upgraded voice evaluation with TF

bob/pad/voice/algorithm/LSTMEval.py → bob/pad/voice/algorithm/TensorflowEval.py

@@ -14,12 +14,13 @@ import logging
 logger = logging.getLogger("bob.pad.voice")
 
 
-class LSTMEval(Algorithm):
+class TensorflowEval(Algorithm):
     """This class is for evaluating data stored in tensorflow tfrecord format using a pre-trained LSTM model."""
 
     def __init__(self,
+                 architecture_name="mlp",
                  input_shape=[200, 81],  # [temporal_length, feature_size]
-                 lstm_network_size=60,  # the output size of LSTM cell
+                 network_size=60,  # the output size of LSTM cell
                  normalization_file=None,  # file with normalization parameters from train set
                  **kwargs):
         """Generates a test value that is read and written"""
@@ -32,17 +33,19 @@ class LSTMEval(Algorithm):
             **kwargs
         )
 
+        self.architecture_name = architecture_name
         self.input_shape = input_shape
         self.num_time_steps = input_shape[0]
-        self.lstm_network_size = lstm_network_size
+        self.network_size = network_size
         self.data_std = None
 #        import ipdb
 #        ipdb.set_trace()
         features_length = input_shape[1]
         if normalization_file and os.path.exists(normalization_file):
+            logger.info("Loading normalization file '%s' " % normalization_file)
             npzfile = numpy.load(normalization_file)
             self.data_mean = npzfile['data_mean']
-            self.data_std = npzfile['data_std']
+            self.data_std = numpy.array(npzfile['data_std'])
             if not self.data_std.shape:  # if std was saved as scalar
                 self.data_std = numpy.ones(features_length)
             # if self.data_mean.shape[0] > input_shape[0]:
@@ -52,6 +55,7 @@ class LSTMEval(Algorithm):
             #     self.data_std = self.data_std[:input_shape[0]]
             # self.data_std = numpy.reshape(self.data_std, input_shape)
         else:
+            logger.warn("Normalization file '%s' does not exist!" % normalization_file)
             self.data_mean = 0
             self.data_std = 1
 
@@ -60,29 +64,29 @@ class LSTMEval(Algorithm):
         self.dnn_model = None
         self.data_placeholder = None
 
-    def simple_lstm_network(self, train_data_shuffler, batch_size=10, lstm_cell_size=64,
-                            num_time_steps=28, num_classes=10, seed=10, reuse=False):
-        import tensorflow as tf
-        from bob.learn.tensorflow.layers import lstm
-        slim = tf.contrib.slim
-
-        if isinstance(train_data_shuffler, tf.Tensor):
-            inputs = train_data_shuffler
-        else:
-            inputs = train_data_shuffler("data", from_queue=False)
-
-        initializer = tf.contrib.layers.xavier_initializer(seed=seed)
-
-        # Creating an LSTM network
-        graph = lstm(inputs, lstm_cell_size, num_time_steps=num_time_steps, batch_size=batch_size,
-                     output_activation_size=num_classes, scope='lstm', name='sync_cell',
-                     weights_initializer=initializer, activation=tf.nn.sigmoid, reuse=reuse)
-
-        # fully connect the LSTM output to the classes
-        graph = slim.fully_connected(graph, num_classes, activation_fn=None, scope='fc1',
-                                     weights_initializer=initializer, reuse=reuse)
-
-        return graph
+    # def simple_lstm_network(self, train_data_shuffler, batch_size=10, lstm_cell_size=64,
+    #                         num_time_steps=28, num_classes=10, seed=10, reuse=False):
+    #     import tensorflow as tf
+    #     from bob.learn.tensorflow.layers import lstm
+    #     slim = tf.contrib.slim
+    #
+    #     if isinstance(train_data_shuffler, tf.Tensor):
+    #         inputs = train_data_shuffler
+    #     else:
+    #         inputs = train_data_shuffler("data", from_queue=False)
+    #
+    #     initializer = tf.contrib.layers.xavier_initializer(seed=seed)
+    #
+    #     # Creating an LSTM network
+    #     graph = lstm(inputs, lstm_cell_size, num_time_steps=num_time_steps, batch_size=batch_size,
+    #                  output_activation_size=num_classes, scope='lstm', name='sync_cell',
+    #                  weights_initializer=initializer, activation=tf.nn.sigmoid, reuse=reuse)
+    #
+    #     # fully connect the LSTM output to the classes
+    #     graph = slim.fully_connected(graph, num_classes, activation_fn=None, scope='fc1',
+    #                                  weights_initializer=initializer, reuse=reuse)
+    #
+    #     return graph
 
