Porting to dask pipelines

bob/pad/face/config/algorithm/video_cascade_svm_pad_algorithm.py

-#!/usr/bin/env python
-
-from bob.pad.base.algorithm import SVMCascadePCA
-
-#=======================================================================================
-# Define instances here:
-
-MACHINE_TYPE = 'ONE_CLASS'
-KERNEL_TYPE = 'RBF'
-SVM_KWARGS = {'nu': 0.001, 'gamma': 0.2}
-N = 2
-POS_SCORES_SLOPE = 0.01
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_n2_gamma_02 = SVMCascadePCA(
-    machine_type=MACHINE_TYPE,
-    kernel_type=KERNEL_TYPE,
-    svm_kwargs=SVM_KWARGS,
-    N=N,
-    pos_scores_slope=POS_SCORES_SLOPE,
-    frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-MACHINE_TYPE = 'ONE_CLASS'
-KERNEL_TYPE = 'RBF'
-SVM_KWARGS = {'nu': 0.001, 'gamma': 0.1}
-N = 2
-POS_SCORES_SLOPE = 0.01
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_n2_gamma_01 = SVMCascadePCA(
-    machine_type=MACHINE_TYPE,
-    kernel_type=KERNEL_TYPE,
-    svm_kwargs=SVM_KWARGS,
-    N=N,
-    pos_scores_slope=POS_SCORES_SLOPE,
-    frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-MACHINE_TYPE = 'ONE_CLASS'
-KERNEL_TYPE = 'RBF'
-SVM_KWARGS = {'nu': 0.001, 'gamma': 0.05}
-N = 2
-POS_SCORES_SLOPE = 0.01
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_n2_gamma_005 = SVMCascadePCA(
-    machine_type=MACHINE_TYPE,
-    kernel_type=KERNEL_TYPE,
-    svm_kwargs=SVM_KWARGS,
-    N=N,
-    pos_scores_slope=POS_SCORES_SLOPE,
-    frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-MACHINE_TYPE = 'ONE_CLASS'
-KERNEL_TYPE = 'RBF'
-SVM_KWARGS = {'nu': 0.001, 'gamma': 0.01}
-N = 2
-POS_SCORES_SLOPE = 0.01
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_n2_gamma_001 = SVMCascadePCA(
-    machine_type=MACHINE_TYPE,
-    kernel_type=KERNEL_TYPE,
-    svm_kwargs=SVM_KWARGS,
-    N=N,
-    pos_scores_slope=POS_SCORES_SLOPE,
-    frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-#=======================================================================================
-
-MACHINE_TYPE = 'ONE_CLASS'
-KERNEL_TYPE = 'RBF'
-SVM_KWARGS = {'nu': 0.001, 'gamma': 0.1}
-N = 10
-POS_SCORES_SLOPE = 0.01
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_n10_gamma_01 = SVMCascadePCA(
-    machine_type=MACHINE_TYPE,
-    kernel_type=KERNEL_TYPE,
-    svm_kwargs=SVM_KWARGS,
-    N=N,
-    pos_scores_slope=POS_SCORES_SLOPE,
-    frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-MACHINE_TYPE = 'ONE_CLASS'
-KERNEL_TYPE = 'RBF'
-SVM_KWARGS = {'nu': 0.001, 'gamma': 0.05}
-N = 10
-POS_SCORES_SLOPE = 0.01
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_n10_gamma_005 = SVMCascadePCA(
-    machine_type=MACHINE_TYPE,
-    kernel_type=KERNEL_TYPE,
-    svm_kwargs=SVM_KWARGS,
-    N=N,
-    pos_scores_slope=POS_SCORES_SLOPE,
-    frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-MACHINE_TYPE = 'ONE_CLASS'
-KERNEL_TYPE = 'RBF'
-SVM_KWARGS = {'nu': 0.001, 'gamma': 0.01}
-N = 10
-POS_SCORES_SLOPE = 0.01
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_n10_gamma_001 = SVMCascadePCA(
-    machine_type=MACHINE_TYPE,
-    kernel_type=KERNEL_TYPE,
-    svm_kwargs=SVM_KWARGS,
-    N=N,
-    pos_scores_slope=POS_SCORES_SLOPE,
-    frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-MACHINE_TYPE = 'ONE_CLASS'
-KERNEL_TYPE = 'RBF'
-SVM_KWARGS = {'nu': 0.001, 'gamma': 0.005}
-N = 10
-POS_SCORES_SLOPE = 0.01
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_n10_gamma_0005 = SVMCascadePCA(
-    machine_type=MACHINE_TYPE,
-    kernel_type=KERNEL_TYPE,
-    svm_kwargs=SVM_KWARGS,
-    N=N,
-    pos_scores_slope=POS_SCORES_SLOPE,
-    frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-#=======================================================================================
-
-MACHINE_TYPE = 'ONE_CLASS'
-KERNEL_TYPE = 'RBF'
-SVM_KWARGS = {'nu': 0.001, 'gamma': 0.5}
-N = 20
