def __init__(vocab, words=None):
vocab.wx_to_word = words
vocab.wordflann = None
vocab.flann_params = vt.get_flann_params(random_seed=42)
vocab.flann_params['checks'] = 1024
vocab.flann_params['trees'] = 8
def subindexer_time_experiment():
"""
builds plot of number of annotations vs indexer build time.
TODO: time experiment
"""
import ibeis
import utool as ut
import pyflann
import plottool as pt
ibs = ibeis.opendb(db='PZ_Master0')
daid_list = ibs.get_valid_aids()
count_list = []
time_list = []
flann_params = vt.get_flann_params()
for count in ut.ProgressIter(range(1, 301)):
daids_ = daid_list[:]
np.random.shuffle(daids_)
daids = daids_[0:count]
vecs = np.vstack(ibs.get_annot_vecs(daids))
with ut.Timer(verbose=False) as t:
flann = pyflann.FLANN()
flann.build_index(vecs, **flann_params)
count_list.append(count)
time_list.append(t.ellapsed)
count_arr = np.array(count_list)
time_arr = np.array(time_list)
pt.plot2(count_arr,
time_arr,
marker='-',
equal_aspect=False,
x_label='num_annotations',
y_label='FLANN build time')
def get_buildtime_data(**kwargs):
flann_params = vt.get_flann_params(**kwargs)
print('flann_params = %r' % (ut.dict_str(flann_params), ))
data_list = []
num = 1000
print('-----')
for count in ut.ProgressIter(itertools.count(),
nTotal=-1,
freq=1,
autoadjust=False):
num = int(num * 1.2)
print('num = %r' % (num, ))
#if num > 1E6:
# break
data = pool.get_testdata(num)
print('object size ' + ut.get_object_size_str(data, 'data'))
flann = pyflann.FLANN(**flann_params)
with ut.Timer(verbose=False) as t:
flann.build_index(data)
print('t.ellapsed = %r' % (t.ellapsed, ))
if t.ellapsed > 5 or count > 1000:
break
data_list.append((count, num, t.ellapsed))
print('-----')
return data_list, flann_params
def get_cfgstr(nnindexer, noquery=False):
r""" returns string which uniquely identified configuration and support data
Args:
noquery (bool): if True cfgstr is only relevant to building the
index. No search params are returned (default = False)
Returns:
str: flann_cfgstr
CommandLine:
python -m wbia.algo.hots.neighbor_index --test-get_cfgstr
Example:
>>> # DISABLE_DOCTEST
>>> from wbia.algo.hots.neighbor_index import * # NOQA
>>> import wbia
>>> cfgdict = dict(fg_on=False)
>>> qreq_ = wbia.testdata_qreq_(defaultdb='testdb1', p='default:fg_on=False')
>>> qreq_.load_indexer()
>>> nnindexer = qreq_.indexer
>>> noquery = True
>>> flann_cfgstr = nnindexer.get_cfgstr(noquery)
>>> result = ('flann_cfgstr = %s' % (str(flann_cfgstr),))
>>> print(result)
flann_cfgstr = _FLANN((algo=kdtree,seed=42,t=8,))_VECS((11260,128)gj5nea@ni0%f3aja)
"""
flann_cfgstr_list = []
use_params_hash = True
use_data_hash = True
if use_params_hash:
flann_defaults = vt.get_flann_params(
nnindexer.flann_params['algorithm'])
# flann_params_clean = flann_defaults.copy()
flann_params_clean = ut.sort_dict(flann_defaults)
ut.update_existing(flann_params_clean, nnindexer.flann_params)
if noquery:
ut.delete_dict_keys(flann_params_clean, ['checks'])
shortnames = dict(algorithm='algo',
checks='chks',
random_seed='seed',
trees='t')
short_params = ut.odict([
(shortnames.get(key, key), str(val)[0:7])
for key, val in six.iteritems(flann_params_clean)
])
flann_valsig_ = ut.repr2(short_params,
nl=False,
explicit=True,
strvals=True)
flann_valsig_ = flann_valsig_.lstrip('dict').replace(' ', '')
# flann_valsig_ = str(list(flann_params.values()))
# flann_valsig = ut.remove_chars(flann_valsig_, ', \'[]')
flann_cfgstr_list.append('_FLANN(' + flann_valsig_ + ')')
