refactor(benchmarks): modularize benchmark system with config-driven execution

mini-nav/benchmarks/__init__.py

@@ -1,59 +1,12 @@
-from typing import Literal, cast
+"""Benchmark evaluation module.
 
-import torch
-from compressors import DinoCompressor, FloatCompressor
-from configs import cfg_manager
-from transformers import AutoImageProcessor, BitImageProcessorFast
-from utils import get_device
+This module provides a modular benchmark system that supports:
+- Multiple dataset sources (HuggingFace, local)
+- Multiple evaluation tasks (retrieval, with extensibility for more)
+- Configuration-driven execution
+"""
 
-from .task_eval import task_eval
+from .base import BaseBenchmarkTask, BaseDataset
+from .runner import run_benchmark
 
-
-def evaluate(
-    compressor_model: Literal["Dinov2", "Dinov2WithCompressor"],
-    dataset: Literal["CIFAR-10", "CIFAR-100"],
-    benchmark: Literal["Recall@1", "Recall@10"],
-):
-    """运行模型评估。
-
-    Args:
-        compressor_model: 压缩模型类型。
-        dataset: 数据集名称。
-        benchmark: 评估指标。
-    """
-    device = get_device()
-
-    match compressor_model:
-        case "Dinov2":
-            processor = cast(
-                BitImageProcessorFast,
-                AutoImageProcessor.from_pretrained(
-                    "facebook/dinov2-large", device_map=device
-                ),
-            )
-            model = DinoCompressor().to(device)
-        case "Dinov2WithCompressor":
-            processor = cast(
-                BitImageProcessorFast,
-                AutoImageProcessor.from_pretrained(
-                    "facebook/dinov2-large", device_map=device
-                ),
-            )
-            output_dir = cfg_manager.get().output.directory
-            compressor = FloatCompressor()
-            compressor.load_state_dict(torch.load(output_dir / "compressor.pt"))
-            model = DinoCompressor(compressor).to(device)
-        case _:
-            raise ValueError(f"Unknown compressor: {compressor_model}")
-
-    # 根据 benchmark 确定 top_k
-    match benchmark:
-        case "Recall@1":
-            task_eval(processor, model, dataset, compressor_model, top_k=1)
-        case "Recall@10":
-            task_eval(processor, model, dataset, compressor_model, top_k=10)
-        case _:
-            raise ValueError(f"Unknown benchmark: {benchmark}")
-
-
-__all__ = ["task_eval", "evaluate"]
+__all__ = ["BaseBenchmarkTask", "BaseDataset", "run_benchmark"]