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PaddleDetection
提交 b5a448f3 编写于 作者: T Tao Luo 提交者： GitHub
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Merge pull request #4154 from luotao1/avg_pool 

refine avg-pooling, which is exclusive. refine related code.
上级 d59295f2 780e2682
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Showing with 159 addition and 188 deletion
paddle/cuda/include/hl_cuda_cudnn.h
浏览文件 @ b5a448f3
......@@ -22,10 +22,10 @@ limitations under the License. */
*/
typedef enum {
HL_POOLING_MAX = 0,
// average includes padded values
HL_POOLING_AVERAGE = 1,
// average does not include padded values
HL_POOLING_AVERAGE_EXCLUDE_PADDING = 2,
HL_POOLING_AVERAGE = 1,
// average includes padded values
HL_POOLING_AVERAGE_INCLUDE_PADDING = 2,
HL_POOLING_END
} hl_pooling_mode_t;
......
paddle/cuda/src/hl_cuda_cnn.cu
浏览文件 @ b5a448f3
......@@ -211,13 +211,11 @@ __global__ void KeAvgPoolForward(const int nthreads,
int hstart = ph * strideH - padH;
int wstart = pw * strideW - padW;
int hend = min(hstart + sizeY, height + padH);
int wend = min(wstart + sizeX, width + padW);
int pool_size = (hend - hstart) * (wend - wstart);
int hend = min(hstart + sizeY, height);
int wend = min(wstart + sizeX, width);
hstart = max(hstart, 0);
wstart = max(wstart, 0);
hend = min(hend, height);
wend = min(wend, width);
int pool_size = (hend - hstart) * (wend - wstart);
real aveval = 0;
inputData += (frameNum * channels + c) * height * width;
......@@ -299,12 +297,14 @@ __global__ void KeAvgPoolBackward(const int nthreads,
outGrad += (frameNum * outStride + offsetC * pooledH * pooledW);
for (int ph = phstart; ph < phend; ++ph) {
int hstart = ph * strideH - padH;
int hend = min(hstart + sizeY, height);
hstart = max(hstart, 0);
for (int pw = pwstart; pw < pwend; ++pw) {
// figure out the pooling size
int hstart = ph * strideH - padH;
int wstart = pw * strideW - padW;
int hend = min(hstart + sizeY, height + padH);
int wend = min(wstart + sizeX, width + padW);
int wend = min(wstart + sizeX, width);
wstart = max(wstart, 0);
int poolsize = (hend - hstart) * (wend - wstart);
gradient += outGrad[ph * pooledW + pw] / poolsize;
}
......@@ -600,16 +600,13 @@ __global__ void KeAvgPool3DForward(const int nthreads,
int dstart = pd * strideD - padD;
int hstart = ph * strideH - padH;
int wstart = pw * strideW - padW;
int dend = min(dstart + sizeZ, depth + padD);
int hend = min(hstart + sizeY, height + padH);
int wend = min(wstart + sizeX, width + padW);
int pool_size = (dend - dstart) * (hend - hstart) * (wend - wstart);
int dend = min(dstart + sizeZ, depth);
int hend = min(hstart + sizeY, height);
int wend = min(wstart + sizeX, width);
dstart = max(dstart, 0);
hstart = max(hstart, 0);
wstart = max(wstart, 0);
dend = min(dend, depth);
hend = min(hend, height);
wend = min(wend, width);
int pool_size = (dend - dstart) * (hend - hstart) * (wend - wstart);
real aveval = 0;
inputData += (frameNum * channels + c) * depth * height * width;
......@@ -712,15 +709,18 @@ __global__ void KeAvgPool3DBackward(const int nthreads,
outGrad += (frameNum * channels + offsetC) * pooledD * pooledH * pooledW;
for (int pd = pdstart; pd < pdend; ++pd) {
int dstart = pd * strideD - padD;
int dend = min(dstart + sizeZ, depth);
dstart = max(dstart, 0);
for (int ph = phstart; ph < phend; ++ph) {
int hstart = ph * strideH - padH;
int hend = min(hstart + sizeY, height);
hstart = max(hstart, 0);
for (int pw = pwstart; pw < pwend; ++pw) {
// figure out the pooling size
int dstart = pd * strideD - padD;
int hstart = ph * strideH - padH;
int wstart = pw * strideW - padW;
int dend = min(dstart + sizeZ, depth + padD);
int hend = min(hstart + sizeY, height + padH);
int wend = min(wstart + sizeX, width + padW);
int wend = min(wstart + sizeX, width);
wstart = max(wstart, 0);
int poolsize = (dend - dstart) * (hend - hstart) * (wend - wstart);
gradient += outGrad[(pd * pooledH + ph) * pooledW + pw] / poolsize;
}
......
