Created a VGGFace for tensorflow

Added download link

Updated the VGG model

bob/ip/tensorflow_extractor/Vgg16.py

+#!/usr/bin/env python
+# vim: set fileencoding=utf-8 :
+# @author: Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
+
+import tensorflow as tf
+from tensorflow.contrib.slim.python.slim.nets import vgg
+import tensorflow.contrib.slim as slim
+from tensorflow.contrib.layers.python.layers import layers as layers_lib
+from tensorflow.contrib import layers
+from tensorflow.contrib.framework.python.ops import arg_scope
+from .Extractor import Extractor
+import numpy
+import bob.extension.download
+import os
+
+def vgg_16(inputs,
+           reuse=None,
+           dropout_keep_prob=0.5,
+           weight_decay=0.0005,
+           mode=tf.estimator.ModeKeys.TRAIN, **kwargs):
+    """
+    Oxford Net VGG 16-Layers version E Example from tf-slim
+
+    Adapted from here.
+    https://raw.githubusercontent.com/tensorflow/models/master/research/slim/nets/vgg.py
+
+    **Parameters**:
+
+        inputs: a 4-D tensor of size [batch_size, height, width, 3].
+
+        reuse: whether or not the network and its variables should be reused. To be
+               able to reuse 'scope' must be given.
+
+        mode:
+           Estimator mode keys
+    """
+    end_points = dict()
+    with tf.variable_scope('vgg_16', reuse=reuse):
+
+        with arg_scope([layers.conv2d, layers_lib.fully_connected],
+                        activation_fn=tf.nn.relu,
+                        weights_regularizer=None,
+                        biases_initializer=tf.zeros_initializer(),
+                        trainable=mode==tf.estimator.ModeKeys.PREDICT):
+ 
+            # Collect outputs for conv2d, fully_connected and max_pool2d.
+                
+            net = layers_lib.repeat(inputs, 2, layers.conv2d, 64, [3, 3], scope='conv1')
+            net = layers_lib.max_pool2d(net, [2, 2], scope='pool1')
+            end_points['conv1'] = net
+
+            net = layers_lib.repeat(net, 2, layers.conv2d, 128, [3, 3], scope='conv2')
+            net = layers_lib.max_pool2d(net, [2, 2], scope='pool2')
+            end_points['conv2'] = net
+
+            net = layers_lib.repeat(net, 3, layers.conv2d, 256, [3, 3], scope='conv3')
+            net = layers_lib.max_pool2d(net, [2, 2], scope='pool3')
+            end_points['conv3'] = net
+
+            net = layers_lib.repeat(net, 3, layers.conv2d, 512, [3, 3], scope='conv4')
+            net = layers_lib.max_pool2d(net, [2, 2], scope='pool4')
+            end_points['conv4'] = net
+           
+            net = layers_lib.repeat(net, 3, layers.conv2d, 512, [3, 3], scope='conv5')
+            net = layers_lib.max_pool2d(net, [2, 2], scope='pool5')
+            end_points['conv5'] = net
+           
+            net = slim.flatten(net)
+
+            net = layers.fully_connected(net, 4096, scope='fc6')
+            end_points['fc6'] = net
+            
+            net = layers.fully_connected(net, 4096, scope='fc7', activation_fn=None)
+            
+            end_points['fc7'] = net
+
+    return net, end_points
+
+
+class VGGFace(Extractor):
+    """
+    Extract features using the VGG model
+    http://www.robots.ox.ac.uk/~vgg/software/vgg_face/
+    
+    This was converted with the script https://github.com/tiagofrepereira2012
+
+    """
+
+    def __init__(self, checkpoint_filename=None, debug=False):
+        # Average image provided in
+        # http://www.robots.ox.ac.uk/~vgg/software/vgg_face/
+        self.average_img = [129.1863, 104.7624, 93.5940]
+
+        if checkpoint_filename is None:
+            checkpoint_filename = os.path.join(VGGFace.get_vggpath(),"vgg_face_tf")
+
+        # Downloading the model if necessary
+        if not os.path.exists(checkpoint_filename):            
+            zip_file = os.path.join(VGGFace.get_vggpath(), "vgg_face_tf.tar.gz")
+
+            urls = [
+                # This is a private link at Idiap to save bandwidth.
+                "https://www.idiap.ch/software/bob/data/bob/bob.ip.tensorflow_extractor/master/"
+                "vgg_face_tf.tar.gz",
+                "http://www.idiap.ch/software/bob/data/bob/bob.ip.tensorflow_extractor/master/"
+                "vgg_face_tf.tar.gz",                
+            ]
+
+            bob.extension.download.download_and_unzip(urls, zip_file)
+
+        input_tensor = tf.placeholder(tf.float32, shape=(1, 224, 224, 3))
+        graph = vgg_16(input_tensor)[0]
+
+        super(VGGFace, self).__init__(checkpoint_filename=os.path.join(checkpoint_filename),
+                                      input_tensor=input_tensor,
+                                      graph=graph,
+                                      debug=debug)
+
+        
+    def __call__(self, image):
+        
+        if len(image.shape) == 3:        
+
+            # Converting from RGB to BGR            
+            R = image[0, :, :] - self.average_img[0]
+            G = image[1, :, :] - self.average_img[1]
+            B = image[2, :, :] - self.average_img[2]
+
+            # Converting to
+            bgr_image = numpy.zeros(shape=image.shape)
+            bgr_image[0, :, :] = B
+            bgr_image[1, :, :] = G
+            bgr_image[2, :, :] = R
+                        
+            # SWAPING TO CxHxW to HxWxC
+            bgr_image = numpy.moveaxis(bgr_image,0,-1)
+            bgr_image  = numpy.expand_dims(bgr_image,0)
+            
+            if self.session is None:
+                self.session = tf.InteractiveSession()            
+            return self.session.run(self.graph, feed_dict={self.input_tensor: bgr_image})[0]
+        else:
+            raise ValueError("Image should have 3 channels")
+        
+            
+    @staticmethod
+    def get_vggpath():
+        import pkg_resources
+        return pkg_resources.resource_filename(__name__, 'data')
+

bob/ip/tensorflow_extractor/__init__.py

@@ -36,6 +36,7 @@ def get_config():
 from .Extractor import Extractor
 from .FaceNet import FaceNet
 from .DrGanMSU import  DrGanMSUExtractor
+from .Vgg16 import VGGFace, vgg_16
 
 
 # gets sphinx autodoc done right - don't remove it
@@ -58,6 +59,7 @@ __appropriate__(
     Extractor,
     FaceNet,
     DrGanMSUExtractor,
+    VGGFace
 )
 
 # gets sphinx autodoc done right - don't remove it

bob/ip/tensorflow_extractor/test.py

@@ -53,3 +53,10 @@ def test_drgan():
     output = extractor(data)
     assert output.size == 320, output.shape
 
+def test_vgg16():
+    from bob.ip.tensorflow_extractor import VGGFace
+    extractor = VGGFace()
+    data = numpy.random.rand(3, 224, 224).astype("uint8")
+    output = extractor(data)
+    assert output.size == 4096, output.shape
+