-POS_SCORES_SLOPE = 0.01
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_n20_gamma_05 = SVMCascadePCA(
-    machine_type=MACHINE_TYPE,
-    kernel_type=KERNEL_TYPE,
-    svm_kwargs=SVM_KWARGS,
-    N=N,
-    pos_scores_slope=POS_SCORES_SLOPE,
-    frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-MACHINE_TYPE = 'ONE_CLASS'
-KERNEL_TYPE = 'RBF'
-SVM_KWARGS = {'nu': 0.001, 'gamma': 0.2}
-N = 20
-POS_SCORES_SLOPE = 0.01
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_n20_gamma_02 = SVMCascadePCA(
-    machine_type=MACHINE_TYPE,
-    kernel_type=KERNEL_TYPE,
-    svm_kwargs=SVM_KWARGS,
-    N=N,
-    pos_scores_slope=POS_SCORES_SLOPE,
-    frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-MACHINE_TYPE = 'ONE_CLASS'
-KERNEL_TYPE = 'RBF'
-SVM_KWARGS = {'nu': 0.001, 'gamma': 0.1}
-N = 20
-POS_SCORES_SLOPE = 0.01
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_n20_gamma_01 = SVMCascadePCA(
-    machine_type=MACHINE_TYPE,
-    kernel_type=KERNEL_TYPE,
-    svm_kwargs=SVM_KWARGS,
-    N=N,
-    pos_scores_slope=POS_SCORES_SLOPE,
-    frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-MACHINE_TYPE = 'ONE_CLASS'
-KERNEL_TYPE = 'RBF'
-SVM_KWARGS = {'nu': 0.001, 'gamma': 0.05}
-N = 20
-POS_SCORES_SLOPE = 0.01
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_n20_gamma_005 = SVMCascadePCA(
-    machine_type=MACHINE_TYPE,
-    kernel_type=KERNEL_TYPE,
-    svm_kwargs=SVM_KWARGS,
-    N=N,
-    pos_scores_slope=POS_SCORES_SLOPE,
-    frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-MACHINE_TYPE = 'ONE_CLASS'
-KERNEL_TYPE = 'RBF'
-SVM_KWARGS = {'nu': 0.001, 'gamma': 0.01}
-N = 20
-POS_SCORES_SLOPE = 0.01
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_n20_gamma_001 = SVMCascadePCA(
-    machine_type=MACHINE_TYPE,
-    kernel_type=KERNEL_TYPE,
-    svm_kwargs=SVM_KWARGS,
-    N=N,
-    pos_scores_slope=POS_SCORES_SLOPE,
-    frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-MACHINE_TYPE = 'ONE_CLASS'
-KERNEL_TYPE = 'RBF'
-SVM_KWARGS = {'nu': 0.001, 'gamma': 0.005}
-N = 20
-POS_SCORES_SLOPE = 0.01
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_n20_gamma_0005 = SVMCascadePCA(
-    machine_type=MACHINE_TYPE,
-    kernel_type=KERNEL_TYPE,
-    svm_kwargs=SVM_KWARGS,
-    N=N,
-    pos_scores_slope=POS_SCORES_SLOPE,
-    frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-MACHINE_TYPE = 'ONE_CLASS'
-KERNEL_TYPE = 'RBF'
-SVM_KWARGS = {'nu': 0.001, 'gamma': 0.001}
-N = 20
-POS_SCORES_SLOPE = 0.01
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_n20_gamma_0001 = SVMCascadePCA(
-    machine_type=MACHINE_TYPE,
-    kernel_type=KERNEL_TYPE,
-    svm_kwargs=SVM_KWARGS,
-    N=N,
-    pos_scores_slope=POS_SCORES_SLOPE,
-    frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-#=======================================================================================
-
-MACHINE_TYPE = 'ONE_CLASS'
-KERNEL_TYPE = 'RBF'
-SVM_KWARGS = {'nu': 0.001, 'gamma': 0.1}
-N = 2
-POS_SCORES_SLOPE = 0.01
-FRAME_LEVEL_SCORES_FLAG = False
-
-algorithm_n2_gamma_01_video_level = SVMCascadePCA(
-    machine_type=MACHINE_TYPE,
-    kernel_type=KERNEL_TYPE,
-    svm_kwargs=SVM_KWARGS,
-    N=N,
-    pos_scores_slope=POS_SCORES_SLOPE,
-    frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-#=======================================================================================
-
-# Test the cascade of two-class SVMs.
-
-MACHINE_TYPE = 'C_SVC'
-KERNEL_TYPE = 'RBF'
-TRAINER_GRID_SEARCH_PARAMS = {'cost': 1, 'gamma': 0.01}
-N = 2
-POS_SCORES_SLOPE = 0.01
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_n2_two_class_svm_c1_gamma_001 = SVMCascadePCA(
-    machine_type=MACHINE_TYPE,
-    kernel_type=KERNEL_TYPE,
-    svm_kwargs=TRAINER_GRID_SEARCH_PARAMS,
-    N=N,
-    pos_scores_slope=POS_SCORES_SLOPE,
-    frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)

