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running in cpu is ok,but when running the code in gpu c10 error happens at box_a.select(2, 0) = prediction.select(2, 0) - prediction.select(2, 2).div(2) in darknet.cpp.how can i fix it?

torch::jit::script::Module module = torch::jit::load(model_path);
How to print out the name and value of parameters in module?

1. 142行 conv_options.with_bias(with_bias);只有这个 conv_options.bias(with_bias)，没有with_bias；
2. at::TensorOptions options = torch::TensorOptions()
   .dtype(torch::kFloat32)
   .is_variable(true)，没有这个is_variable，我改成了at::TensorOptions options = torch::TensorOptions().dtype(torch::kFloat32);去掉了后面的.is_variable(true)；我改了之后，编译通过，GPU运行失败，出现错误的在yolo层，cpu上正常运行

module_list.size() is 24

void Darknet::load_weights(const char *weight_file)
{
...
for (int i = 0; i < module_list.size(); i++)
{

}
...
conv_weights = conv_weights.view_as(conv_imp->weight);//------crah when i == 22
}

How to solve it, thanks #

after executing this command:
cmake -G "Visual Studio 15 2017 Win64" -DCMAKE_PREFIX_PATH=C:/Program Files/libtorch ..
i open yolo-app.sln with visual studio in order to build the project and generate yolo-app.exe
but i can't build because there is some errors in Darknet.cpp file :

Any Help :)

In main.cpp:50, you should change "CV_BGR2RGB" to "cv::COLOR_BGR2RGB", or it will be error while compiling with opencv4.0

have you ever tested your code on VS2015,libTorch1.4.0
seems have a lots of bugs.

I met the same error, did you solve it ?

Yes I have solved it
My cmakelist is error

What's wrong with your cmakelist?

首先 不能用opencv4和libtorch
cmake_minimum_required(VERSION 3.0 FATAL_ERROR)
project(yolo-app)
find_package(OpenCV REQUIRED)
include_directories(${OpenCV _INCLUDE_DIRS})
find_package(Torch REQUIRED)
aux_source_directory(. DIR_sRCS)
add_executable(yolo-app main.cpp)#注 main.cpp可以换成路径xxx/xxx.cpp
target_link_libraries(yolo-app "${TORCH_LIBRARIES}" "${OpenCV_LIBS}")
set_property(TARGET yolo-app PROPERTY CXX_STANDARD 11)

谢啦，我按你的CMakeList.txt改了下，还是有问题，后来又把main.cpp 改成了
${DIR_SRCS} 才解决这个问题。

Originally posted by @Arctanxy in #10 (comment)

./yolo-app ../imgs/person.jpg
loading weight ...
terminate called after throwing an instance of 'c10::Error'
what(): Tensor: invalid storage offset -5 (setStrided at /pytorch/aten/src/ATen/native/Resize.h:87)
frame #0: std::function<std::string ()>::operator()() const + 0x11 (0x7f0957255441 in /home/chenxin/Desktop/libtorch-shared-with-deps-latest/libtorch/lib/libc10.so)
frame #1: c10::Error::Error(c10::SourceLocation, std::string const&) + 0x2a (0x7f0957254d7a in /home/chenxin/Desktop/libtorch-shared-with-deps-latest/libtorch/lib/libc10.so)
frame #2: + 0xa36497 (0x7f094d20b497 in /home/chenxin/Desktop/libtorch-shared-with-deps-latest/libtorch/lib/libcaffe2.so)
frame #3: at::native::as_strided_tensorimpl(at::Tensor const&, c10::ArrayRef, c10::ArrayRef, c10::optional) + 0xbb (0x7f094d2081eb in /home/chenxin/Desktop/libtorch-shared-with-deps-latest/libtorch/lib/libcaffe2.so)
frame #4: + 0xb25b1a (0x7f094d2fab1a in /home/chenxin/Desktop/libtorch-shared-with-deps-latest/libtorch/lib/libcaffe2.so)
frame #5: at::native::slice(at::Tensor const&, long, long, long, long) + 0x1b8 (0x7f094d2000f8 in /home/chenxin/Desktop/libtorch-shared-with-deps-latest/libtorch/lib/libcaffe2.so)
frame #6: at::TypeDefault::slice(at::Tensor const&, long, long, long, long) const + 0x1a (0x7f094d45f1fa in /home/chenxin/Desktop/libtorch-shared-with-deps-latest/libtorch/lib/libcaffe2.so)
frame #7: at::Tensor::slice(long, long, long, long) const + 0x6f (0x55b9b4eb4cf5 in ./yolo-app)
frame #8: Darknet::load_weights(char const*) + 0x596 (0x55b9b4eadfee in ./yolo-app)
frame #9: main + 0x1a1 (0x55b9b4eef14b in ./yolo-app)
frame #10: __libc_start_main + 0xe7 (0x7f08d0fe5b97 in /lib/x86_64-linux-gnu/libc.so.6)
frame #11: _start + 0x2a (0x55b9b4ea848a in ./yolo-app)

Aborted (core dumped)

Solved.
Replace every

torch::TensorFromBlob

to

at::from_blob

like

at::from_blob(weights_src, {length/4}).to(torch::kFloat32)

There will be 3 totally.