if use_data_hash:
vecs_hashstr = ut.hashstr_arr(nnindexer.idx2_vec, '_VECS')
flann_cfgstr_list.append(vecs_hashstr)
flann_cfgstr = ''.join(flann_cfgstr_list)
return flann_cfgstr
def get_cfgstr(nnindexer, noquery=False):
r""" returns string which uniquely identified configuration and support data
Args:
noquery (bool): if True cfgstr is only relevant to building the
index. No search params are returned (default = False)
Returns:
str: flann_cfgstr
CommandLine:
python -m ibeis.algo.hots.neighbor_index --test-get_cfgstr
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.algo.hots.neighbor_index import * # NOQA
>>> import ibeis
>>> cfgdict = dict(fg_on=False)
>>> qreq_ = ibeis.testdata_qreq_(defaultdb='testdb1', p='default:fg_on=False')
>>> qreq_.load_indexer()
>>> nnindexer = qreq_.indexer
>>> noquery = True
>>> flann_cfgstr = nnindexer.get_cfgstr(noquery)
>>> result = ('flann_cfgstr = %s' % (str(flann_cfgstr),))
>>> print(result)
flann_cfgstr = _FLANN((algo=kdtree,seed=42,t=8,))_VECS((11260,128)gj5nea@ni0%f3aja)
"""
flann_cfgstr_list = []
use_params_hash = True
use_data_hash = True
if use_params_hash:
flann_defaults = vt.get_flann_params(nnindexer.flann_params['algorithm'])
#flann_params_clean = flann_defaults.copy()
flann_params_clean = ut.sort_dict(flann_defaults)
ut.updateif_haskey(flann_params_clean, nnindexer.flann_params)
if noquery:
ut.delete_dict_keys(flann_params_clean, ['checks'])
shortnames = dict(algorithm='algo', checks='chks', random_seed='seed', trees='t')
short_params = ut.odict([(shortnames.get(key, key), str(val)[0:7])
for key, val in six.iteritems(flann_params_clean)])
flann_valsig_ = ut.dict_str(
short_params, nl=False, explicit=True, strvals=True)
flann_valsig_ = flann_valsig_.lstrip('dict').replace(' ', '')
#flann_valsig_ = str(list(flann_params.values()))
#flann_valsig = ut.remove_chars(flann_valsig_, ', \'[]')
flann_cfgstr_list.append('_FLANN(' + flann_valsig_ + ')')
if use_data_hash:
vecs_hashstr = ut.hashstr_arr(nnindexer.idx2_vec, '_VECS')
flann_cfgstr_list.append(vecs_hashstr)
flann_cfgstr = ''.join(flann_cfgstr_list)
return flann_cfgstr
def compute_vocab(depc, fid_list, config):
r"""
Args:
depc (dtool.DependencyCache):
fids_list (list):
config (dtool.Config):
CommandLine:
python -m ibeis.core_annots --exec-compute_neighbor_index --show
python -m ibeis.control.IBEISControl --test-show_depc_annot_table_input --show --tablename=neighbor_index
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.new_annots import * # NOQA
>>> import ibeis
>>> ibs, aid_list = ibeis.testdata_aids('testdb1')
>>> depc = ibs.depc_annot
>>> fid_list = depc.get_rowids('feat', aid_list)
>>> config = VocabConfig()
>>> vocab, train_vecs = ut.exec_func_src(compute_vocab, key_list=['vocab', 'train_vecs'])
>>> idx2_vec = depc.d.get_feat_vecs(aid_list)[0]
>>> self = vocab
>>> ut.quit_if_noshow()
>>> data = train_vecs
>>> centroids = vocab.wx2_word
>>> import plottool as pt
>>> vt.plot_centroids(data, centroids, num_pca_dims=2)
>>> ut.show_if_requested()
>>> #config = ibs.depc_annot['vocab'].configclass()
>>>
"""
print('[IBEIS] COMPUTE_VOCAB:')
vecs_list = depc.get_native('feat', fid_list, 'vecs')
train_vecs = np.vstack(vecs_list)
num_words = config['num_words']
max_iters = 100
print('[smk_index] Train Vocab(nWords=%d) using %d annots and %d descriptors' %
(num_words, len(fid_list), len(train_vecs)))
flann_params = vt.get_flann_params(random_seed=42)
kwds = dict(
max_iters=max_iters,
flann_params=flann_params
)
