autoencoders pretraining using RGB faces

Codes allowing to pre-train an autoencoder, using RGB facial images from the CelebA database.

bob/learn/pytorch/architectures/ConvAutoencoder.py

+#!/usr/bin/env python2
+# -*- coding: utf-8 -*-
+"""
+@author: Olegs Nikisins
+"""
+#==============================================================================
+# Import here:
+
+from torch import nn
+
+#==============================================================================
+# Define the network:
+
+class ConvAutoencoder(nn.Module):
+
+    def __init__(self):
+        super(ConvAutoencoder, self).__init__()
+        self.encoder = nn.Sequential(nn.Conv2d(3, 16, 5, padding=2),
+                                     nn.ReLU(True),
+                                     nn.MaxPool2d(2),
+                                     nn.Conv2d(16, 16, 5, padding=2),
+                                     nn.ReLU(True),
+                                     nn.MaxPool2d(2),
+                                     nn.Conv2d(16, 16, 3, padding=2),
+                                     nn.ReLU(True),
+                                     nn.MaxPool2d(2),
+                                     nn.Conv2d(16, 16, 3, padding=2),
+                                     nn.ReLU(True),
+                                     nn.MaxPool2d(2))
+
+        self.decoder = nn.Sequential(nn.ConvTranspose2d(16, 16, 3, stride=2, padding=1),
+                                     nn.ReLU(True),
+                                     nn.ConvTranspose2d(16, 16, 3, stride=2, padding=1),
+                                     nn.ReLU(True),
+                                     nn.ConvTranspose2d(16, 16, 5, stride=2, padding=2),
+                                     nn.ReLU(True),
+                                     nn.ConvTranspose2d(16, 3, 5, stride=2, padding=2),
+                                     nn.ReLU(True),
+                                     nn.ConvTranspose2d(3, 3, 2, stride=1, padding=1),
+                                     nn.Tanh())
+
+    def forward(self, x):
+        """
+        The forward method.
+        """
+        x = self.encoder(x)
+        x = self.decoder(x)
+        return x
+