| 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") | |
| device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") | |
| print(f"Using devide: {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"] | |
| model.load_state_dict(model_ckpt) | |
| return model.to(device).eval() | |
| self.model = _load_model(model_name, model_path) | |
| 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, 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)[:100] | |
| make_submission( | |
| test_metadata=test_metadata, | |
| model_path=MODEL_PATH, | |
| model_name=MODEL_NAME | |
| ) | |