words = vt.akmeans(train_vecs, num_words, **kwds)
vocab = VisualVocab(words)
vocab.reindex()
return vocab
def compute_vocab(depc, fid_list, config):
r"""
Args:
depc (dtool.DependencyCache):
fids_list (list):
config (dtool.Config):
CommandLine:
python -m ibeis.core_annots --exec-compute_neighbor_index --show
python -m ibeis.control.IBEISControl --test-show_depc_annot_table_input --show --tablename=neighbor_index
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.new_annots import * # NOQA
>>> import ibeis
>>> ibs, aid_list = ibeis.testdata_aids('testdb1')
>>> depc = ibs.depc_annot
>>> fid_list = depc.get_rowids('feat', aid_list)
>>> config = VocabConfig()
>>> vocab, train_vecs = ut.exec_func_src(compute_vocab, key_list=['vocab', 'train_vecs'])
>>> idx2_vec = depc.d.get_feat_vecs(aid_list)[0]
>>> self = vocab
>>> ut.quit_if_noshow()
>>> data = train_vecs
>>> centroids = vocab.wx2_word
>>> import plottool as pt
>>> vt.plot_centroids(data, centroids, num_pca_dims=2)
>>> ut.show_if_requested()
>>> #config = ibs.depc_annot['vocab'].configclass()
>>>
"""
print('[IBEIS] COMPUTE_VOCAB:')
vecs_list = depc.get_native('feat', fid_list, 'vecs')
train_vecs = np.vstack(vecs_list)
num_words = config['num_words']
max_iters = 100
print(
'[smk_index] Train Vocab(nWords=%d) using %d annots and %d descriptors'
% (num_words, len(fid_list), len(train_vecs)))
flann_params = vt.get_flann_params(random_seed=42)
kwds = dict(max_iters=max_iters, flann_params=flann_params)
words = vt.akmeans(train_vecs, num_words, **kwds)
vocab = VisualVocab(words)
vocab.reindex()
return vocab
def get_buildtime_data(**kwargs):
flann_params = vt.get_flann_params(**kwargs)
print('flann_params = %r' % (ut.dict_str(flann_params),))
data_list = []
num = 1000
print('-----')
for count in ut.ProgressIter(itertools.count(), nTotal=-1, freq=1, autoadjust=False):
num = int(num * 1.2)
print('num = %r' % (num,))
#if num > 1E6:
# break
data = pool.get_testdata(num)
print('object size ' + ut.get_object_size_str(data, 'data'))
flann = pyflann.FLANN(**flann_params)
with ut.Timer(verbose=False) as t:
flann.build_index(data)
print('t.ellapsed = %r' % (t.ellapsed,))
if t.ellapsed > 5 or count > 1000:
break
data_list.append((count, num, t.ellapsed))
print('-----')
return data_list, flann_params
def compute_vocab(depc, fid_list, config):
r"""
Depcache method for computing a new visual vocab
CommandLine:
python -m wbia.core_annots --exec-compute_neighbor_index --show
python -m wbia show_depc_annot_table_input --show --tablename=neighbor_index
python -m wbia.algo.smk.vocab_indexer --exec-compute_vocab:0
python -m wbia.algo.smk.vocab_indexer --exec-compute_vocab:1
# FIXME make util_tests register
python -m wbia.algo.smk.vocab_indexer compute_vocab:0
Ignore:
>>> # Lev Oxford Debug Example
>>> import wbia
>>> ibs = wbia.opendb('Oxford')
>>> depc = ibs.depc
>>> table = depc['vocab']
>>> # Check what currently exists in vocab table
>>> table.print_configs()
>>> table.print_table()
>>> table.print_internal_info()
>>> # Grab aids used to compute vocab
>>> from wbia.expt.experiment_helpers import get_annotcfg_list
>>> expanded_aids_list = get_annotcfg_list(ibs, ['oxford'])[1]
>>> qaids, daids = expanded_aids_list[0]
>>> vocab_aids = daids
>>> config = {'num_words': 64000}
>>> exists = depc.check_rowids('vocab', [vocab_aids], config=config)
>>> print('exists = %r' % (exists,))
>>> vocab_rowid = depc.get_rowids('vocab', [vocab_aids], config=config)[0]
>>> print('vocab_rowid = %r' % (vocab_rowid,))