paddle/cuda/src/hl_cuda_cudnn.cc
浏览文件 @ b5a448f3
......@@ -432,11 +432,11 @@ void hl_create_pooling_descriptor(hl_pooling_descriptor* pooling_desc,
cudnn_mode = CUDNN_POOLING_MAX;
break;
case HL_POOLING_AVERAGE:
cudnn_mode = CUDNN_POOLING_AVERAGE_COUNT_INCLUDE_PADDING;
break;
case HL_POOLING_AVERAGE_EXCLUDE_PADDING:
cudnn_mode = CUDNN_POOLING_AVERAGE_COUNT_EXCLUDE_PADDING;
break;
case HL_POOLING_AVERAGE_INCLUDE_PADDING:
cudnn_mode = CUDNN_POOLING_AVERAGE_COUNT_INCLUDE_PADDING;
break;
default:
LOG(FATAL) << "parameter mode error";
}
......
paddle/gserver/layers/CudnnPoolLayer.cpp
浏览文件 @ b5a448f3
......@@ -29,9 +29,9 @@ bool CudnnPoolLayer::typeCheck(const std::string &poolType,
if (mode) {
*mode = HL_POOLING_AVERAGE;
}
} else if (poolType == "cudnn-avg-excl-pad-pool") {
} else if (poolType == "cudnn-avg-incl-pad-pool") {
if (mode) {
*mode = HL_POOLING_AVERAGE_EXCLUDE_PADDING;
*mode = HL_POOLING_AVERAGE_INCLUDE_PADDING;
}
} else {
return false;
......
paddle/math/Matrix.cpp
浏览文件 @ b5a448f3
......@@ -1033,17 +1033,15 @@ void GpuMatrix::maxPoolForward(Matrix& inputMat,
real* inputData = inputMat.getData();
size_t frameNum = inputMat.getHeight();
size_t width = imgSizeW;
size_t height = imgSizeH;
CHECK(height * width * channels == inputMat.getWidth());
CHECK(imgSizeH * imgSizeW * channels == inputMat.getWidth());
CHECK(height_ == inputMat.getHeight());
CHECK(width_ == outputH * outputW * channels);
hl_maxpool_forward(frameNum,
inputData,
channels,
height,
width,
imgSizeH,
imgSizeW,
outputH,
outputW,
sizeX,
......@@ -1080,11 +1078,8 @@ void GpuMatrix::maxPoolBackward(Matrix& inputMat,
real* outDiff = outGrad.getData();
size_t frameNum = inputMat.getHeight();
size_t channels = outV.getWidth() / outputH / outputW;
size_t width = imgSizeW;
size_t height = imgSizeH;
CHECK(height * width * channels == inputMat.getWidth());
CHECK(imgSizeH * imgSizeW * channels == inputMat.getWidth());
CHECK(height_ == inputMat.getHeight());
CHECK(width_ == width * height * channels);
CHECK(outGrad.getHeight() == outV.getHeight() &&
outGrad.getWidth() == outV.getWidth());
......@@ -1093,8 +1088,8 @@ void GpuMatrix::maxPoolBackward(Matrix& inputMat,
outData,
outDiff,
channels,
height,
width,
imgSizeH,
imgSizeW,
outputH,
outputW,
sizeX,
......@@ -1125,17 +1120,15 @@ void GpuMatrix::avgPoolForward(Matrix& inputMat,
real* inputData = inputMat.getData();
size_t frameNum = inputMat.getHeight();
size_t height = imgSizeH;
size_t width = imgSizeW;
CHECK(height * width * channels == inputMat.getWidth());
CHECK(imgSizeH * imgSizeW * channels == inputMat.getWidth());
CHECK(height_ == inputMat.getHeight());
CHECK(width_ == outputH * outputW * channels);
hl_avgpool_forward(frameNum,
inputData,
channels,
height,
width,
imgSizeH,
imgSizeW,
outputH,
outputW,
sizeX,
......@@ -1166,17 +1159,15 @@ void GpuMatrix::avgPoolBackward(Matrix& outGrad,
real* outDiff = outGrad.getData();
size_t frameNum = outGrad.getHeight();
size_t channels = outGrad.getWidth() / outputH / outputW;
size_t height = imgSizeH;
size_t width = imgSizeW;
