diff --git a/bob/fusion/base/script/bob_fuse.py b/bob/fusion/base/script/bob_fuse.py
index 1d314b22634a44fdc57e81449663b49810acea48..717f94aff554e09c1f20e85aec314151ad9b0402 100755
--- a/bob/fusion/base/script/bob_fuse.py
+++ b/bob/fusion/base/script/bob_fuse.py
@@ -12,138 +12,169 @@ from bob.measure.load import load_score, dump_score
from bob.bio.base import utils
from ..tools import parse_arguments, write_info, get_gza_from_lines_list, \
- check_consistency, get_scores, remove_nan, get_score_lines, get_2negatives_1positive
+ check_consistency, get_scores, remove_nan, get_score_lines, get_2negatives_1positive
import bob.core
logger = bob.core.log.setup("bob.fusion.base")
+def save_fused_scores(save_path, fused_scores, score_lines):
+ score_lines['score'] = fused_scores
+ gen, zei, atk, _, _, _ = get_2negatives_1positive(score_lines)
+ create_directories_safe(os.path.dirname(save_path))
+ dump_score(save_path, score_lines)
+ dump_score(save_path + '-licit', np.append(gen, zei))
+ dump_score(save_path + '-spoof', np.append(gen, atk))
+
+
+def routine_fusion(algorithm, model_file,
+ scores_train_lines, scores_train, train_neg, train_pos, fused_train_file,
+ scores_dev_lines=None, scores_dev=None, dev_neg=None, dev_pos=None, fused_dev_file=None,
+ scores_eval_lines=None, scores_eval=None, fused_eval_file=None,
+ force=False, min_file_size=1000,
+ ):
+ # train the preprocessors
+ train_scores = np.vstack((train_neg, train_pos))
+ neg_len = train_neg.shape[0]
+ y = np.zeros((train_scores.shape[0],), dtype='bool')
+ y[neg_len:] = True
+ algorithm.train_preprocessors(train_scores, y)
+
+ # preprocess data
+ scores_train = algorithm.preprocess(scores_train)
+ train_neg, train_pos = algorithm.preprocess(train_neg), algorithm.preprocess(train_pos)
+ if scores_dev is not None:
+ scores_dev = algorithm.preprocess(scores_dev)
+ dev_neg, dev_pos = algorithm.preprocess(dev_neg), algorithm.preprocess(dev_pos)
+ if scores_eval is not None:
+ scores_eval = algorithm.preprocess(scores_eval)
+
+ # train the model
+ if utils.check_file(model_file, force, min_file_size):
+ logger.info(
+ "model '%s' already exists.", model_file)
+ algorithm = algorithm.load(model_file)
+ else:
+ algorithm.train(train_neg, train_pos, dev_neg, dev_pos)
+ algorithm.save(model_file)
+
+ # fuse the scores (train)
+ if utils.check_file(fused_train_file, force, min_file_size):
+ logger.info(
+ "score file '%s' already exists.", fused_train_file)
+ else:
+ fused_scores_train = algorithm.fuse(scores_train)
+ save_fused_scores(fused_train_file, fused_scores_train, scores_train_lines)
+
+ if scores_dev is not None:
+ # fuse the scores (dev)
+ if utils.check_file(fused_dev_file, force, min_file_size):
+ logger.info(
+ "score file '%s' already exists.", fused_dev_file)
+ else:
+ fused_scores_dev = algorithm.fuse(scores_dev)
+ save_fused_scores(fused_dev_file, fused_scores_dev, scores_dev_lines)
+
+ if scores_eval is not None:
+ # fuse the scores (eval)
+ if utils.check_file(fused_eval_file, force, min_file_size):
+ logger.info(
+ "score file '%s' already exists.", fused_eval_file)
+ else:
+ fused_scores_eval = algorithm.fuse(scores_eval)
+ save_fused_scores(fused_eval_file, fused_scores_eval, scores_eval_lines)
+
+
def fuse(args, command_line_parameters):
- """Do the actual fusion."""
- algorithm = args.algorithm
-
- write_info(args, command_line_parameters)
-
- # load the scores
- score_lines_list_train = [load_score(path, ncolumns=args.score_type)
- for path in args.train_files]
- if args.dev_files:
- score_lines_list_dev = [load_score(path, ncolumns=args.score_type)
- for path in args.dev_files]
- if args.eval_files:
- score_lines_list_eval = [load_score(path, ncolumns=args.score_type)
- for path in args.eval_files]
-
- # genuine, zero effort impostor, and attack list of
- # train, development and evaluation data.
