803d7e1fc61536c846c811bdee158bd21db36779,models/Pointnet2Cls.py,,,#,92

Before Change

    from torch.autograd import Variable
    model = PointnetCls()
    data = Variable(torch.randn(2, 10, 3))
    print(model(data))

After Change

    N = 32
    inputs = torch.randn(B, N, 9).cuda()
    labels = torch.from_numpy(np.random.randint(0, 3, size=B)).cuda()
    model = Pointnet2SSG(3)
    model.cuda()

    optimizer = optim.Adam(model.parameters(), lr=1e-2)

    model_fn = model_fn_decorator(nn.CrossEntropyLoss())
    for _ in range(20):
        optimizer.zero_grad()
        _, loss, _ = model_fn(model, (inputs, labels))
        loss.backward()
        print(loss.data[0])
        optimizer.step()

Instances