bob/pad/face/config/algorithm/video_gmm_pad_algorithm.py

-#!/usr/bin/env python
-
-from bob.pad.base.algorithm import OneClassGMM
-
-#=======================================================================================
-# Define instances here:
-
-N_COMPONENTS = 2
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_2 = OneClassGMM(
-    n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-N_COMPONENTS = 3
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_3 = OneClassGMM(
-    n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-N_COMPONENTS = 4
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_4 = OneClassGMM(
-    n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-N_COMPONENTS = 5
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_5 = OneClassGMM(
-    n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-N_COMPONENTS = 6
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_6 = OneClassGMM(
-    n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-N_COMPONENTS = 7
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_7 = OneClassGMM(
-    n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-N_COMPONENTS = 8
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_8 = OneClassGMM(
-    n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-N_COMPONENTS = 9
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_9 = OneClassGMM(
-    n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-N_COMPONENTS = 10
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_10 = OneClassGMM(
-    n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-#=======================================================================================
-# above 10 Gaussians:
-
-N_COMPONENTS = 12
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_12 = OneClassGMM(
-    n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-N_COMPONENTS = 14
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_14 = OneClassGMM(
-    n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-N_COMPONENTS = 16
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_16 = OneClassGMM(
-    n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-N_COMPONENTS = 18
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_18 = OneClassGMM(
-    n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-N_COMPONENTS = 20
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_20 = OneClassGMM(
-    n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-#=======================================================================================
-# above 20 Gaussians:
-
-N_COMPONENTS = 25
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_25 = OneClassGMM(
-    n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-N_COMPONENTS = 30
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_30 = OneClassGMM(
-    n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-N_COMPONENTS = 35
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_35 = OneClassGMM(
-    n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-N_COMPONENTS = 40
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_40 = OneClassGMM(
-    n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-N_COMPONENTS = 45
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_45 = OneClassGMM(
-    n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-N_COMPONENTS = 50
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_50 = OneClassGMM(
-    n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-#=======================================================================================
-# above 50 Gaussians:
-
-N_COMPONENTS = 60
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_60 = OneClassGMM(
-    n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-N_COMPONENTS = 70
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_70 = OneClassGMM(
-    n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-N_COMPONENTS = 80
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_80 = OneClassGMM(
-    n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-N_COMPONENTS = 90
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_90 = OneClassGMM(
-    n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-N_COMPONENTS = 100
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_100 = OneClassGMM(
-    n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-#=======================================================================================
-# 50 Gaussians, different random seeds:
-
-N_COMPONENTS = 50
-RANDOM_STATE = 0
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_50_0 = OneClassGMM(
-    n_components=N_COMPONENTS,
-    random_state=RANDOM_STATE,
-    frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-N_COMPONENTS = 50
-RANDOM_STATE = 1
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_50_1 = OneClassGMM(
-    n_components=N_COMPONENTS,
-    random_state=RANDOM_STATE,
-    frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-N_COMPONENTS = 50
-RANDOM_STATE = 2
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_50_2 = OneClassGMM(
-    n_components=N_COMPONENTS,
-    random_state=RANDOM_STATE,
-    frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-N_COMPONENTS = 50
-RANDOM_STATE = 3
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_50_3 = OneClassGMM(
-    n_components=N_COMPONENTS,
-    random_state=RANDOM_STATE,
-    frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-N_COMPONENTS = 50
-RANDOM_STATE = 4
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_50_4 = OneClassGMM(
-    n_components=N_COMPONENTS,
-    random_state=RANDOM_STATE,
-    frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-N_COMPONENTS = 50
-RANDOM_STATE = 5
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_50_5 = OneClassGMM(
-    n_components=N_COMPONENTS,
-    random_state=RANDOM_STATE,
-    frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-N_COMPONENTS = 50
-RANDOM_STATE = 6
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_50_6 = OneClassGMM(
-    n_components=N_COMPONENTS,
-    random_state=RANDOM_STATE,
-    frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-N_COMPONENTS = 50
-RANDOM_STATE = 7
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_50_7 = OneClassGMM(
-    n_components=N_COMPONENTS,
-    random_state=RANDOM_STATE,
-    frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-N_COMPONENTS = 50
-RANDOM_STATE = 8
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_50_8 = OneClassGMM(
-    n_components=N_COMPONENTS,
-    random_state=RANDOM_STATE,
-    frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-N_COMPONENTS = 50
-RANDOM_STATE = 9
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_50_9 = OneClassGMM(
-    n_components=N_COMPONENTS,
-    random_state=RANDOM_STATE,
-    frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)