Originally posted by @zhengli233 in #12 (comment)

After executing make i get the following error. I'm using OpenCV4 on arch linux

[100%] Linking CXX executable yolo_app
/usr/bin/ld: CMakeFiles/yolo_app.dir/yolo_app.cpp.o: in function `main':
yolo_app.cpp:(.text+0x9f0): undefined reference to `cv::imread(std::string const&, int)'
/usr/bin/ld: yolo_app.cpp:(.text+0x137e): undefined reference to `cv::imwrite(std::string const&, cv::_InputArray const&, std::vector<int, std::allocator<int> > const&)'
collect2: error: ld returned 1 exit status
make[2]: *** [CMakeFiles/yolo_app.dir/build.make:125: yolo_app] Error 1
make[1]: *** [CMakeFiles/Makefile2:76: CMakeFiles/yolo_app.dir/all] Error 2
make: *** [Makefile:84: all] Error 2

my CmakeList :
cmake_minimum_required(VERSION 3.0 FATAL_ERROR)
project(yolo-app)

find_package( OpenCV 3.4.9 REQUIRED )
include_directories( ${OpenCV_INCLUDE_DIRS} )
message(${OpenCV_INCLUDE_DIRS})

find_package(Torch REQUIRED)

aux_source_directory(. DIR_SRCS)

add_executable(yolo-app ${DIR_SRCS})
target_link_libraries(yolo-app "${TORCH_LIBRARIES}" "${OpenCV_LIBS}")
set_property(TARGET yolo-app PROPERTY CXX_STANDARD 11)

my terminal show that:

ubuntu@ubuntu-System-Product-Name:/Desktop/coco/libtorch-yolov3-master/build$ cmake -DCMAKE_PREFIX_PATH='/home/ubuntu/Desktop/libtorch'
-- Caffe2: CUDA detected: 10.2
-- Caffe2: CUDA nvcc is: /usr/local/cuda-10.2/bin/nvcc
-- Caffe2: CUDA toolkit directory: /usr/local/cuda-10.2
-- Caffe2: Header version is: 10.2
-- Found cuDNN: v7.6.4 (include: /usr/local/cuda-10.2/include, library: /usr/local/cuda-10.2/lib64/libcudnn.so)
-- Autodetected CUDA architecture(s): 7.5
-- Added CUDA NVCC flags for: -gencode;arch=compute_75,code=sm_75
-- Configuring done
-- Generating done
-- Build files have been written to: /home/ubuntu/Desktop/coco/libtorch-yolov3-master/build
ubuntu@ubuntu-System-Product-Name:/Desktop/coco/libtorch-yolov3-master/build$ make -j4
Scanning dependencies of target yolo-app
[ 66%] Building CXX object CMakeFiles/yolo-app.dir/detect_cuda_compute_capabilities.cpp.o
[ 66%] Building CXX object CMakeFiles/yolo-app.dir/detect_cuda_version.cc.o
[100%] Linking CXX executable yolo-app
CMakeFiles/yolo-app.dir/detect_cuda_version.cc.o: In function main': detect_cuda_version.cc:(.text+0x0): multiple definition of main'
CMakeFiles/yolo-app.dir/detect_cuda_compute_capabilities.cpp.o:detect_cuda_compute_capabilities.cpp:(.text+0x0): first defined here
collect2: error: ld returned 1 exit status
CMakeFiles/yolo-app.dir/build.make:183: recipe for target 'yolo-app' failed
make[2]: *** [yolo-app] Error 1
CMakeFiles/Makefile2:67: recipe for target 'CMakeFiles/yolo-app.dir/all' failed
make[1]: *** [CMakeFiles/yolo-app.dir/all] Error 2
Makefile:83: recipe for target 'all' failed
make: *** [all] Error 2

应该改为
cv::cvtColor(origin_image, resized_image, cv::COLOR_BGR2RGB);

CMakeFiles/yolo-app.dir/main.cpp.o: In function main': main.cpp:(.text+0x1012): multiple definition of main'
CMakeFiles/yolo-app.dir/detect_cuda_version.cc.o:detect_cuda_version.cc:(.text+0x0): first defined here
CMakeFiles/yolo-app.dir/detect_cuda_compute_capabilities.cpp.o: In function main': detect_cuda_compute_capabilities.cpp:(.text+0x0): multiple definition of main'
CMakeFiles/yolo-app.dir/detect_cuda_version.cc.o:detect_cuda_version.cc:(.text+0x0): first defined here
collect2: error: ld returned 1 exit status
CMakeFiles/yolo-app.dir/build.make:233: recipe for target 'yolo-app' failed
make[3]: *** [yolo-app] Error 1
CMakeFiles/Makefile2:67: recipe for target 'CMakeFiles/yolo-app.dir/all' failed
make[2]: *** [CMakeFiles/yolo-app.dir/all] Error 2
CMakeFiles/Makefile2:79: recipe for target 'CMakeFiles/yolo-app.dir/rule' failed
make[1]: *** [CMakeFiles/yolo-app.dir/rule] Error 2
Makefile:118: recipe for target 'yolo-app' failed
make: *** [yolo-app] Error 2

Hi,
I use pytorch 1.2
I could successfully cmake it
but when building with make I get these errors
I don't know why, do you have the same mistakes ?

