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Merge branch 'cnet' of https://github.com/ceccocats/tkDNN into cnet

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This commit is contained in:
xavier
2020-02-05 18:39:32 +01:00
parent d8eeb36d4b c695d8c5d7
commit 37019597af
7 changed files with 555 additions and 15 deletions
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CMakeLists.txt
+3
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@@ -102,6 +102,9 @@ target_link_libraries(test_resnet101_cnet tkDNN)
add_executable(test_dla34 tests/dla34/dla34.cpp)
target_link_libraries(test_dla34 tkDNN)
add_executable(test_dla34_cnet tests/dla34_cnet/dla34_cnet.cpp)
target_link_libraries(test_dla34_cnet tkDNN)
################################################################################
include/tkDNN/Layer.h
+5 -4
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@@ -85,7 +85,7 @@ class LayerWgs : public Layer {
public:
LayerWgs(Network *net, int inputs, int outputs, int kh, int kw, int kt,
std::string fname_weights, bool batchnorm = false, bool additional_bias = false, bool final = false);
std::string fname_weights, bool batchnorm = false, bool additional_bias = false, bool final = false, bool deConv = false, int groups = 1);
virtual ~LayerWgs();
int inputs, outputs;
@@ -165,7 +165,7 @@ class Conv2d : public LayerWgs {
public:
Conv2d( Network *net, int out_ch, int kernelH, int kernelW,
int strideH, int strideW, int paddingH, int paddingW,
std::string fname_weights, bool batchnorm = false, bool deConv = false, bool final = false);
std::string fname_weights, bool batchnorm = false, bool deConv = false, bool final = false, int groups = 1);
virtual ~Conv2d();
virtual layerType_t getLayerType() { return LAYER_CONV2D; };
@@ -173,6 +173,7 @@ public:
int kernelH, kernelW, strideH, strideW, paddingH, paddingW;
bool deConv;
int groups;
protected:
cudnnFilterDescriptor_t filterDesc;
@@ -196,8 +197,8 @@ class DeConv2d : public Conv2d {
public:
DeConv2d( Network *net, int out_ch, int kernelH, int kernelW,
int strideH, int strideW, int paddingH, int paddingW,
std::string fname_weights, bool batchnorm = false) :
Conv2d(net, out_ch, kernelH, kernelW, strideH, strideW, paddingH, paddingW, fname_weights, batchnorm, true) {}
std::string fname_weights, bool batchnorm = false, int groups = 1) :
Conv2d(net, out_ch, kernelH, kernelW, strideH, strideW, paddingH, paddingW, fname_weights, batchnorm, true, false, groups) {}
virtual ~DeConv2d() {}
virtual layerType_t getLayerType() { return LAYER_DECONV2D; };
src/Conv2d.cpp
+9 -7
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@@ -17,8 +17,6 @@ void Conv2d::initCUDNN(bool back) {
idim = output_dim;
odim = input_dim;
}
//idim.print();
//odim.print();
checkCUDNN( cudnnCreateFilterDescriptor(&filterDesc) );
checkCUDNN( cudnnCreateConvolutionDescriptor(&convDesc) );
@@ -29,7 +27,7 @@ void Conv2d::initCUDNN(bool back) {
net->tensorFormat, net->dataType, idim.n, idim.c, idim.h, idim.w) );
checkCUDNN( cudnnSetFilter4dDescriptor(filterDesc,
net->dataType, net->tensorFormat, odim.c, idim.c,
net->dataType, net->tensorFormat, odim.c, idim.c/groups,
kernelH, kernelW) );
checkCUDNN( cudnnSetConvolution2dDescriptor(convDesc,
@@ -38,16 +36,20 @@ void Conv2d::initCUDNN(bool back) {
1,1, // upscale
CUDNN_CROSS_CORRELATION, CUDNN_DATA_FLOAT) );
checkCUDNN( cudnnSetConvolutionGroupCount(convDesc,
groups) );
// check dimension of convolution output
dataDim_t tmpdim;
checkCUDNN( cudnnGetConvolution2dForwardOutputDim(
convDesc, srcTensor, filterDesc,
&tmpdim.n, &tmpdim.c, &tmpdim.h, &tmpdim.w) );
if(odim.n != tmpdim.n || odim.c != tmpdim.c || odim.h != tmpdim.h || odim.w != tmpdim.w) {
std::cout<<"tkdim input: "; idim.print();
std::cout<<"tkdim output: "; odim.print();
std::cout<<"cudnndim: "; tmpdim.print();
FatalError("Eror conv dimension mismatch");
FatalError("Error conv dimension mismatch");
}
checkCUDNN( cudnnSetTensor4dDescriptor(dstTensor,
@@ -119,11 +121,10 @@ void Conv2d::inferCUDNN(dnnType* srcData, bool back) {