bob/pad/face/config/algorithm/video_svm_pad_algorithm.py

-#!/usr/bin/env python
-
-from bob.pad.base.algorithm import SVM
-
-#=======================================================================================
-# Define instances here:
-
-machine_type = 'C_SVC'
-kernel_type = 'RBF'
-n_samples = 10000
-# trainer_grid_search_params = {'cost': [2**p for p in range(-5, 16, 2)], 'gamma': [2**p for p in range(-15, 4, 2)]}
-trainer_grid_search_params = {
-    'cost': [2**p for p in range(-3, 14, 2)],
-    'gamma': [2**p for p in range(-15, 0, 2)]
-}
-mean_std_norm_flag = True
-frame_level_scores_flag = False  # one score per video(!) in this case
-
-video_svm_pad_algorithm_10k_grid_mean_std = SVM(
-    machine_type=machine_type,
-    kernel_type=kernel_type,
-    n_samples=n_samples,
-    trainer_grid_search_params=trainer_grid_search_params,
-    mean_std_norm_flag=mean_std_norm_flag,
-    frame_level_scores_flag=frame_level_scores_flag)
-
-frame_level_scores_flag = True  # one score per frame(!) in this case
-
-video_svm_pad_algorithm_10k_grid_mean_std_frame_level = SVM(
-    machine_type=machine_type,
-    kernel_type=kernel_type,
-    n_samples=n_samples,
-    trainer_grid_search_params=trainer_grid_search_params,
-    mean_std_norm_flag=mean_std_norm_flag,
-    frame_level_scores_flag=frame_level_scores_flag)
-
-trainer_grid_search_params = {
-    'cost': [1],
-    'gamma': [0]
-}  # set the default LibSVM parameters
-
-video_svm_pad_algorithm_default_svm_param_mean_std_frame_level = SVM(
-    machine_type=machine_type,
-    kernel_type=kernel_type,
-    n_samples=n_samples,
-    trainer_grid_search_params=trainer_grid_search_params,
-    mean_std_norm_flag=mean_std_norm_flag,
-    frame_level_scores_flag=frame_level_scores_flag)

bob/pad/face/config/brsu.py

-#!/usr/bin/env python
-# encoding: utf-8
-
-from bob.pad.face.database import BRSUPadDatabase 
+from bob.pad.face.database import BRSUPadDatabase
+from bob.pad.base.pipelines.vanilla_pad import DatabaseConnector
 from bob.extension import rc
 
