def _setup(self):
h, w = self.face_size
self.x = sm.Tensor()
self.mask = sm.Tensor()
self.feat, ho, wo, co = vgg16(self.x, h, w)
self.embed = facenet_head(self.feat, ho, wo, co)
self.loss, self.dis = facenet_loss(self.embed, self.alpha, self.mask)
def _check_validity():
"""Check the validity of vgg16 in smnet."""
lr = 1
epoch = 10000
alpha = 0.5
n = 4 * 3
h, w = 128, 128
x = sm.Tensor()
mask = sm.Tensor()
feat, ho, wo, co = vgg16(x, h, w)
embed = facenet_head(feat, ho, wo, co)
loss = facenet_loss(embed, alpha, mask)
# data in host.
dataHs, maskHs = [], []
dataH = np.random.normal(0, 1, (n, h, w, 3))
maskH = np.zeros([n // 3, 1])
for _ in range(epoch):
dataHs.append(dataH)
maskHs.append(maskH)
# smnet
featHs, embedHs, lossHs = [], [], []
for _ in range(epoch):
featH, embedH, lossH = sm.forward([feat, embed, loss],
feed_dict={
x: dataHs[_],
mask: maskHs[_]
})
loss_sum = np.sum(lossH)
sm.optimize([loss], lr=lr)
featHs.append(featH)
embedHs.append(embedH)
lossHs.append(loss_sum)
print(_, loss_sum)
print(featHs)
print()
print(embedHs)
print()
print(lossHs)
def _setup(self):
"""Prebuild the onet network."""
self.x = sm.Tensor()
self.gt_conf = sm.Tensor()
self.gt_box = sm.Tensor()
self.gt_landmark = sm.Tensor()
self.conf_mask = sm.Tensor()
self.box_mask = sm.Tensor()
self.landmark_mask = sm.Tensor()
self.conf, self.box, self.landmark = onet(self.x)
self.conf_loss, self.box_loss, self.landmark_loss = onet_loss(
self.conf, self.box, self.landmark, self.gt_conf, self.gt_box,
self.gt_landmark, self.conf_mask, self.box_mask,
self.landmark_mask)