.. vim: set fileencoding=utf-8 :
.. Tiago de Freitas Pereira <tiago.pereira@idiap.ch>

======================
 Domain Specific Units
======================

This section contains instructions on how to reproduce the experiments from Section 5 **Results and Discussion**.

Exceptionally, these instructions will cover only the **Thermal** database.
This would avoid this section to be extremely large.
However, the same set of instructions applies to ALL heterogeneous face databases.
To see all the available databases, check::

 $ resources.py --types database
  List of registered databases:

  - bob.bio.htface 1.0.0 @  <experiment>/bob.bio.htface: 
    + casia-nir-vis-2        --> bob.bio.htface.configs.databases.casia_nir_vis: database
    + cuhk-cufs              --> bob.bio.htface.configs.databases.cuhk_cufs: database
    + cuhk-cufsf             --> bob.bio.htface.configs.databases.cuhk_cufsf: database
    + eprip                  --> bob.bio.htface.configs.databases.eprip: database
    + fargo                  --> bob.bio.htface.configs.databases.fargo: database
    + fargo_depth            --> bob.bio.htface.configs.databases.fargo_depth: database
    + ldhf                   --> bob.bio.htface.configs.databases.ldhf: database
    + nivl                   --> bob.bio.htface.configs.databases.nivl: database
    + pola_thermal           --> bob.bio.htface.configs.databases.pola_thermal: database
    + thermal                --> bob.bio.htface.configs.databases.thermal: database


Thermal Experiments
===================

The sequence of experiments in this subsection generates the necessary data that creates Figures 5.16, 5.17 and Table 5.10.
This covers the training using Siamese/Triplet Networks, using Incep. Res. v2 and Incep. Res. v1 as DCNN basis and adaptation of :math:`\beta + W` and :math:`\beta`.


Inception Resnet v2
-------------------

The code below generates the cropped faces that are used to train the DSU for all the cases::

$ bob bio htface htface_baseline htface_idiap_msceleb_inception_v2_centerloss_gray thermal -- preprocess-training-data # generating prior

Siamese training adapting :math:`\beta + W`
...........................................


The code below trains the DSUs for: :math:`\theta_{[1-1](\beta + W)}`, :math:`\theta_{[1-2](\beta + W)}`, :math:`\theta_{[1-4](\beta + W)}`, :math:`\theta_{[1-5](\beta + W)}` and :math:`\theta_{[1-6](\beta + W)}`::

 $ bob bio htface htface_train_dsu siamese_inceptionv2_first_layer_nonshared_batch_norm thermal # Training DSU 1-1
 $ bob bio htface htface_train_dsu siamese_inceptionv2_adapt_1_2_nonshared_batch_norm thermal # Training DSU 1-2
 $ bob bio htface htface_train_dsu siamese_inceptionv2_adapt_1_4_nonshared_batch_norm thermal # Training DSU 1-4
 $ bob bio htface htface_train_dsu siamese_inceptionv2_adapt_1_5_nonshared_batch_norm thermal # Training DSU 1-5
 $ bob bio htface htface_train_dsu siamese_inceptionv2_adapt_1_6_nonshared_batch_norm thermal # Training DSU 1-6

With all the DSUs trained, the corresponding experiments are generated via the following bash commands::

 $ bob bio htface htface_baseline siamese_inceptionv2_first_layer_nonshared_batch_norm thermal -vv
 $ bob bio htface htface_baseline siamese_inceptionv2_adapt_1_2_nonshared_batch_norm thermal -vv
 $ bob bio htface htface_baseline siamese_inceptionv2_adapt_1_4_nonshared_batch_norm thermal -vv
 $ bob bio htface htface_baseline siamese_inceptionv2_adapt_1_5_nonshared_batch_norm thermal -vv
 $ bob bio htface htface_baseline siamese_inceptionv2_adapt_1_6_nonshared_batch_norm thermal -vv


Finally, after the execution of the experiments, Figure 5.16 (a) can be generated::

