8267 fix normalize intensity (#8286)

Fixes #8267 .

### Description

Fix channel-wise intensity normalization for integer type inputs. 

### Types of changes
<!--- Put an `x` in all the boxes that apply, and remove the not
applicable items -->
- [ ] Non-breaking change (fix or new feature that would not break
existing functionality).
- [x] Breaking change (fix or new feature that would cause existing
functionality to change).
- [x] New tests added to cover the changes.
- [x] Integration tests passed locally by running `./runtests.sh -f -u
--net --coverage`.
- [x] Quick tests passed locally by running `./runtests.sh --quick
--unittests --disttests`.
- [ ] In-line docstrings updated.
- [x] Documentation updated, tested `make html` command in the `docs/`
folder.

---------

Signed-off-by: advcu987 <adrianvoicu.tm@gmail.com>
Signed-off-by: advcu <65158236+advcu987@users.noreply.github.com>
Co-authored-by: Eric Kerfoot <17726042+ericspod@users.noreply.github.com>

Author: advcu987

monai/transforms/intensity/array.py

@@ -821,6 +821,7 @@ class NormalizeIntensity(Transform):
     mean and std on each channel separately.
     When `channel_wise` is True, the first dimension of `subtrahend` and `divisor` should
     be the number of image channels if they are not None.
+    If the input is not of floating point type, it will be converted to float32
 
     Args:
         subtrahend: the amount to subtract by (usually the mean).
@@ -907,6 +908,9 @@ def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:
             if self.divisor is not None and len(self.divisor) != len(img):
                 raise ValueError(f"img has {len(img)} channels, but divisor has {len(self.divisor)} components.")
 
+            if not img.dtype.is_floating_point:
+                img, *_ = convert_data_type(img, dtype=torch.float32)
+
             for i, d in enumerate(img):
                 img[i] = self._normalize(  # type: ignore
                     d,

tests/test_normalize_intensity.py

@@ -108,6 +108,27 @@ def test_channel_wise(self, im_type):
         normalized = normalizer(input_data)
         assert_allclose(normalized, im_type(expected), type_test="tensor")
 
+    @parameterized.expand([[p] for p in TEST_NDARRAYS])
+    def test_channel_wise_int(self, im_type):
+        normalizer = NormalizeIntensity(nonzero=True, channel_wise=True)
+        input_data = im_type(torch.arange(1, 25).reshape(2, 3, 4))
+        expected = np.array(
+            [
+                [
+                    [-1.593255, -1.3035723, -1.0138896, -0.7242068],
+                    [-0.4345241, -0.1448414, 0.1448414, 0.4345241],
+                    [0.7242068, 1.0138896, 1.3035723, 1.593255],
+                ],
+                [
+                    [-1.593255, -1.3035723, -1.0138896, -0.7242068],
+                    [-0.4345241, -0.1448414, 0.1448414, 0.4345241],
+                    [0.7242068, 1.0138896, 1.3035723, 1.593255],
+                ],
+            ]
+        )
+        normalized = normalizer(input_data)
+        assert_allclose(normalized, im_type(expected), type_test="tensor", rtol=1e-7, atol=1e-7)  # tolerance
+
     @parameterized.expand([[p] for p in TEST_NDARRAYS])
     def test_value_errors(self, im_type):
         input_data = im_type(np.array([[0.0, 3.0, 0.0, 4.0], [0.0, 4.0, 0.0, 5.0]]))