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add raises description for Cast, Diag, Eye, Fill etc. operators.

tags/v1.2.0-rc1
wangshuide2020 4 years ago
parent
b4a3f3c1ba
commit
8a7fa2e651
4 changed files with 457 additions and 28 deletions
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  1. +273
    -22
      mindspore/ops/operations/array_ops.py
  2. +6
    -1
      mindspore/ops/operations/image_ops.py
  3. +176
    -3
      mindspore/ops/operations/math_ops.py
  4. +2
    -2
      tests/ut/python/ops/test_math_ops.py

+ 273
- 22
mindspore/ops/operations/array_ops.py View File

@@ -140,9 +140,6 @@ class ExpandDims(PrimitiveWithInfer):
If the specified axis is a negative number, the index is counted
backward from the end and starts at 1.

Raises:
ValueError: If axis is not an integer or not in the valid range.

Inputs:
- **input_x** (Tensor) - The shape of tensor is :math:`(x_1, x_2, ..., x_R)`.
- **axis** (int) - Specifies the dimension index at which to expand
@@ -153,6 +150,9 @@ class ExpandDims(PrimitiveWithInfer):
Tensor, the shape of tensor is :math:`(1, x_1, x_2, ..., x_R)` if the
value of `axis` is 0. It has the same type as `input_x`.

Raises:
ValueError: If `axis` is not an int or not in the valid range.

Supported Platforms:
``Ascend`` ``GPU`` ``CPU``

@@ -205,6 +205,9 @@ class DType(PrimitiveWithInfer):
Outputs:
mindspore.dtype, the data type of a tensor.

Raises:
TypeError: If `input_x` is not a Tensor.

Supported Platforms:
``Ascend`` ``GPU`` ``CPU``

@@ -231,10 +234,6 @@ class SameTypeShape(PrimitiveWithInfer):
"""
Checks whether the data type and shape of two tensors are the same.

Raises:
TypeError: If the data types of two tensors are not the same.
ValueError: If the shapes of two tensors are not the same.

Inputs:
- **input_x** (Tensor) - The shape of tensor is :math:`(x_1, x_2, ..., x_R)`.
- **input_y** (Tensor) - The shape of tensor is :math:`(x_1, x_2, ..., x_S)`.
@@ -243,6 +242,10 @@ class SameTypeShape(PrimitiveWithInfer):
Tensor, the shape of tensor is :math:`(x_1, x_2, ..., x_R)`,
if data type and shape of `input_x` and `input_y` are the same.

Raises:
TypeError: If the data types of `input_x` and `input_y` are not the same.
ValueError: If the shapes of `input_x` and `input_y` are not the same.

Supported Platforms:
``Ascend`` ``GPU`` ``CPU``

@@ -287,6 +290,10 @@ class Cast(PrimitiveWithInfer):
Outputs:
Tensor, the shape of tensor is the same as `input_x`, :math:`(x_1, x_2, ..., x_R)`.

Raises:
TypeError: If `input_x` is neither Tensor nor Number.
TypeError: If `type` is not a Number.

Supported Platforms:
``Ascend`` ``GPU`` ``CPU``

@@ -363,6 +370,9 @@ class IsSubClass(PrimitiveWithInfer):
Outputs:
bool, the check result.

Raises:
TypeError: If `sub_type` or `type_` is not a Type.

Supported Platforms:
``Ascend`` ``GPU`` ``CPU``

@@ -402,6 +412,9 @@ class IsInstance(PrimitiveWithInfer):
Outputs:
bool, the check result.

Raises:
TypeError: If `type_` is not a Type.

Supported Platforms:
``Ascend`` ``GPU`` ``CPU``

@@ -439,11 +452,6 @@ class Reshape(PrimitiveWithInfer):
"""
Reshapes the input tensor with the same values based on a given shape tuple.

Raises:
ValueError: Given a shape tuple, if it has several -1; or if the product
of its elements is less than or equal to 0 or cannot be divided by the product
of the input tensor shape; or if it does not match the input's array size.

Inputs:
- **input_x** (Tensor) - The shape of tensor is :math:`(x_1, x_2, ..., x_R)`.
- **input_shape** (tuple[int]) - The input tuple is constructed by multiple
@@ -452,6 +460,11 @@ class Reshape(PrimitiveWithInfer):
Outputs:
Tensor, the shape of tensor is :math:`(y_1, y_2, ..., y_S)`.

Raises:
ValueError: Given a shape tuple, if it has several -1; or if the product
of its elements is less than or equal to 0 or cannot be divided by the product
of the input tensor shape; or if it does not match the input's array size.

