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Added monodepth2.cpp

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perseusdg
2022-01-06 16:30:11 +00:00
parent b75cecb105
commit 7298dcfb2f
2 changed files with 279 additions and 0 deletions
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CMakeLists.txt
+4
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@@ -202,6 +202,10 @@ target_link_libraries(test_shelfnet_berkeley tkDNN)
add_executable(test_shelfnet_mapillary tests/shelfnet/shelfnet_mapillary.cpp)
target_link_libraries(test_shelfnet_mapillary tkDNN)
# MONODEPTH2
add_executable(test_monodepth2 tests/monodepth2/monodepth2.cpp)
target_link_libraries(test_monodepth2 tkDNN)
# DEMOS
add_executable(test_rtinference tests/test_rtinference/rtinference.cpp)
target_link_libraries(test_rtinference tkDNN)
tests/monodepth2/monodepth2.cpp
+275
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@@ -0,0 +1,275 @@
#include <iostream>
#include <vector>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <tkdnn.h>
const char* encoder_conv1_bin = "monodepth2/layers/encoder/encoder-conv1.bin";
const char* encoder_layer1_bin[] = {
"monodepth2/layers/encoder/encoder-layer1-0-conv1.bin",
"monodepth2/layers/encoder/encoder-layer1-0-conv2.bin",
"monodepth2/layers/encoder/encoder-layer1-1-conv1.bin",
"monodepth2/layers/encoder/encoder-layer1-1-conv2.bin",
};
const char* encoder_layer2_bin[] = {
"monodepth2/layers/encoder/encoder-layer2-0-conv1.bin",
"monodepth2/layers/encoder/encoder-layer2-0-conv2.bin",
"monodepth2/layers/encoder/encoder-layer2-0-downsample-0.bin",
"monodepth2/layers/encoder/encoder-layer2-1-conv1.bin",
"monodepth2/layers/encoder/encoder-layer2-1-conv2.bin"
};
const char* encoder_layer3_bin[]={
"monodepth2/layers/encoder/encoder-layer3-0-conv1.bin",
"monodepth2/layers/encoder/encoder-layer3-0-conv2.bin",
"monodepth2/layers/encoder/encoder-layer3-0-downsample-0.bin",
"monodepth2/layers/encoder/encoder-layer3-1-conv1.bin",
"monodepth2/layers/encoder/encoder-layer3-1-conv2.bin"
};
const char* encoder_layer4_bin[] = {
"monodepth2/layers/encoder/encoder-layer4-0-conv1.bin",
"monodepth2/layers/encoder/encoder-layer4-0-conv2.bin",
"monodepth2/layers/encoder/encoder-layer4-0-downsample-0.bin",
"monodepth2/layers/encoder/encoder-layer4-1-conv1.bin",
"monodepth2/layers/encoder/encoder-layer4-1-conv2.bin"
};
const char *encoder_fc_bin = "monodepth2/layers/encoder/encoder-fc.bin";
const char* decoder_layer_bin[] = {
"monodepth2/layers/depth_decoder/decoder-0-conv-conv.bin",
"monodepth2/layers/depth_decoder/decoder-1-conv-conv.bin",
"monodepth2/layers/depth_decoder/decoder-2-conv-conv.bin",
"monodepth2/layers/depth_decoder/decoder-3-conv-conv.bin",
"monodepth2/layers/depth_decoder/decoder-4-conv-conv.bin",
"monodepth2/layers/depth_decoder/decoder-5-conv-conv.bin",
"monodepth2/layers/depth_decoder/decoder-6-conv-conv.bin",
"monodepth2/layers/depth_decoder/decoder-7-conv-conv.bin",
"monodepth2/layers/depth_decoder/decoder-8-conv-conv.bin",
"monodepth2/layers/depth_decoder/decoder-9-conv-conv.bin"
};