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) :
std::string fname_weights, bool batchnorm, bool deConv, bool final, int groups) :
LayerWgs(net, net->getOutputDim().c, out_ch, kernelH, kernelW, 1,
fname_weights, batchnorm, false, final) {
fname_weights, batchnorm, false, final, deConv, groups) {
this->kernelH = kernelH;
this->kernelW = kernelW;
this->strideH = strideH;
@@ -131,6 +132,7 @@ Conv2d::Conv2d( Network *net, int out_ch, int kernelH, int kernelW,
this->paddingH = paddingH;
this->paddingW = paddingW;
this->deConv = deConv;
this->groups = groups;
if(!deConv) {
output_dim.n = input_dim.n;
src/DeformConv2d.cpp
+2 -2
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@@ -76,8 +76,8 @@ DeformConv2d::~DeformConv2d() {
checkCUDNN( cudnnDestroyTensorDescriptor(biasTensorDesc) );
checkCuda( cudaFree(dstData) );
checkCuda( cudaFreeHost(ones_d1) );
checkCuda( cudaFreeHost(ones_d2) );
checkCuda( cudaFree(ones_d1) );
checkCuda( cudaFree(ones_d2) );
checkCuda( cudaFree(offset) );
checkCuda( cudaFree(mask) );
checkCuda( cudaFree(output_conv) );
src/LayerWgs.cpp
+7 -2
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@@ -8,8 +8,13 @@ namespace tk { namespace dnn {
LayerWgs::LayerWgs(Network *net, int inputs, int outputs,
int kh, int kw, int kl,
std::string fname_weights, bool batchnorm, bool additional_bias, bool final) : Layer(net, final) {
std::string fname_weights, bool batchnorm, bool additional_bias, bool final, bool deConv, int groups) : Layer(net, final) {
if(deConv)
inputs = inputs/groups;
else
outputs = outputs/groups;
this->inputs = inputs;
this->outputs = outputs;
this->weights_path = std::string(fname_weights);
src/NetworkRT.cpp
+2
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@@ -245,6 +245,7 @@ ILayer* NetworkRT::convert_layer(ITensor *input, Conv2d *l) {
checkNULL(lRTconv);
lRTconv->setStride(DimsHW{l->strideH, l->strideW});
lRTconv->setPadding(DimsHW{l->paddingH, l->paddingW});
lRTconv->setNbGroups(l->groups);
lRT = (ILayer*) lRTconv;
} else {
IDeconvolutionLayer *lRTconv = networkRT->addDeconvolution(*input,
@@ -252,6 +253,7 @@ ILayer* NetworkRT::convert_layer(ITensor *input, Conv2d *l) {
checkNULL(lRTconv);
lRTconv->setStride(DimsHW{l->strideH, l->strideW});
lRTconv->setPadding(DimsHW{l->paddingH, l->paddingW});
lRTconv->setNbGroups(l->groups);
lRT = (ILayer*) lRTconv;
Dims d = lRTconv->getOutput(0)->getDimensions();
tests/dla34_cnet/dla34_cnet.cpp
+527
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@@ -0,0 +1,527 @@
#include <iostream>
#include "tkdnn.h"
const char *input_bin = "../tests/dla34_cnet/debug/input.bin";
const char *conv1_bin = "../tests/dla34_cnet/layers/base-base_layer-0.bin";
const char *conv2_bin = "../tests/dla34_cnet/layers/base-level0-0.bin";
const char *conv3_bin = "../tests/dla34_cnet/layers/base-level1-0.bin";
// s - stage, t - tree
const char *s1_t1_conv1_bin = "../tests/dla34_cnet/layers/base-level2-tree1-conv1.bin";
const char *s1_t1_conv2_bin = "../tests/dla34_cnet/layers/base-level2-tree1-conv2.bin";
const char *s1_t1_project = "../tests/dla34_cnet/layers/base-level2-project-0.bin";
const char *s1_t2_conv1_bin = "../tests/dla34_cnet/layers/base-level2-tree2-conv1.bin";
const char *s1_t2_conv2_bin = "../tests/dla34_cnet/layers/base-level2-tree2-conv2.bin";
const char *s1_root_conv1_bin = "../tests/dla34_cnet/layers/base-level2-root-conv.bin";
const char *s2_t1_t1_conv1_bin = "../tests/dla34_cnet/layers/base-level3-tree1-tree1-conv1.bin";
const char *s2_t1_t1_conv2_bin = "../tests/dla34_cnet/layers/base-level3-tree1-tree1-conv2.bin";
const char *s2_t1_t1_project = "../tests/dla34_cnet/layers/base-level3-tree1-project-0.bin";
const char *s2_t1_t2_conv1_bin = "../tests/dla34_cnet/layers/base-level3-tree1-tree2-conv1.bin";
const char *s2_t1_t2_conv2_bin = "../tests/dla34_cnet/layers/base-level3-tree1-tree2-conv2.bin";
const char *s2_t1_root_conv1_bin = "../tests/dla34_cnet/layers/base-level3-tree1-root-conv.bin";
