tf 2 compatible

deepface/basemodels/DeepID.py

@@ -1,11 +1,12 @@
 import os
 from pathlib import Path
 import gdown
-import keras
-from keras.models import Model
-from keras.layers import Conv2D, Activation, Input, Add, MaxPooling2D, Flatten, Dense, Dropout
 import zipfile
 
+from tensorflow import keras
+from tensorflow.keras.models import Model
+from tensorflow.keras.layers import Conv2D, Activation, Input, Add, MaxPooling2D, Flatten, Dense, Dropout
+
 #-------------------------------------
 
 def loadModel(url = 'https://drive.google.com/uc?id=1uRLtBCTQQAvHJ_KVrdbRJiCKxU8m5q2J'):
@@ -14,15 +15,15 @@ def loadModel(url = 'https://drive.google.com/uc?id=1uRLtBCTQQAvHJ_KVrdbRJiCKxU8
 	
 	x = Conv2D(20, (4, 4), name='Conv1', activation='relu', input_shape=(55, 47, 3))(myInput)
 	x = MaxPooling2D(pool_size=2, strides=2, name='Pool1')(x)
-	x = Dropout(rate=1, name='D1')(x)
+	x = Dropout(rate=0.99, name='D1')(x)
 	
 	x = Conv2D(40, (3, 3), name='Conv2', activation='relu')(x)
 	x = MaxPooling2D(pool_size=2, strides=2, name='Pool2')(x)
-	x = Dropout(rate=1, name='D2')(x)
+	x = Dropout(rate=0.99, name='D2')(x)
 	
 	x = Conv2D(60, (3, 3), name='Conv3', activation='relu')(x)
 	x = MaxPooling2D(pool_size=2, strides=2, name='Pool3')(x)
-	x = Dropout(rate=1, name='D3')(x)
+	x = Dropout(rate=0.99, name='D3')(x)
 	
 	x1 = Flatten()(x)
 	fc11 = Dense(160, name = 'fc11')(x1)

deepface/basemodels/Facenet.py

@@ -3,19 +3,19 @@ from pathlib import Path
 import gdown
 from functools import partial
 
-from keras.models import Model
+from tensorflow.keras.models import Model
-from keras.layers import Activation
+from tensorflow.keras.layers import Activation
-from keras.layers import BatchNormalization
+from tensorflow.keras.layers import BatchNormalization
-from keras.layers import Concatenate
+from tensorflow.keras.layers import Concatenate
-from keras.layers import Conv2D
+from tensorflow.keras.layers import Conv2D
-from keras.layers import Dense
+from tensorflow.keras.layers import Dense
-from keras.layers import Dropout
+from tensorflow.keras.layers import Dropout
-from keras.layers import GlobalAveragePooling2D
+from tensorflow.keras.layers import GlobalAveragePooling2D
-from keras.layers import Input
+from tensorflow.keras.layers import Input
-from keras.layers import Lambda
+from tensorflow.keras.layers import Lambda
-from keras.layers import MaxPooling2D
+from tensorflow.keras.layers import MaxPooling2D
-from keras.layers import add
+from tensorflow.keras.layers import add
-from keras import backend as K
+from tensorflow.keras import backend as K
 
 def scaling(x, scale):
 	return x * scale

deepface/basemodels/FbDeepFace.py

@@ -1,11 +1,12 @@
 import os
 from pathlib import Path
 import gdown
-import keras
-from keras.models import Model, Sequential
-from keras.layers import Convolution2D, LocallyConnected2D, MaxPooling2D, Flatten, Dense, Dropout
 import zipfile
 
+from tensorflow import keras
+from tensorflow.keras.models import Model, Sequential
+from tensorflow.keras.layers import Convolution2D, LocallyConnected2D, MaxPooling2D, Flatten, Dense, Dropout
+
 #-------------------------------------
 
 def loadModel(url = 'https://github.com/swghosh/DeepFace/releases/download/weights-vggface2-2d-aligned/VGGFace2_DeepFace_weights_val-0.9034.h5.zip'):

deepface/basemodels/OpenFace.py

@@ -3,15 +3,13 @@ from pathlib import Path
 import gdown
 
 import tensorflow as tf
-import keras
+from tensorflow import keras
-from keras.models import Model, Sequential
+from tensorflow.keras.models import Model, Sequential
-from keras.layers import Conv2D, ZeroPadding2D, Activation, Input, concatenate
+from tensorflow.keras.layers import Conv2D, ZeroPadding2D, Activation, Input, concatenate
-from keras.layers.core import Dense, Activation, Lambda, Flatten
+from tensorflow.keras.layers import Dense, Activation, Lambda, Flatten, BatchNormalization
-from keras.layers.pooling import MaxPooling2D, AveragePooling2D
+from tensorflow.keras.layers import MaxPooling2D, AveragePooling2D
-from keras.layers.merge import Concatenate
+from tensorflow.keras.models import load_model
-from keras.layers.normalization import BatchNormalization
+from tensorflow.keras import backend as K
-from keras.models import load_model
-from keras import backend as K
 
 #---------------------------------------
 

deepface/basemodels/VGGFace.py

@@ -1,8 +1,16 @@
 import os
 from pathlib import Path
+import gdown
+
+import tensorflow as tf
+
+if int(tf.__version__.split(".")[0]) == 1:
 	from keras.models import Model, Sequential
 	from keras.layers import Input, Convolution2D, ZeroPadding2D, MaxPooling2D, Flatten, Dense, Dropout, Activation
-import gdown
+else:
+	from tensorflow import keras
+	from tensorflow.keras.models import Model, Sequential
+	from tensorflow.keras.layers import Input, Convolution2D, ZeroPadding2D, MaxPooling2D, Flatten, Dense, Dropout, Activation
 	