const char* decoder_dispconv_layer_bin[] = {
"monodepth2/layers/depth_decoder/decoder-10-conv.bin",
"monodepth2/layers/depth_decoder/decoder-11-conv.bin",
"monodepth2/layers/depth_decoder/decoder-12-conv.bin",
"monodepth2/layers/depth_decoder/decoder-13-conv.bin"
};
const char* output_bin[] = {
"monodepth2/debug/outputs/output-disp-0.bin",
"monodepth2/debug/outputs/output-disp-1.bin",
"monodepth2/debug/outputs/output-disp-2.bin",
"monodepth2/debug/outputs/output-disp-3.bin"
};
const char* input_monodepth2_bin[] = {"monodepth2/debug/input.bin","monodepth2/debug/input2.bin"};
int main(){
tk::dnn::dataDim_t dim(1,3,192,640,1);
tk::dnn::Network net(dim);
std::vector<tk::dnn::Layer*> features;
new tk::dnn::Conv2d(&net,64,7,7,2,2,3,3,encoder_conv1_bin, true,false,1, true);
tk::dnn::Layer *encoder_relu_1 = new tk::dnn::Activation(&net,CUDNN_ACTIVATION_RELU);
features.push_back(encoder_relu_1);
tk::dnn::Layer *last = new tk::dnn::Pooling(&net,3,3,2,2,1,1,tk::dnn::POOLING_MAX);
//layer 1
for(int i=0;i<4;i=i+2){
new tk::dnn::Conv2d(&net,64,3,3,1,1,1,1,encoder_layer1_bin[i], true,false,1, true);
new tk::dnn::Activation(&net,CUDNN_ACTIVATION_RELU);
new tk::dnn::Conv2d(&net,64,3,3,1,1,1,1,encoder_layer1_bin[i+1],true,false,1,true);
new tk::dnn::Shortcut(&net,last);
last = new tk::dnn::Activation(&net,CUDNN_ACTIVATION_RELU);
}
features.push_back(last);
//layer2
new tk::dnn::Conv2d(&net,128,3,3,2,2,1,1,encoder_layer2_bin[0],true,false,1,true);
new tk::dnn::Activation(&net,CUDNN_ACTIVATION_RELU);
tk::dnn::Layer *bn2 = new tk::dnn::Conv2d(&net,128,3,3,1,1,1,1,encoder_layer2_bin[1],true,false,1, true);
new tk::dnn::Route(&net,&last,1);
new tk::dnn::Conv2d(&net,128,1,1,2,2,0,0,encoder_layer2_bin[2], true,false,1, true);
new tk::dnn::Shortcut(&net,bn2);
last = new tk::dnn::Activation(&net,CUDNN_ACTIVATION_RELU);
new tk::dnn::Conv2d(&net,128,3,3,1,1,1,1,encoder_layer2_bin[3],true,false,1, true);
new tk::dnn::Activation(&net,CUDNN_ACTIVATION_RELU);
new tk::dnn::Conv2d(&net,128,3,3,1,1,1,1,encoder_layer2_bin[4],true,false,1, true);
new tk::dnn::Shortcut(&net,last);
last = new tk::dnn::Activation(&net,CUDNN_ACTIVATION_RELU);
features.push_back(last);
//layer3
new tk::dnn::Conv2d(&net,256,3,3,2,2,1,1,encoder_layer3_bin[0],true,false,1, true);
new tk::dnn::Activation(&net,CUDNN_ACTIVATION_RELU);
bn2 = new tk::dnn::Conv2d(&net,256,3,3,1,1,1,1,encoder_layer3_bin[1],true,false,1, true);
new tk::dnn::Route(&net,&last,1);
new tk::dnn::Conv2d(&net,256,1,1,2,2,0,0,encoder_layer3_bin[2], true,false,1,true);
new tk::dnn::Shortcut(&net,bn2);
last = new tk::dnn::Activation(&net,CUDNN_ACTIVATION_RELU);
new tk::dnn::Conv2d(&net,256,3,3,1,1,1,1,encoder_layer3_bin[3],true,false,1, true);
new tk::dnn::Activation(&net,CUDNN_ACTIVATION_RELU);
new tk::dnn::Conv2d(&net,256,3,3,1,1,1,1,encoder_layer3_bin[4],true,false,1, true);
new tk::dnn::Shortcut(&net,last);
last = new tk::dnn::Activation(&net,CUDNN_ACTIVATION_RELU);
features.push_back(last);
//layer4
new tk::dnn::Conv2d(&net,512,3,3,2,2,1,1,encoder_layer4_bin[0],true,false,1, true);
new tk::dnn::Activation(&net,CUDNN_ACTIVATION_RELU);
