From b93381accca8b80130b1fc800c08ae11373bab25 Mon Sep 17 00:00:00 2001
From: SikongJueluo <selfconfusion@gmail.com>
Date: Sat, 7 Feb 2026 15:36:19 +0800
Subject: [PATCH] refactor(project): remove feature compressor module and
 update docs

---
 DEVELOPMENT.md                                |  15 +-
 mini-nav/feature_compressor/__init__.py       |  14 --
 mini-nav/feature_compressor/core/__init__.py  |   7 -
 .../feature_compressor/core/compressor.py     | 135 ----------
 mini-nav/feature_compressor/core/extractor.py | 233 ------------------
 .../examples/basic_usage.py                   |  63 -----
 .../examples/batch_processing.py              |  49 ----
 .../examples/visualization.py                 |  61 -----
 mini-nav/feature_compressor/utils/__init__.py |  19 --
 .../feature_compressor/utils/feature_utils.py |  83 -------
 .../feature_compressor/utils/image_utils.py   |  76 ------
 .../feature_compressor/utils/plot_utils.py    | 167 -------------
 pyproject.toml                                |   1 +
 uv.lock                                       |  24 ++
 14 files changed, 36 insertions(+), 911 deletions(-)
 delete mode 100644 mini-nav/feature_compressor/__init__.py
 delete mode 100644 mini-nav/feature_compressor/core/__init__.py
 delete mode 100644 mini-nav/feature_compressor/core/compressor.py
 delete mode 100644 mini-nav/feature_compressor/core/extractor.py
 delete mode 100644 mini-nav/feature_compressor/examples/basic_usage.py
 delete mode 100644 mini-nav/feature_compressor/examples/batch_processing.py
 delete mode 100644 mini-nav/feature_compressor/examples/visualization.py
 delete mode 100644 mini-nav/feature_compressor/utils/__init__.py
 delete mode 100644 mini-nav/feature_compressor/utils/feature_utils.py
 delete mode 100644 mini-nav/feature_compressor/utils/image_utils.py
 delete mode 100644 mini-nav/feature_compressor/utils/plot_utils.py

diff --git a/DEVELOPMENT.md b/DEVELOPMENT.md
index c1197bf..3c85fdd 100644
--- a/DEVELOPMENT.md
+++ b/DEVELOPMENT.md
@@ -1,4 +1,4 @@
-# 开发者必看文档
+# 开发者必读文档
 
 ## 代码规范
 
@@ -11,7 +11,8 @@
 - 变量说明注释、条件或循环分支注释完全
 - 无需向后兼容，避免添加过多功能
 - 先编写测试集，再实现代码
-- 实现测试集后，先询问意见，在用户修改完成测试集后再实现代码
+- 实现测试集后，先询问用户意见，用户确认后才能继续
+- 如非用户要求，无需编写基准测试代码
 
 ### 测试编写原则
 - 精简、干净、快速
@@ -19,6 +20,11 @@
 - 无需编写测试集的情况
   - UI界面相关的代码
   - 过于复杂或耗时的逻辑
+  - 基准测试相关
+
+### 关键词说明
+- 确认：用户认同当前的实现方案或测试集实现，即可以开始工作
+- 继续：用户需要你重读上下文，继续未完成的工作
 
 ### 文档更新说明
 仅在工程目录变化时，更新此文档的目录说明部分。
@@ -32,6 +38,7 @@
 - mini-nav 为源代码目录
   - mini-nav/configs 为配置文件管理目录，使用python + yaml进行统一的配置管理
   - mini-nav/tests 为pytest测试集目录，用于管理各项测试集
+  - mini-nav/benchmarks 为pytest-benchmark基准测试目录，用于管理各项基准测试，包括速度、准确度等内容
   - mini-nav/visualizer 为plotly、dash的简单数据可视化APP
   - mini-nav/database.py 用于管理lancedb数据库
   - mini-nav/feature_retrieval.py 用于实现图像特征检索
@@ -39,5 +46,5 @@
 - outputs 为默认输出目录
 
 ### Python库
-详细查询pyproject.toml或使用`uv pip list`获取详细的库信息，请基于目前的库实现功能。
-如需添加新库，请先询问，再新增。
+详细可查询pyproject.toml或使用`uv pip list`获取详细的库信息，请基于目前的库实现功能。
+如需添加新库，请先询问，用户确认后才能使用`uv add <package>`新增库。
diff --git a/mini-nav/feature_compressor/__init__.py b/mini-nav/feature_compressor/__init__.py
deleted file mode 100644
index 2590aa1..0000000
--- a/mini-nav/feature_compressor/__init__.py
+++ /dev/null
@@ -1,14 +0,0 @@
-"""DINOv2 Feature Compressor - Extract and compress visual features."""