best regards

(base) n01pham@n01pham-MS-7B48:~/Desktop/libtorch-yolov3$ cmake -DCMAKE_PREFIX_PATH=/home/n01pham/libtorch
-- Looking for pthread.h
-- Looking for pthread.h - found
-- Looking for pthread_create
-- Looking for pthread_create - not found
-- Looking for pthread_create in pthreads
-- Looking for pthread_create in pthreads - not found
-- Looking for pthread_create in pthread
-- Looking for pthread_create in pthread - found
-- Found Threads: TRUE  
-- Found torch: /home/n01pham/libtorch/lib/libtorch.so  
-- Configuring done
-- Generating done
-- Build files have been written to: /home/n01pham/Desktop/libtorch-yolov3
(base) n01pham@n01pham-MS-7B48:~/Desktop/libtorch-yolov3$ make
Scanning dependencies of target yolo-app
[ 33%] Building CXX object CMakeFiles/yolo-app.dir/Darknet.cpp.o
In file included from /home/n01pham/libtorch/include/torch/csrc/api/include/torch/nn/modules/conv.h:5:0,
                 from /home/n01pham/libtorch/include/torch/csrc/api/include/torch/nn/modules.h:12,
                 from /home/n01pham/libtorch/include/torch/csrc/api/include/torch/nn.h:7,
                 from /home/n01pham/libtorch/include/torch/csrc/api/include/torch/all.h:6,
                 from /home/n01pham/libtorch/include/torch/csrc/api/include/torch/torch.h:3,
                 from /home/n01pham/Desktop/libtorch-yolov3/Darknet.h:11,
                 from /home/n01pham/Desktop/libtorch-yolov3/Darknet.cpp:10:
/home/n01pham/libtorch/include/torch/csrc/api/include/torch/nn/options/conv.h: In function ‘torch::nn::Conv2dOptions conv_options(int64_t, int64_t, int64_t, int64_t, int64_t, int64_t, bool)’:
/home/n01pham/libtorch/include/torch/csrc/api/include/torch/nn/options/conv.h:40:42: error: ‘torch::ExpandingArray<2ul, long int> torch::nn::ConvOptions<2ul>::stride_’ is private
   TORCH_ARG(ExpandingArray<D>, stride) = 1;
                                          ^
/home/n01pham/Desktop/libtorch-yolov3/Darknet.cpp:136:18: error: within this context
     conv_options.stride_ = stride;
                  ^
In file included from /home/n01pham/libtorch/include/torch/csrc/api/include/torch/nn/modules/conv.h:5:0,
                 from /home/n01pham/libtorch/include/torch/csrc/api/include/torch/nn/modules.h:12,
                 from /home/n01pham/libtorch/include/torch/csrc/api/include/torch/nn.h:7,
                 from /home/n01pham/libtorch/include/torch/csrc/api/include/torch/all.h:6,
                 from /home/n01pham/libtorch/include/torch/csrc/api/include/torch/torch.h:3,
                 from /home/n01pham/Desktop/libtorch-yolov3/Darknet.h:11,
                 from /home/n01pham/Desktop/libtorch-yolov3/Darknet.cpp:10:
/home/n01pham/libtorch/include/torch/csrc/api/include/torch/nn/options/conv.h:46:43: error: ‘torch::ExpandingArray<2ul, long int> torch::nn::ConvOptions<2ul>::padding_’ is private
   TORCH_ARG(ExpandingArray<D>, padding) = 0;
                                           ^
/home/n01pham/Desktop/libtorch-yolov3/Darknet.cpp:137:18: error: within this context
     conv_options.padding_ = padding;
                  ^
In file included from /home/n01pham/libtorch/include/torch/csrc/api/include/torch/nn/modules/conv.h:5:0,
                 from /home/n01pham/libtorch/include/torch/csrc/api/include/torch/nn/modules.h:12,
                 from /home/n01pham/libtorch/include/torch/csrc/api/include/torch/nn.h:7,
                 from /home/n01pham/libtorch/include/torch/csrc/api/include/torch/all.h:6,
                 from /home/n01pham/libtorch/include/torch/csrc/api/include/torch/torch.h:3,
                 from /home/n01pham/Desktop/libtorch-yolov3/Darknet.h:11,
                 from /home/n01pham/Desktop/libtorch-yolov3/Darknet.cpp:10:
/home/n01pham/libtorch/include/torch/csrc/api/include/torch/nn/options/conv.h:71:32: error: ‘int64_t torch::nn::ConvOptions<2ul>::groups_’ is private
   TORCH_ARG(int64_t, groups) = 1;
                                ^
/home/n01pham/Desktop/libtorch-yolov3/Darknet.cpp:138:18: error: within this context
     conv_options.groups_ = groups;
                  ^
In file included from /home/n01pham/libtorch/include/torch/csrc/api/include/torch/nn/modules/conv.h:5:0,
                 from /home/n01pham/libtorch/include/torch/csrc/api/include/torch/nn/modules.h:12,
                 from /home/n01pham/libtorch/include/torch/csrc/api/include/torch/nn.h:7,
                 from /home/n01pham/libtorch/include/torch/csrc/api/include/torch/all.h:6,
                 from /home/n01pham/libtorch/include/torch/csrc/api/include/torch/torch.h:3,
                 from /home/n01pham/Desktop/libtorch-yolov3/Darknet.h:11,
                 from /home/n01pham/Desktop/libtorch-yolov3/Darknet.cpp:10:
/home/n01pham/libtorch/include/torch/csrc/api/include/torch/nn/options/conv.h:67:32: error: ‘bool torch::nn::ConvOptions<2ul>::with_bias_’ is private
   TORCH_ARG(bool, with_bias) = true;
                                ^
/home/n01pham/Desktop/libtorch-yolov3/Darknet.cpp:139:18: error: within this context
     conv_options.with_bias_ = with_bias;
                  ^
