def batch_query(em, force_recomp=False, test_cxs=None):
'''Runs each test_cxs as a query. If test_cxs is None, then all queries
are run'''
'TODO: Fix up the VM dependencies'
vm, iom, am, cm = em.hs.get_managers('vm', 'iom', 'am', 'cm')
# Compute the matches
qm = vm.hs.qm
vm.sample_train_set()
vm.build_model(force_recomp=force_recomp)
if test_cxs == None:
test_cxs = vm.get_train_cx()
logmsg('Building matching graph. This may take awhile')
depends = ['chiprep', 'preproc', 'model', 'query']
algo_suffix = am.get_algo_suffix(depends)
samp_suffix = vm.get_samp_suffix()
result_dpath = iom.ensure_directory(iom.get_temp_fpath('raw_results'))
rr_fmtstr_cid = os.path.join(
result_dpath, 'rr_cid%07d' + samp_suffix + algo_suffix + '.pkl')
# Find the Queries which need to be run
unsaved_cxs = []
for cx in iter(test_cxs):
cid = cm.cx2_cid[cx]
rr_fpath = rr_fmtstr_cid % cid
if not os.path.exists(rr_fpath):
unsaved_cxs.append(cx)
# Run Unsaved Query
total = len(unsaved_cxs)
for count, cx in enumerate(unsaved_cxs):
logmsg('Query %d/%d' % (count, total))
em.run_and_save_query(cx, rr_fmtstr_cid)
# Read Each Query
cx2_rr = alloc_lists(test_cxs.max() + 1)
total = len(test_cxs)
for count, cx in enumerate(test_cxs):
logmsg('Loading Result %d/%d' % (count, total))
cid = cm.cx2_cid[cx]
rr_fpath = rr_fmtstr_cid % cid
if not os.path.exists(rr_fpath):
logwarn('Result does not exist for CID=%d' % cid)
rr_file = open(rr_fpath, 'rb')
try:
rr = cPickle.load(rr_file)
except EOFError:
rr_file.close()
os.remove(rr_fpath)
logwarn('Result was corrupted for CID=%d' % cid)
rr_file.close()
rr.cx2_cscore_ = []
rr.cx2_fs_ = []
rr.qfdsc = []
rr.qfpts = []
cx2_rr[cx] = rr
return cx2_rr
def batch_query(em, force_recomp=False, test_cxs=None):
'''Runs each test_cxs as a query. If test_cxs is None, then all queries
are run'''
'TODO: Fix up the VM dependencies'
vm, iom, am, cm = em.hs.get_managers('vm','iom','am', 'cm')
# Compute the matches
qm = vm.hs.qm
vm.sample_train_set()
vm.build_model(force_recomp=force_recomp)
if test_cxs == None:
test_cxs = vm.get_train_cx()
logmsg('Building matching graph. This may take awhile')
depends = ['chiprep','preproc','model','query']
algo_suffix = am.get_algo_suffix(depends)
samp_suffix = vm.get_samp_suffix()
result_dpath = iom.ensure_directory(iom.get_temp_fpath('raw_results'))
rr_fmtstr_cid = os.path.join(result_dpath, 'rr_cid%07d'+samp_suffix+algo_suffix+'.pkl')
# Find the Queries which need to be run
unsaved_cxs = []
for cx in iter(test_cxs):
cid = cm.cx2_cid[cx]
rr_fpath = rr_fmtstr_cid % cid
if not os.path.exists(rr_fpath):
unsaved_cxs.append(cx)
# Run Unsaved Query
total = len(unsaved_cxs)
for count, cx in enumerate(unsaved_cxs):
logmsg('Query %d/%d' % (count, total))
em.run_and_save_query(cx, rr_fmtstr_cid)
# Read Each Query
cx2_rr = alloc_lists(test_cxs.max()+1)
total = len(test_cxs)
for count, cx in enumerate(test_cxs):
logmsg('Loading Result %d/%d' % (count, total))
cid = cm.cx2_cid[cx]
rr_fpath = rr_fmtstr_cid % cid
if not os.path.exists(rr_fpath):
logwarn('Result does not exist for CID=%d' % cid)
rr_file = open(rr_fpath,'rb')
try:
rr = cPickle.load(rr_file)
except EOFError:
rr_file.close()
os.remove(rr_fpath)
logwarn('Result was corrupted for CID=%d' % cid)
rr_file.close()
rr.cx2_cscore_ = []
rr.cx2_fs_ = []
rr.qfdsc = []
rr.qfpts = []
cx2_rr[cx] = rr
return cx2_rr
def query_db_vs_db(hsA, hsB):
'Runs cross database queries / reloads cross database queries'
