def test_intensity_only(self):
seq = K.PatchSequential(
K.ImageSequential(
K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=0.5),
K.RandomPerspective(0.2, p=0.5),
K.RandomSolarize(0.1, 0.1, p=0.5),
),
K.ColorJitter(0.1, 0.1, 0.1, 0.1),
K.ImageSequential(
K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=0.5),
K.RandomPerspective(0.2, p=0.5),
K.RandomSolarize(0.1, 0.1, p=0.5),
),
K.ColorJitter(0.1, 0.1, 0.1, 0.1),
grid_size=(2, 2),
)
assert not seq.is_intensity_only()
seq = K.PatchSequential(
K.ImageSequential(K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=0.5)),
K.ColorJitter(0.1, 0.1, 0.1, 0.1),
K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=0.5),
K.ColorJitter(0.1, 0.1, 0.1, 0.1),
grid_size=(2, 2),
)
assert seq.is_intensity_only()
def test_forward(self, shape, padding, patchwise_apply, same_on_batch,
keepdim, random_apply, device, dtype):
torch.manual_seed(11)
try: # skip wrong param settings.
seq = K.PatchSequential(
K.color.RgbToBgr(),
K.ColorJitter(0.1, 0.1, 0.1, 0.1),
K.ImageSequential(
K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=0.5),
K.RandomPerspective(0.2, p=0.5),
K.RandomSolarize(0.1, 0.1, p=0.5),
),
K.RandomMixUp(p=1.0),
grid_size=(2, 2),
padding=padding,
patchwise_apply=patchwise_apply,
same_on_batch=same_on_batch,
keepdim=keepdim,
random_apply=random_apply,
)
# TODO: improve me and remove the exception.
except Exception:
return
input = torch.randn(*shape, device=device, dtype=dtype)
out = seq(input)
if seq.return_label:
out, _ = out
assert out.shape[-3:] == input.shape[-3:]
reproducibility_test(input, seq)
def test_forward(self, shape, padding, patchwise_apply, same_on_batch,
keepdim, device, dtype):
seq = K.PatchSequential(
K.ImageSequential(
K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=0.5),
K.RandomPerspective(0.2, p=0.5),
K.RandomSolarize(0.1, 0.1, p=0.5),
),
K.ColorJitter(0.1, 0.1, 0.1, 0.1),
K.ImageSequential(
K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=0.5),
K.RandomPerspective(0.2, p=0.5),
K.RandomSolarize(0.1, 0.1, p=0.5),
),
K.ColorJitter(0.1, 0.1, 0.1, 0.1),
grid_size=(2, 2),
padding=padding,
patchwise_apply=patchwise_apply,
same_on_batch=same_on_batch,
keepdim=keepdim,
)
input = torch.randn(*shape, device=device, dtype=dtype)
trans = torch.randn(shape[0], 3, 3, device=device, dtype=dtype)
out = seq(input)
assert out.shape[-3:] == input.shape[-3:]
out = seq((input, trans))
assert out[0].shape[-3:] == input.shape[-3:]
assert out[1].shape == trans.shape
def test_exception(self, error_param):
with pytest.raises(Exception): # AssertError and NotImplementedError
K.PatchSequential(
K.ImageSequential(
K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=0.5),
K.RandomPerspective(0.2, p=0.5),
K.RandomSolarize(0.1, 0.1, p=0.5),
),
K.ColorJitter(0.1, 0.1, 0.1, 0.1),
K.ImageSequential(
K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=0.5),
K.RandomPerspective(0.2, p=0.5),
K.RandomSolarize(0.1, 0.1, p=0.5),
),
K.ColorJitter(0.1, 0.1, 0.1, 0.1),
**error_param,
)
def test_forward(self, shape, padding, patchwise_apply, same_on_batch,
keepdim, random_apply, device, dtype):
try: # skip wrong param settings.
seq = K.PatchSequential(
K.ImageSequential(
K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=0.5),
K.RandomPerspective(0.2, p=0.5),
K.RandomSolarize(0.1, 0.1, p=0.5),
),
K.ColorJitter(0.1, 0.1, 0.1, 0.1),
K.ImageSequential(
K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=0.5),
K.RandomPerspective(0.2, p=0.5),
K.RandomSolarize(0.1, 0.1, p=0.5),
),
K.ColorJitter(0.1, 0.1, 0.1, 0.1),
grid_size=(2, 2),
padding=padding,
patchwise_apply=patchwise_apply,
same_on_batch=same_on_batch,
keepdim=keepdim,
random_apply=random_apply,
)
except:
return
input = torch.randn(*shape, device=device, dtype=dtype)
trans = torch.randn(shape[0], 3, 3, device=device, dtype=dtype)
out = seq(input)
assert out.shape[-3:] == input.shape[-3:]
out = seq((input, trans))
assert out[0].shape[-3:] == input.shape[-3:]
assert out[1].shape == trans.shape
reproducibility_test(input, seq)