def test_resize3d_nearest_values(self):
tensorflow_output = np.squeeze(
resize_3d(self.tensorflow_array, (4, 4, 4), 'nearest').numpy())
torch_output = np.squeeze(
resize_3d(self.pytorch_array, (4, 4, 4), 'nearest').numpy())
print(tensorflow_output)
np.testing.assert_array_equal(tensorflow_output[0, 0, :], [0, 0, 1, 1])
np.testing.assert_array_equal(torch_output[0, 0, :], [0, 0, 1, 1])
def test_resize3d_tf(self):
np.testing.assert_equal(
resize_3d(self.tensorflow_array, (4, 4, 4)).numpy().shape,
(1, 4, 4, 4, 1))
def test_resize3d_torch(self):
image_shape = resize_3d(self.pytorch_array, (4, 4, 4)).numpy().shape
np.testing.assert_equal(image_shape, (1, 1, 4, 4, 4))
def test_resize3d_bicubic(self):
with self.assertRaises(AssertionError):
_ = np.squeeze(
resize_3d(self.tensorflow_array, (4, 4, 4), 'bicubic').numpy())
def test_resize3d_nearest(self):
tensorflow_output = np.squeeze(
resize_3d(self.tensorflow_array, (4, 4, 4), 'nearest').numpy())
torch_output = np.squeeze(
resize_3d(self.pytorch_array, (4, 4, 4), 'nearest').numpy())
np.testing.assert_array_almost_equal(tensorflow_output, torch_output)
def forward(self, data: List[Tensor],
state: Dict[str, Any]) -> Union[Tensor, List[Tensor]]:
return [
resize_3d(elem, self.output_shape, self.resize_mode)
for elem in data
]