feat: initial commit with 'working' version

.gitattributes

@@ -33,3 +33,4 @@ 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
+FungiCLEF2024_TestMetadata.csv filter=lfs diff=lfs merge=lfs -text

FungiCLEF2024_TestMetadata.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4e6c2d444ca9fa21fa3f648466513184a5cb9dc0a6e4abf64c74c5139e9ff3ec
+size 4503122

README.md

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+---
+license: mit
+---

script.py

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+import pandas as pd
+import numpy as np
+import onnxruntime as ort
+import os
+from tqdm import tqdm
+import timm
+import torchvision.transforms as T
+from PIL import Image
+import torch
+
+def is_gpu_available():
+    """Check if the python package `onnxruntime-gpu` is installed."""
+    return torch.cuda.is_available()
+
+
+class PytorchWorker:
+    """Run inference using ONNX runtime."""
+
+    def __init__(self, onnx_path: str):
+        print("Setting up Pytorch Model")
+
+        self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+        print(f"Using devide: {self.device}")
+        self.model = timm.create_model("hf-hub:BVRA/tf_efficientnet_b3.in1k_ft_df20_224", pretrained=True)
+        self.model = self.model.eval()
+        self.model.to(self.device)
+
+        self.transforms = T.Compose([T.Resize((224, 224)),
+                                     T.ToTensor(),
+                                     T.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])
+
+    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.model(self.transforms(image).unsqueeze(0))
+
+        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 = PytorchWorker(model_path)
+
+    predictions = []
+
+    for _, row in tqdm(test_metadata.iterrows(), total=len(test_metadata)):
+        image_path = os.path.join(images_root_path, row.image_path)
+
+        test_image = 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__":
+
+    import zipfile
+
+    with zipfile.ZipFile("/tmp/data/private_testset.zip", 'r') as zip_ref:
+        zip_ref.extractall("/tmp/data")
+
+    HFHUB_MODEL_PATH = "hf-hub:BVRA/tf_efficientnet_b3.in1k_ft_df20_224"
+
+    metadata_file_path = "./FungiCLEF2024_TestMetadata.csv"
+    test_metadata = pd.read_csv(metadata_file_path)
+
+    make_submission(
+        test_metadata=test_metadata,
+        model_path=HFHUB_MODEL_PATH,
+    )