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I have convert PyTorch SuperResouloution (.pth) Model to ONNX model (.onnx) with code below:

import torch
import onnx
from onnx_tf.backend import prepare
from basicsr.archs.rrdbnet_arch import RRDBNet

pth_path = "RESRGAN_x4plus_Main.pth"

Model = RRDBNet(num_in_ch=3, num_out_ch=3, scale=4, num_feat=64, num_block=23, num_grow_ch=32)

Model.load_state_dict(torch.load(pth_path)\["params_ema"\])
Model.eval()

X = torch.randn((1, 3, 64, 64))

torch.onnx.export(Model, X,
"Model.onnx",
export_params=True,
opset_version=11,
do_constant_folding=True,
input_names=\["input"\],
output_names=\["output"\])

and now I want to load this model into C++ and run it... I have seen many examples of onnxruntime for loading models but I am very confused. sample codes are very complex and not for Super Resolution models.

I emphasize I want to load the model directly in the C++ program (with onnx-runtime package)

I don't want to convert onnx model into another model format...

I have to load the .onnx model into c++ and pass the image into it and I expect to receive an Image from the model output. (model is a GAN)

Seyed Amirreza Kabodian
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1 Answers1

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You can upload ONNX model to C++ using OpenCV library.

cv::dnn::Net model = cv::dnn::readNetFromONNX("Model.onnx");

// prepare model input
cv::Mat blob = cv::dnn::blobFromImage(originalImage,
    1.f/255.f,
    {64,64});

// set model input
model.setInput(blob);

// get model results
cv::Mat output = model.forward();

// get pointer to raw data
float* ptr = output.ptr<float>();

You can refer to this code to find out how to load GAN model to C++ and create an image from raw data pointer.

gdx9
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