test_nn.py 文件源码

def test_bilinear(self):
        module = nn.Bilinear(10, 10, 8)
        module2 = legacy.Bilinear(10, 10, 8)

        module2.weight.copy_(module.weight.data)
        module2.bias.copy_(module.bias.data)

        input1 = torch.randn(4, 10)
        input2 = torch.randn(4, 10)

        output = module(Variable(input1), Variable(input2))
        output2 = module2.forward([input1, input2])

        input1_1 = Variable(input1, requires_grad=True)
        input2_1 = Variable(input2, requires_grad=True)

        output3 = module(input1_1, input2_1)
        grad = torch.randn(*output3.size())
        output3.backward(grad)
        gi1 = input1_1.grad.data.clone()
        gi2 = input2_1.grad.data.clone()

        self.assertEqual(output.data, output2)
        # TODO: this assertion is incorrect, fix needed
        # self.assertEqual([gi1, gi2], output3)

        self.assertTrue(gradcheck(lambda x1, x2: F.bilinear(x1, x2, module.weight, module.bias), (input1_1, input2_1)))