Create script.py

script.py

@@ -0,0 +1,109 @@
+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 = 1605):
+
+        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)
+            # num_features = model.get_classifier().in_features
+            # model.classifier = nn.Linear(num_features, number_of_categories)
+
+            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])])
+                                    #  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))
+        
+        self.model.eval()
+        
+        outputs = self.model(self.transforms(image).unsqueeze(0).to(self.device))
+        
+        _, preds = torch.max(outputs, 1)
+        
+        preds = preds.cpu()  # Move tensor to CPU    
+        
+        print("preds: ", preds)
+
+        return preds.tolist()  # Convert tensor to list
+
+
+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 = './e21_t152.pth'
+    MODEL_NAME = 'tf_efficientnet_b3_ns' #"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
+    )