def set_edge(self, edge, info_text=None):
aid1, aid2 = edge
if qreq_ is None:
qreq2_ = ibs.new_query_request([aid1], [aid2], cfgdict=cfgdict, verbose=False)
else:
qreq2_ = ibs.new_query_request(
[aid1], [aid2], cfgdict=qreq_.qparams, verbose=False
)
qconfig2_ = qreq2_.extern_query_config2
dconfig2_ = qreq2_.extern_data_config2
annot1 = ibs.annots([aid1], config=qconfig2_)[0]._make_lazy_dict()
annot2 = ibs.annots([aid2], config=dconfig2_)[0]._make_lazy_dict()
match = vt.PairwiseMatch(annot1, annot2)
def on_context():
from wbia.gui import inspect_gui
return inspect_gui.make_annotpair_context_options(ibs, aid1, aid2, None)
self.set_match(match, on_context, info_text)
def bigcache_vsone(qreq_, hyper_params):
"""
Cached output of one-vs-one matches
>>> from wbia.scripts.script_vsone import * # NOQA
>>> self = OneVsOneProblem()
>>> qreq_ = self.qreq_
>>> hyper_params = self.hyper_params
"""
import vtool as vt
import wbia
# Get a set of training pairs
ibs = qreq_.ibs
cm_list = qreq_.execute()
infr = wbia.AnnotInference.from_qreq_(qreq_, cm_list, autoinit=True)
# Per query choose a set of correct, incorrect, and random training pairs
aid_pairs_ = infr._cm_training_pairs(
rng=np.random.RandomState(42), **hyper_params.pair_sample
)
aid_pairs_ = vt.unique_rows(np.array(aid_pairs_), directed=False).tolist()
pb_aid_pairs_ = photobomb_samples(ibs)
# TODO: try to add in more non-comparable samples
aid_pairs_ = pb_aid_pairs_ + aid_pairs_
aid_pairs_ = vt.unique_rows(np.array(aid_pairs_))
# ======================================
# Compute one-vs-one scores and local_measures
# ======================================
# Prepare lazy attributes for annotations
qreq_ = infr.qreq_
ibs = qreq_.ibs
qconfig2_ = qreq_.extern_query_config2
dconfig2_ = qreq_.extern_data_config2
qannot_cfg = ibs.depc.stacked_config(None, 'featweight', qconfig2_)
dannot_cfg = ibs.depc.stacked_config(None, 'featweight', dconfig2_)
# Remove any pairs missing features
if dannot_cfg == qannot_cfg:
unique_annots = ibs.annots(np.unique(np.array(aid_pairs_)), config=dannot_cfg)
bad_aids = unique_annots.compress(~np.array(unique_annots.num_feats) > 0).aids
bad_aids = set(bad_aids)
else:
annots1_ = ibs.annots(ut.unique(ut.take_column(aid_pairs_, 0)), config=qannot_cfg)
annots2_ = ibs.annots(ut.unique(ut.take_column(aid_pairs_, 1)), config=dannot_cfg)
bad_aids1 = annots1_.compress(~np.array(annots1_.num_feats) > 0).aids
bad_aids2 = annots2_.compress(~np.array(annots2_.num_feats) > 0).aids
bad_aids = set(bad_aids1 + bad_aids2)
subset_idxs = np.where(
[not (a1 in bad_aids or a2 in bad_aids) for a1, a2 in aid_pairs_]
)[0]
# Keep only a random subset
if hyper_params.subsample:
rng = np.random.RandomState(3104855634)
num_max = hyper_params.subsample
if num_max < len(subset_idxs):
subset_idxs = rng.choice(subset_idxs, size=num_max, replace=False)
subset_idxs = sorted(subset_idxs)
# Take the current selection
aid_pairs = ut.take(aid_pairs_, subset_idxs)
if True:
# NEW WAY
config = hyper_params.vsone_assign
# TODO: ensure annot probs like chips and features can be appropriately
# set via qreq_ config or whatever
matches = infr.exec_vsone_subset(aid_pairs, config=config)
else:
query_aids = ut.take_column(aid_pairs, 0)
data_aids = ut.take_column(aid_pairs, 1)
# OLD WAY
# Determine a unique set of annots per config
configured_aids = ut.ddict(set)
