merging 6339/6341

monai/transforms/lazy/utils.py

@@ -158,7 +158,7 @@ def resample(data: torch.Tensor, matrix: NdarrayOrTensor, kwargs: dict | None =
             - "lazy_padding_mode"
             - "lazy_interpolation_mode" (this option might be ignored when ``mode="auto"``.)
             - "lazy_align_corners"
-            - "lazy_dtype"
+            - "lazy_dtype" (dtype for resampling computation; this might be ignored when ``mode="auto"``.)
             - "atol" for tolerance for matrix floating point comparison.
             - "lazy_resample_mode" for resampling backend, default to `"auto"`. Setting to other values will use the
               `monai.transforms.SpatialResample` for resampling.
@@ -218,6 +218,7 @@ def resample(data: torch.Tensor, matrix: NdarrayOrTensor, kwargs: dict | None =
             img.affine = call_kwargs["dst_affine"]
             return img
         img = monai.transforms.crop_or_pad_nd(img, matrix_np, out_spatial_size, mode=call_kwargs["padding_mode"])
+        img = img.to(torch.float32)  # consistent with monai.transforms.spatial.functional.spatial_resample
         img.affine = call_kwargs["dst_affine"]
         return img
 

requirements-dev.txt

@@ -19,8 +19,8 @@ mccabe
 pep8-naming
 pycodestyle
 pyflakes
-black
-isort
+black>=22.12
+isort>=5.1
 pytype>=2020.6.1; platform_system != "Windows"
 types-pkg_resources
 mypy>=0.790

tests/test_bundle_trt_export.py

@@ -60,7 +60,7 @@ def tearDown(self):
             del os.environ["CUDA_VISIBLE_DEVICES"]  # previously unset
 
     @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4])
-    @unittest.skipUnless(has_torchtrt and has_tensorrt, "Torch-TensorRT is required for convert!")
+    @unittest.skipUnless(has_torchtrt and has_tensorrt, "Torch-TensorRT is required for conversion!")
     def test_trt_export(self, convert_precision, input_shape, dynamic_batch):
         meta_file = os.path.join(os.path.dirname(__file__), "testing_data", "metadata.json")
         config_file = os.path.join(os.path.dirname(__file__), "testing_data", "inference.json")
@@ -95,7 +95,9 @@ def test_trt_export(self, convert_precision, input_shape, dynamic_batch):
             self.assertTrue("network_def" in json.loads(extra_files["inference.json"]))
 
     @parameterized.expand([TEST_CASE_3, TEST_CASE_4])
-    @unittest.skipUnless(has_onnx, "Onnx is required for onnx-trt conversion!")
+    @unittest.skipUnless(
+        has_onnx and has_torchtrt and has_tensorrt, "Onnx and TensorRT are required for onnx-trt conversion!"
+    )
     def test_onnx_trt_export(self, convert_precision, input_shape, dynamic_batch):
         meta_file = os.path.join(os.path.dirname(__file__), "testing_data", "metadata.json")
         config_file = os.path.join(os.path.dirname(__file__), "testing_data", "inference.json")

tests/test_resample.py

@@ -37,6 +37,11 @@ def test_resample_function_impl(self, img, matrix, expected):
         out = resample(convert_to_tensor(img), matrix, {"lazy_shape": img.shape[1:], "lazy_padding_mode": "border"})
         assert_allclose(out[0], expected, type_test=False)
 
+        img = convert_to_tensor(img, dtype=torch.uint8)
+        out = resample(img, matrix, {"lazy_resample_mode": "auto", "lazy_dtype": torch.float})
+        out_1 = resample(img, matrix, {"lazy_resample_mode": "other value", "lazy_dtype": torch.float})
+        self.assertIs(out.dtype, out_1.dtype)  # testing dtype in different lazy_resample_mode
+
 
 if __name__ == "__main__":
     unittest.main()