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Add shelfnet. Resnet18backbone works

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Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>
This commit is contained in:
Micaela Verucchi
2020-06-19 18:55:42 +02:00
parent 1dfc69ba89
commit 9f1e30eaa9
4 changed files with 219 additions and 1 deletions
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CMakeLists.txt
+4
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@@ -103,6 +103,10 @@ target_link_libraries(test_resnet101_cnet tkDNN)
add_executable(test_dla34_cnet tests/centernet/dla34_cnet/dla34_cnet.cpp)
target_link_libraries(test_dla34_cnet tkDNN)
# SHELFNET
add_executable(test_shelfnet tests/shelfnet/shelfnet.cpp)
target_link_libraries(test_shelfnet tkDNN)
# DEMOS
add_executable(test_rtinference tests/test_rtinference/rtinference.cpp)
target_link_libraries(test_rtinference tkDNN)
src/kernels/activation_leaky.cu
+1 -1
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@@ -9,7 +9,7 @@ void activation_leaky(dnnType *input, dnnType *output, int size) {
if (input[i]>0)
output[i] = input[i];
else
output[i] = 0.1f*input[i];
output[i] = 0.01f*input[i]; //FIME!!
}
}
src/utils.cpp
+1
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@@ -102,6 +102,7 @@ int checkResult(int size, dnnType *data_d, dnnType *correct_d, bool device, int
}
int diffs = 0;
for(int i=0; i<size; i++) {
// data_h[i] = data_h[i]*1e-2;
if(data_h[i] != data_h[i] || correct_h[i] != correct_h[i] || //nan control
fabs(data_h[i] - correct_h[i]) > eps) {
diffs += 1;
tests/shelfnet/shelfnet.cpp
+213
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@@ -0,0 +1,213 @@
#include <iostream>
#include "tkdnn.h"
const char *output_bin1 = "shelfnet/debug/classification_headers-5.bin";
const char *output_bin2 = "shelfnet/debug/regression_headers-5.bin";
const char *input_bin = "shelfnet/debug/input.bin";
const char *backbone[] = {
"shelfnet/layers/backbone-conv1.bin",
"shelfnet/layers/backbone-layer1-0-conv1.bin",
"shelfnet/layers/backbone-layer1-0-conv2.bin",
"shelfnet/layers/backbone-layer1-1-conv1.bin",
"shelfnet/layers/backbone-layer1-1-conv2.bin",
"shelfnet/layers/backbone-layer2-0-conv1.bin",
"shelfnet/layers/backbone-layer2-0-conv2.bin",
"shelfnet/layers/backbone-layer2-0-downsample-0.bin",
"shelfnet/layers/backbone-layer2-1-conv1.bin",
"shelfnet/layers/backbone-layer2-1-conv2.bin",
"shelfnet/layers/backbone-layer3-0-conv1.bin",
"shelfnet/layers/backbone-layer3-0-conv2.bin",
"shelfnet/layers/backbone-layer3-0-downsample-0.bin",
"shelfnet/layers/backbone-layer3-1-conv1.bin",
"shelfnet/layers/backbone-layer3-1-conv2.bin",
"shelfnet/layers/backbone-layer4-0-conv1.bin",
"shelfnet/layers/backbone-layer4-0-conv2.bin",
"shelfnet/layers/backbone-layer4-0-downsample-0.bin",
"shelfnet/layers/backbone-layer4-1-conv1.bin",
"shelfnet/layers/backbone-layer4-1-conv2.bin"};
const char *conv_out[] = {
"shelfnet/layers/conv_out16-conv-conv.bin",
"shelfnet/layers/conv_out16-conv_out.bin",
"shelfnet/layers/conv_out32-conv-conv.bin",
"shelfnet/layers/conv_out32-conv_out.bin",
"shelfnet/layers/conv_out-conv-conv.bin",
