fixes unit tests

Signed-off-by: Wenqi Li <wenqil@nvidia.com>

tests/test_label_to_mask.py

@@ -64,7 +64,7 @@ def test_value(self, argments, image, expected_data):
         self.assertEqual(type(result), type(image))
         if isinstance(result, torch.Tensor):
             self.assertEqual(result.device, image.device)
-        assert_allclose(result, expected_data)
+        assert_allclose(result, expected_data, type_test=False)
 
 
 if __name__ == "__main__":

tests/test_label_to_maskd.py

@@ -65,7 +65,7 @@ def test_value(self, argments, input_data, expected_data):
         self.assertEqual(type(r), type(i))
         if isinstance(r, torch.Tensor):
             self.assertEqual(r.device, i.device)
-        assert_allclose(r, expected_data)
+        assert_allclose(r, expected_data, type_test=False)
 
 
 if __name__ == "__main__":

tests/test_rand_rotate90.py

@@ -23,44 +23,36 @@ def test_default(self):
         for p in TEST_NDARRAYS:
             rotate.set_random_state(123)
             rotated = rotate(p(self.imt[0]))
-            expected = []
-            for channel in self.imt[0]:
-                expected.append(np.rot90(channel, 0, (0, 1)))
+            expected = [np.rot90(channel, 0, (0, 1)) for channel in self.imt[0]]
             expected = np.stack(expected)
-            assert_allclose(rotated, expected, rtol=1.0e-5, atol=1.0e-8)
+            assert_allclose(rotated, p(expected), rtol=1.0e-5, atol=1.0e-8)
 
     def test_k(self):
         rotate = RandRotate90(max_k=2)
         for p in TEST_NDARRAYS:
             rotate.set_random_state(234)
             rotated = rotate(p(self.imt[0]))
-            expected = []
-            for channel in self.imt[0]:
-                expected.append(np.rot90(channel, 0, (0, 1)))
+            expected = [np.rot90(channel, 0, (0, 1)) for channel in self.imt[0]]
             expected = np.stack(expected)
-            assert_allclose(rotated, expected, rtol=1.0e-5, atol=1.0e-8)
+            assert_allclose(rotated, p(expected), rtol=1.0e-5, atol=1.0e-8)
 
     def test_spatial_axes(self):
         rotate = RandRotate90(spatial_axes=(0, 1))
         for p in TEST_NDARRAYS:
             rotate.set_random_state(234)
             rotated = rotate(p(self.imt[0]))
-            expected = []
-            for channel in self.imt[0]:
-                expected.append(np.rot90(channel, 0, (0, 1)))
+            expected = [np.rot90(channel, 0, (0, 1)) for channel in self.imt[0]]
             expected = np.stack(expected)
-            assert_allclose(rotated, expected, rtol=1.0e-5, atol=1.0e-8)
+            assert_allclose(rotated, p(expected), rtol=1.0e-5, atol=1.0e-8)
 
     def test_prob_k_spatial_axes(self):
         rotate = RandRotate90(prob=1.0, max_k=2, spatial_axes=(0, 1))
         for p in TEST_NDARRAYS:
             rotate.set_random_state(234)
             rotated = rotate(p(self.imt[0]))
-            expected = []
-            for channel in self.imt[0]:
-                expected.append(np.rot90(channel, 1, (0, 1)))
+            expected = [np.rot90(channel, 1, (0, 1)) for channel in self.imt[0]]
             expected = np.stack(expected)
-            assert_allclose(rotated, expected, rtol=1.0e-5, atol=1.0e-8)
+            assert_allclose(rotated, p(expected), rtol=1.0e-5, atol=1.0e-8)
 
 
 if __name__ == "__main__":

tests/test_rand_rotate90d.py

@@ -24,47 +24,39 @@ def test_default(self):
         for p in TEST_NDARRAYS:
             rotate.set_random_state(123)
             rotated = rotate({key: p(self.imt[0])})
-            expected = []
-            for channel in self.imt[0]:
-                expected.append(np.rot90(channel, 0, (0, 1)))
+            expected = [np.rot90(channel, 0, (0, 1)) for channel in self.imt[0]]
             expected = np.stack(expected)
-            assert_allclose(rotated[key], expected)
+            assert_allclose(rotated[key], p(expected))
 
