feat(utils): add feature extraction utilities and tests

mini-nav/tests/test_config.py

@@ -25,7 +25,7 @@ class TestConfigModels:
     def test_model_config_defaults(self):
         """Verify ModelConfig creates with correct defaults."""
         config = ModelConfig()
-        assert config.name == "facebook/dinov2-large"
+        assert config.dino_model == "facebook/dinov2-large"
         assert config.compression_dim == 512
         assert config.device == "auto"
 
@@ -73,7 +73,7 @@ class TestYamlLoader:
         config = load_yaml(config_path, Config)
 
         # Verify model config
-        assert config.model.name == "facebook/dinov2-large"
+        assert config.model.dino_model == "facebook/dinov2-large"
         assert config.model.compression_dim == 256
 
         # Verify output config

mini-nav/tests/test_feature_extractor.py

@@ -0,0 +1,50 @@
+"""Tests for feature extraction utilities."""
+
+import pytest
+import torch
+from PIL import Image
+from transformers import AutoImageProcessor, AutoModel
+
+from utils.feature_extractor import (
+    extract_batch_features,
+    extract_single_image_feature,
+    infer_vector_dim,
+)
+
+TEST_MODEL_NAME = "facebook/dinov2-base"
+
+
+@pytest.fixture
+def model_and_processor():
+    processor = AutoImageProcessor.from_pretrained(TEST_MODEL_NAME)
+    model = AutoModel.from_pretrained(TEST_MODEL_NAME)
+    model.eval()
+    yield processor, model
+    del model
+    del processor
+
+
+def test_infer_vector_dim(model_and_processor):
+    """Verify infer_vector_dim returns correct dimension."""
+    processor, model = model_and_processor
+    sample_image = Image.new("RGB", (224, 224), color="blue")
+    dim = infer_vector_dim(processor, model, sample_image)
+    assert dim == 768
+
+
+def test_extract_single_image_feature(model_and_processor):
+    """Verify single image feature extraction."""
+    processor, model = model_and_processor
+    sample_image = Image.new("RGB", (224, 224), color="red")
+    features = extract_single_image_feature(processor, model, sample_image)
+    assert isinstance(features, list)
+    assert len(features) == 768
+
+
+def test_extract_batch_features(model_and_processor):
+    """Verify batch feature extraction."""
+    processor, model = model_and_processor
+    images = [Image.new("RGB", (224, 224), color="red") for _ in range(3)]
+    features = extract_batch_features(processor, model, images)
+    assert isinstance(features, torch.Tensor)
+    assert features.shape == (3, 768)