diff --git a/bob/learn/pytorch/scripts/sample_drgan.py b/bob/learn/pytorch/scripts/sample_drgan.py
new file mode 100644
index 0000000000000000000000000000000000000000..7953f81a9335d0752cef42a41756c9d564c53669
--- /dev/null
+++ b/bob/learn/pytorch/scripts/sample_drgan.py
@@ -0,0 +1,154 @@
+#!/usr/bin/env python
+# encoding: utf-8
+
+
+""" Sample from a DR-GAN
+
+Usage:
+ %(prog)s <input_image> <encoder> <decoder> [--target-pose=<int>] [--light]
+ [--latent-dim=<int>] [--noise-dim=<int>] [--conditional-dim=<int>]
+ [--output-dir=<path>][--verbose ...] [--plot]
+
+Options:
+ -h, --help Show this screen.
+ -V, --version Show version.
+ -l, --light Use a lighter architecture that the original.
+ -p, --target-pose=<int> the target pose of the generated image. [default: 6]
+ -L, --latent-dim=<int> the dimension of the encoded ID [default: 320]
+ -n, --noise-dim=<int> the dimension of the noise [default: 50]
+ -c, --conditional-dim=<int> the dimension of the conditioning variable [default: 13]
+ -o, --output-dir=<path> Dir to save the logs, models and images [default: ./samples/]
+ -v, --verbose Increase the verbosity (may appear multiple times).
+ -P, --plot Show the generated image.
+
+Example:
+
+ To generate a sample of the provided input image with the target pose
+
+ $ %(prog)s <input_image> --target-pose 6 --epochs 25 --output-dir samples
+
+See '%(prog)s --help' for more information.
+
+"""
+
+import os, sys
+import pkg_resources
+
+import bob.core
+logger = bob.core.log.setup("bob.learn.pytorch")
+
+from docopt import docopt
+
+version = pkg_resources.require('bob.learn.pytorch')[0].version
+
+import numpy
+import bob.io.base
+import bob.io.image
+
+# torch
+import torch
+import torch.nn as nn
+import torchvision.transforms as transforms
+import torchvision.utils as vutils
+from torch.autograd import Variable
+
+# data and architecture from the package
+from bob.learn.pytorch.datasets import RollChannels
+from bob.learn.pytorch.datasets import ToTensor
+from bob.learn.pytorch.datasets import Normalize
+
+from bob.learn.pytorch.architectures import weights_init
+
+
+def main(user_input=None):
+
+ # Parse the command-line arguments
+ if user_input is not None:
+ arguments = user_input
+ else:
+ arguments = sys.argv[1:]
+
+ prog = os.path.basename(sys.argv[0])
+ completions = dict(prog=prog, version=version,)
+ args = docopt(__doc__ % completions,argv=arguments,version='Train DR-GAN (%s)' % version,)
+
+ # verbosity
+ verbosity_level = args['--verbose']
+ bob.core.log.set_verbosity_level(logger, verbosity_level)
+
+ # get the arguments
+ encoder_path = args['<encoder>']
+ decoder_path = args['<decoder>']
+
+ noise_dim = int(args['--noise-dim'])
+ latent_dim = int(args['--latent-dim'])
+ conditional_dim = int(args['--conditional-dim'])
+ output_dir = str(args['--output-dir'])
+ plot = bool(args['--plot'])
+
+ if bool(args['--light']):
+ from bob.learn.pytorch.architectures import DRGAN_encoder as drgan_encoder
+ from bob.learn.pytorch.architectures import DRGAN_decoder as drgan_decoder
+ else:
+ from bob.learn.pytorch.architectures import DRGANOriginal_encoder as drgan_encoder
+ from bob.learn.pytorch.architectures import DRGANOriginal_decoder as drgan_decoder
+
+ # process on the arguments / options
+ bob.io.base.create_directories_safe(output_dir)
+
+ # ============
+ # === DATA ===
+ # ============
+ input_image = bob.io.base.read(args['<input_image>'])
+ print input_image.shape
+
+ if bool(args['--plot']):
+ from matplotlib import pyplot
+ pyplot.title("Input Image")
+ pyplot.imshow(numpy.rollaxis(numpy.rollaxis(input_image, 2),2))
+ pyplot.show()
+
+ # check if we have the right image size
+ if bool(args['--light']):
+ assert (input_image.shape == (3, 64, 64)), "Using the DRGAN light model, image size shoud be [3x64x64] (CxHxW)"
+ else:
+ assert input_image.shape == (3, 96, 96), "Using the DRGAN model, image size shoud be [3x96x96] (CxHxW)"
+
+
+ # ===============
+ # === NETWORK ===
+ # ===============
+ encoder = drgan_encoder(input_image.shape, latent_dim)
+ encoder.load_state_dict(torch.load(encoder_path, map_location=lambda storage, loc: storage))
+
+ decoder = drgan_decoder(input_image.shape, noise_dim, latent_dim, conditional_dim)
+ decoder.load_state_dict(torch.load(decoder_path, map_location=lambda storage, loc: storage))
+
+ # ================
+ # === GENERATE ===
+ # ================
+
+ # encode
+ input_image = numpy.rollaxis(numpy.rollaxis(input_image, 2),2)
+ to_tensor = transforms.ToTensor()
+ norm = transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
+ input_image = to_tensor(input_image)
+ input_image = norm(input_image)
+ input_image = input_image.unsqueeze(0)
+ encoded_id = encoder.forward(Variable(input_image))
+
+ # decode
+ noise = torch.FloatTensor(1, noise_dim, 1, 1).normal_(0, 1)
+ one_hot_vector = torch.FloatTensor(1, conditional_dim, 1, 1).zero_()
+ one_hot_vector[0, int(args['--target-pose'])] = 1
+ generated = decoder(Variable(noise), Variable(one_hot_vector), encoded_id)
+ generated = generated.squeeze(0)
+ generated_image = (generated.data + 1)/2.
+
+ if bool(args['--plot']):
+ from matplotlib import pyplot
+ pyplot.title("Generated Image")
+ pyplot.imshow(numpy.rollaxis(numpy.rollaxis(generated_image.numpy(), 2),2))
+ pyplot.show()
+
+
diff --git a/setup.py b/setup.py
index 0086f66ae03614ccf50a1710dbb772ec4383476e..2495cab51fb4ffb5043984f6840af466affd65fe 100644
--- a/setup.py
+++ b/setup.py
@@ -81,6 +81,7 @@ setup(
'train_drgan_mpie_casia.py = bob.learn.pytorch.scripts.train_drgan_mpie_casia:main',
'show_training_images.py = bob.learn.pytorch.scripts.show_training_images:main',
'show_training_stats.py = bob.learn.pytorch.scripts.show_training_stats:main',
+ 'sample_drgan.py = bob.learn.pytorch.scripts.sample_drgan:main',
],