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expressions updated to fit libtorch v1.4.0 and opencv4.0 #4

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Feb 22, 2020
Merged

expressions updated to fit libtorch v1.4.0 and opencv4.0 #4

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162 changes: 80 additions & 82 deletions prediction.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -17,97 +17,95 @@
#define kTOP_K 3

bool LoadImage(std::string file_name, cv::Mat &image) {
image = cv::imread(file_name); // CV_8UC3
if (image.empty() || !image.data) {
return false;
}
cv::cvtColor(image, image, CV_BGR2RGB);
std::cout << "== image size: " << image.size() << " ==" << std::endl;

// scale image to fit
cv::Size scale(kIMAGE_SIZE, kIMAGE_SIZE);
cv::resize(image, image, scale);
std::cout << "== simply resize: " << image.size() << " ==" << std::endl;

// convert [unsigned int] to [float]
image.convertTo(image, CV_32FC3, 1.0f / 255.0f);

return true;
image = cv::imread(file_name); // CV_8UC3
if (image.empty() || !image.data) {
return false;
}
cv::cvtColor(image, image, cv::COLOR_BGR2RGB);
std::cout << "== image size: " << image.size() << " ==" << std::endl;

// scale image to fit
cv::Size scale(kIMAGE_SIZE, kIMAGE_SIZE);
cv::resize(image, image, scale);
std::cout << "== simply resize: " << image.size() << " ==" << std::endl;

// convert [unsigned int] to [float]
image.convertTo(image, CV_32FC3, 1.0f / 255.0f);

return true;
}

bool LoadImageNetLabel(std::string file_name,
std::vector<std::string> &labels) {
std::ifstream ifs(file_name);
if (!ifs) {
return false;
}
std::string line;
while (std::getline(ifs, line)) {
labels.push_back(line);
}
return true;
std::ifstream ifs(file_name);
if (!ifs) {
return false;
}
std::string line;
while (std::getline(ifs, line)) {
labels.push_back(line);
}
return true;
}

int main(int argc, const char *argv[]) {
if (argc != 3) {
std::cerr << "Usage: classifier <path-to-exported-script-module> "
"<path-to-lable-file>"
<< std::endl;
return -1;
}

std::shared_ptr<torch::jit::script::Module> module =
torch::jit::load(argv[1]);
std::cout << "== Switch to GPU mode" << std::endl;
// to GPU
module->to(at::kCUDA);

assert(module != nullptr);
std::cout << "== ResNet50 loaded!\n";
std::vector<std::string> labels;
if (LoadImageNetLabel(argv[2], labels)) {
std::cout << "== Label loaded! Let's try it\n";
} else {
std::cerr << "Please check your label file path." << std::endl;
return -1;
}

std::string file_name = "";
cv::Mat image;
while (true) {
std::cout << "== Input image path: [enter Q to exit]" << std::endl;
std::cin >> file_name;
if (file_name == "Q") {
break;
if (argc != 3) {
std::cerr << "Usage: classifier <path-to-exported-script-module> "
"<path-to-lable-file>"
<< std::endl;
return -1;
}
if (LoadImage(file_name, image)) {
auto input_tensor = torch::from_blob(
image.data, {1, kIMAGE_SIZE, kIMAGE_SIZE, kCHANNELS});
input_tensor = input_tensor.permute({0, 3, 1, 2});
input_tensor[0][0] = input_tensor[0][0].sub_(0.485).div_(0.229);
input_tensor[0][1] = input_tensor[0][1].sub_(0.456).div_(0.224);
input_tensor[0][2] = input_tensor[0][2].sub_(0.406).div_(0.225);

// to GPU
input_tensor = input_tensor.to(at::kCUDA);

torch::Tensor out_tensor = module->forward({input_tensor}).toTensor();

auto results = out_tensor.sort(-1, true);
auto softmaxs = std::get<0>(results)[0].softmax(0);
auto indexs = std::get<1>(results)[0];

for (int i = 0; i < kTOP_K; ++i) {
auto idx = indexs[i].item<int>();
std::cout << " ============= Top-" << i + 1
<< " =============" << std::endl;
std::cout << " Label: " << labels[idx] << std::endl;
std::cout << " With Probability: "
<< softmaxs[i].item<float>() * 100.0f << "%" << std::endl;
}

torch::jit::script::Module module = torch::jit::load(argv[1]);
std::cout << "== Switch to GPU mode" << std::endl;
// to GPU
module.to(at::kCUDA);

std::cout << "== ResNet50 loaded!\n";
std::vector<std::string> labels;
if (LoadImageNetLabel(argv[2], labels)) {
std::cout << "== Label loaded! Let's try it\n";
} else {
std::cout << "Can't load the image, please check your path." << std::endl;
std::cerr << "Please check your label file path." << std::endl;
return -1;
}

std::string file_name = "";
cv::Mat image;
while (true) {
std::cout << "== Input image path: [enter Q to exit]" << std::endl;
std::cin >> file_name;
if (file_name == "Q") {
break;
}
if (LoadImage(file_name, image)) {
auto input_tensor = torch::from_blob(
image.data, {1, kIMAGE_SIZE, kIMAGE_SIZE, kCHANNELS});
input_tensor = input_tensor.permute({0, 3, 1, 2});
input_tensor[0][0] = input_tensor[0][0].sub_(0.485).div_(0.229);
input_tensor[0][1] = input_tensor[0][1].sub_(0.456).div_(0.224);
input_tensor[0][2] = input_tensor[0][2].sub_(0.406).div_(0.225);

// to GPU
input_tensor = input_tensor.to(at::kCUDA);

torch::Tensor out_tensor = module.forward({input_tensor}).toTensor();

auto results = out_tensor.sort(-1, true);
auto softmaxs = std::get<0>(results)[0].softmax(0);
auto indexs = std::get<1>(results)[0];

for (int i = 0; i < kTOP_K; ++i) {
auto idx = indexs[i].item<int>();
std::cout << " ============= Top-" << i + 1
<< " =============" << std::endl;
std::cout << " Label: " << labels[idx] << std::endl;
std::cout << " With Probability: "
<< softmaxs[i].item<float>() * 100.0f << "%" << std::endl;
}

} else {
std::cout << "Can't load the image, please check your path." << std::endl;
}
}
}
}