Supported Platforms:
``Ascend`` ``GPU`` ``CPU``

@@ -544,6 +557,9 @@ class Shape(PrimitiveWithInfer):
tuple[int], the output tuple is constructed by multiple integers,
:math:`(x_1, x_2, ..., x_R)`.

Raises:
TypeError: If `input_x` is not a Tensor.

Supported Platforms:
``Ascend`` ``GPU`` ``CPU``

@@ -577,6 +593,9 @@ class DynamicShape(Primitive):
Outputs:
Tensor[int], 1-dim Tensor of type int32

Raises:
TypeError: If `input_x` is not a Tensor.

Supported Platforms:
``Ascend`` ``GPU`` ``CPU``

@@ -602,9 +621,6 @@ class Squeeze(PrimitiveWithInfer):
Note:
The dimension index starts at 0 and must be in the range `[-input.ndim, input.ndim`.

Raises:
ValueError: If the corresponding dimension of the specified axis does not equal to 1.

Args:
axis (Union[int, tuple(int)]): Specifies the dimension indexes of shape to be removed, which will remove
all the dimensions that are equal to 1. If specified, it must be int32 or int64.
@@ -619,6 +635,7 @@ class Squeeze(PrimitiveWithInfer):
Raises:
TypeError: If `axis` is neither an int nor tuple.
TypeError: If `axis` is a tuple whose elements are not all int.
ValueError: If the corresponding dimension of the specified axis does not equal to 1.

Supported Platforms:
``Ascend`` ``GPU``
@@ -678,6 +695,10 @@ class Transpose(PrimitiveWithInfer):
Tensor, the type of output tensor is the same as `input_x` and the shape of output tensor is decided by the
shape of `input_x` and the value of `input_perm`.

Raises:
TypeError: If `input_perm` is not a tuple.
ValueError: If length of shape of `input_x` is not equal to length of shape of `input_perm`.

Supported Platforms:
``Ascend`` ``GPU`` ``CPU``

@@ -746,6 +767,9 @@ class Unique(Primitive):
ascending order. `idx` is a tensor containing indices of elements in
the input corresponding to the output tensor.

Raises:
TypeError: If `x` is not a Tensor.

Supported Platforms:
``Ascend`` ``GPU`` ``CPU``

@@ -792,6 +816,9 @@ class Gather(PrimitiveWithCheck):
Outputs:
Tensor, the shape of tensor is :math:`(z_1, z_2, ..., z_N)`.

Raises:
TypeError: If `axis` is not an int.

Supported Platforms:
``Ascend`` ``GPU``

@@ -860,6 +887,9 @@ class SparseGatherV2(Gather):
Outputs:
Tensor, the shape of tensor is :math:`(z_1, z_2, ..., z_N)`.

Raises:
TypeError: If `axis` is not an int.

Supported Platforms:
``Ascend`` ``GPU``

@@ -889,6 +919,10 @@ class Padding(PrimitiveWithInfer):
Outputs:
Tensor, the shape of tensor is :math:`(z_1, z_2, ..., z_N)`.

Raises:
TypeError: If `pad_dim_size` is not an int.
ValueError: If `pad_dim_size` is less than 1.

Supported Platforms:
``Ascend``

@@ -934,6 +968,10 @@ class UniqueWithPad(PrimitiveWithInfer):
- y (Tensor) - The unique elements filled with pad_num, the shape and type same as x.
- idx (Tensor) - The index of each value of x in the unique output y, the shape and type same as x.

Raises:
TypeError: If dtype of `x` is neither int32 nor int64.
ValueError: If length of shape of `x` is not equal to 1.

Supported Platforms:
``Ascend`` ``CPU``

@@ -970,10 +1008,6 @@ class Split(PrimitiveWithCheck):
axis (int): Index of the split position. Default: 0.
output_num (int): The number of output tensors. Must be positive int. Default: 1.

Raises:
ValueError: If `axis` is out of the range [-len(`input_x.shape`), len(`input_x.shape`)),
or if the `output_num` is less than or equal to 0.

Inputs:
- **input_x** (Tensor) - The shape of tensor is :math:`(x_1, x_2, ..., x_R)`.

@@ -981,6 +1015,11 @@ class Split(PrimitiveWithCheck):
tuple[Tensor], the shape of each output tensor is the same, which is
:math:`(y_1, y_2, ..., y_S)`.

Raises:
TypeError: If `axis` or `output_num` is not an int.
ValueError: If `axis` is out of the range [-len(`input_x.shape`), len(`input_x.shape`)),
or if the `output_num` is less than or equal to 0.

Supported Platforms:
``Ascend`` ``GPU`` ``CPU``

@@ -1033,6 +1072,9 @@ class Rank(PrimitiveWithInfer):
Outputs:
Tensor. 0-D int32 Tensor representing the rank of input, i.e., :math:`R`.

