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!6118 fix bugs of op Concat, Norm, InTopK, L1Loss and InplaceAdd etc.

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Merge pull request !6118 from lihongkang/v2_master
pull/6118/MERGE
mindspore-ci-bot 5 years ago committed by Gitee
parent bca498eb53 569693af52
commit de9375ba1c
6 changed files with 32 additions and 32 deletions
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6
mindspore/nn/layer/basic.py
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@ -335,7 +335,7 @@ class Norm(Cell):
Computes the norm of vectors, currently including Euclidean norm, i.e., :math:`L_2`-norm.
Args:
axis (tuple): The axis over which to compute vector norms. Default: ().
axis (Union[tuple, int]): The axis over which to compute vector norms. Default: ().
keep_dims (bool): If True, the axis indicated in `axis` are kept with size 1. Otherwise,
the dimensions in `axis` are removed from the output shape. Default: False.
@ -348,12 +348,14 @@ class Norm(Cell):
Examples:
>>> net = nn.Norm(axis=0)
>>> input = Tensor(np.random.randint(0, 10, [4, 16]), mindspore.float32)
>>> input = Tensor(np.random.randint(0, 10, [2, 4]), mindspore.float32)
>>> net(input)
[2.236068 9.848858 4. 5.656854]
"""
def __init__(self, axis=(), keep_dims=False):
super(Norm, self).__init__()
validator.check_value_type("keep_dims", keep_dims, [bool], self.cls_name)
self.axis = axis
self.keep_dims = keep_dims
self.reduce_sum = P.ReduceSum(True)

4
mindspore/nn/layer/conv.py
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@ -884,8 +884,8 @@ class DepthwiseConv2d(Cell):
Tensor of shape :math:`(N, C_{out}, H_{out}, W_{out})`.
Examples:
>>> net = nn.DepthwiseConv2d(120, 240, 4, has_bias=False, weight_init='normal')
>>> input = Tensor(np.ones([1, 120, 1024, 640]), mindspore.float32)
>>> net = nn.DepthwiseConv2d(240, 240, 4, group=None, has_bias=False, weight_init='normal')
>>> input = Tensor(np.ones([1, 240, 1024, 640]), mindspore.float32)
>>> net(input).shape
(1, 240, 1024, 640)
"""

7
mindspore/nn/loss/loss.py
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@ -83,8 +83,10 @@ class L1Loss(_Loss):
Default: "mean".
Inputs:
- **input_data** (Tensor) - Tensor of shape :math:`(x_1, x_2, ..., x_R)`.
- **target_data** (Tensor) - Tensor of shape :math:`(y_1, y_2, ..., y_S)`.
- **input_data** (Tensor) - Tensor of shape :math:`(x_1, x_2, ..., x_R)`. The data type should be float16 or
float32.
- **target_data** (Tensor) - Tensor of shape :math:`(y_1, y_2, ..., y_S)`. The data type should be float16 or
float32.
Outputs:
Tensor, loss float tensor.
@ -94,6 +96,7 @@ class L1Loss(_Loss):
>>> input_data = Tensor(np.array([1, 2, 3]), mindspore.float32)
>>> target_data = Tensor(np.array([1, 2, 2]), mindspore.float32)
>>> loss(input_data, target_data)
0.33333334
"""
def __init__(self, reduction='mean'):
super(L1Loss, self).__init__(reduction)

