/*
* 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.
*/
#include "CpuOperationUtils.h"
#include "OperationResolver.h"
#include "OperationsUtils.h"
#include <cfloat>
#include <cmath>
#include "Tracing.h"
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;
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;
}
} // 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::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) {
inExpectedTypes = {OperandType::TENSOR_QUANT8_ASYMM,
OperandType::TENSOR_QUANT16_ASYMM,
OperandType::TENSOR_INT32,
OperandType::INT32,
OperandType::INT32,
OperandType::FLOAT32,
OperandType::FLOAT32,
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) {
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));
default:
NN_RET_CHECK_FAIL() << "Unsupported tensor type for operation " << kOperationName;
}
}
} // namespace roi_pooling
NN_REGISTER_OPERATION(ROI_POOLING, roi_pooling::kOperationName, roi_pooling::validate,
roi_pooling::prepare, roi_pooling::execute);
} // namespace nn
} // namespace android