 $ bob htface evaluate_and_squash \
   \
   <experiment_path>/thermal/htface_idiap_msceleb_inception_v2_centerloss_gray/ \
   <experiment_path>/thermal/siamese_inceptionv2_first_layer_nonshared_batch_norm/ \
   <experiment_path>/thermal/siamese_inceptionv2_adapt_1_2_nonshared_batch_norm/ \
   <experiment_path>/thermal/siamese_inceptionv2_adapt_1_4_nonshared_batch_norm/ \
   <experiment_path>/thermal/siamese_inceptionv2_adapt_1_5_nonshared_batch_norm/ \
   <experiment_path>/thermal/siamese_inceptionv2_adapt_1_6_nonshared_batch_norm/ \
   \
   --legends "Incep. Res. v2 gray" --legends "$\theta_{t[1-1]}(\beta + W)$" --legends "$\theta_{t[1-2]}(\beta + W)$" \
   --legends "$\theta_{t[1-4]}(\beta + W)$" --legends "$\theta_{t[1-5]}(\beta + W)$" --legends "$\theta_{t[1-6]}(\beta + W)$" \
   --special-linestyle \
   --report-name "v2_siamese.pdf" \
   --x-min 0 \
   --title ""


.. Note::
  It's not necessary to run everything at once. Generate partial plots is a possibility. For more info check::
  
  $ bob bio htface evaluate_and_squash --help

If everything goes alright the following plot should be dumped

.. image:: ./img/chap5/v2_siamese.png
  :scale: 100 %


Siamese training adapting :math:`\beta`
.......................................


The code below trains the DSUs for: :math:`\theta_{[1-1](\beta)}`, :math:`\theta_{[1-2](\beta)}`, :math:`\theta_{[1-4](\beta)}`, :math:`\theta_{[1-5](\beta)}` and :math:`\theta_{[1-6](\beta)}`::

 $ bob bio htface htface_train_dsu siamese_inceptionv2_first_layer_betas_nonshared_batch_norm thermal # Training DSU 1-1
 $ bob bio htface htface_train_dsu siamese_inceptionv2_adapt_1_2_betas_nonshared_batch_norm thermal # Training DSU 1-2
 $ bob bio htface htface_train_dsu siamese_inceptionv2_adapt_1_4_betas_nonshared_batch_norm thermal # Training DSU 1-4
 $ bob bio htface htface_train_dsu siamese_inceptionv2_adapt_1_5_betas_nonshared_batch_norm thermal # Training DSU 1-5
 $ bob bio htface htface_train_dsu siamese_inceptionv2_adapt_1_6_betas_nonshared_batch_norm thermal # Training DSU 1-6

With all the DSUs trained, the corresponding experiments are generated via the following bash commands::

 $ bob bio htface htface_baseline siamese_inceptionv2_first_layer_betas_nonshared_batch_norm thermal -vv
 $ bob bio htface htface_baseline siamese_inceptionv2_adapt_1_2_betas_nonshared_batch_norm thermal -vv
 $ bob bio htface htface_baseline siamese_inceptionv2_adapt_1_4_betas_nonshared_batch_norm thermal -vv
 $ bob bio htface htface_baseline siamese_inceptionv2_adapt_1_5_betas_nonshared_batch_norm thermal -vv
 $ bob bio htface htface_baseline siamese_inceptionv2_adapt_1_6_betas_nonshared_batch_norm thermal -vv


Finally, after the execution of the experiments, Figure 5.17 (a) can be generated::

 $ bob htface evaluate_and_squash \
   \
   <experiment_path>/thermal/htface_idiap_msceleb_inception_v2_centerloss_gray/ \
   <experiment_path>/thermal/siamese_inceptionv2_first_layer_betas_nonshared_batch_norm/ \
   <experiment_path>/thermal/siamese_inceptionv2_adapt_1_2_betas_nonshared_batch_norm/ \
   <experiment_path>/thermal/siamese_inceptionv2_adapt_1_4_betas_nonshared_batch_norm/ \
   <experiment_path>/thermal/siamese_inceptionv2_adapt_1_5_betas_nonshared_batch_norm/ \
   <experiment_path>/thermal/siamese_inceptionv2_adapt_1_6_betas_nonshared_batch_norm/ \
   \
   --legends "Incep. Res. v2 gray" --legends "$\theta_{t[1-1]}(\beta)$" --legends "$\theta_{t[1-2]}(\beta)$" \
   --legends "$\theta_{t[1-4]}(\beta)$" --legends "$\theta_{t[1-5]}(\beta)$" --legends "$\theta_{t[1-6]}(\beta)$" \
   --special-linestyle \
   --report-name "v2_siamese_betas.pdf" \
   --x-min 0 \
   --title ""


