def index_data_annots(annots_df, daids, words):
vecs_list = annots_df['vecs'][daids]
flann_params = {}
wordflann = vtool.nearest_neighbors.flann_cache(words, flann_params=flann_params)
ax2_aid = np.array(daids)
idx2_vec, idx2_ax, idx2_fx = nntool.invertible_stack(vecs_list, np.arange(len(ax2_aid)))
invindex = InvertedIndex(words, wordflann, idx2_vec, idx2_ax, idx2_fx, ax2_aid)
invindex.compute_internals()
return invindex
def invert_index(vecs_list, ax_list):
"""
Aggregates descriptors of input annotations and returns inverted information
"""
if utool.NOT_QUIET:
print('[hsnbrx] stacking descriptors from %d annotations'
% len(ax_list))
try:
idx2_vec, idx2_ax, idx2_fx = nntool.invertible_stack(vecs_list, ax_list)
assert idx2_vec.shape[0] == idx2_ax.shape[0]
assert idx2_vec.shape[0] == idx2_fx.shape[0]
except MemoryError as ex:
utool.printex(ex, 'cannot build inverted index', '[!memerror]')
raise
if utool.NOT_QUIET:
print('stacked nVecs={nVecs} from nAnnots={nAnnots}'.format(
nVecs=len(idx2_vec), nAnnots=len(ax_list)))
return idx2_vec, idx2_ax, idx2_fx
def index_data_annots(annots_df, daids, words, qparams, with_internals=True,
memtrack=None, delete_rawvecs=False):
"""
Builds the initial inverted index from a dataframe, daids, and words.
Optionally builds the internals of the inverted structure
Args:
annots_df ():
daids ():
words ():
qparams ():
with_internals ():
memtrack (): memory debugging object
Returns:
invindex
Example:
>>> from ibeis.algo.hots.smk.smk_repr import * # NOQA
>>> from ibeis.algo.hots.smk import smk_debug
>>> ibs, annots_df, daids, qaids, qreq_, words = smk_debug.testdata_words()
>>> qparams = qreq_.qparams
>>> with_internals = False
>>> invindex = index_data_annots(annots_df, daids, words, qparams, with_internals)
Ignore:
#>>> print(ut.hashstr(repr(list(invindex.__dict__.values()))))
#v8+i5i8+55j0swio
Auto:
from ibeis.algo.hots.smk import smk_repr
import utool as ut
ut.rrrr()
print(ut.make_default_docstr(smk_repr.index_data_annots))
"""
if not ut.QUIET:
print('[smk_repr] index_data_annots')
flann_params = {}
# Compute fast lookup index for the words
wordflann = nntool.flann_cache(words, flann_params=flann_params, appname='smk')
_vecs_list = annots_df['vecs'][daids]
_label_list = annots_df['labels'][daids]
idx2_dvec, idx2_daid, idx2_dfx = nntool.invertible_stack(_vecs_list, daids)
# TODO:
# Need to individually cache residual vectors.
# rvecs_list = annots_df['rvecs'][daids]
#
# Residual vectors depend on
# * nearest word (word assignment)
# * original vectors
# * multiassignment
daid2_label = dict(zip(daids, _label_list))
invindex = InvertedIndex(words, wordflann, idx2_dvec, idx2_daid, idx2_dfx,
daids, daid2_label)
# Decrement reference count so memory can be cleared in the next function
del words, idx2_dvec, idx2_daid, idx2_dfx, daids, daid2_label
del _vecs_list, _label_list
if with_internals:
compute_data_internals_(invindex, qparams, memtrack=memtrack,
delete_rawvecs=delete_rawvecs) # 99%
return invindex
def index_data_annots(annots_df,
daids,
words,
qparams,
with_internals=True,
memtrack=None,
delete_rawvecs=False):
"""
Builds the initial inverted index from a dataframe, daids, and words.
Optionally builds the internals of the inverted structure
Args:
annots_df ():
daids ():
words ():
qparams ():
with_internals ():
memtrack (): memory debugging object
Returns:
invindex
Example:
>>> from ibeis.algo.hots.smk.smk_repr import * # NOQA
>>> from ibeis.algo.hots.smk import smk_debug
>>> ibs, annots_df, daids, qaids, qreq_, words = smk_debug.testdata_words()
>>> qparams = qreq_.qparams
>>> with_internals = False
>>> invindex = index_data_annots(annots_df, daids, words, qparams, with_internals)
Ignore:
#>>> print(ut.hashstr(repr(list(invindex.__dict__.values()))))
#v8+i5i8+55j0swio
Auto:
from ibeis.algo.hots.smk import smk_repr
import utool as ut
ut.rrrr()
print(ut.make_default_docstr(smk_repr.index_data_annots))
"""
if not ut.QUIET:
print('[smk_repr] index_data_annots')
flann_params = {}
# Compute fast lookup index for the words
wordflann = nntool.flann_cache(words,
flann_params=flann_params,
appname='smk')
_vecs_list = annots_df['vecs'][daids]
_label_list = annots_df['labels'][daids]
idx2_dvec, idx2_daid, idx2_dfx = nntool.invertible_stack(_vecs_list, daids)
# TODO:
# Need to individually cache residual vectors.
# rvecs_list = annots_df['rvecs'][daids]
#
# Residual vectors depend on
# * nearest word (word assignment)
# * original vectors
# * multiassignment
daid2_label = dict(zip(daids, _label_list))
invindex = InvertedIndex(words, wordflann, idx2_dvec, idx2_daid, idx2_dfx,
daids, daid2_label)
# Decrement reference count so memory can be cleared in the next function
del words, idx2_dvec, idx2_daid, idx2_dfx, daids, daid2_label
del _vecs_list, _label_list
if with_internals:
compute_data_internals_(invindex,
qparams,
memtrack=memtrack,
delete_rawvecs=delete_rawvecs) # 99%
return invindex