>>> vocab = table.get_row_data([vocab_rowid], 'words')[0]
>>> print('vocab = %r' % (vocab,))
Example:
>>> # DISABLE_DOCTEST
>>> from wbia.algo.smk.vocab_indexer import * # NOQA
>>> # Test depcache access
>>> import wbia
>>> ibs, aid_list = wbia.testdata_aids('testdb1')
>>> depc = ibs.depc_annot
>>> input_tuple = [aid_list]
>>> rowid_kw = {}
>>> tablename = 'vocab'
>>> vocabid_list = depc.get_rowids(tablename, input_tuple, **rowid_kw)
>>> vocab = depc.get(tablename, input_tuple, 'words')[0]
>>> assert vocab.wordflann is not None
>>> assert vocab.wordflann._FLANN__curindex_data is not None
>>> assert vocab.wordflann._FLANN__curindex_data is vocab.wx_to_word
Example:
>>> # DISABLE_DOCTEST
>>> from wbia.algo.smk.vocab_indexer import * # NOQA
>>> import wbia
>>> ibs, aid_list = wbia.testdata_aids('testdb1')
>>> depc = ibs.depc_annot
>>> fid_list = depc.get_rowids('feat', aid_list)
>>> config = VocabConfig()
>>> vocab, train_vecs = ut.exec_func_src(compute_vocab, keys=['vocab', 'train_vecs'])
>>> idx_to_vec = depc.d.get_feat_vecs(aid_list)[0]
>>> self = vocab
>>> ut.quit_if_noshow()
>>> data = train_vecs
>>> centroids = vocab.wx_to_word
>>> import wbia.plottool as pt
>>> vt.plot_centroids(data, centroids, num_pca_dims=2)
>>> ut.show_if_requested()
>>> #config = ibs.depc_annot['vocab'].configclass()
"""
logger.info('[IBEIS] COMPUTE_VOCAB:')
vecs_list = depc.get_native('feat', fid_list, 'vecs')
train_vecs = np.vstack(vecs_list).astype(np.float32)
num_words = config['num_words']
logger.info(
'[smk_index] Train Vocab(nWords=%d) using %d annots and %d descriptors'
% (num_words, len(fid_list), len(train_vecs)))
if config['algorithm'] == 'kdtree':
flann_params = vt.get_flann_params(random_seed=42)
kwds = dict(max_iters=20, flann_params=flann_params)
words = vt.akmeans(train_vecs, num_words, **kwds)
elif config['algorithm'] == 'minibatch':
logger.info('Using minibatch kmeans')
import sklearn.cluster
rng = np.random.RandomState(config['random_seed'])
n_init = config['n_init']
with warnings.catch_warnings():
warnings.simplefilter('ignore')
init_size = int(num_words * 4)
batch_size = 1000
n_batches = ut.get_num_chunks(train_vecs.shape[0], batch_size)
minibatch_params = dict(
n_clusters=num_words,
init='k-means++',
init_size=init_size,
n_init=n_init,
max_iter=30000 // n_batches,
batch_size=batch_size,
tol=0.0,
max_no_improvement=10,
reassignment_ratio=0.01,
)
logger.info('minibatch_params = %s' %
(ut.repr4(minibatch_params), ))
clusterer = sklearn.cluster.MiniBatchKMeans(compute_labels=False,
random_state=rng,
verbose=2,
**minibatch_params)
try:
clusterer.fit(train_vecs)
except (Exception, KeyboardInterrupt) as ex:
ut.printex(ex, tb=True)
if ut.is_developer():
ut.embed()
else:
raise
words = clusterer.cluster_centers_
logger.info('Finished clustering')
# if False:
# flann_params['checks'] = 64
# flann_params['trees'] = 4
# num_words = 128
# centroids = vt.initialize_centroids(num_words, train_vecs, 'akmeans++')
# words, hist = vt.akmeans_iterations(
# train_vecs, centroids, max_iters=1000, monitor=True,
# flann_params=flann_params)
logger.info('Constructing vocab')
vocab = VisualVocab(words)
logger.info('Building vocab index')
vocab.build()
logger.info('Returning vocab')
return (vocab, )
def __init__(self, words=None):
self.wx2_word = words
self.wordflann = pyflann.FLANN()
self.flann_params = vt.get_flann_params(random_seed=42)
def flann_add_time_experiment():
"""
builds plot of number of annotations vs indexer build time.
