From ac00b9063955f578bc14b74fd359b71b24204f84 Mon Sep 17 00:00:00 2001
From: Amir MOHAMMADI <amir.mohammadi@idiap.ch>
Date: Fri, 6 Mar 2020 11:21:52 +0100
Subject: [PATCH] Add license header

---
 .gitlab-ci.yml                                | 26 +++++++++++++++++
 .../Protocol_1.py                             | 26 +++++++++++++++++
 .../icassp2020_domain_guided_pruning/batl.py  | 25 +++++++++++++++++
 .../deep_pix_bis.py                           | 26 +++++++++++++++++
 .../deep_pix_bis_features.py                  | 26 +++++++++++++++++
 .../icassp2020_domain_guided_pruning/dev.py   | 26 +++++++++++++++++
 .../estimator.py                              | 26 +++++++++++++++++
 .../icassp2020_domain_guided_pruning/eval.py  | 26 +++++++++++++++++
 .../face_normalizer_drop_120.py               | 26 +++++++++++++++++
 .../face_video_224.py                         | 26 +++++++++++++++++
 .../feature_divergence.py                     | 26 +++++++++++++++++
 .../filters_multiply_batl.py                  | 26 +++++++++++++++++
 .../filters_multiply_ijbc.py                  | 26 +++++++++++++++++
 .../filters_multiply_replaymobile.py          | 26 +++++++++++++++++
 .../filters_multiply_swan.py                  | 26 +++++++++++++++++
 .../find_filters.py                           | 26 +++++++++++++++++
 .../grandtest.py                              | 26 +++++++++++++++++
 .../grandtest_color_50_PrintReplay.py         | 26 +++++++++++++++++
 .../icassp2020_domain_guided_pruning/ijbc.py  | 26 +++++++++++++++++
 .../input_fn.py                               | 27 +++++++++++++++++-
 .../load_data_with_normalizer.py              | 26 +++++++++++++++++
 .../load_data_without_normalizer.py           | 26 +++++++++++++++++
 .../oulunpu.py                                | 26 +++++++++++++++++
 .../pad_p2_face_f1.py                         | 26 +++++++++++++++++
 .../pad_video_predictions.py                  | 26 +++++++++++++++++
 .../prepare_ijbc_images.py                    | 26 +++++++++++++++++
 .../replaymobile.py                           | 26 +++++++++++++++++
 .../icassp2020_domain_guided_pruning/swan.py  | 26 +++++++++++++++++
 .../icassp2020_domain_guided_pruning/train.py | 26 +++++++++++++++++
 .../train_dev_eval.py                         | 26 +++++++++++++++++
 .../transforms.py                             | 26 +++++++++++++++++
 download_all.py                               | 26 +++++++++++++++++
 evaluate.sh                                   | 26 +++++++++++++++++
 run_part1.sh                                  | 26 +++++++++++++++++
 run_part2.sh                                  | 26 +++++++++++++++++
 run_part3.sh                                  | 27 ++++++++++++++++++
 setup.py                                      | 28 ++++++++++++++++++-
 37 files changed, 963 insertions(+), 2 deletions(-)

diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index f1445b8..f7fc43a 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -1,3 +1,29 @@
+# bob.paper.icassp2020_domain_guided_pruning is part of the signal-processing
+# and machine learning toolbox Bob_. It contains the instruction to reproduce
+# the following paper:
+#
+#    A. Mohammadi, S. Bhattacharjee, and S. Marcel,
+#    “Domain Adaptation For Generalization Of Face Presentation Attack Detection
+#    In Mobile Settings With Minimal Information,” presented at ICASSP 2020.
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Amir Mohammadi <amir.mohammadi@idiap.ch>
+#
+# This file is part of bob.paper.icassp2020_domain_guided_pruning.
+#
+# bob.paper.icassp2020_domain_guided_pruning is free software: you can
+# redistribute it and/or modify it under the terms of the GNU General Public
+# License version 3 as published by the Free Software Foundation.
+#
+# bob.paper.icassp2020_domain_guided_pruning is distributed in the hope that it
+# will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
+# Public License for more details.
+#
+# You should have received a copy of the GNU General Public License along with
+# bob.paper.icassp2020_domain_guided_pruning. If not, see
+# <http://www.gnu.org/licenses/>.
+
 test:
   variables:
     CONDA_ENVS_PATH: "conda-env"
diff --git a/bob/paper/icassp2020_domain_guided_pruning/Protocol_1.py b/bob/paper/icassp2020_domain_guided_pruning/Protocol_1.py
index ac95737..f9de866 100644
--- a/bob/paper/icassp2020_domain_guided_pruning/Protocol_1.py
+++ b/bob/paper/icassp2020_domain_guided_pruning/Protocol_1.py
@@ -1,2 +1,28 @@
+# bob.paper.icassp2020_domain_guided_pruning is part of the signal-processing
+# and machine learning toolbox Bob_. It contains the instruction to reproduce
+# the following paper:
+#
+#    A. Mohammadi, S. Bhattacharjee, and S. Marcel,
+#    “Domain Adaptation For Generalization Of Face Presentation Attack Detection
+#    In Mobile Settings With Minimal Information,” presented at ICASSP 2020.
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Amir Mohammadi <amir.mohammadi@idiap.ch>
+#
+# This file is part of bob.paper.icassp2020_domain_guided_pruning.
+#
+# bob.paper.icassp2020_domain_guided_pruning is free software: you can
+# redistribute it and/or modify it under the terms of the GNU General Public
+# License version 3 as published by the Free Software Foundation.
+#
+# bob.paper.icassp2020_domain_guided_pruning is distributed in the hope that it
+# will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
+# Public License for more details.
+#
+# You should have received a copy of the GNU General Public License along with
+# bob.paper.icassp2020_domain_guided_pruning. If not, see
+# <http://www.gnu.org/licenses/>.
+
 protocol = "Protocol_1"
 database.protocol = protocol
diff --git a/bob/paper/icassp2020_domain_guided_pruning/batl.py b/bob/paper/icassp2020_domain_guided_pruning/batl.py
index d90a664..c0e5954 100644
--- a/bob/paper/icassp2020_domain_guided_pruning/batl.py
+++ b/bob/paper/icassp2020_domain_guided_pruning/batl.py
@@ -1,3 +1,28 @@
+# bob.paper.icassp2020_domain_guided_pruning is part of the signal-processing
+# and machine learning toolbox Bob_. It contains the instruction to reproduce
+# the following paper:
+#
+#    A. Mohammadi, S. Bhattacharjee, and S. Marcel,
+#    “Domain Adaptation For Generalization Of Face Presentation Attack Detection
+#    In Mobile Settings With Minimal Information,” presented at ICASSP 2020.
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Amir Mohammadi <amir.mohammadi@idiap.ch>
+#
+# This file is part of bob.paper.icassp2020_domain_guided_pruning.
+#
+# bob.paper.icassp2020_domain_guided_pruning is free software: you can
+# redistribute it and/or modify it under the terms of the GNU General Public
+# License version 3 as published by the Free Software Foundation.
+#
+# bob.paper.icassp2020_domain_guided_pruning is distributed in the hope that it
+# will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
+# Public License for more details.
+#
+# You should have received a copy of the GNU General Public License along with
+# bob.paper.icassp2020_domain_guided_pruning. If not, see
+# <http://www.gnu.org/licenses/>.
 """
 BATL Db is a database for face PAD experiments.
 """
diff --git a/bob/paper/icassp2020_domain_guided_pruning/deep_pix_bis.py b/bob/paper/icassp2020_domain_guided_pruning/deep_pix_bis.py
index 62ec2e2..bac7e0e 100644
--- a/bob/paper/icassp2020_domain_guided_pruning/deep_pix_bis.py
+++ b/bob/paper/icassp2020_domain_guided_pruning/deep_pix_bis.py
@@ -1,3 +1,29 @@
+# bob.paper.icassp2020_domain_guided_pruning is part of the signal-processing
+# and machine learning toolbox Bob_. It contains the instruction to reproduce
+# the following paper:
+#
+#    A. Mohammadi, S. Bhattacharjee, and S. Marcel,
+#    “Domain Adaptation For Generalization Of Face Presentation Attack Detection
+#    In Mobile Settings With Minimal Information,” presented at ICASSP 2020.
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Amir Mohammadi <amir.mohammadi@idiap.ch>
+#
+# This file is part of bob.paper.icassp2020_domain_guided_pruning.
+#
+# bob.paper.icassp2020_domain_guided_pruning is free software: you can
+# redistribute it and/or modify it under the terms of the GNU General Public
+# License version 3 as published by the Free Software Foundation.
+#
+# bob.paper.icassp2020_domain_guided_pruning is distributed in the hope that it
+# will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
+# Public License for more details.
+#
+# You should have received a copy of the GNU General Public License along with
+# bob.paper.icassp2020_domain_guided_pruning. If not, see
+# <http://www.gnu.org/licenses/>.
+
 from bob.extension import rc
 
