def test_dataloader(self):
dataset = Dataset(
data=[{
"img": np.array([[[0.0, 1.0], [2.0, 3.0]]])
}, {
"img": np.array([[[0.0, 1.0], [2.0, 3.0]]])
}],
transform=IntensityStatsd(keys="img",
ops=["max", "mean"],
key_prefix="orig"),
)
# set num workers = 0 for mac / win
num_workers = 2 if sys.platform == "linux" else 0
dataloader = DataLoader(dataset=dataset,
num_workers=num_workers,
batch_size=2)
orig_method = mp.get_start_method()
mp.set_start_method("spawn", force=True)
for d in dataloader:
meta = d["img_meta_dict"]
np.testing.assert_allclose(meta["orig_max"], [3.0, 3.0], atol=1e-3)
np.testing.assert_allclose(meta["orig_mean"], [1.5, 1.5],
atol=1e-3)
# restore the mp method
mp.set_start_method(orig_method, force=True)
def test_factor(self):
key = "img"
stats = IntensityStatsd(keys=key, ops="max", key_prefix="orig")
shifter = ShiftIntensityd(keys=[key], offset=1.0, factor_key=["orig_max"])
data = {key: self.imt, PostFix.meta(key): {"affine": None}}
result = shifter(stats(data))
expected = self.imt + 1.0 * np.nanmax(self.imt)
np.testing.assert_allclose(result[key], expected)
def test_mask(self):
data = {
"img": np.array([[[0.0, 1.0], [2.0, 3.0]]]),
"img_mask": np.array([[[1, 0], [1, 0]]], dtype=bool)
}
stats = IntensityStatsd(keys="img",
ops=["max", "mean"],
mask_keys="img_mask",
key_prefix="orig")
meta = stats(data)["img_meta_dict"]
np.testing.assert_allclose(meta["orig_max"], 2.0, atol=1e-3)
np.testing.assert_allclose(meta["orig_mean"], 1.0, atol=1e-3)
def test_factor(self):
key = "img"
stats = IntensityStatsd(keys=key, ops="max", key_prefix="orig")
shifter = RandShiftIntensityd(keys=[key],
offsets=1.0,
factor_key=["orig_max"],
prob=1.0)
data = {key: self.imt, key + "_meta_dict": {"affine": None}}
shifter.set_random_state(seed=0)
result = shifter(stats(data))
np.random.seed(0)
# simulate the randomize() of transform
np.random.random()
expected = self.imt + np.random.uniform(low=-1.0,
high=1.0) * np.nanmax(self.imt)
np.testing.assert_allclose(result[key], expected)
def test_value(self, input_param, data, meta_key, expected):
meta = IntensityStatsd(**input_param)(data)[meta_key]
for k, v in expected.items():
self.assertTrue(k in meta)
np.testing.assert_allclose(v, meta[k], atol=1e-3)