const char *s2_t2_t1_conv1_bin = "../tests/dla34_cnet/layers/base-level3-tree2-tree1-conv1.bin";
const char *s2_t2_t1_conv2_bin = "../tests/dla34_cnet/layers/base-level3-tree2-tree1-conv2.bin";
const char *s2_t2_t2_conv1_bin = "../tests/dla34_cnet/layers/base-level3-tree2-tree2-conv1.bin";
const char *s2_t2_t2_conv2_bin = "../tests/dla34_cnet/layers/base-level3-tree2-tree2-conv2.bin";
const char *s2_t2_root_conv1_bin = "../tests/dla34_cnet/layers/base-level3-tree2-root-conv.bin";
const char *s3_t1_t1_conv1_bin = "../tests/dla34_cnet/layers/base-level4-tree1-tree1-conv1.bin";
const char *s3_t1_t1_conv2_bin = "../tests/dla34_cnet/layers/base-level4-tree1-tree1-conv2.bin";
const char *s3_t1_t1_project = "../tests/dla34_cnet/layers/base-level4-tree1-project-0.bin";
const char *s3_t1_t2_conv1_bin = "../tests/dla34_cnet/layers/base-level4-tree1-tree2-conv1.bin";
const char *s3_t1_t2_conv2_bin = "../tests/dla34_cnet/layers/base-level4-tree1-tree2-conv2.bin";
const char *s3_t1_root_conv1_bin = "../tests/dla34_cnet/layers/base-level4-tree1-root-conv.bin";
const char *s3_t2_t1_conv1_bin = "../tests/dla34_cnet/layers/base-level4-tree2-tree1-conv1.bin";
const char *s3_t2_t1_conv2_bin = "../tests/dla34_cnet/layers/base-level4-tree2-tree1-conv2.bin";
const char *s3_t2_t2_conv1_bin = "../tests/dla34_cnet/layers/base-level4-tree2-tree2-conv1.bin";
const char *s3_t2_t2_conv2_bin = "../tests/dla34_cnet/layers/base-level4-tree2-tree2-conv2.bin";
const char *s3_t2_root_conv1_bin = "../tests/dla34_cnet/layers/base-level4-tree2-root-conv.bin";
const char *s4_t1_conv1_bin = "../tests/dla34_cnet/layers/base-level5-tree1-conv1.bin";
const char *s4_t1_conv2_bin = "../tests/dla34_cnet/layers/base-level5-tree1-conv2.bin";
const char *s4_t1_project = "../tests/dla34_cnet/layers/base-level5-project-0.bin";
const char *s4_t2_conv1_bin = "../tests/dla34_cnet/layers/base-level5-tree2-conv1.bin";
const char *s4_t2_conv2_bin = "../tests/dla34_cnet/layers/base-level5-tree2-conv2.bin";
const char *s4_root_conv1_bin = "../tests/dla34_cnet/layers/base-level5-root-conv.bin";
//final
// const char *fc_bin = "../tests/dla34_cnet/layers/output.bin";
const char *ida_0_p_1_dcn_bin = "../tests/dla34_cnet/layers/dla_up-ida_0-proj_1-conv.bin";
const char *ida_0_p_1_conv_bin = "../tests/dla34_cnet/layers/dla_up-ida_0-proj_1-conv-conv_offset_mask.bin";
const char *ida_0_up_1_deconv_bin = "../tests/dla34_cnet/layers/dla_up-ida_0-up_1.bin";
const char *ida_0_n_1_dcn_bin = "../tests/dla34_cnet/layers/dla_up-ida_0-node_1-conv.bin";
const char *ida_0_n_1_conv_bin = "../tests/dla34_cnet/layers/dla_up-ida_0-node_1-conv-conv_offset_mask.bin";
const char *ida_1_p_1_dcn_bin = "../tests/dla34_cnet/layers/dla_up-ida_1-proj_1-conv.bin";
const char *ida_1_p_1_conv_bin = "../tests/dla34_cnet/layers/dla_up-ida_1-proj_1-conv-conv_offset_mask.bin";
const char *ida_1_up_1_deconv_bin = "../tests/dla34_cnet/layers/dla_up-ida_1-up_1.bin";
const char *ida_1_n_1_dcn_bin = "../tests/dla34_cnet/layers/dla_up-ida_1-node_1-conv.bin";
const char *ida_1_n_1_conv_bin = "../tests/dla34_cnet/layers/dla_up-ida_1-node_1-conv-conv_offset_mask.bin";
const char *ida_1_p_2_dcn_bin = "../tests/dla34_cnet/layers/dla_up-ida_1-proj_2-conv.bin";
const char *ida_1_p_2_conv_bin = "../tests/dla34_cnet/layers/dla_up-ida_1-proj_2-conv-conv_offset_mask.bin";
const char *ida_1_up_2_deconv_bin = "../tests/dla34_cnet/layers/dla_up-ida_1-up_2.bin";
const char *ida_1_n_2_dcn_bin = "../tests/dla34_cnet/layers/dla_up-ida_1-node_2-conv.bin";
const char *ida_1_n_2_conv_bin = "../tests/dla34_cnet/layers/dla_up-ida_1-node_2-conv-conv_offset_mask.bin";
const char *ida_2_p_1_dcn_bin = "../tests/dla34_cnet/layers/dla_up-ida_2-proj_1-conv.bin";
const char *ida_2_p_1_conv_bin = "../tests/dla34_cnet/layers/dla_up-ida_2-proj_1-conv-conv_offset_mask.bin";
const char *ida_2_up_1_deconv_bin = "../tests/dla34_cnet/layers/dla_up-ida_2-up_1.bin";
const char *ida_2_n_1_dcn_bin = "../tests/dla34_cnet/layers/dla_up-ida_2-node_1-conv.bin";
