Create script.py

script.py

@@ -0,0 +1,79 @@
+import pandas as pd
+import numpy as np
+import os
+from tqdm import tqdm
+from PIL import Image
+import torch
+import torch.nn as nn
+import torchvision.transforms as T
+from torchvision.models import resnet50
+
+def is_gpu_available():
+    return torch.cuda.is_available()
+
+class ResNetClassifier(nn.Module):
+    def __init__(self, num_classes, metadata_size):
+        super(ResNetClassifier, self).__init__()
+        self.resnet = resnet50(pretrained=True)
+        self.resnet.fc = nn.Identity()  # Remove the fully connected layer
+        self.metadata_fc = nn.Linear(metadata_size, 128)
+        self.classifier = nn.Linear(2048 + 128, num_classes)  # 2048 is the output size of ResNet50
+
+    def forward(self, x, metadata_features):
+        resnet_features = self.resnet(x)
+        metadata_features = self.metadata_fc(metadata_features)
+        combined_features = torch.cat((resnet_features, metadata_features), dim=1)
+        logits = self.classifier(combined_features)
+        return logits
+
+class PytorchWorker:
+    def __init__(self, model_path: str, num_classes: int, metadata_size: int):
+        self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+        print(f"Using device: {self.device}")
+        self.model = self._load_model(model_path, num_classes, metadata_size)
+        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 _load_model(self, model_path, num_classes, metadata_size):
+        model = ResNetClassifier(num_classes, metadata_size)
+        model.load_state_dict(torch.load(model_path, map_location=self.device))
+        return model.to(self.device).eval()
+
+    def predict_image(self, image: Image.Image, metadata_features: np.ndarray) -> list:
+        input_tensor = self.transforms(image).unsqueeze(0).to(self.device)
+        metadata_tensor = torch.tensor(metadata_features).unsqueeze(0).to(self.device)
+        with torch.no_grad():
+            logits = self.model(input_tensor, metadata_tensor)
+        return logits.tolist()
+
+def make_submission(test_metadata, model_path, num_classes, metadata_size, output_csv_path="./submission.csv", images_root_path="/tmp/data/private_testset"):
+    model = PytorchWorker(model_path, num_classes, metadata_size)
+    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")
+        metadata_features = row.drop(['image_path', 'class_id']).values.astype(np.float32)
+        logits = model.predict_image(test_image, metadata_features)
+        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.pth"
+    metadata_file_path = "./SnakeCLEF2024-TestMetadata.csv"
+    test_metadata = pd.read_csv(metadata_file_path)
+    num_classes = 1784
+    metadata_size = len(test_metadata.columns) - 2  # Excluding 'image_path' and 'class_id'
+
+    make_submission(
+        test_metadata=test_metadata,
+        model_path=MODEL_PATH,
+        num_classes=num_classes,
+        metadata_size=metadata_size
+    )