 model_dir = "results/deep_pix_bis"
diff --git a/bob/paper/icassp2020_domain_guided_pruning/deep_pix_bis_features.py b/bob/paper/icassp2020_domain_guided_pruning/deep_pix_bis_features.py
index ff8baf8..a92b8d6 100644
--- a/bob/paper/icassp2020_domain_guided_pruning/deep_pix_bis_features.py
+++ b/bob/paper/icassp2020_domain_guided_pruning/deep_pix_bis_features.py
@@ -1,3 +1,29 @@
+# bob.paper.icassp2020_domain_guided_pruning is part of the signal-processing
+# and machine learning toolbox Bob_. It contains the instruction to reproduce
+# the following paper:
+#
+#    A. Mohammadi, S. Bhattacharjee, and S. Marcel,
+#    “Domain Adaptation For Generalization Of Face Presentation Attack Detection
+#    In Mobile Settings With Minimal Information,” presented at ICASSP 2020.
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Amir Mohammadi <amir.mohammadi@idiap.ch>
+#
+# This file is part of bob.paper.icassp2020_domain_guided_pruning.
+#
+# bob.paper.icassp2020_domain_guided_pruning is free software: you can
+# redistribute it and/or modify it under the terms of the GNU General Public
+# License version 3 as published by the Free Software Foundation.
+#
+# bob.paper.icassp2020_domain_guided_pruning is distributed in the hope that it
+# will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
+# Public License for more details.
+#
+# You should have received a copy of the GNU General Public License along with
+# bob.paper.icassp2020_domain_guided_pruning. If not, see
+# <http://www.gnu.org/licenses/>.
+
 import tensorflow as tf
 from bob.learn.tensorflow.models.densenet import DeepPixBiS
 
diff --git a/bob/paper/icassp2020_domain_guided_pruning/dev.py b/bob/paper/icassp2020_domain_guided_pruning/dev.py
index 2635871..8bc60a0 100644
--- a/bob/paper/icassp2020_domain_guided_pruning/dev.py
+++ b/bob/paper/icassp2020_domain_guided_pruning/dev.py
@@ -1,3 +1,29 @@
+# bob.paper.icassp2020_domain_guided_pruning is part of the signal-processing
+# and machine learning toolbox Bob_. It contains the instruction to reproduce
+# the following paper:
+#
+#    A. Mohammadi, S. Bhattacharjee, and S. Marcel,
+#    “Domain Adaptation For Generalization Of Face Presentation Attack Detection
+#    In Mobile Settings With Minimal Information,” presented at ICASSP 2020.
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Amir Mohammadi <amir.mohammadi@idiap.ch>
+#
+# This file is part of bob.paper.icassp2020_domain_guided_pruning.
+#
+# bob.paper.icassp2020_domain_guided_pruning is free software: you can
+# redistribute it and/or modify it under the terms of the GNU General Public
+# License version 3 as published by the Free Software Foundation.
+#
+# bob.paper.icassp2020_domain_guided_pruning is distributed in the hope that it
+# will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
+# Public License for more details.
+#
+# You should have received a copy of the GNU General Public License along with
+# bob.paper.icassp2020_domain_guided_pruning. If not, see
+# <http://www.gnu.org/licenses/>.
+
 try:
     groups
 except NameError:
diff --git a/bob/paper/icassp2020_domain_guided_pruning/estimator.py b/bob/paper/icassp2020_domain_guided_pruning/estimator.py
index 43b9b87..f67e8f1 100644
--- a/bob/paper/icassp2020_domain_guided_pruning/estimator.py
+++ b/bob/paper/icassp2020_domain_guided_pruning/estimator.py
@@ -1,3 +1,29 @@
+# bob.paper.icassp2020_domain_guided_pruning is part of the signal-processing
+# and machine learning toolbox Bob_. It contains the instruction to reproduce
+# the following paper:
+#
+#    A. Mohammadi, S. Bhattacharjee, and S. Marcel,
+#    “Domain Adaptation For Generalization Of Face Presentation Attack Detection
+#    In Mobile Settings With Minimal Information,” presented at ICASSP 2020.
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Amir Mohammadi <amir.mohammadi@idiap.ch>
+#
+# This file is part of bob.paper.icassp2020_domain_guided_pruning.
+#
+# bob.paper.icassp2020_domain_guided_pruning is free software: you can
+# redistribute it and/or modify it under the terms of the GNU General Public
+# License version 3 as published by the Free Software Foundation.
+#
+# bob.paper.icassp2020_domain_guided_pruning is distributed in the hope that it
+# will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
+# Public License for more details.
+#
+# You should have received a copy of the GNU General Public License along with
+# bob.paper.icassp2020_domain_guided_pruning. If not, see
+# <http://www.gnu.org/licenses/>.
+
 from bob.extension import rc
 from bob.learn.tensorflow.models.densenet import DeepPixBiS
 from bob.learn.tensorflow.utils.reproducible import set_seed
diff --git a/bob/paper/icassp2020_domain_guided_pruning/eval.py b/bob/paper/icassp2020_domain_guided_pruning/eval.py
index 8d47f14..4636cc2 100644
--- a/bob/paper/icassp2020_domain_guided_pruning/eval.py
+++ b/bob/paper/icassp2020_domain_guided_pruning/eval.py
@@ -1,3 +1,29 @@
+# bob.paper.icassp2020_domain_guided_pruning is part of the signal-processing
+# and machine learning toolbox Bob_. It contains the instruction to reproduce
+# the following paper:
+#
+#    A. Mohammadi, S. Bhattacharjee, and S. Marcel,
+#    “Domain Adaptation For Generalization Of Face Presentation Attack Detection
+#    In Mobile Settings With Minimal Information,” presented at ICASSP 2020.
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Amir Mohammadi <amir.mohammadi@idiap.ch>
+#
+# This file is part of bob.paper.icassp2020_domain_guided_pruning.
+#
+# bob.paper.icassp2020_domain_guided_pruning is free software: you can
+# redistribute it and/or modify it under the terms of the GNU General Public
+# License version 3 as published by the Free Software Foundation.
+#
+# bob.paper.icassp2020_domain_guided_pruning is distributed in the hope that it
+# will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
+# Public License for more details.
+#
+# You should have received a copy of the GNU General Public License along with
+# bob.paper.icassp2020_domain_guided_pruning. If not, see
+# <http://www.gnu.org/licenses/>.
+
 try:
     groups
 except NameError:
diff --git a/bob/paper/icassp2020_domain_guided_pruning/face_normalizer_drop_120.py b/bob/paper/icassp2020_domain_guided_pruning/face_normalizer_drop_120.py
index 8a6df46..50e4aa7 100644
--- a/bob/paper/icassp2020_domain_guided_pruning/face_normalizer_drop_120.py
+++ b/bob/paper/icassp2020_domain_guided_pruning/face_normalizer_drop_120.py
@@ -1,3 +1,29 @@
+# bob.paper.icassp2020_domain_guided_pruning is part of the signal-processing
+# and machine learning toolbox Bob_. It contains the instruction to reproduce
+# the following paper:
+#
+#    A. Mohammadi, S. Bhattacharjee, and S. Marcel,
+#    “Domain Adaptation For Generalization Of Face Presentation Attack Detection
+#    In Mobile Settings With Minimal Information,” presented at ICASSP 2020.
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Amir Mohammadi <amir.mohammadi@idiap.ch>
+#
+# This file is part of bob.paper.icassp2020_domain_guided_pruning.
+#
+# bob.paper.icassp2020_domain_guided_pruning is free software: you can
+# redistribute it and/or modify it under the terms of the GNU General Public
+# License version 3 as published by the Free Software Foundation.
+#
+# bob.paper.icassp2020_domain_guided_pruning is distributed in the hope that it
+# will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
+# Public License for more details.
+#
+# You should have received a copy of the GNU General Public License along with
+# bob.paper.icassp2020_domain_guided_pruning. If not, see
+# <http://www.gnu.org/licenses/>.
+
 from bob.pad.face.utils import min_face_size_normalizer
 from functools import partial
 
