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Add Mobilenet2SSDLite test

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The new test works both with TensorRT and cuDNN. Preprocessing and
Postprocessing are missing. Add ClippedReLU (for ReLU6), groups for
Conv2d, additional bias for convolution.

Other minors:
-move the timer in the detector to measure all the
processing time for a given frame (both centernet and yolo);
-add int8 flag.

Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>
Davide Sapienza <sapienza.dav@gmail.com>
This commit is contained in:
xavier
2020-02-21 10:45:46 +01:00
parent 97b88ef52d
commit 38a1b9dcb2
17 changed files with 649 additions and 56 deletions
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src/Conv2d.cpp
+23 -11
View File
@@ -100,7 +100,7 @@ void Conv2d::inferCUDNN(dnnType* srcData, bool back) {
&beta, dstTensorDesc, dstData));
}
if(!batchnorm) {
if(!batchnorm && !additional_bias) { //CHECK WITH IF CORRECT
// bias
alpha = dnnType(1);
beta = dnnType(1);
@@ -108,23 +108,34 @@ void Conv2d::inferCUDNN(dnnType* srcData, bool back) {
&alpha, biasTensorDesc, bias_d,
&beta, dstTensorDesc, dstData) );
} else {
alpha = dnnType(1);
beta = dnnType(0);
checkCUDNN( cudnnBatchNormalizationForwardInference(net->cudnnHandle,
CUDNN_BATCHNORM_SPATIAL, &alpha, &beta,
dstTensorDesc, dstData, dstTensorDesc,
dstData, biasTensorDesc, //same tensor descriptor as bias
scales_d, bias_d, mean_d, variance_d,
TKDNN_BN_MIN_EPSILON) );
if(additional_bias)
{
alpha = dnnType(1);
beta = dnnType(1);
checkCUDNN( cudnnAddTensor(net->cudnnHandle,
&alpha, biasTensorDesc, bias2_d,
&beta, dstTensorDesc, dstData) );
}
if(batchnorm)
{
alpha = dnnType(1);
beta = dnnType(0);
checkCUDNN( cudnnBatchNormalizationForwardInference(net->cudnnHandle,
CUDNN_BATCHNORM_SPATIAL, &alpha, &beta,
dstTensorDesc, dstData, dstTensorDesc,
dstData, biasTensorDesc, //same tensor descriptor as bias
scales_d, bias_d, mean_d, variance_d,
TKDNN_BN_MIN_EPSILON) );
}
}
}
Conv2d::Conv2d( Network *net, int out_ch, int kernelH, int kernelW,
int strideH, int strideW, int paddingH, int paddingW,
std::string fname_weights, bool batchnorm, bool deConv, bool final, int groups) :
std::string fname_weights, bool batchnorm, bool deConv, bool final, int groups, bool additional_bias) :
LayerWgs(net, net->getOutputDim().c, out_ch, kernelH, kernelW, 1,
fname_weights, batchnorm, false, final, deConv, groups) {
fname_weights, batchnorm, additional_bias, final, deConv, groups) {
this->kernelH = kernelH;
this->kernelW = kernelW;
this->strideH = strideH;
@@ -133,6 +144,7 @@ Conv2d::Conv2d( Network *net, int out_ch, int kernelH, int kernelW,
this->paddingW = paddingW;
this->deConv = deConv;
this->groups = groups;
this->additional_bias = additional_bias;
if(!deConv) {
output_dim.n = input_dim.n;