Upload model and script

pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:bed2ba9abe11ad59b65d0ec5521f1ddb5d73d71988986a135322e7a53f77557e
+size 1222078221

script.py

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+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 = 1784):
+
+        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=True)
+
+            # 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_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((336, 336)),
+            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 = "hf-hub:timm/eva02_large_patch14_clip_336.merged2b_ft_inat21"
+
+    metadata_file_path = "./SnakeCLEF2024_TestMetadata.csv"
+    test_metadata = pd.read_csv(metadata_file_path)
+
+    # metadata_file_path = "./unit_test.csv"
+    # test_metadata = pd.read_csv(metadata_file_path)
+    # images_root_path = "SnakeCLEF2023-large_size"
+
+    make_submission(
+        test_metadata=test_metadata,
+        model_path=MODEL_PATH,
+        model_name=MODEL_NAME,
+    )