script.py

import pandas as pd
import numpy as np
from PIL import Image
import onnxruntime as ort
import os
from tqdm import tqdm


def is_gpu_available():
    """Check if the python package `onnxruntime-gpu` is installed."""
    return ort.get_device() == "GPU"


class ONNXWorker:
    """Run inference using ONNX runtime."""

    def __init__(self, onnx_path: str):
        print("Setting up ONNX runtime session.")
        self.use_gpu = is_gpu_available()
        if self.use_gpu:
            providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
        else:
            providers = ["CPUExecutionProvider"]

        print(f"Using {providers}")
        self.ort_session = ort.InferenceSession(onnx_path, providers=providers)

    def _resize_image(self, image: np.ndarray) -> np.ndarray:
        """

        :param image:
        :return:
        """

        newsize = (300, 300)
        im1 = im1.resize(newsize)

    def predict_image(self, image: np.ndarray) -> list():
        """Run inference using ONNX runtime.

        :param image: Input image as numpy array.
        :return: A list with logits and confidences.
        """

        logits, _ = self.ort_session.run(None, {"input": image.astype(dtype=np.uint8)})

        return logits.tolist()


def make_submission(test_metadata, model_path, output_csv_path="./submission.csv", images_root_path="/tmp/data/private_testset"):
    """Make submission with given """

    model = ONNXWorker(model_path)

    predictions = []

    for _, row in tqdm(test_metadata.iterrows(), total=len(test_metadata)):
        image_path = os.path.join(images_root_path, row.filename)

        test_image = Image.open(image_path).convert("RGB")
        test_image_resized = np.asarray(test_image.resize((256, 256)))

        logits = model.predict_image(test_image_resized)

        predictions.append(np.argmax(logits))

    test_metadata["class_id"] = predictions

    user_pred_df = test_metadata.drop_duplicates("observation_id", keep="first")
    user_pred_df[["observation_id", "class_id"]].to_csv(output_csv_path, index=None)


if __name__ == "__main__":

    import zipfile

    with zipfile.ZipFile("/tmp/data/private_testset.zip", 'r') as zip_ref:
        zip_ref.extractall("/tmp/data")

    ONNX_MODEL_PATH = "./swinv2_tiny_window16_256.onnx"

    metadata_file_path = "./SnakeCLEF2024-TestMetadata.csv"
    test_metadata = pd.read_csv(metadata_file_path)

    make_submission(
        test_metadata=test_metadata,
        model_path=ONNX_MODEL_PATH,
    )