def collect_ibeis_training_annotations(ibs, nDaids_basis, verbose=True):
# load a dataset
#dbname = 'PZ_MTEST'
#dbname = 'GZ_ALL'
def get_set_groundfalse(ibs, qaids):
# get groundfalse annots relative to the entire set
valid_nids = ibs.get_valid_nids()
qnids = ibs.get_annot_nids(qaids)
nid_list = list(set(valid_nids) - set(qnids))
aids_list = ibs.get_name_aids(nid_list)
return ut.flatten(aids_list)
# determanism
np.random.seed(0)
random.seed(0)
# TODO: USE ANOT FILTERINGS
import ibeis
qaids_all = ibeis.testdata_aids(
a='default:pername=1,mingt=2,is_known=True')
qaids = qaids_all[::2]
print('nQaids = %r' % len(qaids))
def get_annot_groundtruth_sample(ibs,
aid_list,
per_name=1,
isexemplar=True):
r"""
DEPRICATE
"""
all_trues_list = ibs.get_annot_groundtruth(aid_list,
noself=True,
is_exemplar=isexemplar)
def random_choice(aids):
size = min(len(aids), per_name)
return np.random.choice(aids, size, replace=False).tolist()
sample_trues_list = [
random_choice(aids) if len(aids) > 0 else []
for aids in all_trues_list
]
return sample_trues_list
daids_gt_sample = ut.flatten(
ibs.get_annot_groundtruth_sample(ibs, qaids, isexemplar=None))
daids_gf_all = get_set_groundfalse(ibs, qaids)
ut.assert_eq(len(daids_gt_sample), len(qaids), 'missing gt')
daids_list = []
for target_nDaids in ut.ProgressIter(nDaids_basis, lbl='testing dbsize'):
print('---------------------------')
# Sample one match from the groundtruth with padding
daids_gf_sample = ut.random_sample(
daids_gf_all, max(0, target_nDaids - len(daids_gt_sample)))
daids = sorted(daids_gt_sample + daids_gf_sample)
nDaids = len(daids)
if target_nDaids != nDaids:
continue
daids_list.append(daids)
return qaids, daids_list
def collect_ibeis_training_annotations(ibs, nDaids_basis, verbose=True):
# load a dataset
#dbname = 'PZ_MTEST'
#dbname = 'GZ_ALL'
def get_set_groundfalse(ibs, qaids):
# get groundfalse annots relative to the entire set
valid_nids = ibs.get_valid_nids()
qnids = ibs.get_annot_nids(qaids)
nid_list = list(set(valid_nids) - set(qnids))
aids_list = ibs.get_name_aids(nid_list)
return ut.flatten(aids_list)
# determanism
np.random.seed(0)
random.seed(0)
# TODO: USE ANOT FILTERINGS
import ibeis
qaids_all = ibeis.testdata_aids(a='default:pername=1,mingt=2,is_known=True')
qaids = qaids_all[::2]
print('nQaids = %r' % len(qaids))
def get_annot_groundtruth_sample(ibs, aid_list, per_name=1, isexemplar=True):
r"""
DEPRICATE
"""
all_trues_list = ibs.get_annot_groundtruth(aid_list, noself=True, is_exemplar=isexemplar)
def random_choice(aids):
size = min(len(aids), per_name)
return np.random.choice(aids, size, replace=False).tolist()
sample_trues_list = [random_choice(aids) if len(aids) > 0 else [] for aids in all_trues_list]
return sample_trues_list
daids_gt_sample = ut.flatten(ibs.get_annot_groundtruth_sample(ibs, qaids, isexemplar=None))
daids_gf_all = get_set_groundfalse(ibs, qaids)
ut.assert_eq(len(daids_gt_sample), len(qaids), 'missing gt')
daids_list = []
for target_nDaids in ut.ProgressIter(nDaids_basis, lbl='testing dbsize'):
print('---------------------------')
# Sample one match from the groundtruth with padding
daids_gf_sample = ut.random_sample(daids_gf_all, max(0, target_nDaids - len(daids_gt_sample)))
daids = sorted(daids_gt_sample + daids_gf_sample)
nDaids = len(daids)
if target_nDaids != nDaids:
continue
daids_list.append(daids)
return qaids, daids_list
def sampleone(list_):
return ut.random_sample(list_, 1, rng=rng)[0]
def _cm_training_pairs(
infr,
qreq_=None,
cm_list=None,
top_gt=2,
mid_gt=2,
bot_gt=2,
top_gf=2,
mid_gf=2,
bot_gf=2,
rand_gt=2,
rand_gf=2,
rng=None,
):
"""
Constructs training data for a pairwise classifier
CommandLine:
python -m wbia.algo.graph.core _cm_training_pairs
Example:
>>> # xdoctest: +REQUIRES(--slow)
>>> # ENABLE_DOCTEST
>>> from wbia.algo.graph.core import * # NOQA
>>> infr = testdata_infr('PZ_MTEST')
>>> infr.exec_matching(cfgdict={
>>> 'can_match_samename': True,
>>> 'K': 4,
>>> 'Knorm': 1,
>>> 'prescore_method': 'csum',
>>> 'score_method': 'csum'
>>> })
>>> from wbia.algo.graph.core import * # NOQA
>>> exec(ut.execstr_funckw(infr._cm_training_pairs))
>>> rng = np.random.RandomState(42)
>>> aid_pairs = np.array(infr._cm_training_pairs(rng=rng))
>>> print(len(aid_pairs))
>>> assert np.sum(aid_pairs.T[0] == aid_pairs.T[1]) == 0
"""
if qreq_ is None:
cm_list = infr.cm_list
qreq_ = infr.qreq_
ibs = infr.ibs
aid_pairs = []
dnids = qreq_.get_qreq_annot_nids(qreq_.daids)
# dnids = qreq_.get_qreq_annot_nids(qreq_.daids)
rng = ut.ensure_rng(rng)
for cm in ut.ProgIter(cm_list, lbl='building pairs'):
all_gt_aids = cm.get_top_gt_aids(ibs)
all_gf_aids = cm.get_top_gf_aids(ibs)
gt_aids = ut.take_percentile_parts(all_gt_aids, top_gt, mid_gt, bot_gt)
gf_aids = ut.take_percentile_parts(all_gf_aids, top_gf, mid_gf, bot_gf)
# get unscored examples
unscored_gt_aids = [
aid for aid in qreq_.daids[cm.qnid == dnids] if aid not in cm.daid2_idx
]
rand_gt_aids = ut.random_sample(unscored_gt_aids, rand_gt, rng=rng)
# gf_aids = cm.get_groundfalse_daids()
_gf_aids = qreq_.daids[cm.qnid != dnids]
_gf_aids = qreq_.daids.compress(cm.qnid != dnids)
# gf_aids = ibs.get_annot_groundfalse(cm.qaid, daid_list=qreq_.daids)
rand_gf_aids = ut.random_sample(_gf_aids, rand_gf, rng=rng).tolist()
chosen_daids = ut.unique(gt_aids + gf_aids + rand_gf_aids + rand_gt_aids)
aid_pairs.extend([(cm.qaid, aid) for aid in chosen_daids if cm.qaid != aid])
return aid_pairs
def sampleone(list_):
return ut.random_sample(list_, 1, rng=rng)[0]
