def make_img(self, x, l, num_lm, random=0):
s = xp.log10(xp.ones((1, 1)) * self.gsize / self.img_size) + 1
sm = xp.repeat(s, num_lm, axis=0)
if random == 0:
lm = Variable(xp.clip(l.data, 0, 1))
else:
eps = xp.random.normal(0, 1, size=l.data.shape).astype(xp.float32)
lm = xp.clip(l.data + eps * xp.sqrt(self.vars), 0, 1)
lm = Variable(lm.astype(xp.float32))
if self.use_gpu:
xm = make_sampled_image.generate_xm_rgb_gpu(lm.data, sm, x, num_lm, g_size=self.gsize)
else:
xm = make_sampled_image.generate_xm_rgb(lm.data, sm, x, num_lm, g_size=self.gsize)
return xm, lm
def use_model(self, x, t):
self.reset()
num_lm = x.shape[0]
n_step = self.n_step
s_list = xp.ones((n_step, num_lm, 1)) * (self.gsize / self.img_size)
l_list = xp.empty((n_step, num_lm, 2))
l, b1 = self.first_forward(x, num_lm)
for i in range(n_step):
if i + 1 == n_step:
xm, lm = self.make_img(x, l, num_lm, random=0)
l1, y, b = self.recurrent_forward(xm, lm)
l_list[i] = l1.data
accuracy = y.data * t
return xp.sum(accuracy, axis=1), l_list, s_list
else:
xm, lm = self.make_img(x, l, num_lm, random=0)
l1, y, b = self.recurrent_forward(xm, lm)
l = l1
l_list[i] = l.data
return