def _triplet_mate_frontalpose_nonmate_top1_probe_mixedpose(n_subjects=32):
np.random.seed(42) # for repeatable take
vggface2 = VGGFace2('/proj/janus6/vggface2')
frontalset = [im for im in vggface2.frontalset(n_frontal=1)]
matelist = frontalset[0:n_subjects]
if n_subjects == 16:
matelist[3] = frontalset[n_subjects+1]
matelist[5] = frontalset[n_subjects+5]
matelist[6] = frontalset[n_subjects+4]
matelist[11] = frontalset[n_subjects+7]
matelist[12] = frontalset[n_subjects+2]
matelist[13] = frontalset[n_subjects+9]
matelist[15] = frontalset[n_subjects+6]
d_subjectid_to_topk_frontal_nonmates = vipy.util.load('_vggface2_topk_frontal_nonmates.pkl') # cached
nonmateidlist = []
for m in matelist:
for n in d_subjectid_to_topk_frontal_nonmates[m.category()]:
if n not in nonmateidlist:
nonmateidlist.append(n)
break
d_frontalset = {x.category():x for x in frontalset} # for id lookup
nonmatelist = [d_frontalset[k] for k in nonmateidlist] # ordered
probelist = [vggface2.take(n_subjects, im_mate.category()) for im_mate in matelist]
assert(len(nonmatelist) == n_subjects)
assert(len(probelist) == n_subjects)
assert(len(probelist[0]) == n_subjects)
assert(len(matelist) == n_subjects)
return (matelist, nonmatelist, probelist)
def _triplet_mate_frontalpose_nonmate_top1_probe_frontalpose():
n_subjects = 9
np.random.seed(42) # for repeatable take
vggface2 = VGGFace2('/proj/janus6/vggface2')
frontalset = [im for im in vggface2.frontalset(n_frontal=n_subjects+1)]
subjectid = list(set([im.category() for im in frontalset])) # unique
matelist = [im for im in frontalset if im.category() in subjectid[0:n_subjects]]
d_mate = vipy.util.groupbyasdict(matelist, lambda im: im.category())
matelist = [v[0] for (k,v) in d_mate.items()]
probelist = [v[1:] for (k,v) in d_mate.items()]
d_subjectid_to_topk_frontal_nonmates = vipy.util.load('_vggface2_topk_frontal_nonmates.pkl') # cached
nonmateidlist = []
for m in matelist:
for n in d_subjectid_to_topk_frontal_nonmates[m.category()]:
if n not in nonmateidlist:
# select unique identity from top-k
nonmateidlist.append(n)
break
nonmatelist = [x for x in frontalset if x.category() in nonmateidlist] # ordered
d_nonmate = vipy.util.groupbyasdict(nonmatelist, lambda im: im.category())
nonmatelist = [d_nonmate[k][0] for k in nonmateidlist] # ordered
assert(len(nonmatelist) == n_subjects)
assert(len(probelist) == n_subjects)
assert(len(probelist[0]) == n_subjects)
assert(len(matelist) == n_subjects)
return (matelist, nonmatelist, probelist)
def _all_nonmates(n=None, mateset=set()):
np.random.seed(42) # for repeatable take
vggface2 = VGGFace2('/proj/janus6/vggface2')
subjects = vggface2.subjects()
nonmates = subjects if n is None else subjects[0:n]
nonmatelist = [next(vggface2.subjectset(s)) for s in nonmates if s not in mateset]
return (nonmatelist)
def _n_subjects_k_mates_with_m_probes(n_subjects, k_mates, m_probes, mateset=None):
np.random.seed(42) # for repeatable take
vggface2 = VGGFace2('/proj/janus6/vggface2')
subjects = np.random.choice(vggface2.subjects(), n_subjects) if mateset is None else mateset
imsubjects = {s:list(vggface2.subjectset(s)) for s in subjects}
matelist = [imsubjects[s][0:k_mates] for s in subjects]
probelist = [imsubjects[s][k_mates:m_probes+k_mates] for s in subjects]
return (matelist, probelist)
def _k_mates_with_m_probes(n_subjects, n_probes):
np.random.seed(42) # for repeatable take
vggface2 = VGGFace2('/proj/janus6/vggface2')
subjects = np.random.choice(vggface2.subjects(), n_subjects)
imsubjects = {s:list(vggface2.subjectset(s)) for s in subjects}
matelist = [imsubjects[s][0] for s in subjects]
probelist = [imsubjects[s][1:n_probes+1] for s in subjects]
return (matelist, probelist)
def _vggface2_topk_nonmates(wb, topk):
np.random.seed(42) # for repeatable take
n_minibatch = 2
vggface2 = VGGFace2('/proj/janus6/vggface2')
imlist = vipy.util.chunklistbysize([im for im in vggface2.take_per_subject(n_minibatch)], n_minibatch)
imlist_preprocessed = [torch.cat([wb.net.preprocess(f_detection(im).pil()) for im in iml], dim=0) for iml in imlist] # minibatch tensor
X = [torch.squeeze(torch.sum(wb.net.encode(imchunk), dim=0)).detach().numpy() for imchunk in imlist_preprocessed] # minibatch encode template
X = vipy.linalg.row_normalized(np.array(X))
X_subjectid = [imchunk[0].category() for imchunk in imlist]
d_subjectid_to_topk_frontal_nonmates = {}
for (k, d) in enumerate(squareform(pdist(X, metric='euclidean'))):
j_sorted = np.argsort(d)[1:] # increasing, do not include self distance=0 on diagonal
d_subjectid_to_topk_frontal_nonmates[X_subjectid[k]] = [X_subjectid[j] for j in j_sorted[0:topk]]
vipy.util.save(d_subjectid_to_topk_frontal_nonmates, '_vggface2_topk_nonmates.pkl') # cache
return d_subjectid_to_topk_frontal_nonmates
def _triplet_mate_frontalpose_nonmate_topk_probe_frontalpose():
n_subjects = 9
vggface2 = VGGFace2('/proj/janus6/vggface2', seed=42)
frontalset = [im for im in vggface2.frontalset(n_frontal=n_subjects+1)]
subjectid = sorted(list(set([im.category() for im in frontalset]))) # unique
matelist = [im for im in frontalset if im.category() in subjectid[0:n_subjects]]
d_mate = vipy.util.groupbyasdict(matelist, lambda im: im.category())
matelist = [v[0] for (k,v) in d_mate.items()]
probelist = [v[1:] for (k,v) in d_mate.items()]
d_subjectid_to_topk_frontal_nonmates = vipy.util.load('_vggface2_topk_nonmates.pkl') # cached
nonmateidlist = d_subjectid_to_topk_frontal_nonmates[matelist[8].category()][0:n_subjects]
nonmatelist = [vggface2.take(1, k)[0] for k in nonmateidlist]
matelist = matelist[8]
probelist = [probelist[8]]
return (matelist, nonmatelist, probelist)
def _vggface2_nonmates():
np.random.seed(42) # for repeatable take
return VGGFace2('/proj/janus6/vggface2').take_per_subject(1)