CHECK(height * width * channels == width_);
CHECK(imgSizeH * imgSizeW * channels == width_);
CHECK(height_ == outGrad.getHeight());
CHECK(outGrad.getWidth() == outputH * outputW * channels);
hl_avgpool_backward(frameNum,
outDiff,
channels,
height,
width,
imgSizeH,
imgSizeW,
outputH,
outputW,
sizeX,
......@@ -1214,19 +1205,16 @@ void GpuMatrix::maxPool3DForward(Matrix& inputMat,
real* inputData = inputMat.getData();
real* maxPoolIdxData = maxPoolIdx.getData();
size_t num = inputMat.getHeight();
size_t width = imgSizeW;
size_t height = imgSizeH;
size_t depth = imgSizeD;
CHECK(depth * height * width * channels == inputMat.getWidth());
CHECK(imgSizeD * imgSizeH * imgSizeW * channels == inputMat.getWidth());
CHECK(height_ == inputMat.getHeight());
CHECK(width_ == outputD * outputH * outputW * channels);
hl_maxpool3D_forward(num,
inputData,
channels,
depth,
height,
width,
imgSizeD,
imgSizeH,
imgSizeW,
outputD,
outputH,
outputW,
......@@ -1269,20 +1257,16 @@ void GpuMatrix::maxPool3DBackward(Matrix& outGrad,
real* maxPoolIdxData = maxPoolIdx.getData();
size_t frameNum = getHeight();
size_t channels = outGrad.getWidth() / outputD / outputH / outputW;
size_t width = imgSizeW;
size_t height = imgSizeH;
size_t depth = imgSizeD;
CHECK(depth * height * width * channels == getWidth());
CHECK(width_ == depth * width * height * channels);
CHECK(imgSizeD * imgSizeH * imgSizeW * channels == getWidth());
CHECK(outGrad.getHeight() == maxPoolIdx.getHeight() &&
outGrad.getWidth() == maxPoolIdx.getWidth());
hl_maxpool3D_backward(frameNum,
outDiff,
channels,
depth,
height,
width,
imgSizeD,
imgSizeH,
imgSizeW,
outputD,
outputH,
outputW,
......@@ -1323,19 +1307,16 @@ void GpuMatrix::avgPool3DForward(Matrix& inputMat,
real* inputData = inputMat.getData();
size_t frameNum = inputMat.getHeight();
size_t height = imgSizeH;
size_t width = imgSizeW;
size_t depth = imgSizeD;
CHECK(depth * height * width * channels == inputMat.getWidth());
CHECK(imgSizeD * imgSizeH * imgSizeW * channels == inputMat.getWidth());
CHECK(height_ == inputMat.getHeight());
CHECK(width_ == outputD * outputH * outputW * channels);
hl_avgpool3D_forward(frameNum,
inputData,
channels,
depth,
height,
width,
imgSizeD,
imgSizeH,
imgSizeW,
outputD,
outputH,
outputW,
......@@ -1375,19 +1356,16 @@ void GpuMatrix::avgPool3DBackward(Matrix& outGrad,
real* outDiff = outGrad.getData();
size_t frameNum = outGrad.getHeight();
size_t channels = outGrad.getWidth() / outputD / outputH / outputW;
size_t height = imgSizeH;
size_t width = imgSizeW;
size_t depth = imgSizeD;
CHECK(depth * height * width * channels == width_);
CHECK(imgSizeD * imgSizeH * imgSizeW * channels == width_);
CHECK(height_ == outGrad.getHeight());
CHECK(outGrad.getWidth() == outputD * outputH * outputW * channels);
hl_avgpool3D_backward(frameNum,
outDiff,
channels,
depth,
height,
width,
imgSizeD,
imgSizeH,
imgSizeW,
outputD,
outputH,
outputW,
......@@ -1999,11 +1977,11 @@ void CpuMatrix::maxPoolForward(Matrix& inputMat,
real* inputData = inputMat.getData();
real* outData = data_;
