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MNN/source/core/ConvolutionCommon.cpp

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//
// ConvolutionCommon.cpp
// MNN
//
// Created by MNN on 2020/03/02.
// Copyright © 2018, Alibaba Group Holding Limited
//
#include "ConvolutionCommon.hpp"
#include <math.h>
#include "backend/cpu/compute/CommonOptFunction.h"
#include "half.hpp"
namespace MNN {
static inline void *MNNMemoryAllocAlignZeroAlign(size_t size) {
return MNNMemoryCallocAlign(size, MNN_MEMORY_ALIGN_DEFAULT);
}
static int ReadBlobDim(unsigned char *&myfile, unsigned int* shape, int shapeBufCnt, bool useInt32) {
int uSize = myfile[0];
myfile++;
if (uSize > 4) {
printf("Read shape error!\n");
return 0;
}
int copyLength = uSize;
if (copyLength > shapeBufCnt) {
copyLength = shapeBufCnt;
}
if (useInt32) {
::memcpy(shape, myfile, sizeof(unsigned int) * copyLength);
myfile += copyLength * sizeof(unsigned int);
} else {
auto myfileint16 = (uint16_t*)myfile;
for (int i=0; i<copyLength; ++i) {
shape[i] = myfileint16[i];
}
myfile += copyLength * sizeof(unsigned short);
}
return copyLength;
}
static double _log2(double x) {
return log(x) / log(2);
}
static uint32_t atLestBitsCnt(uint32_t n) {
for (uint32_t i = 0; i < 32; i++) {
int32_t t = n << i;
if (t < 0)
return 32 - i - (((t << 1) == 0) ? 1 : 0);
}
return 0;
}
static void SplitBufToArray(uint8_t *buf, size_t bufLen, uint8_t *arr, size_t arrLen, size_t iNeedBits) {
unsigned char cMask = (1 << (iNeedBits)) - 1;
unsigned char *tmp = (unsigned char *)buf;
int iOffset = 0;
for (unsigned int i = 0; i < arrLen; i++) {
unsigned char idx = 0;
long uShift = 8 - iNeedBits - iOffset % 8;
if (uShift < 0) {
idx = (tmp[iOffset / 8] << (0 - uShift)) & cMask;
idx |= (tmp[(iOffset / 8) + 1] >> (8 + uShift)) & cMask;
} else {
idx = (tmp[iOffset / 8] >> uShift) & cMask;
}
iOffset += iNeedBits;
if (iOffset % 8 == 0) {
tmp += iOffset / 8;
iOffset = 0;
}
arr[i] = idx;
}
}
// fixme!!! not efficiency
typedef struct _SIMPLE_SET {
int8_t *UniSet;
uint32_t UniSetSize;
uint32_t CurUniCnt;
} SIMPLE_SET, *PSIMPLE_SET;
static PSIMPLE_SET CreateSimpleSet(uint32_t maxSize) {
PSIMPLE_SET set = (PSIMPLE_SET)calloc(1, sizeof(SIMPLE_SET));
if (set == nullptr)
return nullptr;
set->UniSet = (int8_t *)calloc(maxSize, sizeof(int8_t));
set->UniSetSize = maxSize;
set->CurUniCnt = 0;
return set;
}
static void SimpleRank(int8_t *data, uint32_t cnt, int up) {
if (up) {
for (uint32_t i = 0; i < cnt; i++) {
for (uint32_t j = i + 1; j < cnt; j++) {
if (data[i] > data[j]) {
int8_t tmp = data[i];
data[i] = data[j];
data[j] = tmp;
}
}
}
} else {
for (uint32_t i = 0; i < cnt; i++) {
for (uint32_t j = i + 1; j < cnt; j++) {
if (data[i] < data[j]) {
int8_t tmp = data[i];
data[i] = data[j];
data[j] = tmp;
}
}
}
}
}
static void InsertSimpleSet(PSIMPLE_SET set, int8_t value) {
if (set->CurUniCnt >= set->UniSetSize)
return;
for (uint32_t i = 0; i < set->CurUniCnt; i++) {
if (set->UniSet[i] == value)
