Feat/batch predict age and gender

deepface/models/demography/Age.py

@@ -1,3 +1,6 @@
+# stdlib dependencies
+from typing import List
+
 # 3rd party dependencies
 import numpy as np
 
@@ -43,6 +46,29 @@ def predict(self, img: np.ndarray) -> np.float64:
         age_predictions = self.model(img, training=False).numpy()[0, :]
         return find_apparent_age(age_predictions)
 
+    def predicts(self, imgs: List[np.ndarray]) -> np.ndarray:
+        """
+        Predict apparent ages of multiple faces
+        Args:
+            imgs (List[np.ndarray]): (n, 224, 224, 3)
+        Returns:
+            apparent_ages (np.ndarray): (n,)
+        """
+        # Convert list to numpy array
+        imgs_:np.ndarray = np.array(imgs)
+        # Remove batch dimension if exists
+        imgs_ = imgs_.squeeze()
+        # Check if the input is a single image
+        if len(imgs_.shape) == 3:
+            # Add batch dimension if not exists
+            imgs_ = np.expand_dims(imgs_, axis=0)
+        # Batch prediction
+        age_predictions = self.model.predict_on_batch(imgs_)
+        apparent_ages = np.array(
+            [find_apparent_age(age_prediction) for age_prediction in age_predictions]
+        )
+        return apparent_ages
+
 
 def load_model(
     url=WEIGHTS_URL,
@@ -65,7 +91,7 @@ def load_model(
 
     # --------------------------
 
-    age_model = Model(inputs=model.input, outputs=base_model_output)
+    age_model = Model(inputs=model.inputs, outputs=base_model_output)
 
     # --------------------------
 

deepface/models/demography/Gender.py

@@ -1,3 +1,6 @@
+# stdlib dependencies
+from typing import List
+
 # 3rd party dependencies
 import numpy as np
 
@@ -42,6 +45,24 @@ def predict(self, img: np.ndarray) -> np.ndarray:
         # return self.model.predict(img, verbose=0)[0, :]
         return self.model(img, training=False).numpy()[0, :]
 
+    def predicts(self, imgs: List[np.ndarray]) -> np.ndarray:
+        """
+        Predict apparent ages of multiple faces
+        Args:
+            imgs (List[np.ndarray]): (n, 224, 224, 3)
+        Returns:
+            apparent_ages (np.ndarray): (n,)
+        """
+        # Convert list to numpy array
+        imgs_:np.ndarray = np.array(imgs)
+        # Remove redundant dimensions
+        imgs_ = imgs_.squeeze()
+        # Check if the input is a single image
+        if len(imgs_.shape) == 3:
+            # Add batch dimension
+            imgs_ = np.expand_dims(imgs_, axis=0)
+        return self.model.predict_on_batch(imgs_)
+
 
 def load_model(
     url=WEIGHTS_URL,
@@ -64,7 +85,7 @@ def load_model(
 
     # --------------------------
 
-    gender_model = Model(inputs=model.input, outputs=base_model_output)
+    gender_model = Model(inputs=model.inputs, outputs=base_model_output)
 
     # --------------------------