Add pet ID model trainer with MobileNetV3-Small transfer learning

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

tests/unit/test_trainer.py

@@ -0,0 +1,55 @@
+"""Tests for pet ID model trainer."""
+
+import os
+from pathlib import Path
+
+import numpy as np
+import pytest
+
+from vigilar.detection.trainer import PetTrainer, TrainingStatus
+
+
+class TestTrainingStatus:
+    def test_initial_status(self):
+        status = TrainingStatus()
+        assert status.is_training is False
+        assert status.progress == 0.0
+        assert status.error is None
+
+
+class TestPetTrainer:
+    def test_check_readiness_no_pets(self, tmp_path):
+        trainer = PetTrainer(training_dir=str(tmp_path), model_output_path=str(tmp_path / "model.pt"))
+        ready, msg = trainer.check_readiness(min_images=20)
+        assert not ready
+        assert "No pet" in msg
+
+    def test_check_readiness_insufficient_images(self, tmp_path):
+        pet_dir = tmp_path / "angel"
+        pet_dir.mkdir()
+        for i in range(5):
+            (pet_dir / f"{i}.jpg").write_bytes(b"fake")
+
+        trainer = PetTrainer(training_dir=str(tmp_path), model_output_path=str(tmp_path / "model.pt"))
+        ready, msg = trainer.check_readiness(min_images=20)
+        assert not ready
+        assert "angel" in msg.lower()
+
+    def test_check_readiness_sufficient_images(self, tmp_path):
+        for name in ["angel", "taquito"]:
+            pet_dir = tmp_path / name
+            pet_dir.mkdir()
+            for i in range(25):
+                (pet_dir / f"{i}.jpg").write_bytes(b"fake")
+
+        trainer = PetTrainer(training_dir=str(tmp_path), model_output_path=str(tmp_path / "model.pt"))
+        ready, msg = trainer.check_readiness(min_images=20)
+        assert ready
+
+    def test_get_class_names(self, tmp_path):
+        for name in ["angel", "milo", "taquito"]:
+            (tmp_path / name).mkdir()
+
+        trainer = PetTrainer(training_dir=str(tmp_path), model_output_path=str(tmp_path / "model.pt"))
+        names = trainer.get_class_names()
+        assert names == ["angel", "milo", "taquito"]  # sorted