Add support for customizing gesture recognizer layers

PiperOrigin-RevId: 496456160

mediapipe/model_maker/python/vision/gesture_recognizer/gesture_recognizer.py

@@ -173,15 +173,20 @@ class GestureRecognizer(classifier.Classifier):
         batch_size=None,
         dtype=tf.float32,
         name='hand_embedding')
-
-    x = tf.keras.layers.BatchNormalization()(inputs)
-    x = tf.keras.layers.ReLU()(x)
+    x = inputs
     dropout_rate = self._model_options.dropout_rate
-    x = tf.keras.layers.Dropout(rate=dropout_rate, name='dropout')(x)
+    for i, width in enumerate(self._model_options.layer_widths):
+      x = tf.keras.layers.BatchNormalization()(x)
+      x = tf.keras.layers.ReLU()(x)
+      x = tf.keras.layers.Dropout(rate=dropout_rate)(x)
+      x = tf.keras.layers.Dense(width, name=f'custom_gesture_recognizer_{i}')(x)
+    x = tf.keras.layers.BatchNormalization()(x)
+    x = tf.keras.layers.ReLU()(x)
+    x = tf.keras.layers.Dropout(rate=dropout_rate)(x)
     outputs = tf.keras.layers.Dense(
         self._num_classes,
         activation='softmax',
-        name='custom_gesture_recognizer')(
+        name='custom_gesture_recognizer_out')(
             x)
 
     self._model = tf.keras.Model(inputs=inputs, outputs=outputs)

mediapipe/model_maker/python/vision/gesture_recognizer/gesture_recognizer_test.py

@@ -60,6 +60,32 @@ class GestureRecognizerTest(tf.test.TestCase):
 
     self._test_accuracy(model)
 
+  @unittest_mock.patch.object(
+      tf.keras.layers, 'Dense', wraps=tf.keras.layers.Dense)
+  def test_gesture_recognizer_model_layer_widths(self, mock_dense):
+    layer_widths = [64, 32]
+    model_options = gesture_recognizer.ModelOptions(layer_widths=layer_widths)
+    hparams = gesture_recognizer.HParams(
+        export_dir=tempfile.mkdtemp(), epochs=2)
+    gesture_recognizer_options = gesture_recognizer.GestureRecognizerOptions(
+        model_options=model_options, hparams=hparams)
+    model = gesture_recognizer.GestureRecognizer.create(
+        train_data=self._train_data,
+        validation_data=self._validation_data,
+        options=gesture_recognizer_options)
+    expected_calls = [
+        unittest_mock.call(w, name=f'custom_gesture_recognizer_{i}')
+        for i, w in enumerate(layer_widths)
+    ]
+    expected_calls.append(
+        unittest_mock.call(
+            len(self._train_data.label_names),
+            activation='softmax',
+            name='custom_gesture_recognizer_out'))
+    self.assertLen(mock_dense.call_args_list, len(expected_calls))
+    mock_dense.assert_has_calls(expected_calls)
+    self._test_accuracy(model)
+
   def test_export_gesture_recognizer_model(self):
     model_options = gesture_recognizer.ModelOptions()
     hparams = gesture_recognizer.HParams(

mediapipe/model_maker/python/vision/gesture_recognizer/model_options.py

@@ -14,6 +14,7 @@
 """Configurable model options for gesture recognizer models."""
 
 import dataclasses
+from typing import List
 
 
 @dataclasses.dataclass
@@ -23,5 +24,10 @@ class GestureRecognizerModelOptions:
   Attributes:
     dropout_rate: The fraction of the input units to drop, used in dropout
       layer.
+    layer_widths: A list of hidden layer widths for the gesture model. Each
+      element in the list will create a new hidden layer with the specified
+      width. The hidden layers are separated with BatchNorm, Dropout, and ReLU.
+      Defaults to an empty list(no hidden layers).
   """
   dropout_rate: float = 0.05
+  layer_widths: List[int] = dataclasses.field(default_factory=list)