script.py

import pandas as pd
import numpy as np
import os
from tqdm import tqdm
import timm
import torchvision.transforms as T
from PIL import Image
import torch

class PytorchWorker:
    """Run inference using PyTorch."""

    def __init__(self, model_path: str, model_name: str, number_of_categories: int):

        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 device: {self.device}")

            model = timm.create_model(model_name, num_classes=number_of_categories, pretrained=False)
            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((224, 224)),
            T.ToTensor(),
            T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
        ])

    def predict_image(self, image: Image.Image) -> list:
        """Run inference using PyTorch.
        :param image: Input image as PIL Image.
        :return: A list with logits.
        """
        image_tensor = self.transforms(image).unsqueeze(0).to(self.device)
        logits = self.model(image_tensor)
        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 metadata and model."""

    model = PytorchWorker(model_path, model_name, number_of_categories=1604)  # Adjust number_of_categories as needed

    predictions = []
    observation_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")
        logits = model.predict_image(test_image)
        predicted_class = np.argmax(logits)
        
        obs_id = row['observation_id']
        if obs_id not in observation_predictions:
            observation_predictions[obs_id] = []
        observation_predictions[obs_id].append(predicted_class)

    final_predictions = {obs_id: max(set(preds), key=preds.count) for obs_id, preds in observation_predictions.items()}

    output_df = pd.DataFrame(list(final_predictions.items()), columns=['observation_id', 'class_id'])
    output_df.to_csv(output_csv_path, index=False)

if __name__ == "__main__":
    import zipfile

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

    MODEL_PATH = "resnet_classifier.pth"  # Ensure this matches the filename of your model
    MODEL_NAME = "resnet50"  # Adjust this to your specific model

    metadata_file_path = "./SnakeCLEF2023_TestMetadata.csv"
    test_metadata = pd.read_csv(metadata_file_path)

    make_submission(
        test_metadata=test_metadata,
        model_path=MODEL_PATH,
        model_name=MODEL_NAME
    )