Modify the problem of dividing by 0

mindspore/dataset/core/validator_helpers.py

@@ -60,6 +60,14 @@ def check_value(value, valid_range, arg_name=""):
                                                                                    valid_range[1]))
 
 
+def check_value_normalize_std(value, valid_range, arg_name=""):
+    arg_name = pad_arg_name(arg_name)
+    if value <= valid_range[0] or value > valid_range[1]:
+        raise ValueError(
+            "Input {0}is not within the required interval of ({1} to {2}).".format(arg_name, valid_range[0],
+                                                                                   valid_range[1]))
+
+
 def check_range(values, valid_range, arg_name=""):
     arg_name = pad_arg_name(arg_name)
     if not valid_range[0] <= values[0] <= values[1] <= valid_range[1]:

mindspore/dataset/transforms/vision/c_transforms.py

@@ -105,7 +105,7 @@ class RandomSharpness(cde.RandomSharpnessOp):
 
     Raises:
         TypeError : If degrees is not a list or tuple.
-        ValueError: If degrees is not positive.
+        ValueError: If degrees is negative.
         ValueError: If degrees is in (max, min) format instead of (min, max).
 
     Examples:

mindspore/dataset/transforms/vision/py_transforms.py

@@ -224,7 +224,7 @@ class Normalize:
     """
     Normalize the input Numpy image array of shape (C, H, W) with the given mean and standard deviation.
 
-    The values of the array need to be in range [0.0, 1.0].
+    The values of the array need to be in range (0.0, 1.0].
 
     Args:
         mean (sequence): List or tuple of mean values for each channel, w.r.t channel order.

mindspore/dataset/transforms/vision/validators.py

@@ -22,7 +22,7 @@ from mindspore._c_dataengine import TensorOp
 from .utils import Inter, Border, ImageBatchFormat
 from ...core.validator_helpers import check_value, check_uint8, FLOAT_MAX_INTEGER, check_pos_float32, \
     check_2tuple, check_range, check_positive, INT32_MAX, parse_user_args, type_check, type_check_list, \
-    check_tensor_op, UINT8_MAX
+    check_tensor_op, UINT8_MAX, check_value_normalize_std
 
 
 def check_crop_size(size):
@@ -92,7 +92,7 @@ def check_normalize_py_param(mean, std):
     for mean_value in mean:
         check_value(mean_value, [0., 1.], "mean_value")
     for std_value in std:
-        check_value(std_value, [0., 1.], "std_value")
+        check_value_normalize_std(std_value, [0., 1.], "std_value")
 
 
 def check_fill_value(fill_value):