def test_batch_random_hflip(self):
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
expected = torch.tensor([[[[0., 0., 0.], [0., 0., 0.],
[1., 1., 0.]]]]) # 1 x 1 x 3 x 3
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 = expected.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.apply_hflip(input,
params=flip_param_0,
return_transform=True)[0] == input).all()
assert (F.apply_hflip(input,
params=flip_param_0,
return_transform=True)[1] == identity).all()
assert (F.apply_hflip(input,
params=flip_param_1,
return_transform=True)[0] == expected).all()
assert (F.apply_hflip(
input, params=flip_param_1,
return_transform=True)[1] == expected_transform).all()
def test_random_hflip(self):
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
expected = torch.tensor([[0., 0., 0., 0.], [0., 0., 0., 0.],
[2., 1., 0., 0.]]) # 3 x 4
assert (F.apply_hflip(input, params=flip_param_0) == input).all()
assert (F.apply_hflip(input, params=flip_param_1) == expected).all()
def test_batch_random_hflip(self):
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
expected = torch.tensor([[[[0., 0., 0.], [0., 0., 0.],
[1., 1., 0.]]]]) # 1 x 1 x 3 x 3
input = input.repeat(batch_size, 3, 1, 1) # 5 x 3 x 3 x 3
expected = expected.repeat(batch_size, 3, 1, 1) # 5 x 3 x 3 x 3
assert (F.apply_hflip(input, params=flip_param_0) == input).all()
assert (F.apply_hflip(input, params=flip_param_1) == expected).all()
def test_random_hflip(self, device, dtype):
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]], device=device, dtype=dtype
) # 3 x 4
expected = torch.tensor(
[[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0], [2.0, 1.0, 0.0, 0.0]], device=device, dtype=dtype
) # 3 x 4
assert (F.apply_hflip(input[None, None]) == expected).all()
def test_random_hflip(self, device, dtype):
input = torch.tensor(
[[0., 0., 0., 0.], [0., 0., 0., 0.], [0., 0., 1., 2.]],
device=device,
dtype=dtype) # 3 x 4
expected = torch.tensor(
[[0., 0., 0., 0.], [0., 0., 0., 0.], [2., 1., 0., 0.]],
device=device,
dtype=dtype) # 3 x 4
assert (F.apply_hflip(input) == expected).all()
def test_random_hflip(self):
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
expected = torch.tensor([[0., 0., 0., 0.], [0., 0., 0., 0.],
[2., 1., 0., 0.]]) # 3 x 4
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.apply_hflip(input,
params=flip_param_0,
return_transform=True)[0] == input).all()
assert (F.apply_hflip(input,
params=flip_param_0,
return_transform=True)[1] == identity).all()
assert (F.apply_hflip(input,
params=flip_param_1,
return_transform=True)[0] == expected).all()
assert (F.apply_hflip(
input, params=flip_param_1,
return_transform=True)[1] == expected_transform).all()
assert (F.apply_hflip(input,
params=flip_param_0,
return_transform=False) == input).all()
assert (F.apply_hflip(input,
params=flip_param_1,
return_transform=False) == expected).all()
def test_batch_random_hflip(self, device, dtype):
batch_size = 5
input = torch.tensor(
[[[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 1.0, 1.0]]]], device=device, dtype=dtype
) # 1 x 1 x 3 x 3
expected = torch.tensor(
[[[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [1.0, 1.0, 0.0]]]], device=device, dtype=dtype
) # 1 x 1 x 3 x 3
input = input.repeat(batch_size, 3, 1, 1) # 5 x 3 x 3 x 3
expected = expected.repeat(batch_size, 3, 1, 1) # 5 x 3 x 3 x 3
assert (F.apply_hflip(input) == expected).all()
def h_flip(x, b):
# b is bool
if b == True:
x = F_k.apply_hflip((x))
return x