const char *ida_2_n_1_conv_bin = "../tests/dla34_cnet/layers/dla_up-ida_2-node_1-conv-conv_offset_mask.bin";
const char *ida_2_p_2_dcn_bin = "../tests/dla34_cnet/layers/dla_up-ida_2-proj_2-conv.bin";
const char *ida_2_p_2_conv_bin = "../tests/dla34_cnet/layers/dla_up-ida_2-proj_2-conv-conv_offset_mask.bin";
const char *ida_2_up_2_deconv_bin = "../tests/dla34_cnet/layers/dla_up-ida_2-up_2.bin";
const char *ida_2_n_2_dcn_bin = "../tests/dla34_cnet/layers/dla_up-ida_2-node_2-conv.bin";
const char *ida_2_n_2_conv_bin = "../tests/dla34_cnet/layers/dla_up-ida_2-node_2-conv-conv_offset_mask.bin";
const char *ida_2_p_3_dcn_bin = "../tests/dla34_cnet/layers/dla_up-ida_2-proj_3-conv.bin";
const char *ida_2_p_3_conv_bin = "../tests/dla34_cnet/layers/dla_up-ida_2-proj_3-conv-conv_offset_mask.bin";
const char *ida_2_up_3_deconv_bin = "../tests/dla34_cnet/layers/dla_up-ida_2-up_3.bin";
const char *ida_2_n_3_dcn_bin = "../tests/dla34_cnet/layers/dla_up-ida_2-node_3-conv.bin";
const char *ida_2_n_3_conv_bin = "../tests/dla34_cnet/layers/dla_up-ida_2-node_3-conv-conv_offset_mask.bin";
const char *ida_up_p_1_dcn_bin = "../tests/dla34_cnet/layers/ida_up-proj_1-conv.bin";
const char *ida_up_p_1_conv_bin = "../tests/dla34_cnet/layers/ida_up-proj_1-conv-conv_offset_mask.bin";
const char *ida_up_up_1_deconv_bin = "../tests/dla34_cnet/layers/ida_up-up_1.bin";
const char *ida_up_n_1_dcn_bin = "../tests/dla34_cnet/layers/ida_up-node_1-conv.bin";
const char *ida_up_n_1_conv_bin = "../tests/dla34_cnet/layers/ida_up-node_1-conv-conv_offset_mask.bin";
const char *ida_up_p_2_dcn_bin = "../tests/dla34_cnet/layers/ida_up-proj_2-conv.bin";
const char *ida_up_p_2_conv_bin = "../tests/dla34_cnet/layers/ida_up-proj_2-conv-conv_offset_mask.bin";
const char *ida_up_up_2_deconv_bin = "../tests/dla34_cnet/layers/ida_up-up_2.bin";
const char *ida_up_n_2_dcn_bin = "../tests/dla34_cnet/layers/ida_up-node_2-conv.bin";
const char *ida_up_n_2_conv_bin = "../tests/dla34_cnet/layers/ida_up-node_2-conv-conv_offset_mask.bin";
const char *hm_conv1_bin = "../tests/dla34_cnet/layers/hm-0.bin";
const char *hm_conv2_bin = "../tests/dla34_cnet/layers/hm-2.bin";
const char *wh_conv1_bin = "../tests/dla34_cnet/layers/wh-0.bin";
const char *wh_conv2_bin = "../tests/dla34_cnet/layers/wh-2.bin";
const char *reg_conv1_bin = "../tests/dla34_cnet/layers/reg-0.bin";
const char *reg_conv2_bin = "../tests/dla34_cnet/layers/reg-2.bin";
const char *output_bin[]={
"../tests/dla34_cnet/debug/hm.bin",
"../tests/dla34_cnet/debug/wh.bin",
"../tests/dla34_cnet/debug/reg.bin"};
int main()
{
// Network layout
tk::dnn::dataDim_t dim(1, 3, 224, 224, 1);
tk::dnn::Network net(dim);
tk::dnn::Layer *last1, *last2, *last3, *last4;
tk::dnn::Layer *base1, *base2, *base3, *base4, *base5, *base6, *ida1, *ida2_1, *ida2_2, *ida3_1, *ida3_2, *ida3_3, *idaup_1, *idaup_2;
tk::dnn::Conv2d conv1(&net, 16, 7, 7, 1, 1, 3, 3, conv1_bin, true);
tk::dnn::Activation relu1(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::Conv2d conv2(&net, 16, 3, 3, 1, 1, 1, 1, conv2_bin, true);
tk::dnn::Activation relu2(&net, CUDNN_ACTIVATION_RELU);
base1 = &relu2;
tk::dnn::Conv2d conv3(&net, 32, 3, 3, 2, 2, 1, 1, conv3_bin, true);
tk::dnn::Activation relu3(&net, CUDNN_ACTIVATION_RELU);
base2 = &relu3;
// level 2
// tree 1
tk::dnn::Conv2d s1_t1_conv1(&net, 64, 3, 3, 2, 2, 1, 1, s1_t1_conv1_bin, true);
tk::dnn::Activation s1_t1_relu1(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::Conv2d s1_t1_conv2(&net, 64, 3, 3, 1, 1, 1, 1, s1_t1_conv2_bin, true);
last2 = &s1_t1_conv2;
// get the basicblock input and apply maxpool conv2d and relu
tk::dnn::Layer *route_s1_t1_layers[1] = { base2 };
tk::dnn::Route route_s1_t1(&net, route_s1_t1_layers, 1);
// downsample
tk::dnn::Pooling s1_t1_maxpool1(&net, 2, 2, 2, 2, 0, 0, tk::dnn::POOLING_MAX);
// project
tk::dnn::Conv2d s1_t1_residual1_conv1(&net, 64, 1, 1, 1, 1, 0, 0, s1_t1_project, true);
tk::dnn::Shortcut s1_t1_s1(&net, last2);
tk::dnn::Activation s1_t1_relu(&net, CUDNN_ACTIVATION_RELU);
last1 = &s1_t1_relu;