configured_aids[qannot_cfg].update(query_aids)
configured_aids[dannot_cfg].update(data_aids)
# Make efficient annot-object representation
configured_obj_annots = {}
for config, aids in configured_aids.items():
annots = ibs.annots(sorted(list(aids)), config=config)
configured_obj_annots[config] = annots
annots1 = configured_obj_annots[qannot_cfg].loc(query_aids)
annots2 = configured_obj_annots[dannot_cfg].loc(data_aids)
# Get hash based on visual annotation appearence of each pair
# as well as algorithm configurations used to compute those properties
qvuuids = annots1.visual_uuids
dvuuids = annots2.visual_uuids
qcfgstr = annots1._config.get_cfgstr()
dcfgstr = annots2._config.get_cfgstr()
annots_cfgstr = ut.hashstr27(qcfgstr) + ut.hashstr27(dcfgstr)
vsone_uuids = [
ut.combine_uuids(uuids, salt=annots_cfgstr)
for uuids in ut.ProgIter(
zip(qvuuids, dvuuids), length=len(qvuuids), label='hashing ids'
)
]
# Combine into a big cache for the entire 1-v-1 matching run
big_uuid = ut.hashstr_arr27(vsone_uuids, '', pathsafe=True)
cacher = ut.Cacher('vsone_v7', cfgstr=str(big_uuid), appname='vsone_rf_train')
cached_data = cacher.tryload()
if cached_data is not None:
# Caching doesn't work 100% for PairwiseMatch object, so we need to do
# some postprocessing
configured_lazy_annots = ut.ddict(dict)
for config, annots in configured_obj_annots.items():
annot_dict = configured_lazy_annots[config]
for _annot in ut.ProgIter(annots.scalars(), label='make lazy dict'):
annot_dict[_annot.aid] = _annot._make_lazy_dict()
# Extract pairs of annot objects (with shared caches)
lazy_annots1 = ut.take(configured_lazy_annots[qannot_cfg], query_aids)
lazy_annots2 = ut.take(configured_lazy_annots[dannot_cfg], data_aids)
# Create a set of PairwiseMatches with the correct annot properties
matches = [
vt.PairwiseMatch(annot1, annot2)
for annot1, annot2 in zip(lazy_annots1, lazy_annots2)
]
# Updating a new matches dictionary ensure the annot1/annot2 properties
# are set correctly
for key, cached_matches in list(cached_data.items()):
fixed_matches = [match.copy() for match in matches]
for fixed, internal in zip(fixed_matches, cached_matches):
dict_ = internal.__dict__
ut.delete_dict_keys(dict_, ['annot1', 'annot2'])
fixed.__dict__.update(dict_)
cached_data[key] = fixed_matches
else:
cached_data = vsone_(
qreq_,
query_aids,
data_aids,
qannot_cfg,
dannot_cfg,
configured_obj_annots,
hyper_params,
)
cacher.save(cached_data)
# key_ = 'SV_LNBNN'
key_ = 'RAT_SV'
# for key in list(cached_data.keys()):
# if key != 'SV_LNBNN':
# del cached_data[key]
matches = cached_data[key_]
return matches, infr
def vsone_(
qreq_,
query_aids,
data_aids,
qannot_cfg,
dannot_cfg,
configured_obj_annots,
hyper_params,
):
# Do vectorized preload before constructing lazy dicts
# Then make sure the lazy dicts point to this subset
unique_obj_annots = list(configured_obj_annots.values())
for annots in ut.ProgIter(unique_obj_annots, 'vectorized preload'):
annots.set_caching(True)
annots.chip_size
annots.vecs
annots.kpts
annots.yaw
annots.qual
annots.gps
annots.time
if qreq_.qparams.featweight_enabled:
annots.fgweights
# annots._internal_attrs.clear()
# Make convinient lazy dict representations (after loading pre info)
configured_lazy_annots = ut.ddict(dict)
for config, annots in configured_obj_annots.items():