vs_str = get_results_name(hsA, hsB)
print('Running/Loading ' + vs_str)
query_cxs = hsA.cm.get_valid_cxs()
total = len(query_cxs)
cx2_rr = alloc_lists(total)
for count, qcx in enumerate(query_cxs):
#with Timer() as t:
#print(('Query %d / %d ' % (count, total)) + vs_str)
rr = hsB.qm.cx2_rr(qcx, hsA)
cx2_rr[count] = rr
return cx2_rr
def query_db_vs_db(hsA, hsB):
'Runs cross database queries / reloads cross database queries'
vs_str = get_results_name(hsA, hsB)
print('Running/Loading '+vs_str)
query_cxs = hsA.cm.get_valid_cxs()
total = len(query_cxs)
cx2_rr = alloc_lists(total)
for count, qcx in enumerate(query_cxs):
#with Timer() as t:
#print(('Query %d / %d ' % (count, total)) + vs_str)
rr = hsB.qm.cx2_rr(qcx, hsA)
cx2_rr[count] = rr
return cx2_rr
def approximate_kmeans(data, K=1e6, max_iters=1000, flann_pref=None):
if flann_pref == None:
flann_pref = Pref()
flann_pref.algorithm = Pref("kdtree")
flann_pref.trees = Pref(8)
flann_pref.checks = Pref(128)
flann_args = flann_pref.to_dict()
float_data = np.array(data, dtype=np.float32)
N = float_data.shape[0]
print ("Approximately clustering %d data vectors into %d clusters" % (N, K))
np.random.seed(seed=0) # For Reproducibility
# Initialize to Random Cluster Centers
centx = np.random.choice(N, size=K, replace=False)
cent = np.copy(float_data[centx])
assign = alloc_lists(K) # List for each cluster center with assigned indexes
for iterx in xrange(0, max_iters):
print "Iteration " + str(iterx)
# Step 1: Find Nearest Neighbors
flann = FLANN()
flann.build_index(data_vecs, **flann_args)
(index_list, dist_list) = flann.nn_index(query_vecs, K, checks=flann_args["checks"])
return (index_list, dist_list)
datax2_centx, _ = flann_one_time(cent, float_data, 1, flann_args)
# Step 2: Assign data to cluster centers
datax_sort = datax2_centx.argsort()
centx_sort = datax2_centx[datax_sort]
# Efficiently Trace over sorted centers with two pointers. Take care
# To include the last batch of datavecs with the same center_index
converged = True
prev_centx = -1
_L = 0
dbg_total_assigned = 0
dbg_assigned_list = []
for _R in xrange(N + 1): # Loop over datapoints, going 1 past the end, and group them
# data = 0[ . . . . . . . . . . . . .]N
# ptrs = L R
# |- k -|L R
# |- k+1 |L R
# |_K|
if _R == N or centx_sort[_L] != centx_sort[_R]: # We found a group
centx = centx_sort[_L] # Assign this group cluster index: centx
# SPECIAL CASE: ( akmeans might not assign everything )
if centx - prev_centx > 1: # Check if a cluster got skipped
for skipx in xrange(prev_centx + 1, centx):
print (" Skipping Index:" + str(skipx))
if len(assign[skipx]) != 0:
converged = False
assign[skipx] = []
prev_centx = centx
# Set Assignments
num_members = np.float32(_R - _L)
dbg_total_assigned += num_members
centx_membx = datax_sort[_L:_R]
# DBG CODE, keep track of data vectors you've assigned
# print(' Assigning %d data vectors to center index: %d' % (num_members, centx) )
# for x in centx_membx:
# dbg_assigned_list.append(x)
# /DBGCODE
if np.all(assign[centx] != centx_membx):
converged = False
assign[centx] = centx_membx
# Recompute Centers
cent[centx] = float_data[centx_membx, :].sum(axis=0) / num_members
_L = _R
# print(' Did Assignment of %d centers' % prev_centx)
# print(' Assigned %d datavectors in total' % dbg_total_assigned)
# SPECIAL CASE: has to run at the end again
if prev_centx < K: # Check if a cluster got skipped at the end
for skipx in xrange(prev_centx + 1, K):
