fix HistogramFixedWidth

mindspore/ops/operations/array_ops.py

@@ -1371,11 +1371,9 @@ class UnsortedSegmentMin(PrimitiveWithInfer):
     """
     Computes the minimum along segments of a tensor.
 
-    If the given segment_ids is negative, the value will be ignored.
-
     Inputs:
         - **input_x** (Tensor) - The shape is :math:`(x_1, x_2, ..., x_R)`.
-        - **segment_ids** (Tensor) - A `1-D` tensor whose shape is :math:`(x_1)`.
+        - **segment_ids** (Tensor) - A `1-D` tensor whose shape is :math:`(x_1)`, the value should be >= 0.
         - **num_segments** (int) - The value spcifies the number of distinct `segment_ids`.
 
     Outputs:

mindspore/ops/operations/math_ops.py

@@ -1356,7 +1356,7 @@ class HistogramFixedWidth(PrimitiveWithInfer):
 
     Args:
         dtype (string): An optional attribute. Must be one of the following types: "int32", "int64". Default: "int32".
-        nbins (Tensor): Number of histogram bins, the type is int32.
+        nbins (int): Number of histogram bins, the type is positive integer.
 
     Inputs:
         - **x** (Tensor) - Numeric Tensor. Must be one of the following types: int32, float32, float16.
@@ -1377,6 +1377,7 @@ class HistogramFixedWidth(PrimitiveWithInfer):
     @prim_attr_register
     def __init__(self, nbins, dtype='int32'):
         self.nbins = validator.check_value_type("nbins", nbins, [int], self.name)
+        validator.check_integer("nbins", nbins, 1, Rel.GE, self.name)
         valid_values = ['int32', 'int64']
         self.dtype = validator.check_string("dtype", dtype, valid_values, self.name)
         self.init_prim_io_names(inputs=['x', 'range'], outputs=['y'])

mindspore/ops/operations/nn_ops.py

@@ -1738,6 +1738,8 @@ class SGD(PrimitiveWithInfer):
     @prim_attr_register
     def __init__(self, dampening=0.0, weight_decay=0.0, nesterov=False):
         validator.check_value_type("nesterov", nesterov, [bool], self.name)
+        if nesterov and dampening != 0:
+            raise ValueError(f"Nesterov need zero dampening!")
         self.init_prim_io_names(inputs=['parameters', 'gradient', 'learning_rate', 'accum', 'momentum', 'stat'],
                                 outputs=['output'])
 
@@ -2151,7 +2153,8 @@ class ResizeBilinear(PrimitiveWithInfer):
                        rescale by `new_height / height`. Default: False.
 
     Inputs:
-        - **input** (Tensor) - Image to be resized. Tensor of shape `(N_i, ..., N_n, height, width)`.
+        - **input** (Tensor) - Image to be resized. Tensor of shape `(N_i, ..., N_n, height, width)`,
+          with data type of float32 or float16.
 
     Outputs:
         Tensor, resized image. Tensor of shape `(N_i, ..., N_n, new_height, new_width)` in `float32`.