.. Note::
  It's not necessary to run everything at once. Generate partial plots is a possibility. For more info check::
  
  $ bob bio htface evaluate_and_squash --help

If everything goes alright the following plot should be dumped

.. image:: ./img/chap5/v2_siamese_bias.png
  :scale: 100 %


Triplet training adapting :math:`\beta + W`
...........................................


The code below trains the DSUs for: :math:`\theta_{[1-1](\beta + W)}`, :math:`\theta_{[1-2](\beta + W)}`, :math:`\theta_{[1-4](\beta + W)}`, :math:`\theta_{[1-5](\beta + W)}` and :math:`\theta_{[1-6](\beta + W)}`::

 $ bob bio htface htface_train_dsu triplet_inceptionv2_first_layer_nonshared_batch_norm thermal # Training DSU 1-1
 $ bob bio htface htface_train_dsu triplet_inceptionv2_layers_1_2_nonshared_batch_norm thermal # Training DSU 1-2
 $ bob bio htface htface_train_dsu triplet_inceptionv2_layers_1_4_nonshared_batch_norm thermal # Training DSU 1-4
 $ bob bio htface htface_train_dsu triplet_inceptionv2_layers_1_5_nonshared_batch_norm thermal # Training DSU 1-5
 $ bob bio htface htface_train_dsu triplet_inceptionv2_layers_1_6_nonshared_batch_norm thermal # Training DSU 1-6

With all the DSUs trained, the corresponding experiments are generated via the following bash commands::

 $ bob bio htface htface_baseline triplet_inceptionv2_first_layer_nonshared_batch_norm thermal -vv
 $ bob bio htface htface_baseline triplet_inceptionv2_layers_1_2_nonshared_batch_norm thermal -vv
 $ bob bio htface htface_baseline triplet_inceptionv2_layers_1_4_nonshared_batch_norm thermal -vv
 $ bob bio htface htface_baseline triplet_inceptionv2_layers_1_5_nonshared_batch_norm thermal -vv
 $ bob bio htface htface_baseline triplet_inceptionv2_layers_1_6_nonshared_batch_norm thermal -vv


Finally, after the execution of the experiments the CMC curves can be generated as::

 $ bob htface evaluate_and_squash \
   \
   <experiment_path>/thermal/htface_idiap_msceleb_inception_v2_centerloss_gray/ \
   <experiment_path>/thermal/triplet_inceptionv2_first_layer_nonshared_batch_norm/ \
   <experiment_path>/thermal/triplet_inceptionv2_layers_1_2_nonshared_batch_norm/ \
   <experiment_path>/thermal/triplet_inceptionv2_layers_1_4_nonshared_batch_norm/ \
   <experiment_path>/thermal/triplet_inceptionv2_layers_1_5_nonshared_batch_norm/ \
   <experiment_path>/thermal/triplet_inceptionv2_layers_1_6_nonshared_batch_norm/ \
   \
   --legends "Incep. Res. v2 gray" --legends "$\theta_{t[1-1]}(\beta + W)$" --legends "$\theta_{t[1-2]}(\beta + W)$" \
   --legends "$\theta_{t[1-4]}(\beta + W)$" --legends "$\theta_{t[1-5]}(\beta + W)$" --legends "$\theta_{t[1-6]}(\beta + W)$" \
   --special-linestyle \
   --report-name "v2_triplet.pdf" \
   --x-min 0 \
   --title ""


.. Note::
  It's not necessary to run everything at once. Generate partial plots is a possibility. For more info check::
  
  $ bob bio htface evaluate_and_squash --help


Triplet training adapting :math:`\beta`
.......................................


