[algorithms] added MLP, and corresponding unit test

bob/pad/base/algorithm/MLP.py

+#!/usr/bin/env python
+# vim: set fileencoding=utf-8 :
+
+import numpy
+
+from bob.pad.base.algorithm import Algorithm
+import bob.learn.mlp
+import bob.io.base
+
+from bob.bio.video.utils import FrameContainer
+from bob.pad.base.utils import convert_frame_cont_to_array
+
+from bob.core.log import setup
+logger = setup("bob.pad.base")
+
+
+class MLP(Algorithm):
+    """Interfaces an MLP classifier used for PAD
+
+    Attributes
+    ----------
+    hidden_units : :py:obj:`tuple` of int
+      The number of hidden units in each hidden layer
+    max_iter : int
+      The maximum number of training iterations
+    """
+
+    def __init__(self, hidden_units=(10, 10), max_iter=1000, precision=0.001, **kwargs):
+        """Init function
+
+        Parameters
+        ----------
+        hidden_units : :py:obj:`tuple` of int
+          The number of hidden units in each hidden layer
+        max_iter : int
+          The maximum number of training iterations
+        precision : float
+          criterion to stop the training: if the difference
+          between current and last loss is smaller than
+          this number, then stop training.
+        
+        """
+        Algorithm.__init__(self,  
+                           performs_projection=True,
+                           requires_projector_training=True, 
+                           **kwargs)
+
+        self.hidden_units = hidden_units
+        self.max_iter = max_iter
+        self.precision = precision
+        self.mlp = None
+
+
+    def train_projector(self, training_features, projector_file):
+        """Trains the MLP
+
+        Parameters:
+        -----------
+        training_features : :py:obj:`list` of :py:class:`numpy.ndarray` or :py:class:`bob.bio.video.utils.FrameContainer`
+          Data used to train the MLP. The real data are in training_features[0] and the attacks are in training_features[1]
+        projector_file : str
+          Filename where to save the trained model.
+        
+        """
+        # training is done in batch (i.e. using all training data)
+        batch_size = len(training_features[0]) + len(training_features[1])
+        
+        # The labels
+        label_real = numpy.zeros((len(training_features[0]), 2), dtype='float64')
+        label_real[:, 0] = 1
+        label_attack = numpy.zeros((len(training_features[1]), 2), dtype='float64')
+        label_attack[:, 1] = 0
+        
+        #if isinstance(training_features[0][0], FrameContainer): 
+        #    real = convert_frame_cont_to_array(training_features[0])
+        #if isinstance(training_features[1][0], FrameContainer): 
+        #    attack = convert_frame_cont_to_array(training_features[1])
+
+        real = numpy.array(training_features[0])
+        attack = numpy.array(training_features[1])
+        X = numpy.vstack([real, attack])
+        Y = numpy.vstack([label_real, label_attack])
+      
+        # Building MLP architecture
+        input_dim = real.shape[1]
+        shape = []
+        shape.append(input_dim)
+        for i in range(len(self.hidden_units)):
+            shape.append(self.hidden_units[i])
+        # last layer contains two units: one for each class (i.e. real and attack)
+        shape.append(2)
+        shape = tuple(shape)
+    
+        self.mlp = bob.learn.mlp.Machine(shape)
+        self.mlp.output_activation = bob.learn.activation.Logistic()
+        self.mlp.randomize()
+        trainer = bob.learn.mlp.BackProp(batch_size, bob.learn.mlp.CrossEntropyLoss(self.mlp.output_activation), self.mlp, train_biases=True)
+    
+        n_iter = 0
+        previous_cost = 0
+        current_cost = 1
+        while (n_iter < self.max_iter) or (abs(previous_cost - current_cost) > self.precision): 
+            previous_cost = current_cost
+            trainer.train(self.mlp, X, Y)
+            current_cost = trainer.cost(self.mlp, X, Y)
+            n_iter += 1
+            logger.debug("Iteration {} -> cost = {} (previous = {})".format(n_iter, trainer.cost(self.mlp, X, Y), previous_cost))
+
+        f = bob.io.base.HDF5File(projector_file, 'w')
+        self.mlp.save(f)
+    
+
+    def project(self, feature):
+        """Project the given feature
+        
+        Parameters
+        ----------
+        feature : :py:class:`numpy.ndarray` or :py:class:`bob.bio.video.utils.FrameContainer`
+
+        Returns
+        -------
+        numpy.ndarray
+          The value of the two units in the last layer of the MLP.
+        """
+       # if isinstance(feature, FrameContainer):
+       #     feature = convert_frame_cont_to_array(feature)
+        return self.mlp(feature)
+
+
+    def score(self, toscore):
+        """Returns the probability of the real class.
+
+        Parameters
+        ----------
+        toscore : :py:class:`numpy.ndarray`
+       
+        Returns
+        -------
+        float
+         probability of the authentication attempt to be real. 
+        """
+        if toscore.ndim == 1:
+            return [toscore[0]]
+        else:
+            return numpy.mean([toscore[:, 0]])

bob/pad/base/algorithm/__init__.py

@@ -5,6 +5,7 @@ from .LogRegr import LogRegr
 from .SVMCascadePCA import SVMCascadePCA
 from .Predictions import Predictions
 
+from .MLP import MLP
 
 # to fix sphinx warnings of not able to find classes, when path is shortened
 def __appropriate__(*args):

bob/pad/base/test/test_algorithms.py

@@ -12,6 +12,7 @@ import bob.pad.base
 
 from bob.pad.base.algorithm import SVM
 from bob.pad.base.algorithm import OneClassGMM
+from bob.pad.base.algorithm import MLP
 
 import random
 
@@ -173,3 +174,38 @@ def test_convert_list_of_frame_cont_to_array():
   assert isinstance(features_array[0], np.ndarray)
   features_fm = convert_array_to_list_of_frame_cont(real_array)
   assert isinstance(features_fm[0], bob.bio.video.FrameContainer)
+
+
+def test_MLP():
+    """
+    Test the MLP PAD algorithm.
+    """
+
+    random.seed(7)
+
+    N = 20000
+    mu = 1
+    sigma = 1
+    real_array = np.transpose(
+        np.vstack([[random.gauss(mu, sigma) for _ in range(N)],
+                   [random.gauss(mu, sigma) for _ in range(N)]]))
+
+    mu = 5
+    sigma = 1
+    attack_array = np.transpose(
+        np.vstack([[random.gauss(mu, sigma) for _ in range(N)],
+                   [random.gauss(mu, sigma) for _ in range(N)]]))
+
+
+    training_features = [real_array, attack_array]
+
+    mlp = MLP(max_iter=100)
+    mlp.train_projector(training_features, '/tmp/mlp.hdf5')
+
+    real_sample = real_array[0]
+
+    prob = mlp.project(real_sample)
+
+    assert prob[0] > prob[1]
+
+