size_t num = inputMat.getHeight();
size_t inWidth = imgSizeW;
size_t inHeight = imgSizeH;
CHECK(inHeight * inWidth == inputMat.getWidth() / channels);
size_t inLength = imgSizeH * imgSizeW;
size_t outLength = outputH * outputW;
CHECK(inLength == inputMat.getWidth() / channels);
CHECK_EQ(num, this->getHeight());
CHECK_EQ(channels * outputH * outputW, this->getWidth());
CHECK_EQ(channels * outLength, this->getWidth());
size_t outStride = getStride();
/* initialize the data_ */
......@@ -2020,24 +1998,24 @@ void CpuMatrix::maxPoolForward(Matrix& inputMat,
}
for (size_t c = 0; c < channels; ++c) { // channel by channel
for (size_t ph = 0; ph < outputH; ++ph) {
for (size_t pw = 0; pw < outputW; ++pw) {
int hstart = ph * strideH - paddingH;
int wstart = pw * strideW - paddingW;
int hend = std::min(hstart + sizeY, inHeight);
int wend = std::min(wstart + sizeX, inWidth);
int hend = std::min(hstart + sizeY, imgSizeH);
hstart = std::max(hstart, 0);
for (size_t pw = 0; pw < outputW; ++pw) {
int wstart = pw * strideW - paddingW;
int wend = std::min(wstart + sizeX, imgSizeW);
wstart = std::max(wstart, 0);
for (int h = hstart; h < hend; ++h) {
for (int w = wstart; w < wend; ++w) {
outData[ph * outputW + pw] = std::max(outData[ph * outputW + pw],
inputData[h * inWidth + w]);
outData[ph * outputW + pw] = std::max(
outData[ph * outputW + pw], inputData[h * imgSizeW + w]);
}
}
}
}
// compute offset
inputData += inHeight * inWidth;
outData += outputH * outputW;
inputData += inLength;
outData += outLength;
}
}
}
......@@ -2058,8 +2036,10 @@ void CpuMatrix::maxPoolBackward(Matrix& image,
size_t paddingH,
size_t paddingW) {
size_t num = image.getHeight();
size_t channels = size_t(width_ / imgSizeH / imgSizeW);
CHECK(image.getWidth() == imgSizeH * imgSizeW * channels);
size_t inLength = imgSizeH * imgSizeW;
size_t outLength = outputH * outputW;
size_t channels = size_t(width_ / inLength);
CHECK(image.getWidth() == inLength * channels);
CHECK(image.getHeight() == height_ && image.getWidth() == width_);
CHECK(outV.getHeight() == outGrad.getHeight() &&
outV.getWidth() == outGrad.getWidth());
......@@ -2080,12 +2060,12 @@ void CpuMatrix::maxPoolBackward(Matrix& image,
}
for (size_t c = 0; c < channels; ++c) {
for (size_t ph = 0; ph < outputH; ++ph) {
for (size_t pw = 0; pw < outputW; ++pw) {
int hstart = ph * strideH - paddingH;
int wstart = pw * strideW - paddingW;
int hend = std::min(hstart + sizeY, imgSizeH);
int wend = std::min(wstart + sizeX, imgSizeW);
hstart = std::max(hstart, 0);
for (size_t pw = 0; pw < outputW; ++pw) {
int wstart = pw * strideW - paddingW;
int wend = std::min(wstart + sizeX, imgSizeW);
wstart = std::max(wstart, 0);
for (int h = hstart; h < hend; ++h) {
for (int w = wstart; w < wend; ++w) {
......@@ -2098,10 +2078,10 @@ void CpuMatrix::maxPoolBackward(Matrix& image,
}
}
// offset
inData += imgSizeH * imgSizeW;
tgtGrad += imgSizeH * imgSizeW;
otData += outputH * outputW;
otGrad += outputH * outputW;
inData += inLength;
tgtGrad += inLength;
otData += outLength;
otGrad += outLength;
}
