def test_invalid_param_combinations(self, kernel_size, angle, direction,
device, dtype):
random_motion_blur_generator3d(batch_size=8,
kernel_size=kernel_size,
angle=angle.to(device=device,
dtype=dtype),
direction=direction.to(device=device,
dtype=dtype))
def test_same_on_batch(self, device, dtype):
torch.manual_seed(42)
angle = torch.tensor([(10, 30), (30, 60), (60, 90)])
direction = torch.tensor([-1, 1])
res = random_motion_blur_generator3d(
batch_size=2,
kernel_size=3,
angle=angle.to(device=device, dtype=dtype),
direction=direction.to(device=device, dtype=dtype),
same_on_batch=True,
)
expected = dict(
ksize_factor=torch.tensor([3, 3], device=device,
dtype=torch.int32),
angle_factor=torch.tensor(
[[27.6454, 57.4501, 71.4859], [27.6454, 57.4501, 71.4859]],
device=device,
dtype=dtype),
direction_factor=torch.tensor([0.9186, 0.9186],
device=device,
dtype=dtype),
)
assert res.keys() == expected.keys()
assert_close(res['ksize_factor'],
expected['ksize_factor'],
rtol=1e-4,
atol=1e-4)
assert_close(res['angle_factor'],
expected['angle_factor'],
rtol=1e-4,
atol=1e-4)
assert_close(res['direction_factor'],
expected['direction_factor'],
rtol=1e-4,
atol=1e-4)
def test_random_gen(self, device, dtype):
torch.manual_seed(42)
angle = torch.tensor([(10, 30), (30, 60), (60, 90)])
direction = torch.tensor([-1, 1])
res = random_motion_blur_generator3d(
batch_size=2,
kernel_size=3,
angle=angle.to(device=device, dtype=dtype),
direction=direction.to(device=device, dtype=dtype),
same_on_batch=False,
)
expected = dict(
ksize_factor=torch.tensor([3, 3], device=device,
dtype=torch.int32),
angle_factor=torch.tensor(
[[27.6454, 41.4859, 71.7134], [28.3001, 58.7792, 78.0269]],
device=device,
dtype=dtype),
direction_factor=torch.tensor([-0.4869, 0.5873],
device=device,
dtype=dtype),
)
assert res.keys() == expected.keys()
assert_close(res['ksize_factor'],
expected['ksize_factor'],
rtol=1e-4,
atol=1e-4)
assert_close(res['angle_factor'],
expected['angle_factor'],
rtol=1e-4,
atol=1e-4)
assert_close(res['direction_factor'],
expected['direction_factor'],
rtol=1e-4,
atol=1e-4)
def test_same_on_batch(self, device, dtype):
torch.manual_seed(42)
angle = torch.tensor([(10, 30), (30, 60), (60, 90)])
direction = torch.tensor([-1, 1])
res = random_motion_blur_generator3d(batch_size=2,
kernel_size=3,
angle=angle.to(device=device,
dtype=dtype),
direction=direction.to(
device=device, dtype=dtype),
same_on_batch=True)
expected = dict(
ksize_factor=torch.tensor([3., 3.],
device=device,
dtype=torch.int32),
angle_factor=torch.tensor(
[[11.1631, 31.8873, 63.7076], [11.1631, 31.8873, 63.7076]],
device=device,
dtype=dtype),
direction_factor=torch.tensor([-0.8948, -0.8948],
device=device,
dtype=dtype))
assert res.keys() == expected.keys()
assert_allclose(res['ksize_factor'],
expected['ksize_factor'],
rtol=1e-4,
atol=1e-4)
assert_allclose(res['angle_factor'],
expected['angle_factor'],
rtol=1e-4,
atol=1e-4)
assert_allclose(res['direction_factor'],
expected['direction_factor'],
rtol=1e-4,
atol=1e-4)
def test_random_gen(self, device, dtype):
torch.manual_seed(42)
angle = torch.tensor([(10, 30), (30, 60), (60, 90)])
direction = torch.tensor([-1, 1])
res = random_motion_blur_generator3d(batch_size=2,
kernel_size=3,
angle=angle.to(device=device,
dtype=dtype),
direction=direction.to(
device=device, dtype=dtype),
same_on_batch=False)
expected = dict(
ksize_factor=torch.tensor([3., 3.],
device=device,
dtype=torch.int32),
angle_factor=torch.tensor(
[[11.1631, 33.7076, 75.7852], [11.2582, 31.5774, 74.3035]],
device=device,
dtype=dtype),
direction_factor=torch.tensor([0.9105, 0.8575],
device=device,
dtype=dtype))
assert res.keys() == expected.keys()
assert_allclose(res['ksize_factor'],
expected['ksize_factor'],
rtol=1e-4,
atol=1e-4)
assert_allclose(res['angle_factor'],
expected['angle_factor'],
rtol=1e-4,
atol=1e-4)
assert_allclose(res['direction_factor'],
expected['direction_factor'],
rtol=1e-4,
atol=1e-4)