print (" Cluster Index %d was empty:" % skipx)
if len(assign[skipx]) != 0:
converged = False
assign[skipx] = []
prev_centx = centx
if converged: # Assignments have not changed
print "akmeans converged in " + str(iterx) + " iterations"
break
return cent, assign
def approximate_kmeans(data, K=1e6, max_iters=1000, flann_pref=None):
if flann_pref == None:
flann_pref = Pref()
flann_pref.algorithm = Pref('kdtree')
flann_pref.trees = Pref(8)
flann_pref.checks = Pref(128)
flann_args = flann_pref.to_dict()
float_data = np.array(data, dtype=np.float32)
N = float_data.shape[0]
print('Approximately clustering %d data vectors into %d clusters' % (N, K))
np.random.seed(seed=0) # For Reproducibility
# Initialize to Random Cluster Centers
centx = np.random.choice(N, size=K, replace=False)
cent = np.copy(float_data[centx])
assign = alloc_lists(
K) # List for each cluster center with assigned indexes
for iterx in xrange(0, max_iters):
print "Iteration " + str(iterx)
# Step 1: Find Nearest Neighbors
flann = FLANN()
flann.build_index(data_vecs, **flann_args)
(index_list, dist_list) = flann.nn_index(query_vecs,
K,
checks=flann_args['checks'])
return (index_list, dist_list)
datax2_centx, _ = flann_one_time(cent, float_data, 1, flann_args)
# Step 2: Assign data to cluster centers
datax_sort = datax2_centx.argsort()
centx_sort = datax2_centx[datax_sort]
# Efficiently Trace over sorted centers with two pointers. Take care
# To include the last batch of datavecs with the same center_index
converged = True
prev_centx = -1
_L = 0
dbg_total_assigned = 0
dbg_assigned_list = []
for _R in xrange(
N + 1
): #Loop over datapoints, going 1 past the end, and group them
# data = 0[ . . . . . . . . . . . . .]N
# ptrs = L R
# |- k -|L R
# |- k+1 |L R
# |_K|
if _R == N or centx_sort[_L] != centx_sort[_R]: # We found a group
centx = centx_sort[
_L] # Assign this group cluster index: centx
# SPECIAL CASE: ( akmeans might not assign everything )
if centx - prev_centx > 1: #Check if a cluster got skipped
for skipx in xrange(prev_centx + 1, centx):
print(" Skipping Index:" + str(skipx))
if len(assign[skipx]) != 0:
converged = False
assign[skipx] = []
prev_centx = centx
# Set Assignments
num_members = np.float32(_R - _L)
dbg_total_assigned += num_members
centx_membx = datax_sort[_L:_R]
#DBG CODE, keep track of data vectors you've assigned
#print(' Assigning %d data vectors to center index: %d' % (num_members, centx) )
#for x in centx_membx:
#dbg_assigned_list.append(x)
#/DBGCODE
if np.all(assign[centx] != centx_membx):
converged = False
assign[centx] = centx_membx
# Recompute Centers
cent[centx] = float_data[centx_membx, :].sum(
axis=0) / num_members
_L = _R
#print(' Did Assignment of %d centers' % prev_centx)
#print(' Assigned %d datavectors in total' % dbg_total_assigned)
# SPECIAL CASE: has to run at the end again
if prev_centx < K: #Check if a cluster got skipped at the end
for skipx in xrange(prev_centx + 1, K):
print(' Cluster Index %d was empty:' % skipx)
if len(assign[skipx]) != 0:
converged = False
assign[skipx] = []
prev_centx = centx
if converged: # Assignments have not changed
print 'akmeans converged in ' + str(iterx) + ' iterations'
break
return cent, assign
def assign_feature_matches_1vM(rr, hs, K, method, cids_to_remove):
'''Assigns each query feature to its K nearest database features
with a similarity-score. Each feature votes for its assigned
chip with this weight.'''