diff --git a/bob/paper/icassp2020_domain_guided_pruning/face_video_224.py b/bob/paper/icassp2020_domain_guided_pruning/face_video_224.py
index 0e4d086..493a1a5 100644
--- a/bob/paper/icassp2020_domain_guided_pruning/face_video_224.py
+++ b/bob/paper/icassp2020_domain_guided_pruning/face_video_224.py
@@ -1,3 +1,29 @@
+# bob.paper.icassp2020_domain_guided_pruning is part of the signal-processing
+# and machine learning toolbox Bob_. It contains the instruction to reproduce
+# the following paper:
+#
+#    A. Mohammadi, S. Bhattacharjee, and S. Marcel,
+#    “Domain Adaptation For Generalization Of Face Presentation Attack Detection
+#    In Mobile Settings With Minimal Information,” presented at ICASSP 2020.
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Amir Mohammadi <amir.mohammadi@idiap.ch>
+#
+# This file is part of bob.paper.icassp2020_domain_guided_pruning.
+#
+# bob.paper.icassp2020_domain_guided_pruning is free software: you can
+# redistribute it and/or modify it under the terms of the GNU General Public
+# License version 3 as published by the Free Software Foundation.
+#
+# bob.paper.icassp2020_domain_guided_pruning is distributed in the hope that it
+# will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
+# Public License for more details.
+#
+# You should have received a copy of the GNU General Public License along with
+# bob.paper.icassp2020_domain_guided_pruning. If not, see
+# <http://www.gnu.org/licenses/>.
+
 from bob.bio.face.preprocessor import FaceCrop
 from bob.bio.video.preprocessor import Wrapper
 from bob.bio.video import FrameSelector
diff --git a/bob/paper/icassp2020_domain_guided_pruning/feature_divergence.py b/bob/paper/icassp2020_domain_guided_pruning/feature_divergence.py
index a92ab3a..936d45b 100644
--- a/bob/paper/icassp2020_domain_guided_pruning/feature_divergence.py
+++ b/bob/paper/icassp2020_domain_guided_pruning/feature_divergence.py
@@ -1,3 +1,29 @@
+# bob.paper.icassp2020_domain_guided_pruning is part of the signal-processing
+# and machine learning toolbox Bob_. It contains the instruction to reproduce
+# the following paper:
+#
+#    A. Mohammadi, S. Bhattacharjee, and S. Marcel,
+#    “Domain Adaptation For Generalization Of Face Presentation Attack Detection
+#    In Mobile Settings With Minimal Information,” presented at ICASSP 2020.
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Amir Mohammadi <amir.mohammadi@idiap.ch>
+#
+# This file is part of bob.paper.icassp2020_domain_guided_pruning.
+#
+# bob.paper.icassp2020_domain_guided_pruning is free software: you can
+# redistribute it and/or modify it under the terms of the GNU General Public
+# License version 3 as published by the Free Software Foundation.
+#
+# bob.paper.icassp2020_domain_guided_pruning is distributed in the hope that it
+# will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
+# Public License for more details.
+#
+# You should have received a copy of the GNU General Public License along with
+# bob.paper.icassp2020_domain_guided_pruning. If not, see
+# <http://www.gnu.org/licenses/>.
+
 import tensorflow as tf
 import numpy as np
 import os
diff --git a/bob/paper/icassp2020_domain_guided_pruning/filters_multiply_batl.py b/bob/paper/icassp2020_domain_guided_pruning/filters_multiply_batl.py
index b6dfd4e..4354342 100644
--- a/bob/paper/icassp2020_domain_guided_pruning/filters_multiply_batl.py
+++ b/bob/paper/icassp2020_domain_guided_pruning/filters_multiply_batl.py
@@ -1,3 +1,29 @@
+# bob.paper.icassp2020_domain_guided_pruning is part of the signal-processing
+# and machine learning toolbox Bob_. It contains the instruction to reproduce
+# the following paper:
+#
+#    A. Mohammadi, S. Bhattacharjee, and S. Marcel,
+#    “Domain Adaptation For Generalization Of Face Presentation Attack Detection
+#    In Mobile Settings With Minimal Information,” presented at ICASSP 2020.
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Amir Mohammadi <amir.mohammadi@idiap.ch>
+#
+# This file is part of bob.paper.icassp2020_domain_guided_pruning.
+#
+# bob.paper.icassp2020_domain_guided_pruning is free software: you can
+# redistribute it and/or modify it under the terms of the GNU General Public
+# License version 3 as published by the Free Software Foundation.
+#
+# bob.paper.icassp2020_domain_guided_pruning is distributed in the hope that it
+# will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
+# Public License for more details.
+#
+# You should have received a copy of the GNU General Public License along with
+# bob.paper.icassp2020_domain_guided_pruning. If not, see
+# <http://www.gnu.org/licenses/>.
+
 import numpy as np
 filters_multiply = np.load("results/filters/oulunpu_vs_batl.npy")
 model_dir = "results/deep_pix_bis_pruned_by_batl"
diff --git a/bob/paper/icassp2020_domain_guided_pruning/filters_multiply_ijbc.py b/bob/paper/icassp2020_domain_guided_pruning/filters_multiply_ijbc.py
index 92a0573..a7a1820 100644
--- a/bob/paper/icassp2020_domain_guided_pruning/filters_multiply_ijbc.py
+++ b/bob/paper/icassp2020_domain_guided_pruning/filters_multiply_ijbc.py
@@ -1,3 +1,29 @@
+# bob.paper.icassp2020_domain_guided_pruning is part of the signal-processing
+# and machine learning toolbox Bob_. It contains the instruction to reproduce
+# the following paper:
+#
+#    A. Mohammadi, S. Bhattacharjee, and S. Marcel,
+#    “Domain Adaptation For Generalization Of Face Presentation Attack Detection
+#    In Mobile Settings With Minimal Information,” presented at ICASSP 2020.
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Amir Mohammadi <amir.mohammadi@idiap.ch>
+#
+# This file is part of bob.paper.icassp2020_domain_guided_pruning.
+#
+# bob.paper.icassp2020_domain_guided_pruning is free software: you can
+# redistribute it and/or modify it under the terms of the GNU General Public
+# License version 3 as published by the Free Software Foundation.
+#
+# bob.paper.icassp2020_domain_guided_pruning is distributed in the hope that it
+# will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
+# Public License for more details.
+#
+# You should have received a copy of the GNU General Public License along with
+# bob.paper.icassp2020_domain_guided_pruning. If not, see
+# <http://www.gnu.org/licenses/>.
+
 import numpy as np
 filters_multiply = np.load("results/filters/oulunpu_vs_ijbc.npy")
 model_dir = "results/deep_pix_bis_pruned_by_ijbc"
diff --git a/bob/paper/icassp2020_domain_guided_pruning/filters_multiply_replaymobile.py b/bob/paper/icassp2020_domain_guided_pruning/filters_multiply_replaymobile.py
index 91c40f8..3d1e94c 100644
--- a/bob/paper/icassp2020_domain_guided_pruning/filters_multiply_replaymobile.py
+++ b/bob/paper/icassp2020_domain_guided_pruning/filters_multiply_replaymobile.py
@@ -1,3 +1,29 @@
+# bob.paper.icassp2020_domain_guided_pruning is part of the signal-processing
+# and machine learning toolbox Bob_. It contains the instruction to reproduce
+# the following paper:
+#
+#    A. Mohammadi, S. Bhattacharjee, and S. Marcel,
+#    “Domain Adaptation For Generalization Of Face Presentation Attack Detection
+#    In Mobile Settings With Minimal Information,” presented at ICASSP 2020.
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Amir Mohammadi <amir.mohammadi@idiap.ch>
+#
+# This file is part of bob.paper.icassp2020_domain_guided_pruning.
+#
+# bob.paper.icassp2020_domain_guided_pruning is free software: you can
+# redistribute it and/or modify it under the terms of the GNU General Public
+# License version 3 as published by the Free Software Foundation.
+#
+# bob.paper.icassp2020_domain_guided_pruning is distributed in the hope that it
+# will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
+# Public License for more details.
+#
+# You should have received a copy of the GNU General Public License along with
+# bob.paper.icassp2020_domain_guided_pruning. If not, see
+# <http://www.gnu.org/licenses/>.
+
 import numpy as np
 filters_multiply = np.load("results/filters/oulunpu_vs_replaymobile.npy")
 model_dir = "results/deep_pix_bis_pruned_by_replaymobile"
diff --git a/bob/paper/icassp2020_domain_guided_pruning/filters_multiply_swan.py b/bob/paper/icassp2020_domain_guided_pruning/filters_multiply_swan.py
index ce3661d..50fb91a 100644
--- a/bob/paper/icassp2020_domain_guided_pruning/filters_multiply_swan.py
+++ b/bob/paper/icassp2020_domain_guided_pruning/filters_multiply_swan.py
@@ -1,3 +1,29 @@
+# bob.paper.icassp2020_domain_guided_pruning is part of the signal-processing
+# and machine learning toolbox Bob_. It contains the instruction to reproduce
+# the following paper:
+#
+#    A. Mohammadi, S. Bhattacharjee, and S. Marcel,
+#    “Domain Adaptation For Generalization Of Face Presentation Attack Detection
+#    In Mobile Settings With Minimal Information,” presented at ICASSP 2020.
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Amir Mohammadi <amir.mohammadi@idiap.ch>
+#
+# This file is part of bob.paper.icassp2020_domain_guided_pruning.
+#
+# bob.paper.icassp2020_domain_guided_pruning is free software: you can
+# redistribute it and/or modify it under the terms of the GNU General Public
+# License version 3 as published by the Free Software Foundation.
+#
+# bob.paper.icassp2020_domain_guided_pruning is distributed in the hope that it
+# will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
+# Public License for more details.
+#
+# You should have received a copy of the GNU General Public License along with
+# bob.paper.icassp2020_domain_guided_pruning. If not, see
+# <http://www.gnu.org/licenses/>.
+
 import numpy as np
 filters_multiply = np.load("results/filters/oulunpu_vs_swan.npy")
 model_dir = "results/deep_pix_bis_pruned_by_swan"
diff --git a/bob/paper/icassp2020_domain_guided_pruning/find_filters.py b/bob/paper/icassp2020_domain_guided_pruning/find_filters.py
index 2516f9c..25f5321 100644
--- a/bob/paper/icassp2020_domain_guided_pruning/find_filters.py
+++ b/bob/paper/icassp2020_domain_guided_pruning/find_filters.py
@@ -1,3 +1,29 @@
+# bob.paper.icassp2020_domain_guided_pruning is part of the signal-processing
+# and machine learning toolbox Bob_. It contains the instruction to reproduce
+# the following paper:
+#
+#    A. Mohammadi, S. Bhattacharjee, and S. Marcel,
+#    “Domain Adaptation For Generalization Of Face Presentation Attack Detection
+#    In Mobile Settings With Minimal Information,” presented at ICASSP 2020.
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Amir Mohammadi <amir.mohammadi@idiap.ch>
+#
+# This file is part of bob.paper.icassp2020_domain_guided_pruning.
+#
+# bob.paper.icassp2020_domain_guided_pruning is free software: you can
+# redistribute it and/or modify it under the terms of the GNU General Public
+# License version 3 as published by the Free Software Foundation.
+#
+# bob.paper.icassp2020_domain_guided_pruning is distributed in the hope that it
+# will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
+# Public License for more details.
+#
+# You should have received a copy of the GNU General Public License along with
+# bob.paper.icassp2020_domain_guided_pruning. If not, see
+# <http://www.gnu.org/licenses/>.
+
 import numpy as np
 import os
 import click
diff --git a/bob/paper/icassp2020_domain_guided_pruning/grandtest.py b/bob/paper/icassp2020_domain_guided_pruning/grandtest.py
index cf6057f..1fb695e 100644
--- a/bob/paper/icassp2020_domain_guided_pruning/grandtest.py
+++ b/bob/paper/icassp2020_domain_guided_pruning/grandtest.py
@@ -1,2 +1,28 @@
+# bob.paper.icassp2020_domain_guided_pruning is part of the signal-processing
+# and machine learning toolbox Bob_. It contains the instruction to reproduce
+# the following paper:
+#
+#    A. Mohammadi, S. Bhattacharjee, and S. Marcel,
+#    “Domain Adaptation For Generalization Of Face Presentation Attack Detection
+#    In Mobile Settings With Minimal Information,” presented at ICASSP 2020.
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Amir Mohammadi <amir.mohammadi@idiap.ch>
+#
+# This file is part of bob.paper.icassp2020_domain_guided_pruning.
+#
+# bob.paper.icassp2020_domain_guided_pruning is free software: you can
+# redistribute it and/or modify it under the terms of the GNU General Public
+# License version 3 as published by the Free Software Foundation.
+#
+# bob.paper.icassp2020_domain_guided_pruning is distributed in the hope that it
+# will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
+# Public License for more details.
+#
+# You should have received a copy of the GNU General Public License along with
+# bob.paper.icassp2020_domain_guided_pruning. If not, see
+# <http://www.gnu.org/licenses/>.
+
 protocol = "grandtest"
 database.protocol = protocol
diff --git a/bob/paper/icassp2020_domain_guided_pruning/grandtest_color_50_PrintReplay.py b/bob/paper/icassp2020_domain_guided_pruning/grandtest_color_50_PrintReplay.py
index 1b406c8..20d1598 100644
--- a/bob/paper/icassp2020_domain_guided_pruning/grandtest_color_50_PrintReplay.py
+++ b/bob/paper/icassp2020_domain_guided_pruning/grandtest_color_50_PrintReplay.py
@@ -1,2 +1,28 @@
+# bob.paper.icassp2020_domain_guided_pruning is part of the signal-processing
+# and machine learning toolbox Bob_. It contains the instruction to reproduce
+# the following paper:
+#
+#    A. Mohammadi, S. Bhattacharjee, and S. Marcel,
+#    “Domain Adaptation For Generalization Of Face Presentation Attack Detection
+#    In Mobile Settings With Minimal Information,” presented at ICASSP 2020.
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Amir Mohammadi <amir.mohammadi@idiap.ch>
+#
+# This file is part of bob.paper.icassp2020_domain_guided_pruning.
+#
+# bob.paper.icassp2020_domain_guided_pruning is free software: you can
+# redistribute it and/or modify it under the terms of the GNU General Public
+# License version 3 as published by the Free Software Foundation.
+#
+# bob.paper.icassp2020_domain_guided_pruning is distributed in the hope that it
+# will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
+# Public License for more details.
+#
+# You should have received a copy of the GNU General Public License along with
+# bob.paper.icassp2020_domain_guided_pruning. If not, see
+# <http://www.gnu.org/licenses/>.
+
 protocol = "grandtest-color-50-PrintReplay"
 database.protocol = protocol
diff --git a/bob/paper/icassp2020_domain_guided_pruning/ijbc.py b/bob/paper/icassp2020_domain_guided_pruning/ijbc.py
index cf4e9a9..b1e0b45 100644
--- a/bob/paper/icassp2020_domain_guided_pruning/ijbc.py
+++ b/bob/paper/icassp2020_domain_guided_pruning/ijbc.py
@@ -1,3 +1,29 @@
+# bob.paper.icassp2020_domain_guided_pruning is part of the signal-processing
+# and machine learning toolbox Bob_. It contains the instruction to reproduce
+# the following paper:
+#
+#    A. Mohammadi, S. Bhattacharjee, and S. Marcel,
+#    “Domain Adaptation For Generalization Of Face Presentation Attack Detection
+#    In Mobile Settings With Minimal Information,” presented at ICASSP 2020.
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Amir Mohammadi <amir.mohammadi@idiap.ch>
+#
+# This file is part of bob.paper.icassp2020_domain_guided_pruning.
+#
+# bob.paper.icassp2020_domain_guided_pruning is free software: you can
+# redistribute it and/or modify it under the terms of the GNU General Public
+# License version 3 as published by the Free Software Foundation.
+#
+# bob.paper.icassp2020_domain_guided_pruning is distributed in the hope that it
+# will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
+# Public License for more details.
+#
+# You should have received a copy of the GNU General Public License along with
+# bob.paper.icassp2020_domain_guided_pruning. If not, see
+# <http://www.gnu.org/licenses/>.
+
 from bob.extension import rc
 from bob.pad.base.database import PadDatabase, PadFile
 from glob import glob
diff --git a/bob/paper/icassp2020_domain_guided_pruning/input_fn.py b/bob/paper/icassp2020_domain_guided_pruning/input_fn.py
index ab31f06..8905af0 100644
--- a/bob/paper/icassp2020_domain_guided_pruning/input_fn.py
+++ b/bob/paper/icassp2020_domain_guided_pruning/input_fn.py
@@ -1,4 +1,29 @@
-# coding: utf-8
+# bob.paper.icassp2020_domain_guided_pruning is part of the signal-processing
+# and machine learning toolbox Bob_. It contains the instruction to reproduce
+# the following paper:
+#
+#    A. Mohammadi, S. Bhattacharjee, and S. Marcel,
+#    “Domain Adaptation For Generalization Of Face Presentation Attack Detection
+#    In Mobile Settings With Minimal Information,” presented at ICASSP 2020.
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Amir Mohammadi <amir.mohammadi@idiap.ch>
+#
+# This file is part of bob.paper.icassp2020_domain_guided_pruning.
+#
+# bob.paper.icassp2020_domain_guided_pruning is free software: you can
+# redistribute it and/or modify it under the terms of the GNU General Public
+# License version 3 as published by the Free Software Foundation.
+#
+# bob.paper.icassp2020_domain_guided_pruning is distributed in the hope that it
+# will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
+# Public License for more details.
+#
+# You should have received a copy of the GNU General Public License along with
+# bob.paper.icassp2020_domain_guided_pruning. If not, see
+# <http://www.gnu.org/licenses/>.
+
 from .transforms import (
     deep_pix_pre_transform,
     deep_pix_train_transform,
diff --git a/bob/paper/icassp2020_domain_guided_pruning/load_data_with_normalizer.py b/bob/paper/icassp2020_domain_guided_pruning/load_data_with_normalizer.py
index c415f4c..108e4e0 100644
--- a/bob/paper/icassp2020_domain_guided_pruning/load_data_with_normalizer.py
+++ b/bob/paper/icassp2020_domain_guided_pruning/load_data_with_normalizer.py
@@ -1,3 +1,29 @@
+# bob.paper.icassp2020_domain_guided_pruning is part of the signal-processing
+# and machine learning toolbox Bob_. It contains the instruction to reproduce
+# the following paper:
+#
+#    A. Mohammadi, S. Bhattacharjee, and S. Marcel,
+#    “Domain Adaptation For Generalization Of Face Presentation Attack Detection
+#    In Mobile Settings With Minimal Information,” presented at ICASSP 2020.
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Amir Mohammadi <amir.mohammadi@idiap.ch>
+#
+# This file is part of bob.paper.icassp2020_domain_guided_pruning.
+#
+# bob.paper.icassp2020_domain_guided_pruning is free software: you can
+# redistribute it and/or modify it under the terms of the GNU General Public
+# License version 3 as published by the Free Software Foundation.
+#
+# bob.paper.icassp2020_domain_guided_pruning is distributed in the hope that it
+# will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
+# Public License for more details.
+#
+# You should have received a copy of the GNU General Public License along with
+# bob.paper.icassp2020_domain_guided_pruning. If not, see
+# <http://www.gnu.org/licenses/>.
+
 from bob.pad.face.utils import the_giant_video_loader
 from functools import partial
 from .face_video_224 import cropper
diff --git a/bob/paper/icassp2020_domain_guided_pruning/load_data_without_normalizer.py b/bob/paper/icassp2020_domain_guided_pruning/load_data_without_normalizer.py
index 91d44e3..d13069c 100644
--- a/bob/paper/icassp2020_domain_guided_pruning/load_data_without_normalizer.py
+++ b/bob/paper/icassp2020_domain_guided_pruning/load_data_without_normalizer.py
@@ -1,3 +1,29 @@
+# bob.paper.icassp2020_domain_guided_pruning is part of the signal-processing
+# and machine learning toolbox Bob_. It contains the instruction to reproduce
+# the following paper:
+#
+#    A. Mohammadi, S. Bhattacharjee, and S. Marcel,
+#    “Domain Adaptation For Generalization Of Face Presentation Attack Detection
+#    In Mobile Settings With Minimal Information,” presented at ICASSP 2020.
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Amir Mohammadi <amir.mohammadi@idiap.ch>
+#
+# This file is part of bob.paper.icassp2020_domain_guided_pruning.
+#
+# bob.paper.icassp2020_domain_guided_pruning is free software: you can
+# redistribute it and/or modify it under the terms of the GNU General Public
+# License version 3 as published by the Free Software Foundation.
+#
+# bob.paper.icassp2020_domain_guided_pruning is distributed in the hope that it
+# will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
+# Public License for more details.
+#
+# You should have received a copy of the GNU General Public License along with
+# bob.paper.icassp2020_domain_guided_pruning. If not, see
+# <http://www.gnu.org/licenses/>.
+
 from bob.pad.face.utils import the_giant_video_loader
 from functools import partial
 from .face_video_224 import cropper
diff --git a/bob/paper/icassp2020_domain_guided_pruning/oulunpu.py b/bob/paper/icassp2020_domain_guided_pruning/oulunpu.py
index d99d051..e8e35bb 100644
--- a/bob/paper/icassp2020_domain_guided_pruning/oulunpu.py
+++ b/bob/paper/icassp2020_domain_guided_pruning/oulunpu.py
@@ -1,3 +1,29 @@
+# bob.paper.icassp2020_domain_guided_pruning is part of the signal-processing
+# and machine learning toolbox Bob_. It contains the instruction to reproduce
+# the following paper:
+#
+#    A. Mohammadi, S. Bhattacharjee, and S. Marcel,
+#    “Domain Adaptation For Generalization Of Face Presentation Attack Detection
+#    In Mobile Settings With Minimal Information,” presented at ICASSP 2020.
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Amir Mohammadi <amir.mohammadi@idiap.ch>
+#
+# This file is part of bob.paper.icassp2020_domain_guided_pruning.
+#
+# bob.paper.icassp2020_domain_guided_pruning is free software: you can
+# redistribute it and/or modify it under the terms of the GNU General Public
+# License version 3 as published by the Free Software Foundation.
+#
+# bob.paper.icassp2020_domain_guided_pruning is distributed in the hope that it
+# will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
+# Public License for more details.
+#
+# You should have received a copy of the GNU General Public License along with
+# bob.paper.icassp2020_domain_guided_pruning. If not, see
+# <http://www.gnu.org/licenses/>.
+
 from bob.db.oulunpu.config import database
 from bob.extension import rc
 
