192e33a99505947aedd3dd8b081d71b04f14eb85,examples/equivariance_error/main.py,,,#,34

Before Change

y = foo(x)

// L_R \Phi(x)
y1 = so3_rotation(y, alpha, beta, gamma)

// \Phi(L_R x)
y2 = foo(so3_rotation(x, alpha, beta, gamma))


y = y.data.cpu().numpy()
y1 = y1.data.cpu().numpy()
y2 = y2.data.cpu().numpy()

relative_error = np.std(y1 - y2) / np.std(y)

After Change

    return torch.cat([x[:, :, :, n:], x[:, :, :, :n]], dim=3)

// Create random input
x = torch.autograd.Variable(torch.randn(1, 12, 128, 128), volatile=True).cuda() // [batch, feature, beta, alpha]

y = phi(x)
y1 = rot(phi(x), angle=45)

Instances