Added docs

doc/guide_mccnn_pad.rst

@@ -28,6 +28,84 @@ All the parameters required to train MCCNN are defined in the configuration file
 The ``config.py`` file should contain atleast the network definition and the dataset class to be used for training. 
 It can also define the transforms, number of channels in mccnn, training parameters such as number of epochs, learning rate and so on.  
 
+a. Structure of the config file
+
+An example configuration file to train MCCNN with WMCA dataset is shown below
+
+.. code-block:: python
+
+
+    from torchvision import transforms
+
+    from bob.learn.pytorch.architectures import MCCNN
+
+    from bob.learn.pytorch.datasets import DataFolder
+
+    from bob.pad.face.database import BatlPadDatabase
+
+    from bob.learn.pytorch.datasets import ChannelSelect
+
+    ### initialize bob database instance ###
+
+    data_folder_train='<PREPROCESSED_FOLDER>'
+
+    frames=50
+
+    extension='.h5'
+
+    train_groups=['train'] # only 'train' group is used for training the network
+
+    protocols="grandtest-color*depth*infrared*thermal-{}".format(frames) # makeup is excluded anyway here
+
+    exlude_attacks_list=["makeup"]
+
+    bob_hldi_instance_train = BatlPadDatabase(
+        protocol=protocols,
+        original_directory=data_folder_train,
+        original_extension=extension,
+        landmark_detect_method="mtcnn",  # detect annotations using mtcnn
+        exclude_attacks_list=exlude_attacks_list,
+        exclude_pai_all_sets=True,  # exclude makeup from all the sets, which is the default behavior for grandtest protocol
+        append_color_face_roi_annot=False) 
+
+    #==============================================================================
+    # Initialize the torch dataset, subselect channels from the pretrained files if needed.
+
+    SELECTED_CHANNELS = [0,1,2] # selects only color, depth and infrared
+
+    img_transform_train = transforms.Compose([ChannelSelect(selected_channels = SELECTED_CHANNELS),transforms.ToPILImage(),transforms.RandomHorizontalFlip(),transforms.ToTensor()])# Add p=0.5 later
+
+    dataset = DataFolder(data_folder=data_folder_train,
+                         transform=img_transform_train,
+                         extension='.hdf5',
+                         bob_hldi_instance=bob_hldi_instance_train,
+                         groups=train_groups,
+                         protocol=protocols,
+                         purposes=['real', 'attack'],
+                         allow_missing_files=True)
+
+    #==============================================================================
+    # Load the architecture
+
+    NUM_CHANNELS = 3
+
+    network=MCCNN(num_channels = NUM_CHANNELS)
+
+    #==============================================================================
+    # Specify other training parameters
+
+    batch_size = 64
+    epochs=25
+    learning_rate=0.0001
+    seed = 3
+    output_dir = 'training_mccn'
+    use_gpu = False
+    adapted_layers = 'conv1-ffc'
+    adapt_reference_channel = False
+    verbose = 2
+
+
+
 Once the config file is defined, training the network can be done with the following code:
 
 .. code-block:: sh