annot_dict = configured_lazy_annots[config]
for _annot in ut.ProgIter(annots.scalars(), label='make lazy dict'):
annot = _annot._make_lazy_dict()
annot_dict[_annot.aid] = annot
unique_lazy_annots = ut.flatten([x.values() for x in configured_lazy_annots.values()])
flann_params = {'algorithm': 'kdtree', 'trees': 4}
for annot in ut.ProgIter(unique_lazy_annots, label='lazy flann'):
vt.matching.ensure_metadata_flann(annot, flann_params)
vt.matching.ensure_metadata_normxy(annot)
for annot in ut.ProgIter(unique_lazy_annots, 'preload kpts'):
annot['kpts']
for annot in ut.ProgIter(unique_lazy_annots, 'preload normxy'):
annot['norm_xys']
for annot in ut.ProgIter(unique_lazy_annots, 'preload vecs'):
annot['vecs']
# Extract pairs of annot objects (with shared caches)
lazy_annots1 = ut.take(configured_lazy_annots[qannot_cfg], query_aids)
lazy_annots2 = ut.take(configured_lazy_annots[dannot_cfg], data_aids)
# TODO: param search over grid
# 'use_sv': [0, 1],
# 'use_fg': [0, 1],
# 'use_ratio_test': [0, 1],
matches_RAT = [
vt.PairwiseMatch(annot1, annot2)
for annot1, annot2 in zip(lazy_annots1, lazy_annots2)
]
# Construct global measurements
global_keys = ['yaw', 'qual', 'gps', 'time']
for match in ut.ProgIter(matches_RAT, label='setup globals'):
match.add_global_measures(global_keys)
# Preload flann for only specific annots
for match in ut.ProgIter(matches_RAT, label='preload FLANN'):
match.annot1['flann']
cfgdict = hyper_params.vsone_assign
# Find one-vs-one matches
# cfgdict = {'checks': 20, 'symmetric': False}
for match in ut.ProgIter(matches_RAT, label='assign vsone'):
match.assign(cfgdict=cfgdict)
# gridsearch_ratio_thresh()
# vt.matching.gridsearch_match_operation(matches_RAT, 'apply_ratio_test', {
# 'ratio_thresh': np.linspace(.6, .7, 50)
# })
for match in ut.ProgIter(matches_RAT, label='apply ratio thresh'):
match.apply_ratio_test({'ratio_thresh': 0.638}, inplace=True)
# TODO gridsearch over sv params
# vt.matching.gridsearch_match_operation(matches_RAT, 'apply_sver', {
# 'xy_thresh': np.linspace(0, 1, 3)
# })
matches_RAT_SV = [
match.apply_sver(inplace=True) for match in ut.ProgIter(matches_RAT, label='sver')
]
# Add keypoint spatial information to local features
for match in matches_RAT_SV:
match.add_local_measures()
# key_ = 'norm_xys'
# norm_xy1 = match.annot1[key_].take(match.fm.T[0], axis=1)
# norm_xy2 = match.annot2[key_].take(match.fm.T[1], axis=1)
# match.local_measures['norm_x1'] = norm_xy1[0]
# match.local_measures['norm_y1'] = norm_xy1[1]
# match.local_measures['norm_x2'] = norm_xy2[0]
# match.local_measures['norm_y2'] = norm_xy2[1]
# match.local_measures['scale1'] = vt.get_scales(
# match.annot1['kpts'].take(match.fm.T[0], axis=0))
# match.local_measures['scale2'] = vt.get_scales(
# match.annot2['kpts'].take(match.fm.T[1], axis=0))
# Create another version where we find global normalizers for the data
# qreq_.load_indexer()
# matches_SV_LNBNN = batch_apply_lnbnn(matches_RAT_SV, qreq_, inplace=True)
# if 'weight' in cfgdict:
# for match in matches_SV_LNBNN[::-1]:
# lnbnn_dist = match.local_measures['lnbnn']
# ndist = match.local_measures['lnbnn_norm_dist']
# weights = match.local_measures[cfgdict['weight']]
# match.local_measures['weighted_lnbnn'] = weights * lnbnn_dist
# match.local_measures['weighted_lnbnn_norm_dist'] = weights * ndist
# match.fs = match.local_measures['weighted_lnbnn']
cached_data = {
