New place to put extactors

Created a place to hold the code for extractors based on architectures defined in bob.learn.pytorch.architectures.

@ageorge @onikisins @ssarfjoo : If you implement a new extractor, it should be placed here (don't worry about what has already been done in bob.ip.pytorch_extractor, I'll take care of that when I'll have some more time)

Thanks

bob/learn/pytorch/extractor/image/LightCNN29.py

+import numpy
+
+import torch
+from torch.autograd import Variable
+
+import torchvision.transforms as transforms
+
+from bob.learn.pytorch.architectures import LightCNN29
+from bob.bio.base.extractor import Extractor
+
+class LightCNN29Extractor(Extractor):
+  """ The class implementing the feature extraction of LightCNN29 embeddings.
+
+  Attributes
+  ----------
+  network: :py:class:`torch.nn.Module`
+      The network architecture
+  to_tensor: :py:mod:`torchvision.transforms`
+      The transform from numpy.array to torch.Tensor
+  norm: :py:mod:`torchvision.transforms`
+      The transform to normalize the input 
+
+  """
+  
+  def __init__(self, model_file=None, num_classes=79077):
+    """ Init method
+
+    Parameters
+    ----------
+    model_file: str
+        The path of the trained network to load
+    num_classes: int
+        The number of classes.
+    
+    """
+        
+    Extractor.__init__(self, skip_extractor_training=True)
+    
+    # model
+    self.network = LightCNN29(num_classes=num_classes)
+    if model_file is None:
+      # do nothing (used mainly for unit testing) 
+      pass
+    else:
+
+      cp = torch.load(model_file, map_location='cpu')
+      
+      # checked if pre-trained model was saved using nn.DataParallel ...
+      saved_with_nnDataParallel = False
+      for k, v in cp['state_dict'].items():
+        if 'module' in k:
+          saved_with_nnDataParallel = True
+          break
+
+      # if it was, you have to rename the keys of state_dict ... (i.e. remove 'module.')
+      if saved_with_nnDataParallel:
+        if 'state_dict' in cp:
+          from collections import OrderedDict
+          new_state_dict = OrderedDict()
+          for k, v in cp['state_dict'].items():
+            name = k[7:]
+            new_state_dict[name] = v
+          self.network.load_state_dict(new_state_dict)
+      else:
+          self.network.load_state_dict(cp['state_dict'])
+    self.network.eval()
+
+    # image pre-processing
+    self.to_tensor = transforms.ToTensor()
+    self.norm = transforms.Normalize((0.5,), (0.5,))
+
+  def __call__(self, image):
+    """ Extract features from an image
+
+    Parameters
+    ----------
+    image : 2D :py:class:`numpy.ndarray` (floats)
+      The grayscale image to extract the features from. Its size must be 128x128
+
+    Returns
+    -------
+    feature : :py:class:`numpy.ndarray` (floats)
+      The extracted features as a 1d array of size 320 
+    
+    """
+  
+    # torchvision.transforms expect a numpy array of size HxWxC
+    input_image = numpy.expand_dims(image, axis=2)
+    input_image = self.to_tensor(input_image)
+    input_image = self.norm(input_image)
+    input_image = input_image.unsqueeze(0)
+    
+    # to be compliant with the loaded model, where weight and biases are torch.FloatTensor
+    input_image = input_image.float()
+
+    _ , features = self.network.forward(Variable(input_image))
+    features = features.data.numpy().flatten()
+    return features