Rename index_by_label to label_names.

PiperOrigin-RevId: 484956259

mediapipe/model_maker/python/core/data/classification_dataset.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 """Common classification dataset library."""
 
-from typing import Any, Tuple
+from typing import List, Tuple
 
 import tensorflow as tf
 
@@ -21,19 +21,20 @@ from mediapipe.model_maker.python.core.data import dataset as ds
 
 
 class ClassificationDataset(ds.Dataset):
-  """DataLoader for classification models."""
+  """Dataset Loader for classification models."""
 
-  def __init__(self, dataset: tf.data.Dataset, size: int, index_by_label: Any):
+  def __init__(self, dataset: tf.data.Dataset, size: int,
+               label_names: List[str]):
     super().__init__(dataset, size)
-    self._index_by_label = index_by_label
+    self._label_names = label_names
 
   @property
   def num_classes(self: ds._DatasetT) -> int:
-    return len(self._index_by_label)
+    return len(self._label_names)
 
   @property
-  def index_by_label(self: ds._DatasetT) -> Any:
-    return self._index_by_label
+  def label_names(self: ds._DatasetT) -> List[str]:
+    return self._label_names
 
   def split(self: ds._DatasetT,
             fraction: float) -> Tuple[ds._DatasetT, ds._DatasetT]:
@@ -48,4 +49,4 @@ class ClassificationDataset(ds.Dataset):
     Returns:
       The splitted two sub datasets.
     """
-    return self._split(fraction, self._index_by_label)
+    return self._split(fraction, self._label_names)

mediapipe/model_maker/python/core/data/classification_dataset_test.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import Any, Tuple, TypeVar
+from typing import Any, List, Tuple, TypeVar
 
 # Dependency imports
 
@@ -37,26 +37,22 @@ class ClassificationDatasetTest(tf.test.TestCase):
       """
 
       def __init__(self, dataset: tf.data.Dataset, size: int,
-                   index_by_label: Any, value: Any):
-        super().__init__(
-            dataset=dataset, size=size, index_by_label=index_by_label)
+                   label_names: List[str], value: Any):
+        super().__init__(dataset=dataset, size=size, label_names=label_names)
         self.value = value
 
       def split(self, fraction: float) -> Tuple[_DatasetT, _DatasetT]:
-        return self._split(fraction, self.index_by_label, self.value)
+        return self._split(fraction, self.label_names, self.value)
 
     # Some dummy inputs.
     magic_value = 42
     num_classes = 2
-    index_by_label = (False, True)
+    label_names = ['foo', 'bar']
 
     # Create data loader from sample data.
     ds = tf.data.Dataset.from_tensor_slices([[0, 1], [1, 1], [0, 0], [1, 0]])
     data = MagicClassificationDataset(
-        dataset=ds,
-        size=len(ds),
-        index_by_label=index_by_label,
-        value=magic_value)
+        dataset=ds, size=len(ds), label_names=label_names, value=magic_value)
 
     # Train/Test data split.
     fraction = .25
@@ -73,7 +69,7 @@ class ClassificationDatasetTest(tf.test.TestCase):
 
     # Make sure attributes propagated correctly.
     self.assertEqual(train_data.num_classes, num_classes)
-    self.assertEqual(test_data.index_by_label, index_by_label)
+    self.assertEqual(test_data.label_names, label_names)
     self.assertEqual(train_data.value, magic_value)
     self.assertEqual(test_data.value, magic_value)
 

mediapipe/model_maker/python/core/tasks/classifier.py

@@ -29,22 +29,22 @@ from mediapipe.model_maker.python.core.tasks import custom_model
 class Classifier(custom_model.CustomModel):
   """An abstract base class that represents a TensorFlow classifier."""
 
-  def __init__(self, model_spec: Any, index_by_label: List[str], shuffle: bool,
+  def __init__(self, model_spec: Any, label_names: List[str], shuffle: bool,
                full_train: bool):
     """Initilizes a classifier with its specifications.
 
     Args:
         model_spec: Specification for the model.
-        index_by_label: A list that map from index to label class name.
+        label_names: A list of label names for the classes.
         shuffle: Whether the dataset should be shuffled.
         full_train: If true, train the model end-to-end including the backbone
           and the classification layers on top. Otherwise, only train the top
           classification layers.
     """
     super(Classifier, self).__init__(model_spec, shuffle)
-    self._index_by_label = index_by_label
+    self._label_names = label_names
     self._full_train = full_train
-    self._num_classes = len(index_by_label)
+    self._num_classes = len(label_names)
 
   def evaluate(self, data: dataset.Dataset, batch_size: int = 32) -> Any:
     """Evaluates the classifier with the provided evaluation dataset.
@@ -74,4 +74,4 @@ class Classifier(custom_model.CustomModel):
     label_filepath = os.path.join(export_dir, label_filename)
     tf.compat.v1.logging.info('Saving labels in %s', label_filepath)
     with tf.io.gfile.GFile(label_filepath, 'w') as f:
-      f.write('\n'.join(self._index_by_label))
+      f.write('\n'.join(self._label_names))

mediapipe/model_maker/python/core/tasks/classifier_test.py

@@ -36,10 +36,10 @@ class ClassifierTest(tf.test.TestCase):
 