In file included from /home/n01pham/libtorch/include/torch/csrc/api/include/torch/nn/modules/batchnorm.h:4:0,
                 from /home/n01pham/libtorch/include/torch/csrc/api/include/torch/nn/modules.h:11,
                 from /home/n01pham/libtorch/include/torch/csrc/api/include/torch/nn.h:7,
                 from /home/n01pham/libtorch/include/torch/csrc/api/include/torch/all.h:6,
                 from /home/n01pham/libtorch/include/torch/csrc/api/include/torch/torch.h:3,
                 from /home/n01pham/Desktop/libtorch-yolov3/Darknet.h:11,
                 from /home/n01pham/Desktop/libtorch-yolov3/Darknet.cpp:10:
/home/n01pham/libtorch/include/torch/csrc/api/include/torch/nn/options/batchnorm.h: In function ‘torch::nn::BatchNormOptions bn_options(int64_t)’:
/home/n01pham/libtorch/include/torch/csrc/api/include/torch/nn/options/batchnorm.h:19:29: error: ‘bool torch::nn::BatchNormOptions::affine_’ is private
   TORCH_ARG(bool, affine) = true;
                             ^
/home/n01pham/Desktop/libtorch-yolov3/Darknet.cpp:145:16: error: within this context
     bn_options.affine_ = true;
                ^
In file included from /home/n01pham/libtorch/include/torch/csrc/api/include/torch/nn/modules/batchnorm.h:4:0,
                 from /home/n01pham/libtorch/include/torch/csrc/api/include/torch/nn/modules.h:11,
                 from /home/n01pham/libtorch/include/torch/csrc/api/include/torch/nn.h:7,
                 from /home/n01pham/libtorch/include/torch/csrc/api/include/torch/all.h:6,
                 from /home/n01pham/libtorch/include/torch/csrc/api/include/torch/torch.h:3,
                 from /home/n01pham/Desktop/libtorch-yolov3/Darknet.h:11,
                 from /home/n01pham/Desktop/libtorch-yolov3/Darknet.cpp:10:
/home/n01pham/libtorch/include/torch/csrc/api/include/torch/nn/options/batchnorm.h:24:31: error: ‘bool torch::nn::BatchNormOptions::stateful_’ is private
   TORCH_ARG(bool, stateful) = true;
                               ^
/home/n01pham/Desktop/libtorch-yolov3/Darknet.cpp:146:16: error: within this context
     bn_options.stateful_ = true;
                ^
/home/n01pham/Desktop/libtorch-yolov3/Darknet.cpp: In member function ‘at::Tensor UpsampleLayer::forward(at::Tensor)’:
/home/n01pham/Desktop/libtorch-yolov3/Darknet.cpp:170:21: warning: ‘using IntList = class c10::ArrayRef<long int>’ is deprecated [-Wdeprecated-declarations]
      torch::IntList sizes = x.sizes();
                     ^
In file included from /home/n01pham/libtorch/include/c10/util/Exception.h:6:0,
                 from /home/n01pham/libtorch/include/c10/core/Device.h:5,
                 from /home/n01pham/libtorch/include/c10/core/Allocator.h:6,
                 from /home/n01pham/libtorch/include/ATen/ATen.h:3,
                 from /home/n01pham/libtorch/include/torch/csrc/api/include/torch/types.h:3,
                 from /home/n01pham/libtorch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4,
                 from /home/n01pham/libtorch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3,
                 from /home/n01pham/libtorch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3,
                 from /home/n01pham/libtorch/include/torch/csrc/api/include/torch/data/dataloader.h:3,
                 from /home/n01pham/libtorch/include/torch/csrc/api/include/torch/data.h:3,
                 from /home/n01pham/libtorch/include/torch/csrc/api/include/torch/all.h:4,
                 from /home/n01pham/libtorch/include/torch/csrc/api/include/torch/torch.h:3,
                 from /home/n01pham/Desktop/libtorch-yolov3/Darknet.h:11,
                 from /home/n01pham/Desktop/libtorch-yolov3/Darknet.cpp:10:
/home/n01pham/libtorch/include/c10/util/ArrayRef.h:278:1: note: declared here
 C10_DEFINE_DEPRECATED_USING(IntList, ArrayRef<int64_t>)
 ^
/home/n01pham/Desktop/libtorch-yolov3/Darknet.cpp: In member function ‘void Darknet::load_weights(const char*)’:
/home/n01pham/Desktop/libtorch-yolov3/Darknet.cpp:527:54: error: ‘class at::DeprecatedTypeProperties’ has no member named ‘tensorFromBlob’
     at::Tensor weights = torch::CPU(torch::kFloat32).tensorFromBlob(weights_src
                                                      ^
/home/n01pham/Desktop/libtorch-yolov3/Darknet.cpp:569:52: error: ‘class torch::nn::BatchNormImpl’ has no member named ‘running_variance’
    bn_running_var = bn_running_var.view_as(bn_imp->running_variance);
                                                    ^
/home/n01pham/Desktop/libtorch-yolov3/Darknet.cpp:574:12: error: ‘class torch::nn::BatchNormImpl’ has no member named ‘running_variance’
    bn_imp->running_variance.set_data(bn_running_var);
            ^
CMakeFiles/yolo-app.dir/build.make:62: recipe for target 'CMakeFiles/yolo-app.dir/Darknet.cpp.o' failed
make[2]: *** [CMakeFiles/yolo-app.dir/Darknet.cpp.o] Error 1
CMakeFiles/Makefile2:67: recipe for target 'CMakeFiles/yolo-app.dir/all' failed
make[1]: *** [CMakeFiles/yolo-app.dir/all] Error 2
Makefile:83: recipe for target 'all' failed
make: *** [all] Error 2

