MMMU, MMMU-Pro, MMBench, MMStar, AI2D, MMVP, OCRBench, MMVet, CountBench, POPE, HallusionBench, DynaMath, MMT-Bench, SEED-Bench2, BLINK, and VLMBlind evals

environments/eval_environments/seedbench2_plus_environment.py

@@ -0,0 +1,193 @@
+"""SEED-Bench2-Plus 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 openai import AsyncOpenAI
+from PIL import Image
+
+from environments.eval_environments.eval_base import EvalBase, eval_runner
+from environments.eval_environments.eval_helpers import (
+    extract_letter_from_answer_tag,
+    extract_mcqa_answer_with_fallback,
+)
+
+
+class SEEDBench2Plus(EvalBase):
+    """SEED-Bench2-Plus evaluation - comprehensive visual understanding benchmark."""
+
+    def setup_data(self) -> list:
+        split = getattr(self, "split", "test")
+        max_samples = getattr(self, "max_samples", None)
+
+        try:
+            # Use streaming to avoid memory issues with this large dataset
+            dataset = load_dataset("lmms-lab/SEED-Bench-2", split=split, streaming=True)
+
+            # Take samples from streaming dataset
+            if max_samples:
+                data = list(dataset.take(max_samples))
+            else:
+                # Default to 1000 samples to avoid loading entire 24k dataset
+                data = list(dataset.take(1000))
+
+            print(f"Loaded {len(data)} examples from SEED-Bench2 ({split}, streaming)")
+            return data
+        except Exception as e:
+            print(f"Warning: Could not load SEED-Bench2: {e}")
+            try:
+                dataset = load_dataset("lmms-lab/SEED-Bench", split=split, streaming=True)
+                if max_samples:
+                    data = list(dataset.take(max_samples))
+                else:
+                    data = list(dataset.take(1000))
+                print(f"Loaded {len(data)} examples from SEED-Bench ({split}, streaming)")
+                return data
+            except Exception:
+                raise ValueError(f"Could not load SEED-Bench2-Plus 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, list) and len(val) > 0:
+                    # SEED-Bench-2 stores images as a list of PIL images
+                    if isinstance(val[0], Image.Image):
+                        return self.encode_image(val[0])
+        return None
+
+    def build_messages(self, item: dict) -> List[dict]:
+        image_base64 = self.get_image_base64(item)
+        question = item.get("question", "")
+
+        options = {}
+        for letter in ascii_uppercase[:6]:
+            # Check for choice_a, choice_b format
+            choice_key = f"choice_{letter.lower()}"
+            if choice_key in item and item[choice_key] is not None:
+                val = item[choice_key]
+                if isinstance(val, str) and val.strip():
+                    options[letter] = val
+            elif letter in item and item[letter] is not None:
+                val = item[letter]
+                if isinstance(val, str) and val.strip():
+                    options[letter] = val
+
+        if not options:
+            choices = item.get("choices", [])
+            if isinstance(choices, str):
+                try:
+                    choices = eval(choices)
+                except Exception:
+                    choices = []
+            for i, choice in enumerate(choices):
+                options[ascii_uppercase[i]] = choice
+
+        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, client: AsyncOpenAI, data_item: dict) -> Tuple[dict, dict]:
+        try:
+            messages = self.build_messages(data_item)
+
+            gen_params = self.get_generation_params()
+            completion = await client.chat.completions.create(
+                model=self.model_name,
+                messages=messages,
+                temperature=gen_params["temperature"],
+                max_tokens=gen_params["max_tokens"],
+            )
+
+            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", "")
+
+            choices = data_item.get("choices", [])
+            if isinstance(choices, str):
+                try:
+                    choices = eval(choices)
+                except Exception:
+                    choices = []
+
+            num_choices = len(choices) if choices else 4
+            if num_choices == 0:
+                num_choices = sum(
+                    1 for letter in ascii_uppercase[:6]
+                    if letter in data_item and data_item[letter] is not None
+                )
+                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("question_id", "")),
+                "question": data_item.get("question", "")[:200],
+                "category": data_item.get("question_type_id", data_item.get("category", "")),
+                "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(
+            SEEDBench2Plus,
+            split="test",
+            temperature=0.0,
+            max_tokens=256,
+        )
+    )