evals moved + readme

environments/eval_environments/vision_evals/countbench_environment.py

@@ -0,0 +1,135 @@
+"""CountBench evaluation environment."""
+
+import asyncio
+import base64
+import io
+import re
+from typing import List, Optional, Tuple
+
+from datasets import load_dataset
+from PIL import Image
+
+from atroposlib.envs.server_handling.server_manager import ServerManager
+from environments.eval_environments.eval import EvalBase, eval_runner
+
+
+class CountBench(EvalBase):
+    """CountBench evaluation - object counting benchmark."""
+
+    def setup_data(self) -> list:
+        split = getattr(self, "split", "train")  # CountBench only has train split
+
+        try:
+            dataset = load_dataset("nielsr/countbench", split=split)
+            print(f"Loaded {len(dataset)} examples from CountBench ({split})")
+            return list(dataset)
+        except Exception as e:
+            print(f"Warning: Could not load CountBench: {e}")
+            try:
+                # Try train split explicitly
+                dataset = load_dataset("nielsr/countbench", split="train")
+                print(f"Loaded {len(dataset)} examples from CountBench (train)")
+                return list(dataset)
+            except Exception:
+                try:
+                    dataset = load_dataset(
+                        "google-research/countbenchqa", split="train"
+                    )
+                    print(f"Loaded {len(dataset)} examples from CountBench (train)")
+                    return list(dataset)
+                except Exception:
+                    raise ValueError(f"Could not load CountBench dataset: {e}")
+
+    def encode_image(self, pil_image: Image.Image) -> str:
+        buffer = io.BytesIO()
+        pil_image.save(buffer, format="PNG")
+        return base64.b64encode(buffer.getvalue()).decode("utf-8")
+
+    def get_image_base64(self, item: dict) -> Optional[str]:
+        for key in ["image", "decoded_image"]:
+            if key in item and item[key] is not None:
+                if isinstance(item[key], Image.Image):
+                    return self.encode_image(item[key])
+        return None
+
+    def build_messages(self, item: dict) -> List[dict]:
+        image_base64 = self.get_image_base64(item)
+        question = item.get("question", "")
+
+        prompt = f"{question}\n\nNote: Answer with a number directly, e.g., 3. Do not include any additional text."
+
+        content = []
+        if image_base64:
+            content.append(
+                {
+                    "type": "image_url",
+                    "image_url": {"url": f"data:image/png;base64,{image_base64}"},
+                }
+            )
+        content.append({"type": "text", "text": prompt})
+
+        return [{"role": "user", "content": content}]
+
+    def extract_number(self, response: str) -> Optional[str]:
+        """Extract a number from the response."""
+        numbers = re.findall(r"\b(\d+)\b", response)
+        if numbers:
+            return numbers[0]
+        return None
+
+    def score(self, prediction: str, answer: str) -> bool:
+        """Score counting answer - check if answer appears in prediction."""
+        answer_str = str(answer).strip()
+
+        if answer_str in prediction:
+            return True
+
+        extracted = self.extract_number(prediction)
+        if extracted and extracted == answer_str:
+            return True
+
+        try:
+            pred_num = int(self.extract_number(prediction) or prediction.strip())
+            ans_num = int(answer_str)
+            return pred_num == ans_num
+        except (ValueError, TypeError):
+            pass
+
+        return False
+
+    async def run_item(self, server: ServerManager, data_item: dict) -> Tuple[dict, dict]:
+        try:
+            messages = self.build_messages(data_item)
+            completion = await self.chat_completion(server, messages)
+
+            if not completion.choices:
+                return {"accuracy": 0.0}, {"error": "Empty response"}
+
+            message = completion.choices[0].message
+            response = message.content or ""
+
+            if not response:
+                return {"accuracy": 0.0}, {"error": "Empty response"}
+
+            answer = data_item.get("answer", data_item.get("number", ""))
+
+            correct = self.score(response, answer)
+            extracted = self.extract_number(response)
+
+            sample = {
+                "id": data_item.get("index", data_item.get("id", "")),
+                "question": data_item.get("question", "")[:200],
+                "answer": answer,
+                "prediction": extracted or response[:50],
+                "raw_response": response[:200],
+                "correct": correct,
+            }
+
+            return {"accuracy": 1.0 if correct else 0.0}, sample
+
+        except Exception as e:
+            return {"accuracy": 0.0}, {"error": str(e)}
+
+
+if __name__ == "__main__":
+    asyncio.run(eval_runner(CountBench(split="test", temperature=0.0, max_tokens=64)))