如題

error code in 537 line at Darknet.cpp( bn_imp->bias.set_data(bn_bias)), are you test in window?

libtorch 1.0,cuda9.0,

torch::Tensor Darknet::forward(torch::Tensor x)
{
int module_count = module_list.size();

std::vector<torch::Tensor> outputs(module_count);

torch::Tensor result;
int write = 0;

for (int i = 0; i < module_count; i++)
{
	map<string, string> block = blocks[i+1];

	string layer_type = block["type"];

	cout << layer_type << endl;

	if (layer_type == "net")
		continue;

	if (layer_type == "convolutional" || layer_type == "upsample" || layer_type == "maxpool")
	{
		torch::nn::SequentialImpl *seq_imp = dynamic_cast<torch::nn::SequentialImpl *>(module_list[i].ptr().get());
		
		x = seq_imp->forward(x);//这一句出错
		
		outputs[i] = x;
	}
	else if (layer_type == "route")
	{
		int start = std::stoi(block["start"]);
		int end = std::stoi(block["end"]);

		if (start > 0) start = start - i;

		if (end == 0)
		{
			x = outputs[i + start];
		}
		else
		{
			if (end > 0) end = end - i;

			torch::Tensor map_1 = outputs[i + start];
			torch::Tensor map_2 = outputs[i + end];

			x = torch::cat({map_1, map_2}, 1);
		}

		outputs[i] = x;
	}
	else if (layer_type == "shortcut")
	{
		int from = std::stoi(block["from"]);
		x = outputs[i-1] + outputs[i+from];
        outputs[i] = x;
	}
	else if (layer_type == "yolo")
	{
		torch::nn::SequentialImpl *seq_imp = dynamic_cast<torch::nn::SequentialImpl *>(module_list[i].ptr().get());

		map<string, string> net_info = blocks[0];
		int inp_dim = get_int_from_cfg(net_info, "height", 0);
		int num_classes = get_int_from_cfg(block, "classes", 0);

		x = seq_imp->forward(x, inp_dim, num_classes, *_device);

		if (write == 0)
		{
			result = x;
			write = 1;
		}
		else
		{
			result = torch::cat({result,x}, 1);
		}

		outputs[i] = outputs[i-1];
	}
}
return result;

}

OS: Win 10 64
Libtorch: 1.6
Visual Studio 2017 (VC15)
OpenCV: 4.4 (win pre-build)
使用该库调用最新的libtorch的话会出现接口不兼容的问题，出现下面这些错误：

1. Darknet.cpp(18): error C2039: “isspace”: 不是“std”的成员

2. Darknet.cpp(139): error C2039: “with_bias”: 不是“torch::nn::ConvOptions<2>”的成员

3. Darknet.cpp(146): error C2039: “stateful”: 不是“torch::nn::BatchNormOptions

4. Darknet.cpp(380): error C2039: “BatchNorm”: 不是“torch::nn”的成员

5. Darknet.cpp(526): error C2039: “is_variable”: 不是“c10::TensorOptions”的成员

6. Darknet.cpp(550): error C2039: “BatchNormImpl”: 不是“torch::nn”的成员

相应的解决办法：

1. 添加头文件#include
2. 将with_bias改为bias
   
3. 修改为track_running_stats:
   
4. 修改为BathNorm2d
   
5. 没找到对应的属性，直接注释了
   
6. 修改为BatchNorm2dImpl：
   

其实就是libtorch的接口还不稳定导致不同版本之间不兼容。
另外注意事项：

1. cmakelists.txt需要手动添加opencv地址
2. 64位框架编译：cmake -G "Visual Studio 15 2017 Win64" -T host=x64 -DCMAKE_PREFIX_PATH="E:\MachineLearn ing\libtorch" ..
3. 运行程序的时候要复制libtorch的dll文件到执行路径

最后将修改后的程序托管在地址：https://github.com/ouening/libtorch1.6-yolov3

hi， can you tell me how to run in Linux？

What to do about it

template <typename ReturnType, typename... Ts, typename Function, typename Accessor>
ReturnType unpack(Function function, Accessor accessor) {
return ReturnType(unpack<ReturnType, Ts...>(
std::move(function),
std::move(accessor),
typename MakeIndices<sizeof...(Ts)>::indices()));
}

template <typename ReturnType, typename... Ts, typename Function, typename Accessor, size_t... Is>
ReturnType unpack(Function function, Accessor accessor, Indices<Is...>) {
return ReturnType(function(accessor.template operator()(Is)...));
}

错误 C2059 语法错误:“template” yolo-app E:\correct\libtorch\include\torch\csrc\utils\variadic.h 195
严重性 代码 说明 项目 文件 行 禁止显示状态
错误 C2951 模板 声明只能在全局、命名空间或类范围内使用 yolo-app E:\correct\libtorch\include\torch\csrc\utils\variadic.h 195

Hi, I can't compile it with the latest version of libtorch

I use TX2，this is my list:
Ubuntu 16.04.5 LTS
gcc (Ubuntu/Linaro 5.4.0-6ubuntu1~16.04.11) 5.4.0 20160609
cmake version 3.5.1
pytorch C++ v1.1

can you tell me what happened?

i already built the project using cmake , then i am trying to generate the .exe file by build .sln file using visual studio 2017 ,
but i got those errors in the code :

attribute does not apply to any entity
'split': must return a value

Any Help :)

hi,thank your code.
I trained a model that only detects pedestrians，nums=1(not 80)
Does the code need to be changed？

i train my own data. i want result have classes and confidence but only result have bounding box
thanks!!!

