Fine-tuning of autoencoders on multi-channel data

This MR contains just configs and documentation allowing to fine-tune an AE on the multi-channel (BW-NIR-D) data.

@heusch can you please review and merge if fine with you. Thanks!

bob/learn/pytorch/config/autoencoder/net1_batl.py

+#!/usr/bin/env python2
+# -*- coding: utf-8 -*-
+"""
+@author: Olegs Nikisins
+"""
+#==============================================================================
+# Import here:
+
+from torchvision import transforms
+
+from bob.pad.face.database import BatlPadDatabase
+
+from torch import nn
+
+
+#==============================================================================
+# Define parameters here:
+
+"""
+Note: do not change names of the below constants.
+"""
+NUM_EPOCHS = 50 # Maximum number of epochs
+BATCH_SIZE =  32 # Size of the batch
+LEARNING_RATE = 1e-3 # Learning rate
+NUM_WORKERS = 8  # The number of workers for the DataLoader
+
+
+"""
+Transformations to be applied sequentially to the input PIL image.
+Note: the variable name ``transform`` must be the same in all configuration files.
+"""
+
+transform = transforms.Compose([transforms.ToTensor(),
+                                transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
+                                ])
+
+
+"""
+Set the parameters of the DataFolder dataset class.
+Note: do not change the name ``kwargs``.
+"""
+
+ORIGINAL_DIRECTORY = ""
+ORIGINAL_EXTENSION = ".h5"  # extension of the data files
+
+PROTOCOL = 'grandtest-color*infrared*depth-10'  # use 10 frames for PAD experiments
+
+annotations_temp_dir = ""
+
+bob_hldi_instance = BatlPadDatabase(protocol=PROTOCOL,
+                                    original_directory=ORIGINAL_DIRECTORY,
+                                    original_extension=ORIGINAL_EXTENSION,
+                                    annotations_temp_dir = annotations_temp_dir,
+                                    landmark_detect_method="mtcnn",  # detect annotations using mtcnn
+                                    exclude_attacks_list=['makeup'],
+                                    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)
+
+kwargs = {}
+kwargs["data_folder"] = "NO NEED TO SET HERE, WILL BE SET IN THE TRAINING SCRIPT"
+kwargs["transform"] = transform
+kwargs["extension"] = '.hdf5'
+kwargs["bob_hldi_instance"] = bob_hldi_instance
+kwargs["hldi_type"] = "pad"
+kwargs["groups"] = ['train']
+kwargs["protocol"] = 'grandtest'
+kwargs["purposes"] = ['real']
+kwargs["allow_missing_files"] = True
+
+
+"""
+Define the network to be trained as a class, named ``Network``.
+Note: Do not change the name of the below class.
+"""
+
+from bob.learn.pytorch.architectures import ConvAutoencoder as Network
+
+
+"""
+Define the loss to be used for training.
+Note: do not change the name of the below variable.
+"""
+loss_type = nn.MSELoss()
+
+
+"""
+OPTIONAL: if not defined loss will be computed in the training script.
+See training script for details
+
+Define the function to compute the loss. Don't change the signature of this
+function.
+"""
+# we don't define the loss_function for this configuration
+#def loss_function(output, img, target):
+
+