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!39203 【算子】【mirror pad】修复资料问题 

Merge pull request !39203 from yefeng/op_mirror_pad_fix_doc
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i-robot 2022-07-30 07:26:40 +00:00 committed by Gitee
parent 17675daf15 c7fff34215
commit 85f64335fe
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2 changed files with 7 additions and 28 deletions
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1
mindspore/python/mindspore/ops/_vmap/vmap_nn_ops.py
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@ -1016,7 +1016,6 @@ def get_mirror_pad_vmap_rule(prim, axis_size):
pad_dim = F.shape(paddings)[0]
x_ndim = F.rank(x)
# pylint: disable=chained-comparison
if pad_dim == x_ndim and x_ndim <= input_max_dim:
out = prim(x, paddings)
elif x_ndim > input_max_dim:

34
mindspore/python/mindspore/ops/function/nn_func.py
View File

@ -1195,19 +1195,17 @@ def mirror_pad(input_x, paddings, mode):
Pads the input tensor according to the paddings and mode.
Args:
mode (str): Specifies the padding mode. The optional values are "REFLECT" and "SYMMETRIC".
Default: "REFLECT".
Inputs:
- **input_x** (Tensor) - Tensor of shape :math:`(N, *)`, where :math:`*` means, any number of
**input_x** (Tensor) - Tensor of shape :math:`(N, *)`, where :math:`*` means, any number of
additional dimensions.
- **paddings** (Tensor) - Paddings requires constant tensor. The value of `paddings` is a
**paddings** (Tensor) - Paddings requires constant tensor. The value of `paddings` is a
matrix(list), and its shape is (N, 2). N is the rank of input data. All elements of paddings
are int type. For the input in the `D` th dimension, paddings[D, 0] indicates how many sizes
to be extended ahead of the input tensor in the `D` th dimension, and paddings[D, 1]
indicates how many sizes to be extended behind the input tensor in the `D` th dimension. Both
paddings[D, 0] and paddings[D, 1] must be no greater than input_x.dim_size(D)
(or input_x.dim_size(D) - 1) if mode is SYMMETRIC (if REFLECT, respectively).
mode (str): Specifies the padding mode. The optional values are "REFLECT" and "SYMMETRIC".
Default: "REFLECT".
Outputs:
@ -1232,34 +1230,16 @@ def mirror_pad(input_x, paddings, mode):
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> from mindspore import Tensor, nn, ops
>>> # case1: mode="REFLECT"
>>> class Net(nn.Cell):
... def __init__(self, mode):
... super(Net, self).__init__()
... self.pad = ops.MirrorPad(mode=mode)
... self.paddings = Tensor([[1, 1], [2, 2]])
... def construct(self, input_x):
... return self.pad(input_x, self.paddings)
...
>>> input_x = Tensor([[1,2,3], [4,5,6], [7,8,9]])
>>> pad = Net("REFLECT")
>>> output = pad(input_x)
>>> mode = "REFLECT"
>>> paddings = Tensor([[1, 1], [2, 2]])
>>> output = ops.mirror_pad(input_x, paddings, mode)
>>> print(output)
[[6 5 4 5 6 5 4]
[3 2 1 2 3 2 1]
[6 5 4 5 6 5 4]
[9 8 7 8 9 8 7]
[6 5 4 5 6 5 4]]
>>> # case2: mode="SYMMETRIC"
>>> pad = Net("SYMMETRIC")
>>> output = pad(input_x)
>>> print(output)
[[2 1 1 2 3 3 2]
[2 1 1 2 3 3 2]
[5 4 4 5 6 6 5]
[8 7 7 8 9 9 8]
[8 7 7 8 9 9 8]]
"""
_mirror_pad = _get_cache_prim(P.MirrorPad)(mode)