def test_invalid_param_combinations(self, input_size, size, resize_to,
device, dtype):
batch_size = 2
random_crop_generator3d(batch_size=batch_size,
input_size=input_size,
size=size.to(device=device, dtype=dtype)
if isinstance(size, torch.Tensor) else size,
resize_to=resize_to)
def test_valid_param_combinations(self, batch_size, input_size, size,
resize_to, same_on_batch, device, dtype):
if isinstance(size, torch.Tensor):
size = size.repeat(batch_size, 1).to(device=device, dtype=dtype)
random_crop_generator3d(batch_size=batch_size,
input_size=input_size,
size=size.to(device=device, dtype=dtype)
if isinstance(size, torch.Tensor) else size,
resize_to=resize_to,
same_on_batch=same_on_batch)
def test_same_on_batch(self, device, dtype):
torch.manual_seed(42)
res = random_crop_generator3d(batch_size=2,
input_size=(200, 200, 200),
size=torch.tensor(
[[50, 60, 70], [50, 60, 70]],
device=device,
dtype=dtype),
resize_to=(100, 100, 100),
same_on_batch=True)
expected = dict(
src=torch.tensor([[[7., 8., 18.], [76., 8., 18.], [76., 67., 18.],
[7., 67., 18.], [7., 8., 67.], [76., 8., 67.],
[76., 67., 67.], [7., 67., 67.]],
[[7., 8., 18.], [76., 8., 18.], [76., 67., 18.],
[7., 67., 18.], [7., 8., 67.], [76., 8., 67.],
[76., 67., 67.], [7., 67., 67.]]],
device=device,
dtype=dtype),
dst=torch.tensor([[[0., 0., 0.], [99., 0., 0.], [99., 99., 0.],
[0., 99., 0.], [0., 0., 99.], [99., 0., 99.],
[99., 99., 99.], [0., 99., 99.]],
[[0., 0., 0.], [99., 0., 0.], [99., 99., 0.],
[0., 99., 0.], [0., 0., 99.], [99., 0., 99.],
[99., 99., 99.], [0., 99., 99.]]],
device=device,
dtype=dtype),
)
assert res.keys() == expected.keys()
assert_allclose(res['src'], expected['src'], atol=1e-4, rtol=1e-4)
assert_allclose(res['dst'], expected['dst'], atol=1e-4, rtol=1e-4)
def test_random_gen(self, device, dtype):
torch.manual_seed(42)
res = random_crop_generator3d(batch_size=2,
input_size=(200, 200, 200),
size=torch.tensor(
[[50, 60, 70], [50, 60, 70]],
device=device,
dtype=dtype),
resize_to=(100, 100, 100))
expected = dict(
src=torch.tensor(
[[[115, 53, 58], [184, 53, 58], [184, 112, 58], [115, 112, 58],
[115, 53, 107], [184, 53, 107], [184, 112, 107],
[115, 112, 107]],
[[119, 135, 90], [188, 135, 90], [188, 194, 90],
[119, 194, 90], [119, 135, 139], [188, 135, 139],
[188, 194, 139], [119, 194, 139]]],
device=device,
dtype=dtype),
dst=torch.tensor(
[[[0, 0, 0], [99, 0, 0], [99, 99, 0], [0, 99, 0], [0, 0, 99],
[99, 0, 99], [99, 99, 99], [0, 99, 99]],
[[0, 0, 0], [99, 0, 0], [99, 99, 0], [0, 99, 0], [0, 0, 99],
[99, 0, 99], [99, 99, 99], [0, 99, 99]]],
device=device,
dtype=dtype),
)
assert res.keys() == expected.keys()
assert_allclose(res['src'], expected['src'], atol=1e-4, rtol=1e-4)
assert_allclose(res['dst'], expected['dst'], atol=1e-4, rtol=1e-4)
def test_same_on_batch(self, device, dtype):
torch.manual_seed(42)
res = random_crop_generator3d(batch_size=2,
input_size=(200, 200, 200),
size=torch.tensor(
[[50, 60, 70], [50, 60, 70]],
device=device,
dtype=dtype),
resize_to=(100, 100, 100),
same_on_batch=True)
expected = dict(
src=torch.tensor(
[[[115, 129, 57], [184, 129, 57], [184, 188, 57],
[115, 188, 57], [115, 129, 106], [184, 129, 106],
[184, 188, 106], [115, 188, 106]],
[[115, 129, 57], [184, 129, 57], [184, 188, 57],
[115, 188, 57], [115, 129, 106], [184, 129, 106],
[184, 188, 106], [115, 188, 106]]],
device=device,
dtype=torch.long),
dst=torch.tensor(
[[[0, 0, 0], [99, 0, 0], [99, 99, 0], [0, 99, 0], [0, 0, 99],
[99, 0, 99], [99, 99, 99], [0, 99, 99]],
[[0, 0, 0], [99, 0, 0], [99, 99, 0], [0, 99, 0], [0, 0, 99],
[99, 0, 99], [99, 99, 99], [0, 99, 99]]],
device=device,
dtype=torch.long),
)
assert res.keys() == expected.keys()
assert_allclose(res['src'], expected['src'], atol=1e-4, rtol=1e-4)
assert_allclose(res['dst'], expected['dst'], atol=1e-4, rtol=1e-4)
