Added VGG networks

bob/learn/tensorflow/network/Vgg.py

+#!/usr/bin/env python
+# vim: set fileencoding=utf-8 :
+# @author: Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
+
+
+"""
+VGG16 and VGG19 wrappers
+"""
+
+import tensorflow as tf
+from tensorflow.contrib.slim.python.slim.nets import vgg
+import tensorflow.contrib.slim as slim
+
+
+def vgg_19(inputs,
+           reuse=None,                      
+           mode=tf.estimator.ModeKeys.TRAIN, **kwargs):
+    """
+    Oxford Net VGG 19-Layers version E Example from tf-slim
+
+    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
+    """
+
+    with slim.arg_scope(
+        [slim.conv2d],
+            trainable=mode==tf.estimator.ModeKeys.TRAIN):
+
+        return vgg.vgg_19(inputs, spatial_squeeze=False)
+
+
+def vgg_16(inputs,
+           reuse=None,                      
+           mode=tf.estimator.ModeKeys.TRAIN, **kwargs):
+    """
+    Oxford Net VGG 16-Layers version E Example from tf-slim
+
+    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
+    """
+
+    with slim.arg_scope(
+        [slim.conv2d],
+            trainable=mode==tf.estimator.ModeKeys.TRAIN):
+
+        return vgg.vgg_16(inputs, spatial_squeeze=False)
+

bob/learn/tensorflow/network/__init__.py

@@ -5,6 +5,7 @@ from .MLP import mlp
 from .InceptionResnetV2 import inception_resnet_v2, inception_resnet_v2_batch_norm
 from .InceptionResnetV1 import inception_resnet_v1, inception_resnet_v1_batch_norm
 from . import SimpleCNN
+from .Vgg import vgg_19, vgg_16
 
 
 # gets sphinx autodoc done right - don't remove it

bob/learn/tensorflow/test/test_architectures.py

@@ -4,7 +4,8 @@
 
 import tensorflow as tf
 from bob.learn.tensorflow.network import inception_resnet_v2, inception_resnet_v2_batch_norm,\
-                                         inception_resnet_v1, inception_resnet_v1_batch_norm
+                                         inception_resnet_v1, inception_resnet_v1_batch_norm,\
+                                         vgg_19, vgg_16
 
 
 def test_inceptionv2():
@@ -43,3 +44,41 @@ def test_inceptionv1():
 
     tf.reset_default_graph()
     assert len(tf.global_variables()) == 0
+
+
+def test_vgg():
+    # Testing VGG19 Training mode
+    inputs = tf.placeholder(tf.float32, shape=(1, 224, 224, 3))
+    graph, _ = vgg_19(inputs)
+    assert len(tf.trainable_variables()) == 38
+
+    tf.reset_default_graph()
+    assert len(tf.global_variables()) == 0
+
+
+    # Testing VGG19 predicting mode
+    inputs = tf.placeholder(tf.float32, shape=(1, 224, 224, 3))
+    graph, _ = vgg_19(inputs, mode=tf.estimator.ModeKeys.PREDICT)
+    assert len(tf.trainable_variables()) == 0
+
+    tf.reset_default_graph()
+    assert len(tf.global_variables()) == 0
+
+
+   # Testing VGG 16 training mode
+    inputs = tf.placeholder(tf.float32, shape=(1, 224, 224, 3))
+    graph, _ = vgg_16(inputs)
+    assert len(tf.trainable_variables()) == 32
+
+    tf.reset_default_graph()
+    assert len(tf.global_variables()) == 0
+
+
+    # Testing VGG 16 predicting mode
+    inputs = tf.placeholder(tf.float32, shape=(1, 224, 224, 3))
+    graph, _ = vgg_16(inputs, mode=tf.estimator.ModeKeys.PREDICT)
+    assert len(tf.trainable_variables()) == 0
+
+    tf.reset_default_graph()
+    assert len(tf.global_variables()) == 0
+