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Fix pooling, add return code in each test, add return code handling in test_all_tests script

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Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>
This commit is contained in:
Micaela Verucchi
2020-04-09 15:25:05 +02:00
parent ed0596a52c
commit 3502c5b676
31 changed files with 293 additions and 202 deletions
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src/Pooling.cpp
+13 -21
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@@ -7,7 +7,7 @@ namespace tk { namespace dnn {
Pooling::Pooling( Network *net, int winH, int winW, int strideH, int strideW,
int paddingH, int paddingW,
tkdnnPoolingMode_t pool_mode, bool final, bool maxpoolfixedsize) :
tkdnnPoolingMode_t pool_mode, bool final) :
Layer(net, final) {
this->winH = winH;
@@ -17,7 +17,6 @@ Pooling::Pooling( Network *net, int winH, int winW, int strideH, int strideW,
this->pool_mode = pool_mode;
this->paddingH = paddingH;
this->paddingW = paddingW;
this->maxpoolfixedsize = maxpoolfixedsize;
checkCUDNN( cudnnCreatePoolingDescriptor(&poolingDesc) );
@@ -40,9 +39,10 @@ Pooling::Pooling( Network *net, int winH, int winW, int strideH, int strideW,
n = l;
}
cudnnPoolingMode_t cudnn_pool_mode = cudnnPoolingMode_t(pool_mode);
if(pool_mode == POOLING_MAX_FIXEDSIZE) cudnn_pool_mode = cudnnPoolingMode_t(tkdnnPoolingMode_t::POOLING_MAX);
checkCUDNN( cudnnSetPooling2dDescriptor(poolingDesc, cudnnPoolingMode_t(pool_mode),
checkCUDNN( cudnnSetPooling2dDescriptor(poolingDesc, cudnn_pool_mode,
CUDNN_NOT_PROPAGATE_NAN, winH, winW, paddingH, paddingW, strideH, strideW) );
checkCUDNN( cudnnSetTensor4dDescriptor(srcTensorDesc,
@@ -52,18 +52,16 @@ Pooling::Pooling( Network *net, int winH, int winW, int strideH, int strideW,
// checkCUDNN( cudnnGetPooling2dForwardOutputDim(poolingDesc, srcTensorDesc, &n, &c, &h, &w));
//compute w and h as in darknet
int padH = paddingH == 0? winH -1 : paddingH;
int padW = paddingW == 0? winW -1 : paddingW;
if(final){
h = (h + 2*paddingH - winH)/strideH +1 ;
w = (w + 2*paddingW - winW)/strideW +1;
}
else{
if(pool_mode == tkdnnPoolingMode_t::POOLING_MAX_FIXEDSIZE){
int padH = paddingH == 0? winH -1 : paddingH;
int padW = paddingW == 0? winW -1 : paddingW;
h = (h + padH - winH)/strideH +1;
w = (w + padW - winW)/strideW +1;
}
else{
h = (h + 2*paddingH - winH)/strideH +1 ;
w = (w + 2*paddingW - winW)/strideW +1;
}
// h = (h + winH*this->paddingH)/strideH;
// w = (w + winW*this->paddingW)/strideW;
@@ -112,22 +110,16 @@ dnnType* Pooling::infer(dataDim_t &dim, dnnType* srcData) {
poolDst = tmpOutputData;
}
if(this->maxpoolfixedsize)
{
if(pool_mode == tkdnnPoolingMode_t::POOLING_MAX_FIXEDSIZE){
MaxPoolingForward(poolSrc, poolDst, dim.n, dim.c, dim.h, dim.w, this->strideH, this->strideW, this->winH, this->winH-1);
}
else
{
else{
dnnType alpha = dnnType(1);
dnnType beta = dnnType(0);
checkCUDNN( cudnnPoolingForward(net->cudnnHandle, poolingDesc,
&alpha, srcTensorDesc, poolSrc,
&beta, dstTensorDesc, poolDst) );
}
//update dim
dim = output_dim;