputWidthScalar = 4;
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constexpr uint32_t kHeightStrideSalar = 5;
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constexpr uint32_t kWidthStrideScalar = 6;
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constexpr uint32_t kLayoutScalar = 7;
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constexpr uint32_t kNumOutputs = 1;
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constexpr uint32_t kOutputTensor = 0;
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#ifdef NN_INCLUDE_CPU_IMPLEMENTATION
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namespace {
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template <typename T_Input, typename T_Roi>
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inline bool roiPoolingNhwc(const T_Input* inputData, const Shape& inputShape, const T_Roi* roiData,
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const Shape& roiShape, const int32_t* batchSplitData,
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const Shape& batchSplitShape, float heightStride, float widthStride,
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T_Input* outputData, const Shape& outputShape) {
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NNTRACE_TRANS("RoiPooling");
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const uint32_t kRoiDim = 4;
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const T_Roi heightScale = 1.0f / heightStride;
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const T_Roi widthScale = 1.0f / widthStride;
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uint32_t numBatches = getSizeOfDimension(inputShape, 0);
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uint32_t inHeight = getSizeOfDimension(inputShape, 1);
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uint32_t inWidth = getSizeOfDimension(inputShape, 2);
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uint32_t inDepth = getSizeOfDimension(inputShape, 3);
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uint32_t outHeight = getSizeOfDimension(outputShape, 1);
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uint32_t outWidth = getSizeOfDimension(outputShape, 2);
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uint32_t numRois = getSizeOfDimension(roiShape, 0);
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uint32_t roiInfoLength = getSizeOfDimension(roiShape, 1);
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T_Input* outPtr = outputData;
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const T_Roi* roiDataEnd = roiData + numRois * roiInfoLength;
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uint32_t roiIndex = 0;
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for (const T_Roi* roiInfo = roiData; roiInfo < roiDataEnd; roiInfo += kRoiDim, roiIndex++) {
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uint32_t batchId = batchSplitData[roiIndex];
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// Check for malformed data
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// 1. invalid batch id
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// 2. Region out of bound: x1|x2|y1|y2 < 0 || x1|x2 > inWidth || y1|y2 > inHeight
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// 3. Invalid region: x2 < x1 || y2 < y1
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NN_RET_CHECK_GE(batchId, 0);
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NN_RET_CHECK_LT(batchId, numBatches);
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NN_RET_CHECK(roiInfo[0] >= 0);
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NN_RET_CHECK(roiInfo[1] >= 0);
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NN_RET_CHECK(roiInfo[2] >= 0);
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NN_RET_CHECK(roiInfo[3] >= 0);
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NN_RET_CHECK(roiInfo[0] * widthScale <= inWidth);
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NN_RET_CHECK(roiInfo[1] * heightScale <= inHeight);
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NN_RET_CHECK(roiInfo[2] * widthScale <= inWidth);
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NN_RET_CHECK(roiInfo[3] * heightScale <= inHeight);
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NN_RET_CHECK(roiInfo[0] <= roiInfo[2]);
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NN_RET_CHECK(roiInfo[1] <= roiInfo[3]);
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int32_t wRoiStart = std::round(static_cast<float>(roiInfo[0] * widthScale));
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int32_t hRoiStart = std::round(static_cast<float>(roiInfo[1] * heightScale));
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int32_t wRoiEnd = std::round(static_cast<float>(roiInfo[2] * widthScale));
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int32_t hRoiEnd = std::round(static_cast<float>(roiInfo[3] * heightScale));
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// Rois with width/height < 1 are considered malformed and are forced to be 1
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T_Roi roiWidth = static_cast<T_Roi>(std::max(wRoiEnd - wRoiStart + 1, 1));
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T_Roi roiHeight = static_cast<T_Roi>(std::max(hRoiEnd - hRoiStart + 1, 1));
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T_Roi wStepSize = roiWidth / static_cast<T_Roi>(outWidth);
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T_Roi hStepSize = roiHeight / static_cast<T_Roi>(outHeight);
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const T_Input* batchBase = inputData + batchId * inHeight * inWidth * inDepth;
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for (uint32_t i = 0; i < outHeight; i++) {
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for (uint32_t j = 0; j < outWidth; j++) {
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// Take floor on start, ceil on end, start included, end excluded, i.e. [start, end)
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// end is guaranteed to larger than start by at least 1
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uint32_t wStart = std::floor(static_cast<float>(wStepSize * j + wRoiStart));