The code below trains the DSUs for: :math:`\theta_{[1-1](\beta)}`, :math:`\theta_{[1-2](\beta)}`, :math:`\theta_{[1-4](\beta)}`, :math:`\theta_{[1-5](\beta)}` and :math:`\theta_{[1-6](\beta)}`::

 $ bob bio htface htface_train_dsu triplet_inceptionv2_first_layer_nonshared_betas_batch_norm thermal # Training DSU 1-1
 $ bob bio htface htface_train_dsu triplet_inceptionv2_layers_1_2_betas_nonshared_batch_norm thermal # Training DSU 1-2
 $ bob bio htface htface_train_dsu triplet_inceptionv2_layers_1_4_betas_nonshared_batch_norm thermal # Training DSU 1-4
 $ bob bio htface htface_train_dsu triplet_inceptionv2_layers_1_5_betas_nonshared_batch_norm thermal # Training DSU 1-5
 $ bob bio htface htface_train_dsu triplet_inceptionv2_layers_1_6_betas_nonshared_batch_norm thermal # Training DSU 1-6

With all the DSUs trained, the corresponding experiments are generated via the following bash commands::

 $ bob bio htface htface_baseline triplet_inceptionv2_first_layer_nonshared_betas_batch_norm thermal -vv
 $ bob bio htface htface_baseline triplet_inceptionv2_layers_1_2_betas_nonshared_batch_norm thermal -vv
 $ bob bio htface htface_baseline triplet_inceptionv2_layers_1_4_betas_nonshared_batch_norm thermal -vv
 $ bob bio htface htface_baseline triplet_inceptionv2_layers_1_5_betas_nonshared_batch_norm thermal -vv
 $ bob bio htface htface_baseline triplet_inceptionv2_layers_1_6_betas_nonshared_batch_norm thermal -vv


Finally, after the execution of the experiments the CMC curves can be generated as::

 $ bob htface evaluate_and_squash \
   \
   <experiment_path>/thermal/htface_idiap_msceleb_inception_v2_centerloss_gray/ \
   <experiment_path>/thermal/triplet_inceptionv2_first_layer_nonshared_betas_batch_norm/ \
   <experiment_path>/thermal/triplet_inceptionv2_layers_1_2_betas_nonshared_batch_norm/ \
   <experiment_path>/thermal/triplet_inceptionv2_layers_1_4_betas_nonshared_batch_norm/ \
   <experiment_path>/thermal/triplet_inceptionv2_layers_1_5_betas_nonshared_batch_norm/ \
   <experiment_path>/thermal/triplet_inceptionv2_layers_1_6_betas_nonshared_batch_norm/ \
   \
   --legends "Incep. Res. v2 gray" --legends "$\theta_{t[1-1]}(\beta)$" --legends "$\theta_{t[1-2]}(\beta)$" \
   --legends "$\theta_{t[1-4]}(\beta)$" --legends "$\theta_{t[1-5]}(\beta)$" --legends "$\theta_{t[1-6]}(\beta)$" \
   --special-linestyle \
   --report-name "v2_triplet_betas.pdf" \
   --x-min 0 \
   --title ""


.. Note::
  It's not necessary to run everything at once. Generate partial plots is a possibility. For more info check::
  
  $ bob bio htface evaluate_and_squash --help



Inception Resnet v1
-------------------

The code below generates the cropped faces that are used to train the DSU for all the cases::

$ bob bio htface htface_baseline htface_idiap_msceleb_inception_v1_centerloss_gray thermal -- preprocess-training-data # generating prior

Siamese training adapting :math:`\beta + W`
...........................................


The code below trains the DSUs for: :math:`\theta_{[1-1](\beta + W)}`, :math:`\theta_{[1-2](\beta + W)}`, :math:`\theta_{[1-4](\beta + W)}`, :math:`\theta_{[1-5](\beta + W)}` and :math:`\theta_{[1-6](\beta + W)}`::

 $ bob bio htface htface_train_dsu siamese_inceptionv1_first_layer_nonshared_batch_norm thermal # Training DSU 1-1
 $ bob bio htface htface_train_dsu siamese_inceptionv1_adapt_1_2_nonshared_batch_norm thermal # Training DSU 1-2
 $ bob bio htface htface_train_dsu siamese_inceptionv1_adapt_1_4_nonshared_batch_norm thermal # Training DSU 1-4
 $ bob bio htface htface_train_dsu siamese_inceptionv1_adapt_1_5_nonshared_batch_norm thermal # Training DSU 1-5
 $ bob bio htface htface_train_dsu siamese_inceptionv1_adapt_1_6_nonshared_batch_norm thermal # Training DSU 1-6

