def test_random_hflip(self, device):
flip_param_0 = {'batch_prob': torch.tensor(False)}
flip_param_1 = {'batch_prob': torch.tensor(True)}
input = torch.tensor([[0., 0., 0., 0.], [0., 0., 0., 0.],
[0., 0., 1., 2.]]) # 3 x 4
input.to(device)
expected_transform = torch.tensor([[-1., 0., 4.], [0., 1., 0.],
[0., 0., 1.]]) # 3 x 3
identity = torch.tensor([[1., 0., 0.], [0., 1., 0.], [0., 0.,
1.]]) # 3 x 3
assert (F.compute_hflip_transformation(
input, params=flip_param_0) == identity).all()
assert (F.compute_hflip_transformation(
input, params=flip_param_1) == expected_transform).all()
def test_random_hflip(self, device):
input = torch.tensor([[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 1.0, 2.0]]) # 3 x 4
input.to(device)
expected_transform = torch.tensor([[-1.0, 0.0, 3.0], [0.0, 1.0, 0.0],
[0.0, 0.0, 1.0]]) # 3 x 3
assert (F.compute_hflip_transformation(
input[None, None]) == expected_transform).all()
def test_random_hflip(self, device):
input = torch.tensor([[0., 0., 0., 0.],
[0., 0., 0., 0.],
[0., 0., 1., 2.]]) # 3 x 4
input.to(device)
expected_transform = torch.tensor([[-1., 0., 3.],
[0., 1., 0.],
[0., 0., 1.]]) # 3 x 3
assert (F.compute_hflip_transformation(input) == expected_transform).all()
def test_batch_random_hflip(self, device):
batch_size = 5
flip_param_0 = {'batch_prob': torch.tensor([False] * 5)}
flip_param_1 = {'batch_prob': torch.tensor([True] * 5)}
input = torch.tensor([[[[0., 0., 0.], [0., 0., 0.],
[0., 1., 1.]]]]) # 1 x 1 x 3 x 3
input.to(device)
expected_transform = torch.tensor([[[-1., 0., 3.], [0., 1., 0.],
[0., 0., 1.]]]) # 1 x 3 x 3
identity = torch.tensor([[[1., 0., 0.], [0., 1., 0.],
[0., 0., 1.]]]) # 1 x 3 x 3
input = input.repeat(batch_size, 3, 1, 1) # 5 x 3 x 3 x 3
expected_transform = expected_transform.repeat(batch_size, 1,
1) # 5 x 3 x 3
identity = identity.repeat(batch_size, 1, 1) # 5 x 3 x 3
assert (F.compute_hflip_transformation(
input, params=flip_param_0) == identity).all()
assert (F.compute_hflip_transformation(
input, params=flip_param_1) == expected_transform).all()
def test_batch_random_hflip(self, device):
batch_size = 5
input = torch.tensor([[[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0],
[0.0, 1.0, 1.0]]]]) # 1 x 1 x 3 x 3
input.to(device)
expected_transform = torch.tensor([[[-1.0, 0.0, 2.0], [0.0, 1.0, 0.0],
[0.0, 0.0, 1.0]]]) # 1 x 3 x 3
input = input.repeat(batch_size, 3, 1, 1) # 5 x 3 x 3 x 3
expected_transform = expected_transform.repeat(batch_size, 1,
1) # 5 x 3 x 3
assert (
F.compute_hflip_transformation(input) == expected_transform).all()