def _gmm_to_numpy(gmm):
d, k = gmm.d, gmm.k
w = yael.fvec_to_numpy(gmm.w, k)
mu = yael.fvec_to_numpy(gmm.mu, d * k)
mu = mu.reshape((k, d))
sigma = yael.fvec_to_numpy(gmm.sigma, d * k)
sigma = sigma.reshape((k, d))
return w, mu, sigma
def siftgeo_read(filename):
# I/O via double pointers (too lazy to make proper swig interface)
v_out = yael.BytePtrArray(1)
meta_out = yael.FloatPtrArray(1)
d_out = yael.ivec(2)
n = yael.bvecs_new_from_siftgeo(filename, d_out, v_out.cast(),
d_out.plus(1), meta_out.cast())
if n < 0:
raise IOError("cannot read " + filename)
if n == 0:
v = numpy.array([[]], dtype = numpy.uint8)
meta = numpy.array([[]*9], dtype = numpy.float32)
return v, meta
v_out = yael.bvec.acquirepointer(v_out[0])
meta_out = yael.fvec.acquirepointer(meta_out[0])
d = d_out[0]
d_meta = d_out[1]
assert d_meta == 9
v = yael.bvec_to_numpy(v_out, n * d)
v = v.reshape((n, d))
meta = yael.fvec_to_numpy(meta_out, n * d_meta)
meta = meta.reshape((n, d_meta))
return v, meta
def flushBuffer(x, y, t): c = int(cnt[x, y, t]) fvs = [] for cutFrom, cutTo, fvSize, gmm, partName in parts: desc = np.ascontiguousarray(buffer[x, y, t, :c, cutFrom:(1 + cutTo)]) fv = yael.fvec_new_0(fvSize) yael.gmm_fisher(c, yael.FloatArray.acquirepointer(yael.numpy_to_fvec(desc)), gmm, flags, fv) fvs.append(yael.fvec_to_numpy(fv, fvSize).flatten()) ndescr[x, y, t] += c cnt[x, y, t] = 0 return np.sqrt(c) * np.hstack(tuple(fvs))
def fvecs_read(filename, nmax = -1):
if nmax < 0:
(fvecs, n, d) = yael.fvecs_new_read(filename)
else:
(fvecs, n, d) = yael.fvecs_new_fread_max(open(filename, "r"), nmax)
if n == -1:
raise IOError("could not read " + filename)
elif n == 0: d = 0
fvecs = yael.fvec.acquirepointer(fvecs)
# TODO find a way to avoid copy
a = yael.fvec_to_numpy(fvecs, n * d)
return a.reshape((n, d))
def flushBuffer(x, y, t):
c = int(cnt[x, y, t])
fvs = []
for cutFrom, cutTo, fvSize, gmm, partName in parts:
desc = np.ascontiguousarray(buffer[x, y, t, :c, cutFrom:(1 + cutTo)])
fv = yael.fvec_new_0(fvSize)
yael.gmm_fisher(
c, yael.FloatArray.acquirepointer(yael.numpy_to_fvec(desc)), gmm,
flags, fv)
fvs.append(yael.fvec_to_numpy(fv, fvSize).flatten())
ndescr[x, y, t] += c
cnt[x, y, t] = 0
return np.sqrt(c) * np.hstack(tuple(fvs))
def load_features(filename, file_format, total_nuse, dimension, lsh, index_folder, offset = 0, run_index = 'n'):
np_feature_vecs = None
actual_total_nuse = 0
for feature_idx_begin in range(offset, total_nuse + offset, 10000000):
print "loading from " + str(feature_idx_begin)
nuse = 0
if (total_nuse + offset) > (feature_idx_begin + 10000000):
nuse = 10000000
else:
nuse = (total_nuse + offset) - feature_idx_begin
(feature_vecs, actual_nuse) = yutils.load_vectors_fmt(filename, file_format, dimension, nuse, feature_idx_begin , verbose = True)
part_np_feature_vecs = None
if file_format == 'fvecs':
part_np_feature_vecs = yael.fvec_to_numpy(feature_vecs, int(actual_nuse) * dimension)
elif file_format == 'bvecs':
part_np_feature_vecs = yael.bvec_to_numpy(feature_vecs, int(actual_nuse) * dimension)
# for CUDA-based batch indexing, skip the reshaping
#part_np_feature_vecs = part_np_feature_vecs.reshape((int(actual_nuse), dimension))
if run_index != 'y':
part_np_feature_vecs = part_np_feature_vecs.reshape((int(actual_nuse), dimension))
if np_feature_vecs != None:
np_feature_vecs = numpy.concatenate((np_feature_vecs, part_np_feature_vecs))
else:
np_feature_vecs = part_np_feature_vecs
else:
index(lsh, part_np_feature_vecs, actual_total_nuse)
del part_np_feature_vecs
if index_folder != None:
save_index(lsh, index_folder, feature_idx_begin)
actual_total_nuse += int(actual_nuse)
if run_index != 'y':
print np_feature_vecs.shape
return np_feature_vecs
def partial_pca(mat, nev=6, nt=1):
_check_row_float32(mat)
n, d = mat.shape
avg = mat.mean(axis=0)
mat = mat - avg[numpy.newaxis, :]
singvals = numpy.empty(nev, dtype=numpy.float32)
# pdb.set_trace()
pcamat = yael.fmat_new_pca_part(d, n, nev, yael.numpy_to_fvec_ref(mat), yael.numpy_to_fvec_ref(singvals))
assert pcamat != None
# print "SVs", singvals
pcamat = yael.fvec.acquirepointer(pcamat)
pcamat = yael.fvec_to_numpy(pcamat, (nev, d))
return avg, singvals, pcamat
def partial_pca(mat, nev=6, nt=1):
_check_row_float32(mat)
n, d = mat.shape
avg = mat.mean(axis=0)