"shelfnet/layers/conv_out-conv_out.bin"};
const char *decoder[] = {
"shelfnet/layers/decoder-bottom-conv1.bin",
"shelfnet/layers/decoder-up_conv_list-0-conv_atten.bin",
"shelfnet/layers/decoder-up_conv_list-0-conv-conv.bin",
"shelfnet/layers/decoder-up_conv_list-1-conv_atten.bin",
"shelfnet/layers/decoder-up_conv_list-1-conv-conv.bin",
"shelfnet/layers/decoder-up_dense_list-0-conv.bin",
"shelfnet/layers/decoder-up_dense_list-1-conv.bin"};
const char *ladder[] = {
"shelfnet/layers/ladder-bottom-conv1.bin",
"shelfnet/layers/ladder-down_conv_list-0.bin",
"shelfnet/layers/ladder-down_conv_list-1.bin",
"shelfnet/layers/ladder-down_module_list-0-conv1.bin",
"shelfnet/layers/ladder-down_module_list-1-conv1.bin",
"shelfnet/layers/ladder-inconv-conv1.bin",
"shelfnet/layers/ladder-up_conv_list-0-conv_atten.bin",
"shelfnet/layers/ladder-up_conv_list-0-conv-conv.bin",
"shelfnet/layers/ladder-up_conv_list-1-conv_atten.bin",
"shelfnet/layers/ladder-up_conv_list-1-conv-conv.bin",
"shelfnet/layers/ladder-up_dense_list-0-conv.bin",
"shelfnet/layers/ladder-up_dense_list-1-conv.bin"};
const char *trans[] = {
"shelfnet/layers/trans1-conv.bin",
"shelfnet/layers/trans2-conv.bin",
"shelfnet/layers/trans3-conv.bin"};
int main()
{
// downloadWeightsifDoNotExist(input_bin, "shelfnet", "https://cloud.hipert.unimore.it/s/x4ZfxBKN23zAJQp/download");
int classes = 19;
// Network layout
tk::dnn::dataDim_t dim(1, 3, 1024, 1024, 1);
tk::dnn::Network net(dim);
int bi = 0;
new tk::dnn::Conv2d(&net, 64, 7, 7, 2, 2, 3, 3, backbone[bi++], true);
new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Layer* last = new tk::dnn::Pooling (&net, 3, 3, 2, 2, 1, 1, tk::dnn::POOLING_MAX);
for(int i=0; i<2; ++i){
new tk::dnn::Conv2d (&net, 64, 3, 3, 1, 1, 1, 1, backbone[bi++], true);
new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY);
new tk::dnn::Conv2d (&net, 64, 3, 3, 1, 1, 1, 1, backbone[bi++], true);
new tk::dnn::Shortcut(&net, last);
last = new tk::dnn::Activation (&net, CUDNN_ACTIVATION_RELU);
}
std::vector<tk::dnn::Layer*> features;
for(int i=0;i<3;++i){
int out_channel = pow(2,7+i);
std::cout<<out_channel<<std::endl;
new tk::dnn::Conv2d (&net, out_channel, 3, 3, 2, 2, 1, 1, backbone[bi++], true);
new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Layer* bn2 = new tk::dnn::Conv2d (&net, out_channel, 3, 3, 1, 1, 1, 1, backbone[bi++], true);
new tk::dnn::Route(&net, &last, 1);
new tk::dnn::Conv2d (&net, out_channel, 1, 1, 2, 2, 0, 0, backbone[bi++], true);
new tk::dnn::Shortcut(&net, bn2);
last = new tk::dnn::Activation (&net, CUDNN_ACTIVATION_RELU);
new tk::dnn::Conv2d (&net, out_channel, 3, 3, 1, 1, 1, 1, backbone[bi++], true);
new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY);
new tk::dnn::Conv2d (&net, out_channel, 3, 3, 1, 1, 1, 1, backbone[bi++], true);
if(i != 2)
{new tk::dnn::Shortcut(&net, last);
last = new tk::dnn::Activation (&net, CUDNN_ACTIVATION_RELU);
features.push_back(last);}
}
// for(int i=0; i<features.size(); ++i){
// new tk::dnn::Route(&net, &features[i], 1);
// int out_channel = pow(2,6+i);
// new tk::dnn::Conv2d (&net, out_channel, 1, 1, 1, 1, 0, 0, trans[i], true);