thanks for your great work. I could run it with cpu and gpu in my PC, but when i planted the code from PC to NVIDIA TX2, errors came. I could run with cpu but not with cuda. details was that:
terminate called after throwing an instance of 'std::runtime_error'
what():
Tensor for argument #4 'running_mean' is on CPU, but expected it to be on GPU (while checking arguments for cudnn_batch_norm) (checkSameGPU at /home/nvidia/workspace/wangh/libraries/pytorch/aten/src/ATen/TensorUtils.cpp:122)
frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits, std::allocator > const&) + 0x7c (0x7f7dc40374 in /home/nvidia/workspace/wangh/libraries/pytorch/torch/lib/libc10.so)
frame #1: at::checkSameGPU(char const*, at::TensorArg const&, at::TensorArg const&) + 0xbec (0x7f904c771c in /home/nvidia/workspace/wangh/libraries/pytorch/torch/lib/libcaffe2.so)
frame #2: at::checkAllSameGPU(char const*, c10::ArrayRefat::TensorArg) + 0x44 (0x7f904c8374 in /home/nvidia/workspace/wangh/libraries/pytorch/torch/lib/libcaffe2.so)
frame #3: at::native::cudnn_batch_norm(at::Tensor const&, at::Tensor const&, at::Tensor const&, at::Tensor const&, at::Tensor const&, bool, double, double) + 0x3b8 (0x7f7e9ef918 in /home/nvidia/workspace/wangh/libraries/pytorch/torch/lib/libcaffe2_gpu.so)
frame #4: at::CUDAFloatType::cudnn_batch_norm(at::Tensor const&, at::Tensor const&, at::Tensor const&, at::Tensor const&, at::Tensor const&, bool, double, double) const + 0xd8 (0x7f7eadd968 in /home/nvidia/workspace/wangh/libraries/pytorch/torch/lib/libcaffe2_gpu.so)
frame #5: torch::autograd::VariableType::cudnn_batch_norm(at::Tensor const&, at::Tensor const&, at::Tensor const&, at::Tensor const&, at::Tensor const&, bool, double, double) const + 0x568 (0x7f93da2160 in /home/nvidia/workspace/wangh/libraries/pytorch/torch/lib/libtorch.so.1)
frame #6: at::native::_batch_norm_impl_index(at::Tensor const&, at::Tensor const&, at::Tensor const&, at::Tensor const&, at::Tensor const&, bool, double, double, bool) + 0x3a0 (0x7f905463e0 in /home/nvidia/workspace/wangh/libraries/pytorch/torch/lib/libcaffe2.so)
frame #7: at::TypeDefault::_batch_norm_impl_index(at::Tensor const&, at::Tensor const&, at::Tensor const&, at::Tensor const&, at::Tensor const&, bool, double, double, bool) const + 0xe4 (0x7f90976f74 in /home/nvidia/workspace/wangh/libraries/pytorch/torch/lib/libcaffe2.so)
frame #8: torch::autograd::VariableType::_batch_norm_impl_index(at::Tensor const&, at::Tensor const&, at::Tensor const&, at::Tensor const&, at::Tensor const&, bool, double, double, bool) const + 0x2a4 (0x7f93d1d1ac in /home/nvidia/workspace/wangh/libraries/pytorch/torch/lib/libtorch.so.1)
frame #9: at::native::batch_norm(at::Tensor const&, at::Tensor const&, at::Tensor const&, at::Tensor const&, at::Tensor const&, bool, double, double, bool) + 0x8c (0x7f90544dac in /home/nvidia/workspace/wangh/libraries/pytorch/torch/lib/libcaffe2.so)
frame #10: at::TypeDefault::batch_norm(at::Tensor const&, at::Tensor const&, at::Tensor const&, at::Tensor const&, at::Tensor const&, bool, double, double, bool) const + 0xe4 (0x7f90976e04 in /home/nvidia/workspace/wangh/libraries/pytorch/torch/lib/libcaffe2.so)
frame #11: torch::autograd::VariableType::batch_norm(at::Tensor const&, at::Tensor const&, at::Tensor const&, at::Tensor const&, at::Tensor const&, bool, double, double, bool) const + 0x21c (0x7f93d1ecdc in /home/nvidia/workspace/wangh/libraries/pytorch/torch/lib/libtorch.so.1)
frame #12: + 0x7cc8ac (0x7f93e6d8ac in /home/nvidia/workspace/wangh/libraries/pytorch/torch/lib/libtorch.so.1)
frame #13: + 0x88a660 (0x7f93f2b660 in /home/nvidia/workspace/wangh/libraries/pytorch/torch/lib/libtorch.so.1)
frame #14: torch::jit::InterpreterState::run(std::vector<c10::IValue, std::allocatorc10::IValue >&) + 0x38 (0x7f93f26838 in /home/nvidia/workspace/wangh/libraries/pytorch/torch/lib/libtorch.so.1)
frame #15: torch::jit::GraphExecutor::run(std::vector<c10::IValue, std::allocatorc10::IValue >&) + 0x188 (0x7f93f06cd0 in /home/nvidia/workspace/wangh/libraries/pytorch/torch/lib/libtorch.so.1)
frame #16: torch::jit::script::Method::run(std::vector<c10::IValue, std::allocatorc10::IValue >&) + 0x98 (0x4dfe64 in ./yolo-app)
frame #17: torch::jit::script::Method::operator()(std::vector<c10::IValue, std::allocatorc10::IValue >) + 0x30 (0x4dfec0 in ./yolo-app)
frame #18: torch::jit::script::Module::forward(std::vector<c10::IValue, std::allocatorc10::IValue >) + 0x84 (0x4e0fa4 in ./yolo-app)