mat = mat - avg[numpy.newaxis, :]
singvals = numpy.empty(nev, dtype=numpy.float32)
# pdb.set_trace()
pcamat = yael.fmat_new_pca_part(d, n, nev, yael.numpy_to_fvec_ref(mat),
yael.numpy_to_fvec_ref(singvals))
assert pcamat != None
#print "SVs", singvals
pcamat = yael.fvec.acquirepointer(pcamat)
pcamat = yael.fvec_to_numpy(pcamat, (nev, d))
return avg, singvals, pcamat
def fvecs_read(filename, nmax = -1, c_contiguous = True):
if nmax < 0:
fv = numpy.fromfile(filename, dtype = numpy.float32)
if fv.size == 0:
return numpy.zeros((0,0))
dim = fv.view(numpy.int32)[0]
assert dim>0
fv = fv.reshape(-1,1+dim)
if not all(fv.view(numpy.int32)[:,0]==dim):
raise IOError("non-uniform vector sizes in " + filename)
fv = fv[:,1:]
if c_contiguous:
fv = fv.copy()
return fv
(fvecs, n, d) = yael.fvecs_new_fread_max(open(filename, "r"), nmax)
if n == -1:
raise IOError("could not read " + filename)
elif n == 0: d = 0
fvecs = yael.fvec.acquirepointer(fvecs)
# TODO find a way to avoid copy
a = yael.fvec_to_numpy(fvecs, n * d)
return a.reshape((n, d))
def fvecs_read(filename, nmax=-1, c_contiguous=True):
if nmax < 0:
fv = numpy.fromfile(filename, dtype=numpy.float32)
if fv.size == 0:
return numpy.zeros((0, 0))
dim = fv.view(numpy.int32)[0]
assert dim > 0
fv = fv.reshape(-1, 1 + dim)
if not all(fv.view(numpy.int32)[:, 0] == dim):
raise IOError("non-uniform vector sizes in " + filename)
fv = fv[:, 1:]
if c_contiguous:
fv = fv.copy()
return fv
(fvecs, n, d) = yael.fvecs_new_fread_max(open(filename, "r"), nmax)
if n == -1:
raise IOError("could not read " + filename)
elif n == 0:
d = 0
fvecs = yael.fvec.acquirepointer(fvecs)
# TODO find a way to avoid copy
a = yael.fvec_to_numpy(fvecs, n * d)
return a.reshape((n, d))
def load_features(filename, file_format, total_nuse, dimension, offset = 0):
np_feature_vecs = None
actual_total_nuse = 0
for feature_idx_begin in range(offset, total_nuse + offset, 10000000):
print "loading from " + str(feature_idx_begin)
nuse = 0
if (total_nuse + offset) > (feature_idx_begin + 10000000):
nuse = 10000000
else:
nuse = (total_nuse + offset) - feature_idx_begin
(feature_vecs, actual_nuse) = yutils.load_vectors_fmt(filename, file_format, dimension, nuse, feature_idx_begin , verbose = True)
part_np_feature_vecs = None
if file_format == 'fvecs':
part_np_feature_vecs = yael.fvec_to_numpy(feature_vecs, int(actual_nuse) * dimension)
elif file_format == 'bvecs':
part_np_feature_vecs = yael.bvec_to_numpy(feature_vecs, int(actual_nuse) * dimension)
part_np_feature_vecs = part_np_feature_vecs.reshape((int(actual_nuse), dimension))
if np_feature_vecs != None:
np_feature_vecs = numpy.concatenate((np_feature_vecs, part_np_feature_vecs))
else:
np_feature_vecs = part_np_feature_vecs
actual_total_nuse += int(actual_nuse)
print np_feature_vecs.shape
return np_feature_vecs
def load_features(filename,
file_format,
total_nuse,
dimension,
lsh,
index_folder,
offset=0,
run_index='n'):
np_feature_vecs = None
actual_total_nuse = 0
for feature_idx_begin in range(offset, total_nuse + offset, 10000000):
print "loading from " + str(feature_idx_begin)
nuse = 0
if (total_nuse + offset) > (feature_idx_begin + 10000000):
nuse = 10000000
else:
nuse = (total_nuse + offset) - feature_idx_begin
(feature_vecs,
actual_nuse) = yutils.load_vectors_fmt(filename,
file_format,
dimension,
nuse,
feature_idx_begin,
verbose=True)
part_np_feature_vecs = None
if file_format == 'fvecs':
part_np_feature_vecs = yael.fvec_to_numpy(
feature_vecs,
int(actual_nuse) * dimension)
elif file_format == 'bvecs':
part_np_feature_vecs = yael.bvec_to_numpy(
feature_vecs,
int(actual_nuse) * dimension)
# for CUDA-based batch indexing, skip the reshaping
#part_np_feature_vecs = part_np_feature_vecs.reshape((int(actual_nuse), dimension))
if run_index != 'y':
part_np_feature_vecs = part_np_feature_vecs.reshape(
(int(actual_nuse), dimension))
if np_feature_vecs != None:
np_feature_vecs = numpy.concatenate(
(np_feature_vecs, part_np_feature_vecs))
else:
np_feature_vecs = part_np_feature_vecs
else:
index(lsh, part_np_feature_vecs, actual_total_nuse)
del part_np_feature_vecs
if index_folder != None:
save_index(lsh, index_folder, feature_idx_begin)
actual_total_nuse += int(actual_nuse)
if run_index != 'y':
print np_feature_vecs.shape
return np_feature_vecs
