minimal pytorch 1.0 pytorch -> C++ full example demo image at: https://i.imgur.com/hiWRITj.jpg

CMakeLists.txt

cmake_minimum_required(VERSION 3.0 FATAL_ERROR)
project(cpp_shim)
set(CMAKE_PREFIX_PATH ../libtorch)
find_package(Torch REQUIRED)
find_package(OpenCV REQUIRED)

add_executable(testing main.cpp)

message(STATUS "OpenCV library status:")
message(STATUS "    config: ${OpenCV_DIR}")
message(STATUS "    version: ${OpenCV_VERSION}")
message(STATUS "    libraries: ${OpenCV_LIBS}")
message(STATUS "    include path: ${OpenCV_INCLUDE_DIRS}")

message(STATUS "TORCHLIB: ${TORCH_LIBRARIES}")
#target_include_directories(testing PRIVATE ${TORCH_INCLUDE_DIRS} ${OpenCV_INCLUDE_DIRS})
target_link_libraries(testing ${OpenCV_LIBS})
target_link_libraries(testing ${TORCH_LIBRARIES})

target_compile_definitions(testing PRIVATE -D_GLIBCXX_USE_CXX11_ABI=0)

generator.py

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.jit import ScriptModule, script_method, trace



class MyScriptModule(ScriptModule):
# class MyScriptModule(nn.Module):
    def __init__(self):
        super(MyScriptModule, self).__init__()
        # trace produces a ScriptModule's conv1 and conv2
        self.conv1 = trace(nn.Conv2d(3, 2, 5).to("cpu"), torch.rand(1, 3, 1266, 1900))
        self.conv2 = trace(nn.Conv2d(2, 1, 5).to("cpu"), torch.rand(1, 2, 1266, 1900))
        self.lin = trace(nn.Linear(1258*1892, 5), torch.rand(1258*1892))

    @script_method
    def forward(self, input):
      input = F.relu(self.conv1(input))
      input = F.relu(self.conv2(input))
      input = input.squeeze()
      input = input.view(1258*1892)
      output = self.lin(input)
      return output

test_module = MyScriptModule()
print(test_module.graph)
if __name__ == "__main__":
    test_module.save("/tmp/model.pl")

# if __name__ == "__main__":
#     import numpy as np
#     from PIL import Image
#     img_path = "/tmp/cat_image.jpg"
#     img = np.asarray(Image.open(img_path))
#     tensor = torch.from_numpy(img).float()
#     tensor = tensor.view(1, 3, tensor.shape[0], tensor.shape[1])
#     test_module.forward(tensor)

main.cpp

//
// Created by zeryx on 10/5/18.
//
#include <torch/script.h>
#include <iostream>
#include <memory>

#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>

using namespace cv;

int main() {
    std::string model_path = "/tmp/model.pl";
    std::string image_path = "/tmp/cat_image.jpg";

    Mat image = imread(image_path);
    std::vector<int64_t> sizes = {1, 3, image.rows, image.cols};
    at::TensorOptions options(at::ScalarType::Byte);
    at::Tensor tensor_image = torch::from_blob(image.data, at::IntList(sizes), options);
    tensor_image = tensor_image.toType(at::kFloat);

    std::ifstream is (model_path, std::ifstream::binary);
    std::shared_ptr<torch::jit::script::Module> module = torch::jit::load(is);
    std::vector<torch::jit::IValue> inputs;
    inputs.emplace_back(tensor_image);
    at::Tensor result = module->forward(inputs).toTensor();
    auto max_result = result.max(0, true);
    auto max_index = std::get<1>(max_result).item<float>();


    std::cout << max_index << std::endl;
}

@zeryx thanks for providing your example.

• however i get this error with generator.py

$ python3 generator.py 
Traceback (most recent call last):
  File "generator.py", line 26, in <module>
    test_module = MyScriptModule()
  File "/Users/geri/.pyenv/versions/3.6.0/lib/python3.6/site-packages/torch/jit/__init__.py", line 594, in init_then_register
    self._create_methods(asts, rcbs)
RuntimeError: 
arguments for call are not valid:
  
  for operator aten::view(Tensor self, int[] size):
  expected a value of type int[] for argument 'size' but found int
  @script_method
  def forward(self, input):
    input = F.relu(self.conv1(input))
    input = F.relu(self.conv2(input))
    input = input.squeeze()
    input = input.view(1258*1892)
                       ~~~~~~~~~ <--- HERE
    output = self.lin(input)
    return output
for call at:
@script_method
def forward(self, input):
  input = F.relu(self.conv1(input))
  input = F.relu(self.conv2(input))
  input = input.squeeze()
  input = input.view(1258*1892)
          ~~~~~~~~~~ <--- HERE
  output = self.lin(input)
  return output

