evals moved + readme

environments/eval_environments/vision_evals/mmt_bench_environment.py

@@ -0,0 +1,174 @@
+"""MMT-Bench evaluation environment."""
+
+import asyncio
+import base64
+import io
+from string import ascii_uppercase
+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
+from environments.eval_environments.eval_helpers import (
+    extract_letter_from_answer_tag,
+    extract_mcqa_answer_with_fallback,
+)
+
+
+class MMTBench(EvalBase):
+    """MMT-Bench evaluation - multi-task multimodal benchmark."""
+
+    def setup_data(self) -> list:
+        split = getattr(self, "split", "train")
+        max_samples = getattr(self, "max_samples", None)  # None = use all samples
+
+        try:
+            # Try full dataset download first
+            dataset = load_dataset("OpenGVLab/MMT-Bench", split=split)
+            data = list(dataset)
+            if max_samples:
+                data = data[:max_samples]
+            print(f"Loaded {len(data)} examples from MMT-Bench ({split})")
+            return data
+        except Exception as e:
+            print(f"Warning: Full download failed, using streaming: {e}")
+            # Fallback to streaming if full download fails (known column mismatch issue)
+            try:
+                dataset = load_dataset(
+                    "OpenGVLab/MMT-Bench", split=split, streaming=True
+                )
+                if max_samples:
+                    data = list(dataset.take(max_samples))
+                else:
+                    # Stream all available samples
+                    data = []
+                    for i, item in enumerate(dataset):
+                        data.append(item)
+                        if i % 5000 == 0 and i > 0:
+                            print(f"  Streamed {i} samples...")
+                print(
+                    f"Loaded {len(data)} examples from MMT-Bench ({split}, streaming)"
+                )
+                return data
+            except Exception:
+                raise ValueError(f"Could not load MMT-Bench 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:
+                val = item[key]
+                if isinstance(val, Image.Image):
+                    return self.encode_image(val)
+                elif isinstance(val, str) and len(val) > 100:
+                    # Already base64-encoded string
+                    return val
+        return None
+
+    def build_messages(self, item: dict) -> List[dict]:
+        image_base64 = self.get_image_base64(item)
+        question = item.get("question", "")
+        hint = item.get("hint", "")
+
+        options = {}
+        for letter in ascii_uppercase[:8]:  # Support up to 8 options
+            if letter in item and item[letter] is not None:
+                val = item[letter]
+                if isinstance(val, str) and val.strip():
+                    options[letter] = val
+
+        prompt = ""
+        if hint and str(hint).strip() and str(hint).lower() != "nan":
+            prompt += f"Hint: {hint}\n"
+        prompt += f"Question: {question}\n"
+
+        if options:
+            prompt += "Options:\n"
+            for letter in sorted(options.keys()):
+                prompt += f"{letter}. {options[letter]}\n"
+            prompt += "\nPlease select the correct answer from the options above."
+
+        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_answer(
+        self, response: str, num_choices: int
+    ) -> Tuple[Optional[str], str]:
+        valid_letters = set(ascii_uppercase[:num_choices])
+
+        letter, method = extract_letter_from_answer_tag(response, valid_letters)
+        if letter:
+            return letter, method
+
+        letter, method = extract_mcqa_answer_with_fallback(response, num_choices)
+        return letter, method
+
+    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", "")
+
+            num_choices = sum(
+                1
+                for letter in ascii_uppercase[:8]
+                if letter in data_item
+                and data_item[letter] is not None
+                and isinstance(data_item[letter], str)
+                and data_item[letter].strip()
+            )
+            num_choices = max(num_choices, 4)
+
+            extracted, method = self.extract_answer(response, num_choices)
+
+            correct = False
+            if extracted and answer:
+                correct = extracted.upper() == str(answer).upper()
+
+            sample = {
+                "id": data_item.get("index", data_item.get("id", "")),
+                "question": data_item.get("question", "")[:200],
+                "task": data_item.get("task", ""),
+                "answer": answer,
+                "prediction": extracted,
+                "raw_response": response[:500],
+                "correct": correct,
+                "extraction_method": method,
+            }
+
+            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(MMTBench(split="val", temperature=0.0, max_tokens=256))
+    )