diff --git a/bob/paper/icassp2020_domain_guided_pruning/pad_p2_face_f1.py b/bob/paper/icassp2020_domain_guided_pruning/pad_p2_face_f1.py
index caa2ffb..35045d3 100644
--- a/bob/paper/icassp2020_domain_guided_pruning/pad_p2_face_f1.py
+++ b/bob/paper/icassp2020_domain_guided_pruning/pad_p2_face_f1.py
@@ -1,2 +1,28 @@
+# bob.paper.icassp2020_domain_guided_pruning is part of the signal-processing
+# and machine learning toolbox Bob_. It contains the instruction to reproduce
+# the following paper:
+#
+#    A. Mohammadi, S. Bhattacharjee, and S. Marcel,
+#    “Domain Adaptation For Generalization Of Face Presentation Attack Detection
+#    In Mobile Settings With Minimal Information,” presented at ICASSP 2020.
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Amir Mohammadi <amir.mohammadi@idiap.ch>
+#
+# This file is part of bob.paper.icassp2020_domain_guided_pruning.
+#
+# bob.paper.icassp2020_domain_guided_pruning is free software: you can
+# redistribute it and/or modify it under the terms of the GNU General Public
+# License version 3 as published by the Free Software Foundation.
+#
+# bob.paper.icassp2020_domain_guided_pruning is distributed in the hope that it
+# will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
+# Public License for more details.
+#
+# You should have received a copy of the GNU General Public License along with
+# bob.paper.icassp2020_domain_guided_pruning. If not, see
+# <http://www.gnu.org/licenses/>.
+
 protocol = "pad_p2_face_f1"
 database.protocol = protocol
diff --git a/bob/paper/icassp2020_domain_guided_pruning/pad_video_predictions.py b/bob/paper/icassp2020_domain_guided_pruning/pad_video_predictions.py
index 683f35a..b11c673 100644
--- a/bob/paper/icassp2020_domain_guided_pruning/pad_video_predictions.py
+++ b/bob/paper/icassp2020_domain_guided_pruning/pad_video_predictions.py
@@ -1,3 +1,29 @@
+# bob.paper.icassp2020_domain_guided_pruning is part of the signal-processing
+# and machine learning toolbox Bob_. It contains the instruction to reproduce
+# the following paper:
+#
+#    A. Mohammadi, S. Bhattacharjee, and S. Marcel,
+#    “Domain Adaptation For Generalization Of Face Presentation Attack Detection
+#    In Mobile Settings With Minimal Information,” presented at ICASSP 2020.
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Amir Mohammadi <amir.mohammadi@idiap.ch>
+#
+# This file is part of bob.paper.icassp2020_domain_guided_pruning.
+#
+# bob.paper.icassp2020_domain_guided_pruning is free software: you can
+# redistribute it and/or modify it under the terms of the GNU General Public
+# License version 3 as published by the Free Software Foundation.
+#
+# bob.paper.icassp2020_domain_guided_pruning is distributed in the hope that it
+# will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
+# Public License for more details.
+#
+# You should have received a copy of the GNU General Public License along with
+# bob.paper.icassp2020_domain_guided_pruning. If not, see
+# <http://www.gnu.org/licenses/>.
+
 from bob.pad.base.algorithm import VideoPredictions
 from bob.bio.base.preprocessor import CallablePreprocessor
 from bob.bio.base.extractor import CallableExtractor
diff --git a/bob/paper/icassp2020_domain_guided_pruning/prepare_ijbc_images.py b/bob/paper/icassp2020_domain_guided_pruning/prepare_ijbc_images.py
index 1e7b4ee..2d35124 100644
--- a/bob/paper/icassp2020_domain_guided_pruning/prepare_ijbc_images.py
+++ b/bob/paper/icassp2020_domain_guided_pruning/prepare_ijbc_images.py
@@ -1,3 +1,29 @@
+# bob.paper.icassp2020_domain_guided_pruning is part of the signal-processing
+# and machine learning toolbox Bob_. It contains the instruction to reproduce
+# the following paper:
+#
+#    A. Mohammadi, S. Bhattacharjee, and S. Marcel,
+#    “Domain Adaptation For Generalization Of Face Presentation Attack Detection
+#    In Mobile Settings With Minimal Information,” presented at ICASSP 2020.
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Amir Mohammadi <amir.mohammadi@idiap.ch>
+#
+# This file is part of bob.paper.icassp2020_domain_guided_pruning.
+#
+# bob.paper.icassp2020_domain_guided_pruning is free software: you can
+# redistribute it and/or modify it under the terms of the GNU General Public
+# License version 3 as published by the Free Software Foundation.
+#
+# bob.paper.icassp2020_domain_guided_pruning is distributed in the hope that it
+# will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
+# Public License for more details.
+#
+# You should have received a copy of the GNU General Public License along with
+# bob.paper.icassp2020_domain_guided_pruning. If not, see
+# <http://www.gnu.org/licenses/>.
+
 import click
 