// new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY);
// }
const char *output_bin = "shelfnet/debug/backbone-layer4-1-bn2.bin";
// Load input
dnnType *data;
dnnType *input_h;
readBinaryFile(input_bin, dim.tot(), &input_h, &data);
std::cout<<"Input:"<<std::endl;
// printDeviceVector(64, data, true);
//print network model
net.print();
// // convert network to tensorRT
// tk::dnn::NetworkRT netRT(&net, net.getNetworkRTName("shelfnet"));
tk::dnn::dataDim_t dim1 = dim; //input dim
dnnType *cudnn_out = nullptr;
printCenteredTitle(" CUDNN inference ", '=', 30);
{
dim1.print();
TKDNN_TSTART
cudnn_out = net.infer(dim1, data);
TKDNN_TSTOP
dim1.print();
}
// tk::dnn::dataDim_t out_dim1 = conf5[0]->output_dim;
// dnnType *cudnn_out2 = loc5[0]->dstData;
// tk::dnn::dataDim_t out_dim2 = loc5[0]->output_dim;
// tk::dnn::dataDim_t dim2 = dim;
// printCenteredTitle(" TENSORRT inference ", '=', 30);
// {
// dim2.print();
// TKDNN_TSTART
// netRT.infer(dim2, data);
// TKDNN_TSTOP
// dim2.print();
// }
// dnnType *rt_out1 = (dnnType *)netRT.buffersRT[1];
// dnnType *rt_out2 = (dnnType *)netRT.buffersRT[2];
// dnnType *rt_out3 = (dnnType *)netRT.buffersRT[3];
// dnnType *rt_out4 = (dnnType *)netRT.buffersRT[4];
printCenteredTitle(std::string(" RESNET CHECK RESULTS ").c_str(), '=', 30);
dnnType *out1, *out1_h;
int odim1 = dim1.tot();
readBinaryFile(output_bin, odim1, &out1_h, &out1);
printDeviceVector(64, out1);
// dnnType *out2, *out2_h;
// int odim2 = out_dim2.tot();
// readBinaryFile(output_bin2, odim2, &out2_h, &out2);
// int ret_cudnn = 0, ret_tensorrt = 0, ret_cudnn_tensorrt = 0;
std::cout << "CUDNN vs correct" << std::endl;
checkResult(odim1, cudnn_out, out1, true, 20) == 0 ? 0 : ERROR_CUDNN;
// std::cout << "TRT vs correct" << std::endl;
// checkResult(odim1, rt_out1, out1) == 0 ? 0 : ERROR_TENSORRT;
// ret_tensorrt |= checkResult(odim2, rt_out2, out2) == 0 ? 0 : ERROR_TENSORRT;
// std::cout << "CUDNN vs TRT " << std::endl;
// ret_cudnn_tensorrt |= checkResult(odim1, cudnn_out1, rt_out1) == 0 ? 0 : ERROR_CUDNNvsTENSORRT;
// ret_cudnn_tensorrt |= checkResult(odim2, cudnn_out2, rt_out2) == 0 ? 0 : ERROR_CUDNNvsTENSORRT;
// std::cout << "---------------------------------------------------" << std::endl;
// std::cout << "Confidence CUDNN" << std::endl;
// printDeviceVector(64, conf->dstData, true);
// std::cout << "Locations CUDNN" << std::endl;
// printDeviceVector(64, loc->dstData, true);
// std::cout << "---------------------------------------------------" << std::endl;
// std::cout << "Confidence tensorRT" << std::endl;
// printDeviceVector(64, rt_out3, true);
// std::cout << "Locations tensorRT" << std::endl;
// printDeviceVector(64, rt_out4, true);
// std::cout << "---------------------------------------------------" << std::endl;
// std::cout << "CUDNN vs TRT " << std::endl;
// ret_cudnn_tensorrt |= checkResult(conf->output_dim.tot(), conf->dstData, rt_out3) == 0 ? 0 : ERROR_CUDNNvsTENSORRT;
// ret_cudnn_tensorrt |= checkResult(loc->output_dim.tot(), loc->dstData, rt_out4) == 0 ? 0 : ERROR_CUDNNvsTENSORRT;
// return ret_cudnn | ret_tensorrt | ret_cudnn_tensorrt;
}