mods to config

bob/learn/pytorch/config/mccnn/wmca_mccnn.py

@@ -13,7 +13,7 @@ from bob.learn.pytorch.datasets import ChannelSelect
 #==============================================================================
 # Load the dataset
 
-""" The steps in initializing the dataset is as follows
+""" The steps are as follows
 
 1. Initialize a databae instance, with the protocol, groups and number of frames 
 	(currently for the ones in 'bob.pad.face', and point 'data_folder_train' to the preprocessed directory )	
@@ -27,8 +27,13 @@ from bob.learn.pytorch.datasets import ChannelSelect
 
 4. Initialize the network architecture with required arguments.
 
+5. Define the parameters for the trainer. 
+
 """
 
+#==============================================================================
+# Initialize the bob database instance 
+
 data_folder_train='/idiap/temp/ageorge/WMCA/preprocessed/'
 
 frames=50
@@ -41,11 +46,6 @@ protocols="grandtest-color*depth*infrared*thermal-{}".format(frames) # makeup is
 
 exlude_attacks_list=["makeup"]
 
-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
-
-
 bob_hldi_instance_train = BatlPadDatabase(
     protocol=protocols,
     original_directory=data_folder_train,
@@ -55,6 +55,13 @@ bob_hldi_instance_train = BatlPadDatabase(
     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',