fixes unit tests

Signed-off-by: Wenqi Li <wenqil@nvidia.com>
Project-MONAI · Feb 1, 2021 · cd500e9 · cd500e9
1 parent 9115b74
commit cd500e9
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Showing 3 changed files with 7 additions and 7 deletions.
diff --git a/monai/data/png_saver.py b/monai/data/png_saver.py
@@ -95,7 +95,7 @@ def save(self, data: Union[torch.Tensor, np.ndarray], meta_data: Optional[Dict]
         if data.shape[0] == 1:
             data = data.squeeze(0)
         elif 2 < data.shape[0] < 5:
-            data = np.moveaxis(data, 0, -1)  # type: ignore
+            data = np.moveaxis(np.asarray(data), 0, -1)
         else:
             raise ValueError(f"Unsupported number of channels: {data.shape[0]}, available options are [1, 3, 4]")
 

diff --git a/monai/networks/nets/varautoencoder.py b/monai/networks/nets/varautoencoder.py
@@ -46,7 +46,7 @@ def __init__(
         self.in_channels, *self.in_shape = in_shape
 
         self.latent_size = latent_size
-        self.final_size = np.asarray(self.in_shape, int)
+        self.final_size = np.asarray(self.in_shape, dtype=int)
 
         super().__init__(
             dimensions,
@@ -68,7 +68,7 @@ def __init__(
         padding = same_padding(self.kernel_size)
 
         for s in strides:
-            self.final_size = np.asarray(calculate_out_shape(self.final_size, self.kernel_size, s, padding), dtype=int)
+            self.final_size = calculate_out_shape(self.final_size, self.kernel_size, s, padding)  # type: ignore
 
         linear_size = int(np.product(self.final_size)) * self.encoded_channels
         self.mu = nn.Linear(linear_size, self.latent_size)

diff --git a/tests/test_crop_foregroundd.py b/tests/test_crop_foregroundd.py
@@ -65,14 +65,14 @@ def test_value(self, argments, image, expected_data):
     @parameterized.expand([TEST_CASE_1])
     def test_foreground_position(self, argments, image, _):
         result = CropForegroundd(**argments)(image)
-        self.assertListEqual(result["foreground_start_coord"], [1, 1])
-        self.assertListEqual(result["foreground_end_coord"], [4, 4])
+        np.testing.assert_allclose(result["foreground_start_coord"], np.array([1, 1]))
+        np.testing.assert_allclose(result["foreground_end_coord"], np.array([4, 4]))
 
         argments["start_coord_key"] = "test_start_coord"
         argments["end_coord_key"] = "test_end_coord"
         result = CropForegroundd(**argments)(image)
-        self.assertListEqual(result["test_start_coord"], [1, 1])
-        self.assertListEqual(result["test_end_coord"], [4, 4])
+        np.testing.assert_allclose(result["test_start_coord"], np.array([1, 1]))
+        np.testing.assert_allclose(result["test_end_coord"], np.array([4, 4]))
 
 
 if __name__ == "__main__":