Gan

Ok, so I think that the modifications I made here can be merged ...

Two main changes as compared to the master:

1. Added some layers
2. Modified the way variable name and scope is handled in layers (and in SequenceNetwork as well)

Change 1 should by no means affect what has been done until now (on the master). However, change 2 may require a bit of adaptation, especially when defining architectures and layers ...

@tiago.pereira I'll let you check and then we can discuss

Also, I think that this will be irrelevant when we'll go to tf-slim anyway !

bob/learn/tensorflow/datashuffler/DrGanDisk.py

@@ -25,9 +25,11 @@ class DrGanDisk(Base):
   data:
     Input data
 
-  labels:
-    List of list containing labels 
-    (we consider several labels per example)
+  id_labels:
+    id labels of the retrieved faces. 
+
+  pose_labels:
+    pose labels of the retrieved faces. 
 
   input_shape:
     The shape of the inputs
@@ -110,6 +112,19 @@ class DrGanDisk(Base):
     return self.data_placeholder, self.id_label_placeholder, self.pose_label_placeholder
 
   def load_from_file(self, file_name):
+    """load_from_file(file_name) -> data
+    
+    Load an image from a file, and rescale it if it does not fit the input data format
+    Optionnally, data augmentation is performed.
+
+    **Parameters**
+      file_name: path
+        The name of the (image) file to load.
+
+    **Returns**
+      data: numpy array
+        The image data
+    """
     d = bob.io.base.load(file_name)
 
     # Applying the data augmentation
@@ -132,6 +147,22 @@ class DrGanDisk(Base):
 
 
   def get_batch(self):
+    """get_batch() -> selected_data, selected_pose_labels, selected_id_labels
+
+     This function selects and returns data to be used in a minibatch iteration.
+     Note that returned data is randomly selected in the training set
+    
+    **Returns**
+
+    selected_data:
+      The face images.
+
+    selected_pose_labels:
+      The pose labels
+
+    selected_id_labels:
+      The id labels
+    """
 
     # Shuffling samples
     indexes = numpy.array(range(self.data.shape[0]))
@@ -155,13 +186,30 @@ class DrGanDisk(Base):
 
 
   def get_batch_epoch(self):
+    """get_batch_epoch() -> selected_data, selected_pose_labels, selected_id_labels
+
+    This function selects and returns data to be used in a minibatch iterations.
+    Note that it works in epochs, i.e. all the training data should be seen
+    during one epoch, which consists in several minibatch iterations.
+
+    **Returns**
 
+    selected_data:
+      The face images.
+
+    selected_pose_labels:
+      The pose labels
+
+    selected_id_labels:
+      The id labels
+    """
     # this is done to rebuild the whole list (i.e. at the end of one epoch)
     epoch_done = False
 
     # returned mini-batch
     selected_data = numpy.zeros(shape=self.shape)
-    selected_labels = [] 
+    selected_id_labels = [] 
+    selected_pose_labels = [] 
 
     # if there is not enough available data to fill the current mini-batch
     # add randomly some examples THAT ARE NOT STILL PRESENT in the dataset !
@@ -202,18 +250,22 @@ class DrGanDisk(Base):
       selected_data[i, ...] = self.normalizer(selected_data[i, ...])
         
       # label
-      selected_labels.append(self.labels[self.indexes[current_index]])
+      selected_id_labels.append(self.id_labels[self.indexes[current_index]])
+      selected_pose_labels.append(self.pose_labels[self.indexes[current_index]])
 
       # remove this example from the training set - used once in the epoch
       new_indexes = numpy.delete(self.indexes, current_index)
       self.indexes = new_indexes
 
-    if isinstance(selected_labels, list):
-      selected_labels = numpy.array(selected_labels)
+    if isinstance(selected_id_labels, list):
+      selected_id_labels = numpy.array(selected_id_labels)
+
+    if isinstance(selected_pose_labels, list):
+      selected_pose_labels = numpy.array(selected_pose_labels)
 
     # rebuild whole randomly shuffled training dataset
     if epoch_done:
       self.indexes = numpy.array(range(self.data.shape[0]))
       numpy.random.shuffle(self.indexes)
 
-    return [selected_data.astype("float32"), selected_labels.astype("int64"), epoch_done]
+    return [selected_data.astype("float32"), selected_id_labels.astype("int64"), selected_pose_labels.astype("int64"), epoch_done]