     def test_k(self):
         key = "test"
         rotate = RandRotate90d(keys=key, max_k=2)
         for p in TEST_NDARRAYS:
             rotate.set_random_state(234)
             rotated = rotate({key: p(self.imt[0])})
-            expected = []
-            for channel in self.imt[0]:
-                expected.append(np.rot90(channel, 0, (0, 1)))
+            expected = [np.rot90(channel, 0, (0, 1)) for channel in self.imt[0]]
             expected = np.stack(expected)
-            assert_allclose(rotated[key], expected)
+            assert_allclose(rotated[key], p(expected))
 
     def test_spatial_axes(self):
         key = "test"
         rotate = RandRotate90d(keys=key, spatial_axes=(0, 1))
         for p in TEST_NDARRAYS:
             rotate.set_random_state(234)
             rotated = rotate({key: p(self.imt[0])})
-            expected = []
-            for channel in self.imt[0]:
-                expected.append(np.rot90(channel, 0, (0, 1)))
+            expected = [np.rot90(channel, 0, (0, 1)) for channel in self.imt[0]]
             expected = np.stack(expected)
-            assert_allclose(rotated[key], expected)
+            assert_allclose(rotated[key], p(expected))
 
     def test_prob_k_spatial_axes(self):
         key = "test"
         rotate = RandRotate90d(keys=key, prob=1.0, max_k=2, spatial_axes=(0, 1))
         for p in TEST_NDARRAYS:
             rotate.set_random_state(234)
             rotated = rotate({key: p(self.imt[0])})
-            expected = []
-            for channel in self.imt[0]:
-                expected.append(np.rot90(channel, 1, (0, 1)))
+            expected = [np.rot90(channel, 1, (0, 1)) for channel in self.imt[0]]
             expected = np.stack(expected)
-            assert_allclose(rotated[key], expected)
+            assert_allclose(rotated[key], p(expected))
 
     def test_no_key(self):
         key = "unknown"

tests/test_rand_scale_intensity.py

@@ -25,7 +25,7 @@ def test_value(self):
             result = scaler(p(self.imt))
             np.random.seed(0)
             expected = p((self.imt * (1 + np.random.uniform(low=-0.5, high=0.5))).astype(np.float32))
-            assert_allclose(result, expected, rtol=1e-7, atol=0)
+            assert_allclose(result, p(expected), rtol=1e-7, atol=0)
 
 
 if __name__ == "__main__":

tests/test_rand_scale_intensityd.py

@@ -19,14 +19,14 @@
 
 class TestRandScaleIntensityd(NumpyImageTestCase2D):
     def test_value(self):
+        key = "img"
         for p in TEST_NDARRAYS:
-            key = "img"
             scaler = RandScaleIntensityd(keys=[key], factors=0.5, prob=1.0)
             scaler.set_random_state(seed=0)
             result = scaler({key: p(self.imt)})
             np.random.seed(0)
             expected = (self.imt * (1 + np.random.uniform(low=-0.5, high=0.5))).astype(np.float32)
-            assert_allclose(result[key], expected)
+            assert_allclose(result[key], p(expected))
 
 
 if __name__ == "__main__":

tests/test_scale_intensity_range.py

@@ -22,7 +22,7 @@ def test_image_scale_intensity_range(self):
             scaled = scaler(p(self.imt))
             expected = (self.imt - 20) / 88
             expected = expected * 30 + 50
-            assert_allclose(scaled, expected)
+            assert_allclose(scaled, p(expected))
 
 
 if __name__ == "__main__":

tests/test_scale_intensity_ranged.py

@@ -23,7 +23,7 @@ def test_image_scale_intensity_ranged(self):
             scaled = scaler({key: p(self.imt)})
             expected = (self.imt - 20) / 88
             expected = expected * 30 + 50
-            assert_allclose(scaled[key], expected)
+            assert_allclose(scaled[key], p(expected))
 