TODO: time experiment
CommandLine:
python -m wbia.algo.hots._neighbor_experiment --test-flann_add_time_experiment --db PZ_MTEST --show
python -m wbia.algo.hots._neighbor_experiment --test-flann_add_time_experiment --db PZ_Master0 --show
utprof.py -m wbia.algo.hots._neighbor_experiment --test-flann_add_time_experiment --show
valgrind --tool=memcheck --suppressions=valgrind-python.supp python -m wbia.algo.hots._neighbor_experiment --test-flann_add_time_experiment --db PZ_MTEST --no-with-reindex
Example:
>>> # DISABLE_DOCTEST
>>> from wbia.algo.hots._neighbor_experiment import * # NOQA
>>> import wbia
>>> #ibs = wbia.opendb('PZ_MTEST')
>>> result = flann_add_time_experiment()
>>> # verify results
>>> print(result)
>>> ut.show_if_requested()
"""
import wbia
import utool as ut
import numpy as np
import wbia.plottool as pt
def make_flann_index(vecs, flann_params):
flann = pyflann.FLANN()
flann.build_index(vecs, **flann_params)
return flann
db = ut.get_argval('--db')
ibs = wbia.opendb(db=db)
# Input
if ibs.get_dbname() == 'PZ_MTEST':
initial = 1
reindex_stride = 16
addition_stride = 4
max_ceiling = 120
elif ibs.get_dbname() == 'PZ_Master0':
# ibs = wbia.opendb(db='GZ_ALL')
initial = 32
reindex_stride = 32
addition_stride = 16
max_ceiling = 300001
else:
assert False
# max_ceiling = 32
all_daids = ibs.get_valid_aids()
max_num = min(max_ceiling, len(all_daids))
flann_params = vt.get_flann_params()
# Output
count_list, time_list_reindex = [], []
count_list2, time_list_addition = [], []
# Setup
# all_randomize_daids_ = ut.deterministic_shuffle(all_daids[:])
all_randomize_daids_ = all_daids
# ensure all features are computed
ibs.get_annot_vecs(all_randomize_daids_)
def reindex_step(count, count_list, time_list_reindex):
daids = all_randomize_daids_[0:count]
vecs = np.vstack(ibs.get_annot_vecs(daids))
with ut.Timer(verbose=False) as t:
flann = make_flann_index(vecs, flann_params) # NOQA
count_list.append(count)
time_list_reindex.append(t.ellapsed)
def addition_step(count, flann, count_list2, time_list_addition):
daids = all_randomize_daids_[count:count + 1]
vecs = np.vstack(ibs.get_annot_vecs(daids))
with ut.Timer(verbose=False) as t:
flann.add_points(vecs)
count_list2.append(count)
time_list_addition.append(t.ellapsed)
def make_initial_index(initial):
daids = all_randomize_daids_[0:initial + 1]
vecs = np.vstack(ibs.get_annot_vecs(daids))
flann = make_flann_index(vecs, flann_params)
return flann
WITH_REINDEX = not ut.get_argflag('--no-with-reindex')
if WITH_REINDEX:
# Reindex Part
reindex_lbl = 'Reindexing'
_reindex_iter = range(1, max_num, reindex_stride)
reindex_iter = ut.ProgressIter(_reindex_iter, lbl=reindex_lbl, freq=1)
for count in reindex_iter:
reindex_step(count, count_list, time_list_reindex)
# Add Part
flann = make_initial_index(initial)
addition_lbl = 'Addition'
_addition_iter = range(initial + 1, max_num, addition_stride)
addition_iter = ut.ProgressIter(_addition_iter, lbl=addition_lbl)
for count in addition_iter:
addition_step(count, flann, count_list2, time_list_addition)
logger.info('---')
logger.info('Reindex took time_list_reindex %.2s seconds' %
sum(time_list_reindex))
logger.info('Addition took time_list_reindex %.2s seconds' %
sum(time_list_addition))
logger.info('---')
statskw = dict(precision=2, newlines=True)
logger.info('Reindex stats ' +
ut.get_stats_str(time_list_reindex, **statskw))
logger.info('Addition stats ' +
ut.get_stats_str(time_list_addition, **statskw))
logger.info('Plotting')
# with pt.FigureContext:
next_fnum = iter(range(0, 2)).next # python3 PY3
pt.figure(fnum=next_fnum())
if WITH_REINDEX:
pt.plot2(
count_list,
time_list_reindex,
marker='-o',
equal_aspect=False,
x_label='num_annotations',
label=reindex_lbl + ' Time',
dark=False,
)
# pt.figure(fnum=next_fnum())
pt.plot2(
count_list2,
time_list_addition,
marker='-o',
equal_aspect=False,
x_label='num_annotations',
label=addition_lbl + ' Time',
)
pt
pt.legend()
def __init__(self, words=None):
self.wx2_word = words
self.wordflann = pyflann.FLANN()
self.flann_params = vt.get_flann_params(random_seed=42)