def queryhash(self, qdata): Y = np.zeros((len(qdata), self.r), dtype=np.int8) for rr in xrange(self.r): print rr res = self.classifier[rr].predict(qdata) Y[:,rr] = np.where(res>0, 1, 0) return hash_value(Y)
def queryhash(self, qdata): if self.type != 'kernel': Kdata = np.dot(qdata-self.mean, self.W) else: Kdata = np.dot(self.kernel_param['kernel'](qdata, self.anchors), self.W) Y = np.dot(Kdata, self.R) Y = np.where(Y>=0, 1, 0) return hash_value(Y)
def queryhash(self, qdata): if self.classifier == 'CNN': return self.cls.predict(qdata) Kdata = self.kernel(qdata, self.anchors) Kdata -= self.mvec Y = np.dot(Kdata, self.W) + self.b Y = np.where(Y>=0, 1, 0) return hash_value(Y)
def basehash(self, data): H = np.where(self.B>=0, 1, 0) return hash_value(H)
def queryhash(self, qdata): Kdata = self.kernel(qdata, self.anchors) Kdata -= self.mvec Y = np.dot(Kdata, self.P) Y = np.where(Y>=0, 1, 0) return hash_value(Y)
def train(self, traindata, trainlabel): n = len(traindata) # shuffle data indexes = np.arange(n, dtype=np.int32) np.random.shuffle(indexes) traindata = traindata[indexes] trainlabel = trainlabel[indexes] # determine anchors anchoridx = np.copy(indexes) np.random.shuffle(anchoridx) anchoridx = anchoridx[:self.m] self.anchors = traindata[anchoridx] # kernel matrix and mean KK = self.kernel(traindata, self.anchors) self.mvec = np.mean(KK, axis=0).reshape((1, self.m)) KK = KK - self.mvec # pairwise label matrix, rS=PQ^T l = self.numlabel + 1 # PH = [P, +H], QH = [Q, -H] PH = np.zeros((n,l+self.r), dtype=np.float32) if len(trainlabel.shape) >= 2: assert trainlabel.shape[1] == self.numlabel PH[:,:self.numlabel] = trainlabel else: PH[np.arange(n, dtype=np.int32), trainlabel] = 1 QH = np.copy(PH) PH[:,:l-1] *= 2*self.r PH[:,l-1] = self.r QH[:,l-1] = -1 S = np.dot(PH[:,:l], QH.T[:l]) / self.r print 'Init Obj:', np.sum(np.sum(S*S,axis=1)), np.sum(S) # projection optimization RM = np.dot(KK.T, KK) A = np.zeros((self.m, self.r), dtype=np.float32) # parameter A LM = np.dot(np.dot(KK.T, PH[:,:l]), np.dot(QH.T[:l], KK)) # hash matrix H = np.zeros((n, self.r), dtype=np.int8) # greedy optimization for rr in range(0, self.r): print "No:", rr # step 1: spectral relaxation if rr > 0: tmp = np.dot(KK.T, h0.reshape((n,1))) LM -= np.dot(tmp, tmp.T) (V, U) = eigh(LM, RM, eigvals_only=False) A[:,rr] = U[:,self.m-1] tmp = np.dot(np.dot(A[:,rr].T, RM), A[:,rr]) A[:,rr] *= np.sqrt(n/tmp) # step 2: sigmoid smoothing get_vec, cost = OptProjectionFast(KK, PH[:,:l+rr], QH[:,:l+rr], A[:,rr], 500) h0 = np.dot(KK, A[:,rr]) h0 = np.where(h0>=0, 1, -1) h1 = np.dot(KK, get_vec) h1 = np.where(h1>=0, 1, -1) if np.dot(np.dot(PH.T[:l+rr], h1), np.dot(QH.T[:l+rr], h1)) > \ np.dot(np.dot(PH.T[:l+rr], h0), np.dot(QH.T[:l+rr], h0)): A[:,rr] = get_vec h0 = h1 # update PH and QH PH[:,l+rr] = h0 QH[:,l+rr] = -1 * h0 Tmp = S + np.dot(PH[:,l:l+rr+1], QH[:,l:l+rr+1].T) / (rr+1) print "Obj:", np.sum(np.sum(Tmp*Tmp,axis=1)) H[:,rr] = (h0 > 0).astype(np.int8) self.A = A return hash_value(H)
def queryhash(self, qdata): Y = self.cls.predict(qdata) Y = np.where(Y>=0, 1, 0) return hash_value(Y)
def queryhash(self, qdata): Y = np.dot(qdata, self.W) Y = np.where(Y>=0, 1, 0) return hash_value(Y)
def queryhash(self, data): (Z, _) = self._Z(data, self.anchors, self.nnanchors, self.sigma) print Z.shape Y = Z.dot(self.W) Y = np.where(Y>=0, 1, 0) return hash_value(Y)
def queryhash(self, qdata):
Kdata = self.kernel(qdata, self.anchors)
Kdata -= self.mvec
Y = np.dot(Kdata, self.W) + self.b
Y = np.where(Y >= 0, 1, 0)
return hash_value(Y)
def train(self):
trainlabel = np.load(root+'trainlabel.npy')
n = len(trainlabel)
self.anchors = np.load('anchors.npy')
mu = self.mu * n
KK = np.zeros((n, self.m))
pt = 0
print 'making kernel vector...'
for i in xrange(13):
print 'loading', i+1, '...'
