def test_contrastive_loss_value(self):
count = 0
for i in range(0, 100):
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))
if (round(loss_expect, 6) == round(loss_value, 6)):
count += 1
#print (self.assertAlmostEqual(loss_expect, loss_value, places=5))
print(count * 1. / 100)
def test_contrastive_loss(self):
input1 = Variable(torch.randn(4, 4), requires_grad=True)
input2 = Variable(torch.randn(4, 4), requires_grad=True)
target = Variable(torch.randn(4), requires_grad=True)
tml = ContrastiveLoss(margin=self.margin)
self.assertTrue(
gradcheck(lambda x1, x2, t: tml.forward(x1, x2, t),
(input1, input2, target)))