Update feature extraction to use last_hidden_state in submission_task2.py

medvqa/competitions/gi-2025/task_2.py

@@ -24,7 +24,7 @@ os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
 submission_file = "submission_task2.py"
 file_from_validation = "predictions_2.json"
 
-min_library = ["datasets>=3.4.1", "transformers", "evaluate", "scipy", "scikit-learn",
+min_library = ["datasets>=3.4.1", "transformers", "evaluate", "scipy", "scikit-learn", "diffusers", "peft",
                "rouge_score", 'tqdm', "gradio_client>=1.8.0"]
 
 print("🌟 ImageCLEFmed-MEDVQA-GI-2025 🌟",

medvqa/submission_samples/gi-2025/submission_task2.py

@@ -122,7 +122,7 @@ modelx.eval()
 def extract_features(batch):
     inputs = processor(images=batch['image'], return_tensors="pt").to(DEVICE)
     with torch.no_grad():
-        feats = modelx(**inputs).pooler_output
+        feats = modelx(**inputs).last_hidden_state[:, 0, :]
         feats = feats / feats.norm(p=2, dim=-1, keepdim=True)
     return {'features': feats.cpu().numpy()}