 
diff --git a/bob/paper/icassp2020_domain_guided_pruning/replaymobile.py b/bob/paper/icassp2020_domain_guided_pruning/replaymobile.py
index 23e2573..747e52e 100644
--- a/bob/paper/icassp2020_domain_guided_pruning/replaymobile.py
+++ b/bob/paper/icassp2020_domain_guided_pruning/replaymobile.py
@@ -1,3 +1,29 @@
+# bob.paper.icassp2020_domain_guided_pruning is part of the signal-processing
+# and machine learning toolbox Bob_. It contains the instruction to reproduce
+# the following paper:
+#
+#    A. Mohammadi, S. Bhattacharjee, and S. Marcel,
+#    “Domain Adaptation For Generalization Of Face Presentation Attack Detection
+#    In Mobile Settings With Minimal Information,” presented at ICASSP 2020.
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Amir Mohammadi <amir.mohammadi@idiap.ch>
+#
+# This file is part of bob.paper.icassp2020_domain_guided_pruning.
+#
+# bob.paper.icassp2020_domain_guided_pruning is free software: you can
+# redistribute it and/or modify it under the terms of the GNU General Public
+# License version 3 as published by the Free Software Foundation.
+#
+# bob.paper.icassp2020_domain_guided_pruning is distributed in the hope that it
+# will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
+# Public License for more details.
+#
+# You should have received a copy of the GNU General Public License along with
+# bob.paper.icassp2020_domain_guided_pruning. If not, see
+# <http://www.gnu.org/licenses/>.
+
 from bob.pad.face.config.replay_mobile import database
 from bob.extension import rc
 
