refactor(benchmarks): overhaul evaluation pipeline with LanceDB integration

mini-nav/benchmarks/__init__.py

@@ -2,8 +2,9 @@ from typing import Literal, cast
 
 import torch
 from compressors import DinoCompressor, FloatCompressor
+from configs import cfg_manager
 from transformers import AutoImageProcessor, BitImageProcessorFast
-from utils import get_device, get_output_diretory
+from utils import get_device
 
 from .task_eval import task_eval
 
@@ -13,35 +14,44 @@ def evaluate(
     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=get_device()
+                    "facebook/dinov2-large", device_map=device
                 ),
             )
-            model = DinoCompressor().to(get_device())
+            model = DinoCompressor().to(device)
         case "Dinov2WithCompressor":
             processor = cast(
                 BitImageProcessorFast,
                 AutoImageProcessor.from_pretrained(
-                    "facebook/dinov2-large", device_map=get_device()
+                    "facebook/dinov2-large", device_map=device
                 ),
             )
-
-            compressor = FloatCompressor().load_state_dict(
-                torch.load(get_output_diretory() / "compressor.pt")
-            )
-            model = DinoCompressor(compressor).to(get_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, 1)
+            task_eval(processor, model, dataset, compressor_model, top_k=1)
         case "Recall@10":
-            task_eval(processor, model, dataset, 10)
+            task_eval(processor, model, dataset, compressor_model, top_k=10)
         case _:
             raise ValueError(f"Unknown benchmark: {benchmark}")