   def setUp(self):
     super(ClassifierTest, self).setUp()
-    index_by_label = ['cat', 'dog']
+    label_names = ['cat', 'dog']
     self.model = MockClassifier(
         model_spec=None,
-        index_by_label=index_by_label,
+        label_names=label_names,
         shuffle=False,
         full_train=False)
     self.model.model = test_util.build_model(input_shape=[4], num_classes=2)

mediapipe/model_maker/python/vision/image_classifier/dataset.py

@@ -106,4 +106,4 @@ class Dataset(classification_dataset.ClassificationDataset):
         'Load image with size: %d, num_label: %d, labels: %s.', all_image_size,
         all_label_size, ', '.join(label_names))
     return Dataset(
-        dataset=image_label_ds, size=all_image_size, index_by_label=label_names)
+        dataset=image_label_ds, size=all_image_size, label_names=label_names)

mediapipe/model_maker/python/vision/image_classifier/dataset_test.py

@@ -49,27 +49,27 @@ class DatasetTest(tf.test.TestCase):
 
   def test_split(self):
     ds = tf.data.Dataset.from_tensor_slices([[0, 1], [1, 1], [0, 0], [1, 0]])
-    data = dataset.Dataset(dataset=ds, size=4, index_by_label=['pos', 'neg'])
+    data = dataset.Dataset(dataset=ds, size=4, label_names=['pos', 'neg'])
     train_data, test_data = data.split(fraction=0.5)
 
     self.assertLen(train_data, 2)
     for i, elem in enumerate(train_data._dataset):
       self.assertTrue((elem.numpy() == np.array([i, 1])).all())
     self.assertEqual(train_data.num_classes, 2)
-    self.assertEqual(train_data.index_by_label, ['pos', 'neg'])
+    self.assertEqual(train_data.label_names, ['pos', 'neg'])
 
     self.assertLen(test_data, 2)
     for i, elem in enumerate(test_data._dataset):
       self.assertTrue((elem.numpy() == np.array([i, 0])).all())
     self.assertEqual(test_data.num_classes, 2)
-    self.assertEqual(test_data.index_by_label, ['pos', 'neg'])
+    self.assertEqual(test_data.label_names, ['pos', 'neg'])
 
   def test_from_folder(self):
     data = dataset.Dataset.from_folder(dirname=self.image_path)
 
     self.assertLen(data, 2)
     self.assertEqual(data.num_classes, 2)
-    self.assertEqual(data.index_by_label, ['daisy', 'tulips'])
+    self.assertEqual(data.label_names, ['daisy', 'tulips'])
     for image, label in data.gen_tf_dataset():
       self.assertTrue(label.numpy() == 1 or label.numpy() == 0)
       if label.numpy() == 0:
@@ -88,19 +88,19 @@ class DatasetTest(tf.test.TestCase):
     self.assertIsInstance(train_data.gen_tf_dataset(), tf.data.Dataset)
     self.assertLen(train_data, 1034)
     self.assertEqual(train_data.num_classes, 3)
-    self.assertEqual(train_data.index_by_label,
+    self.assertEqual(train_data.label_names,
                      ['angular_leaf_spot', 'bean_rust', 'healthy'])
 
     self.assertIsInstance(validation_data.gen_tf_dataset(), tf.data.Dataset)
     self.assertLen(validation_data, 133)
     self.assertEqual(validation_data.num_classes, 3)
-    self.assertEqual(validation_data.index_by_label,
+    self.assertEqual(validation_data.label_names,
                      ['angular_leaf_spot', 'bean_rust', 'healthy'])
 
     self.assertIsInstance(test_data.gen_tf_dataset(), tf.data.Dataset)
     self.assertLen(test_data, 128)
     self.assertEqual(test_data.num_classes, 3)
-    self.assertEqual(test_data.index_by_label,
+    self.assertEqual(test_data.label_names,
                      ['angular_leaf_spot', 'bean_rust', 'healthy'])
 
 

mediapipe/model_maker/python/vision/image_classifier/image_classifier.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 """APIs to train image classifier model."""
 
-from typing import Any, List, Optional
+from typing import List, Optional
 
 import tensorflow as tf
 import tensorflow_hub as hub
@@ -31,18 +31,18 @@ from mediapipe.model_maker.python.vision.image_classifier import train_image_cla
 class ImageClassifier(classifier.Classifier):
   """ImageClassifier for building image classification model."""
 
-  def __init__(self, model_spec: ms.ModelSpec, index_by_label: List[Any],
+  def __init__(self, model_spec: ms.ModelSpec, label_names: List[str],
                hparams: hp.HParams):
     """Initializes ImageClassifier class.
 
     Args:
       model_spec: Specification for the model.
-      index_by_label: A list that maps from index to label class name.
+      label_names: A list of label names for the classes.
       hparams: The hyperparameters for training image classifier.
     """
     super().__init__(
         model_spec=model_spec,
-        index_by_label=index_by_label,
+        label_names=label_names,
         shuffle=hparams.shuffle,
         full_train=hparams.do_fine_tuning)
     self._hparams = hparams
@@ -80,9 +80,7 @@ class ImageClassifier(classifier.Classifier):
 
     spec = ms.SupportedModels.get(model_spec)
     image_classifier = cls(
-        model_spec=spec,
-        index_by_label=train_data.index_by_label,
-        hparams=hparams)
+        model_spec=spec, label_names=train_data.label_names, hparams=hparams)
 
     image_classifier._create_model()