// tree 2
tk::dnn::Conv2d s1_t2_conv1(&net, 64, 3, 3, 1, 1, 1, 1, s1_t2_conv1_bin, true);
tk::dnn::Activation s1_t2_relu1(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::Conv2d s1_t2_conv2(&net, 64, 3, 3, 1, 1, 1, 1, s1_t2_conv2_bin, true);
tk::dnn::Shortcut s1_t2_s1(&net, last1);
tk::dnn::Activation s1_t2_relu(&net, CUDNN_ACTIVATION_RELU);
last2 = &s1_t2_relu;
// root
// join last1 and net in single input 128, 56, 56
tk::dnn::Layer *route_s1_root_layers[2] = { last2, last1 };
tk::dnn::Route route_s1_root(&net, route_s1_root_layers, 2);
tk::dnn::Conv2d s1_root_conv1(&net, 64, 1, 1, 1, 1, 0, 0, s1_root_conv1_bin, true);
tk::dnn::Activation s1_root_relu(&net, CUDNN_ACTIVATION_RELU);
base3 = &s1_root_relu;
// level 3
// tree 1
// tree 1
tk::dnn::Conv2d s2_t1_t1_conv1(&net, 128, 3, 3, 2, 2, 1, 1, s2_t1_t1_conv1_bin, true);
tk::dnn::Activation s2_t1_t1_relu1(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::Conv2d s2_t1_t1_conv2(&net, 128, 3, 3, 1, 1, 1, 1, s2_t1_t1_conv2_bin, true);
last2 = &s2_t1_t1_conv2;
// get the basicblock input and apply maxpool conv2d and relu
tk::dnn::Layer *route_s2_t1_t1_layers[1] = { base3 };
tk::dnn::Route route_s2_t1_t1(&net, route_s2_t1_t1_layers, 1);
// downsample
tk::dnn::Pooling s2_t1_t1_maxpool1(&net, 2, 2, 2, 2, 0, 0, tk::dnn::POOLING_MAX);
last4 = &s2_t1_t1_maxpool1;
// project
tk::dnn::Conv2d s2_t1_t1_residual1_conv1(&net, 128, 1, 1, 1, 1, 0, 0, s2_t1_t1_project, true);
tk::dnn::Shortcut s2_t1_t1_s1(&net, last2);
tk::dnn::Activation s2_t1_t1_relu(&net, CUDNN_ACTIVATION_RELU);
last1 = &s2_t1_t1_relu;
// tree 2
tk::dnn::Conv2d s2_t1_t2_conv1(&net, 128, 3, 3, 1, 1, 1, 1, s2_t1_t2_conv1_bin, true);
tk::dnn::Activation s2_t1_t2_relu1(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::Conv2d s2_t1_t2_conv2(&net, 128, 3, 3, 1, 1, 1, 1, s2_t1_t2_conv2_bin, true);
tk::dnn::Shortcut s2_t1_t2_s1(&net, last1);
tk::dnn::Activation s2_t1_t2_relu(&net, CUDNN_ACTIVATION_RELU);
last2 = &s2_t1_t2_relu;
// root
// join last1 and net in single input 128, 56, 56
tk::dnn::Layer *route_s2_t1_root_layers[2] = { last2, last1 };
tk::dnn::Route route_s2_t1_root(&net, route_s2_t1_root_layers, 2);
tk::dnn::Conv2d s2_t1_root_conv1(&net, 128, 1, 1, 1, 1, 0, 0, s2_t1_root_conv1_bin, true);
tk::dnn::Activation s2_t1_root_relu(&net, CUDNN_ACTIVATION_RELU);
last1 = &s2_t1_root_relu;
last3 = &s2_t1_root_relu;
// tree 2
// tree 1
tk::dnn::Conv2d s2_t2_t1_conv1(&net, 128, 3, 3, 1, 1, 1, 1, s2_t2_t1_conv1_bin, true);
tk::dnn::Activation s2_t2_t1_relu1(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::Conv2d s2_t2_t1_conv2(&net, 128, 3, 3, 1, 1, 1, 1, s2_t2_t1_conv2_bin, true);
tk::dnn::Shortcut s2_t2_t1_s1(&net, last1);
tk::dnn::Activation s2_t2_t1_relu(&net, CUDNN_ACTIVATION_RELU);
last1 = &s2_t2_t1_relu;
// tree 2
tk::dnn::Conv2d s2_t2_t2_conv1(&net, 128, 3, 3, 1, 1, 1, 1, s2_t2_t2_conv1_bin, true);
tk::dnn::Activation s2_t2_t2_relu1(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::Conv2d s2_t2_t2_conv2(&net, 128, 3, 3, 1, 1, 1, 1, s2_t2_t2_conv2_bin, true);
tk::dnn::Shortcut s2_t2_t2_s1(&net, last1);
tk::dnn::Activation s2_t2_t2_relu(&net, CUDNN_ACTIVATION_RELU);
last2 = &s2_t2_t2_relu;
// root
// join last1 and net in single input 128, 56, 56
tk::dnn::Layer *route_s2_t2_root_layers[4] = { last2, last1, last4, last3};
tk::dnn::Route route_s2_t2_root(&net, route_s2_t2_root_layers, 4);
tk::dnn::Conv2d s2_t2_root_conv1(&net, 128, 1, 1, 1, 1, 0, 0, s2_t2_root_conv1_bin, true);
tk::dnn::Activation s2_t2_root_relu(&net, CUDNN_ACTIVATION_RELU);
base4 = &s2_t2_root_relu;
// level 4
// tree 1
// tree 1
tk::dnn::Conv2d s3_t1_t1_conv1(&net, 256, 3, 3, 2, 2, 1, 1, s3_t1_t1_conv1_bin, true);
tk::dnn::Activation s3_t1_t1_relu1(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::Conv2d s3_t1_t1_conv2(&net, 256, 3, 3, 1, 1, 1, 1, s3_t1_t1_conv2_bin, true);
last2 = &s3_t1_t1_conv2;
// get the basicblock input and apply maxpool conv2d and relu
tk::dnn::Layer *route_s3_t1_t1_layers[1] = { base4 };
