!5946 fix bugs of op Exp, IOU, GroupNorm and Dropout

Merge pull request !5946 from lihongkang/lhk_master

mindspore/nn/layer/basic.py

@@ -65,18 +65,22 @@ class Dropout(Cell):
         dtype (:class:`mindspore.dtype`): Data type of input. Default: mindspore.float32.
 
     Raises:
-        ValueError: If `keep_prob` is not in range (0, 1).
+        ValueError: If `keep_prob` is not in range (0, 1].
 
     Inputs:
-        - **input** (Tensor) - An N-D Tensor.
+        - **input** (Tensor) - The input tensor.
 
     Outputs:
         Tensor, output tensor with the same shape as the input.
 
     Examples:
-        >>> x = Tensor(np.ones([20, 16, 50]), mindspore.float32)
+        >>> x = Tensor(np.ones([2, 2, 3]), mindspore.float32)
         >>> net = nn.Dropout(keep_prob=0.8)
         >>> net(x)
+        [[[1.0, 1.0, 1.0],
+          [1.0, 1.0, 1.0]],
+         [[1.0, 1.0, 1.0],
+          [1.0, 1.0, 1.0]]]
     """
 
     def __init__(self, keep_prob=0.5, seed0=0, seed1=0, dtype=mstype.float32):
@@ -84,6 +88,7 @@ class Dropout(Cell):
         if keep_prob <= 0 or keep_prob > 1:
             raise ValueError("dropout probability should be a number in range (0, 1], but got {}".format(keep_prob))
         validator.check_subclass("dtype", dtype, mstype.number_type, self.cls_name)
+        validator.check_value_type('keep_prob', keep_prob, [float], self.cls_name)
         self.keep_prob = keep_prob
         self.seed0 = seed0
         self.seed1 = seed1
@@ -107,8 +112,7 @@ class Dropout(Cell):
             return x
 
         shape = self.get_shape(x)
-        dtype = P.DType()(x)
-        keep_prob = self.cast(self.keep_prob, dtype)
+        keep_prob = self.cast(self.keep_prob, mstype.float32)
         output = self.dropout_gen_mask(shape, keep_prob)
         return self.dropout_do_mask(x, output, keep_prob)
 

mindspore/nn/layer/normalization.py

@@ -585,9 +585,18 @@ class GroupNorm(Cell):
         Tensor, the normalized and scaled offset tensor, has the same shape and data type as the `input_x`.
 
     Examples:
-        >>> goup_norm_op = nn.GroupNorm(16, 64)
-        >>> x = Tensor(np.ones([1, 64, 256, 256], np.float32))
+        >>> goup_norm_op = nn.GroupNorm(2, 2)
+        >>> x = Tensor(np.ones([1, 2, 4, 4], np.float32))
         >>> goup_norm_op(x)
+        [[[[0. 0. 0. 0.]
+           [0. 0. 0. 0.]
+           [0. 0. 0. 0.]
+           [0. 0. 0. 0.]]
+
+          [[0. 0. 0. 0.]
+           [0. 0. 0. 0.]
+           [0. 0. 0. 0.]
+           [0. 0. 0. 0.]]]]
     """
 
     def __init__(self, num_groups, num_channels, eps=1e-05, affine=True, gamma_init='ones', beta_init='zeros'):

mindspore/ops/operations/array_ops.py

@@ -1360,7 +1360,7 @@ class Tile(PrimitiveWithInfer):
 
         - **multiples** (tuple[int]) - The input tuple is constructed by multiple
           integers, i.e., :math:`(y_1, y_2, ..., y_S)`. The length of `multiples`
-          can't be smaller than the length of shape in `input_x`.
+          can't be smaller than the length of shape in `input_x`. Only constant value is allowed.
 
     Outputs:
         Tensor, has the same type as the `input_x`.
@@ -1400,7 +1400,7 @@ class Tile(PrimitiveWithInfer):
     def __infer__(self, x, multiples):
         multiples_v = multiples['value']
         x_shp = x['shape']
-        validator.check_value_type("shape", multiples_v, [tuple], self.name)
+        validator.check_value_type("multiples", multiples_v, [tuple], self.name)
         for i, multiple in enumerate(multiples_v):
             validator.check_value_type("multiples[%d]" % i, multiple, [int], self.name)
         validator.check_value_type("x[\'dtype\']", x["dtype"], mstype.tensor_type, self.name)

mindspore/ops/operations/math_ops.py

@@ -1382,10 +1382,10 @@ class Exp(PrimitiveWithInfer):
     Returns exponential of a tensor element-wise.
 
     Inputs:
-        - **input_x** (Tensor) - The input tensor.
+        - **input_x** (Tensor) - The input tensor. The data type mast be float16 or float32.
 
     Outputs:
-        Tensor, has the same shape as the `input_x`.
+        Tensor, has the same shape and dtype as the `input_x`.
 
     Examples:
         >>> input_x = Tensor(np.array([1.0, 2.0, 4.0]), mindspore.float32)
@@ -1452,7 +1452,7 @@ class HistogramFixedWidth(PrimitiveWithInfer):
     width and determined by the arguments range and nbins.
 
     Args:
-        dtype (string): An optional attribute. Must be one of the following types: "int32", "int64". Default: "int32".
+        dtype (str): An optional attribute. Must be one of the following types: "int32", "int64". Default: "int32".
         nbins (int): The number of histogram bins, the type is a positive integer.
 
     Inputs:

mindspore/ops/operations/other_ops.py

@@ -264,6 +264,9 @@ class IOU(PrimitiveWithInfer):
         >>> anchor_boxes = Tensor(np.random.randint(1.0, 5.0, [3, 4]), mindspore.float16)
         >>> gt_boxes = Tensor(np.random.randint(1.0, 5.0, [3, 4]), mindspore.float16)
         >>> iou(anchor_boxes, gt_boxes)
+        [[0.0, 65504, 65504],
+         [0.0, 0.0, 0.0],
+         [0.22253, 0.0, 0.0]]
     """
 
     @prim_attr_register