return;
}
set->UniSet[set->CurUniCnt++] = value;
// SimpleRank(set->UniSet, set->CurUniCnt, 1);
}
void DestorySimpleSet(PSIMPLE_SET set) {
if (set->UniSet != nullptr)
free(set->UniSet);
free(set);
}
typedef struct _SIMPLE_MAP {
int8_t *CharCharMap;
uint32_t CharMapSize;
uint32_t CurMapCnt;
} SIMPLE_MAP, *PSIMPLE_MAP;
static PSIMPLE_MAP CreateSimpleMap(uint32_t MaxCnt) {
PSIMPLE_MAP map = (PSIMPLE_MAP)calloc(1, sizeof(SIMPLE_MAP));
if (map == nullptr)
return nullptr;
map->CharMapSize = MaxCnt * sizeof(int8_t);
map->CurMapCnt = 0;
map->CharCharMap = (int8_t *)calloc(1, MaxCnt * 2);
return map;
}
static void DestroySimpleMap(PSIMPLE_MAP map) {
if (map->CharCharMap)
free(map->CharCharMap);
free(map);
}
static void InsertMap(PSIMPLE_MAP map, int8_t k, int8_t v) {
for (uint32_t i = 0; i < map->CurMapCnt; i++) {
if (map->CharCharMap[i * 2] == k) {
map->CharCharMap[i * 2 + 1] = v;
return;
}
}
if (map->CurMapCnt >= map->CharMapSize)
return;
map->CharCharMap[map->CurMapCnt * 2] = k;
map->CharCharMap[map->CurMapCnt * 2 + 1] = v;
map->CurMapCnt++;
}
static int8_t FindInMap(PSIMPLE_MAP map, int8_t k, int *found) {
for (uint32_t i = 0; i < map->CurMapCnt; i++) {
if (map->CharCharMap[i * 2] == k) {
if (found != nullptr)
*found = 1;
return map->CharCharMap[i * 2 + 1];
}
}
if (found != nullptr)
*found = 0;
return 0;
}
static void StreamSizeRead(void *dst, int unit, size_t count, unsigned char *&file) {
::memcpy(dst, file, unit * count);
file += (unit * count);
}
static bool isFastSample(const std::vector<int8_t>& sample, int bit) {
if (bit != 4) {
return false;
}
if (sample.size() != (1 << bit) - 1) {
return false;
}
for (int i = 0; i < sample.size(); i++) {
if (static_cast<int>(sample[i]) != i - 7) {
return false;
}
}
return true;
}
static int8_t *ReadQuanData_c(unsigned char *&s, size_t* len, ConvolutionCommon::Int8Common* result, bool shapeInt32) {
int8_t *blob = nullptr;
uint8_t *idxBuf = nullptr;
uint8_t *idxBytes = nullptr;
uint32_t dataCnt = 1;
do {
// blob shape
unsigned int shape[32] = {0};
uint32_t shapeDim = (uint32_t)ReadBlobDim(s, shape, 32, shapeInt32);
if (shapeDim == 0 || shapeDim > 32)
break;
for (uint32_t i = 0; i < shapeDim; i++)
dataCnt *= shape[i];
// sample
uint32_t sampleCnt = 0;
StreamSizeRead(&sampleCnt, 1, 1, s);
if (0 == sampleCnt) {
sampleCnt = 256;
}
result->weightMap.resize(sampleCnt);
auto samples = result->weightMap.data();
if (samples == nullptr)
break;
StreamSizeRead(samples, 1, sampleCnt, s);
SimpleRank(samples, sampleCnt, 1);
// index
uint32_t idxBitsCnt = atLestBitsCnt(sampleCnt);
idxBitsCnt = idxBitsCnt < 1 ? 1 : idxBitsCnt;
size_t idxBufSize = ceil(idxBitsCnt * dataCnt * 0.125);
idxBuf = (uint8_t *)MNNMemoryAllocAlignZeroAlign(idxBufSize);
if (nullptr == idxBuf) {
MNN_ERROR("Not enought memory\n");
break;
}
StreamSizeRead(idxBuf, 1, idxBufSize, s);
blob = (int8_t *)MNNMemoryAllocAlignZeroAlign((size_t)dataCnt);
if (nullptr == blob) {
break;
}
