added the CNN8 architecture + feature extraction from Xiaojiang

bob/ip/pytorch_extractor/CNN8.py

+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+import torchvision.transforms as transforms
+
+CNN8_CONFIG = [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M']
+
+def make_conv_layers(cfg, input_c = 3):
+    layers = []
+    in_channels = input_c
+    for v in cfg:
+        if v == 'M':
+            layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
+        else:
+            conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)
+            layers += [conv2d, nn.ReLU()]
+            in_channels = v
+    return nn.Sequential(*layers)
+
+class CNN8(nn.Module):
+
+    def __init__(self, num_cls, drop_rate=0.5):
+        super(CNN8, self).__init__()
+        self.num_classes = num_cls
+        self.drop_rate = float(drop_rate)
+        self.conv = make_conv_layers(CNN8_CONFIG)
+        self.avgpool = nn.AvgPool2d(8)
+        self.classifier = nn.Linear(512, self.num_classes)
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.avgpool(x)
+        x = x.view(x.size(0), -1)
+        x = F.dropout(x, p = self.drop_rate, training=self.training)
+        return x 
+
+
+from bob.bio.base.extractor import Extractor
+
+class CNN8Extractor(Extractor):
+  
+  def __init__(self, model_file, num_classes=10575):
+
+    Extractor.__init__(self, skip_extractor_training=True)
+    
+    # model
+    self.network = CNN8(num_classes)
+    cp = torch.load(model_file)
+    if 'state_dict' in cp:
+        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, 0.5), (0.5, 0.5, 0.5))
+
+  def __call__(self, image):
+    """__call__(image) -> feature
+
+    Extract features
+
+    **Parameters:**
+
+    image : 3D :py:class:`numpy.ndarray` (floats)
+      The image to extract the features from.
+
+    **Returns:**
+
+    feature : 2D :py:class:`numpy.ndarray` (floats)
+      The extracted features
+    """
+    input_image = numpy.rollaxis(numpy.rollaxis(image, 2),2)
+    input_image = self.to_tensor(input_image)
+    input_image = self.norm(input_image)
+    input_image = input_image.unsqueeze(0)
+    features = self.network.forward(Variable(input_image))
+    feat = feat.data.cpu().numpy().flatten() 
+    features = features.data.numpy().flatten()
+    
+    print features.shape
+    return features
+
+

bob/ip/pytorch_extractor/__init__.py

 
 from .DRGANLight import DRGANLight
 from .DRGANOriginal import DRGANOriginal
+from .CNN8 import CNN8Extractor
 
 
 # gets sphinx autodoc done right - don't remove it
@@ -22,6 +23,7 @@ def __appropriate__(*args):
 __appropriate__(
     DRGANLight,
     DRGANOriginal,
+    CNN8Extractor,
 )
 
 # gets sphinx autodoc done right - don't remove it