| 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 |
| import torch.nn as nn |
|
|
| 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, strict=False) |
| msg = model.load_state_dict(model_ckpt, strict=False) |
| print("load_state_dict: ", msg) |
| |
| |
|
|
| 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([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]) |
| |
|
|
|
|
| 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. |
| """ |
|
|
| |
| |
| self.model.eval() |
| |
| outputs = self.model(self.transforms(image).unsqueeze(0).to(self.device)) |
| |
| _, preds = torch.max(outputs, 1) |
| |
| preds = preds.cpu() |
| |
| print("preds: ", preds) |
|
|
| return preds.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) |
| |
| pred_class_id = logits[0] if logits[0] !=1604 else -1 |
| |
| predictions.append(pred_class_id) |
|
|
| 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 = './e25_t144.pth' |
| MODEL_NAME = 'tf_efficientnet_b3_ns' |
|
|
| 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 |
| ) |
