Add test_double_flatfield for cuda backend

nabu/preproc/tests/test_double_flatfield.py

@@ -7,7 +7,10 @@ from silx.io.url import DataUrl
 from tomoscan.esrf.mock import MockHDF5
 from nabu.io.reader import HDF5Reader
 from nabu.preproc.double_flatfield import DoubleFlatField, __have_scipy__
+from nabu.preproc.double_flatfield_cuda import CudaDoubleFlatField, __has_pycuda__
 
+if __has_pycuda__:
+    import pycuda.gpuarray as garray
 
 @pytest.fixture(scope="class")
 def bootstrap(request):
@@ -35,7 +38,10 @@ def bootstrap(request):
         file_path=path.join(cls.dname, "dff.h5"),
         data_path="/entry/double_flatfield/results/data"
     )
-
+    cls.ff_cuda_dump_url = DataUrl(
+        file_path=path.join(cls.dname, "dff_cuda.h5"),
+        data_path="/entry/double_flatfield/results/data"
+    )
     golden = 0
     for i in range(10):
         golden += exp(-i)
@@ -64,3 +70,26 @@ class TestDoubleFlatField:
 
         assert np.max(np.abs(mydf2 - mydf)) < self.tol
         assert np.max(np.abs(mydf - self.golden)) < self.tol
+
+
+    @pytest.mark.skipif(not(__has_pycuda__), reason="Need pycuda for double flatfield with cuda backend")
+    def test_dff_cuda(self):
+        import pycuda.autoinit
+        dff = CudaDoubleFlatField(
+            self.radios.shape,
+            result_url=self.ff_cuda_dump_url
+        )
+        d_radios = garray.to_gpu(self.radios)
+        mydf = dff.get_double_flatfield(radios=d_radios).get()
+
+
+        assert path.isfile(dff.result_url.file_path())
+
+        dff2 = CudaDoubleFlatField(
+            self.radios.shape,
+            result_url=self.ff_cuda_dump_url
+        )
+        mydf2 = dff2.get_double_flatfield(radios=d_radios).get()
+
+        assert np.max(np.abs(mydf2 - mydf)) < self.tol
+        assert np.max(np.abs(mydf - self.golden)) < self.tol