i installed pytorch:

pip3 install torch_nightly -f https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html
Looking in links: https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html
Collecting torch_nightly
  Downloading https://download.pytorch.org/whl/nightly/cpu/torch_nightly-1.0.0.dev20181203-cp36-none-macosx_10_7_x86_64.whl (61.3MB)
    100% |████████████████████████████████| 61.3MB 33kB/s 
Installing collected packages: torch-nightly
Successfully installed torch-nightly-1.0.0.dev20181203

i installed libtorch following these steps:
https://pytorch.org/cppdocs/installing.html
but i downloaded the libtorch from here:
https://download.pytorch.org/libtorch/nightly/cpu/libtorch-macos-latest.zip

instead:
https://download.pytorch.org/libtorch/nightly/cpu/libtorch-shared-with-deps-latest.zip

generator.py works now, here the output:

$ python3 generator.py
graph(%input.1 : Tensor
      %1 : Tensor
      %2 : Tensor
      %25 : Tensor
      %26 : Tensor
      %55 : Tensor
      %56 : Tensor) {
  %19 : bool = prim::Constant[value=1](), scope: Conv2d
  %12 : bool = prim::Constant[value=0](), scope: Conv2d
  %6 : int = prim::Constant[value=0](), scope: Conv2d
  %3 : int = prim::Constant[value=1](), scope: Conv2d
  %50 : int = prim::Constant[value=1258]()
  %51 : int = prim::Constant[value=1892]()
  %5 : int[] = prim::ListConstruct(%3, %3), scope: Conv2d
  %8 : int[] = prim::ListConstruct(%6, %6), scope: Conv2d
  %11 : int[] = prim::ListConstruct(%3, %3), scope: Conv2d
  %15 : int[] = prim::ListConstruct(%6, %6), scope: Conv2d
  %20 : Float(1, 2, 1262, 1896) = aten::_convolution(%input.1, %1, %2, %5, %8, %11, %12, %15, %3, %12, %12, %19), scope: Conv2d
  %result.3 : Tensor = prim::If(%12)
    block0() {
      %result.4 : Tensor = aten::relu_(%20)
      -> (%result.4)
    }
    block1() {
      %result.5 : Tensor = aten::relu(%20)
      -> (%result.5)
    }
  %29 : int[] = prim::ListConstruct(%3, %3), scope: Conv2d
  %32 : int[] = prim::ListConstruct(%6, %6), scope: Conv2d
  %35 : int[] = prim::ListConstruct(%3, %3), scope: Conv2d
  %39 : int[] = prim::ListConstruct(%6, %6), scope: Conv2d
  %44 : Float(1, 1, 1262, 1896) = aten::_convolution(%result.3, %25, %26, %29, %32, %35, %12, %39, %3, %12, %12, %19), scope: Conv2d
  %result : Tensor = prim::If(%12)
    block0() {
      %result.1 : Tensor = aten::relu_(%44)
      -> (%result.1)
    }
    block1() {
      %result.2 : Tensor = aten::relu(%44)
      -> (%result.2)
    }
  %input.2 : Tensor = aten::squeeze(%result)
  %52 : int = aten::mul(%50, %51)
  %53 : int[] = prim::ListConstruct(%52)
  %input : Tensor = aten::view(%input.2, %53)
  %57 : Float(2380136!, 5!) = aten::t(%55), scope: Linear
  %output.1 : Float(5) = aten::matmul(%input, %57), scope: Linear
  %output : Float(5) = aten::add_(%output.1, %56, %3), scope: Linear
  return (%output);
}

@zeryx when i call the binary and try to load the model i get, why isnt it able to load the model?

$ make clean; make; ./testing
Scanning dependencies of target testing
[ 50%] Building CXX object CMakeFiles/testing.dir/main.cpp.o
[100%] Linking CXX executable testing
[100%] Built target testing
libc++abi.dylib: terminating with uncaught exception of type c10::Error: INVALID_ARGUMENT:tensors[5]: Cannot find field. (deserialize at /Users/administrator/nightlies/2018_12_03/wheel_build_dirs/libtorch_2.7/pytorch/torch/csrc/jit/import.cpp:92)
frame #0: c10::Error::Error(c10::SourceLocation, std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> > const&) + 135 (0x11351df87 in libc10.dylib)
frame #1: torch::jit::(anonymous namespace)::ScriptModuleDeserializer::deserialize(std::__1::function<std::__1::shared_ptr<torch::jit::script::Module> (std::__1::vector<std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> >, std::__1::allocator<std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> > > > const&)>) + 6408 (0x10ca94c78 in libtorch.1.dylib)
frame #2: torch::jit::load(std::__1::basic_istream<char, std::__1::char_traits<char> >&) + 538 (0x10ca95d4a in libtorch.1.dylib)
frame #3: main + 2188 (0x10b61f13c in testing)
frame #4: start + 1 (0x7fff8a8605ad in libdyld.dylib)

