Delete script.py

script.py

@@ -1,97 +0,0 @@
-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.float32)})
-
-        return logits
-
-
-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.image_path)
-
-        test_image = Image.open(image_path).convert("RGB")
-        test_image_resized = np.asarray(test_image.resize((256, 256)))
-        mean=np.array([0.485, 0.456, 0.406])
-        std=np.array([0.229, 0.224, 0.225])
-        mean=mean[None,None,:]
-        std=std[None,None,:]
-        test_image_resized=test_image_resized/255
-        test_image_resized=(test_image_resized-mean)/std
-        test_image_resized=test_image_resized.astype(np.float32)
-        test_image_resized=test_image_resized[None,:,:,:].transpose(0,3,1,2)
-        
-
-        logits = model.predict_image(test_image_resized)[0]
-
-        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 = "./convt_gem.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,
-    )