tk::dnn::Route route_s3_t1_t1(&net, route_s3_t1_t1_layers, 1);
// downsample
tk::dnn::Pooling s3_t1_t1_maxpool1(&net, 2, 2, 2, 2, 0, 0, tk::dnn::POOLING_MAX);
last4 = &s3_t1_t1_maxpool1;
// project
tk::dnn::Conv2d s3_t1_t1_residual1_conv1(&net, 256, 1, 1, 1, 1, 0, 0, s3_t1_t1_project, true);
tk::dnn::Shortcut s3_t1_t1_s1(&net, last2);
tk::dnn::Activation s3_t1_t1_relu(&net, CUDNN_ACTIVATION_RELU);
last1 = &s3_t1_t1_relu;
// tree 2
tk::dnn::Conv2d s3_t1_t2_conv1(&net, 256, 3, 3, 1, 1, 1, 1, s3_t1_t2_conv1_bin, true);
tk::dnn::Activation s3_t1_t2_relu1(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::Conv2d s3_t1_t2_conv2(&net, 256, 3, 3, 1, 1, 1, 1, s3_t1_t2_conv2_bin, true);
tk::dnn::Shortcut s3_t1_t2_s1(&net, last1);
tk::dnn::Activation s3_t1_t2_relu(&net, CUDNN_ACTIVATION_RELU);
last2 = &s3_t1_t2_relu;
// root
// join last1 and net in single input 256, 56, 56
tk::dnn::Layer *route_s3_t1_root_layers[2] = { last2, last1 };
tk::dnn::Route route_s3_t1_root(&net, route_s3_t1_root_layers, 2);
tk::dnn::Conv2d s3_t1_root_conv1(&net, 256, 1, 1, 1, 1, 0, 0, s3_t1_root_conv1_bin, true);
tk::dnn::Activation s3_t1_root_relu(&net, CUDNN_ACTIVATION_RELU);
last1 = &s3_t1_root_relu;
last3 = &s3_t1_root_relu;
// tree 2
// tree 1
tk::dnn::Conv2d s3_t2_t1_conv1(&net, 256, 3, 3, 1, 1, 1, 1, s3_t2_t1_conv1_bin, true);
tk::dnn::Activation s3_t2_t1_relu1(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::Conv2d s3_t2_t1_conv2(&net, 256, 3, 3, 1, 1, 1, 1, s3_t2_t1_conv2_bin, true);
tk::dnn::Shortcut s3_t2_t1_s1(&net, last1);
tk::dnn::Activation s3_t2_t1_relu(&net, CUDNN_ACTIVATION_RELU);
last1 = &s3_t2_t1_relu;
// tree 2
tk::dnn::Conv2d s3_t2_t2_conv1(&net, 256, 3, 3, 1, 1, 1, 1, s3_t2_t2_conv1_bin, true);
tk::dnn::Activation s3_t2_t2_relu1(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::Conv2d s3_t2_t2_conv2(&net, 256, 3, 3, 1, 1, 1, 1, s3_t2_t2_conv2_bin, true);
tk::dnn::Shortcut s3_t2_t2_s1(&net, last1);
tk::dnn::Activation s3_t2_t2_relu(&net, CUDNN_ACTIVATION_RELU);
last2 = &s3_t2_t2_relu;
// root
// join last1 and net in single input 256, 56, 56
tk::dnn::Layer *route_s3_t2_root_layers[4] = { last2, last1, last4, last3};
tk::dnn::Route route_s3_t2_root(&net, route_s3_t2_root_layers, 4);
tk::dnn::Conv2d s3_t2_root_conv1(&net, 256, 1, 1, 1, 1, 0, 0, s3_t2_root_conv1_bin, true);
tk::dnn::Activation s3_t2_root_relu(&net, CUDNN_ACTIVATION_RELU);
base5 = &s3_t2_root_relu;
// level 5
// tree 1
tk::dnn::Conv2d s4_t1_conv1(&net, 512, 3, 3, 2, 2, 1, 1, s4_t1_conv1_bin, true);
tk::dnn::Activation s4_t1_relu1(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::Conv2d s4_t1_conv2(&net, 512, 3, 3, 1, 1, 1, 1, s4_t1_conv2_bin, true);
last2 = &s4_t1_conv2;
// get the basicblock input and apply maxpool conv2d and relu
tk::dnn::Layer *route_s4_t1_layers[1] = { base5 };
tk::dnn::Route route_s4_t1(&net, route_s4_t1_layers, 1);
// downsample
tk::dnn::Pooling s4_t1_maxpool1(&net, 2, 2, 2, 2, 0, 0, tk::dnn::POOLING_MAX);
last4 = &s4_t1_maxpool1;
// project
tk::dnn::Conv2d s4_t1_residual1_conv1(&net, 512, 1, 1, 1, 1, 0, 0, s4_t1_project, true);
tk::dnn::Shortcut s4_t1_s1(&net, last2);
tk::dnn::Activation s4_t1_relu(&net, CUDNN_ACTIVATION_RELU);
last1 = &s4_t1_relu;
// tree 2
tk::dnn::Conv2d s4_t2_conv1(&net, 512, 3, 3, 1, 1, 1, 1, s4_t2_conv1_bin, true);
tk::dnn::Activation s4_t2_relu1(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::Conv2d s4_t2_conv2(&net, 512, 3, 3, 1, 1, 1, 1, s4_t2_conv2_bin, true);
tk::dnn::Shortcut s4_t2_s1(&net, last1);
tk::dnn::Activation s4_t2_relu(&net, CUDNN_ACTIVATION_RELU);
last2 = &s4_t2_relu;
// root
// join last1 and net in single input 128, 56, 56
tk::dnn::Layer *route_s4_root_layers[3] = { last2, last1, last4 };
tk::dnn::Route route_s4_root(&net, route_s4_root_layers, 3);
tk::dnn::Conv2d s4_root_conv1(&net, 512, 1, 1, 1, 1, 0, 0, s4_root_conv1_bin, true);
tk::dnn::Activation s4_root_relu(&net, CUDNN_ACTIVATION_RELU);
base6 = &s4_root_relu;
//final
// tk::dnn::Pooling avgpool(&net, 7, 7, 7, 7, 0, 0, tk::dnn::POOLING_AVERAGE);
