evals moved + readme

environments/eval_environments/vision_evals/vlmblind_environment.py

@@ -0,0 +1,165 @@
+"""VLMBlind (VLMs are Blind) 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 VLMBlind(EvalBase):
+    """VLMBlind evaluation - tests basic visual perception abilities of VLMs."""
+
+    TASK_PATTERNS = {
+        "Subway Connections": r"\{([^}]+)\}",
+        "Nested Squares": r"\{([^}]+)\}",
+        "Line Plot Intersections": r"\{([^}]+)\}",
+        "Touching Circles": None,  # Substring match
+        "Counting Grid": r"(\d+)\s*(?:rows?|r).*?(\d+)\s*(?:columns?|cols?|c)|(\d+)\s*[xX×]\s*(\d+)",
+        "Olympic Counting": None,  # Substring match
+        "Circled Letter": r"\{([^}]+)\}",
+    }
+
+    def setup_data(self) -> list:
+        # XAI/vlmsareblind only has 'valid' split
+        split = getattr(self, "split", "valid")
+
+        try:
+            dataset = load_dataset("XAI/vlmsareblind", split=split)
+            print(f"Loaded {len(dataset)} examples from VLMBlind ({split})")
+            return list(dataset)
+        except Exception as e:
+            print(f"Warning: Could not load VLMBlind: {e}")
+            try:
+                # Try valid split explicitly
+                dataset = load_dataset("XAI/vlmsareblind", split="valid")
+                print(f"Loaded {len(dataset)} examples from VLMBlind (valid)")
+                return list(dataset)
+            except Exception:
+                raise ValueError(f"Could not load VLMBlind 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)
+        # XAI/vlmsareblind uses 'prompt' instead of 'question'
+        question = item.get("prompt", item.get("question", ""))
+
+        prompt = f"{question}\n\nProvide your answer."
+
+        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_and_score(
+        self, response: str, answer: str, task: str
+    ) -> Tuple[bool, str]:
+        """Task-specific answer extraction and scoring."""
+        response_lower = response.lower().strip()
+        answer_lower = str(answer).lower().strip()
+
+        if task in [
+            "Subway Connections",
+            "Nested Squares",
+            "Line Plot Intersections",
+            "Circled Letter",
+        ]:
+            match = re.search(r"\{([^}]+)\}", response)
+            if match:
+                extracted = match.group(1).strip().lower()
+                return extracted == answer_lower, extracted
+            return answer_lower in response_lower, response_lower[:50]
+
+        elif task == "Touching Circles":
+            return answer_lower in response_lower, response_lower[:50]
+
+        elif "Counting Grid" in task or "Grid" in task:
+            patterns = [
+                r"(\d+)\s*[xX×]\s*(\d+)",
+                r"(\d+)\s*(?:rows?|r).*?(\d+)\s*(?:columns?|cols?|c)",
+                r"(\d+)\s*(?:columns?|cols?|c).*?(\d+)\s*(?:rows?|r)",
+            ]
+            for pattern in patterns:
+                match = re.search(pattern, response)
+                if match:
+                    groups = match.groups()
+                    extracted = f"{groups[0]}x{groups[1]}"
+                    ans_match = re.search(r"(\d+)\s*[xX×,]\s*(\d+)", answer)
+                    if ans_match:
+                        answer_parsed = f"{ans_match.group(1)}x{ans_match.group(2)}"
+                        return extracted == answer_parsed, extracted
+            return answer_lower in response_lower, response_lower[:50]
+
+        elif "Olympic" in task or "Counting" in task:
+            return answer_lower in response_lower, response_lower[:50]
+
+        else:
+            return answer_lower in response_lower, response_lower[:50]
+
+    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"}
+
+            # XAI/vlmsareblind uses 'groundtruth' instead of 'answer'
+            answer = data_item.get("groundtruth", data_item.get("answer", ""))
+            task = data_item.get("task", data_item.get("category", ""))
+
+            correct, extracted = self.extract_and_score(response, answer, task)
+
+            sample = {
+                "id": data_item.get("index", data_item.get("id", "")),
+                "question": data_item.get("prompt", data_item.get("question", ""))[
+                    :200
+                ],
+                "task": task,
+                "answer": answer,
+                "prediction": extracted,
+                "raw_response": response[:500],
+                "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(VLMBlind(split="test", temperature=0.0, max_tokens=512))
+    )