if (isFastSample(result->weightMap, idxBitsCnt)) {
for (int i = 0; i < idxBufSize; i++) {
int val = idxBuf[i];
int x1 = val / 16;
int x2 = val % 16;
blob[2 * i] = x1 - 7;
blob[2 * i + 1] = x2 - 7;
}
} else {
// split index value into bytes
idxBytes = (uint8_t *)MNNMemoryAllocAlignZeroAlign(dataCnt * sizeof(uint8_t));
if (idxBitsCnt == 0 || nullptr == idxBytes) {
break;
}
SplitBufToArray(idxBuf, (uint32_t)idxBufSize, idxBytes, (uint32_t)dataCnt, (uint32_t)idxBitsCnt);
int i = 0;
for (; i < dataCnt; i++) {
if (idxBytes[i] >= sampleCnt) {
MNN_PRINT("iNeedBits is %u\nRead quan weights error with idx:%d\n", idxBitsCnt, (int)idxBytes[i]);
break;
}
blob[i] = samples[idxBytes[i]];
}
if (i < dataCnt) {
MNNMemoryFreeAlign(blob);
blob = nullptr;
break;
}
MNNMemoryFreeAlign(idxBytes);
idxBytes = nullptr;
}
} while (0);
if (idxBuf != nullptr)
MNNMemoryFreeAlign(idxBuf);
if (idxBytes != nullptr)
MNNMemoryFreeAlign(idxBytes);
if (len)
*len = blob ? dataCnt : 0;
return blob;
}
static int8_t *ReadSparseQuanData_c(unsigned char *&myfile, size_t* len, const flatbuffers::Vector<float> *alpha, ConvolutionCommon::Int8Common* result, bool useInt32) {
// MNN_ERROR("sparse:%d\n", 1);
unsigned int shape[32];
uint32_t ucMapSize = 0;
PSIMPLE_SET setWeight = CreateSimpleSet(256);
if (setWeight == nullptr) {
return nullptr;
}
std::shared_ptr<unsigned int> __autoReleaseSetWeight(nullptr, [setWeight](void *) { DestorySimpleSet(setWeight); });
unsigned int nnz;
unsigned char iIdxNeedBits;
int8_t *blob = nullptr;
// 1. weights blob shape(unsigned int32)
int ShapeDim = ReadBlobDim(myfile, shape, 32, useInt32);
size_t Size = sizeof(int8_t);
for (int i = 0; i < ShapeDim; i++)
Size *= shape[i];
blob = (int8_t *)MNNMemoryAllocAlignZeroAlign((size_t)Size);
if (blob == nullptr)
return nullptr;
// 2. nnz
StreamSizeRead(&nnz, 4, 1, myfile);
// 3. max_step use # bits () (unsigned char)
StreamSizeRead(&iIdxNeedBits, 1, 1, myfile);
// read idx array
// 4. buf for steps ceil(nnz*step need bits/8)
AutoStorage<unsigned char> arrIdxBuffer(nnz);
unsigned char *arrIdx = arrIdxBuffer.get();
if (nullptr == arrIdx) {
return nullptr;
}
{
size_t bufLen = (size_t)(ceil(0.125 * iIdxNeedBits * nnz));
char *buf = (char *)MNNMemoryAllocAlignZeroAlign(bufLen * sizeof(char));
if (nullptr == buf) {
return nullptr;
}
StreamSizeRead(buf, 1, bufLen, myfile);
SplitBufToArray((uint8_t *)buf, (uint32_t)bufLen, (uint8_t *)arrIdx, (uint32_t)nnz, (uint32_t)iIdxNeedBits);
MNNMemoryFreeAlign(buf);
}
// 5. Avalable values Count(unsigned char)
StreamSizeRead(&ucMapSize, 1, 1, myfile);
if (0 == ucMapSize) {
ucMapSize = 256;
}
result->weightMap.resize(ucMapSize);
// 6. valueset(signed char * valueset_size)
for (int i = 0; i < ucMapSize; i++) {
int8_t tmp;
StreamSizeRead(&tmp, 1, 1, myfile);
InsertSimpleSet(setWeight, tmp);
result->weightMap[i] = tmp;
}
SimpleRank(setWeight->UniSet, setWeight->CurUniCnt, 1);
// map<unsigned char, signed char> mapWeight;
PSIMPLE_MAP mapWeight = CreateSimpleMap(256);
