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

mini-nav/main.py

@@ -16,9 +16,51 @@ if __name__ == "__main__":
             epoch_size=10, batch_size=64, lr=1e-4, checkpoint_path="hash_checkpoint.pt"
         )
     elif args.action == "benchmark":
-        from benchmarks import evaluate
+        from typing import cast
 
-        evaluate("Dinov2", "CIFAR-10", "Recall@10")
+        import torch
+        from benchmarks import run_benchmark
+        from compressors import DinoCompressor
+        from configs import cfg_manager
+        from transformers import AutoImageProcessor, BitImageProcessorFast
+        from utils import get_device
+
+        config = cfg_manager.get()
+        benchmark_cfg = config.benchmark
+
+        if not benchmark_cfg.enabled:
+            print("Benchmark is not enabled. Set benchmark.enabled=true in config.yaml")
+            exit(1)
+
+        device = get_device()
+
+        # Load model and processor based on config
+        model_cfg = config.model
+        processor = cast(
+            BitImageProcessorFast,
+            AutoImageProcessor.from_pretrained(model_cfg.name, device_map=device),
+        )
+
+        # Load compressor weights if specified in model config
+        model = DinoCompressor().to(device)
+        if model_cfg.compressor_path is not None:
+            from compressors import HashCompressor
+
+            compressor = HashCompressor(
+                input_dim=model_cfg.compression_dim,
+                output_dim=model_cfg.compression_dim,
+            )
+            compressor.load_state_dict(torch.load(model_cfg.compressor_path))
+            # Wrap with compressor if path is specified
+            model.compressor = compressor
+
+        # Run benchmark
+        run_benchmark(
+            model=model,
+            processor=processor,
+            config=benchmark_cfg,
+            model_name="dinov2",
+        )
     elif args.action == "visualize":
         from visualizer import app