frame #19: classifier(std::shared_ptrtorch::jit::script::Module, at::Tensor) + 0xfc (0x4d6760 in ./yolo-app)
frame #20: main + 0xc88 (0x4d8954 in ./yolo-app)
frame #21: __libc_start_main + 0xe0 (0x7f7cf718a0 in /lib/aarch64-linux-gnu/libc.so.6)
:
operation failed in interpreter:
op_version_set = 0
def forward(self,
input_1: Tensor) -> Tensor:
input_2 = torch.convolution(input_1, getattr(self.layer1, "0").weight, None, [1, 3], [0, 0], [1, 1], False, [0, 0], 1, False, False, True)
0 = torch.add(getattr(self.layer1, "1").num_batches_tracked, CONSTANTS.c0, alpha=1)
input_3 = torch.batch_norm(input_2, getattr(self.layer1, "1").weight, getattr(self.layer1, "1").bias, getattr(self.layer1, "1").running_mean, getattr(self.layer1, "1").running_var, True, 0.10000000000000001, 1.0000000000000001e-05, True)
~~~~~~~~~~~~~~~~ <--- HERE
input_4 = torch.threshold(input_3, 0., 0.)
input_5, _1 = torch.max_pool2d_with_indices(input_4, [2, 2], [2, 2], [0, 0], [1, 1], False)
input_6 = torch.convolution(input_5, getattr(self.layer2, "0").weight, None, [1, 1], [2, 0], [1, 1], False, [0, 0], 2, False, False, True)
2 = torch.add(getattr(self.layer2, "1").num_batches_tracked, CONSTANTS.c0, alpha=1)
input_7 = torch.batch_norm(input_6, getattr(self.layer2, "1").weight, getattr(self.layer2, "1").bias, getattr(self.layer2, "1").running_mean, getattr(self.layer2, "1").running_var, True, 0.10000000000000001, 1.0000000000000001e-05, True)
input_8 = torch.threshold(input_7, 0., 0.)
input_9, _3 = torch.max_pool2d_with_indices(input_8, [3, 3], [2, 2], [1, 0], [1, 1], False)
input_10 = torch._convolution(input_9, getattr(self.layer3, "0").weight, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True)
4 = torch.add(getattr(self.layer3, "1").num_batches_tracked, CONSTANTS.c0, alpha=1)
Aborted (core dumped)
why?

Hi,

thanks for the nice work, it seems however, that there is still a performance problem when running on GPU, i.e. the code cannot fully utilize the GPU and has high CPU usage when running on GPU. Steps to reproduce:

• modify the code so that you create an endless loop around these two calls - net.forward and net.write_results,

• on my GPU the GPU load goes up to 60 - 70 percent, not higher and CPU goes up to 20 percent.

Expected result: GPU load should go up to 100 percent and CPU usage should be small.

Any thoughts on the CPU consumption? It also might be a libtorch issue though...

Thanks.

loading weight ...
terminate called after throwing an instance of 'c10::Error'
what(): !is_variable() ASSERT FAILED at /pytorch/c10/core/TensorImpl.h:922, please report a bug to PyTorch. (shallow_copy_and_detach at /pytorch/c10/core/TensorImpl.h:922)
frame #0: std::function<std::string ()>::operator()() const + 0x11 (0x7efc2cd3f441 in /home/youyi/Documents/packages/libtorch/lib/libc10.so)
frame #1: c10::Error::Error(c10::SourceLocation, std::string const&) + 0x2a (0x7efc2cd3ed7a in /home/youyi/Documents/packages/libtorch/lib/libc10.so)
frame #2: + 0x95063d (0x7efc2d8a863d in /home/youyi/Documents/packages/libtorch/lib/libtorch.so.1)
frame #3: torch::autograd::Variable::Impl::set_data(at::Tensor const&) + 0x268 (0x7efc2d8a4b58 in /home/youyi/Documents/packages/libtorch/lib/libtorch.so.1)
frame #4: at::Tensor::set_data(at::Tensor) + 0x61 (0x564f2799e459 in ./yolo-app)
frame #5: Darknet::load_weights(char const*) + 0x816 (0x564f279980c0 in ./yolo-app)
frame #6: main + 0x1a1 (0x564f279d670d in ./yolo-app)
frame #7: __libc_start_main + 0xe7 (0x7efc1de31b97 in /lib/x86_64-linux-gnu/libc.so.6)
frame #8: _start + 0x2a (0x564f2799262a in ./yolo-app)

based on pytorch 1.1.0

请问这个一定得用libtorch重写yolov3么，能不能用torch.jit.trace导出pytorch的yolov3模型进行调用

I downlown the code and run in Ubuntu16.06
my cmakelist is
`
cmake_minimum_required(VERSION 3.0 FATAL_ERROR)
project(yolo-app)

set(CMAKE_CXX_STANDARD 14)
set(Torch_DIR /home/zyy/libtorch/share/cmake/Torch)
set(OpenCV_DIR /home/tusvision/Downloads/opencv-3.4/opencv/build)

find_package(Torch REQUIRED)
find_package(OpenCV REQUIRED)

add_executable(yolo-app main.cpp)
target_link_libraries(yolo-app "${TORCH_LIBRARIES}" "${OpenCV_LIBS}")
set_property(TARGET yolo-app PROPERTY CXX_STANDARD 11)
`
My folder is
yolo-app/main.cpp 、darknet.h darknet.cpp

cmake .
Usage

cmake [options]
cmake [options]

Specify a source directory to (re-)generate a build system for it in the
current working directory. Specify an existing build directory to
re-generate its build system.

Run 'cmake --help' for more information.