Raises:
TypeError: If `input_x` is not a Tensor.

Supported Platforms:
``Ascend`` ``GPU`` ``CPU``

@@ -1108,6 +1150,9 @@ class Size(PrimitiveWithInfer):
int, a scalar representing the elements size of `input_x`, tensor is the number of elements
in a tensor, :math:`size=x_1*x_2*...x_R`.

Raises:
TypeError: If `input_x` is not a Tensor.

Supported Platforms:
``Ascend`` ``GPU`` ``CPU``

@@ -1151,6 +1196,9 @@ class Fill(PrimitiveWithInfer):
Outputs:
Tensor, has the same type and shape as input value.

Raises:
TypeError: If `shape` is not a tuple.

Supported Platforms:
``Ascend`` ``GPU`` ``CPU``

@@ -1200,6 +1248,9 @@ class Ones(PrimitiveWithInfer):
Outputs:
Tensor, has the same type and shape as input shape value.

Raises:
TypeError: If `shape` is neither tuple nor int.

Supported Platforms:
``Ascend`` ``GPU`` ``CPU``

@@ -1253,6 +1304,10 @@ class Zeros(PrimitiveWithInfer):
Outputs:
Tensor, has the same type and shape as input shape value.

Raises:
TypeError: If `shape` is neither int nor tuple.
TypeError: If `shape` is a tuple whose elements are not all int.

Supported Platforms:
``Ascend`` ``GPU`` ``CPU``

@@ -1304,6 +1359,9 @@ class OnesLike(PrimitiveWithInfer):
Outputs:
Tensor, has the same shape and type as `input_x` but filled with ones.

Raises:
TypeError: If `input_x` is not a Tensor.

Supported Platforms:
``Ascend`` ``GPU`` ``CPU``

@@ -1340,6 +1398,9 @@ class ZerosLike(PrimitiveWithCheck):
Outputs:
Tensor, has the same shape and data type as `input_x` but filled with zeros.

Raises:
TypeError: If `input_x` is not a Tensor.

Supported Platforms:
``Ascend`` ``GPU`` ``CPU``

@@ -1374,6 +1435,10 @@ class TupleToArray(PrimitiveWithInfer):
Outputs:
Tensor, if the input tuple contains `N` numbers, then the shape of the output tensor is (N,).

Raises:
TypeError: If `input_x` is not a tuple.
ValueError: If length of `input_x` is less than or equal to 0.

Supported Platforms:
``Ascend`` ``GPU`` ``CPU``

@@ -1420,6 +1485,9 @@ class ScalarToArray(PrimitiveWithInfer):
Outputs:
Tensor. 0-D Tensor and the content is the input.

Raises:
TypeError: If `input_x` is neither int nor float.

Supported Platforms:
``Ascend`` ``GPU`` ``CPU``

@@ -1456,6 +1524,9 @@ class ScalarToTensor(PrimitiveWithInfer):
Outputs:
Tensor. 0-D Tensor and the content is the input.

Raises:
TypeError: If `input_x` is neither int nor float.

Supported Platforms:
``Ascend`` ``GPU`` ``CPU``

@@ -1502,6 +1573,10 @@ class InvertPermutation(PrimitiveWithInfer):
Outputs:
tuple[int]. It has the same length as the input.

Raises:
TypeError: If `input_x` is neither tuple nor list.
TypeError: If element of `input_x` is not an int.

Supported Platforms:
``Ascend`` ``GPU`` ``CPU``

@@ -1570,6 +1645,10 @@ class Argmax(PrimitiveWithInfer):
Outputs:
Tensor, indices of the max value of input tensor across the axis.

Raises:
TypeError: If `axis` is not an int.
TypeError: If `output_type` is neither int32 nor int64.

Supported Platforms:
``Ascend`` ``GPU`` ``CPU``

@@ -1622,6 +1701,10 @@ class Argmin(PrimitiveWithInfer):
Outputs:
Tensor, indices of the min value of input tensor across the axis.

Raises:
TypeError: If `axis` is not an int.
TypeError: If `output_type` is neither int32 nor int64.

Supported Platforms:
``Ascend``

@@ -1683,6 +1766,10 @@ class ArgMaxWithValue(PrimitiveWithInfer):
:math:`(x_1, x_2, ..., x_{axis-1}, x_{axis+1}, ..., x_N)`.
- output_x (Tensor) - The maximum value of input tensor, with the same shape as index.

Raises:
TypeError: If `keep_dims` is not a bool.
TypeError: If `axis` is not an int.

