Adds more unit tests and updated doc for mccnn

bob/learn/pytorch/test/test.py

@@ -220,6 +220,7 @@ class DummyDataSetMCCNN(Dataset):
     sample = data, label
     return sample 
 
+
 def test_MCCNNtrainer():
 
   from ..architectures import MCCNN
@@ -229,13 +230,44 @@ def test_MCCNNtrainer():
   dataloader['train'] = torch.utils.data.DataLoader(DummyDataSetMCCNN(), batch_size=32, shuffle=True)
 
   from ..trainers import MCCNNTrainer
-  trainer = MCCNNTrainer(net, verbosity_level=3)
+  trainer = MCCNNTrainer(net, verbosity_level=3, do_crossvalidation=False)
   trainer.train(dataloader, n_epochs=1, output_dir='.')
 
   import os
   assert os.path.isfile('model_1_0.pth')
 
   os.remove('model_1_0.pth')   
+
+
+class DummyDataSetFASNet(Dataset):
+  def __init__(self):
+    pass
+  def __len__(self):
+    return 100
+  def __getitem__(self, idx):
+    data =  numpy.random.rand(3, 224,224).astype("float32")
+    label = numpy.random.randint(2)
+    sample = data, label
+    return sample 
+
+def test_FASNettrainer():
+
+  from ..architectures import FASNet
+  net = FASNet()
+
+  dataloader={}
+  dataloader['train'] = torch.utils.data.DataLoader(DummyDataSetFASNet(), batch_size=32, shuffle=True)
+
+  from ..trainers import FASNetTrainer
+  trainer = FASNetTrainer(net, verbosity_level=3,do_crossvalidation=False)
+  trainer.train(dataloader, n_epochs=1, output_dir='.')
+
+  import os
+  assert os.path.isfile('model_1_0.pth')
+
+  os.remove('model_1_0.pth') 
+
+
 class DummyDataSetGAN(Dataset):
   def __init__(self):
     pass
@@ -357,6 +389,14 @@ def test_extractors():
   data = numpy.random.rand(4, 128, 128).astype("float32")
   output = extractor(data)
   assert output.shape[0] == 1
+
+  # FASNet
+  from ..extractor.image import FASNetExtractor
+  extractor = FASNetExtractor(num_channels_used=4)
+  # this architecture expects RGB images of size 3x224x224 channel images
+  data = numpy.random.rand(3, 224, 224).astype("float32")
+  output = extractor(data)
+  assert output.shape[0] == 1
   
 def test_two_layer_mlp():
     """

doc/guide_mccnn_pad.rst

@@ -37,7 +37,6 @@ 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
@@ -46,66 +45,119 @@ An example configuration file to train MCCNN with WMCA dataset is shown below
 
     from bob.pad.face.database import BatlPadDatabase
 
-    from bob.learn.pytorch.datasets import ChannelSelect
+    from bob.learn.pytorch.datasets import ChannelSelect, RandomHorizontalFlipImage
+
+    #==============================================================================
+    # Load the dataset
+
+    """ The steps are as follows
 
-    ### initialize bob database instance ###
+    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 )    
+        Note: Here we assume that we have already preprocessed the with `spoof.py` script and dumped it to location 
+        pointed to by 'data_folder_train'.
 
-    data_folder_train='<PREPROCESSED_FOLDER>'
+    2. Specify the transform to be used on the images. It can be instances of `torchvision.transforms.Compose` or custom functions.
 
-    frames=50
+    3. Initialize the `data_folder` class with the database instance and all other parameters. This dataset instance is used in
+     the trainer class
+
+    4. Initialize the network architecture with required arguments.
+
+    5. Define the parameters for the trainer. 
+
+    """
+
+    #==============================================================================
+    # Initialize the bob database instance 
+
+    data_folder_train= <PREPROCESSED_FOLDER>
+
+    output_base_path= <OUTPUT_PATH>
 
     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
+    val_groups=['dev']
+
+    do_crossvalidation=True
+    #=======================
+
+    if do_crossvalidation:
+        phases=['train','val']
+    else:
+        phases=['train']
+
+    groups={"train":['train'],"val":['dev']}
+
+    protocols="grandtest-color-50"
 
     exlude_attacks_list=["makeup"]
 
-    bob_hldi_instance_train = BatlPadDatabase(
+    bob_hldi_instance = 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
+        exclude_pai_all_sets=True,  
         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
+    SELECTED_CHANNELS = [0,1,2,3] 
+    
+    img_transform={}
 
-    img_transform_train = transforms.Compose([ChannelSelect(selected_channels = SELECTED_CHANNELS),transforms.ToPILImage(),transforms.RandomHorizontalFlip(),transforms.ToTensor()])# Add p=0.5 later
+    img_transform['train'] = transforms.Compose([ChannelSelect(selected_channels = SELECTED_CHANNELS),RandomHorizontalFlipImage(p=0.5),transforms.ToTensor()])
 
-    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)
+    img_transform['val'] = transforms.Compose([ChannelSelect(selected_channels = SELECTED_CHANNELS),transforms.ToTensor()])
 
-    #==============================================================================
-    # Load the architecture
+    dataset={}
 
-    NUM_CHANNELS = 3
+    for phase in phases:
+
+        dataset[phase] = DataFolder(data_folder=data_folder_train,
+                             transform=img_transform[phase],
+                             extension='.hdf5',
+                             bob_hldi_instance=bob_hldi_instance,
+                             groups=groups[phase],
+                             protocol=protocols,
+                             purposes=['real', 'attack'],
+                             allow_missing_files=True)
 
-    network=MCCNN(num_channels = NUM_CHANNELS)
 
     #==============================================================================
     # Specify other training parameters
 
-    batch_size = 64
+    NUM_CHANNELS = len(SELECTED_CHANNELS)
+
+    ADAPTED_LAYERS = 'conv1-block1-group1-ffc'
+    ADAPT_REF_CHANNEL = False
+
+    batch_size = 32
+    num_workers = 0
     epochs=25
     learning_rate=0.0001
     seed = 3
-    output_dir = 'training_mccn'
     use_gpu = False
-    adapted_layers = 'conv1-ffc'
-    adapt_reference_channel = False
+    adapted_layers = ADAPTED_LAYERS
+    adapt_reference_channel = ADAPT_REF_CHANNEL
     verbose = 2
+    UID = "_".join([str(i) for i in SELECTED_CHANNELS])+"_"+str(ADAPT_REF_CHANNEL)+"_"+ADAPTED_LAYERS+"_"+str(NUM_CHANNELS)+"_"+protocols
+    training_logs= output_base_path+UID+'/train_log_dir/'
+    output_dir = output_base_path+UID
+
+
+    #==============================================================================
+    # Load the architecture
+
+    assert(len(SELECTED_CHANNELS)==NUM_CHANNELS)
+
+    network=MCCNN(num_channels = NUM_CHANNELS)
+    #==============================================================================