if (mapWeight == nullptr) {
return nullptr;
}
std::shared_ptr<unsigned int> __autoReleaseMapWeight(nullptr, [mapWeight](void *) { DestroySimpleMap(mapWeight); });
for (int i = 0; i < setWeight->CurUniCnt; i++) {
InsertMap(mapWeight, i, setWeight->UniSet[i]);
}
// unsigned char iIdx = 0;
// 7. none zero weights indexes(nnz*ceil(log2(Avalable_values_Count))/8)
AutoStorage<unsigned char> arrWeightIdxBuffer(nnz);
unsigned char *arrWeightIdx = arrWeightIdxBuffer.get();
if (nullptr == arrWeightIdx) {
return nullptr;
}
{
int iDataNeedBits = (int)ceil(_log2(ucMapSize));
iDataNeedBits = iDataNeedBits < 1 ? 1 : iDataNeedBits;
size_t bufLen = (size_t)(ceil(0.125 * iDataNeedBits * nnz));
char *buf = (char *)MNNMemoryAllocAlignZeroAlign(bufLen * sizeof(char));
if (nullptr == buf) {
return nullptr;
}
StreamSizeRead(buf, 1, bufLen, myfile);
SplitBufToArray((uint8_t *)buf, (uint32_t)bufLen, (uint8_t *)arrWeightIdx, (uint32_t)nnz,
(uint32_t)iDataNeedBits);
MNNMemoryFreeAlign(buf);
}
// set blob data with idx and weight idx
{
if (alpha->size() == 2 * shape[0]) {
auto alphaPtr = alpha->data();
int area = Size / shape[0];
for (int i = 0; i < shape[0]; i++) {
float min = alphaPtr[2*i];
float scale = alphaPtr[2*i+1];
int zeroQuant = -128;
if (scale > 1e-6) {
zeroQuant = round((0.0f - min) / scale) + (-128);
}
memset(blob+area*i, zeroQuant, area * sizeof(signed char));
}
} else {
memset(blob, 0, Size * sizeof(signed char)); //backward compability with previous symmetric weight quant
}
int iPreIdx = 0;
for (int i = 0; i < nnz; i++) {
iPreIdx += arrIdx[i];
int found = 0;
int8_t value = FindInMap(mapWeight, arrWeightIdx[i], &found);
if (!found) {
MNN_ERROR("Read quan weights error with idx:%d\n", arrWeightIdx[i]);
MNNMemoryFreeAlign(blob);
return nullptr;
}
blob[iPreIdx] = value;
}
}
*len = Size;
return blob;
}
std::shared_ptr<ConvolutionCommon::Int8Common> ConvolutionCommon::load(const IDSTQuan *quan, bool forceFloat, bool forceInt8) {
auto result = std::make_shared<Int8Common>();
result->quan = quan;
if (quan->index() != nullptr) {
if (forceFloat) {
// Expand sparse to dense
result->weightFloat.reset(quan->weightSize());
if (nullptr == result->weightFloat.get()) {
return nullptr;
}
::memset(result->weightFloat.get(), 0, quan->weightSize() * sizeof(float));
auto index = quan->index()->data();
auto indexSize = quan->index()->size();
if (nullptr == quan->alpha() || quan->alpha()->size() != indexSize) {
MNN_ERROR("The model is error, don't has alpha but has index\n");
return nullptr;
}
auto weightRaw = quan->alpha()->data();
for (uint32_t i=0; i<indexSize; ++i) {
result->weightFloat.get()[index[i]] = weightRaw[i];
}
} // Otherwise needn't treat, just return result with quan info
return result;
}
size_t weightLength = 0;
int8_t *buffer = nullptr;
auto originBuffer = (unsigned char *)quan->buffer()->data();
if (1 == quan->type()) {
buffer = ReadQuanData_c(originBuffer, &weightLength, result.get(), quan->shapeInt32());