logdbg('Assigning feature matches and initial scores')
# Get managers
cm = hs.cm
nm = hs.nm
vm = hs.vm
# Get intermediate results
qcx = rr.qcx
qcid = rr.qcid
qfdsc = rr.qfdsc
qfpts = rr.qfpts
num_qf = qfpts.shape[0]
# define: Prefix K = list of K+1 nearest; k = K nearest
# Everything is done in a flat manner, and reshaped at the end.
if len(cids_to_remove) > 0:
K += len(cids_to_remove)
logdbg('K = %d. Increased by %d to account for removing results' %
(K, len(cids_to_remove)))
# qfx = Query Feature Index
# Kwxs = the Kth result word index ; Kdists = the Kth result distance
(qfx2_Kwxs, qfx2_Kdists) = vm.nearest_neighbors(qfdsc, K + 1)
# ---
# Candidate score the nearest neighbor matches
# p - pth nearest ; o - k+1th nearest
score_fn_dict = {
'DIFF': lambda p, o: o - p,
'RAT': lambda p, o: o / p,
'LNRAT': lambda p, o: np.log2(o / p),
'COUNT': lambda p, o: 1,
'NDIST': lambda p, o: 10e16 - p,
'TFIDF': lambda wx2_tf, wx_idf, wx: wx2_tf[wx] * wx_idf[wx]
}
score_fn = score_fn_dict[method]
if method == 'TFIDF':
# The wx2_qtf could really be per k or as agged across all K
w_histo = bincount(qfx2_wxs, minlength=vm.numWords())
wx2_qtf = np.array(w_histo, dtype=np.float32) / num_qf
qfx2_vweight = score_fn(wx2_qtf, vm.wx2_idf, qfx2_wxs)
else:
# Distances to the 0-K results
p_vote = qfx2_Kdists[:, 0:K] + 1
# Distance to the K+1th result
o_norm = np.tile(qfx2_Kdists[:, -1].reshape(num_qf, 1) + 1, (1, K))
# Use score method to get weight
qfx2_kweight = np.array(
[score_fn(p, o) for (p, o) in iter(zip(p_vote.flat, o_norm.flat))],
dtype=np.float32)
qfx2_kweight.shape = (num_qf, K)
# ---
# Use the scores to cast weighted votes for database chips
#
if len(cids_to_remove) > 0:
# Remove the query from results
# query feature index 2 agg descriptor indexes -> cids -> self_query_bit -> clean_axs
#
# Feature Matches -> Chip Ids
logdbg('Query qcid=%r are being removed from results ' %
cids_to_remove)
qfx2_Kaxs_ = vm.wx2_axs[qfx2_Kwxs]
qfx2_Kcids_ = [vm.ax2_cid[axs] for axs in qfx2_Kaxs_.flat]
# Test if each FeatureMatch-ChipId is the Query-ChipId.
qfx2_Ksqbit_ = [
True - np.in1d(cids, cids_to_remove) for cids in qfx2_Kcids_
]
# Remove FeatureMatches to the Query-ChipId
qfx2_Kaxs = [np.array(axs)[sqbit].tolist() for (axs, sqbit) in\
iter(zip(qfx2_Kaxs_.flat, qfx2_Ksqbit_))]
else:
qfx2_Kaxs_ = vm.wx2_axs[qfx2_Kwxs]
qfx2_Kaxs = [np.array(axs).tolist() for axs in qfx2_Kaxs_.flat]
# Clean Vote for Info
qfx2_Kcxs = np.array([vm.ax2_cx(axs) for axs in qfx2_Kaxs])
qfx2_Kfxs = np.array([vm.ax2_fx[axs] for axs in qfx2_Kaxs])
qfx2_Knxs = np.array([cm.cx2_nx[cxs] for cxs in qfx2_Kcxs])
if qfx2_Kfxs.size == 0:
logerr('Cannot query when there is one chip in database')
# Reshape Vote for Info
qfx2_Kcxs = np.array(qfx2_Kcxs).reshape(num_qf, K + 1)
qfx2_Kfxs = np.array(qfx2_Kfxs).reshape(num_qf, K + 1)
qfx2_Knxs = np.array(qfx2_Knxs).reshape(num_qf, K + 1)
# Using the K=K+1 results, make k=K scores
qfx2_kcxs_vote = qfx2_Kcxs[:, 0:K] # vote for cx
qfx2_kfxs_vote = qfx2_Kfxs[:, 0:K] # vote for fx
qfx2_knxs_vote = qfx2_Knxs[:, 0:K] # check with nx
# Attempt to recover from problems where K is too small
qfx2_knxs_norm = np.tile(qfx2_Knxs[:, K].reshape(num_qf, 1), (1, K))
qfx2_knxs_norm[qfx2_knxs_norm ==
nm.UNIDEN_NX()] = 0 # Remove Unidentifieds from this test
qfx2_kcxs_norm = np.tile(qfx2_Kcxs[:, K].reshape(num_qf, 1), (1, K))
