I converted a torch model into a torchscript. Then I was planning to get the outputs of my torchscript model.
#include <iostream>
#include <torch/script.h>
#include <torch/torch.h>
#include <opencv2/opencv.hpp>
using namespace std;
using namespace cv;
int main(int argc,char**argv)
{
torch::jit::script::Module decoder = torch::jit::load("/opt/depth/model.pt");
std::cout << "monodepth loaded!\n";
decoder.to(at::kCUDA);
cv::Mat src=cv::imread("/opt/depth/test_image.jpg");
cv::Mat input_mat;
int w=640;
int h=192;
while (1) {
cv::resize(src,input_mat,cv::Size(w,h));
input_mat.convertTo(input_mat,CV_32FC3,1./255.);
torch::Tensor tensor_image = torch::from_blob(input_mat.data, {1,input_mat.rows, input_mat.cols,3}, torch::kF32);
tensor_image = tensor_image.permute({0,3,1,2});
tensor_image = tensor_image.to(at::kCUDA);
std::vector<torch::IValue> batch;
batch.push_back(tensor_image);
//this model actually return four ouputs, I only need to use one of them,
torch::IValue result_decoder=decoder.forward(batch);
torch::Tensor tensor_result = result_decoder[3].toTensor().to(at::kCPU);
tensor_result = tensor_result.permute({0,3,2,1});
cv::Mat disp=cv::Mat(h,w,CV_32FC1,tensor_result.data_ptr());
cv::resize(disp,disp,cv::Size(src.cols,src.rows));
disp*=512;
disp.convertTo(disp,CV_8UC1);
cv::cvtColor(disp,disp,cv::COLOR_BGR2GRAY);
src.push_back(disp);
// vector<cv::Mat> channels={disp,disp,disp};
// cv::merge(channels,disp);
cv::resize(src,src,cv::Size(),0.5,0.5);
// cv::imshow("result",disp);
cv::imwrite("src.jpg",src);
if(cv::waitKey(1)==27)
break;
}
cout<<"Done"<<endl;
return 0;
}
cmake_minimum_required(VERSION 2.8 FATAL_ERROR)
PROJECT ("DEPTH_EXAMPLE")
set(CMAKE_BUILD_TYPE Debug)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11")
SET(CMAKE_CXX_STANDARD 11)
SET(CMAKE_CXX_STANDARD_REQUIRED ON)
set(CMAKE_PREFIX_PATH ${CMAKE_CURRENT_SOURCE_DIR}/libtorch)
find_package(Torch REQUIRED)
find_package(CUDA)
find_package(OpenCV REQUIRED)
add_executable(monodepth main.cpp)
target_link_libraries(monodepth ${OpenCV_LIBS} ${TORCH_LIBRARIES})
Just as the middle part of this code said, my model returns four outputs, but I don’t know how to set the types of return values. Originally, I used result_decoder[3] to represent the output that I want to use. However, I got an error as below,
no match for ‘operator[]’ (operand types are ‘c10::IValue’ and ‘int’)
Next, I removed and recompile my project.
isTensor() INTERNAL ASSERT FAILED at /opt/depth/libtorch/include/ATen/core/ivalue_inl.h:90, please report a bug to PyTorch. Expected Tensor but got Tuple (toTensor at /opt/depth/libtorch/include/ATen/core/ivalue_inl.h:90)
this error means that this operation result_decoder.toTensor(). couldn’t be executed because result_decoder is not a single tensor.
finally, how to fix this error? any cues would be highly appreciated.