# 'RAT': matches_RAT,
'RAT_SV': matches_RAT_SV,
# 'SV_LNBNN': matches_SV_LNBNN,
}
return cached_data
from sklearn.metrics.classification import coo_matrix
def quick_cm(y_true, y_pred, labels, sample_weight):
n_labels = len(labels)
C = coo_matrix(
(sample_weight, (y_true, y_pred)), shape=(n_labels, n_labels)
).toarray()
return C
def quick_mcc(C):
""" assumes y_true and y_pred are in index/encoded format """
t_sum = C.sum(axis=1)
p_sum = C.sum(axis=0)
n_correct = np.diag(C).sum()
n_samples = p_sum.sum()
cov_ytyp = n_correct * n_samples - np.dot(t_sum, p_sum)
cov_ypyp = n_samples ** 2 - np.dot(p_sum, p_sum)
cov_ytyt = n_samples ** 2 - np.dot(t_sum, t_sum)
mcc = cov_ytyp / np.sqrt(cov_ytyt * cov_ypyp)
return mcc
def mcc_hack():
sample_weight = np.ones(len(self.samples), dtype=np.int)
task_mccs = ut.ddict(dict)
# Determine threshold levels per score type
score_to_order = {}
for scoretype in score_dict.keys():
y_score = score_dict[scoretype].values
sortx = np.argsort(y_score, kind='mergesort')[::-1]
y_score = y_score[sortx]
distinct_value_indices = np.where(np.diff(y_score))[0]
threshold_idxs = np.r_[distinct_value_indices, y_score.size - 1]
thresh = y_score[threshold_idxs]
score_to_order[scoretype] = (sortx, y_score, thresh)
classes_ = np.array([0, 1], dtype=np.int)
for task in task_list:
labels = self.samples.subtasks[task]
for sublabels in labels.gen_one_vs_rest_labels():
for scoretype in score_dict.keys():
sortx, y_score, thresh = score_to_order[scoretype]
y_true = sublabels.y_enc[sortx]
mcc = -np.inf
for t in thresh:
y_pred = (y_score > t).astype(np.int)
C1 = quick_cm(y_true, y_pred, classes_, sample_weight)
mcc1 = quick_mcc(C1)
if mcc1 < 0:
C2 = quick_cm(y_true, 1 - y_pred, classes_, sample_weight)
mcc1 = quick_mcc(C2)
mcc = max(mcc1, mcc)
# logger.info('mcc = %r' % (mcc,))
task_mccs[sublabels.task_name][scoretype] = mcc
return task_mccs
if 0:
with ut.Timer('mcc'):
task_mccs = mcc_hack()
logger.info('\nMCC of simple scoring measures:')
df = pd.DataFrame.from_dict(task_mccs, orient='index')
from utool.experimental.pandas_highlight import to_string_monkey
logger.info(to_string_monkey(df, highlight_cols=np.arange(len(df.columns))))
def demo_classes(pblm):
r"""
CommandLine:
python -m ibeis.algo.verif.vsone demo_classes --saveparts --save=classes.png --clipwhite
python -m ibeis.algo.verif.vsone demo_classes --saveparts --save=figures/classes.png --clipwhite --dpath=~/latex/crall-iccv-2017
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.algo.verif.vsone import * # NOQA
>>> pblm = OneVsOneProblem.from_empty(defaultdb='PZ_PB_RF_TRAIN')
>>> pblm.load_features()
>>> pblm.load_samples()
>>> pblm.build_feature_subsets()
>>> pblm.demo_classes()
>>> ut.show_if_requested()
"""
task_key = 'match_state'
labels = pblm.samples.subtasks[task_key]
pb_labels = pblm.samples.subtasks['photobomb_state']
classname_offset = {
POSTV: 0,
NEGTV: 0,
INCMP: 0,
}
class_name = POSTV
class_name = NEGTV
class_name = INCMP
feats = pblm.samples.X_dict['learn(sum,glob)']
offset = 0
class_to_edge = {}
for class_name in labels.class_names:
print('Find example of %r' % (class_name, ))
# Find an example of each class (that is not a photobomb)
pbflags = pb_labels.indicator_df['notpb']