// tk::dnn::Dense fc(&net, 1000, fc_bin);
//ida 0
tk::dnn::DeformConv2d ida_0_p_1_dcn(&net, 256, 1, 3, 3, 1, 1, 1, 1, ida_0_p_1_dcn_bin, ida_0_p_1_conv_bin, true);
tk::dnn::Activation ida_0_p_1_relu(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::DeConv2d ida_0_up_1_deconv(&net, 256, 4, 4, 2, 2, 1, 1, ida_0_up_1_deconv_bin, false, 256);
tk::dnn::Shortcut ida_0_shortcut(&net, base5);
tk::dnn::DeformConv2d ida_0_n_1_dcn(&net, 256, 1, 3, 3, 1, 1, 1, 1, ida_0_n_1_dcn_bin, ida_0_n_1_conv_bin, true);
tk::dnn::Activation ida_0_n_1_relu(&net, CUDNN_ACTIVATION_RELU);
ida1 = &ida_0_n_1_relu;
//ida1-1
tk::dnn::Layer *route_ida1_layers_1[1] = { base5 };
tk::dnn::Route route_ida1_1(&net, route_ida1_layers_1, 1);
tk::dnn::DeformConv2d ida_1_p_1_dcn(&net, 128, 1, 3, 3, 1, 1, 1, 1, ida_1_p_1_dcn_bin, ida_1_p_1_conv_bin, true);
tk::dnn::Activation ida_1_p_1_relu(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::DeConv2d ida_1_up_1_deconv(&net, 128, 4, 4, 2, 2, 1, 1, ida_1_up_1_deconv_bin, false, 128);
tk::dnn::Shortcut ida_1_shortcut1(&net, base4);
tk::dnn::DeformConv2d ida_1_n_1_dcn(&net, 128, 1, 3, 3, 1, 1, 1, 1, ida_1_n_1_dcn_bin, ida_1_n_1_conv_bin, true);
tk::dnn::Activation ida_1_n_1_relu(&net, CUDNN_ACTIVATION_RELU);
ida2_1 = &ida_1_n_1_relu;
//ida1-2
tk::dnn::Layer *route_ida1_layers_2[1] = { ida1 };
tk::dnn::Route route_ida1_2(&net, route_ida1_layers_2, 1);
tk::dnn::DeformConv2d ida_1_p_2_dcn(&net, 128, 1, 3, 3, 1, 1, 1, 1, ida_1_p_2_dcn_bin, ida_1_p_2_conv_bin, true);
tk::dnn::Activation ida_1_p_2_relu(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::DeConv2d ida_1_up_2_deconv(&net, 128, 4, 4, 2, 2, 1, 1, ida_1_up_2_deconv_bin, false, 128);
tk::dnn::Shortcut ida_1_shortcut2(&net, ida2_1);
tk::dnn::DeformConv2d ida_1_n_2_dcn(&net, 128, 1, 3, 3, 1, 1, 1, 1, ida_1_n_2_dcn_bin, ida_1_n_2_conv_bin, true);
tk::dnn::Activation ida_1_n_2_relu(&net, CUDNN_ACTIVATION_RELU);
ida2_2 = &ida_1_n_2_relu;
//ida2-1
tk::dnn::Layer *route_ida2_layers_1[1] = { base4 };
tk::dnn::Route route_ida2_1(&net, route_ida2_layers_1, 1);
tk::dnn::DeformConv2d ida_2_p_1_dcn(&net, 64, 1, 3, 3, 1, 1, 1, 1, ida_2_p_1_dcn_bin, ida_2_p_1_conv_bin, true);
tk::dnn::Activation ida_2_p_1_relu(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::DeConv2d ida_2_up_1_deconv(&net, 64, 4, 4, 2, 2, 1, 1, ida_2_up_1_deconv_bin, false, 64);
tk::dnn::Shortcut ida_2_shortcut1(&net, base3);
tk::dnn::DeformConv2d ida_2_n_1_dcn(&net, 64, 1, 3, 3, 1, 1, 1, 1, ida_2_n_1_dcn_bin, ida_2_n_1_conv_bin, true);
tk::dnn::Activation ida_2_n_1_relu(&net, CUDNN_ACTIVATION_RELU);
ida3_1 = &ida_2_n_1_relu;
//ida2-2
tk::dnn::Layer *route_ida2_layers_2[1] = { ida2_1 };
tk::dnn::Route route_ida2_2(&net, route_ida2_layers_2, 1);
tk::dnn::DeformConv2d ida_2_p_2_dcn(&net, 64, 1, 3, 3, 1, 1, 1, 1, ida_2_p_2_dcn_bin, ida_2_p_2_conv_bin, true);
tk::dnn::Activation ida_2_p_2_relu(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::DeConv2d ida_2_up_2_deconv(&net, 64, 4, 4, 2, 2, 1, 1, ida_2_up_2_deconv_bin, false, 64);
tk::dnn::Shortcut ida_2_shortcut2(&net, ida3_1);
tk::dnn::DeformConv2d ida_2_n_2_dcn(&net, 64, 1, 3, 3, 1, 1, 1, 1, ida_2_n_2_dcn_bin, ida_2_n_2_conv_bin, true);
tk::dnn::Activation ida_2_n_2_relu(&net, CUDNN_ACTIVATION_RELU);
ida3_2 = &ida_2_n_2_relu;
//ida2-3
tk::dnn::Layer *route_ida2_layers_3[1] = { ida2_2 };
tk::dnn::Route route_ida2_3(&net, route_ida2_layers_3, 1);
tk::dnn::DeformConv2d ida_2_p_3_dcn(&net, 64, 1, 3, 3, 1, 1, 1, 1, ida_2_p_3_dcn_bin, ida_2_p_3_conv_bin, true);
tk::dnn::Activation ida_2_p_3_relu(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::DeConv2d ida_2_up_3_deconv(&net, 64, 4, 4, 2, 2, 1, 1, ida_2_up_3_deconv_bin, false, 64);
tk::dnn::Shortcut ida_2_shortcut3(&net, ida3_2);
tk::dnn::DeformConv2d ida_2_n_3_dcn(&net, 64, 1, 3, 3, 1, 1, 1, 1, ida_2_n_3_dcn_bin, ida_2_n_3_conv_bin, true);
tk::dnn::Activation ida_2_n_3_relu(&net, CUDNN_ACTIVATION_RELU);
ida3_3 = &ida_2_n_3_relu;
//idaup-1