-database = BRSUPadDatabase(
-    protocol='test',
-    original_directory=rc['bob.db.brsu.directory'],
+database = DatabaseConnector(
+    BRSUPadDatabase(
+        protocol="test",
+        original_directory=rc["bob.db.brsu.directory"],
+    )
 )

bob/pad/face/config/casiafasd.py

-#!/usr/bin/env python
-
 """Config file for the CASIA FASD dataset.
 Please run ``bob config set bob.db.casia_fasd.directory /path/to/casia_fasd_files``
 in terminal to point to the original files of the dataset on your computer."""
 
 from bob.pad.face.database import CasiaFasdPadDatabase
-database = CasiaFasdPadDatabase()
+from bob.pad.base.pipelines.vanilla_pad import DatabaseConnector
+database = DatabaseConnector(CasiaFasdPadDatabase())

bob/pad/face/config/casiasurf_color.py

-#!/usr/bin/env python
-# encoding: utf-8
-
 from bob.pad.face.database import CasiaSurfPadDatabase
+from bob.pad.base.pipelines.vanilla_pad import DatabaseConnector
 from bob.extension import rc
 
-database = CasiaSurfPadDatabase(
-    protocol='color',
-    original_directory=rc['bob.db.casiasurf.directory'],
-    original_extension=".jpg",
+database = DatabaseConnector(
+    CasiaSurfPadDatabase(
+        protocol="color",
+        original_directory=rc.get("bob.db.casiasurf.directory"),
+        original_extension=".jpg",
+    )
 )

bob/pad/face/config/celeb_a.py

-#!/usr/bin/env python
 """`CELEBA`_ is a face makeup spoofing database adapted for face PAD experiments.
 
 
@@ -9,48 +8,15 @@ the link.
 
 """
 
+from bob.extension import rc
+from bob.pad.base.pipelines.vanilla_pad import DatabaseConnector
 from bob.pad.face.database.celeb_a import CELEBAPadDatabase
 
-# Directory where the data files are stored.
-# This directory is given in the .bob_bio_databases.txt file located in your home directory
-ORIGINAL_DIRECTORY = "[YOUR_CELEB_A_DATABASE_DIRECTORY]"
-"""Value of ``~/.bob_bio_databases.txt`` for this database"""
-
-ORIGINAL_EXTENSION = ""  # extension of the data files
-
-database = CELEBAPadDatabase(
-    protocol='grandtest',
-    original_directory=ORIGINAL_DIRECTORY,
-    original_extension=ORIGINAL_EXTENSION,
-    training_depends_on_protocol=True
+database = DatabaseConnector(
+    CELEBAPadDatabase(
+        protocol="grandtest",
+        original_directory=rc.get("bob.db.celeba.directory"),
+        original_extension="",
+        training_depends_on_protocol=True,
+    )
 )
-"""The :py:class:`bob.pad.base.database.PadDatabase` derivative with CELEBA
-database settings.
-
-.. warning::
-
-   This class only provides a programmatic interface to load data in an orderly
-   manner, respecting usage protocols. It does **not** contain the raw
-   data files. You should procure those yourself.
-
-Notice that ``original_directory`` is set to ``[YOUR_CELEBA_DATABASE_DIRECTORY]``.
-You must make sure to create ``${HOME}/.bob_bio_databases.txt`` setting this
-value to the place where you actually installed the CELEBA Database, as
-explained in the section :ref:`bob.pad.face.baselines`.
-"""
-
-protocol = 'grandtest'
-"""The default protocol to use for reproducing the baselines.
-
-You may modify this at runtime by specifying the option ``--protocol`` on the
-command-line of ``spoof.py`` or using the keyword ``protocol`` on a
-configuration file that is loaded **after** this configuration resource.
-"""
-
-groups = ["train", "dev", "eval"]
-"""The default groups to use for reproducing the baselines.
-
-You may modify this at runtime by specifying the option ``--groups`` on the
-command-line of ``spoof.py`` or using the keyword ``groups`` on a
-configuration file that is loaded **after** this configuration resource.
-"""

