Support tensorflow 2

.vscode/settings.json

@@ -0,0 +1,3 @@
+{
+    "python.pythonPath": "C:\\Users\\Den\\Miniconda3\\envs\\dl\\python.exe"
+}

realtime_demo.py

@@ -7,7 +7,7 @@
 import numpy as np
 import argparse
 from wide_resnet import WideResNet
-from keras.utils.data_utils import get_file
+from tensorflow.keras.utils import get_file
 
 class FaceCV(object):
     """

requirements.txt

@@ -1,4 +1,45 @@
-numpy==1.13.3+mkl
-Keras==2.0.8
-opencv==1.0.1
-opencv-python==3.3.0+contrib
+absl-py==0.9.0
+astor==0.8.1
+astroid==2.2.5
+autopep8==1.4.4
+cachetools==4.0.0
+certifi==2019.11.28
+chardet==3.0.4
+colorama==0.4.1
+gast==0.2.2
+google-auth==1.11.2
+google-auth-oauthlib==0.4.1
+google-pasta==0.1.8
+grpcio==1.27.2
+h5py==2.10.0
+idna==2.9
+isort==4.3.21
+Keras-Applications==1.0.8
+Keras-Preprocessing==1.1.0
+lazy-object-proxy==1.4.2
+Markdown==3.2.1
+mccabe==0.6.1
+numpy==1.18.1
+oauthlib==3.1.0
+opencv-python==4.2.0.32
+opt-einsum==3.2.0
+protobuf==3.11.3
+pyasn1==0.4.8
+pyasn1-modules==0.2.8
+pycodestyle==2.5.0
+pylint==2.3.1
+requests==2.23.0
+requests-oauthlib==1.3.0
+rope==0.14.0
+rsa==4.0
+scipy==1.4.1
+six==1.12.0
+tensorboard==2.1.1
+tensorflow==2.1.0
+tensorflow-estimator==2.1.0
+termcolor==1.1.0
+typed-ast==1.4.0
+urllib3==1.25.8
+Werkzeug==1.0.0
+wincertstore==0.2
+wrapt==1.11.2

wide_resnet.py

@@ -3,12 +3,10 @@
 import logging
 import sys
 import numpy as np
-from keras.models import Model
-from keras.layers import Input, Activation, add, Dense, Flatten, Dropout
-from keras.layers.convolutional import Conv2D, AveragePooling2D
-from keras.layers.normalization import BatchNormalization
-from keras.regularizers import l2
-from keras import backend as K
+from tensorflow.keras.models import Model
+from tensorflow.keras.layers import Input, Activation, add, Dense, Flatten, Dropout, Conv2D, AveragePooling2D, BatchNormalization
+from tensorflow.keras.regularizers import l2
+from tensorflow.keras import backend as K
 
 sys.setrecursionlimit(2 ** 20)
 np.random.seed(2 ** 10)
@@ -23,7 +21,8 @@ def __init__(self, image_size, depth=16, k=8):
         self._use_bias = False
         self._weight_init = "he_normal"
 
-        if K.image_dim_ordering() == "th":
+        # if K.image_dim_ordering() == "th":
+        if K.image_data_format() == "th":
             logging.debug("image_dim_ordering = 'th'")
             self._channel_axis = 1
             self._input_shape = (3, image_size, image_size)