}
if (2 == quan->type()) {
buffer = ReadSparseQuanData_c(originBuffer, &weightLength, quan->alpha(), result.get(), quan->shapeInt32());
}
if (result->weightMap.size() > 0 && result->weightMap.size() <= 16) {
// Compute Remap for int4
result->canUseInt4 = true;
result->weightReverseMap.resize(256);
::memset(result->weightReverseMap.data(), 0, 256 * sizeof(int8_t));
for (int i=0; i<result->weightMap.size(); ++i) {
int value = result->weightMap[i];
value = value + 128;
result->weightReverseMap[value] = i;
}
#ifdef MNN_TEST_REMAPQUANT
// Test reverse
std::vector<int8_t> originBuffer(weightLength);
for (int i=0; i<weightLength; ++i) {
originBuffer[i] = buffer[i];
buffer[i] = result->weightReverseMap[(int)buffer[i] + 128];
}
for (int i=0; i<weightLength; ++i) {
buffer[i] = result->weightMap[buffer[i]];
}
for (int i=0; i<weightLength; ++i) {
MNN_ASSERT(buffer[i] == originBuffer[i]);
}
#endif
}
// read fp16 data
if (3 == quan->type()) {
weightLength = quan->buffer()->size() / sizeof(half_float::half);
std::vector<int8_t> tempHalfWeight(quan->buffer()->size());
::memcpy(tempHalfWeight.data(), quan->buffer()->data(), quan->buffer()->size());
auto halfWeight = reinterpret_cast<half_float::half *>(tempHalfWeight.data());
result->weightFloat.reset(weightLength);
if (nullptr == result->weightFloat.get()) {
MNN_PRINT("Alloc memory error for extract fp16 back to float\n");
return nullptr;
}
std::transform(halfWeight, halfWeight + weightLength, result->weightFloat.get(),
[](half_float::half h) { return float(h); });
return result;
}
// weight int8 only
if (4 == quan->type()) {
weightLength = quan->buffer()->size();
result->weight.reset(weightLength);
::memcpy(result->weight.get(), quan->buffer()->data(), weightLength);
}
if (result->weight.get() == nullptr) {
if (nullptr == buffer) {
MNN_PRINT("Alloc memory error for extract idst int8\n");
return nullptr;
}
result->weight.set(buffer, weightLength);
}
result->alpha.reset(quan->alpha()->size());
if (nullptr == result->alpha.get()) {
MNN_PRINT("Alloc memory error for extract idst int8\n");
return nullptr;
}
::memcpy(result->alpha.get(), quan->alpha()->data(), quan->alpha()->size() * sizeof(float));
{
int outputCount = 0;
bool oldType4 = (quan->type() == 4 && quan->aMin() == 0 && std::abs(quan->quantScale()) < 1e-6);
if (quan->readType() != 0 || oldType4) {
result->asymmetric = true;
outputCount = result->alpha.size() / 2;
} else {
result->asymmetric = false;
outputCount = result->alpha.size(); // backward compability with previous symmetric quantization
}
if (result->asymmetric) {
// clampMin is minVal in asymmetric quant, clampMin = -(2^(bit))
// and old version clampMin is -128
float clampMin = quan->aMin() == 0 ? -128 : quan->aMin();
for (int o = 0; o < outputCount; ++o) {
result->alpha.get()[2 * o] = result->alpha.get()[2 * o] - clampMin * result->alpha.get()[2 * o + 1];
}
}
if (!quan->has_scaleInt()) {
float extraFactor = quan->quantScale();