X = np.load(root+'traindata_fc7_norm_{}.npy'.format(i+1))
t = X.shape[0]
KK[pt:pt+t,:] = self.kernel(X, self.anchors)
pt += t
del X
# kernel matrix and mean
self.mvec = np.mean(KK, axis=0).reshape((1, self.m))
KK = KK - self.mvec
np.save('mean_vec.npy', self.mvec)
# pairwise label matrix, S = 2*P*P.T-1_{n*n}
if len(trainlabel.shape) >= 2:
assert trainlabel.shape[1] == self.numlabel
P = csc_matrix(trainlabel, dtype=np.float32)
P = P.T
else:
P = csc_matrix((np.ones(n),[np.arange(n, dtype=np.int32), trainlabel]), shape=(n,self.numlabel), dtype=np.float32)
P = P.T
H = np.zeros((n,self.r))
print 'preparing step 1...'
# projection optimization
RM = np.dot(KK.T, KK)
W = np.zeros((self.m, self.r), dtype=np.float32) # parameter W
LM = self.r*(2*np.dot(P.dot(KK).T, P.dot(KK)) - np.dot(np.sum(KK.T, axis=1, keepdims=True), np.sum(KK, axis=0, keepdims=True)))
# Evaluator
# step 1: initialize with spectral relaxation
# step 1.1: batch coordinate optimization
h0 = np.zeros(n)
print '\nSTEP 1: Initialize with spectral relaxation...'
print 'step 1.1...'
for rr in range(self.r):
if rr > 0:
tmp = np.dot(KK.T, h0.reshape((n,1)))
LM -= np.dot(tmp, tmp.T)
(V, U) = eigh(LM, RM, eigvals_only=False)
W[:,rr] = U[:,self.m-1]
tmp = np.dot(np.dot(W[:,rr].T, RM), W[:,rr])
W[:,rr] *= np.sqrt(n/tmp)
h0 = np.where(np.dot(KK, W[:,rr]) >= 0, 1, -1)
H[:,rr] = h0
# step 1.2: batch coordinate optimization for some loops
h1 = np.zeros(n)
for t in range(5):
print 'step 1.{}...'.format(t+2)
for rr in range(self.r):
h0[:] = H[:,rr]
tmp = np.dot(KK.T, h0.reshape((n,1)))
LM += np.dot(tmp, tmp.T)
(V, U) = eigh(LM, RM, eigvals_only=False)
W[:,rr] = U[:,self.m-1]
tmp = np.dot(np.dot(W[:,rr].T, RM), W[:,rr])
W[:,rr] *= np.sqrt(n/tmp)
# JUST FOR EXPERIMENT
h1[:] = np.where(np.dot(KK, W[:,rr]) > 0, 1, -1)
H[:,rr] = 0
fun = lambda x: -self.r*(2*np.sum(P.dot(x)**2) - np.sum(x)**2) + np.sum(np.dot(x,H)**2)
if fun(h1) <= fun(h0):
h0[:] = h1[:]
# END FOR EXPERIMENT
tmp = np.dot(KK.T, h0.reshape((n,1)))
LM -= np.dot(tmp, tmp.T)
H[:,rr] = h0
np.save('hash_value.npy', hash_value(np.where(H>=0, 1, 0)))
np.save('weight.npy', W)
# step 2: discrete optimization
print '\nSTEP 2: Discrete Optimization...'
RM += self.lmda * np.eye(self.m)
invRM = np.linalg.inv(RM)
h = np.zeros(n)
h1 = np.zeros(n)
bnds = [(-1,1) for i in xrange(n)]
for t in range(5):
print '\nIter No: %d' % t
# step 2.1: fix Hp, Hq, optimize W
W = np.dot(invRM, np.dot(KK.T, H))
# step 2.2: fix W, optimize H
KK_W = np.dot(KK, W)
np.save('hash_value.npy', hash_value(np.where(KK_W>=0, 1, 0)))
np.save('weight.npy', W)
for rr in range(self.r):
if (rr+1) % 8 == 0:
print 'rr:', rr
h[:] = H[:,rr]
H[:,rr] = 0
fun = lambda x: -self.r*(2*np.sum(P.dot(x)**2) - np.sum(x)**2) + np.sum(np.dot(x,H)**2) - mu * np.dot(KK_W[:,rr], x)
gra = lambda x: -2*self.r*(2*P.T.dot(P.dot(x)) - np.sum(x)) + 2*np.dot(H,np.dot(x,H)) - mu * KK_W[:,rr]
res = minimize(fun, h, method='L-BFGS-B', jac=gra, bounds=bnds, options={'disp': False, 'maxiter':500, 'maxfun':500})
h1[:] = np.where(res.x>=0, 1, -1)
# JUST FOR EXPERIMENT
if fun(h1) <= fun(h):
H[:,rr] = h1
else:
H[:,rr] = h
# END FOR EXPERIMENT
self.W = W
self.trainlabel = trainlabel
Y = np.dot(KK, self.W)
Y = np.where(Y>=0, 1, 0)
np.save('base_hash_value.npy', hash_value(Y))
np.save('weight.npy', self.W)
