learnopencv/PyTorch-ONNX-TensorRT-CPP/pytorch_model.py

import cv2
import onnx
import torch
from albumentations import (Compose,Resize,)
from albumentations.augmentations.transforms import Normalize
from albumentations.pytorch.transforms import ToTensor
from torchvision import models


def preprocess_image(img_path):
    # transformations for the input data
    transforms = Compose([
        Resize(224, 224, interpolation=cv2.INTER_NEAREST),
        Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
        ToTensor(),
    ])

    # read input image
    input_img = cv2.imread(img_path)
    # do transformations
    input_data = transforms(image=input_img)["image"]
    # prepare batch
    batch_data = torch.unsqueeze(input_data, 0)

    return batch_data


def postprocess(output_data):
    # get class names
    with open("imagenet_classes.txt") as f:
        classes = [line.strip() for line in f.readlines()]
    # calculate human-readable value by softmax
    confidences = torch.nn.functional.softmax(output_data, dim=1)[0] * 100
    # find top predicted classes
    _, indices = torch.sort(output_data, descending=True)
    i = 0
    # print the top classes predicted by the model
    while confidences[indices[0][i]] > 0.5:
        class_idx = indices[0][i]
        print(
            "class:",
            classes[class_idx],
            ", confidence:",
            confidences[class_idx].item(),
            "%, index:",
            class_idx.item(),
        )
        i += 1


def main():
    # load pre-trained model -------------------------------------------------------------------------------------------
    model = models.resnet50(pretrained=True)

    # preprocessing stage ----------------------------------------------------------------------------------------------
    input = preprocess_image("turkish_coffee.jpg").cuda()

    # inference stage --------------------------------------------------------------------------------------------------
    model.eval()
    model.cuda()
    output = model(input)

    # post-processing stage --------------------------------------------------------------------------------------------
    postprocess(output)

    # convert to ONNX --------------------------------------------------------------------------------------------------
    ONNX_FILE_PATH = "resnet50.onnx"
    torch.onnx.export(model, input, ONNX_FILE_PATH, input_names=["input"], output_names=["output"], export_params=True)

    onnx_model = onnx.load(ONNX_FILE_PATH)
    # check that the model converted fine
    onnx.checker.check_model(onnx_model)

    print("Model was successfully converted to ONNX format.")
    print("It was saved to", ONNX_FILE_PATH)


if __name__ == '__main__':
    main()