- idx1, gen_lt, zei_lt, atk_lt = get_gza_from_lines_list(score_lines_list_train)
- if args.dev_files:
- _, gen_ld, zei_ld, atk_ld = get_gza_from_lines_list(score_lines_list_dev)
- if args.eval_files:
- _, gen_le, zei_le, atk_le = get_gza_from_lines_list(score_lines_list_eval)
-
- # check if score lines are consistent
- if not args.skip_check:
- logger.info('Checking the training files for consistency ...')
- check_consistency(gen_lt, zei_lt, atk_lt)
+ """Do the actual fusion."""
+ algorithm = args.algorithm
+
+ write_info(args, command_line_parameters)
+
+ # load the scores
+ score_lines_list_train = [load_score(path, ncolumns=args.score_type)
+ for path in args.train_files]
if args.dev_files:
- logger.info('Checking the development files for consistency ...')
- check_consistency(gen_ld, zei_ld, atk_ld)
+ score_lines_list_dev = [load_score(path, ncolumns=args.score_type)
+ for path in args.dev_files]
if args.eval_files:
- logger.info('Checking the evaluation files for consistency ...')
- check_consistency(gen_le, zei_le, atk_le)
-
- scores_train = get_scores(gen_lt, zei_lt, atk_lt)
- train_neg = get_scores(zei_lt, atk_lt)
- train_pos = get_scores(gen_lt)
- if args.dev_files:
- scores_dev = get_scores(gen_ld, zei_ld, atk_ld)
- scores_dev_lines = get_score_lines(gen_ld[0:1], zei_ld[0:1], atk_ld[0:1])
- dev_neg = get_scores(zei_ld, atk_ld)
- dev_pos = get_scores(gen_ld)
- else:
- dev_neg, dev_pos = None, None
- if args.eval_files:
- scores_eval = get_scores(gen_le, zei_le, atk_le)
- scores_eval_lines = get_score_lines(gen_le[0:1], zei_le[0:1], atk_le[0:1])
-
- # check for nan values
- found_nan = False
- found_nan, _, scores_train = remove_nan(scores_train, found_nan)
- found_nan, _, train_neg = remove_nan(train_neg, found_nan)
- found_nan, _, train_pos = remove_nan(train_pos, found_nan)
- if args.dev_files:
- found_nan, nan_dev, scores_dev = remove_nan(scores_dev, found_nan)
- found_nan, _, dev_neg = remove_nan(dev_neg, found_nan)
- found_nan, _, dev_pos = remove_nan(dev_pos, found_nan)
- if args.eval_files:
- found_nan, nan_eval, scores_eval = remove_nan(scores_eval, found_nan)
-
- if found_nan:
- logger.warn('Some nan values were removed.')
-
- # train the preprocessors
- algorithm.train_preprocessors(scores_train)
-
- # preprocess data
- train_neg, train_pos = algorithm.preprocess(train_neg), algorithm.preprocess(train_pos)
- if args.dev_files:
- scores_dev = algorithm.preprocess(scores_dev)
- dev_neg, dev_pos = algorithm.preprocess(dev_neg), algorithm.preprocess(dev_pos)
- if args.eval_files:
- scores_eval = algorithm.preprocess(scores_eval)
-
- # train the model
- if utils.check_file(args.model_file, args.force, 1000):
- logger.info(
- "- Fusion: model '%s' already exists.", args.model_file)
- algorithm = algorithm.load(args.model_file)
- else:
- algorithm.train(train_neg, train_pos, dev_neg, dev_pos)
- algorithm.save(args.model_file)
-
- # fuse the scores (dev)
- if utils.check_file(args.fused_dev_file, args.force, 1000):
- logger.info(
- "- Fusion: scores '%s' already exists.", args.fused_dev_file)
- elif args.dev_files:
- fused_scores_dev = algorithm.fuse(scores_dev)
- score_lines = scores_dev_lines[~nan_dev]
- score_lines['score'] = fused_scores_dev
- gen, zei, atk, _, _, _ = get_2negatives_1positive(score_lines)
- create_directories_safe(os.path.dirname(args.fused_dev_file))
- dump_score(args.fused_dev_file, score_lines)
- dump_score(args.fused_dev_file + '-licit', np.append(gen, zei))
- dump_score(args.fused_dev_file + '-spoof', np.append(gen, atk))
-
- # fuse the scores (eval)
- if args.eval_files:
- if utils.check_file(args.fused_eval_file, args.force, 1000):
- logger.info(
- "- Fusion: scores '%s' already exists.", args.fused_eval_file)
+ score_lines_list_eval = [load_score(path, ncolumns=args.score_type)
+ for path in args.eval_files]
+
+ # genuine, zero effort impostor, and attack list of
+ # train, development and evaluation data.
