def update_core(self):
xp = self.gen.xp
self._iter += 1
opt_g = self.get_optimizer('gen')
opt_d = self.get_optimizer('dis')
data_z0 = self.get_latent_code_batch()
x_fake0 = self.gen(Variable(data_z0))
data_z1 = self.get_latent_code_batch()
x_fake1 = self.gen(Variable(data_z1))
data_x = self.get_real_image_batch()
x_real = Variable(data_x)
eta = np.random.rand()
x_inter = Variable((data_x * eta + (1.0 - eta) * x_fake0.data).astype('f'))
dis_x_fake0 = self.dis(x_fake0)
dis_x_fake1 = self.dis(x_fake1)
dis_x_real = self.dis(x_real)
loss_gen = loss_l2_norm(dis_x_fake0, dis_x_real) + \
loss_l2_norm(dis_x_fake1, dis_x_real) - \
loss_l2_norm(dis_x_fake0, dis_x_fake1)
#print(loss_gen.data)
chainer.report({'loss': loss_gen}, self.gen)
opt_g.zero_grads()
loss_gen.backward()
opt_g.update()
x_fake0.unchain_backward()
x_fake1.unchain_backward()
loss_surrogate = loss_l2_norm(dis_x_fake0, dis_x_fake1) - \
loss_l2_norm(dis_x_fake0, 0.0) + \
loss_l2_norm(dis_x_real, 0.0) - \
loss_l2_norm(dis_x_real, dis_x_fake1)
dis_x_inter = self.dis(x_inter, retain_forward=True)
g = xp.ones_like(dis_x_inter.data)
t0 = dis_x_inter.data - dis_x_fake1.data
t0_norm = xp.sum(t0**2, axis=(1)) ** 0.5
t1_norm = xp.sum(dis_x_inter.data**2, axis=(1)) ** 0.5
t_g = ((t0.transpose() / t0_norm) - (dis_x_inter.data.transpose()) / t1_norm).transpose()
g = g * t_g
grad = self.dis.differentiable_backward(Variable(g))
grad_l2 = F.sqrt(F.sum(grad**2, axis=(1, 2, 3)))
loss_gp = self._lambda_gp * loss_l2(grad_l2, 1.0)
loss_dis = loss_surrogate + loss_gp
opt_d.zero_grads()
loss_dis.backward()
opt_d.update()
chainer.report({'loss': loss_dis, 'loss_gp': loss_gp}, self.dis)
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