Minor fix

CMakeLists.txt

@@ -176,7 +176,7 @@ if(ENABLE_CUDA)
       add_definitions(-DZED_STEREO)
       message("ZED Camera support enabled")
     else()
-      message(WARNING "ZED SDK not found !")
+      message("ZED SDK not found !")
       set(ENABLE_ZED_CAMERA "FALSE" CACHE BOOL "Enable ZED Camera support" FORCE)
     endif()
   endif()

README.md

@@ -49,6 +49,10 @@ More details: http://pjreddie.com/darknet/yolo/
 * **GPU with CC >= 3.0**: https://en.wikipedia.org/wiki/CUDA#GPUs_supported
 * on Linux **GCC or Clang**, on Windows **MSVS 2017 (v15)** https://visualstudio.microsoft.com/thank-you-downloading-visual-studio/?sku=Community&rel=15#
 
+Compiling on **Windows** by using `Cmake-GUI` as on this [**IMAGE**](https://user-images.githubusercontent.com/4096485/55107892-6becf380-50e3-11e9-9a0a-556a943c429a.png): Configure -> Optional platform for generator (Set: x64) -> Finish -> Generate -> Open Project -> x64 & Release -> Build -> Build solution
+
+Compiling on **Linux** by using command `make` (or alternative way by using command: `cmake . && make` )
+
 #### Pre-trained models
 
 There are weights-file for different cfg-files (smaller size -> faster speed & lower accuracy:
@@ -69,7 +73,7 @@ You can get cfg-files by path: `darknet/cfg/`
 
 #### Yolo v3 in other frameworks
 
-* Convert `yolov3.weights`/`cfg` model to **TensorFlow**: by using [mystic123](https://github.com/mystic123/tensorflow-yolo-v3) or [jinyu121](https://github.com/jinyu121/DW2TF) projects
+* Convert `yolov3.weights`/`cfg` model to **TensorFlow**: by using [mystic123](https://github.com/mystic123/tensorflow-yolo-v3) or [jinyu121](https://github.com/jinyu121/DW2TF) projects, and [TensorFlow-lite](https://www.tensorflow.org/lite/guide/get_started#2_convert_the_model_format)
 * To use Yolo v3 model in **Intel OpenVINO** (Myriad X / USB Neural Compute Stick / Arria FPGA): read this [manual](https://software.intel.com/en-us/articles/OpenVINO-Using-TensorFlow#converting-a-darknet-yolo-model)
 * **OpenCV-dnn** is very fast DNN implementation on CPU (x86/ARM-Android), use `yolov3.weights`/`cfg` with: [C++ example](https://github.com/opencv/opencv/blob/8c25a8eb7b10fb50cda323ee6bec68aa1a9ce43c/samples/dnn/object_detection.cpp#L192-L221), [Python example](https://github.com/opencv/opencv/blob/8c25a8eb7b10fb50cda323ee6bec68aa1a9ce43c/samples/dnn/object_detection.py#L129-L150)
 
@@ -165,6 +169,10 @@ To run Darknet on Linux use examples from this article, just use `./darknet` ins
 
 ### How to compile on Windows (using `vcpkg`)
 
+If you have installed: MSVS 2015/2017, CUDA 10, cuDNN 7.x, OpenCV 3.x. Then compile Darknet by using `C:\Program Files\CMake\bin\cmake-gui.exe` as on this [**IMAGE**](https://user-images.githubusercontent.com/4096485/55107892-6becf380-50e3-11e9-9a0a-556a943c429a.png): Configure -> Optional platform for generator (Set: x64) -> Finish -> Generate -> Open Project -> x64 & Release -> Build -> Build solution
+
+Otherwise:
+
 1. Install or update Visual Studio to at least version 2017, making sure to have it fully patched (run again the installer if not sure to automatically update to latest version). If you need to install from scratch, download VS from here: [Visual Studio 2017 Community](http://visualstudio.com)
 
 2. Install CUDA and cuDNN
@@ -387,7 +395,7 @@ It will create `.txt`-file for each `.jpg`-image-file - in the same directory an
 
 9. After training is complete - get result `yolo-obj_final.weights` from path `build\darknet\x64\backup\`
 
- * After each 100 iterations you can stop and later start training from this point. For example, after 2000 iterations you can stop training, and later just copy `yolo-obj_2000.weights` from `build\darknet\x64\backup\` to `build\darknet\x64\` and start training using: `darknet.exe detector train data/obj.data yolo-obj.cfg yolo-obj_2000.weights`
+ * After each 100 iterations you can stop and later start training from this point. For example, after 2000 iterations you can stop training, and later just start training using: `darknet.exe detector train data/obj.data yolo-obj.cfg backup\yolo-obj_2000.weights`
 
     (in the original repository https://github.com/pjreddie/darknet the weights-file is saved only once every 10 000 iterations `if(iterations > 1000)`)
 

src/crnn_layer.c

@@ -269,7 +269,7 @@ void forward_crnn_layer_gpu(layer l, network_state state)
     layer self_layer = *(l.self_layer);
     layer output_layer = *(l.output_layer);
 
-/*
+
 #ifdef CUDNN_HALF   // slow and bad for training
     if (!state.train && state.net.cudnn_half) {
         s.index = state.index;
@@ -278,7 +278,6 @@ void forward_crnn_layer_gpu(layer l, network_state state)
         cuda_convert_f32_to_f16(output_layer.weights_gpu, output_layer.c*output_layer.n*output_layer.size*output_layer.size, output_layer.weights_gpu16);
     }
 #endif  //CUDNN_HALF
-*/
 
 
     if (state.train) {

src/dark_cuda.c

@@ -25,6 +25,10 @@ int gpu_index = 0;
 #endif  // USE_CMAKE_LIBS
 #endif  // CUDNN
 
+#if defined(CUDNN_HALF) && !defined(CUDNN)
+#error "If you set CUDNN_HALF=1 then you must set CUDNN=1"
+#endif
+
 void cuda_set_device(int n)
 {
     gpu_index = n;

src/network_kernels.cu

@@ -156,7 +156,7 @@ void forward_backward_network_gpu(network net, float *x, float *y)
     state.delta = 0;
     state.truth = *net.truth_gpu;
     state.train = 1;
-#ifdef CUDNN_HALF
+#if defined(CUDNN_HALF) && defined(CUDNN)
     int i;
     for (i = 0; i < net.n; ++i) {
         layer l = net.layers[i];