Delete script.py

script.py

@@ -1,97 +0,0 @@
-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, model_path: str, model_name: str, number_of_categories: int = 1604):
-
-        def _load_model(model_name, model_path):
-
-            print("Setting up Pytorch Model")
-            self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-            print(f"Using devide: {self.device}")
-
-            model = timm.create_model(model_name, num_classes=number_of_categories, pretrained=False)
-
-            # if not torch.cuda.is_available():
-            #     model_ckpt = torch.load(model_path, map_location=torch.device("cpu"))
-            # else:
-            #     model_ckpt = torch.load(model_path)
-
-            model_ckpt = torch.load(model_path, map_location=self.device)
-            model.load_state_dict(model_ckpt)
-
-            return model.to(self.device).eval()
-
-        self.model = _load_model(model_name, model_path)
-
-        self.transforms = T.Compose([T.Resize((299, 299)),
-                                     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).to(self.device))
-
-        return logits.tolist()
-
-
-def make_submission(test_metadata, model_path, model_name, output_csv_path="./submission.csv", images_root_path="/tmp/data/private_testset"):
-    """Make submission with given """
-
-    model = PytorchWorker(model_path, model_name)
-
-    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")
-
-    MODEL_PATH = "pytorch_model.bin"
-    MODEL_NAME = "tf_efficientnet_b1.ap_in1k"
-
-    metadata_file_path = "./FungiCLEF2024_TestMetadata.csv"
-    test_metadata = pd.read_csv(metadata_file_path)
-
-    make_submission(
-        test_metadata=test_metadata,
-        model_path=MODEL_PATH,
-        model_name=MODEL_NAME
-    )