Commit 679b71ad authored by Amir MOHAMMADI's avatar Amir MOHAMMADI

Porting to dask pipelines

parent 0b334636
Pipeline #45314 failed with stage
in 4 minutes and 48 seconds
#!/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)
#!/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)
#!/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)
#!/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"],
)
)
#!/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())
#!/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",
)
)
#!/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`.
"""