bn2 = new tk::dnn::Conv2d(&net,512,3,3,1,1,1,1,encoder_layer4_bin[1],true,false,1, true);
new tk::dnn::Route(&net,&last,1);
new tk::dnn::Conv2d(&net,512,1,1,2,2,0,0,encoder_layer4_bin[2], true,false,1, true);
new tk::dnn::Shortcut(&net,bn2);
last = new tk::dnn::Activation(&net,CUDNN_ACTIVATION_RELU);
new tk::dnn::Conv2d(&net,512,3,3,1,1,1,1,encoder_layer4_bin[3],true,false,1, true);
new tk::dnn::Activation(&net,CUDNN_ACTIVATION_RELU);
new tk::dnn::Conv2d(&net,512,3,3,1,1,1,1,encoder_layer4_bin[4],true,false,1, true);
new tk::dnn::Shortcut(&net,last);
last = new tk::dnn::Activation(&net,CUDNN_ACTIVATION_RELU);
features.push_back(last);
std::vector<tk::dnn::Layer*> depth_conv_features;
//decoders
new tk::dnn::Shortcut(&net,features[4]);
new tk::dnn::Padding(&net,1,1,tk::dnn::PADDING_MODE_REFLECTION);
new tk::dnn::Conv2d(&net,256,3,3,1,1,0,0,decoder_layer_bin[0], false,false,1, false);
new tk::dnn::Activation(&net,CUDNN_ACTIVATION_ELU);
tk::dnn::Layer *upsample_layer_1 = new tk::dnn::Upsample(&net, 2);
tk::dnn::Layer *layer_1[2] = {features[3],upsample_layer_1};
new tk::dnn::Route(&net,layer_1,2);
new tk::dnn::Padding(&net,1,1,tk::dnn::PADDING_MODE_REFLECTION);
new tk::dnn::Conv2d(&net,256,3,3,1,1,0,0,decoder_layer_bin[1], false,false,1,false);
new tk::dnn::Activation(&net,CUDNN_ACTIVATION_ELU);
new tk::dnn::Padding(&net,1,1,tk::dnn::PADDING_MODE_REFLECTION);
new tk::dnn::Conv2d(&net,128,3,3,1,1,0,0,decoder_layer_bin[2], false,false,1,false);
new tk::dnn::Activation(&net,CUDNN_ACTIVATION_ELU);
tk::dnn::Layer *upsample_layer_2 = new tk::dnn::Upsample(&net,2);
tk::dnn::Layer *layer_2[2] = {features[2],upsample_layer_2};
new tk::dnn::Route(&net,layer_2,2);
new tk::dnn::Padding(&net,1,1,tk::dnn::PADDING_MODE_REFLECTION);
new tk::dnn::Conv2d(&net,128,3,3,1,1,0,0,decoder_layer_bin[3], false,false,1,false);
last = new tk::dnn::Activation(&net,CUDNN_ACTIVATION_ELU);
depth_conv_features.push_back(last);
new tk::dnn::Padding(&net,1,1,tk::dnn::PADDING_MODE_REFLECTION);
new tk::dnn::Conv2d(&net,64,3,3,1,1,0,0,decoder_layer_bin[4],false,false,1,false);
new tk::dnn::Activation(&net,CUDNN_ACTIVATION_ELU);
tk::dnn::Layer* upsample_layer_3 = new tk::dnn::Upsample(&net,2);
tk::dnn::Layer *layer_3[2] = {features[1],upsample_layer_3};
new tk::dnn::Route(&net,layer_3,2);
new tk::dnn::Padding(&net,1,1,tk::dnn::PADDING_MODE_REFLECTION);
new tk::dnn::Conv2d(&net,64,3,3,1,1,0,0,decoder_layer_bin[5], false,false,1, false);
last = new tk::dnn::Activation(&net,CUDNN_ACTIVATION_ELU);
depth_conv_features.push_back(last);
new tk::dnn::Padding(&net,1,1,tk::dnn::PADDING_MODE_REFLECTION);
new tk::dnn::Conv2d(&net,32,3,3,1,1,0,0,decoder_layer_bin[6], false,false,1, false);
new tk::dnn::Activation(&net,CUDNN_ACTIVATION_ELU);
tk::dnn::Layer* upsample_layer_4 = new tk::dnn::Upsample(&net,2);
tk::dnn::Layer *layer_4[2] = {features[0],upsample_layer_4};
new tk::dnn::Route(&net,layer_4,2);
new tk::dnn::Padding(&net,1,1,tk::dnn::PADDING_MODE_REFLECTION);
new tk::dnn::Conv2d(&net,32,3,3,1,1,0,0,decoder_layer_bin[7], false,false,1,false);