diff --git a/bob/paper/icassp2020_domain_guided_pruning/swan.py b/bob/paper/icassp2020_domain_guided_pruning/swan.py
index e92391a..01b6987 100644
--- a/bob/paper/icassp2020_domain_guided_pruning/swan.py
+++ b/bob/paper/icassp2020_domain_guided_pruning/swan.py
@@ -1,3 +1,29 @@
+# bob.paper.icassp2020_domain_guided_pruning is part of the signal-processing
+# and machine learning toolbox Bob_. It contains the instruction to reproduce
+# the following paper:
+#
+#    A. Mohammadi, S. Bhattacharjee, and S. Marcel,
+#    “Domain Adaptation For Generalization Of Face Presentation Attack Detection
+#    In Mobile Settings With Minimal Information,” presented at ICASSP 2020.
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Amir Mohammadi <amir.mohammadi@idiap.ch>
+#
+# This file is part of bob.paper.icassp2020_domain_guided_pruning.
+#
+# bob.paper.icassp2020_domain_guided_pruning is free software: you can
+# redistribute it and/or modify it under the terms of the GNU General Public
+# License version 3 as published by the Free Software Foundation.
+#
+# bob.paper.icassp2020_domain_guided_pruning is distributed in the hope that it
+# will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
+# Public License for more details.
+#
+# You should have received a copy of the GNU General Public License along with
+# bob.paper.icassp2020_domain_guided_pruning. If not, see
+# <http://www.gnu.org/licenses/>.
+
 from bob.db.swan.config_pad_video import database
 