bob/pad/face/config/database/batl/batl_db.py

-#!/usr/bin/env python
-"""
-BATL Db is a database for face PAD experiments.
-"""
-
-from bob.pad.face.database import BatlPadDatabase
-
-# Directory where the data files are stored.
-# This directory is given in the .bob_bio_databases.txt file located in your home directory
-ORIGINAL_DIRECTORY = "[YOUR_BATL_DB_DIRECTORY]"
-"""Value of ``~/.bob_bio_databases.txt`` for this database"""
-
-ORIGINAL_EXTENSION = ".h5"  # extension of the data files
-
-ANNOTATIONS_TEMP_DIR = ""
-
-PROTOCOL = 'nowig-color-50'
-
-database = BatlPadDatabase(
-    protocol=PROTOCOL,
-    original_directory=ORIGINAL_DIRECTORY,
-    original_extension=ORIGINAL_EXTENSION,
-    annotations_temp_dir=ANNOTATIONS_TEMP_DIR,
-    landmark_detect_method="mtcnn",
-    training_depends_on_protocol=True,
-)
-"""The :py:class:`bob.pad.base.database.BatlPadDatabase` derivative with BATL Db
-database settings.
-
-.. warning::
-
-   This class only provides a programmatic interface to load data in an orderly
-   manner, respecting usage protocols. It does **not** contain the raw
-   data files. You should procure those yourself.
-
-Notice that ``original_directory`` is set to ``[BatlPadDatabase]``.
-You must make sure to create ``${HOME}/.bob_bio_databases.txt`` file setting this
-value to the places where you actually installed the BATL database.
-"""
-
-protocol = PROTOCOL
-"""
-You may modify this at runtime by specifying the option ``--protocol`` on the
-command-line of ``spoof.py`` or using the keyword ``protocol`` on a
-configuration file that is loaded **after** this configuration resource.
-"""
-
-groups = ["train", "dev", "eval"]
-"""The default groups to use for reproducing the baselines.
-
-You may modify this at runtime by specifying the option ``--groups`` on the
-command-line of ``spoof.py`` or using the keyword ``groups`` on a
-configuration file that is loaded **after** this configuration resource.
-"""

bob/pad/face/config/database/batl/batl_db_depth.py

-#!/usr/bin/env python
-"""
-BATL Db is a database for face PAD experiments.
-"""
-
-from bob.pad.face.database import BatlPadDatabase
-
-# Directory where the data files are stored.
-# This directory is given in the .bob_bio_databases.txt file located in your home directory
-ORIGINAL_DIRECTORY = "[YOUR_BATL_DB_DIRECTORY]"
-"""Value of ``~/.bob_bio_databases.txt`` for this database"""
-
-ORIGINAL_EXTENSION = ".h5"  # extension of the data files
-
-ANNOTATIONS_TEMP_DIR = ""
-
-PROTOCOL = 'nowig-depth-50'
-
-database = BatlPadDatabase(
-    protocol=PROTOCOL,
-    original_directory=ORIGINAL_DIRECTORY,
-    original_extension=ORIGINAL_EXTENSION,
-    annotations_temp_dir=ANNOTATIONS_TEMP_DIR,
-    landmark_detect_method="mtcnn",
-    training_depends_on_protocol=True,
-)
-"""The :py:class:`bob.pad.base.database.BatlPadDatabase` derivative with BATL Db
-database settings.
-
-.. warning::
-
-   This class only provides a programmatic interface to load data in an orderly
-   manner, respecting usage protocols. It does **not** contain the raw
-   data files. You should procure those yourself.
-
-Notice that ``original_directory`` is set to ``[BatlPadDatabase]``.
-You must make sure to create ``${HOME}/.bob_bio_databases.txt`` file setting this
-value to the places where you actually installed the BATL database.
-"""
-
-protocol = PROTOCOL
-"""
-You may modify this at runtime by specifying the option ``--protocol`` on the
-command-line of ``spoof.py`` or using the keyword ``protocol`` on a
-configuration file that is loaded **after** this configuration resource.
-"""
-
-groups = ["train", "dev", "eval"]
-"""The default groups to use for reproducing the baselines.
-
-You may modify this at runtime by specifying the option ``--groups`` on the
-command-line of ``spoof.py`` or using the keyword ``groups`` on a
-configuration file that is loaded **after** this configuration resource.
-"""

