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resnet_classifier.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f8ca5ff2798636210fe50f065081559a6de41d940ff8a1b6c3439fcca1f646f5
+size 109894058

script.py

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+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
+    )