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