Abort trap: 6

generator.py works now, here the output:

$ python3 generator.py
graph(%input.1 : Tensor
      %1 : Tensor
      %2 : Tensor
      %25 : Tensor
      %26 : Tensor
      %55 : Tensor
      %56 : Tensor) {
  %19 : bool = prim::Constant[value=1](), scope: Conv2d
  %12 : bool = prim::Constant[value=0](), scope: Conv2d
  %6 : int = prim::Constant[value=0](), scope: Conv2d
  %3 : int = prim::Constant[value=1](), scope: Conv2d
  %50 : int = prim::Constant[value=1258]()
  %51 : int = prim::Constant[value=1892]()
  %5 : int[] = prim::ListConstruct(%3, %3), scope: Conv2d
  %8 : int[] = prim::ListConstruct(%6, %6), scope: Conv2d
  %11 : int[] = prim::ListConstruct(%3, %3), scope: Conv2d
  %15 : int[] = prim::ListConstruct(%6, %6), scope: Conv2d
  %20 : Float(1, 2, 1262, 1896) = aten::_convolution(%input.1, %1, %2, %5, %8, %11, %12, %15, %3, %12, %12, %19), scope: Conv2d
  %result.3 : Tensor = prim::If(%12)
    block0() {
      %result.4 : Tensor = aten::relu_(%20)
      -> (%result.4)
    }
    block1() {
      %result.5 : Tensor = aten::relu(%20)
      -> (%result.5)
    }
  %29 : int[] = prim::ListConstruct(%3, %3), scope: Conv2d
  %32 : int[] = prim::ListConstruct(%6, %6), scope: Conv2d
  %35 : int[] = prim::ListConstruct(%3, %3), scope: Conv2d
  %39 : int[] = prim::ListConstruct(%6, %6), scope: Conv2d
  %44 : Float(1, 1, 1262, 1896) = aten::_convolution(%result.3, %25, %26, %29, %32, %35, %12, %39, %3, %12, %12, %19), scope: Conv2d
  %result : Tensor = prim::If(%12)
    block0() {
      %result.1 : Tensor = aten::relu_(%44)
      -> (%result.1)
    }
    block1() {
      %result.2 : Tensor = aten::relu(%44)
      -> (%result.2)
    }
  %input.2 : Tensor = aten::squeeze(%result)
  %52 : int = aten::mul(%50, %51)
  %53 : int[] = prim::ListConstruct(%52)
  %input : Tensor = aten::view(%input.2, %53)
  %57 : Float(2380136!, 5!) = aten::t(%55), scope: Linear
  %output.1 : Float(5) = aten::matmul(%input, %57), scope: Linear
  %output : Float(5) = aten::add_(%output.1, %56, %3), scope: Linear
  return (%output);
}

@zeryx when i call the binary and try to load the model i get, why isnt it able to load the model?

$ make clean; make; ./testing
Scanning dependencies of target testing
[ 50%] Building CXX object CMakeFiles/testing.dir/main.cpp.o
[100%] Linking CXX executable testing
[100%] Built target testing
libc++abi.dylib: terminating with uncaught exception of type c10::Error: INVALID_ARGUMENT:tensors[5]: Cannot find field. (deserialize at /Users/administrator/nightlies/2018_12_03/wheel_build_dirs/libtorch_2.7/pytorch/torch/csrc/jit/import.cpp:92)
frame #0: c10::Error::Error(c10::SourceLocation, std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> > const&) + 135 (0x11351df87 in libc10.dylib)
frame #1: torch::jit::(anonymous namespace)::ScriptModuleDeserializer::deserialize(std::__1::function<std::__1::shared_ptr<torch::jit::script::Module> (std::__1::vector<std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> >, std::__1::allocator<std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> > > > const&)>) + 6408 (0x10ca94c78 in libtorch.1.dylib)
frame #2: torch::jit::load(std::__1::basic_istream<char, std::__1::char_traits<char> >&) + 538 (0x10ca95d4a in libtorch.1.dylib)
frame #3: main + 2188 (0x10b61f13c in testing)
frame #4: start + 1 (0x7fff8a8605ad in libdyld.dylib)

Abort trap: 6

This is due to the different CUDA version between LibTorch and PyTorch. For example, maybe your PyTorch is under CUDA10 but LibTorch is under CUDA9.