Modified code for Tensorflow 2.4 and scipy>1.0.0

requirements.txt

@@ -1,9 +1,45 @@
-tensorflow==1.7
-scipy
-scikit-learn
-opencv-python
-h5py
-matplotlib
-Pillow
-requests
-psutil
+absl-py==0.11.0
+astunparse==1.6.3
+cachetools==4.2.1
+certifi==2020.12.5
+chardet==4.0.0
+cycler==0.10.0
+flatbuffers==1.12
+gast==0.3.3
+google-auth==1.24.0
+google-auth-oauthlib==0.4.2
+google-pasta==0.2.0
+grpcio==1.32.0
+h5py==2.10.0
+idna==2.10
+joblib==1.0.0
+Keras-Preprocessing==1.1.2
+kiwisolver==1.3.1
+Markdown==3.3.3
+matplotlib==3.3.4
+numpy==1.19.5
+oauthlib==3.1.0
+opencv-python==4.5.1.48
+opt-einsum==3.3.0
+Pillow==8.1.0
+protobuf==3.14.0
+pyasn1==0.4.8
+pyasn1-modules==0.2.8
+pyparsing==2.4.7
+python-dateutil==2.8.1
+requests==2.25.1
+requests-oauthlib==1.3.0
+rsa==4.7
+scikit-learn==0.24.1
+scipy==1.6.0
+six==1.15.0
+tensorboard==2.4.1
+tensorboard-plugin-wit==1.8.0
+tensorflow==2.4.1
+tensorflow-estimator==2.4.0
+termcolor==1.1.0
+threadpoolctl==2.1.0
+typing-extensions==3.7.4.3
+urllib3==1.26.3
+Werkzeug==1.0.1
+wrapt==1.12.1

src/align/align_dataset_mtcnn.py

@@ -35,6 +35,7 @@
 import align.detect_face
 import random
 from time import sleep
+from PIL import Image
 
 def main(args):
     sleep(random.random())
@@ -49,8 +50,8 @@ def main(args):
     print('Creating networks and loading parameters')
 
     with tf.Graph().as_default():
-        gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=args.gpu_memory_fraction)
-        sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False))
+        gpu_options = tf.compat.v1.GPUOptions(per_process_gpu_memory_fraction=args.gpu_memory_fraction)
+        sess = tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(gpu_options=gpu_options, log_device_placement=False))
         with sess.as_default():
             pnet, rnet, onet = align.detect_face.create_mtcnn(sess, None)
 
@@ -80,7 +81,7 @@ def main(args):
                 print(image_path)
                 if not os.path.exists(output_filename):
                     try:
-                        img = misc.imread(image_path)
+                        img = np.array(Image.open(image_path))
                     except (IOError, ValueError, IndexError) as e:
                         errorMessage = '{}: {}'.format(image_path, e)
                         print(errorMessage)
@@ -121,14 +122,15 @@ def main(args):
                                 bb[2] = np.minimum(det[2]+args.margin/2, img_size[1])
                                 bb[3] = np.minimum(det[3]+args.margin/2, img_size[0])
                                 cropped = img[bb[1]:bb[3],bb[0]:bb[2],:]
-                                scaled = misc.imresize(cropped, (args.image_size, args.image_size), interp='bilinear')
+                                cropped = Image.fromarray(np.uint8(cropped))
+                                scaled = cropped.resize((args.image_size, args.image_size), Image.ANTIALIAS)
                                 nrof_successfully_aligned += 1
                                 filename_base, file_extension = os.path.splitext(output_filename)
                                 if args.detect_multiple_faces:
                                     output_filename_n = "{}_{}{}".format(filename_base, i, file_extension)
                                 else:
                                     output_filename_n = "{}{}".format(filename_base, file_extension)
-                                misc.imsave(output_filename_n, scaled)
+                                scaled.save(output_filename_n)
                                 text_file.write('%s %d %d %d %d\n' % (output_filename_n, bb[0], bb[1], bb[2], bb[3]))
                         else:
                             print('Unable to align "%s"' % image_path)

src/align/detect_face.py

@@ -82,13 +82,13 @@ def load(self, data_path, session, ignore_missing=False):
         session: The current TensorFlow session
         ignore_missing: If true, serialized weights for missing layers are ignored.
         """
-        data_dict = np.load(data_path, encoding='latin1').item() #pylint: disable=no-member
+        data_dict = np.load(data_path, encoding='latin1', allow_pickle=True).item() #pylint: disable=no-member
 
         for op_name in data_dict:
-            with tf.variable_scope(op_name, reuse=True):
+            with tf.compat.v1.variable_scope(op_name, reuse=True):
                 for param_name, data in iteritems(data_dict[op_name]):
                     try:
-                        var = tf.get_variable(param_name)
+                        var = tf.compat.v1.get_variable(param_name)
                         session.run(var.assign(data))
                     except ValueError:
                         if not ignore_missing:
@@ -122,7 +122,7 @@ def get_unique_name(self, prefix):
 
     def make_var(self, name, shape):
         """Creates a new TensorFlow variable."""
-        return tf.get_variable(name, shape, trainable=self.trainable)
+        return tf.compat.v1.get_variable(name, shape, trainable=self.trainable)
 
     def validate_padding(self, padding):
         """Verifies that the padding is one of the supported ones."""
@@ -150,7 +150,7 @@ def conv(self,
         assert c_o % group == 0
         # Convolution for a given input and kernel
         convolve = lambda i, k: tf.nn.conv2d(i, k, [1, s_h, s_w, 1], padding=padding)
-        with tf.variable_scope(name) as scope:
+        with tf.compat.v1.variable_scope(name) as scope:
             kernel = self.make_var('weights', shape=[k_h, k_w, c_i // group, c_o])
             # This is the common-case. Convolve the input without any further complications.
             output = convolve(inp, kernel)
@@ -165,7 +165,7 @@ def conv(self,
 