# If the normalizer has the same name, but is a different chip, there is a good chance
# it is a correct match and was peanalized by the scoring function
qfx2_normgood_bit = np.logical_and(qfx2_kcxs_vote != qfx2_kcxs_norm, \
qfx2_knxs_vote == qfx2_knxs_norm)
#qfx2_kweight[qfx2_normgood_bit] = 2
# -----
# Build FeatureMatches and FeaturesScores
#
cx2_fm = alloc_lists(cm.max_cx + 1)
cx2_fs_ = alloc_lists(cm.max_cx + 1)
qfx2_qfx = np.tile(np.arange(0, num_qf).reshape(num_qf, 1), (1, K))
# Add matches and scores
for (qfx, qfs, cxs, fxs)\
in iter(zip(qfx2_qfx.flat, \
qfx2_kweight.flat, \
qfx2_kcxs_vote.flat, \
qfx2_kfxs_vote.flat)):
if cxs.size == 0:
continue
for (vote_cx, vote_fx) in iter(zip(np.nditer(cxs), np.nditer(fxs))):
cx2_fm[vote_cx].append((qfx, vote_fx))
cx2_fs_[vote_cx].append(qfs)
# Convert correspondences to to numpy
for cx in xrange(len(cx2_fs_)):
num_m = len(cx2_fm[cx])
cx2_fs_[cx] = np.array(cx2_fs_[cx], dtype=np.float32)
cx2_fm[cx] = np.array(cx2_fm[cx], dtype=np.uint32).reshape(num_m, 2)
logdbg('Setting feature assignments')
rr.cx2_fm = cx2_fm
rr.cx2_fs_ = cx2_fs_
def assign_feature_matches_1vM(rr, hs, K, method, cids_to_remove):
'''Assigns each query feature to its K nearest database features
with a similarity-score. Each feature votes for its assigned
chip with this weight.'''
logdbg('Assigning feature matches and initial scores')
# Get managers
cm = hs.cm
nm = hs.nm
vm = hs.vm
# Get intermediate results
qcx = rr.qcx
qcid = rr.qcid
qfdsc = rr.qfdsc
qfpts = rr.qfpts
num_qf = qfpts.shape[0]
# define: Prefix K = list of K+1 nearest; k = K nearest
# Everything is done in a flat manner, and reshaped at the end.
if len(cids_to_remove) > 0:
K += len(cids_to_remove)
logdbg('K = %d. Increased by %d to account for removing results'
% (K, len(cids_to_remove)))
# qfx = Query Feature Index
# Kwxs = the Kth result word index ; Kdists = the Kth result distance
(qfx2_Kwxs, qfx2_Kdists) = vm.nearest_neighbors(qfdsc, K+1)
# ---
# Candidate score the nearest neighbor matches
# p - pth nearest ; o - k+1th nearest
score_fn_dict = {
'DIFF' : lambda p, o: o - p,
'RAT' : lambda p, o: o / p,
'LNRAT' : lambda p, o: np.log2(o / p),
'COUNT' : lambda p, o: 1,
'NDIST' : lambda p, o: 10e16 - p,
'TFIDF' : lambda wx2_tf, wx_idf, wx: wx2_tf[wx] * wx_idf[wx] }
score_fn = score_fn_dict[method]
if method == 'TFIDF':
# The wx2_qtf could really be per k or as agged across all K
w_histo = bincount(qfx2_wxs, minlength=vm.numWords())
wx2_qtf = np.array(w_histo, dtype=np.float32) / num_qf
qfx2_vweight = score_fn(wx2_qtf, vm.wx2_idf, qfx2_wxs)
else:
# Distances to the 0-K results
p_vote = qfx2_Kdists[:, 0:K] + 1
# Distance to the K+1th result
o_norm = np.tile(
qfx2_Kdists[:, -1].reshape(num_qf, 1) + 1, (1, K))
# Use score method to get weight
qfx2_kweight = np.array(
[score_fn(p, o) for (p, o) in
iter(zip(p_vote.flat, o_norm.flat))],
dtype=np.float32)
qfx2_kweight.shape = (num_qf, K)
# ---
# Use the scores to cast weighted votes for database chips
#
if len(cids_to_remove) > 0:
# Remove the query from results
# query feature index 2 agg descriptor indexes -> cids -> self_query_bit -> clean_axs
#
# Feature Matches -> Chip Ids
logdbg('Query qcid=%r are being removed from results ' % cids_to_remove)
qfx2_Kaxs_ = vm.wx2_axs[qfx2_Kwxs]
qfx2_Kcids_ = [vm.ax2_cid[axs] for axs in qfx2_Kaxs_.flat]