With all the DSUs trained, the corresponding experiments are generated via the following bash commands::

 $ bob bio htface htface_baseline siamese_inceptionv1_first_layer_nonshared_batch_norm thermal -vv
 $ bob bio htface htface_baseline siamese_inceptionv1_adapt_1_2_nonshared_batch_norm thermal -vv
 $ bob bio htface htface_baseline siamese_inceptionv1_adapt_1_4_nonshared_batch_norm thermal -vv
 $ bob bio htface htface_baseline siamese_inceptionv1_adapt_1_5_nonshared_batch_norm thermal -vv
 $ bob bio htface htface_baseline siamese_inceptionv1_adapt_1_6_nonshared_batch_norm thermal -vv


Finally, after the execution of the experiments, Figure 5.16 (b) can be generated::

 $ bob htface evaluate_and_squash \
   \
   <experiment_path>/thermal/htface_idiap_msceleb_inception_v1_centerloss_gray/ \
   <experiment_path>/thermal/siamese_inceptionv1_first_layer_nonshared_batch_norm/ \
   <experiment_path>/thermal/siamese_inceptionv1_adapt_1_2_nonshared_batch_norm/ \
   <experiment_path>/thermal/siamese_inceptionv1_adapt_1_4_nonshared_batch_norm/ \
   <experiment_path>/thermal/siamese_inceptionv1_adapt_1_5_nonshared_batch_norm/ \
   <experiment_path>/thermal/siamese_inceptionv1_adapt_1_6_nonshared_batch_norm/ \
   \
   --legends "Incep. Res. v1 gray" --legends "$\theta_{t[1-1]}(\beta + W)$" --legends "$\theta_{t[1-2]}(\beta + W)$" \
   --legends "$\theta_{t[1-4]}(\beta + W)$" --legends "$\theta_{t[1-5]}(\beta + W)$" --legends "$\theta_{t[1-6]}(\beta + W)$" \
   --special-linestyle \
   --report-name "v1_siamese.pdf" \
   --x-min 0 \
   --title ""


.. Note::
  It's not necessary to run everything at once. Generate partial plots is a possibility. For more info check::
  
  $ bob bio htface evaluate_and_squash --help

If everything goes alright the following plot should be dumped

.. image:: ./img/chap5/v1_siamese.png
  :scale: 100 %


Siamese training adapting :math:`\beta`
.......................................


The code below trains the DSUs for: :math:`\theta_{[1-1](\beta)}`, :math:`\theta_{[1-2](\beta)}`, :math:`\theta_{[1-4](\beta)}`, :math:`\theta_{[1-5](\beta)}` and :math:`\theta_{[1-6](\beta)}`::

 $ bob bio htface htface_train_dsu siamese_inceptionv1_first_layer_betas_nonshared_batch_norm thermal # Training DSU 1-1
 $ bob bio htface htface_train_dsu siamese_inceptionv1_adapt_1_2_betas_nonshared_batch_norm thermal # Training DSU 1-2
 $ bob bio htface htface_train_dsu siamese_inceptionv1_adapt_1_4_betas_nonshared_batch_norm thermal # Training DSU 1-4
 $ bob bio htface htface_train_dsu siamese_inceptionv1_adapt_1_5_betas_nonshared_batch_norm thermal # Training DSU 1-5
 $ bob bio htface htface_train_dsu siamese_inceptionv1_adapt_1_6_betas_nonshared_batch_norm thermal # Training DSU 1-6

With all the DSUs trained, the corresponding experiments are generated via the following bash commands::

 $ bob bio htface htface_baseline siamese_inceptionv1_first_layer_betas_nonshared_batch_norm thermal -vv
 $ bob bio htface htface_baseline siamese_inceptionv1_adapt_1_2_betas_nonshared_batch_norm thermal -vv
 $ bob bio htface htface_baseline siamese_inceptionv1_adapt_1_4_betas_nonshared_batch_norm thermal -vv
 $ bob bio htface htface_baseline siamese_inceptionv1_adapt_1_5_betas_nonshared_batch_norm thermal -vv
 $ bob bio htface htface_baseline siamese_inceptionv1_adapt_1_6_betas_nonshared_batch_norm thermal -vv