     @layer
     def prelu(self, inp, name):
-        with tf.variable_scope(name):
+        with tf.compat.v1.variable_scope(name):
             i = int(inp.get_shape()[-1])
             alpha = self.make_var('alpha', shape=(i,))
             output = tf.nn.relu(inp) + tf.multiply(alpha, -tf.nn.relu(-inp))
@@ -182,7 +182,7 @@ def max_pool(self, inp, k_h, k_w, s_h, s_w, name, padding='SAME'):
 
     @layer
     def fc(self, inp, num_out, name, relu=True):
-        with tf.variable_scope(name):
+        with tf.compat.v1.variable_scope(name):
             input_shape = inp.get_shape()
             if input_shape.ndims == 4:
                 # The input is spatial. Vectorize it first.
@@ -191,10 +191,10 @@ def fc(self, inp, num_out, name, relu=True):
                     dim *= int(d)
                 feed_in = tf.reshape(inp, [-1, dim])
             else:
-                feed_in, dim = (inp, input_shape[-1].value)
+                feed_in, dim = (inp, input_shape.as_list()[-1])
             weights = self.make_var('weights', shape=[dim, num_out])
             biases = self.make_var('biases', [num_out])
-            op = tf.nn.relu_layer if relu else tf.nn.xw_plus_b
+            op = tf.nn.relu_layer if relu else tf.compat.v1.nn.xw_plus_b
             fc = op(feed_in, weights, biases, name=name)
             return fc
 
@@ -210,7 +210,7 @@ def softmax(self, target, axis, name=None):
         max_axis = tf.reduce_max(target, axis, keepdims=True)
         target_exp = tf.exp(target-max_axis)
         normalize = tf.reduce_sum(target_exp, axis, keepdims=True)
-        softmax = tf.div(target_exp, normalize, name)
+        softmax = tf.compat.v1.div(target_exp, normalize, name)
         return softmax
 
 class PNet(Network):
@@ -277,16 +277,16 @@ def create_mtcnn(sess, model_path):
     if not model_path:
         model_path,_ = os.path.split(os.path.realpath(__file__))
 
-    with tf.variable_scope('pnet'):
-        data = tf.placeholder(tf.float32, (None,None,None,3), 'input')
+    with tf.compat.v1.variable_scope('pnet'):
+        data = tf.compat.v1.placeholder(tf.float32, (None,None,None,3), 'input')
         pnet = PNet({'data':data})
         pnet.load(os.path.join(model_path, 'det1.npy'), sess)
-    with tf.variable_scope('rnet'):
-        data = tf.placeholder(tf.float32, (None,24,24,3), 'input')
+    with tf.compat.v1.variable_scope('rnet'):
+        data = tf.compat.v1.placeholder(tf.float32, (None,24,24,3), 'input')
         rnet = RNet({'data':data})
         rnet.load(os.path.join(model_path, 'det2.npy'), sess)
-    with tf.variable_scope('onet'):
-        data = tf.placeholder(tf.float32, (None,48,48,3), 'input')
+    with tf.compat.v1.variable_scope('onet'):
+        data = tf.compat.v1.placeholder(tf.float32, (None,48,48,3), 'input')
         onet = ONet({'data':data})
         onet.load(os.path.join(model_path, 'det3.npy'), sess)
 

src/classifier.py

@@ -40,7 +40,7 @@ def main(args):
 
     with tf.Graph().as_default():
 
-        with tf.Session() as sess:
+        with tf.compat.v1.Session() as sess:
 
             np.random.seed(seed=args.seed)
 
@@ -69,9 +69,9 @@ def main(args):
             facenet.load_model(args.model)
 
             # Get input and output tensors
-            images_placeholder = tf.get_default_graph().get_tensor_by_name("input:0")
-            embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0")
-            phase_train_placeholder = tf.get_default_graph().get_tensor_by_name("phase_train:0")
+            images_placeholder = tf.compat.v1.get_default_graph().get_tensor_by_name("input:0")
+            embeddings = tf.compat.v1.get_default_graph().get_tensor_by_name("embeddings:0")
+            phase_train_placeholder = tf.compat.v1.get_default_graph().get_tensor_by_name("phase_train:0")
             embedding_size = embeddings.get_shape()[1]
 
             # Run forward pass to calculate embeddings

src/facenet.py

@@ -40,6 +40,7 @@
 from tensorflow.python.platform import gfile
 import math
 from six import iteritems
+from PIL import Image
 
 def triplet_loss(anchor, positive, negative, alpha):
     """Calculate the triplet loss according to the FaceNet paper
@@ -244,7 +245,7 @@ def load_data(image_paths, do_random_crop, do_random_flip, image_size, do_prewhi
     nrof_samples = len(image_paths)
     images = np.zeros((nrof_samples, image_size, image_size, 3))
     for i in range(nrof_samples):
-        img = misc.imread(image_paths[i])
+        img = np.array(Image.open(image_paths[i]))
         if img.ndim == 2:
             img = to_rgb(img)
         if do_prewhiten:
@@ -368,7 +369,7 @@ def load_model(model, input_map=None):
     if (os.path.isfile(model_exp)):
         print('Model filename: %s' % model_exp)
         with gfile.FastGFile(model_exp,'rb') as f:
-            graph_def = tf.GraphDef()
+            graph_def = tf.compat.v1.GraphDef()
             graph_def.ParseFromString(f.read())
             tf.import_graph_def(graph_def, input_map=input_map, name='')
     else: