Implemented a simple version of Facenet

bob/learn/tensorflow/network/FaceNetSimple.py

+#!/usr/bin/env python
+# vim: set fileencoding=utf-8 :
+# @author: Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
+# @date: Wed 11 May 2016 09:39:36 CEST 
+
+import tensorflow as tf
+from .SequenceNetwork import SequenceNetwork
+from ..layers import Conv2D, FullyConnected, MaxPooling
+from bob.learn.tensorflow.initialization import Xavier
+from bob.learn.tensorflow.initialization import Constant
+
+
+class FaceNetSimple(SequenceNetwork):
+    """
+    Class that creates the The FaceNet architecture used in
+
+    Schroff, Florian, Dmitry Kalenichenko, and James Philbin.
+    "Facenet: A unified embedding for face recognition and clustering."
+    Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015.
+
+    This CNN DOES NOT HAVE THE 2d convolution of 1x1 described in:
+
+    Ahuja, Ravindra K., Thomas L. Magnanti, and James B. Orlin.
+    "Network flows: theory, algorithms, and applications." (1993).
+
+    """
+
+    def __init__(self,
+                 ### First macro layer
+                 conv1_kernel_size=7,
+                 conv1_output=64,
+                 conv1_stride=[1, 2, 2, 1], #[1, 2, 2, 1],
+
+                 pool1_shape=[1, 3, 3, 1],
+                 pool1_stride=[1, 2, 2, 1],
+
+                 ### Second macro layer
+                 conv2_kernel_size=3,
+                 conv2_output=192,
+
+                 pool2_shape=[1, 3, 3, 1],
+                 pool2_stride=[1, 2, 2, 1],
+
+                 ### Third macro layer
+                 conv3_kernel_size=3,
+                 conv3_output=192,
+
+                 pool3_shape=[1, 3, 3, 1],
+                 pool3_stride=[1, 2, 2, 1],
+
+                 ### Forth macro layer
+                 conv4_kernel_size=3,
+                 conv4_output=384,
+
+                 ### Fifth macro layer
+                 conv5_kernel_size=3,
+                 conv5_output=256,
+
+                 ### Sixth macro layer
+                 conv6_kernel_size=3,
+                 conv6_output=256,
+
+                 pool6_shape=[1, 3, 3, 1],
+                 pool6_stride=[1, 2, 2, 1],
+
+
+                 fc1_output=256,
+                 fc2_output=128,
+
+                 fc7128_output=128,
+
+                 default_feature_layer="fc7128",
+                 seed=10,
+                 use_gpu=False):
+        """
+        Create all the necessary variables for this CNN
+
+        **Parameters**
+            conv1_kernel_size=5,
+            conv1_output=32,
+
+            conv2_kernel_size=5,
+            conv2_output=64,
+
+            fc1_output=400,
+            n_classes=10
+
+            seed = 10
+        """
+        super(FaceNetSimple, self).__init__(default_feature_layer=default_feature_layer,
+                                    use_gpu=use_gpu)
+
+        self.add(Conv2D(name="conv1", kernel_size=conv1_kernel_size,
+                        filters=conv1_output,
+                        activation=tf.nn.relu,
+                        stride=conv1_stride,
+                        weights_initialization=Xavier(seed=seed, use_gpu=self.use_gpu),
+                        bias_initialization=Constant(use_gpu=self.use_gpu)
+                        ))
+        self.add(MaxPooling(name="pooling1", shape=pool1_shape, strides=pool1_stride))
+
+        ##########
+        self.add(Conv2D(name="conv2", kernel_size=conv2_kernel_size,
+                        filters=conv2_output,
+                        activation=tf.nn.relu,
+                        weights_initialization=Xavier(seed=seed, use_gpu=self.use_gpu),
+                        bias_initialization=Constant(use_gpu=self.use_gpu)
+                        ))
+        self.add(MaxPooling(name="pooling2", shape=pool2_shape, strides=pool2_stride))
+
+        ##########
+        self.add(Conv2D(name="conv3", kernel_size=conv3_kernel_size,
+                        filters=conv3_output,
+                        activation=tf.nn.relu,
+                        weights_initialization=Xavier(seed=seed, use_gpu=self.use_gpu),
+                        bias_initialization=Constant(use_gpu=self.use_gpu)
+                        ))
+        self.add(MaxPooling(name="pooling3", shape=pool3_shape, strides=pool3_stride))
+
+        ##########
+        self.add(Conv2D(name="conv4", kernel_size=conv4_kernel_size,
+                        filters=conv4_output,
+                        activation=tf.nn.relu,
+                        weights_initialization=Xavier(seed=seed, use_gpu=self.use_gpu),
+                        bias_initialization=Constant(use_gpu=self.use_gpu)
+                        ))
+
+        ##########
+        self.add(Conv2D(name="conv5", kernel_size=conv5_kernel_size,
+                        filters=conv5_output,
+                        activation=tf.nn.relu,
+                        weights_initialization=Xavier(seed=seed, use_gpu=self.use_gpu),
+                        bias_initialization=Constant(use_gpu=self.use_gpu)
+                        ))
+
+        ##########
+        self.add(Conv2D(name="conv6", kernel_size=conv6_kernel_size,
+                        filters=conv6_output,
+                        activation=tf.nn.relu,
+                        weights_initialization=Xavier(seed=seed, use_gpu=self.use_gpu),
+                        bias_initialization=Constant(use_gpu=self.use_gpu)
+                        ))
+        self.add(MaxPooling(name="pooling6", shape=pool6_shape, strides=pool6_stride))
+
+        self.add(FullyConnected(name="fc1", output_dim=fc1_output,
+                                activation=tf.nn.relu,
+                                weights_initialization=Xavier(seed=seed, use_gpu=self.use_gpu),
+                                bias_initialization=Constant(use_gpu=self.use_gpu)
+                                ))
+
+        self.add(FullyConnected(name="fc2", output_dim=fc2_output,
+                                activation=tf.nn.relu,
+                                weights_initialization=Xavier(seed=seed, use_gpu=self.use_gpu),
+                                bias_initialization=Constant(use_gpu=self.use_gpu)
+                                ))
+
+        self.add(FullyConnected(name="fc7128", output_dim=fc7128_output,
+                                activation=None,
+                                weights_initialization=Xavier(seed=seed, use_gpu=self.use_gpu),
+                                bias_initialization=Constant(use_gpu=self.use_gpu)
+                                ))