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Merge pull request #18323 from liqi-c:tengine-lite-update

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Tengine lite update

* update tengine

* Modify for arm32 build.

* format optimization

* add teng_ befor some tengine api

* update graph_t to teng_graph_t

* update graph_t to teng_graph_t

* Code structure optimization

* optimization

* optimization

* remove space

* update tengine url

Co-authored-by: liqi <qli@openailab.com>
This commit is contained in:
NesQl
2020-09-23 17:34:29 +08:00
committed by GitHub
parent 48ddb53332
commit 3fc1487cc9
6 changed files with 208 additions and 152 deletions
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modules/dnn/src/layers/convolution_layer.cpp
+62 -15
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@@ -248,6 +248,10 @@ public:
float power;
#endif
#ifdef HAVE_TENGINE
teng_graph_t tengine_graph;
#endif
#ifdef HAVE_CUDA
cuda4dnn::ConvolutionConfiguration::FusionMode cudaFusionMode;
cuda4dnn::ConvolutionConfiguration::ActivationType cudaActType;
@@ -266,8 +270,20 @@ public:
#ifdef HAVE_CUDA
cudaFusionMode = cuda4dnn::ConvolutionConfiguration::FusionMode::NONE;
cudaActType = cuda4dnn::ConvolutionConfiguration::ActivationType::IDENTITY;
#endif
#ifdef HAVE_TENGINE
tengine_graph=NULL;
#endif
}
#ifdef HAVE_TENGINE
~ConvolutionLayerImpl()
{
if(NULL != tengine_graph )
{
tengine_release(tengine_graph);
}
}
#endif
MatShape computeColRowShape(const MatShape &inpShape, const MatShape &outShape) const CV_OVERRIDE
{
@@ -391,6 +407,13 @@ public:
for(int i = 0; i < numOutput; i++ )
biasvec[i] = biasMat.at<float>(i);
}
#ifdef HAVE_TENGINE
if(NULL != tengine_graph )
{
tengine_release(tengine_graph);
tengine_graph = NULL ;
}
#endif
#ifdef HAVE_OPENCL
convolutionOp.release();
#endif
@@ -1765,26 +1788,50 @@ public:
}
#ifdef HAVE_TENGINE
int inch = inputs[0].size[1]; // inch
int in_h = inputs[0].size[2]; // in_h
int in_w = inputs[0].size[3]; // in_w
bool tengine_ret = false; ;
int out_b = outputs[0].size[0]; // out batch size
int outch = outputs[0].size[1]; // outch
int out_h = outputs[0].size[2]; // out_h
int out_w = outputs[0].size[3]; // out_w
std::vector<Mat> teng_in, teng_out;
inputs_arr.getMatVector(teng_in);
outputs_arr.getMatVector(teng_out);
float *input_ = inputs[0].ptr<float>();
float *output_ = outputs[0].ptr<float>();
int inch = teng_in[0].size[1]; // inch
int in_h = teng_in[0].size[2]; // in_h
int in_w = teng_in[0].size[3]; // in_w
int out_b = teng_out[0].size[0]; // out batch size
int outch = teng_out[0].size[1]; // outch
int out_h = teng_out[0].size[2]; // out_h
int out_w = teng_out[0].size[3]; // out_w
float *input_ = teng_in[0].ptr<float>();
float *output_ = teng_out[0].ptr<float>();
float *kernel_ = weightsMat.ptr<float>();
float *teg_bias = &biasvec[0];
bool tengine_ret = tengine_forward(input_, inch, ngroups, in_h, in_w,
output_, out_b, outch, out_h, out_w,
kernel_, kernel_size.size(), kernel.height, kernel.width,
teg_bias, stride.height, stride.width,
pad.height, pad.width, dilation.height, dilation.width,
weightsMat.step1(), padMode);
int nstripes = std::max(getNumThreads(), 1);
/* tengine_init will run when first time. */
if(NULL == tengine_graph)
{
tengine_graph = tengine_init(name.c_str(), input_, inch, ngroups, in_h, in_w,
output_, out_b, outch, out_h, out_w,
kernel_, kernel_size.size(), kernel.height, kernel.width,
teg_bias, stride.height, stride.width,
pad.height, pad.width, dilation.height, dilation.width,
weightsMat.step1(), padMode, tengine_graph, nstripes);
/*printf("Init(%s): input=%p(%d %d %d %d ),output=%p(%d %d %d %d ),kernel=%p(%ld %d %d ), bias=%p ,"
"stride(%d %d), pad(%d %d), dilation(%d %d) ,weightsMat=%ld, padMode=%s ,tengine_graph = %p \n",
name.c_str(),input_, inch, ngroups, in_h, in_w,
output_, out_b, outch, out_h, out_w,
kernel_, kernel_size.size(), kernel.height, kernel.width,
teg_bias, stride.height, stride.width,
pad.height, pad.width, dilation.height, dilation.width,
weightsMat.step1(), padMode.c_str() ,tengine_graph);*/
}
if(NULL != tengine_graph)
{
tengine_ret = tengine_forward(tengine_graph);
}
/* activation */
if((true == tengine_ret) && activ )
{