feat:sample script with onnx model prediction

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+SnakeCLEF2024-TestMetadata.csv filter=lfs diff=lfs merge=lfs -text
+swinv2_tiny_window16_256.onnx filter=lfs diff=lfs merge=lfs -text

SnakeCLEF2024-TestMetadata.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:84a019142f4f674985599deaf0d705d3d30069eaddc1191b247d5cefd779f08a
+size 404453

script.py

@@ -0,0 +1,70 @@
+import glob
+import traceback
+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"]
+        self.ort_session = ort.InferenceSession(onnx_path, providers=providers)
+
+    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", data_root_path="/tmp/data"):
+    """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(data_root_path, row.filename)
+        test_image = np.asarray(Image.open(image_path).convert("RGB"))
+        logits = model.predict_image(test_image)
+
+        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__":
+
+    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,
+    )

swinv2_tiny_window16_256.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ff53172bace1485a6e582e1c3dc9719fa4b6acf7ba4481061a220220faaa2eb2
+size 122122210