     def normalize_data(self, features):
         mean = numpy.mean(features, axis=0)
@@ -97,16 +101,34 @@ class LSTMEval(Algorithm):
 
     def restore_trained_model(self, projector_file):
         import tensorflow as tf
-        from bob.learn.tensorflow.network import LightCNN9
 
         if self.session is None:
             self.session = tf.Session()
         data_pl = tf.placeholder(tf.float32, shape=(None,) + tuple(self.input_shape), name="data")
-#        network = LightCNN9(n_classes=2, device="/cpu:0")
-#        graph = network(data_pl)
-        graph = self.simple_lstm_network(data_pl, batch_size=1,
-                                         lstm_cell_size=self.lstm_network_size, num_time_steps=self.num_time_steps,
-                                         num_classes=2, reuse=False)
+
+        # create an empty graph of the correct architecture but with needed batch_size==1
+        if self.architecture_name == 'lstm':
+            from bob.learn.tensorflow.network import simple_lstm_network
+            graph = simple_lstm_network(data_pl, batch_size=1,
+                                        lstm_cell_size=self.network_size, num_time_steps=self.num_time_steps,
+                                        num_classes=2, reuse=False)
+
+        elif self.architecture_name == 'mlp':
+            from bob.learn.tensorflow.network import mlp_network
+            graph = mlp_network(data_pl,
+                                hidden_layer_size=self.network_size,
+                                num_time_steps=self.num_time_steps,
+                                num_classes=2, reuse=False)
+        elif self.architecture_name == 'simplecnn':
+            from bob.learn.tensorflow.network import simple2Dcnn_network
+            graph = simple2Dcnn_network(data_pl,
+                                        num_classes=2, reuse=False)
+        elif self.architecture_name == 'lightcnn':
+            from bob.learn.tensorflow.network import LightCNN9
+            network = LightCNN9(n_classes=2, device="/cpu:0")
+            graph = network(data_pl, reuse=False)
+        else:
+            return None
 
         self.session.run(tf.global_variables_initializer())
         saver = tf.train.Saver()
@@ -201,4 +223,4 @@ class LSTMEval(Algorithm):
         return [toscore[0]]
 
 
-algorithm = LSTMEval()
+algorithm = TensorflowEval()

bob/pad/voice/algorithm/__init__.py

 from .GMM import GMM
 from .LogRegr import LogRegr
-from .LSTMEval import LSTMEval
+from .TensorflowEval import TensorflowEval
 
 # gets sphinx autodoc done right - don't remove it
 def __appropriate__(*args):
@@ -19,6 +19,6 @@ def __appropriate__(*args):
 __appropriate__(
     GMM,
     LogRegr,
-    LSTMEval,
+    TensorflowEval,
     )
 __all__ = [_ for _ in dir() if not _.startswith('_')]

develop.cfg

@@ -13,6 +13,7 @@ eggs =  bob.pad.voice
         bob.pad.base
         bob.db.asvspoof
         bob.db.avspoof
+        bob.learn.tensorflow
         gridtk
 
 extensions = bob.buildout
@@ -26,6 +27,8 @@ develop = src/bob.bio.spear
          src/bob.db.avspoof
          src/bob.bio.base
          src/bob.pad.base
+         bob.learn.tensorflow
+         /remote/idiap.svm/home.active/pkorshunov/src/bob.learn.tensorflow
          .
          
 ; options for bob.buildout
@@ -41,6 +44,7 @@ bob.db.asvspoof = git git@gitlab.idiap.ch:bob/bob.db.asvspoof.git
 bob.bio.base = git git@gitlab.idiap.ch:bob/bob.bio.base.git
 bob.pad.base = git git@gitlab.idiap.ch:bob/bob.pad.base.git
 bob.bio.spear = git git@gitlab.idiap.ch:bob/bob.bio.spear.git
+bob.learn.tensorflow = fs bob.learn.tensorflow full-path=/remote/idiap.svm/home.active/pkorshunov/src/bob.learn.tensorflow
 
 [scripts]
 recipe = bob.buildout:scripts

setup.py

@@ -116,7 +116,7 @@ setup(
         ],
 
         'bob.pad.algorithm': [
-            'tensorflow  = bob.pad.voice.algorithm.LSTMEval:algorithm',
+            'tensorflow  = bob.pad.voice.algorithm.TensorflowEval:algorithm',
             'dummy-algo  = bob.pad.voice.algorithm.dummy:algorithm',
             # compute scores based on different energy bands
             'logregr  = bob.pad.voice.algorithm.LogRegr:algorithm',