-
-__version__ = "0.1.0"
-
-from .core.compressor import PoolNetCompressor
-from .core.extractor import DINOv2FeatureExtractor
-from .core.visualizer import FeatureVisualizer
-
-__all__ = [
-    "PoolNetCompressor",
-    "DINOv2FeatureExtractor",
-    "FeatureVisualizer",
-    "__version__",
-]
diff --git a/mini-nav/feature_compressor/core/__init__.py b/mini-nav/feature_compressor/core/__init__.py
deleted file mode 100644
index 81ea385..0000000
--- a/mini-nav/feature_compressor/core/__init__.py
+++ /dev/null
@@ -1,7 +0,0 @@
-"""Core compression, extraction, and visualization modules."""
-
-from .compressor import PoolNetCompressor
-from .extractor import DINOv2FeatureExtractor
-from .visualizer import FeatureVisualizer
-
-__all__ = ["PoolNetCompressor", "DINOv2FeatureExtractor", "FeatureVisualizer"]
diff --git a/mini-nav/feature_compressor/core/compressor.py b/mini-nav/feature_compressor/core/compressor.py
deleted file mode 100644
index 8441ac3..0000000
--- a/mini-nav/feature_compressor/core/compressor.py
+++ /dev/null
@@ -1,135 +0,0 @@
-"""Feature compression module with attention-based pooling and MLP."""
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-class PoolNetCompressor(nn.Module):
-    """Pool + Network feature compressor for DINOv2 embeddings.
-
-    Combines attention-based Top-K token pooling with a 2-layer MLP to compress
-    DINOv2's last_hidden_state from [batch, seq_len, hidden_dim] to [batch, compression_dim].
-
-    Args:
-        input_dim: Input feature dimension (e.g., 1024 for DINOv2-large)
-        compression_dim: Output feature dimension (default: 256)
-        top_k_ratio: Ratio of tokens to keep via attention pooling (default: 0.5)
-        hidden_ratio: Hidden layer dimension as multiple of compression_dim (default: 2.0)
-        dropout_rate: Dropout probability (default: 0.1)
-        use_residual: Whether to use residual connection (default: True)
-        device: Device to place model on ('auto', 'cpu', or 'cuda')
-    """
-
-    def __init__(
-        self,
-        input_dim: int,
-        compression_dim: int = 256,
-        top_k_ratio: float = 0.5,
-        hidden_ratio: float = 2.0,
-        dropout_rate: float = 0.1,
-        use_residual: bool = True,
-        device: str = "auto",
-    ):
-        super().__init__()
-
-        self.input_dim = input_dim
-        self.compression_dim = compression_dim
-        self.top_k_ratio = top_k_ratio
-        self.use_residual = use_residual
-
-        # Attention mechanism for token selection
-        self.attention = nn.Sequential(
-            nn.Linear(input_dim, input_dim // 4),
-            nn.Tanh(),
-            nn.Linear(input_dim // 4, 1),
-        )
-
-        # Compression network: 2-layer MLP
-        hidden_dim = int(compression_dim * hidden_ratio)
-        self.net = nn.Sequential(
-            nn.Linear(input_dim, hidden_dim),
-            nn.LayerNorm(hidden_dim),
-            nn.GELU(),
-            nn.Dropout(dropout_rate),
-            nn.Linear(hidden_dim, compression_dim),
-        )
-
-        # Residual projection if dimensions don't match
-        if use_residual and input_dim != compression_dim:
-            self.residual_proj = nn.Linear(input_dim, compression_dim)
-        else:
-            self.residual_proj = None
-
-        # Set device
-        if device == "auto":
-            device = "cuda" if torch.cuda.is_available() else "cpu"
-        self.device = torch.device(device)
-        self.to(self.device)
-
-    def _compute_attention_scores(self, x: torch.Tensor) -> torch.Tensor:
-        """Compute attention scores for each token.
-
-        Args:
-            x: Input tensor [batch, seq_len, input_dim]
-
-        Returns:
-            Attention scores [batch, seq_len, 1]
-        """
-        scores = self.attention(x)  # [batch, seq_len, 1]
-        return scores.squeeze(-1)  # [batch, seq_len]
-
-    def _apply_pooling(self, x: torch.Tensor, scores: torch.Tensor) -> torch.Tensor:
-        """Apply Top-K attention pooling to select important tokens.
-
-        Args:
-            x: Input tensor [batch, seq_len, input_dim]
-            scores: Attention scores [batch, seq_len]
-
-        Returns:
-            Pooled features [batch, k, input_dim] where k = ceil(seq_len * top_k_ratio)
-        """
-        batch_size, seq_len, _ = x.shape
-        k = max(1, int(seq_len * self.top_k_ratio))
-
-        # Get top-k indices
-        top_k_values, top_k_indices = torch.topk(scores, k=k, dim=-1)  # [batch, k]
-
-        # Select top-k tokens
-        batch_indices = (
-            torch.arange(batch_size, device=x.device).unsqueeze(1).expand(-1, k)
-        )
-        pooled = x[batch_indices, top_k_indices, :]  # [batch, k, input_dim]
-
-        return pooled
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        """Forward pass through compressor.
-
-        Args:
-            x: Input features [batch, seq_len, input_dim]
-
-        Returns:
-            Compressed features [batch, compression_dim]
-        """
-        # Compute attention scores
-        scores = self._compute_attention_scores(x)
-
-        # Apply Top-K pooling
-        pooled = self._apply_pooling(x, scores)
-
-        # Average pool over selected tokens to get [batch, input_dim]
-        pooled = pooled.mean(dim=1)  # [batch, input_dim]
-
-        # Apply compression network
-        compressed = self.net(pooled)  # [batch, compression_dim]
-
-        # Add residual connection if enabled
-        if self.use_residual:
-            if self.residual_proj is not None:
-                residual = self.residual_proj(pooled)
-            else:
-                residual = pooled[:, : self.compression_dim]
-            compressed = compressed + residual
-
-        return compressed
diff --git a/mini-nav/feature_compressor/core/extractor.py b/mini-nav/feature_compressor/core/extractor.py
deleted file mode 100644
index 00f886d..0000000
--- a/mini-nav/feature_compressor/core/extractor.py
+++ /dev/null
@@ -1,233 +0,0 @@
-"""DINOv2 feature extraction and compression pipeline."""
-
-import time
-from pathlib import Path
-from typing import Dict, List, Optional, Union
-
-import torch
-from configs import FeatureCompressorConfig, cfg_manager, load_yaml
-from transformers import AutoImageProcessor, AutoModel
-
-from ..utils.image_utils import load_image, preprocess_image
-from .compressor import PoolNetCompressor
-
-
-class DINOv2FeatureExtractor:
-    """End-to-end DINOv2 feature extraction with compression.
-
-    Loads DINOv2 model, extracts last_hidden_state features,
-    and applies PoolNetCompressor for dimensionality reduction.
-
-    Args:
-        config_path: Path to YAML configuration file
-        device: Device to use ('auto', 'cpu', or 'cuda')
-    """
-
-    def __init__(self, config_path: Optional[str] = None, device: str = "auto"):
-        self.config: FeatureCompressorConfig = self._load_config(config_path)
-
-        # Set device
-        if device == "auto":
-            device = self.config.model.device
-        if device == "auto":
-            device = "cuda" if torch.cuda.is_available() else "cpu"
-        self.device = torch.device(device)
-
-        # Load DINOv2 model and processor
-        model_name = self.config.model.name
-        self.processor = AutoImageProcessor.from_pretrained(model_name)
-        self.model = AutoModel.from_pretrained(model_name).to(self.device)
-        self.model.eval()
-
-        # Initialize compressor
-        self.compressor = PoolNetCompressor(
-            input_dim=self.model.config.hidden_size,
-            compression_dim=self.config.model.compression_dim,
-            top_k_ratio=self.config.model.top_k_ratio,
-            hidden_ratio=self.config.model.hidden_ratio,
-            dropout_rate=self.config.model.dropout_rate,
-            use_residual=self.config.model.use_residual,
-            device=str(self.device),
-        )
-
-    def _load_config(
-        self, config_path: Optional[str] = None
-    ) -> FeatureCompressorConfig:
-        """Load configuration from YAML file.
-
-        Args:
-            config_path: Path to config file, or None for default
-
-        Returns:
-            FeatureCompressorConfig instance
-        """
-        if config_path is None:
-            return cfg_manager.get()
-        else:
-            return load_yaml(Path(config_path), FeatureCompressorConfig)
-
-    def _extract_dinov2_features(self, images: List) -> torch.Tensor:
-        """Extract DINOv2 last_hidden_state features.
-
-        Args:
-            images: List of PIL Images
-
-        Returns:
-            last_hidden_state [batch, seq_len, hidden_dim]
-        """
-        with torch.no_grad():
-            inputs = self.processor(images=images, return_tensors="pt").to(self.device)
-            outputs = self.model(**inputs)
-            features = outputs.last_hidden_state
-
-        return features
-
-    def _compress_features(self, features: torch.Tensor) -> torch.Tensor:
-        """Compress features using PoolNetCompressor.
-
-        Args:
-            features: [batch, seq_len, hidden_dim]
-
-        Returns:
-            compressed [batch, compression_dim]
-        """
-        with torch.no_grad():
-            compressed = self.compressor(features)
-
-        return compressed
-
-    def process_image(
-        self, image_path: str, visualize: bool = False
-    ) -> Dict[str, object]:
-        """Process a single image and extract compressed features.
-
-        Args:
-            image_path: Path to image file
-            visualize: Whether to generate visualizations
-
-        Returns:
-            Dictionary with original_features, compressed_features, metadata
-        """
-        start_time = time.time()
-
-        # Load and preprocess image
-        image = load_image(image_path)
-        image = preprocess_image(image, size=224)
-
-        # Extract DINOv2 features
-        original_features = self._extract_dinov2_features([image])
-
-        # Compute feature stats for compression ratio
-        original_dim = original_features.shape[-1]
-        compressed_dim = self.compressor.compression_dim
-        compression_ratio = original_dim / compressed_dim
-
-        # Compress features
-        compressed_features = self._compress_features(original_features)
-
-        # Get pooled features (before compression) for analysis
-        pooled_features = self.compressor._apply_pooling(
-            original_features,
-            self.compressor._compute_attention_scores(original_features),
-        )
-        pooled_features = pooled_features.mean(dim=1)
-
-        # Compute feature norm
-        feature_norm = torch.norm(compressed_features, p=2, dim=-1).mean().item()
-
-        processing_time = time.time() - start_time
-
-        # Build result dictionary
-        result = {
-            "original_features": original_features.cpu(),
-            "compressed_features": compressed_features.cpu(),
-            "pooled_features": pooled_features.cpu(),
-            "metadata": {
-                "image_path": str(image_path),
-                "compression_ratio": compression_ratio,
-                "processing_time": processing_time,
-                "feature_norm": feature_norm,
-                "device": str(self.device),
-                "model_name": self.config.model.name,
-            },
-        }
-
-        return result
-
-    def process_batch(
-        self,
-        image_dir: Union[str, Path],
-        batch_size: int = 8,
-        save_features: bool = True,
-    ) -> List[Dict[str, object]]:
-        """Process multiple images in batches.
-
-        Args:
-            image_dir: Directory containing images
-            batch_size: Number of images per batch
-            save_features: Whether to save features to disk
-
-        Returns:
-            List of result dictionaries, one per image
-        """
-        image_dir = Path(image_dir)
-        image_files = sorted(image_dir.glob("*.*"))
-
-        results = []
-
-        # Process in batches
-        for i in range(0, len(image_files), batch_size):
-            batch_files = image_files[i : i + batch_size]
-
-            # Load and preprocess batch
-            images = [preprocess_image(load_image(f), size=224) for f in batch_files]
-
-            # Extract features for batch
-            original_features = self._extract_dinov2_features(images)
-            compressed_features = self._compress_features(original_features)
-
-            # Create individual results
-            for j, file_path in enumerate(batch_files):
-                pooled_features = self.compressor._apply_pooling(
-                    original_features[j : j + 1],
-                    self.compressor._compute_attention_scores(
-                        original_features[j : j + 1]
-                    ),
-                ).mean(dim=1)
-
-                result = {
-                    "original_features": original_features[j : j + 1].cpu(),
-                    "compressed_features": compressed_features[j : j + 1].cpu(),
-                    "pooled_features": pooled_features.cpu(),
-                    "metadata": {
-                        "image_path": str(file_path),
-                        "compression_ratio": original_features.shape[-1]
-                        / self.compressor.compression_dim,
-                        "processing_time": 0.0,
-                        "feature_norm": torch.norm(
-                            compressed_features[j : j + 1], p=2, dim=-1
-                        )
-                        .mean()
-                        .item(),
-                        "device": str(self.device),
-                        "model_name": self.config.model.name,
-                    },
-                }
-
-                results.append(result)
-
-                # Save features if requested
-                if save_features:
-                    output_dir = Path(self.config.output.directory)
-                    output_dir.mkdir(parents=True, exist_ok=True)
-
-                    output_path = output_dir / f"{file_path.stem}_features.json"
-                    from ..utils.feature_utils import save_features_to_json
-
-                    save_features_to_json(
-                        result["compressed_features"],
-                        output_path,
-                        result["metadata"],
-                    )
-
-        return results
diff --git a/mini-nav/feature_compressor/examples/basic_usage.py b/mini-nav/feature_compressor/examples/basic_usage.py
deleted file mode 100644
index e06589a..0000000
--- a/mini-nav/feature_compressor/examples/basic_usage.py
+++ /dev/null
@@ -1,63 +0,0 @@
-"""Basic usage example for DINOv2 Feature Compressor."""
-
-import sys
-from pathlib import Path
-
-# Add parent to path for imports
-sys.path.insert(0, str(Path(__file__).parent.parent.parent))
-
-import requests
-from PIL import Image
-import io
-
-from dino_feature_compressor import DINOv2FeatureExtractor, FeatureVisualizer
-
-
-def main():
-    # Initialize extractor
-    print("Initializing DINOv2FeatureExtractor...")
-    extractor = DINOv2FeatureExtractor()
-
-    # Download and save test image
-    print("Downloading test image...")
-    url = "http://images.cocodataset.org/val2017/000000039769.jpg"
-    response = requests.get(url)
-    img = Image.open(io.BytesIO(response.content))
-
-    test_image_path = "/tmp/test_image.jpg"
-    img.save(test_image_path)
-    print(f"Image saved to {test_image_path}")
-
-    # Extract features
-    print("Extracting features...")
-    result = extractor.process_image(test_image_path)
-
-    print(f"\n=== Feature Extraction Results ===")
-    print(f"Original features shape: {result['original_features'].shape}")
-    print(f"Compressed features shape: {result['compressed_features'].shape}")
-    print(f"Processing time: {result['metadata']['processing_time']:.3f}s")
-    print(f"Compression ratio: {result['metadata']['compression_ratio']:.2f}x")
-    print(f"Feature norm: {result['metadata']['feature_norm']:.4f}")
-    print(f"Device: {result['metadata']['device']}")
-
-    # Visualize
-    print("\nGenerating visualization...")