# Test if each FeatureMatch-ChipId is the Query-ChipId.
qfx2_Ksqbit_ = [True - np.in1d(cids, cids_to_remove) for cids in qfx2_Kcids_]
# Remove FeatureMatches to the Query-ChipId
qfx2_Kaxs = [np.array(axs)[sqbit].tolist() for (axs, sqbit) in\
iter(zip(qfx2_Kaxs_.flat, qfx2_Ksqbit_))]
else:
qfx2_Kaxs_ = vm.wx2_axs[qfx2_Kwxs]
qfx2_Kaxs = [np.array(axs).tolist() for axs in qfx2_Kaxs_.flat]
# Clean Vote for Info
qfx2_Kcxs = np.array([vm.ax2_cx(axs) for axs in qfx2_Kaxs])
qfx2_Kfxs = np.array([vm.ax2_fx[axs] for axs in qfx2_Kaxs])
qfx2_Knxs = np.array([cm.cx2_nx[cxs] for cxs in qfx2_Kcxs])
if qfx2_Kfxs.size == 0:
logerr('Cannot query when there is one chip in database')
# Reshape Vote for Info
qfx2_Kcxs = np.array(qfx2_Kcxs).reshape(num_qf, K+1)
qfx2_Kfxs = np.array(qfx2_Kfxs).reshape(num_qf, K+1)
qfx2_Knxs = np.array(qfx2_Knxs).reshape(num_qf, K+1)
# Using the K=K+1 results, make k=K scores
qfx2_kcxs_vote = qfx2_Kcxs[:, 0:K] # vote for cx
qfx2_kfxs_vote = qfx2_Kfxs[:, 0:K] # vote for fx
qfx2_knxs_vote = qfx2_Knxs[:, 0:K] # check with nx
# Attempt to recover from problems where K is too small
qfx2_knxs_norm = np.tile(qfx2_Knxs[:, K].reshape(num_qf, 1), (1, K))
qfx2_knxs_norm[qfx2_knxs_norm == nm.UNIDEN_NX()] = 0 # Remove Unidentifieds from this test
qfx2_kcxs_norm = np.tile(qfx2_Kcxs[:, K].reshape(num_qf, 1), (1, K))
# If the normalizer has the same name, but is a different chip, there is a good chance
# it is a correct match and was peanalized by the scoring function
qfx2_normgood_bit = np.logical_and(qfx2_kcxs_vote != qfx2_kcxs_norm, \
qfx2_knxs_vote == qfx2_knxs_norm)
#qfx2_kweight[qfx2_normgood_bit] = 2
# -----
# Build FeatureMatches and FeaturesScores
#
cx2_fm = alloc_lists(cm.max_cx + 1)
cx2_fs_ = alloc_lists(cm.max_cx + 1)
qfx2_qfx = np.tile(np.arange(0, num_qf).reshape(num_qf, 1), (1, K))
# Add matches and scores
for (qfx, qfs, cxs, fxs)\
in iter(zip(qfx2_qfx.flat, \
qfx2_kweight.flat, \
qfx2_kcxs_vote.flat, \
qfx2_kfxs_vote.flat)):
if cxs.size == 0:
continue
for (vote_cx, vote_fx) in iter(zip(np.nditer(cxs), np.nditer(fxs))):
cx2_fm[vote_cx].append((qfx, vote_fx))
cx2_fs_[vote_cx].append(qfs)
# Convert correspondences to to numpy
for cx in xrange(len(cx2_fs_)):
num_m = len(cx2_fm[cx])
cx2_fs_[cx] = np.array(cx2_fs_[cx], dtype=np.float32)
cx2_fm[cx] = np.array(cx2_fm[cx], dtype=np.uint32).reshape(num_m, 2)
logdbg('Setting feature assignments')
rr.cx2_fm = cx2_fm
rr.cx2_fs_ = cx2_fs_