Builds on windows successfully,issues with deserialization and downloading weights

.gitignore

@@ -12,5 +12,6 @@ build/
 *.hdf5
 *.pk
 *.table
+cmake-build-release/
 demo/COCO_val2017
-demo/BDD100K_val
+demo/BDD100K_val

CMakeLists.txt

@@ -1,13 +1,14 @@
 cmake_minimum_required(VERSION 3.5)
 
-project (tkDNN)
+project (tkDNN CUDA)
 set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} ${CMAKE_CURRENT_SOURCE_DIR}/cmake)
 if(UNIX)
 set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11 -fPIC -Wno-deprecated-declarations -Wno-unused-variable")
 endif()
 if(WIN32)
 set(CMAKE_CXX_STANDARD 14)
-set(CMAKE_CXX_FLAGS "/O2 /FS ")
+set(CMAKE_CXX_FLAGS "/O2 ")
+set(CMAKE_WINDOWS_EXPORT_ALL_SYMBOLS ON)
 endif(WIN32)
 include_directories(${CMAKE_CURRENT_SOURCE_DIR}/include/tkDNN)
 
@@ -33,7 +34,7 @@ include_directories(${CUDNN_INCLUDE_DIR})
 # compile
 file(GLOB tkdnn_CUSRC "src/kernels/*.cu" "src/sorting.cu")
 cuda_include_directories(${CMAKE_CURRENT_SOURCE_DIR}/include ${CUDA_INCLUDE_DIRS} ${CUDNN_INCLUDE_DIRS})
-cuda_add_library(kernels SHARED ${tkdnn_CUSRC})
+add_library(kernels SHARED ${tkdnn_CUSRC})
 target_link_libraries(kernels ${CUDA_CUBLAS_LIBRARIES})
 
 
@@ -47,7 +48,7 @@ find_package(OpenCV REQUIRED)
 set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DOPENCV")
 
 # gives problems in cross-compiling, probably malformed cmake config
-#find_package(yaml-cpp REQUIRED)
+find_package(yaml-cpp REQUIRED)
 
 #-------------------------------------------------------------------------------
 # Build Libraries

demo/demo/demo.cpp

@@ -1,7 +1,7 @@
 #include <iostream>
 #include <signal.h>
 #include <stdlib.h>     /* srand, rand */
-#include <unistd.h>
+//#include <unistd.h>
 #include <mutex>
 
 #include "CenternetDetection.h"

include/tkDNN/test.h

@@ -29,7 +29,8 @@ int testInference(std::vector<std::string> input_bins, std::vector<std::string>
     readBinaryFile(input_bins[0], net->input_dim.tot(), &input_h, &data);
 
     // outputs
-    dnnType *cudnn_out[outputs.size()], *rt_out[outputs.size()]; 
+    //dnnType *cudnn_out[outputs.size()], *rt_out[outputs.size()];
+    std::vector<dnnType *> cudnn_out,rt_out;
 
     tk::dnn::dataDim_t dim1 =  net->input_dim; //input dim
     printCenteredTitle(" CUDNN inference ", '=', 30); {