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!18125 fix DynamicStitch check and StandardNormal docs 

Merge pull request !18125 from yanzhenxiang2020/fix_dynamicstitch_standard
This commit is contained in:
i-robot 2021-06-11 10:19:45 +08:00 committed by Gitee
parent 7c85442e50 32481cdb74
commit f87f2ea121
4 changed files with 11 additions and 9 deletions
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4
mindspore/core/abstract/prim_arrays.cc
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@ -1187,8 +1187,8 @@ AbstractBasePtr InferImplDynamicStitch(const AnalysisEnginePtr &, const Primitiv
for (size_t i = 1; i < data.size(); ++i) {
auto indicesi_shape = CheckAndConvertUtils::ConvertShapePtrToShapeMap(indices[i]->BuildShape())[kShape];
auto datai_shape = CheckAndConvertUtils::ConvertShapePtrToShapeMap(data[i]->BuildShape())[kShape];
if (indicesi_shape.size() >= datai_shape.size()) {
MS_LOG(EXCEPTION) << "The rank of indices[i] must be < rank of data[i]!";
if (indicesi_shape.size() > datai_shape.size()) {
MS_LOG(EXCEPTION) << "The rank of indices[i] must be <= rank of data[i]!";
}
indices_total_size += indicesi_shape.size();
}

2
mindspore/ops/operations/_inner_ops.py
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@ -1082,7 +1082,7 @@ class DynamicStitch(PrimitiveWithCheck):
for i in range(0, indices_num):
indices_dim = len(indices_shape[i])
data_dim = len(data_shape[i])
validator.check(f"dim of indices[{i}]", indices_dim, f"dim of data[{i}]", data_dim, Rel.LT, self.name)
validator.check(f"dim of indices[{i}]", indices_dim, f"dim of data[{i}]", data_dim, Rel.LE, self.name)
if data_shape[i][:indices_dim] != data_shape[i][:indices_dim]:
raise ValueError(f"data[{i}].shape: {data_shape} does not start with indices[{i}].shape: {data_shape}")

2
mindspore/ops/operations/nn_ops.py
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@ -6893,8 +6893,6 @@ class Dropout(PrimitiveWithCheck):
>>> output, mask = dropout(x)
>>> print(output)
[0. 32. 0. 0.]
>>> print(mask)
[0. 1. 0. 0.]
"""
@prim_attr_register

12
mindspore/ops/operations/random_ops.py
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@ -24,6 +24,9 @@ class StandardNormal(PrimitiveWithInfer):
r"""
Generates random numbers according to the standard Normal (or Gaussian) random number distribution.
Returns the tensor with the given shape, the random numbers in it drawn from normal distributions
whose mean is 0 and standard deviation is 1.
Args:
seed (int): Random seed, must be non-negative. Default: 0.
seed2 (int): Random seed2, must be non-negative. Default: 0.
@ -43,12 +46,13 @@ class StandardNormal(PrimitiveWithInfer):
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> shape = (4, 16)
>>> shape = (3, 4)
>>> stdnormal = ops.StandardNormal(seed=2)
>>> output = stdnormal(shape)
>>> result = output.shape
>>> print(result)
(4, 16)
>>> print(output)
[[-1.3031056 0.64198005 -0.65207404 -1.767485 ]
[-0.91792876 0.6508565 -0.9098478 -0.14092612]
[ 0.7806437 1.1585592 1.9676613 -0.00440959]]
"""
@prim_attr_register