last = new tk::dnn::Activation(&net,CUDNN_ACTIVATION_ELU);
depth_conv_features.push_back(last);
new tk::dnn::Padding(&net,1,1,tk::dnn::PADDING_MODE_REFLECTION);
new tk::dnn::Conv2d(&net,16,3,3,1,1,0,0,decoder_layer_bin[8], false,false,1,false);
new tk::dnn::Activation(&net,CUDNN_ACTIVATION_ELU);
new tk::dnn::Upsample(&net,2);
new tk::dnn::Padding(&net,1,1,tk::dnn::PADDING_MODE_REFLECTION);
new tk::dnn::Conv2d(&net,16,3,3,1,1,0,0,decoder_layer_bin[9], false, false,1, false);
last = new tk::dnn::Activation(&net,CUDNN_ACTIVATION_ELU);
depth_conv_features.push_back(last);
new tk::dnn::Route(&net,&depth_conv_features[3],1);
new tk::dnn::Padding(&net,1,1,tk::dnn::PADDING_MODE_REFLECTION);
new tk::dnn::Conv2d(&net,1,3,3,1,1,0,0,decoder_dispconv_layer_bin[0], false, false,1, false);
tk::dnn::Layer *disp0 = new tk::dnn::Activation(&net,CUDNN_ACTIVATION_SIGMOID);
disp0->setFinal();
new tk::dnn::Route(&net,&depth_conv_features[2],1);
new tk::dnn::Padding(&net,1,1,tk::dnn::PADDING_MODE_REFLECTION);
new tk::dnn::Conv2d(&net,1,3,3,1,1,0,0,decoder_dispconv_layer_bin[1],false,false,1,false);
tk::dnn::Layer *disp1 = new tk::dnn::Activation(&net,CUDNN_ACTIVATION_SIGMOID);
disp1->setFinal();
new tk::dnn::Route(&net,&depth_conv_features[1],1);
new tk::dnn::Padding(&net,1,1,tk::dnn::PADDING_MODE_REFLECTION);
new tk::dnn::Conv2d(&net,1,3,3,1,1,0,0,decoder_dispconv_layer_bin[2], false,false,1, false);
tk::dnn::Layer *disp2 = new tk::dnn::Activation(&net,CUDNN_ACTIVATION_SIGMOID);
disp2->setFinal();
new tk::dnn::Route(&net,&depth_conv_features[0],1);
new tk::dnn::Padding(&net,1,1,tk::dnn::PADDING_MODE_REFLECTION);
new tk::dnn::Conv2d(&net,1,3,3,1,1,0,0,decoder_dispconv_layer_bin[3], false,false,1, false);
tk::dnn::Layer *disp3 = new tk::dnn::Activation(&net,CUDNN_ACTIVATION_SIGMOID);
disp3->setFinal();
dnnType *data;
dnnType *input_H;
readBinaryFile(input_monodepth2_bin[1],dim.tot(),&input_H,&data);
std::cout<<"INPUT DIMENSIONS : "<<dim.tot()<<std::endl;
net.print();
tk::dnn::NetworkRT netRT(&net, net.getNetworkRTName("monodepth2"));
tk::dnn::dataDim_t dim1 = dim;
dnnType *cudnn_out = nullptr;
printCenteredTitle(" CUDNN inference ", '=', 30);
{
dim1.print();
TKDNN_TSTART
net.infer(dim1, data);
TKDNN_TSTOP
dim1.print();
}
tk::dnn::dataDim_t dim2 = dim;
printCenteredTitle(" TENSORRT inference ", '=', 30);
{
dim2.print();
TKDNN_TSTART
netRT.infer(dim2, data);
TKDNN_TSTOP
dim2.print();
}
tk::dnn::Layer *outs[4] = {disp0,disp1,disp2,disp3};
std::cout<<std::endl<<std::endl;
disp3->output_dim.print();
int ret_cudnn = 0, ret_tensorrt = 0, ret_cudnn_tensorrt = 0;
for(int i=0;i<4;i++){
printCenteredTitle((std::string("MONODEPTH2 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];
std::cout<<"CUDNN vs correct";
ret_cudnn |= checkResult(odim, cudnn_out, out) == 0 ? 0: ERROR_CUDNN;
std::cout<<"TRT vs correct";
ret_tensorrt |= checkResult(odim, rt_out, out) == 0 ? 0 : ERROR_TENSORRT;
std::cout<<"CUDNN vs TRT ";
ret_cudnn_tensorrt |= checkResult(odim, cudnn_out, rt_out) == 0 ? 0 : ERROR_CUDNNvsTENSORRT;
}
return ret_cudnn | ret_tensorrt | ret_cudnn_tensorrt;
}