30
mindspore/ops/operations/array_ops.py
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@ -300,6 +300,7 @@ class IsSubClass(PrimitiveWithInfer):
Examples:
>>> result = P.IsSubClass()(mindspore.int32, mindspore.intc)
True
"""
@prim_attr_register
@ -1099,7 +1100,7 @@ class InvertPermutation(PrimitiveWithInfer):
- **input_x** (Union(tuple[int], list[int]) - The input is constructed by multiple
integers, i.e., :math:`(y_1, y_2, ..., y_S)` representing the indices.
The values must include 0. There can be no duplicate values or negative values.
Only constant value is allowed.
Only constant value is allowed. The maximum value msut be equal to length of input_x.
Outputs:
tuple[int]. It has the same length as the input.
@ -1599,7 +1600,7 @@ class Concat(PrimitiveWithInfer):
Note:
The input data is a tuple of tensors. These tensors have the same rank `R`. Set the given axis as `m`, and
:math:`0 \le m < N`. Set the number of input tensors as `N`. For the :math:`i`-th tensor :math:`t_i`, it has
:math:`0 \le m < R`. Set the number of input tensors as `N`. For the :math:`i`-th tensor :math:`t_i`, it has
the shape of :math:`(x_1, x_2, ..., x_{mi}, ..., x_R)`. :math:`x_{mi}` is the :math:`m`-th dimension of the
:math:`i`-th tensor. Then, the shape of the output tensor is
@ -3451,7 +3452,8 @@ class InplaceUpdate(PrimitiveWithInfer):
Updates specified rows with values in `v`.
Args:
indices (Union[int, tuple]): Indices into the left-most dimension of `x`.
indices (Union[int, tuple]): Indices into the left-most dimension of `x`, and determines which rows of x
to update with v. It is a int or tuple, whose value is in [0, the first dimension size of x).
Inputs:
- **x** (Tensor) - A tensor which to be inplace updated. It can be one of the following data types:
@ -3463,22 +3465,14 @@ class InplaceUpdate(PrimitiveWithInfer):
Tensor, with the same type and shape as the input `x`.
Examples:
>>> x = Tensor(np.arange(24).reshape(3, 4, 2), mindspore.float32)
>>> v = Tensor(np.arange(-8, 8).reshape(2, 4, 2), mindspore.float32)
>>> inplace_update = P.InplaceUpdate((0, 2))
>>> indices = (0, 1)
>>> x = Tensor(np.array([[1, 2], [3, 4], [5, 6]]), mindspore.float32)
>>> v = Tensor(np.array([[0.5, 1.0], [1.0, 1.5]]), mindspore.float32)
>>> inplace_update = P.InplaceUpdate(indices)
>>> result = inplace_update(x, v)
[[[-8. -7.]
[-6. -5.]
[-4. -3.]
[-2. -1.]]
[[ 8. 9.]
[10. 11.]
[12. 13.]
[14. 15.]]
[[ 0. 1.]
[ 2. 3.]
[ 4. 5.]
[ 6. 7.]]]
[[0.5, 1.0],
[1.0, 1.5],
[5.0, 6.0]]
"""
@prim_attr_register

10
mindspore/ops/operations/math_ops.py
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@ -965,7 +965,7 @@ class InplaceAdd(PrimitiveWithInfer):
the first dimension, which must be the same as indices's size. It has the same data type with `input_x`.
Outputs:
Tensor, has the same shape and dtype as input.
Tensor, has the same shape and dtype as input_x.
Examples:
>>> indices = (0, 1)
@ -1011,7 +1011,7 @@ class InplaceAdd(PrimitiveWithInfer):
class InplaceSub(PrimitiveWithInfer):
"""
Subtracts v into specified rows of x. Computes y = x; y[i, :] -= v; return y.
Subtracts v into specified rows of x. Computes y = x; y[i, :] -= v.
Args:
indices (Union[int, tuple]): Indices into the left-most dimension of x, and determines which rows of x
@ -1023,7 +1023,7 @@ class InplaceSub(PrimitiveWithInfer):
the first dimension, which must be the same as indices's size. It has the same data type with `input_x`.
Outputs:
Tensor, has the same shape and dtype as input.
Tensor, has the same shape and dtype as input_x.
Examples:
>>> indices = (0, 1)
@ -1496,7 +1496,7 @@ class Log(PrimitiveWithInfer):
Returns the natural logarithm of a tensor element-wise.
Inputs:
- **input_x** (Tensor) - The input tensor.
- **input_x** (Tensor) - The input tensor. With float16 or float32 data type. The value must be greater than 0.
Outputs:
Tensor, has the same shape as the `input_x`.
@ -1533,7 +1533,7 @@ class Log1p(PrimitiveWithInfer):
Returns the natural logarithm of one plus the input tensor element-wise.
Inputs:
- **input_x** (Tensor) - The input tensor. With float16 or float32 data type.
- **input_x** (Tensor) - The input tensor. With float16 or float32 data type. The value must be greater than -1.
Outputs:
Tensor, has the same shape as the `input_x`.

7
mindspore/ops/operations/nn_ops.py
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@ -5541,7 +5541,8 @@ class InTopK(PrimitiveWithInfer):
Inputs:
- **x1** (Tensor) - A 2D Tensor defines the predictions of a batch of samples with float16 or float32 data type.
- **x2** (Tensor) - A 1D Tensor defines the labels of a batch of samples with int32 data type.
- **x2** (Tensor) - A 1D Tensor defines the labels of a batch of samples with int32 data type. The size of x2
must be equal to x1's first dimension.
Outputs:
Tensor has 1 dimension of type bool and the same shape with `x2`. For labeling sample `i` in `x2`,
@ -5568,8 +5569,8 @@ class InTopK(PrimitiveWithInfer):
return mstype.tensor_type(mstype.bool_)
def infer_shape(self, x1_shape, x2_shape):
validator.check("x1", len(x1_shape), "", 2, Rel.EQ, self.name)
validator.check("x2", len(x2_shape), "", 1, Rel.EQ, self.name)
validator.check("x1 shape", len(x1_shape), "", 2, Rel.EQ, self.name)
validator.check("x2 shape", len(x2_shape), "", 1, Rel.EQ, self.name)
validator.check("size of x2", x2_shape[0], "x1's first dimension", x1_shape[0], Rel.EQ, self.name)
return x2_shape

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