 #---------------------------------------
 

deepface/commons/functions.py

@@ -1,9 +1,6 @@
 import os
 import numpy as np
 import pandas as pd
-from keras.preprocessing.image import load_img, save_img, img_to_array
-from keras.applications.imagenet_utils import preprocess_input
-from keras.preprocessing import image
 import cv2
 from pathlib import Path
 import gdown
@@ -14,12 +11,26 @@ import copy
 import base64
 import multiprocessing
 import subprocess
-import tensorflow as tf
-import keras
 import bz2
 from deepface.commons import distance
 from mtcnn import MTCNN #0.1.0
 
+import tensorflow as tf
+tf_version = int(tf.__version__.split(".")[0])
+
+if tf_version == 1:
+	import keras
+	from keras.preprocessing.image import load_img, save_img, img_to_array
+	from keras.applications.imagenet_utils import preprocess_input
+	from keras.preprocessing import image
+elif tf_version == 2:
+	from tensorflow import keras
+	from tensorflow.keras.preprocessing.image import load_img, save_img, img_to_array
+	from tensorflow.keras.applications.imagenet_utils import preprocess_input
+	from tensorflow.keras.preprocessing import image
+
+#--------------------------------------------------
+
 def initialize_input(img1_path, img2_path = None):
 	
 	if type(img1_path) == list:

deepface/extendedmodels/Age.py

@@ -1,12 +1,20 @@
-#from basemodels import VGGFace
 from deepface.basemodels import VGGFace
-
 import os
 from pathlib import Path
 import gdown
 import numpy as np
+
+import tensorflow as tf
+tf_version = int(tf.__version__.split(".")[0])
+
+if tf_version == 1:
+	import keras
 	from keras.models import Model, Sequential
 	from keras.layers import Convolution2D, Flatten, Activation	
+elif tf_version == 2:
+	from tensorflow import keras
+	from tensorflow.keras.models import Model, Sequential
+	from tensorflow.keras.layers import Convolution2D, Flatten, Activation
 
 def loadModel():
 	

deepface/extendedmodels/Emotion.py

@@ -1,9 +1,19 @@
 import os
 import gdown
 from pathlib import Path
+import zipfile
+
+import tensorflow as tf
+tf_version = int(tf.__version__.split(".")[0])
+
+if tf_version == 1:
+	import keras
 	from keras.models import Model, Sequential
 	from keras.layers import Conv2D, MaxPooling2D, AveragePooling2D, Flatten, Dense, Dropout
-import zipfile
+elif tf_version == 2:
+	from tensorflow import keras
+	from tensorflow.keras.models import Model, Sequential
+	from tensorflow.keras.layers import Conv2D, MaxPooling2D, AveragePooling2D, Flatten, Dense, Dropout
 	
 def loadModel():
 	

deepface/extendedmodels/Gender.py

@@ -1,12 +1,18 @@
-#from basemodels import VGGFace
 from deepface.basemodels import VGGFace
-
 import os
 from pathlib import Path
 import gdown
 import numpy as np
+
+import tensorflow as tf
+tf_version = int(tf.__version__.split(".")[0])
+
+if tf_version == 1:
 	from keras.models import Model, Sequential
 	from keras.layers import Convolution2D, Flatten, Activation
+elif tf_version == 2:
+	from tensorflow.keras.models import Model, Sequential
+	from tensorflow.keras.layers import Convolution2D, Flatten, Activation
 
 def loadModel():
 	

deepface/extendedmodels/Race.py

@@ -1,13 +1,20 @@
-#from basemodels import VGGFace
 from deepface.basemodels import VGGFace
 
 import os
 from pathlib import Path
 import gdown
 import numpy as np
+import zipfile
+
+import tensorflow as tf
+tf_version = int(tf.__version__.split(".")[0])
+
+if tf_version == 1:
 	from keras.models import Model, Sequential
 	from keras.layers import Convolution2D, Flatten, Activation
-import zipfile
+elif tf_version == 2:
+	from tensorflow.keras.models import Model, Sequential
+	from tensorflow.keras.layers import Convolution2D, Flatten, Activation
 
 def loadModel():
 	

requirements.txt

@@ -9,3 +9,4 @@ keras>=2.2.0
 Flask>=1.1.2
 mtcnn>=0.1.0
 lightgbm>=2.3.1
+dlib>=19.20.0

tests/unit_tests.py

@@ -1,10 +1,20 @@
+import warnings
+warnings.filterwarnings("ignore")
+
+import os
+#os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
 from deepface import DeepFace
 from deepface.commons import functions
 import json
 import time
 
-import os
+import tensorflow as tf
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
+
+#-----------------------------------------
+
+tf_version = int(tf.__version__.split(".")[0])
 
 #-----------------------------------------
 
@@ -136,7 +146,8 @@ dataset = [
 ]
 
 models = ['VGG-Face', 'Facenet', 'OpenFace', 'DeepFace', 'DeepID', 'Dlib']
-metrics = ['cosine', 'euclidean', 'euclidean_l2']
+#metrics = ['cosine', 'euclidean', 'euclidean_l2']
+metrics = ['cosine']
 
 passed_tests = 0; test_cases = 0