def sumidxtab(self, D, ivfidx):
"""
Compute distance to database items based on distances to centroids.
D: nsubq x ksub
"""
ids = self.storage[ivfidx].get_keys()
if ids.shape[0] == 0:
dis = np.ndarray(0)
else:
dis = cext.sumidxtab_core(D, self.storage[ivfidx].get_codes())
return np.array(ids), np.array(dis)
"""
Deprecated code
"""
num_candidates = self.storage[ivfidx].get_num_items()
dis = np.zeros(num_candidates)
ids = np.arange(0)
start_id = 0
for keys, blk in self.storage[ivfidx]:
cur_num = blk.shape[0]
# dis[start_id:start_id+cur_num] = self.sumidxtab_core(D, blk)
dis[start_id : start_id + cur_num] = cext.sumidxtab_core(D, blk)
start_id += cur_num
ids = np.hstack((ids, keys))
return ids, dis
def sumidxtab(self, D, ivfidx):
"""
Compute distance to database items based on distances to centroids.
D: nsubq x ksub
"""
ids = self.storage[ivfidx].get_keys()
if ids.shape[0] == 0:
dis = np.ndarray(0)
else:
dis = cext.sumidxtab_core(D, self.storage[ivfidx].get_codes())
return np.array(ids), np.array(dis)
"""
Deprecated code
"""
num_candidates = self.storage[ivfidx].get_num_items()
dis = np.zeros(num_candidates)
ids = np.arange(0)
start_id = 0
for keys, blk in self.storage[ivfidx]:
cur_num = blk.shape[0]
# dis[start_id:start_id+cur_num] = self.sumidxtab_core(D, blk)
dis[start_id:start_id + cur_num] = cext.sumidxtab_core(D, blk)
start_id += cur_num
ids = np.hstack((ids, keys))
return ids, dis
def sumidxtab(self, D):
"""
Compute distance to database items based on distances to centroids.
D: nsubq x ksub
"""
ids = self.storage.get_keys()
dis = cext.sumidxtab_core(D, self.storage.get_codes())
return np.array(ids), np.array(dis)
"""
def sumidxtab(self, D):
"""
Compute distance to database items based on distances to centroids.
D: nsubq x ksub
"""
ids = self.storage.get_keys()
dis = cext.sumidxtab_core(D, self.storage.get_codes())
return np.array(ids), np.array(dis)
"""
def test_sumidxtab_core(self):
nsq = 8
ksub = 256
cur_num = 10
for iround in range(10):
raw_D = np.random.random((nsq, ksub))
raw_blk = np.random.random_integers(0, ksub-1, (cur_num, nsq))
D = np.require(raw_D, np.float32, "C")
blk = np.require(raw_blk, np.uint8, "C")
self.assertLessEqual(np.abs(raw_D - D).sum(), 1e-4)
self.assertEqual(np.abs(raw_blk - blk).sum(), 0)
py_res = self.sumidxtab_core(D, blk)
c_res = cext.sumidxtab_core(D, blk)
self.assertLessEqual(np.abs(py_res - c_res).sum(), 1e-4)
