Modified config file

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

@@ -7,6 +7,8 @@ from bob.learn.pytorch.datasets import DataFolder
 
 from bob.pad.face.database import BatlPadDatabase
 
+from bob.learn.pytorch.datasets import ChannelSelect
+
 
 #==============================================================================
 # Load the dataset
@@ -39,7 +41,9 @@ protocols="grandtest-color*depth*infrared*thermal-{}".format(frames) # makeup is
 
 exlude_attacks_list=["makeup"]
 
-img_transform_train = transforms.Compose([transforms.ToPILImage(),transforms.RandomHorizontalFlip(),transforms.ToTensor()])# Add p=0.5 later
+SELECTED_CHANNELS = [0] # 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(
@@ -63,7 +67,9 @@ dataset = DataFolder(data_folder=data_folder_train,
 #==============================================================================
 # Load the architecture
 
-network=MCCNN(num_channels=4)
+NUM_CHANNELS = 1
+
+network=MCCNN(num_channels = NUM_CHANNELS)
 
 #==============================================================================
 # Specify other training parameters

bob/learn/pytorch/trainers/MCCNNTrainer.py

@@ -15,13 +15,12 @@ import time
 import os
 """
 #TODO: 
-#0. Add class balancing
-#1. Logging to tensorboardX or a simpler logger
+#0. Add class balancing - done with the weights ; import the function from utils
+#1. Logging to tensorboardX or a simpler logger: 
 #2. Support for Validation set and validation loss
-#3. Use to(device) instead of .cuda()?
-#4. Functionality to select channels from the dataloader: may be move this to the datafolder class
-#5. Moving more arguments to config?
-#6. Implement the selection of channels as a transform
+#3. Use to(device) instead of .cuda()?: Eventually migrate everything to this!
+#4. Moving more arguments to config?
+#5. Implement the selection of channels as a transform: Added now ChannelSelect
 
 """
 def comp_bce_loss_weights(target):