From 9f1e30eaa9c637e9ef30ab78e9f4809f4caac4e2 Mon Sep 17 00:00:00 2001
From: Micaela Verucchi <micaelaverucchi@gmail.com>
Date: Fri, 19 Jun 2020 18:55:42 +0200
Subject: [PATCH] Add shelfnet. Resnet18backbone works

Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>
---
 CMakeLists.txt                  |   4 +
 src/kernels/activation_leaky.cu |   2 +-
 src/utils.cpp                   |   1 +
 tests/shelfnet/shelfnet.cpp     | 213 ++++++++++++++++++++++++++++++++
 4 files changed, 219 insertions(+), 1 deletion(-)
 create mode 100644 tests/shelfnet/shelfnet.cpp

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 8c8619d..a2a8a06 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -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)
diff --git a/src/kernels/activation_leaky.cu b/src/kernels/activation_leaky.cu
index a9029ad..47dc18f 100644
--- a/src/kernels/activation_leaky.cu
+++ b/src/kernels/activation_leaky.cu
@@ -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!!
     }
  }
 
diff --git a/src/utils.cpp b/src/utils.cpp
index 65030f0..87aad06 100644
--- a/src/utils.cpp
+++ b/src/utils.cpp
@@ -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;
diff --git a/tests/shelfnet/shelfnet.cpp b/tests/shelfnet/shelfnet.cpp
new file mode 100644
index 0000000..3f8834a
--- /dev/null
+++ b/tests/shelfnet/shelfnet.cpp
@@ -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;
+}