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fix some issues

This commit is contained in:
lilinjie 2022-09-05 02:22:47 +08:00
parent 1b06078d05
commit deb6f502e3
3 changed files with 24 additions and 14 deletions
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17
docs/api/api_python/ops/mindspore.ops.StridedSlice.rst
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@ -3,15 +3,18 @@
.. py:class:: mindspore.ops.StridedSlice(begin_mask=0, end_mask=0, ellipsis_mask=0, new_axis_mask=0, shrink_axis_mask=0)
输入Tensor根据步长和索引进行切片提取。
对输入Tensor根据步长和索引进行切片提取。
给定一个输入Tensor，此操作会插入长度为1的维度。从给定的 `input_tensor` 中提取大小为 `(end-begin)/stride` 的片段。从起始位置开始，根据步长和索引进行提取，直到所有维度都不小于结束位置为止。
该算子在给定的 `input_tensor` 中提取大小为 `(end-begin)/stride` 的片段。从起始位置开始，根据步长和索引进行提取，直到所有维度的都不小于结束位置为止。
给定一个 `input_x[m1, m2, ...、mn]` 。 `begin` 、 `end` 和 `strides` 是长度为n的向量。
在每个掩码字段中（`begin_mask`、`end_mask`、`ellipsis_mask`、`new_axis_mask`、`shrink_ax_mask`)，第i位将对应于第i个m。
在每个掩码字段中（`begin_mask`、`end_mask`、`ellipsis_mask`、`new_axis_mask`、`shrink_axis_mask`)，第i位将对应于第i个m。
如果设置了 `begin_mask` 的第i位，则忽略 `begin[i]` ，而使用该维度中最有可能的取值范围。除了结尾的取值范围， `end_mask` 是类似的。
对于每个特定的mask，内部先将其转化为二进制表示， 然后倒序排布后进行计算。比如说对于一个5*6*7的Tensor，mask设置为3， 3转化为二进制表示为ob011， 倒序
后为ob110， 则该mask只在第0维和第1维产生作用， 下面各自举例说明。
如果设置了 `begin_mask` 的第i位，则忽略 `begin[i]`，而使用该维度的最大可能取值范围， `begin_mask` 使用方法与之类似。
对于5*6*7的Tensor， `x[2:,:3,:]` 等同于 `x[2:5,0:3,0:7]` 。
@ -21,11 +24,11 @@
如果设置了 `new_axis_mask` 的第i位，则忽略 `begin` 、 `end` 和 `strides` ，并在输出Tensor的指定位置添加新的长度为1的维度。
对于5*6*7的Tensor， `x[:2, newaxis, :6]` 将产生一个shape为 :math:`(2, 1, 7)` 的Tensor。
对于5*6*7的Tensor， `x[:2, newaxis, :6]` 将产生一个shape为 :math:`(2, 1, 6, 7)` 的Tensor。
如果设置了 `shrink_axis_mask` 的第i位，则第i个大小将维度收缩1，并忽略 `begin[i]` 、 `end[i]` 和 `strides[i]` 索引处的值。
如果设置了 `shrink_axis_mask` 的第i位，则第i维被收缩掉，并忽略 `begin[i]` 、 `end[i]` 和 `strides[i]` 索引处的值。
对于5*6*7的Tensor， `x[:, 5, :]` 将使得 `shrink_axis_mask` 等于4。
对于5*6*7的Tensor， `x[:, 5, :]` 相当于将 `shrink_axis_mask` 设置为2, 使得输出shape为:math:`(5, 7)` 。
.. note::
步长可能为负值，这会导致反向切片。 `begin` 、 `end` 和 `strides` 的shape必须相同。 `begin` 和 `end` 是零索引。 `strides` 的元素必须非零。

4
mindspore/core/ops/sign.cc
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@ -54,7 +54,9 @@ AbstractBasePtr SignInfer(const abstract::AnalysisEnginePtr &, const PrimitivePt
const int64_t input_num = 1;
CheckAndConvertUtils::CheckInputArgs(input_args, kEqual, input_num, primitive->name());
return abstract::MakeAbstract(SignInferShape(primitive, input_args), SignInferType(primitive, input_args));
auto infer_type = SignInferType(primitive, input_args);
auto infer_shape = SignInferShape(primitive, input_args);
return abstract::MakeAbstract(infer_shape, infer_type);
}
REGISTER_PRIMITIVE_EVAL_IMPL(Sign, prim::kPrimSign, SignInfer, nullptr, true);
} // namespace ops

17
mindspore/python/mindspore/ops/operations/array_ops.py
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@ -3129,7 +3129,6 @@ class StridedSlice(PrimitiveWithInfer):
Extracts a strided slice of a tensor.
Given an input tensor, this operation inserts a dimension of length 1 at the dimension.
This operation extracts a fragment of size (end-begin)/stride from the given 'input_tensor'.
Starting from the beginning position, the fragment continues adding stride to the index until
all dimensions are not less than the ending position.
@ -3139,26 +3138,32 @@ class StridedSlice(PrimitiveWithInfer):
In each mask field (`begin_mask`, `end_mask`, `ellipsis_mask`, `new_axis_mask`, `shrink_axis_mask`)
the ith bit will correspond to the ith m.
For each specific mask, it will be converted to a binary representation internally, and then
reverse the result to start the calculation. For a 5*6*7 tensor with a given mask value of 3 which
can be represented as ob011. Reverse that we get ob110, which implies the first and second dim of the
original tensor will be effected by this mask. See examples below:
If the ith bit of `begin_mask` is set, `begin[i]` is ignored and the fullest possible range in that dimension
is used instead. `end_mask` is analogous, except with the end range.
As for a 5*6*7 tensor, `x[2:,:3,:]` is equivalent to `x[2:5,0:3,0:7]`.
For a 5*6*7 tensor, `x[2:,:3,:]` is equivalent to `x[2:5,0:3,0:7]`.
If the ith bit of `ellipsis_mask` is set, as many unspecified dimensions as needed will be inserted between
other dimensions. Only one non-zero bit is allowed in `ellipsis_mask`.
As for a 5*6*7*8 tensor, `x[2:,...,:6]` is equivalent to `x[2:5,:,:,0:6]`.
For a 5*6*7*8 tensor, `x[2:,...,:6]` is equivalent to `x[2:5,:,:,0:6]`.
`x[2:,...]` is equivalent to `x[2:5,:,:,:]`.
If the ith bit of `new_axis_mask` is set, `begin`, `end` and `strides` are ignored and a new length 1
dimension is added at the specified position in the output tensor.
As for a 5*6*7 tensor, `x[:2, newaxis, :6]` will produce a tensor with shape :math:`(2, 1, 7)` .
For a 5*6*7 tensor, `x[:2, newaxis, :6]` will produce a tensor with shape :math:`(2, 1, 6, 7)` .
If the ith bit of `shrink_axis_mask` is set, ith size shrinks the dimension by 1, taking on the value
If the ith bit of `shrink_axis_mask` is set, dimension i will be shrunk to 0, taking on the value
at index `begin[i]`, `end[i]` and `strides[i]` are ignored.
As for a 5*6*7 tensor, `x[:, 5, :]` will result in `shrink_axis_mask` equal to 4.
For a 5*6*7 tensor, `x[:, 5, :]` is equivalent to setting the `shrink_axis_mask` to 2 and results in
an output shape of :math:`(5, 7)`.
Note:
The stride may be negative value, which causes reverse slicing.