| 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) |
|
|
| |
| |
| |
| |
|
|
| 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 |
| ) |
|
|