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uint32_t wEnd = std::ceil(static_cast<float>(wStepSize * (j + 1) + wRoiStart));
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uint32_t hStart = std::floor(static_cast<float>(hStepSize * i + hRoiStart));
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uint32_t hEnd = std::ceil(static_cast<float>(hStepSize * (i + 1) + hRoiStart));
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wStart = std::min(wStart, inWidth);
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wEnd = std::min(wEnd, inWidth);
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hStart = std::min(hStart, inHeight);
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hEnd = std::min(hEnd, inHeight);
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for (uint32_t k = 0; k < inDepth; k++) {
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T_Input maxValue = static_cast<T_Input>(inputShape.offset);
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bool first = true;
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for (uint32_t h = hStart; h < hEnd; h++) {
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for (uint32_t w = wStart; w < wEnd; w++) {
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T_Input inputValue = batchBase[h * inWidth * inDepth + w * inDepth + k];
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if (first || inputValue > maxValue) {
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maxValue = inputValue;
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first = false;
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}
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}
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}
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outPtr[k] = maxValue;
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}
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outPtr += inDepth;
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}
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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_Input, typename T_Roi>
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inline bool roiPooling(const T_Input* inputData, const Shape& inputShape, const T_Roi* roiData,
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const Shape& roiShape, const int32_t* batchSplitData,
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const Shape& batchSplitShape, float heightStride, float widthStride,
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bool useNchw, T_Input* outputData, const Shape& outputShape) {
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InputWithLayout<T_Input> input(useNchw);
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OutputWithLayout<T_Input> output(useNchw);
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NN_RET_CHECK(input.initialize(inputData, inputShape));
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NN_RET_CHECK(output.initialize(outputData, outputShape));
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NN_RET_CHECK(roiPoolingNhwc(input.getNhwcBuffer(), input.getNhwcShape(), roiData, roiShape,
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batchSplitData, batchSplitShape, heightStride, widthStride,
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output.getNhwcBuffer(), output.getNhwcShape()));
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NN_RET_CHECK(output.commit());
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return true;
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}
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template <>
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inline bool roiPooling<uint8_t, uint16_t>(const uint8_t* inputData, const Shape& inputShape,
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const uint16_t* roiData, const Shape& roiShape,
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const int32_t* batchSplitData,
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const Shape& batchSplitShape, float heightStride,
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float widthStride, bool useNchw, uint8_t* outputData,
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const Shape& outputShape) {
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std::vector<float> roi_float32(getNumberOfElements(roiShape));
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convertQuantToFloat32(roiData, roiShape.scale, roiShape.offset, &roi_float32);
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NN_RET_CHECK(roiPooling(inputData, inputShape, roi_float32.data(), roiShape, batchSplitData,
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batchSplitShape, heightStride, widthStride, useNchw, outputData,
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outputShape));
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return true;
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}
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template <>
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inline bool roiPooling<int8_t, uint16_t>(const int8_t* inputData, const Shape& inputShape,
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const uint16_t* roiData, const Shape& roiShape,
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const int32_t* batchSplitData,
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const Shape& batchSplitShape, float heightStride,
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float widthStride, bool useNchw, int8_t* outputData,
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const Shape& outputShape) {
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std::vector<float> roi_float32(getNumberOfElements(roiShape));
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convertQuantToFloat32(roiData, roiShape.scale, roiShape.offset, &roi_float32);
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NN_RET_CHECK(roiPooling(inputData, inputShape, roi_float32.data(), roiShape, batchSplitData,
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batchSplitShape, heightStride, widthStride, useNchw, outputData,
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outputShape));
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return true;