 
 if __name__ == "__main__":

tests/test_shift_intensityd.py

@@ -24,7 +24,7 @@ def test_value(self):
             shifter = ShiftIntensityd(keys=[key], offset=1.0)
             result = shifter({key: p(self.imt)})
             expected = self.imt + 1.0
-            assert_allclose(result[key], expected)
+            assert_allclose(result[key], p(expected))
 
     def test_factor(self):
         key = "img"

tests/test_threshold_intensity.py

@@ -29,7 +29,7 @@ class TestThresholdIntensity(unittest.TestCase):
     def test_value(self, in_type, input_param, expected_value):
         test_data = in_type(np.arange(10))
         result = ThresholdIntensity(**input_param)(test_data)
-        assert_allclose(result, expected_value)
+        assert_allclose(result, in_type(expected_value))
 
 
 if __name__ == "__main__":

tests/test_threshold_intensityd.py

@@ -47,9 +47,9 @@ class TestThresholdIntensityd(unittest.TestCase):
     def test_value(self, in_type, input_param, expected_value):
         test_data = {"image": in_type(np.arange(10)), "label": in_type(np.arange(10)), "extra": in_type(np.arange(10))}
         result = ThresholdIntensityd(**input_param)(test_data)
-        assert_allclose(result["image"], expected_value)
-        assert_allclose(result["label"], expected_value)
-        assert_allclose(result["extra"], expected_value)
+        assert_allclose(result["image"], in_type(expected_value))
+        assert_allclose(result["label"], in_type(expected_value))
+        assert_allclose(result["extra"], in_type(expected_value))
 
 
 if __name__ == "__main__":

tests/test_to_tensor.py

@@ -37,14 +37,14 @@ class TestToTensor(unittest.TestCase):
     def test_array_input(self, test_data, expected_shape):
         result = ToTensor()(test_data)
         self.assertTrue(isinstance(result, Tensor))
-        assert_allclose(result, test_data)
+        assert_allclose(result, test_data, type_test=False)
         self.assertTupleEqual(result.shape, expected_shape)
 
     @parameterized.expand(TESTS_SINGLE)
     def test_single_input(self, test_data):
         result = ToTensor()(test_data)
         self.assertTrue(isinstance(result, Tensor))
-        assert_allclose(result, test_data)
+        assert_allclose(result, test_data, type_test=False)
         self.assertEqual(result.ndim, 0)
 
     @unittest.skipUnless(has_cp, "CuPy is required.")
@@ -53,7 +53,7 @@ def test_cupy(self):
         cupy_array = cp.ascontiguousarray(cp.asarray(test_data))
         result = ToTensor()(cupy_array)
         self.assertTrue(isinstance(result, Tensor))
-        assert_allclose(result, test_data)
+        assert_allclose(result, test_data, type_test=False)
 
 
 if __name__ == "__main__":

tests/test_transpose.py

@@ -42,7 +42,7 @@ def test_transpose(self, im, indices):
         if isinstance(im, torch.Tensor):
             im = im.cpu().numpy()
         out2 = np.transpose(im, indices)
-        assert_allclose(out1, out2)
+        assert_allclose(out1, out2, type_test=False)
 
 
 if __name__ == "__main__":

tests/test_transposed.py

@@ -57,13 +57,13 @@ def test_transpose(self, im, indices):
         if isinstance(im, torch.Tensor):
             im = im.cpu().numpy()
         out_gt = np.transpose(im, indices)
-        assert_allclose(out_im1, out_gt)
-        assert_allclose(out_im2, out_gt)
+        assert_allclose(out_im1, out_gt, type_test=False)
+        assert_allclose(out_im2, out_gt, type_test=False)
 
         # test inverse
         fwd_inv_data = tr.inverse(out_data)
         for i, j in zip(data.values(), fwd_inv_data.values()):
-            assert_allclose(i, j)
+            assert_allclose(i, j, type_test=False)
 
 
 if __name__ == "__main__":