}
}
......@@ -2120,10 +2100,10 @@ void CpuMatrix::avgPoolForward(Matrix& input,
size_t paddingW) {
// The main loop
size_t num = input.getHeight();
size_t inHeight = imgSizeH;
size_t inWidth = imgSizeW;
CHECK(inHeight * inWidth * channels == input.getWidth());
CHECK(outputH * outputW * channels * num == height_ * width_);
size_t inLength = imgSizeH * imgSizeW;
size_t outLength = outputH * outputW;
CHECK(inLength * channels == input.getWidth());
CHECK(outLength * channels * num == height_ * width_);
real* tgtData = data_;
real* inData = input.getData();
......@@ -2133,30 +2113,27 @@ void CpuMatrix::avgPoolForward(Matrix& input,
}
for (size_t c = 0; c < channels; ++c) {
for (size_t ph = 0; ph < outputH; ++ph) {
for (size_t pw = 0; pw < outputW; ++pw) {
int hstart = ph * strideH - paddingH;
int wstart = pw * strideW - paddingW;
int hend = std::min(hstart + sizeY, inHeight + paddingH);
int wend = std::min(wstart + sizeX, inWidth + paddingW);
int poolSize = (hend - hstart) * (wend - wstart);
int hend = std::min(hstart + sizeY, imgSizeH);
hstart = std::max(hstart, 0);
for (size_t pw = 0; pw < outputW; ++pw) {
int wstart = pw * strideW - paddingW;
int wend = std::min(wstart + sizeX, imgSizeW);
wstart = std::max(wstart, 0);
hend = std::min(hend, static_cast<int>(inHeight));
wend = std::min(wend, static_cast<int>(inWidth));
CHECK(poolSize);
tgtData[ph * outputW + pw] = 0; // clear
for (int h = hstart; h < hend; ++h) {
for (int w = wstart; w < wend; ++w) {
tgtData[ph * outputW + pw] += inData[h * inWidth + w];
tgtData[ph * outputW + pw] += inData[h * imgSizeW + w];
}
}
int poolSize = (hend - hstart) * (wend - wstart);
CHECK(poolSize);
tgtData[ph * outputW + pw] /= poolSize;
}
}
// compute offset
inData += inHeight * inWidth;
tgtData += outputH * outputW;
inData += inLength;
tgtData += outLength;
}
}
}
......@@ -2176,7 +2153,9 @@ void CpuMatrix::avgPoolBackward(Matrix& input,
size_t paddingW) {
size_t num = input.getHeight();
size_t channels = input.getWidth() / outputH / outputW;
CHECK(imgSizeH * imgSizeW * channels == getWidth());
size_t inLength = imgSizeH * imgSizeW;
size_t outLength = outputH * outputW;
CHECK(inLength * channels == getWidth());
real* inData = input.getData();
real* outData = getData();
......@@ -2186,16 +2165,14 @@ void CpuMatrix::avgPoolBackward(Matrix& input,
}
for (size_t c = 0; c < channels; ++c) {
for (size_t ph = 0; ph < outputH; ++ph) {
for (size_t pw = 0; pw < outputW; ++pw) {
int hstart = ph * strideH - paddingH;
int wstart = pw * strideW - paddingW;
int hend = std::min(hstart + sizeY, imgSizeH + paddingH);
int wend = std::min(wstart + sizeX, imgSizeW + paddingW);
int poolSize = (hend - hstart) * (wend - wstart);
int hend = std::min(hstart + sizeY, imgSizeH);
hstart = std::max(hstart, 0);
for (size_t pw = 0; pw < outputW; ++pw) {
int wstart = pw * strideW - paddingW;
int wend = std::min(wstart + sizeX, imgSizeW);
wstart = std::max(wstart, 0);
hend = std::min(hend, static_cast<int>(imgSizeH));
wend = std::min(wend, static_cast<int>(imgSizeW));