// for old type 4 models, their quan->quantScale is 0. which will introduce a bug here
if (oldType4) {
extraFactor = 1.0f;
}
for (int o=0; o<result->alpha.size(); ++o) {
result->alpha.get()[o] *= extraFactor;
}
}
}
if (forceInt8) {
return result;
}
if (!quan->has_scaleInt() || forceFloat) {
// Back to float
result->weightFloat.reset(weightLength);
if (nullptr == result->weightFloat.get()) {
MNN_PRINT("Alloc memory error for extract idst int8/ Back to float\n");
return nullptr;
}
int outputCount = 0;
if (result->asymmetric) {
outputCount = result->alpha.size() / 2;
} else {
outputCount = result->alpha.size();
}
int partWeightSize = weightLength / outputCount;
for (int o = 0; o < outputCount; ++o) {
float min = 0.0f;
float alpha = 0.0f;
if (result->asymmetric) {
min = result->alpha.get()[2*o];
alpha = result->alpha.get()[2*o+1];
} else {
alpha = result->alpha.get()[o];
}
auto dstW = result->weightFloat.get() + o * partWeightSize;
auto srcW = result->weight.get() + o * partWeightSize;
for (int v=0; v < partWeightSize; ++v) {
dstW[v] = (float)srcW[v] * alpha + min;
}
}
result->weight.release();
result->alpha.release();
}
return result;
}
void ConvolutionCommon::getConvParameters(std::shared_ptr<Int8Common> *quanCommon, const MNN::Convolution2D *conv2d, const float** originWeight, int* originWeightSize) {
*originWeight = nullptr;
*originWeightSize = 0;
if (nullptr != conv2d->quanParameter()) {
bool forceFloat = conv2d->quanParameter()->index() != nullptr;
*quanCommon = load(conv2d->quanParameter(), forceFloat);
*originWeight = (*quanCommon)->weightFloat.get();
*originWeightSize = (*quanCommon)->weightFloat.size();
}
if (*originWeight == nullptr) {
*originWeight = conv2d->weight()->data();
*originWeightSize = conv2d->weight()->size();
}
}
bool ConvolutionCommon::getConvInt8Parameters(const MNN::Convolution2D* conv2d, std::shared_ptr<Int8Common>& quanCommon,
const int8_t*& weight, int& weightSize, float*& scale, int32_t*& bias) {
int outputCount = conv2d->common()->outputCount();
weightSize = 0;
// fix xcode UndefinedBehaviorSanitizer
if (conv2d->symmetricQuan()->weight() != nullptr) {
weight = conv2d->symmetricQuan()->weight()->data();
weightSize = conv2d->symmetricQuan()->weight()->size();
}
if (conv2d->quanParameter() && conv2d->quanParameter()->buffer()) {
quanCommon = ConvolutionCommon::load(conv2d->quanParameter(), false, true);
weight = quanCommon->weight.get();
weightSize = quanCommon->weight.size();
}
if (weight == nullptr) {
MNN_ERROR("ConvolutionCommon::getConvInt8Parameters: No weight data!");
return false;
}
if (conv2d->symmetricQuan()->bias() && conv2d->symmetricQuan()->scale()) {
// Compability for old model
MNN_ASSERT(conv2d->symmetricQuan()->bias()->size() == outputCount && conv2d->symmetricQuan()->scale()->size() == outputCount);
::memcpy(bias, conv2d->symmetricQuan()->bias()->data(), outputCount * sizeof(int32_t));
::memcpy(scale, conv2d->symmetricQuan()->scale()->data(), outputCount * sizeof(float));