-    viz = FeatureVisualizer()
-
-    fig = viz.plot_histogram(
-        result["compressed_features"], title="Compressed Features Distribution"
-    )
-
-    output_path = (
-        Path(__file__).parent.parent.parent / "outputs" / "basic_usage_histogram"
-    )
-    output_path.parent.mkdir(parents=True, exist_ok=True)
-
-    viz.save(fig, str(output_path), formats=["html"])
-    print(f"Visualization saved to {output_path}.html")
-
-    print("\nDone!")
-
-
-if __name__ == "__main__":
-    main()
diff --git a/mini-nav/feature_compressor/examples/batch_processing.py b/mini-nav/feature_compressor/examples/batch_processing.py
deleted file mode 100644
index 6a36caa..0000000
--- a/mini-nav/feature_compressor/examples/batch_processing.py
+++ /dev/null
@@ -1,49 +0,0 @@
-"""Batch processing example for DINOv2 Feature Compressor."""
-
-import sys
-from pathlib import Path
-
-# Add parent to path for imports
-sys.path.insert(0, str(Path(__file__).parent.parent.parent))
-
-from dino_feature_compressor import DINOv2FeatureExtractor
-
-
-def main():
-    # Initialize extractor
-    print("Initializing DINOv2FeatureExtractor...")
-    extractor = DINOv2FeatureExtractor()
-
-    # Create a test directory with sample images
-    # In practice, use your own directory
-    image_dir = "/tmp/test_images"
-    Path(image_dir).mkdir(parents=True, exist_ok=True)
-
-    # Create 3 test images
-    print("Creating test images...")
-    import numpy as np
-    from PIL import Image
-
-    for i in range(3):
-        img_array = np.random.randint(0, 255, (224, 224, 3), dtype=np.uint8)
-        img = Image.fromarray(img_array)
-        img.save(f"{image_dir}/test_{i}.jpg")
-    print(f"Created 3 test images in {image_dir}")
-
-    # Process batch
-    print("\nProcessing images in batch...")
-    results = extractor.process_batch(image_dir, batch_size=2, save_features=True)
-
-    print(f"\n=== Batch Processing Results ===")
-    print(f"Processed {len(results)} images")
-
-    for i, result in enumerate(results):
-        print(f"\nImage {i + 1}: {result['metadata']['image_path']}")
-        print(f"  Compressed shape: {result['compressed_features'].shape}")
-        print(f"  Feature norm: {result['metadata']['feature_norm']:.4f}")
-
-    print("\nDone! Features saved to outputs/ directory.")
-
-
-if __name__ == "__main__":
-    main()
diff --git a/mini-nav/feature_compressor/examples/visualization.py b/mini-nav/feature_compressor/examples/visualization.py
deleted file mode 100644
index 18d18a0..0000000
--- a/mini-nav/feature_compressor/examples/visualization.py
+++ /dev/null
@@ -1,61 +0,0 @@
-"""Visualization example for DINOv2 Feature Compressor."""
-
-import sys
-from pathlib import Path
-
-# Add parent to path for imports
-sys.path.insert(0, str(Path(__file__).parent.parent.parent))
-
-import numpy as np
-import torch
-
-from dino_feature_compressor import FeatureVisualizer
-
-
-def main():
-    # Generate synthetic features for demonstration
-    print("Generating synthetic features...")
-    n_samples = 100
-    n_features = 256
-
-    # Create two clusters
-    cluster1 = np.random.randn(50, n_features) + 2
-    cluster2 = np.random.randn(50, n_features) - 2
-    features = np.vstack([cluster1, cluster2])
-
-    labels = ["Cluster A"] * 50 + ["Cluster B"] * 50
-
-    features_tensor = torch.tensor(features, dtype=torch.float32)
-
-    # Initialize visualizer
-    print("Initializing FeatureVisualizer...")
-    viz = FeatureVisualizer()
-
-    output_dir = Path(__file__).parent.parent.parent / "outputs"
-    output_dir.mkdir(parents=True, exist_ok=True)
-
-    # Create histogram
-    print("Creating histogram...")
-    fig_hist = viz.plot_histogram(features_tensor, title="Feature Distribution")
-    viz.save(fig_hist, str(output_dir / "feature_histogram"), formats=["html", "json"])
-    print(f"Saved histogram to {output_dir / 'feature_histogram.html'}")
-
-    # Create PCA 2D projection
-    print("Creating PCA 2D projection...")
-    fig_pca = viz.plot_pca_2d(features_tensor, labels=labels)
-    viz.save(fig_pca, str(output_dir / "feature_pca_2d"), formats=["html", "json"])
-    print(f"Saved PCA to {output_dir / 'feature_pca_2d.html'}")
-
-    # Create comparison plot
-    print("Creating comparison plot...")
-    features_list = [torch.tensor(cluster1), torch.tensor(cluster2)]
-    names = ["Cluster A", "Cluster B"]
-    fig_comp = viz.plot_comparison(features_list, names)
-    viz.save(fig_comp, str(output_dir / "feature_comparison"), formats=["html", "json"])
-    print(f"Saved comparison to {output_dir / 'feature_comparison.html'}")
-
-    print("\nDone! All visualizations saved to outputs/ directory.")
-
-
-if __name__ == "__main__":
-    main()
diff --git a/mini-nav/feature_compressor/utils/__init__.py b/mini-nav/feature_compressor/utils/__init__.py
deleted file mode 100644
index bc553c2..0000000
--- a/mini-nav/feature_compressor/utils/__init__.py
+++ /dev/null
@@ -1,19 +0,0 @@
-"""Utility modules for image, feature, and plot operations."""
-
-from .feature_utils import (
-    compute_feature_stats,
-    normalize_features,
-    save_features_to_csv,
-    save_features_to_json,
-)
-from .image_utils import load_image, load_images_from_directory, preprocess_image
-
-__all__ = [
-    "load_image",
-    "load_images_from_directory",
-    "preprocess_image",
-    "normalize_features",
-    "compute_feature_stats",
-    "save_features_to_json",
-    "save_features_to_csv",
-]
diff --git a/mini-nav/feature_compressor/utils/feature_utils.py b/mini-nav/feature_compressor/utils/feature_utils.py
deleted file mode 100644
index ce27c9c..0000000
--- a/mini-nav/feature_compressor/utils/feature_utils.py
+++ /dev/null
@@ -1,83 +0,0 @@
-"""Feature processing utilities."""
-
-from pathlib import Path
-from typing import Dict
-
-import numpy as np
-import torch
-import yaml
-
-
-def normalize_features(features: torch.Tensor) -> torch.Tensor:
-    """L2-normalize features.
-
-    Args:
-        features: Tensor of shape [batch, dim] or [batch, seq, dim]
-
-    Returns:
-        L2-normalized features
-    """
-    norm = torch.norm(features, p=2, dim=-1, keepdim=True)
-    return features / (norm + 1e-8)
-
-
-def compute_feature_stats(features: torch.Tensor) -> Dict[str, float]:
-    """Compute basic statistics for features.
-
-    Args:
-        features: Tensor of shape [batch, dim] or [batch, seq, dim]
-
-    Returns:
-        Dictionary with mean, std, min, max
-    """
-    with torch.no_grad():
-        return {
-            "mean": float(features.mean().item()),
-            "std": float(features.std().item()),
-            "min": float(features.min().item()),
-            "max": float(features.max().item()),
-        }
-
-
-def save_features_to_json(
-    features: torch.Tensor, path: Path, metadata: Dict = None
-) -> None:
-    """Save features to JSON file.
-
-    Args:
-        features: Tensor to save
-        path: Output file path
-        metadata: Optional metadata dictionary
-    """
-    path = Path(path)
-    path.parent.mkdir(parents=True, exist_ok=True)
-
-    features_np = features.cpu().numpy()
-
-    data = {
-        "features": features_np.tolist(),
-        "shape": list(features.shape),
-    }
-
-    if metadata:
-        data["metadata"] = metadata
-
-    with open(path, "w") as f:
-        import json
-
-        json.dump(data, f, indent=2)
-
-
-def save_features_to_csv(features: torch.Tensor, path: Path) -> None:
-    """Save features to CSV file.
-
-    Args:
-        features: Tensor to save
-        path: Output file path
-    """
-    path = Path(path)
-    path.parent.mkdir(parents=True, exist_ok=True)
-
-    features_np = features.cpu().numpy()
-
-    np.savetxt(path, features_np, delimiter=",", fmt="%.6f")
diff --git a/mini-nav/feature_compressor/utils/image_utils.py b/mini-nav/feature_compressor/utils/image_utils.py
deleted file mode 100644
index 722f522..0000000
--- a/mini-nav/feature_compressor/utils/image_utils.py
+++ /dev/null
@@ -1,76 +0,0 @@
-"""Image loading and preprocessing utilities."""
-
-from pathlib import Path
-from typing import List, Optional, Union
-
-import requests
-from PIL import Image
-
-
-def load_image(path: Union[str, Path]) -> Image.Image:
-    """Load an image from file path or URL.
-
-    Args:
-        path: File path or URL to image
-
-    Returns:
-        PIL Image object
-
-    Raises:
-        FileNotFoundError: If file doesn't exist
-        ValueError: If image cannot be loaded
-    """
-    path_str = str(path)
-
-    if path_str.startswith(("http://", "https://")):
-        response = requests.get(path_str, stream=True)
-        response.raise_for_status()
-        img = Image.open(response.raw)
-    else:
-        img = Image.open(path)
-
-    return img
-
-
-def preprocess_image(image: Image.Image, size: int = 224) -> Image.Image:
-    """Preprocess image to square format with resizing.
-
-    Args:
-        image: PIL Image
-        size: Target size for shortest dimension (default: 224)
-
-    Returns:
-        Resized PIL Image
-    """
-    if image.mode != "RGB":
-        image = image.convert("RGB")
-
-    # Resize while maintaining aspect ratio, then center crop
-    image = image.resize((size, size), Image.Resampling.LANCZOS)
-
-    return image
-
-
-def load_images_from_directory(
-    dir_path: Union[str, Path], extensions: Optional[List[str]] = None
-) -> List[Image.Image]:
-    """Load all images from a directory.
-
-    Args:
-        dir_path: Path to directory
-        extensions: List of file extensions to include (e.g., ['.jpg', '.png'])
-
-    Returns:
-        List of PIL Images
-    """
-    if extensions is None:
-        extensions = [".jpg", ".jpeg", ".png", ".bmp", ".tiff", ".webp"]
-
-    dir_path = Path(dir_path)
-    images = []
-
-    for ext in extensions:
-        images.extend([load_image(p) for p in dir_path.glob(f"*{ext}")])
-        images.extend([load_image(p) for p in dir_path.glob(f"*{ext.upper()}")])
-
-    return images
diff --git a/mini-nav/feature_compressor/utils/plot_utils.py b/mini-nav/feature_compressor/utils/plot_utils.py
deleted file mode 100644
index 0131455..0000000
--- a/mini-nav/feature_compressor/utils/plot_utils.py
+++ /dev/null
@@ -1,167 +0,0 @@
-"""Plotting utility functions for feature visualization."""
-
-from pathlib import Path
-from typing import List, Optional
-
-import numpy as np
-import plotly.graph_objects as go
-from plotly.subplots import make_subplots
-
-
-def create_histogram(data: np.ndarray, title: str = None, **kwargs) -> go.Figure:
-    """Create a histogram plot.
-
-    Args:
-        data: 1D array of values
-        title: Plot title
-        **kwargs: Additional histogram arguments
-
-    Returns:
-        Plotly Figure object
-    """
-    fig = go.Figure()
-
-    fig.add_trace(
-        go.Histogram(
-            x=data.flatten(),
-            name="Feature Values",
-            **kwargs,
-        )
-    )
-
-    if title:
-        fig.update_layout(title=title)
-
-    fig.update_layout(
-        xaxis_title="Value",
-        yaxis_title="Count",
-        hovermode="x unified",
-    )
-
-    return fig
-
-
-def create_pca_scatter_2d(
-    features: np.ndarray, labels: List = None, **kwargs
-) -> go.Figure:
-    """Create a 2D PCA scatter plot.
-
-    Args:
-        features: 2D array [n_samples, n_features]
-        labels: Optional list of labels for coloring
-        **kwargs: Additional scatter arguments
-
-    Returns:
-        Plotly Figure object
-    """
-    from sklearn.decomposition import PCA
-
-    # Apply PCA
-    pca = PCA(n_components=2)
-    components = pca.fit_transform(features)
-
-    explained_var = pca.explained_variance_ratio_ * 100
-
-    fig = go.Figure()
-
-    if labels is None:
-        fig.add_trace(
-            go.Scatter(
-                x=components[:, 0],
-                y=components[:, 1],
-                mode="markers",
-                marker=dict(size=8, opacity=0.7),
-                **kwargs,
-            )
-        )
-    else:
-        for label in set(labels):
-            mask = np.array(labels) == label
-            fig.add_trace(
-                go.Scatter(
-                    x=components[mask, 0],
-                    y=components[mask, 1],
-                    mode="markers",
-                    name=str(label),
-                    marker=dict(size=8, opacity=0.7),
-                )
-            )
-
-    fig.update_layout(
-        title=f"PCA 2D Projection (Total Variance: {explained_var.sum():.1f}%)",
-        xaxis_title=f"PC 1 ({explained_var[0]:.1f}%)",
-        yaxis_title=f"PC 2 ({explained_var[1]:.1f}%)",
-        hovermode="closest",
-    )
-
-    return fig
-
-
-def create_comparison_plot(
-    features_list: List[np.ndarray], names: List[str], **kwargs
-) -> go.Figure:
-    """Create a comparison plot of multiple feature sets.
-
-    Args:
-        features_list: List of feature arrays
-        names: List of names for each feature set
-        **kwargs: Additional histogram arguments
-
-    Returns:
-        Plotly Figure object
-    """
-    fig = make_subplots(rows=1, cols=len(features_list), subplot_titles=names)
-
-    for i, features in enumerate(features_list, 1):
-        fig.add_trace(
-            go.Histogram(
-                x=features.flatten(),
-                name=names[i - 1],
-                showlegend=False,
-                **kwargs,
-            ),
-            row=1,
-            col=i,
-        )
-
-    fig.update_layout(
-        title="Feature Distribution Comparison",
-        hovermode="x unified",
-    )
-
-    return fig
-
-
-def save_figure(fig: go.Figure, path: str, format: str = "html") -> None:
-    """Save figure to file.