int poolSize = (hend - hstart) * (wend - wstart);
CHECK(poolSize);
for (int h = hstart; h < hend; ++h) {
......@@ -2206,8 +2183,8 @@ void CpuMatrix::avgPoolBackward(Matrix& input,
}
}
// offset
outData += imgSizeH * imgSizeW;
inData += outputH * outputW;
outData += inLength;
inData += outLength;
}
}
}
......@@ -2234,12 +2211,11 @@ void CpuMatrix::maxPool3DForward(Matrix& inputMat,
real* outData = getData();
real* maxPoolIdxData = maxPoolIdx.getData();
size_t num = inputMat.getHeight();
size_t inWidth = imgSizeW;
size_t inHeight = imgSizeH;
size_t inDepth = imgSizeD;
CHECK(inHeight * inWidth * inDepth == inputMat.getWidth() / channels);
size_t inLength = imgSizeH * imgSizeW * imgSizeD;
size_t outLength = outputH * outputW * outputD;
CHECK(inLength == inputMat.getWidth() / channels);
CHECK_EQ(num, this->getHeight());
CHECK_EQ(channels * outputH * outputW * outputD, this->getWidth());
CHECK_EQ(channels * outLength, this->getWidth());
size_t outStride = getStride();
/* initialize the data_ */
......@@ -2258,16 +2234,16 @@ void CpuMatrix::maxPool3DForward(Matrix& inputMat,
}
for (size_t c = 0; c < channels; ++c) { // channel by channel
for (size_t pd = 0; pd < outputD; ++pd) {
for (size_t ph = 0; ph < outputH; ++ph) {
for (size_t pw = 0; pw < outputW; ++pw) {
int dstart = pd * strideD - paddingD;
int hstart = ph * strideH - paddingH;
int wstart = pw * strideW - paddingW;
int dend = std::min(dstart + sizeZ, inDepth);
int hend = std::min(hstart + sizeY, inHeight);
int wend = std::min(wstart + sizeX, inWidth);
int dend = std::min(dstart + sizeZ, imgSizeD);
dstart = std::max(dstart, 0);
for (size_t ph = 0; ph < outputH; ++ph) {
int hstart = ph * strideH - paddingH;
int hend = std::min(hstart + sizeY, imgSizeH);
hstart = std::max(hstart, 0);
for (size_t pw = 0; pw < outputW; ++pw) {
int wstart = pw * strideW - paddingW;
int wend = std::min(wstart + sizeX, imgSizeW);
wstart = std::max(wstart, 0);
int maxIdx = -1;
real maxOutData = outData[(pd * outputH + ph) * outputW + pw];
......@@ -2275,9 +2251,9 @@ void CpuMatrix::maxPool3DForward(Matrix& inputMat,
for (int h = hstart; h < hend; ++h) {
for (int w = wstart; w < wend; ++w) {
if (maxOutData <
inputData[(d * inHeight + h) * inWidth + w]) {
maxOutData = inputData[(d * inHeight + h) * inWidth + w];
maxIdx = (d * inHeight + h) * inWidth + w;
inputData[(d * imgSizeH + h) * imgSizeW + w]) {
maxOutData = inputData[(d * imgSizeH + h) * imgSizeW + w];
maxIdx = (d * imgSizeH + h) * imgSizeW + w;
}
}
}
......@@ -2288,9 +2264,9 @@ void CpuMatrix::maxPool3DForward(Matrix& inputMat,
}
}
// compute offset
inputData += inDepth * inHeight * inWidth;
outData += outputD * outputH * outputW;
maxPoolIdxData += outputD * outputH * outputW;
inputData += inLength;
outData += outLength;
maxPoolIdxData += outLength;
}
}
}
......@@ -2315,7 +2291,9 @@ void CpuMatrix::maxPool3DBackward(Matrix& outGrad,
real scaleTargets,
real scaleOutput) {
size_t num = getHeight();
size_t channels = size_t(width_ / imgSizeD / imgSizeH / imgSizeW);
size_t inLength = imgSizeH * imgSizeW * imgSizeD;
