def test_default_intensity(self, im_shape, im_type, channel_wise):
im = self.get_data(im_shape, im_type)
t = RandKSpaceSpikeNoise(1.0,
intensity_range=None,
channel_wise=channel_wise)
out = t(deepcopy(im))
self.assertEqual(out.shape, im.shape)
def test_intensity(self, im_shape, im_type, channel_wise):
im = self.get_data(im_shape, im_type)
intensity_range = [14, 14.1]
t = RandKSpaceSpikeNoise(1.0, intensity_range, channel_wise)
_ = t(deepcopy(im))
self.assertGreaterEqual(t.sampled_k_intensity[0], 14)
self.assertLessEqual(t.sampled_k_intensity[0], 14.1)
def test_0_prob(self, im_shape, as_tensor_output, as_tensor_input,
channel_wise):
im = self.get_data(im_shape, as_tensor_input)
intensity_range = [14, 15]
t = RandKSpaceSpikeNoise(0.0, intensity_range, channel_wise,
as_tensor_output)
out = t(im)
np.testing.assert_allclose(im, out)
def test_1_prob(self, im_shape, im_type, channel_wise):
im = self.get_data(im_shape, im_type)
intensity_range = [14, 14]
t = RandKSpaceSpikeNoise(1.0, intensity_range, channel_wise)
out = t(im)
base_t = KSpaceSpikeNoise(t.sampled_locs, [14])
out = out - base_t(im)
assert_allclose(out, im * 0, type_test="tensor")
def test_0_prob(self, im_shape, im_type, channel_wise):
im = self.get_data(im_shape, im_type)
intensity_range = [14, 15]
t = RandKSpaceSpikeNoise(0.0, intensity_range, channel_wise)
out = t(im)
self.assertEqual(type(im), type(out))
if isinstance(out, torch.Tensor):
self.assertEqual(out.device, im.device)
im, out = im.cpu(), out.cpu()
np.testing.assert_allclose(im, out)
def test_1_prob(self, im_shape, as_tensor_output, as_tensor_input,
channel_wise):
im = self.get_data(im_shape, as_tensor_input)
intensity_range = [14, 14]
t = RandKSpaceSpikeNoise(1.0, intensity_range, channel_wise,
as_tensor_output)
out = t(im)
base_t = KSpaceSpikeNoise(t.sampled_locs, [14], as_tensor_output)
out = out - base_t(im)
np.testing.assert_allclose(out, im * 0)
def test_1_prob(self, im_shape, im_type, channel_wise):
im = self.get_data(im_shape, im_type)
intensity_range = [14, 14]
t = RandKSpaceSpikeNoise(1.0, intensity_range, channel_wise)
out = t(im)
base_t = KSpaceSpikeNoise(t.sampled_locs, [14])
out = out - base_t(im)
self.assertEqual(type(im), type(out))
if isinstance(out, torch.Tensor):
self.assertEqual(out.device, im.device)
im, out = im.cpu(), out.cpu()
np.testing.assert_allclose(out, im * 0)
def test_same_result(self, im_shape, im_type, channel_wise):
im = self.get_data(im_shape, im_type)
intensity_range = [14, 15]
t = RandKSpaceSpikeNoise(0.0, intensity_range, channel_wise)
t.set_random_state(42)
out1 = t(deepcopy(im))
t.set_random_state(42)
out2 = t(deepcopy(im))
assert_allclose(out1, out2, type_test="tensor")
def test_same_result(self, im_shape, im_type, channel_wise):
im = self.get_data(im_shape, im_type)
intensity_range = [14, 15]
t = RandKSpaceSpikeNoise(0.0, intensity_range, channel_wise)
t.set_random_state(42)
out1 = t(deepcopy(im))
t.set_random_state(42)
out2 = t(deepcopy(im))
self.assertEqual(type(im), type(out1))
if isinstance(out1, torch.Tensor):
self.assertEqual(out1.device, im.device)
out1, out2 = out1.cpu(), out2.cpu()
np.testing.assert_allclose(out1, out2)
def test_same_result(self, im_shape, as_tensor_output, as_tensor_input,
channel_wise):
im = self.get_data(im_shape, as_tensor_input)
intensity_range = [14, 15]
t = RandKSpaceSpikeNoise(0.0, intensity_range, channel_wise,
as_tensor_output)
t.set_random_state(42)
out1 = t(deepcopy(im))
t.set_random_state(42)
out2 = t(deepcopy(im))
np.testing.assert_allclose(out1, out2)
self.assertIsInstance(out1,
torch.Tensor if as_tensor_output else np.ndarray)
def test_0_prob(self, im_shape, im_type, channel_wise):
im = self.get_data(im_shape, im_type)
intensity_range = [14, 15]
t = RandKSpaceSpikeNoise(0.0, intensity_range, channel_wise)
out = t(im)
assert_allclose(out, im, type_test="tensor")