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}
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} // namespace
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#endif // NN_INCLUDE_CPU_IMPLEMENTATION
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Result<Version> validate(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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std::vector<OperandType> inExpectedTypes;
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auto inputType = context->getInputType(kInputTensor);
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if (inputType == OperandType::TENSOR_FLOAT32) {
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inExpectedTypes = {OperandType::TENSOR_FLOAT32, OperandType::TENSOR_FLOAT32,
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OperandType::TENSOR_INT32, OperandType::INT32,
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OperandType::INT32, OperandType::FLOAT32,
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OperandType::FLOAT32, OperandType::BOOL};
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} else if (inputType == OperandType::TENSOR_FLOAT16) {
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inExpectedTypes = {OperandType::TENSOR_FLOAT16, OperandType::TENSOR_FLOAT16,
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OperandType::TENSOR_INT32, OperandType::INT32,
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OperandType::INT32, OperandType::FLOAT16,
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OperandType::FLOAT16, OperandType::BOOL};
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} else if (inputType == OperandType::TENSOR_QUANT8_ASYMM ||
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inputType == OperandType::TENSOR_QUANT8_ASYMM_SIGNED) {
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inExpectedTypes = {inputType,
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OperandType::TENSOR_QUANT16_ASYMM,
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OperandType::TENSOR_INT32,
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OperandType::INT32,
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OperandType::INT32,
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OperandType::FLOAT32,
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OperandType::FLOAT32,
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OperandType::BOOL};
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} else {
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return NN_ERROR() << "Unsupported input tensor type for operation " << kOperationName;
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}
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NN_RET_CHECK(validateInputTypes(context, inExpectedTypes));
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NN_RET_CHECK(validateOutputTypes(context, {inputType}));
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if (inputType == OperandType::TENSOR_QUANT8_ASYMM_SIGNED) {
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return Version::ANDROID_R;
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} else {
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return Version::ANDROID_Q;
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}
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}
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#ifdef NN_INCLUDE_CPU_IMPLEMENTATION
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bool prepare(IOperationExecutionContext* context) {
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bool useNchw = context->getInputValue<bool>(kLayoutScalar);
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Shape input = context->getInputShape(kInputTensor);
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Shape roiShape = context->getInputShape(kRoiTensor);
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Shape batchSplitShape = context->getInputShape(kBatchSplitTensor);
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NN_RET_CHECK_EQ(getNumberOfDimensions(input), 4);
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NN_RET_CHECK_EQ(getNumberOfDimensions(roiShape), 2);
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uint32_t numBatches = getSizeOfDimension(input, 0);
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uint32_t inHeight = getSizeOfDimension(input, useNchw ? 2 : 1);
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uint32_t inWidth = getSizeOfDimension(input, useNchw ? 3 : 2);
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uint32_t inDepth = getSizeOfDimension(input, useNchw ? 1 : 3);
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uint32_t numRois = getSizeOfDimension(roiShape, 0);
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NN_RET_CHECK_EQ(getSizeOfDimension(roiShape, 1), 4);
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NN_RET_CHECK_EQ(getSizeOfDimension(batchSplitShape, 0), numRois);
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auto outputHeight = context->getInputValue<int32_t>(kOutputHeightScalar);
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auto outputWidth = context->getInputValue<int32_t>(kOutputWidthScalar);
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float heightStride, widthStride;
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if (context->getInputType(kInputTensor) == OperandType::TENSOR_FLOAT16) {
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heightStride = context->getInputValue<_Float16>(kHeightStrideSalar);
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widthStride = context->getInputValue<_Float16>(kWidthStrideScalar);
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} else {
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heightStride = context->getInputValue<float>(kHeightStrideSalar);
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widthStride = context->getInputValue<float>(kWidthStrideScalar);
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}
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NN_RET_CHECK_GT(outputHeight, 0);
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NN_RET_CHECK_GT(outputWidth, 0);
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NN_RET_CHECK_GT(heightStride, 0);