size_t outLength = outputH * outputW * outputD;
size_t channels = size_t(width_ / inLength);
CHECK(maxPoolIdx.getHeight() == outGrad.getHeight() &&
maxPoolIdx.getWidth() == outGrad.getWidth());
......@@ -2341,9 +2319,9 @@ void CpuMatrix::maxPool3DBackward(Matrix& outGrad,
}
}
// offset
tgtGrad += imgSizeD * imgSizeH * imgSizeW;
otGrad += outputD * outputH * outputW;
maxPoolIdxData += outputD * outputH * outputW;
tgtGrad += inLength;
otGrad += outLength;
maxPoolIdxData += outLength;
}
}
}
......@@ -2367,11 +2345,10 @@ void CpuMatrix::avgPool3DForward(Matrix& input,
size_t paddingW) {
// The main loop
size_t num = input.getHeight();
size_t inDepth = imgSizeD;
size_t inHeight = imgSizeH;
size_t inWidth = imgSizeW;
CHECK(inDepth * inHeight * inWidth * channels == input.getWidth());
CHECK(outputD * outputH * outputW * channels * num == height_ * width_);
size_t inLength = imgSizeH * imgSizeW * imgSizeD;
size_t outLength = outputH * outputW * outputD;
CHECK(inLength * channels == input.getWidth());
CHECK(outLength * channels * num == height_ * width_);
real* tgtData = getData();
real* inData = input.getData();
......@@ -2381,39 +2358,36 @@ void CpuMatrix::avgPool3DForward(Matrix& input,
}
for (size_t c = 0; c < channels; ++c) {
for (size_t pd = 0; pd < outputD; ++pd) {
for (size_t ph = 0; ph < outputH; ++ph) {
for (size_t pw = 0; pw < outputW; ++pw) {
int dstart = pd * strideD - paddingD;
int hstart = ph * strideH - paddingH;
int wstart = pw * strideW - paddingW;
int dend = std::min(dstart + sizeZ, inDepth + paddingD);
int hend = std::min(hstart + sizeY, inHeight + paddingH);
int wend = std::min(wstart + sizeX, inWidth + paddingW);
int poolSize = (dend - dstart) * (hend - hstart) * (wend - wstart);
int dend = std::min(dstart + sizeZ, imgSizeD);
dstart = std::max(dstart, 0);
for (size_t ph = 0; ph < outputH; ++ph) {
int hstart = ph * strideH - paddingH;
int hend = std::min(hstart + sizeY, imgSizeH);
hstart = std::max(hstart, 0);
for (size_t pw = 0; pw < outputW; ++pw) {
int wstart = pw * strideW - paddingW;
int wend = std::min(wstart + sizeX, imgSizeW);
wstart = std::max(wstart, 0);
dend = std::min(dend, static_cast<int>(inDepth));
hend = std::min(hend, static_cast<int>(inHeight));
wend = std::min(wend, static_cast<int>(inWidth));
CHECK(poolSize);
tgtData[(pd * outputH + ph) * outputW + pw] = 0; // clear
for (int d = dstart; d < dend; ++d) {
for (int h = hstart; h < hend; ++h) {
for (int w = wstart; w < wend; ++w) {
tgtData[(pd * outputH + ph) * outputW + pw] +=
inData[(d * inHeight + h) * inWidth + w];
inData[(d * imgSizeH + h) * imgSizeW + w];
}
}
}
int poolSize = (dend - dstart) * (hend - hstart) * (wend - wstart);
CHECK(poolSize);
tgtData[(pd * outputH + ph) * outputW + pw] /= poolSize;
}
}
}
// compute offset
inData += inDepth * inHeight * inWidth;
tgtData += outputD * outputH * outputW;
inData += inLength;
tgtData += outLength;
}
}
}
......@@ -2437,8 +2411,10 @@ void CpuMatrix::avgPool3DBackward(Matrix& input,
real scaleTargets,
real scaleOutput) {
size_t num = input.getHeight();
