[doc] add the need for already preprocessed face images in the GANs doc

doc/guide_conditionalgan.rst

@@ -48,10 +48,10 @@ network definition:
   from bob.learn.pytorch.datasets import Normalize
   import torchvision.transforms as transforms
 
-  dataset = MultiPIEDataset(root_dir='/idiap/temp/heusch/data/multipie-cropped-64x64', 
+  dataset = MultiPIEDataset(root_dir='path/to/multipie/data', 
                                    frontal_only=False, 
                                    transform=transforms.Compose([
-                                     RollChannels(), # bob to skimage:  
+                                     RollChannels(), 
                                      ToTensor(),
                                      Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
                                      ])
@@ -72,7 +72,9 @@ network definition:
 
 .. note::
 
-  You should have the ``bob.db.multipie`` package installed on your environment
+  You should have the ``bob.db.multipie`` package installed on your environment. Note also
+  that this model acts on color images of face of size 64x64. It's up to you to preprocess
+  your data to make them fit these constraints ...
 
 To train the conditional GAN to generate faces with different poses, you should execute::
 

doc/guide_dcgan.rst

@@ -53,7 +53,9 @@ file. The configuration file should be as follow:
 
 .. note::
 
-  You should have the ``bob.db.multipie`` package installed on your environment
+  You should have the ``bob.db.multipie`` package installed on your environment. Note also
+  that this model acts on color images of face of size 64x64. It's up to you to preprocess
+  your data to make them fit these constraints ...
 
 Once your configuration file is done. You can learn a model to generate 
 frontal face images by invoking the following script::
@@ -87,6 +89,4 @@ Here are some examples of saved images.
    After 20 epoch
 
 
-.. todo:: script that samples from the model
-
 .. [dcgan]  *A.Radford, L. Metz, S. Chintala*. **Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks** Intl Conf. on Learning Representation, 2016. `arXiv <https://arxiv.org/abs/1511.06434>`__