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NN_RET_CHECK_GT(widthStride, 0);
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if (roiShape.type == OperandType::TENSOR_QUANT16_ASYMM) {
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NN_RET_CHECK_EQ(roiShape.scale, 0.125f);
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NN_RET_CHECK_EQ(roiShape.offset, 0);
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}
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Shape output = input;
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if (useNchw) {
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output.dimensions = {numRois, inDepth, static_cast<uint32_t>(outputHeight),
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static_cast<uint32_t>(outputWidth)};
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} else {
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output.dimensions = {numRois, static_cast<uint32_t>(outputHeight),
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static_cast<uint32_t>(outputWidth), inDepth};
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}
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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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switch (context->getInputType(kInputTensor)) {
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case OperandType::TENSOR_FLOAT16:
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return roiPooling(context->getInputBuffer<_Float16>(kInputTensor),
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context->getInputShape(kInputTensor),
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context->getInputBuffer<_Float16>(kRoiTensor),
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context->getInputShape(kRoiTensor),
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context->getInputBuffer<int32_t>(kBatchSplitTensor),
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context->getInputShape(kBatchSplitTensor),
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context->getInputValue<_Float16>(kHeightStrideSalar),
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context->getInputValue<_Float16>(kWidthStrideScalar),
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context->getInputValue<bool>(kLayoutScalar),
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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 roiPooling(context->getInputBuffer<float>(kInputTensor),
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context->getInputShape(kInputTensor),
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context->getInputBuffer<float>(kRoiTensor),
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context->getInputShape(kRoiTensor),
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context->getInputBuffer<int32_t>(kBatchSplitTensor),
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context->getInputShape(kBatchSplitTensor),
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context->getInputValue<float>(kHeightStrideSalar),
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context->getInputValue<float>(kWidthStrideScalar),
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context->getInputValue<bool>(kLayoutScalar),
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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 roiPooling(context->getInputBuffer<uint8_t>(kInputTensor),
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context->getInputShape(kInputTensor),
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context->getInputBuffer<uint16_t>(kRoiTensor),
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context->getInputShape(kRoiTensor),
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context->getInputBuffer<int32_t>(kBatchSplitTensor),
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context->getInputShape(kBatchSplitTensor),
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context->getInputValue<float>(kHeightStrideSalar),
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context->getInputValue<float>(kWidthStrideScalar),
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context->getInputValue<bool>(kLayoutScalar),
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context->getOutputBuffer<uint8_t>(kOutputTensor),
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context->getOutputShape(kOutputTensor));
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case OperandType::TENSOR_QUANT8_ASYMM_SIGNED:
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return roiPooling(context->getInputBuffer<int8_t>(kInputTensor),
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context->getInputShape(kInputTensor),
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context->getInputBuffer<uint16_t>(kRoiTensor),
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context->getInputShape(kRoiTensor),
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context->getInputBuffer<int32_t>(kBatchSplitTensor),
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context->getInputShape(kBatchSplitTensor),
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context->getInputValue<float>(kHeightStrideSalar),
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context->getInputValue<float>(kWidthStrideScalar),
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context->getInputValue<bool>(kLayoutScalar),
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context->getOutputBuffer<int8_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 " << kOperationName;
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}
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}
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#endif // NN_INCLUDE_CPU_IMPLEMENTATION
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} // namespace roi_pooling
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NN_REGISTER_OPERATION(ROI_POOLING, roi_pooling::kOperationName, roi_pooling::validate,
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roi_pooling::prepare, roi_pooling::execute);
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} // namespace nn
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} // namespace android
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