tk::dnn::Layer *route_idaup_layers_1[1] = { ida2_2 };
tk::dnn::Route route_idaup_1(&net, route_idaup_layers_1, 1);
tk::dnn::DeformConv2d idaup_p_1_dcn(&net, 64, 1, 3, 3, 1, 1, 1, 1, ida_up_p_1_dcn_bin, ida_up_p_1_conv_bin, true);
tk::dnn::Activation idaup_p_1_relu(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::DeConv2d idaup_up_1_deconv(&net, 64, 4, 4, 2, 2, 1, 1, ida_up_up_1_deconv_bin, false, 64);
tk::dnn::Shortcut idaup_shortcut1(&net, ida3_3);
tk::dnn::DeformConv2d idaup_n_1_dcn(&net, 64, 1, 3, 3, 1, 1, 1, 1, ida_up_n_1_dcn_bin, ida_up_n_1_conv_bin, true);
tk::dnn::Activation idaup_n_1_relu(&net, CUDNN_ACTIVATION_RELU);
idaup_1 = &idaup_n_1_relu;
//idaup-2
tk::dnn::Layer *route_idaup_layers_2[1] = { ida1 };
tk::dnn::Route route_idaup_2(&net, route_idaup_layers_2, 1);
tk::dnn::DeformConv2d idaup_p_2_dcn(&net, 64, 1, 3, 3, 1, 1, 1, 1, ida_up_p_2_dcn_bin, ida_up_p_2_conv_bin, true);
tk::dnn::Activation idaup_p_2_relu(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::DeConv2d idaup_up_2_deconv(&net, 64, 8, 8, 4, 4, 2, 2, ida_up_up_2_deconv_bin, false, 64);
tk::dnn::Shortcut idaup_shortcut2(&net, idaup_1);
tk::dnn::DeformConv2d idaup_n_2_dcn(&net, 64, 1, 3, 3, 1, 1, 1, 1, ida_up_n_2_dcn_bin, ida_up_n_2_conv_bin, true);
tk::dnn::Activation idaup_n_2_relu(&net, CUDNN_ACTIVATION_RELU);
idaup_2 = &idaup_n_2_relu;
tk::dnn::Layer *route_1_0_layers[1] = { idaup_2 };
// hm
tk::dnn::Conv2d *hm_conv1 = new tk::dnn::Conv2d(&net, 256, 3, 3, 1, 1, 1, 1, hm_conv1_bin, false);
tk::dnn::Activation *hm_relu1 = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::Conv2d *hm = new tk::dnn::Conv2d(&net, 80, 1, 1, 1, 1, 0, 0, hm_conv2_bin, false, false, true);
int kernel = 3;
int pad = (kernel - 1)/2;
tk::dnn::Activation *hm_sig = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_SIGMOID);
tk::dnn::Pooling *hmax = new tk::dnn::Pooling(&net, kernel, kernel, 1, 1, pad, pad, tk::dnn::POOLING_MAX, true);
// // wh
tk::dnn::Route *route_1_0 = new tk::dnn::Route(&net, route_1_0_layers, 1);
tk::dnn::Conv2d *wh_conv1 = new tk::dnn::Conv2d(&net, 256, 3, 3, 1, 1, 1, 1, wh_conv1_bin, false);
tk::dnn::Activation *wh_relu1 = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::Conv2d *wh = new tk::dnn::Conv2d(&net, 2, 1, 1, 1, 1, 0, 0, wh_conv2_bin, false, false, true);
// // reg
tk::dnn::Route *route_2_0 = new tk::dnn::Route(&net, route_1_0_layers, 1);
tk::dnn::Conv2d *reg_conv1 = new tk::dnn::Conv2d(&net, 256, 3, 3, 1, 1, 1, 1, reg_conv1_bin, false);
tk::dnn::Activation *reg_relu1 = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::Conv2d *reg = new tk::dnn::Conv2d(&net, 2, 1, 1, 1, 1, 0, 0, reg_conv2_bin, false, false, true);
// Load input
dnnType *data;
dnnType *input_h;
readBinaryFile(input_bin, dim.tot(), &input_h, &data);
//printDeviceVector(64, data, true);
//print network model
net.print();
//convert network to tensorRT
tk::dnn::NetworkRT netRT(&net, "dla34_cnet.rt");
tk::dnn::dataDim_t dim1 = dim; //input dim
printCenteredTitle(" CUDNN inference ", '=', 30);
{
dim1.print();
TIMER_START
net.infer(dim1, data);
TIMER_STOP
dim1.print();
}
tk::dnn::dataDim_t dim2 = dim;
printCenteredTitle(" TENSORRT inference ", '=', 30);
{
dim2.print();
TIMER_START
netRT.infer(dim2, data);
TIMER_STOP
dim2.print();
}
tk::dnn::Layer *outs[3] = { hm, wh, reg };
int out_count = 1;
for(int i=0; i<3; i++) {
printCenteredTitle((std::string(" RESNET CHECK RESULTS ") + std::to_string(i) + " ").c_str(), '=', 30);
outs[i]->output_dim.print();
dnnType *out, *out_h;
int odim = outs[i]->output_dim.tot();
readBinaryFile(output_bin[i], odim, &out_h, &out);
dnnType *cudnn_out, *rt_out;
cudnn_out = outs[i]->dstData;
rt_out = (dnnType *)netRT.buffersRT[i+out_count];
// there is the maxpool. It isn't an output but it is necessary for the process section
if(i==0)
out_count ++;
std::cout << "CUDNN vs correct";
checkResult(odim, cudnn_out, out);
std::cout << "TRT vs correct";
checkResult(odim, rt_out, out);
std::cout << "CUDNN vs TRT ";
checkResult(odim, cudnn_out, rt_out);
}
return 0;
}