return true;
}
if (conv2d->bias() && conv2d->quanParameter()->alpha()) {
::memcpy(bias, conv2d->bias()->data(), outputCount * sizeof(float));
::memcpy(scale, conv2d->quanParameter()->alpha()->data(), outputCount * sizeof(float));
return true;
}
MNN_ERROR("ConvolutionCommon::getConvInt8Parameters: No bias & scale data!");
return false;
}
std::pair<int, int> ConvolutionCommon::convolutionPad(const Tensor *input, const Tensor *output,
const Convolution2DCommon *mCommon) {
if (mCommon->padMode() == PadMode_SAME) {
int kernelWidthSize = (mCommon->kernelX() - 1) * mCommon->dilateX() + 1;
int kernelHeightSize = (mCommon->kernelY() - 1) * mCommon->dilateY() + 1;
int padNeededWidth = (output->width() - 1) * mCommon->strideX() + kernelWidthSize - input->width();
int padNeededHeight = (output->height() - 1) * mCommon->strideY() + kernelHeightSize - input->height();
auto mPadX = padNeededWidth / 2;
auto mPadY = padNeededHeight / 2;
return std::make_pair(mPadX, mPadY);
}
auto mPadX = mCommon->padX();
auto mPadY = mCommon->padY();
if (nullptr != mCommon->pads() && mCommon->pads()->size() >= 2) {
mPadX = mCommon->pads()->data()[1];
mPadY = mCommon->pads()->data()[0];
}
return std::make_pair(mPadX, mPadY);
}
std::tuple<int, int, int, int> ConvolutionCommon::convolutionPadFull(const Tensor* input, const Tensor* output,
const Convolution2DCommon* common) {
auto pad = convolutionPad(input, output, common);
int iw = input->width();
int ih = input->height();
int ow = output->width();
int oh = output->height();
int right = (ow - 1) * common->strideX() + (common->kernelX() - 1) * common->dilateX() - pad.first;
int padRight = 0;
if (right >= iw) {
padRight = right - iw + 1;
}
int bottom = (oh - 1) * common->strideY() + (common->kernelY() - 1) * common->dilateY() - pad.second;
int padBottom = 0;
if (bottom >= ih) {
padBottom = bottom - ih + 1;
}
return std::make_tuple(pad.first, pad.second, padRight, padBottom);
}
std::pair<int, int> ConvolutionCommon::convolutionTransposePad(const Tensor *input, const Tensor *output,
const Convolution2DCommon *mCommon) {
if (mCommon->padMode() == PadMode_SAME) {
const int outputWidth = output->width();
const int outputHeight = output->height();
const int outputWidthPadded = (input->width() - 1) * mCommon->strideX() + mCommon->kernelX();
const int outputHeightPadded = (input->height() - 1) * mCommon->strideY() + mCommon->kernelY();
const int padNeededWidth = outputWidthPadded - outputWidth;
const int padNeededHeight = outputHeightPadded - outputHeight;
auto mPadX = padNeededWidth / 2;
auto mPadY = padNeededHeight / 2;
return std::make_pair(mPadX, mPadY);
}
auto mPadX = mCommon->padX();
auto mPadY = mCommon->padY();
if (nullptr != mCommon->pads() && mCommon->pads()->size() >= 2) {
mPadY = mCommon->pads()->data()[0];
mPadX = mCommon->pads()->data()[1];
}
return std::make_pair(mPadX, mPadY);
}
} // namespace MNN
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