test_contrastive.py 文件源码

def test_contrastive_loss_value(self):
        x0_val = Variable(self.x0)
        x1_val = Variable(self.x1)
        t_val = Variable(self.t)
        tml = ContrastiveLoss(margin=self.margin)
        loss = tml.forward(x0_val, x1_val, t_val)
        self.assertEqual(loss.data.numpy().shape, (1, ))
        self.assertEqual(loss.data.numpy().dtype, np.float32)
        loss_value = float(loss.data.numpy())

        # Compute expected value
        loss_expect = 0
        for i in range(self.x0.size()[0]):
            x0d, x1d, td = self.x0[i], self.x1[i], self.t[i]
            d = torch.sum(torch.pow(x0d - x1d, 2))
            if td == 1:  # similar pair
                loss_expect += d
            elif td == 0:  # dissimilar pair
                loss_expect += max(1 - np.sqrt(d), 0)**2
        loss_expect /= 2.0 * self.t.size()[0]
        print("expected %s got %s" % (loss_expect, loss_value))
        self.assertAlmostEqual(loss_expect, loss_value, places=5)