Finally, after the execution of the experiments, Figure 5.17 (a) can be generated::

 $ bob htface evaluate_and_squash \
   \
   <experiment_path>/thermal/htface_idiap_msceleb_inception_v1_centerloss_gray/ \
   <experiment_path>/thermal/siamese_inceptionv1_first_layer_betas_nonshared_batch_norm/ \
   <experiment_path>/thermal/siamese_inceptionv1_adapt_1_2_betas_nonshared_batch_norm/ \
   <experiment_path>/thermal/siamese_inceptionv1_adapt_1_4_betas_nonshared_batch_norm/ \
   <experiment_path>/thermal/siamese_inceptionv1_adapt_1_5_betas_nonshared_batch_norm/ \
   <experiment_path>/thermal/siamese_inceptionv1_adapt_1_6_betas_nonshared_batch_norm/ \
   \
   --legends "Incep. Res. v1 gray" --legends "$\theta_{t[1-1]}(\beta)$" --legends "$\theta_{t[1-2]}(\beta)$" \
   --legends "$\theta_{t[1-4]}(\beta)$" --legends "$\theta_{t[1-5]}(\beta)$" --legends "$\theta_{t[1-6]}(\beta)$" \
   --special-linestyle \
   --report-name "v1_siamese_betas.pdf" \
   --x-min 0 \
   --title ""


.. Note::
  It's not necessary to run everything at once. Generate partial plots is a possibility. For more info check::
  
  $ bob bio htface evaluate_and_squash --help

If everything goes alright the following plot should be dumped

.. image:: ./img/chap5/v1_siamese_bias.png
  :scale: 100 %


Triplet training adapting :math:`\beta + W`
...........................................


The code below trains the DSUs for: :math:`\theta_{[1-1](\beta + W)}`, :math:`\theta_{[1-2](\beta + W)}`, :math:`\theta_{[1-4](\beta + W)}`, :math:`\theta_{[1-5](\beta + W)}` and :math:`\theta_{[1-6](\beta + W)}`::

 $ bob bio htface htface_train_dsu triplet_inceptionv1_first_layer_nonshared_batch_norm thermal # Training DSU 1-1
 $ bob bio htface htface_train_dsu triplet_inceptionv1_layers_1_2_nonshared_batch_norm thermal # Training DSU 1-2
 $ bob bio htface htface_train_dsu triplet_inceptionv1_layers_1_4_nonshared_batch_norm thermal # Training DSU 1-4
 $ bob bio htface htface_train_dsu triplet_inceptionv1_layers_1_5_nonshared_batch_norm thermal # Training DSU 1-5
 $ bob bio htface htface_train_dsu triplet_inceptionv1_layers_1_6_nonshared_batch_norm thermal # Training DSU 1-6

With all the DSUs trained, the corresponding experiments are generated via the following bash commands::

 $ bob bio htface htface_baseline triplet_inceptionv1_first_layer_nonshared_batch_norm thermal -vv
 $ bob bio htface htface_baseline triplet_inceptionv1_layers_1_2_nonshared_batch_norm thermal -vv
 $ bob bio htface htface_baseline triplet_inceptionv1_layers_1_4_nonshared_batch_norm thermal -vv
 $ bob bio htface htface_baseline triplet_inceptionv1_layers_1_5_nonshared_batch_norm thermal -vv
 $ bob bio htface htface_baseline triplet_inceptionv1_layers_1_6_nonshared_batch_norm thermal -vv


Finally, after the execution of the experiments the CMC curves can be generated as::