flags = labels.indicator_df[class_name]
assert np.all(pbflags.index == flags.index)
flags = flags & pbflags
ratio = feats['sum(ratio)']
if class_name == INCMP:
# flags &= feats['global(delta_yaw)'] > 3
flags &= feats['global(delta_view)'] > 2
# flags &= feats['sum(ratio)'] > 0
if class_name == NEGTV:
low = ratio[flags].max()
flags &= feats['sum(ratio)'] >= low
if class_name == POSTV:
low = ratio[flags].median() / 2
high = ratio[flags].median()
flags &= feats['sum(ratio)'] < high
flags &= feats['sum(ratio)'] > low
# flags &= pblm.samples.simple_scores[flags]['score_lnbnn_1vM'] > 0
idxs = np.where(flags)[0]
print('Found %d candidates' % (len(idxs)))
offset = classname_offset[class_name]
idx = idxs[offset]
series = labels.indicator_df.iloc[idx]
assert series[class_name]
edge = series.name
class_to_edge[class_name] = edge
import plottool as pt
import guitool as gt
gt.ensure_qapp()
pt.qtensure()
fnum = 1
pt.figure(fnum=fnum, pnum=(1, 3, 1))
pnum_ = pt.make_pnum_nextgen(1, 3)
# classname_alias = {
# POSTV: 'positive',
# NEGTV: 'negative',
# INCMP: 'incomparable',
# }
ibs = pblm.infr.ibs
for class_name in class_to_edge.keys():
edge = class_to_edge[class_name]
aid1, aid2 = edge
# alias = classname_alias[class_name]
print('class_name = %r' % (class_name, ))
annot1 = ibs.annots([aid1])[0]._make_lazy_dict()
annot2 = ibs.annots([aid2])[0]._make_lazy_dict()
vt.matching.ensure_metadata_normxy(annot1)
vt.matching.ensure_metadata_normxy(annot2)
match = vt.PairwiseMatch(annot1, annot2)
cfgdict = pblm.hyper_params.vsone_match.asdict()
match.apply_all(cfgdict)
pt.figure(fnum=fnum, pnum=pnum_())
match.show(show_ell=False, show_ori=False)
def demo_single_pairwise_feature_vector():
r"""
CommandLine:
python -m ibeis.algo.verif.vsone demo_single_pairwise_feature_vector
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.algo.verif.vsone import * # NOQA
>>> match = demo_single_pairwise_feature_vector()
>>> print(match)
"""
import vtool as vt
import ibeis
ibs = ibeis.opendb('testdb1')
qaid, daid = 1, 2
annot1 = ibs.annots([qaid])[0]._make_lazy_dict()
annot2 = ibs.annots([daid])[0]._make_lazy_dict()
vt.matching.ensure_metadata_normxy(annot1)
vt.matching.ensure_metadata_normxy(annot2)
match = vt.PairwiseMatch(annot1, annot2)
cfgdict = {'checks': 200, 'symmetric': False}
match.assign(cfgdict=cfgdict)
match.apply_ratio_test({'ratio_thresh': .638}, inplace=True)
match.apply_sver(inplace=True)
# match.add_global_measures(['yaw', 'qual', 'gps', 'time'])
match.add_global_measures(['view', 'qual', 'gps', 'time'])
match.add_local_measures()
# sorters = ['ratio', 'norm_dist', 'match_dist']
match.make_feature_vector()
return match
def demo_classes(pblm):
r"""
CommandLine:
python -m ibeis.algo.verif.vsone demo_classes --saveparts --save=classes.png --clipwhite
python -m ibeis.algo.verif.vsone demo_classes --saveparts --save=figures/classes.png --clipwhite --dpath=~/latex/crall-iccv-2017
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.algo.verif.vsone import * # NOQA
>>> pblm = OneVsOneProblem.from_empty(defaultdb='PZ_PB_RF_TRAIN')
>>> pblm.load_features()
>>> pblm.load_samples()
>>> pblm.build_feature_subsets()
>>> pblm.demo_classes()
>>> ut.show_if_requested()
"""
task_key = 'match_state'
labels = pblm.samples.subtasks[task_key]
pb_labels = pblm.samples.subtasks['photobomb_state']
classname_offset = {