zyy@TusVision:~/yolo-app$ cmake .
-- Looking for pthread.h
-- Looking for pthread.h - found
-- Looking for pthread_create
-- Looking for pthread_create - not found
-- Looking for pthread_create in pthreads
-- Looking for pthread_create in pthreads - not found
-- Looking for pthread_create in pthread
-- Looking for pthread_create in pthread - found
-- Found Threads: TRUE
-- Found CUDA: /usr/local/cuda (found suitable version "9.0", minimum required is "7.0")
-- Caffe2: CUDA detected: 9.0
-- Caffe2: CUDA nvcc is: /usr/local/cuda/bin/nvcc
-- Caffe2: CUDA toolkit directory: /usr/local/cuda
-- Caffe2: Header version is: 9.0
-- Found CUDNN: /usr/local/cuda/include
-- Found cuDNN: v7.2.1 (include: /usr/local/cuda/include, library: /usr/local/cuda/lib64/libcudnn.so)
-- Autodetected CUDA architecture(s): 6.1;6.1
-- Added CUDA NVCC flags for: -gencode;arch=compute_61,code=sm_61
-- Found torch: /home/zyy/libtorch/lib/libtorch.so
-- Found OpenCV: /home/tusvision/Downloads/opencv-3.4/opencv/build (found version "3.4.1")
-- Configuring done
-- Generating done
-- Build files have been written to: /home/zyy/yolo-app

make
zyy@TusVision:~/yolo-app$ make
Scanning dependencies of target yolo-app
[ 50%] Building CXX object CMakeFiles/yolo-app.dir/main.cpp.o
[100%] Linking CXX executable yolo-app
CMakeFiles/yolo-app.dir/main.cpp.o: In function main': main.cpp:(.text+0xc1b): undefined reference to Darknet::Darknet(char const*, c10::Device*)'
main.cpp:(.text+0xc2a): undefined reference to Darknet::get_net_info()' main.cpp:(.text+0xd09): undefined reference to Darknet::load_weights(char const*)'
main.cpp:(.text+0x11ab): undefined reference to Darknet::forward(at::Tensor)' main.cpp:(.text+0x1205): undefined reference to Darknet::write_results(at::Tensor, int, float, float)'
collect2: error: ld returned 1 exit status
CMakeFiles/yolo-app.dir/build.make:168: recipe for target 'yolo-app' failed
make[2]: *** [yolo-app] Error 1
CMakeFiles/Makefile2:67: recipe for target 'CMakeFiles/yolo-app.dir/all' failed
make[1]: *** [CMakeFiles/yolo-app.dir/all] Error 2
Makefile:83: recipe for target 'all' failed
make: *** [all] Error 2

the main.cpp error

Do you want to train with pytorch or Python interface?Thank you!

i hava libtorch1.0.0 cuda10 GPU1050Ti, why i test a picture need cost 375ms,I think it run on the cpu
thanks!

error: ‘class at::DeprecatedTypeProperties’ has no member named ‘tensorFromBlob’
at::Tensor weights = torch::CPU(torch::kFloat32).tensorFromBlob(weights_src, {length/4});

Can you please guide a little how to modify it according to the newer version

Since the original weights are for size 416.

nn::Sequential generator(
// Layer 1
nn::Conv2d(nn::Conv2dOptions(3, 256, 4)
.with_bias(false)
.transposed(true)));

the question is the title

I have a question.
In inference stage, I want to set batch to four, processing four channels of video at the same time.
How should I do? Thank you !

I try to load yolov3-tiny cfg and weights, but failed

This error showed when loading the weights file:

terminate called after throwing an instance of 'c10::Error'
what(): !is_variable() INTERNAL ASSERT FAILED at /pytorch/c10/core/TensorImpl.h:927, please report a bug to PyTorch. (shallow_copy_and_detach at /pytorch/c10/core/TensorImpl.h:927)
frame #0: std::function<std::string ()>::operator()() const + 0x11 (0x7f54a6c17961 in /home/zhengli/Pytorch/libtorch-shared-with-deps-latest/libtorch/lib/libc10.so)
frame #1: c10::Error::Error(c10::SourceLocation, std::string const&) + 0x2a (0x7f54a6c1729a in /home/zhengli/Pytorch/libtorch-shared-with-deps-latest/libtorch/lib/libc10.so)
frame #2: + 0x2f662b9 (0x7f549de102b9 in /home/zhengli/Pytorch/libtorch-shared-with-deps-latest/libtorch/lib/libcaffe2.so)
frame #3: torch::autograd::Variable::Impl::set_data(at::Tensor const&) + 0x278 (0x7f549de0d5b8 in /home/zhengli/Pytorch/libtorch-shared-with-deps-latest/libtorch/lib/libcaffe2.so)
frame #4: at::Tensor::set_data(at::Tensor) + 0x53 (0x558b6a423363 in ./yolo-app)
frame #5: Darknet::load_weights(char const*) + 0x625 (0x558b6a416335 in ./yolo-app)
frame #6: main + 0x206 (0x558b6a40ef06 in ./yolo-app)
frame #7: __libc_start_main + 0xe7 (0x7f5499755b97 in /lib/x86_64-linux-gnu/libc.so.6)
frame #8: _start + 0x2a (0x558b6a40ff7a in ./yolo-app)

Aborted (core dumped)

Does anyone have the same problem?

thanks your work, it's great.
I just want to detect a specific kind of object and what to do.(For example, only people are tested.)
hope your reply,thanks