 $ bob htface evaluate_and_squash \
   \
   <experiment_path>/thermal/htface_idiap_msceleb_inception_v1_centerloss_gray/ \
   <experiment_path>/thermal/triplet_inceptionv1_first_layer_nonshared_batch_norm/ \
   <experiment_path>/thermal/triplet_inceptionv1_layers_1_2_nonshared_batch_norm/ \
   <experiment_path>/thermal/triplet_inceptionv1_layers_1_4_nonshared_batch_norm/ \
   <experiment_path>/thermal/triplet_inceptionv1_layers_1_5_nonshared_batch_norm/ \
   <experiment_path>/thermal/triplet_inceptionv1_layers_1_6_nonshared_batch_norm/ \
   \
   --legends "Incep. Res. v1 gray" --legends "$\theta_{t[1-1]}(\beta + W)$" --legends "$\theta_{t[1-2]}(\beta + W)$" \
   --legends "$\theta_{t[1-4]}(\beta + W)$" --legends "$\theta_{t[1-5]}(\beta + W)$" --legends "$\theta_{t[1-6]}(\beta + W)$" \
   --special-linestyle \
   --report-name "v1_triplet.pdf" \
   --x-min 0 \
   --title ""


.. Note::
  It's not necessary to run everything at once. Generate partial plots is a possibility. For more info check::
  
  $ bob bio htface evaluate_and_squash --help


Triplet training adapting :math:`\beta`
.......................................


The code below trains the DSUs for: :math:`\theta_{[1-1](\beta)}`, :math:`\theta_{[1-2](\beta)}`, :math:`\theta_{[1-4](\beta)}`, :math:`\theta_{[1-5](\beta)}` and :math:`\theta_{[1-6](\beta)}`::

 $ bob bio htface htface_train_dsu triplet_inceptionv1_first_layer_nonshared_betas_batch_norm thermal # Training DSU 1-1
 $ bob bio htface htface_train_dsu triplet_inceptionv1_layers_1_2_betas_nonshared_batch_norm thermal # Training DSU 1-2
 $ bob bio htface htface_train_dsu triplet_inceptionv1_layers_1_4_betas_nonshared_batch_norm thermal # Training DSU 1-4
 $ bob bio htface htface_train_dsu triplet_inceptionv1_layers_1_5_betas_nonshared_batch_norm thermal # Training DSU 1-5
 $ bob bio htface htface_train_dsu triplet_inceptionv1_layers_1_6_betas_nonshared_batch_norm thermal # Training DSU 1-6

With all the DSUs trained, the corresponding experiments are generated via the following bash commands::

 $ bob bio htface htface_baseline triplet_inceptionv1_first_layer_nonshared_betas_batch_norm thermal -vv
 $ bob bio htface htface_baseline triplet_inceptionv1_layers_1_2_betas_nonshared_batch_norm thermal -vv
 $ bob bio htface htface_baseline triplet_inceptionv1_layers_1_4_betas_nonshared_batch_norm thermal -vv
 $ bob bio htface htface_baseline triplet_inceptionv1_layers_1_5_betas_nonshared_batch_norm thermal -vv
 $ bob bio htface htface_baseline triplet_inceptionv1_layers_1_6_betas_nonshared_batch_norm thermal -vv


Finally, after the execution of the experiments the CMC curves can be generated as::

 $ bob htface evaluate_and_squash \
   \
   <experiment_path>/thermal/htface_idiap_msceleb_inception_v1_centerloss_gray/ \
   <experiment_path>/thermal/triplet_inceptionv1_first_layer_nonshared_betas_batch_norm/ \
   <experiment_path>/thermal/triplet_inceptionv1_layers_1_2_betas_nonshared_batch_norm/ \
   <experiment_path>/thermal/triplet_inceptionv1_layers_1_4_betas_nonshared_batch_norm/ \
   <experiment_path>/thermal/triplet_inceptionv1_layers_1_5_betas_nonshared_batch_norm/ \
   <experiment_path>/thermal/triplet_inceptionv1_layers_1_6_betas_nonshared_batch_norm/ \
   \
   --legends "Incep. Res. v1 gray" --legends "$\theta_{t[1-1]}(\beta)$" --legends "$\theta_{t[1-2]}(\beta)$" \
   --legends "$\theta_{t[1-4]}(\beta)$" --legends "$\theta_{t[1-5]}(\beta)$" --legends "$\theta_{t[1-6]}(\beta)$" \
   --special-linestyle \
   --report-name "v1_triplet_betas.pdf" \
   --x-min 0 \
   --title ""


.. Note::
  It's not necessary to run everything at once. Generate partial plots is a possibility. For more info check::
  
  $ bob bio htface evaluate_and_squash --help