working version of tf classifier

bob/pad/voice/algorithm/tensorflow_algorithm.py

@@ -6,11 +6,9 @@
 from bob.pad.base.algorithm import Algorithm
 import numpy
 
-import bob.io.base
 
 # import tensorflow as tf
 
-import os
 import logging
 
 logger = logging.getLogger("bob.pad.voice")
@@ -53,18 +51,18 @@ class TensorflowAlgo(Algorithm):
 
         # Creating an LSTM network
         graph = lstm(inputs, lstm_cell_size, num_time_steps=num_time_steps, batch_size=batch_size,
-                     output_activation_size=lstm_cell_size, scope='lstm',
-                     weights_initializer=initializer, activation=tf.nn.relu, reuse=reuse)
+                     output_activation_size=num_classes, scope='lstm',
+                     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)
+#        graph = slim.fully_connected(graph, num_classes, activation_fn=None, scope='fc1',
+#                                     weights_initializer=initializer, reuse=reuse)
 
         return graph
 
     def _check_feature(self, feature):
         """Checks that the features are appropriate."""
-        if not isinstance(feature, numpy.ndarray) or feature.ndim != 1 or feature.dtype != numpy.float32:
+        if not isinstance(feature, numpy.ndarray) or feature.ndim != 2 or feature.dtype != numpy.float32:
             raise ValueError("The given feature is not appropriate", feature)
         return True
 
@@ -72,13 +70,16 @@ class TensorflowAlgo(Algorithm):
         import tensorflow as tf
         if self.session is None:
             self.session = tf.Session()
-        data_pl = tf.placeholder(tf.float32, shape=(None, 28, 28, 1))
-        graph = self.simple_lstm_network(data_pl, reuse=False)
+        data_pl = tf.placeholder(tf.float32, shape=(None, 60, 102))
+        graph = self.simple_lstm_network(data_pl, batch_size=1,
+                                         lstm_cell_size=60, num_time_steps=60,
+                                         num_classes=2, reuse=False)
 
         self.session.run(tf.global_variables_initializer())
-        saver = tf.train.import_meta_graph(projector_file + ".meta", clear_devices=True)
+        saver = tf.train.Saver()
+#        saver = tf.train.import_meta_graph(projector_file + ".meta", clear_devices=True)
         saver.restore(self.session, projector_file)
-        return graph, data_pl
+        return tf.nn.softmax(graph, name="softmax"), data_pl
 
     def load_projector(self, projector_file):
         logger.info("Loading pretrained model from {0}".format(projector_file))
@@ -87,21 +88,27 @@ class TensorflowAlgo(Algorithm):
 
     def project_feature(self, feature):
 
-        logger.debug(" .... Projecting %d features vector" % feature.shape[0])
+        logger.info(" .... Projecting %d features vector" % feature.shape[0])
         from bob.learn.tensorflow.datashuffler import DiskAudio
         if not self.data_reader:
-            self.data_reader = DiskAudio([0], [0])
+            self.data_reader = DiskAudio([0], [0], [1, 60, 102])
         # frames, labels = self.data_reader.extract_frames_from_wav(feature, 0)
-        frames, labels = self.data_reader.split_features_in_windows(feature, 0, )
-        frames = numpy.asarray(frames)
-        logger.debug(" .... And %d frames are extracted to pass into DNN model" % frames.shape[0])
-        frames = numpy.reshape(frames, (frames.shape[0], -1, 1))
-
-        if self.session is not None:
-            forward_output = self.session.run(self.dnn_model, feed_dict={self.data_placeholder: frames})
-        else:
-            raise ValueError("Tensorflow session was not initialized, so cannot project on DNN model!")
-        return forward_output
+        frames, labels = self.data_reader.split_features_in_windows(feature, 0, 60)
+#        frames = numpy.asarray(frames)
+#        logger.info(" .... And frames of shape {0} are extracted to pass into DNN model".format(frames.shape))
+        projections = numpy.zeros((len(frames), 2), dtype=numpy.float32)
+        for frame, i in zip(frames, range(len(frames))):
+            frame = numpy.reshape(frame, ([1] + list(frames[0].shape)))
+#        frames = numpy.reshape(frames, (frames.shape[0], -1, 1))
+            logger.info(" .... projecting frame of shape {0} onto DNN model".format(frame.shape))
+
+            if self.session is not None:
+                forward_output = self.session.run(self.dnn_model, feed_dict={self.data_placeholder: frame})
+                projections[i]=forward_output[0]
+            else:
+                raise ValueError("Tensorflow session was not initialized, so cannot project on DNN model!")
+        logger.info("Projected scores {0}".format(projections))
+        return numpy.asarray(projections, dtype=numpy.float32)
 
     def project(self, feature):
         """project(feature) -> projected