 
diff --git a/bob/paper/icassp2020_domain_guided_pruning/train.py b/bob/paper/icassp2020_domain_guided_pruning/train.py
index e3d8d53..4f7406b 100644
--- a/bob/paper/icassp2020_domain_guided_pruning/train.py
+++ b/bob/paper/icassp2020_domain_guided_pruning/train.py
@@ -1,3 +1,29 @@
+# bob.paper.icassp2020_domain_guided_pruning is part of the signal-processing
+# and machine learning toolbox Bob_. It contains the instruction to reproduce
+# the following paper:
+#
+#    A. Mohammadi, S. Bhattacharjee, and S. Marcel,
+#    “Domain Adaptation For Generalization Of Face Presentation Attack Detection
+#    In Mobile Settings With Minimal Information,” presented at ICASSP 2020.
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Amir Mohammadi <amir.mohammadi@idiap.ch>
+#
+# This file is part of bob.paper.icassp2020_domain_guided_pruning.
+#
+# bob.paper.icassp2020_domain_guided_pruning is free software: you can
+# redistribute it and/or modify it under the terms of the GNU General Public
+# License version 3 as published by the Free Software Foundation.
+#
+# bob.paper.icassp2020_domain_guided_pruning is distributed in the hope that it
+# will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
+# Public License for more details.
+#
+# You should have received a copy of the GNU General Public License along with
+# bob.paper.icassp2020_domain_guided_pruning. If not, see
+# <http://www.gnu.org/licenses/>.
+
 try:
     groups
 except NameError:
diff --git a/bob/paper/icassp2020_domain_guided_pruning/train_dev_eval.py b/bob/paper/icassp2020_domain_guided_pruning/train_dev_eval.py
index 595ce9b..52a0658 100644
--- a/bob/paper/icassp2020_domain_guided_pruning/train_dev_eval.py
+++ b/bob/paper/icassp2020_domain_guided_pruning/train_dev_eval.py
@@ -1 +1,27 @@
+# bob.paper.icassp2020_domain_guided_pruning is part of the signal-processing
+# and machine learning toolbox Bob_. It contains the instruction to reproduce
+# the following paper:
+#
+#    A. Mohammadi, S. Bhattacharjee, and S. Marcel,
+#    “Domain Adaptation For Generalization Of Face Presentation Attack Detection
+#    In Mobile Settings With Minimal Information,” presented at ICASSP 2020.
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Amir Mohammadi <amir.mohammadi@idiap.ch>
+#
+# This file is part of bob.paper.icassp2020_domain_guided_pruning.
+#
+# bob.paper.icassp2020_domain_guided_pruning is free software: you can
+# redistribute it and/or modify it under the terms of the GNU General Public
+# License version 3 as published by the Free Software Foundation.
+#
+# bob.paper.icassp2020_domain_guided_pruning is distributed in the hope that it
+# will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
+# Public License for more details.
+#
+# You should have received a copy of the GNU General Public License along with
+# bob.paper.icassp2020_domain_guided_pruning. If not, see
+# <http://www.gnu.org/licenses/>.
+
 groups = ["train", "dev", "eval"]
diff --git a/bob/paper/icassp2020_domain_guided_pruning/transforms.py b/bob/paper/icassp2020_domain_guided_pruning/transforms.py
index 3480c71..8cc8799 100644
--- a/bob/paper/icassp2020_domain_guided_pruning/transforms.py
+++ b/bob/paper/icassp2020_domain_guided_pruning/transforms.py
@@ -1,3 +1,29 @@
+# bob.paper.icassp2020_domain_guided_pruning is part of the signal-processing
+# and machine learning toolbox Bob_. It contains the instruction to reproduce
+# the following paper:
+#
+#    A. Mohammadi, S. Bhattacharjee, and S. Marcel,
+#    “Domain Adaptation For Generalization Of Face Presentation Attack Detection
+#    In Mobile Settings With Minimal Information,” presented at ICASSP 2020.
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Amir Mohammadi <amir.mohammadi@idiap.ch>
+#
+# This file is part of bob.paper.icassp2020_domain_guided_pruning.
+#
+# bob.paper.icassp2020_domain_guided_pruning is free software: you can
+# redistribute it and/or modify it under the terms of the GNU General Public
+# License version 3 as published by the Free Software Foundation.
+#
+# bob.paper.icassp2020_domain_guided_pruning is distributed in the hope that it
+# will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
+# Public License for more details.
+#
+# You should have received a copy of the GNU General Public License along with
+# bob.paper.icassp2020_domain_guided_pruning. If not, see
+# <http://www.gnu.org/licenses/>.
+
 import tensorflow as tf
 from bob.learn.tensorflow.utils import to_channels_last
 
diff --git a/download_all.py b/download_all.py
index 028126d..bd4c4aa 100755
--- a/download_all.py
+++ b/download_all.py
@@ -1,5 +1,31 @@
 #!/usr/bin/env python
 
+# bob.paper.icassp2020_domain_guided_pruning is part of the signal-processing
+# and machine learning toolbox Bob_. It contains the instruction to reproduce
+# the following paper:
+#
+#    A. Mohammadi, S. Bhattacharjee, and S. Marcel,
+#    “Domain Adaptation For Generalization Of Face Presentation Attack Detection
+#    In Mobile Settings With Minimal Information,” presented at ICASSP 2020.
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Amir Mohammadi <amir.mohammadi@idiap.ch>
+#
+# This file is part of bob.paper.icassp2020_domain_guided_pruning.
+#
+# bob.paper.icassp2020_domain_guided_pruning is free software: you can
+# redistribute it and/or modify it under the terms of the GNU General Public
+# License version 3 as published by the Free Software Foundation.
+#
+# bob.paper.icassp2020_domain_guided_pruning is distributed in the hope that it
+# will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
+# Public License for more details.
+#
+# You should have received a copy of the GNU General Public License along with
+# bob.paper.icassp2020_domain_guided_pruning. If not, see
+# <http://www.gnu.org/licenses/>.
+
 from bob.extension.download import download_and_unzip
 import os
 
diff --git a/evaluate.sh b/evaluate.sh
index b0f717e..812430e 100755
--- a/evaluate.sh
+++ b/evaluate.sh
@@ -1,5 +1,31 @@
 #!/bin/bash
 
