Working training loop and testing of trained CharacterModel.

1 files changed, 17 insertions, 2 deletions

diff --git a/src/text_recognizer/tests/test_character_predictor.py b/src/text_recognizer/tests/test_character_predictor.py
index 7c094ef..c603a3a 100644
--- a/src/text_recognizer/tests/test_character_predictor.py
+++ b/src/text_recognizer/tests/test_character_predictor.py
@@ -1,9 +1,14 @@
 """Test for CharacterPredictor class."""
+import importlib
 import os
 from pathlib import Path
 import unittest
 
+import click
+from loguru import logger
+
 from text_recognizer.character_predictor import CharacterPredictor
+from text_recognizer.networks import MLP
 
 SUPPORT_DIRNAME = Path(__file__).parents[0].resolve() / "support" / "emnist"
 
@@ -13,13 +18,23 @@ os.environ["CUDA_VISIBLE_DEVICES"] = ""
 class TestCharacterPredictor(unittest.TestCase):
     """Tests for the CharacterPredictor class."""
 
+    # @click.command()
+    # @click.option(
+    #     "--network", type=str, help="Network to load, e.g. MLP or LeNet.", default="MLP"
+    # )
     def test_filename(self) -> None:
         """Test that CharacterPredictor correctly predicts on a single image, for serveral test images."""
-        predictor = CharacterPredictor()
+        network_module = importlib.import_module("text_recognizer.networks")
+        network_fn_ = getattr(network_module, "MLP")
+        # network_args = {"input_size": [28, 28], "output_size": 62, "dropout_rate": 0}
+        network_args = {"input_size": 784, "output_size": 62, "dropout_rate": 0.2}
+        predictor = CharacterPredictor(
+            network_fn=network_fn_, network_args=network_args
+        )
 
         for filename in SUPPORT_DIRNAME.glob("*.png"):
             pred, conf = predictor.predict(str(filename))
-            print(
+            logger.info(
                 f"Prediction: {pred} at confidence: {conf} for image with character {filename.stem}"
             )
             self.assertEqual(pred, filename.stem)