bob/pad/face/config/database/batl/batl_db_infrared.py

-#!/usr/bin/env python
-"""
-BATL Db is a database for face PAD experiments.
-"""
-
-from bob.pad.face.database import BatlPadDatabase
-
-# Directory where the data files are stored.
-# This directory is given in the .bob_bio_databases.txt file located in your home directory
-ORIGINAL_DIRECTORY = "[YOUR_BATL_DB_DIRECTORY]"
-"""Value of ``~/.bob_bio_databases.txt`` for this database"""
-
-ORIGINAL_EXTENSION = ".h5"  # extension of the data files
-
-ANNOTATIONS_TEMP_DIR = ""
-
-PROTOCOL = 'nowig-infrared-50'
-
-database = BatlPadDatabase(
-    protocol=PROTOCOL,
-    original_directory=ORIGINAL_DIRECTORY,
-    original_extension=ORIGINAL_EXTENSION,
-    annotations_temp_dir=ANNOTATIONS_TEMP_DIR,
-    landmark_detect_method="mtcnn",
-    training_depends_on_protocol=True,
-)
-"""The :py:class:`bob.pad.base.database.BatlPadDatabase` derivative with BATL Db
-database settings.
-
-.. warning::
-
-   This class only provides a programmatic interface to load data in an orderly
-   manner, respecting usage protocols. It does **not** contain the raw
-   data files. You should procure those yourself.
-
-Notice that ``original_directory`` is set to ``[BatlPadDatabase]``.
-You must make sure to create ``${HOME}/.bob_bio_databases.txt`` file setting this
-value to the places where you actually installed the BATL database.
-"""
-
-protocol = PROTOCOL
-"""
-You may modify this at runtime by specifying the option ``--protocol`` on the
-command-line of ``spoof.py`` or using the keyword ``protocol`` on a
-configuration file that is loaded **after** this configuration resource.
-"""
-
-groups = ["train", "dev", "eval"]
-"""The default groups to use for reproducing the baselines.
-
-You may modify this at runtime by specifying the option ``--groups`` on the
-command-line of ``spoof.py`` or using the keyword ``groups`` on a
-configuration file that is loaded **after** this configuration resource.
-"""

bob/pad/face/config/database/batl/batl_db_rgb_ir_d_grandtest.py

-#!/usr/bin/env python
-"""
-Idiap BATL DB is a database for face PAD experiments.
-"""
-
-from bob.pad.face.database import BatlPadDatabase
-
-# Directory where the data files are stored.
-# This directory is given in the .bob_bio_databases.txt file located in your home directory
-ORIGINAL_DIRECTORY = "[YOUR_BATL_DB_DIRECTORY]"
-"""Value of ``~/.bob_bio_databases.txt`` for this database"""
-
-ORIGINAL_EXTENSION = ".h5"  # extension of the data files
-
-ANNOTATIONS_TEMP_DIR = ""  # NOTE: this variable is NOT assigned in the instance of the BatlPadDatabase, thus "rc" functionality defined in bob.extension will be involved
-
-PROTOCOL = 'grandtest-color*infrared*depth-10'  # use 10 frames for PAD experiments
-
-database = BatlPadDatabase(
-    protocol=PROTOCOL,
-    original_directory=ORIGINAL_DIRECTORY,
-    original_extension=ORIGINAL_EXTENSION,
-    landmark_detect_method="mtcnn",  # detect annotations using mtcnn
-    exclude_attacks_list=['makeup'],
-    exclude_pai_all_sets=True,  # exclude makeup from all the sets, which is the default behavior for grandtest protocol
-    append_color_face_roi_annot=False)  # do not append annotations, defining ROI in the cropped face image, to the dictionary of annotations
-
-"""The :py:class:`bob.pad.base.database.BatlPadDatabase` derivative with BATL Db
-database settings.
-
-.. warning::
-
-   This class only provides a programmatic interface to load data in an orderly
-   manner, respecting usage protocols. It does **not** contain the raw
-   data files. You should procure those yourself.
-
-Notice that ``original_directory`` is set to ``[YOUR_BATL_DB_DIRECTORY]``.
-You must make sure to create ``${HOME}/.bob_bio_databases.txt`` file setting this
-value to the places where you actually installed the BATL Govt database.
-"""
-
-protocol = PROTOCOL
-"""
-You may modify this at runtime by specifying the option ``--protocol`` on the
-command-line of ``spoof.py`` or using the keyword ``protocol`` on a
-configuration file that is loaded **after** this configuration resource.
-"""
-
-groups = ["train", "dev", "eval"]
-"""The default groups to use for reproducing the baselines.
-
-You may modify this at runtime by specifying the option ``--groups`` on the
-command-line of ``spoof.py`` or using the keyword ``groups`` on a
-configuration file that is loaded **after** this configuration resource.
-"""