size_t channels = input.getWidth() / outputD / outputH / outputW;
CHECK(imgSizeD * imgSizeH * imgSizeW * channels == getWidth());
size_t inLength = imgSizeH * imgSizeW * imgSizeD;
size_t outLength = outputH * outputW * outputD;
size_t channels = input.getWidth() / outLength;
CHECK(inLength * channels == getWidth());
real* inData = input.getData();
real* outData = getData();
......@@ -2448,21 +2424,18 @@ void CpuMatrix::avgPool3DBackward(Matrix& input,
}
for (size_t c = 0; c < channels; ++c) {
for (size_t pd = 0; pd < outputD; ++pd) {
for (size_t ph = 0; ph < outputH; ++ph) {
for (size_t pw = 0; pw < outputW; ++pw) {
int dstart = pd * strideD - paddingD;
int hstart = ph * strideH - paddingH;
int wstart = pw * strideW - paddingW;
int dend = std::min(dstart + sizeZ, imgSizeD + paddingD);
int hend = std::min(hstart + sizeY, imgSizeH + paddingH);
int wend = std::min(wstart + sizeX, imgSizeW + paddingW);
int poolSize = (dend - dstart) * (hend - hstart) * (wend - wstart);
int dend = std::min(dstart + sizeZ, imgSizeD);
dstart = std::max(dstart, 0);
for (size_t ph = 0; ph < outputH; ++ph) {
int hstart = ph * strideH - paddingH;
int hend = std::min(hstart + sizeY, imgSizeH);
hstart = std::max(hstart, 0);
for (size_t pw = 0; pw < outputW; ++pw) {
int wstart = pw * strideW - paddingW;
int wend = std::min(wstart + sizeX, imgSizeW);
wstart = std::max(wstart, 0);
dend = std::min(dend, static_cast<int>(imgSizeD));
hend = std::min(hend, static_cast<int>(imgSizeH));
wend = std::min(wend, static_cast<int>(imgSizeW));
int poolSize = (dend - dstart) * (hend - hstart) * (wend - wstart);
CHECK(poolSize);
for (int d = dstart; d < dend; ++d) {
for (int h = hstart; h < hend; ++h) {
......@@ -2476,8 +2449,8 @@ void CpuMatrix::avgPool3DBackward(Matrix& input,
}
}
// offset
outData += imgSizeD * imgSizeH * imgSizeW;
inData += outputD * outputH * outputW;
outData += inLength;
inData += outLength;
}
}
}
......
paddle/math/tests/test_matrixCompare.cpp
浏览文件 @ b5a448f3
......@@ -825,9 +825,8 @@ void testMaxPoolFwdBwd(int numSamples,
int strideW,
int padH,
int padW) {
int outH = 0, outW = 0;
outH = (imgSizeH - ksizeH + 2 * padH + strideH - 1) / strideH + 1;
outW = (imgSizeW - ksizeW + 2 * padW + strideW - 1) / strideW + 1;
int outH = outputSize(imgSizeH, ksizeH, padH, strideH, true);
int outW = outputSize(imgSizeW, ksizeW, padW, strideW, true);
int inWidth = imgSizeH * imgSizeW * channels;
MatrixPtr input = CpuMatrix::create(numSamples, inWidth, false, false);
......@@ -927,9 +926,8 @@ void testAvgPoolFwdBwd(int numSamples,
int strideW,
int padH,
int padW) {
int outH = 0, outW = 0;
outH = (imgSizeH - ksizeH + 2 * padH + strideH - 1) / strideH + 1;
outW = (imgSizeW - ksizeW + 2 * padW + strideW - 1) / strideW + 1;
int outH = outputSize(imgSizeH, ksizeH, padH, strideH, true);
int outW = outputSize(imgSizeW, ksizeW, padW, strideW, true);
int inWidth = imgSizeH * imgSizeW * channels;
MatrixPtr input = CpuMatrix::create(numSamples, inWidth, false, false);
......
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