+ idx1, gen_lt, zei_lt, atk_lt = get_gza_from_lines_list(score_lines_list_train)
+ if args.dev_files:
+ _, gen_ld, zei_ld, atk_ld = get_gza_from_lines_list(score_lines_list_dev)
+ if args.eval_files:
+ _, gen_le, zei_le, atk_le = get_gza_from_lines_list(score_lines_list_eval)
+
+ # check if score lines are consistent
+ if not args.skip_check:
+ logger.info('Checking the training files for consistency ...')
+ check_consistency(gen_lt, zei_lt, atk_lt)
+ if args.dev_files:
+ logger.info('Checking the development files for consistency ...')
+ check_consistency(gen_ld, zei_ld, atk_ld)
+ if args.eval_files:
+ logger.info('Checking the evaluation files for consistency ...')
+ check_consistency(gen_le, zei_le, atk_le)
+
+ scores_train = get_scores(gen_lt, zei_lt, atk_lt)
+ scores_train_lines = get_score_lines(gen_lt[0:1], zei_lt[0:1], atk_lt[0:1])
+ train_neg = get_scores(zei_lt, atk_lt)
+ train_pos = get_scores(gen_lt)
+ if args.dev_files:
+ scores_dev = get_scores(gen_ld, zei_ld, atk_ld)
+ scores_dev_lines = get_score_lines(gen_ld[0:1], zei_ld[0:1], atk_ld[0:1])
+ dev_neg = get_scores(zei_ld, atk_ld)
+ dev_pos = get_scores(gen_ld)
else:
- fused_scores_eval = algorithm.fuse(scores_eval)
- score_lines = scores_eval_lines[~nan_eval]
- score_lines['score'] = fused_scores_eval
- gen, zei, atk, _, _, _ = get_2negatives_1positive(score_lines)
- create_directories_safe(os.path.dirname(args.fused_eval_file))
- dump_score(args.fused_eval_file, score_lines)
- dump_score(args.fused_eval_file + '-licit', np.append(gen, zei))
- dump_score(args.fused_eval_file + '-spoof', np.append(gen, atk))
+ scores_dev, scores_dev_lines, dev_neg, dev_pos = None, None, None, None
+ if args.eval_files:
+ scores_eval = get_scores(gen_le, zei_le, atk_le)
+ scores_eval_lines = get_score_lines(gen_le[0:1], zei_le[0:1], atk_le[0:1])
+ else:
+ scores_eval, scores_eval_lines = None, None
+
+ # check for nan values
+ found_nan = False
+ found_nan, nan_train, scores_train = remove_nan(scores_train, found_nan)
+ scores_train_lines = scores_train_lines[~nan_train]
+ found_nan, _, train_neg = remove_nan(train_neg, found_nan)
+ found_nan, _, train_pos = remove_nan(train_pos, found_nan)
+ if args.dev_files:
+ found_nan, nan_dev, scores_dev = remove_nan(scores_dev, found_nan)
+ scores_dev_lines = scores_dev_lines[~nan_dev]
+ found_nan, _, dev_neg = remove_nan(dev_neg, found_nan)
+ found_nan, _, dev_pos = remove_nan(dev_pos, found_nan)
+ if args.eval_files:
+ found_nan, nan_eval, scores_eval = remove_nan(scores_eval, found_nan)
+ scores_eval_lines = scores_eval_lines[~nan_eval]
+
+ if found_nan:
+ logger.warn('Some nan values were removed.')
+
+ routine_fusion(algorithm, args.model_file,
+ scores_train_lines, scores_train, train_neg, train_pos, args.fused_train_file,
+ scores_dev_lines, scores_dev, dev_neg, dev_pos, args.fused_dev_file,
+ scores_eval_lines, scores_eval, args.fused_eval_file,
+ args.force)
def main(command_line_parameters=None):
- """Executes the main function"""
- try:
- # do the command line parsing
- args = parse_arguments(command_line_parameters)
-
- # perform face verification test
- fuse(args, command_line_parameters)
- except Exception as e:
- # track any exceptions as error logs (i.e., to get a time stamp)
- logger.error("During the execution, an exception was raised: %s" % e)
- raise
+ """Executes the main function"""
+ try:
+ # do the command line parsing
+ args = parse_arguments(command_line_parameters)
+
+ # perform face verification test
+ fuse(args, command_line_parameters)
+ except Exception as e:
+ # track any exceptions as error logs (i.e., to get a time stamp)
+ logger.error("During the execution, an exception was raised: %s" % e)
+ raise
+
if __name__ == "__main__":
- main()
+ main()