def test_smoke_transform(self, device):
x_data = torch.rand(1, 2, 4, 5).to(device)
out = F.random_affine(x_data, 0., return_transform=True)
assert isinstance(out, tuple)
assert len(out) == 2
assert out[0].shape == x_data.shape
assert out[1].shape == (1, 3, 3)
def test_batch_multi_params(self, device):
x_data = torch.rand(2, 2, 8, 9).to(device)
out = F.random_affine(x_data, 0., (0., 0.))
assert out.shape == x_data.shape
def test_smoke_no_transform_batch(self, device):
x_data = torch.rand(2, 2, 8, 9).to(device)
out = F.random_affine(x_data, 0.)
assert out.shape == x_data.shape