bob/pad/face/config/database/batl/batl_db_thermal.py

-#!/usr/bin/env python
-"""
-BATL Db is a database for face PAD experiments.
-"""
-
-from bob.pad.face.database import BatlPadDatabase
-
-# Directory where the data files are stored.
-# This directory is given in the .bob_bio_databases.txt file located in your home directory
-ORIGINAL_DIRECTORY = "[YOUR_BATL_DB_DIRECTORY]"
-"""Value of ``~/.bob_bio_databases.txt`` for this database"""
-
-ORIGINAL_EXTENSION = ".h5"  # extension of the data files
-
-ANNOTATIONS_TEMP_DIR = ""
-
-PROTOCOL = 'nowig-thermal-50'
-
-database = BatlPadDatabase(
-    protocol=PROTOCOL,
-    original_directory=ORIGINAL_DIRECTORY,
-    original_extension=ORIGINAL_EXTENSION,
-    annotations_temp_dir=ANNOTATIONS_TEMP_DIR,
-    landmark_detect_method="mtcnn",
-    training_depends_on_protocol=True,
-)
-"""The :py:class:`bob.pad.base.database.BatlPadDatabase` derivative with BATL Db
-database settings.
-
-.. warning::
-
-   This class only provides a programmatic interface to load data in an orderly
-   manner, respecting usage protocols. It does **not** contain the raw
-   data files. You should procure those yourself.
-
-Notice that ``original_directory`` is set to ``[BatlPadDatabase]``.
-You must make sure to create ``${HOME}/.bob_bio_databases.txt`` file setting this
-value to the places where you actually installed the BATL database.
-"""
-
-protocol = PROTOCOL
-"""
-You may modify this at runtime by specifying the option ``--protocol`` on the
-command-line of ``spoof.py`` or using the keyword ``protocol`` on a
-configuration file that is loaded **after** this configuration resource.
-"""
-
-groups = ["train", "dev", "eval"]
-"""The default groups to use for reproducing the baselines.
-
-You may modify this at runtime by specifying the option ``--groups`` on the
-command-line of ``spoof.py`` or using the keyword ``groups`` on a
-configuration file that is loaded **after** this configuration resource.
-"""

bob/pad/face/config/extractor/frame_diff_features.py

-#!/usr/bin/env python
-
-from bob.pad.face.extractor import FrameDiffFeatures
-
-#=======================================================================================
-# Define instances here:
-
-window_size = 20
-overlap = 0
-
-frame_diff_feat_extr_w20_over0 = FrameDiffFeatures(
-    window_size=window_size, overlap=overlap)

bob/pad/face/config/extractor/optical_flow.py

-from bob.bio.base.extractor import CallableExtractor
-from bob.pad.face.extractor import OpticalFlow
-
-extractor = CallableExtractor(OpticalFlow())

bob/pad/face/config/extractor/video_lbp_histogram.py

-#!/usr/bin/env python
-
-from bob.pad.face.extractor import LBPHistogram
-
-from bob.bio.video.extractor import Wrapper
-
-#=======================================================================================
-# Define instances here:
-
-lbptype = 'uniform'
-elbptype = 'regular'
-rad = 1
-neighbors = 8
-circ = False
-dtype = None
-
-video_lbp_histogram_extractor_n8r1_uniform = Wrapper(LBPHistogram(
-    lbptype=lbptype,
-    elbptype=elbptype,
-    rad=rad,
-    neighbors=neighbors,
-    circ=circ,
-    dtype=dtype))

bob/pad/face/config/extractor/video_quality_measure.py

-#!/usr/bin/env python
-
-from bob.pad.face.extractor import ImageQualityMeasure
-
-import bob.bio.video
-
-#=======================================================================================
-# Define instances here:
-
-galbally = True
-msu = True
-dtype = None
-
-video_quality_measure_galbally_msu = bob.bio.video.extractor.Wrapper(ImageQualityMeasure(galbally=galbally, msu=msu, dtype=dtype))