-
-    Args:
-        fig: Plotly Figure object
-        path: Output file path (without extension)
-        format: Output format ('html', 'png', 'json')
-    """
-    path = Path(path)
-    path.parent.mkdir(parents=True, exist_ok=True)
-
-    if format == "html":
-        fig.write_html(str(path) + ".html", include_plotlyjs="cdn")
-    elif format == "png":
-        fig.write_image(str(path) + ".png", scale=2)
-    elif format == "json":
-        fig.write_json(str(path) + ".json")
-    else:
-        raise ValueError(f"Unsupported format: {format}")
-
-
-def apply_theme(fig: go.Figure, theme: str = "plotly_white") -> go.Figure:
-    """Apply a theme to the figure.
-
-    Args:
-        fig: Plotly Figure object
-        theme: Theme name
-
-    Returns:
-        Updated Plotly Figure object
-    """
-    fig.update_layout(template=theme)
-    return fig
diff --git a/pyproject.toml b/pyproject.toml
index 7a9929b..ece73b7 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -15,6 +15,7 @@ dependencies = [
     "polars[database,numpy,pandas,pydantic]>=1.37.1",
     "pyarrow>=23.0.0",
     "pydantic>=2.12.5",
+    "pytest-benchmark>=5.2.3",
     "scikit-learn>=1.7.2",
     "torch>=2.10.0",
     "torchvision>=0.25.0",
diff --git a/uv.lock b/uv.lock
index 820ed27..753d2df 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1114,6 +1114,7 @@ dependencies = [
     { name = "polars", extra = ["database", "numpy", "pandas", "pydantic"] },
     { name = "pyarrow" },
     { name = "pydantic" },
+    { name = "pytest-benchmark" },
     { name = "scikit-learn", version = "1.7.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
     { name = "scikit-learn", version = "1.8.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
     { name = "torch", version = "2.10.0", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform != 'linux' and sys_platform != 'win32'" },
@@ -1141,6 +1142,7 @@ requires-dist = [
     { name = "polars", extras = ["database", "numpy", "pandas", "pydantic"], specifier = ">=1.37.1" },
     { name = "pyarrow", specifier = ">=23.0.0" },
     { name = "pydantic", specifier = ">=2.12.5" },
+    { name = "pytest-benchmark", specifier = ">=5.2.3" },
     { name = "scikit-learn", specifier = ">=1.7.2" },
     { name = "torch", marker = "sys_platform != 'linux' and sys_platform != 'win32'", specifier = ">=2.10.0" },
     { name = "torch", marker = "sys_platform == 'linux' or sys_platform == 'win32'", specifier = ">=2.10.0", index = "https://download.pytorch.org/whl/cu130" },
@@ -2139,6 +2141,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/8c/c7/7bb2e321574b10df20cbde462a94e2b71d05f9bbda251ef27d104668306a/psutil-7.2.2-cp37-abi3-win_arm64.whl", hash = "sha256:8c233660f575a5a89e6d4cb65d9f938126312bca76d8fe087b947b3a1aaac9ee", size = 134617, upload-time = "2026-01-28T18:15:36.514Z" },
 ]
 
+[[package]]
+name = "py-cpuinfo"
+version = "9.0.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/37/a8/d832f7293ebb21690860d2e01d8115e5ff6f2ae8bbdc953f0eb0fa4bd2c7/py-cpuinfo-9.0.0.tar.gz", hash = "sha256:3cdbbf3fac90dc6f118bfd64384f309edeadd902d7c8fb17f02ffa1fc3f49690", size = 104716, upload-time = "2022-10-25T20:38:06.303Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/e0/a9/023730ba63db1e494a271cb018dcd361bd2c917ba7004c3e49d5daf795a2/py_cpuinfo-9.0.0-py3-none-any.whl", hash = "sha256:859625bc251f64e21f077d099d4162689c762b5d6a4c3c97553d56241c9674d5", size = 22335, upload-time = "2022-10-25T20:38:27.636Z" },
+]
+
 [[package]]
 name = "pyarrow"
 version = "23.0.0"
@@ -2356,6 +2367,19 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/3b/ab/b3226f0bd7cdcf710fbede2b3548584366da3b19b5021e74f5bde2a8fa3f/pytest-9.0.2-py3-none-any.whl", hash = "sha256:711ffd45bf766d5264d487b917733b453d917afd2b0ad65223959f59089f875b", size = 374801, upload-time = "2025-12-06T21:30:49.154Z" },
 ]
 
+[[package]]
+name = "pytest-benchmark"
+version = "5.2.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "py-cpuinfo" },
+    { name = "pytest" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/24/34/9f732b76456d64faffbef6232f1f9dbec7a7c4999ff46282fa418bd1af66/pytest_benchmark-5.2.3.tar.gz", hash = "sha256:deb7317998a23c650fd4ff76e1230066a76cb45dcece0aca5607143c619e7779", size = 341340, upload-time = "2025-11-09T18:48:43.215Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/33/29/e756e715a48959f1c0045342088d7ca9762a2f509b945f362a316e9412b7/pytest_benchmark-5.2.3-py3-none-any.whl", hash = "sha256:bc839726ad20e99aaa0d11a127445457b4219bdb9e80a1afc4b51da7f96b0803", size = 45255, upload-time = "2025-11-09T18:48:39.765Z" },
+]
+
 [[package]]
 name = "pytest-mpi"
 version = "0.6"