POSTV: 0,
NEGTV: 0,
INCMP: 0,
}
class_name = POSTV
class_name = NEGTV
class_name = INCMP
feats = pblm.samples.X_dict['learn(sum,glob)']
offset = 0
class_to_edge = {}
for class_name in labels.class_names:
print('Find example of %r' % (class_name, ))
# Find an example of each class (that is not a photobomb)
pbflags = pb_labels.indicator_df['notpb']
flags = labels.indicator_df[class_name]
assert np.all(pbflags.index == flags.index)
flags = flags & pbflags
ratio = feats['sum(ratio)']
if class_name == INCMP:
# flags &= feats['global(delta_yaw)'] > 3
flags &= feats['global(delta_view)'] > 2
# flags &= feats['sum(ratio)'] > 0
if class_name == NEGTV:
low = ratio[flags].max()
flags &= feats['sum(ratio)'] >= low
if class_name == POSTV:
low = ratio[flags].median() / 2
high = ratio[flags].median()
flags &= feats['sum(ratio)'] < high
flags &= feats['sum(ratio)'] > low
# flags &= pblm.samples.simple_scores[flags]['score_lnbnn_1vM'] > 0
idxs = np.where(flags)[0]
print('Found %d candidates' % (len(idxs)))
offset = classname_offset[class_name]
idx = idxs[offset]
series = labels.indicator_df.iloc[idx]
assert series[class_name]
edge = series.name
class_to_edge[class_name] = edge
import plottool as pt
import guitool as gt
gt.ensure_qapp()
pt.qtensure()
fnum = 1
pt.figure(fnum=fnum, pnum=(1, 3, 1))
pnum_ = pt.make_pnum_nextgen(1, 3)
# classname_alias = {
# POSTV: 'positive',
# NEGTV: 'negative',
# INCMP: 'incomparable',
# }
ibs = pblm.infr.ibs
for class_name in class_to_edge.keys():
edge = class_to_edge[class_name]
aid1, aid2 = edge
# alias = classname_alias[class_name]
print('class_name = %r' % (class_name, ))
annot1 = ibs.annots([aid1])[0]._make_lazy_dict()
annot2 = ibs.annots([aid2])[0]._make_lazy_dict()
vt.matching.ensure_metadata_normxy(annot1)
vt.matching.ensure_metadata_normxy(annot2)
match = vt.PairwiseMatch(annot1, annot2)
cfgdict = pblm.hyper_params.vsone_match.asdict()
match.apply_all(cfgdict)
pt.figure(fnum=fnum, pnum=pnum_())
match.show(show_ell=False, show_ori=False)
# pt.set_title(alias)
def find_opt_ratio(pblm):
"""
script to help find the correct value for the ratio threshold
>>> from ibeis.algo.verif.vsone import * # NOQA
>>> pblm = OneVsOneProblem.from_empty('PZ_PB_RF_TRAIN')
>>> pblm = OneVsOneProblem.from_empty('GZ_Master1')
"""
# Find best ratio threshold
pblm.load_samples()
infr = pblm.infr
edges = ut.emap(tuple, pblm.samples.aid_pairs.tolist())
task = pblm.samples['match_state']
pos_idx = task.class_names.tolist().index(POSTV)
config = {'ratio_thresh': 1.0, 'sv_on': False}
matches = infr._exec_pairwise_match(edges, config)
import plottool as pt
pt.qtensure()
thresholds = np.linspace(0, 1.0, 100)
pos_truth = task.y_bin.T[pos_idx]
ratio_fs = [m.local_measures['ratio'] for m in matches]
aucs = []
# Given the current correspondences: Find the optimal
# correspondence threshold.
for thresh in ut.ProgIter(thresholds, 'computing thresh'):
scores = np.array([fs[fs < thresh].sum() for fs in ratio_fs])
roc = sklearn.metrics.roc_auc_score(pos_truth, scores)
aucs.append(roc)
aucs = np.array(aucs)
opt_auc = aucs.max()
opt_thresh = thresholds[aucs.argmax()]
if True:
pt.plt.plot(thresholds, aucs, 'r-', label='')
pt.plt.plot(opt_thresh,
opt_auc,
'ro',
label='L opt=%r' % (opt_thresh, ))
pt.set_ylabel('auc')
pt.set_xlabel('ratio threshold')
pt.legend()
