fix mssim precision when dtype is uint32.

mindspore/nn/layer/image.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 # ============================================================================
 """image"""
+import numbers
 import numpy as np
 import mindspore.common.dtype as mstype
 from mindspore.common.tensor import Tensor
@@ -93,6 +94,16 @@ def _convert_img_dtype_to_float32(img, max_val):
         ret = ret * scale
     return ret
 
+@constexpr
+def _get_dtype_max(dtype):
+    """get max of the dtype"""
+    np_type = mstype.dtype_to_nptype(dtype)
+    if issubclass(np_type, numbers.Integral):
+        dtype_max = np.float64(np.iinfo(np_type).max)
+    else:
+        dtype_max = 1.0
+    return dtype_max
+
 @constexpr
 def _check_input_4d(input_shape, param_name, func_name):
     if len(input_shape) != 4:
@@ -224,9 +235,11 @@ class SSIM(Cell):
         _check_input_dtype(F.dtype(img1), "img1", [mstype.float32, mstype.float16], self.cls_name)
         _check_input_filter_size(F.shape(img1), "img1", self.filter_size, self.cls_name)
         P.SameTypeShape()(img1, img2)
-        max_val = _convert_img_dtype_to_float32(self.max_val, self.max_val)
-        img1 = _convert_img_dtype_to_float32(img1, self.max_val)
-        img2 = _convert_img_dtype_to_float32(img2, self.max_val)
+        dtype_max_val = _get_dtype_max(F.dtype(img1))
+        max_val = F.scalar_cast(self.max_val, F.dtype(img1))
+        max_val = _convert_img_dtype_to_float32(max_val, dtype_max_val)
+        img1 = _convert_img_dtype_to_float32(img1, dtype_max_val)
+        img2 = _convert_img_dtype_to_float32(img2, dtype_max_val)
 
         c1 = (self.k1 * max_val) ** 2
         c2 = (self.k2 * max_val) ** 2
@@ -309,10 +322,13 @@ class MSSSIM(Cell):
     def construct(self, img1, img2):
         _check_input_4d(F.shape(img1), "img1", self.cls_name)
         _check_input_4d(F.shape(img2), "img2", self.cls_name)
+        _check_input_dtype(F.dtype(img1), 'img1', mstype.number_type, self.cls_name)
         P.SameTypeShape()(img1, img2)
-        max_val = _convert_img_dtype_to_float32(self.max_val, self.max_val)
-        img1 = _convert_img_dtype_to_float32(img1, self.max_val)
-        img2 = _convert_img_dtype_to_float32(img2, self.max_val)
+        dtype_max_val = _get_dtype_max(F.dtype(img1))
+        max_val = F.scalar_cast(self.max_val, F.dtype(img1))
+        max_val = _convert_img_dtype_to_float32(max_val, dtype_max_val)
+        img1 = _convert_img_dtype_to_float32(img1, dtype_max_val)
+        img2 = _convert_img_dtype_to_float32(img2, dtype_max_val)
 
         c1 = (self.k1 * max_val) ** 2
         c2 = (self.k2 * max_val) ** 2
@@ -375,9 +391,11 @@ class PSNR(Cell):
         _check_input_4d(F.shape(img1), "img1", self.cls_name)
         _check_input_4d(F.shape(img2), "img2", self.cls_name)
         P.SameTypeShape()(img1, img2)
-        max_val = _convert_img_dtype_to_float32(self.max_val, self.max_val)
-        img1 = _convert_img_dtype_to_float32(img1, self.max_val)
-        img2 = _convert_img_dtype_to_float32(img2, self.max_val)
+        dtype_max_val = _get_dtype_max(F.dtype(img1))
+        max_val = F.scalar_cast(self.max_val, F.dtype(img1))
+        max_val = _convert_img_dtype_to_float32(max_val, dtype_max_val)
+        img1 = _convert_img_dtype_to_float32(img1, dtype_max_val)
+        img2 = _convert_img_dtype_to_float32(img2, dtype_max_val)
 
         mse = P.ReduceMean()(F.square(img1 - img2), (-3, -2, -1))
         psnr = 10 * P.Log()(F.square(max_val) / mse) / F.scalar_log(10.0)