Update script.py

script.py

@@ -2,78 +2,90 @@ 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
-import torch.nn as nn
-import torchvision.transforms as T
-from torchvision.models import resnet50
 
 def is_gpu_available():
+    """Check if the python package `onnxruntime-gpu` is installed."""
     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)
+    """Run inference using PyTorch."""
+
+    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 device: {self.device}")
+
+            model = timm.create_model(model_name, num_classes=number_of_categories, pretrained=False)
+
+            # Load model weights
+            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 _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) -> list:
+        """Run inference using PyTorch.
 
-    def predict_image(self, image: Image.Image, metadata_features: np.ndarray) -> list:
+        :param image: Input image as PIL Image.
+        :return: A list with logits.
+        """
+        # Transform the image
         input_tensor = self.transforms(image).unsqueeze(0).to(self.device)
-        metadata_tensor = torch.tensor(metadata_features).unsqueeze(0).to(self.device)
+        
+        # Get logits
         with torch.no_grad():
-            logits = self.model(input_tensor, metadata_tensor)
+            logits = self.model(input_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)
+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'])
+        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)
+
+        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.pth"
+
+    MODEL_PATH = "resnet_classifier.pth"
+    MODEL_NAME = "tf_efficientnet_b1.ap_in1k"
+
     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
+        model_name=MODEL_NAME
     )