+# bob.paper.icassp2020_domain_guided_pruning is part of the signal-processing
+# and machine learning toolbox Bob_. It contains the instruction to reproduce
+# the following paper:
+#
+#    A. Mohammadi, S. Bhattacharjee, and S. Marcel,
+#    “Domain Adaptation For Generalization Of Face Presentation Attack Detection
+#    In Mobile Settings With Minimal Information,” presented at ICASSP 2020.
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Amir Mohammadi <amir.mohammadi@idiap.ch>
+#
+# This file is part of bob.paper.icassp2020_domain_guided_pruning.
+#
+# bob.paper.icassp2020_domain_guided_pruning is free software: you can
+# redistribute it and/or modify it under the terms of the GNU General Public
+# License version 3 as published by the Free Software Foundation.
+#
+# bob.paper.icassp2020_domain_guided_pruning is distributed in the hope that it
+# will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
+# Public License for more details.
+#
+# You should have received a copy of the GNU General Public License along with
+# bob.paper.icassp2020_domain_guided_pruning. If not, see
+# <http://www.gnu.org/licenses/>.
+
 set -ex
 
 databases=(oulunpu replaymobile swan batl)
diff --git a/run_part1.sh b/run_part1.sh
index f8079cb..cc8a95f 100755
--- a/run_part1.sh
+++ b/run_part1.sh
@@ -1,5 +1,31 @@
 #!/bin/bash
 
+# bob.paper.icassp2020_domain_guided_pruning is part of the signal-processing
+# and machine learning toolbox Bob_. It contains the instruction to reproduce
+# the following paper:
+#
+#    A. Mohammadi, S. Bhattacharjee, and S. Marcel,
+#    “Domain Adaptation For Generalization Of Face Presentation Attack Detection
+#    In Mobile Settings With Minimal Information,” presented at ICASSP 2020.
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Amir Mohammadi <amir.mohammadi@idiap.ch>
+#
+# This file is part of bob.paper.icassp2020_domain_guided_pruning.
+#
+# bob.paper.icassp2020_domain_guided_pruning is free software: you can
+# redistribute it and/or modify it under the terms of the GNU General Public
+# License version 3 as published by the Free Software Foundation.
+#
+# bob.paper.icassp2020_domain_guided_pruning is distributed in the hope that it
+# will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
+# Public License for more details.
+#
+# You should have received a copy of the GNU General Public License along with
+# bob.paper.icassp2020_domain_guided_pruning. If not, see
+# <http://www.gnu.org/licenses/>.
+
 # The experiments are done in 3 stages:
 # 1. Caching where all the face PAD datasets are cached for faster training and evaluation in future.
 # 2. Training deep_pix_bis on oulunpu
diff --git a/run_part2.sh b/run_part2.sh
index bc9a701..a626f88 100755
--- a/run_part2.sh
+++ b/run_part2.sh
@@ -1,5 +1,31 @@
 #!/bin/bash
 
+# bob.paper.icassp2020_domain_guided_pruning is part of the signal-processing
+# and machine learning toolbox Bob_. It contains the instruction to reproduce
+# the following paper:
+#
+#    A. Mohammadi, S. Bhattacharjee, and S. Marcel,
+#    “Domain Adaptation For Generalization Of Face Presentation Attack Detection
+#    In Mobile Settings With Minimal Information,” presented at ICASSP 2020.
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Amir Mohammadi <amir.mohammadi@idiap.ch>
+#
+# This file is part of bob.paper.icassp2020_domain_guided_pruning.
+#
+# bob.paper.icassp2020_domain_guided_pruning is free software: you can
+# redistribute it and/or modify it under the terms of the GNU General Public
+# License version 3 as published by the Free Software Foundation.
+#
+# bob.paper.icassp2020_domain_guided_pruning is distributed in the hope that it
+# will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
+# Public License for more details.
+#
+# You should have received a copy of the GNU General Public License along with
+# bob.paper.icassp2020_domain_guided_pruning. If not, see
+# <http://www.gnu.org/licenses/>.
+
 # The experiments are done in 3 stages:
 # 1. Caching where all the face PAD datasets are cached for faster training and evaluation in future.
 # 2. Training deep_pix_bis on oulunpu
diff --git a/run_part3.sh b/run_part3.sh
index e9a3480..c4c788d 100755
--- a/run_part3.sh
+++ b/run_part3.sh
@@ -1,5 +1,32 @@
 #!/bin/bash
 
+# bob.paper.icassp2020_domain_guided_pruning is part of the signal-processing
+# and machine learning toolbox Bob_. It contains the instruction to reproduce
+# the following paper:
+#
+#    A. Mohammadi, S. Bhattacharjee, and S. Marcel,
+#    “Domain Adaptation For Generalization Of Face Presentation Attack Detection
+#    In Mobile Settings With Minimal Information,” presented at ICASSP 2020.
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Amir Mohammadi <amir.mohammadi@idiap.ch>
+#
+# This file is part of bob.paper.icassp2020_domain_guided_pruning.
+#
+# bob.paper.icassp2020_domain_guided_pruning is free software: you can
+# redistribute it and/or modify it under the terms of the GNU General Public
+# License version 3 as published by the Free Software Foundation.
+#
+# bob.paper.icassp2020_domain_guided_pruning is distributed in the hope that it
+# will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
+# Public License for more details.
+#
+# You should have received a copy of the GNU General Public License along with
+# bob.paper.icassp2020_domain_guided_pruning. If not, see
+# <http://www.gnu.org/licenses/>.
+
+
 # The experiments are done in 3 stages:
 # 1. Caching where all the face PAD datasets are cached for faster training and evaluation in future.
 # 2. Training deep_pix_bis on oulunpu
diff --git a/setup.py b/setup.py
index 3785800..a6ed13e 100644
--- a/setup.py
+++ b/setup.py
@@ -1,5 +1,31 @@
 #!/usr/bin/env python
-# -*- coding: utf-8 -*-
+
+# bob.paper.icassp2020_domain_guided_pruning is part of the signal-processing
+# and machine learning toolbox Bob_. It contains the instruction to reproduce
+# the following paper:
+#
+#    A. Mohammadi, S. Bhattacharjee, and S. Marcel,
+#    “Domain Adaptation For Generalization Of Face Presentation Attack Detection
+#    In Mobile Settings With Minimal Information,” presented at ICASSP 2020.
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Amir Mohammadi <amir.mohammadi@idiap.ch>
+#
+# This file is part of bob.paper.icassp2020_domain_guided_pruning.
+#
+# bob.paper.icassp2020_domain_guided_pruning is free software: you can
+# redistribute it and/or modify it under the terms of the GNU General Public
+# License version 3 as published by the Free Software Foundation.
+#
+# bob.paper.icassp2020_domain_guided_pruning is distributed in the hope that it
+# will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
+# Public License for more details.
+#
+# You should have received a copy of the GNU General Public License along with
+# bob.paper.icassp2020_domain_guided_pruning. If not, see
+# <http://www.gnu.org/licenses/>.
+
 
 from setuptools import setup, dist
 
-- 
GitLab