Supported Platforms:
``Ascend`` ``GPU``

@@ -1740,6 +1827,10 @@ class ArgMinWithValue(PrimitiveWithInfer):
:math:`(x_1, x_2, ..., x_{axis-1}, x_{axis+1}, ..., x_N)`.
- output_x (Tensor) - The minimum value of input tensor, with the same shape as index.

Raises:
TypeError: If `keep_dims` is not a bool.
TypeError: If `axis` is not an int.

Supported Platforms:
``Ascend`` ``CPU``

@@ -1798,6 +1889,10 @@ class Tile(PrimitiveWithInfer):
then the shape of their corresponding positions can be multiplied, and
the shape of Outputs is :math:`(1*y_1, ..., x_S*y_R)`.

Raises:
TypeError: If `multiples` is not a tuple or its elements are not all int.
ValueError: If the elements of `multiples` are not all greater than 0.

Supported Platforms:
``Ascend`` ``GPU`` ``CPU``

@@ -1876,6 +1971,10 @@ class UnsortedSegmentSum(PrimitiveWithInfer):
Outputs:
Tensor, the shape is :math:`(z, x_{N+1}, ..., x_R)`.

Raises:
TypeError: If `num_segments` is not an int.
ValueError: If length of shape of `segment_ids` is less than 1.

Supported Platforms:
``Ascend`` ``GPU``

@@ -1966,6 +2065,10 @@ class UnsortedSegmentMin(PrimitiveWithCheck):
Outputs:
Tensor, set the number of `num_segments` as `N`, the shape is :math:`(N, x_2, ..., x_R)`.

Raises:
TypeError: If `num_segments` is not an int.
ValueError: If length of shape of `segment_ids` is not equal to 1.

Supported Platforms:
``Ascend`` ``GPU``

@@ -2021,6 +2124,10 @@ class UnsortedSegmentMax(PrimitiveWithCheck):
Outputs:
Tensor, set the number of `num_segments` as `N`, the shape is :math:`(N, x_2, ..., x_R)`.

Raises:
TypeError: If `num_segments` is not an int.
ValueError: If length of shape of `segment_ids` is not equal to 1.

Supported Platforms:
``Ascend`` ``GPU``

@@ -2074,6 +2181,10 @@ class UnsortedSegmentProd(PrimitiveWithInfer):
Outputs:
Tensor, set the number of `num_segments` as `N`, the shape is :math:`(N, x_2, ..., x_R)`.

Raises:
TypeError: If `num_segments` is not an int.
ValueError: If length of shape of `segment_ids` is not equal to 1.

Supported Platforms:
``Ascend``

@@ -2140,6 +2251,9 @@ class Concat(PrimitiveWithInfer):
Outputs:
Tensor, the shape is :math:`(x_1, x_2, ..., \sum_{i=1}^Nx_{mi}, ..., x_R)`.

Raises:
TypeError: If `axis` is not an int.

Supported Platforms:
``Ascend`` ``GPU`` ``CPU``

@@ -2206,6 +2320,9 @@ class ParallelConcat(PrimitiveWithInfer):
Outputs:
Tensor, data type is the same as `values`.

Raises:
ValueError: If length of shape of `values` is less than 1.

Supported Platforms:
``Ascend``

@@ -2469,6 +2586,9 @@ class Slice(PrimitiveWithInfer):
Outputs:
Tensor, the shape is : input `size`, the data type is the same as input `x`.

Raises:
TypeError: If `begin` or `size` is neither tuple nor list.

Supported Platforms:
``Ascend`` ``GPU`` ``CPU``

@@ -2526,6 +2646,10 @@ class ReverseV2(PrimitiveWithInfer):
Outputs:
Tensor, has the same shape and type as `input_x`.

Raises:
TypeError: If `axis` is neither list nor tuple.
TypeError: If element of `axis` is not an int.

Supported Platforms:
``Ascend``

@@ -2568,6 +2692,9 @@ class Rint(PrimitiveWithInfer):
Outputs:
Tensor, has the same shape and type as `input_x`.

Raises:
TypeError: If dtype of `input_x` is neither float16 nor float32.

Supported Platforms:
``Ascend``

@@ -2631,6 +2758,10 @@ class Select(PrimitiveWithInfer):
Outputs:
Tensor, has the same shape as `input_x`. The shape is :math:`(x_1, x_2, ..., x_N, ..., x_R)`.

Raises:
TypeError: If `input_x` or `input_y` is not a Tensor.
ValueError: If shape of `input_x` is not equal to shape of `input_y` or shape of `input_cond`.

Supported Platforms:
``Ascend`` ``GPU`` ``CPU``

@@ -2768,6 +2899,11 @@ class StridedSlice(PrimitiveWithInfer):
i.e., [3, 3, 3].
- Finally, the output is [3, 3, 3].

Raises:
TypeError: If `begin_mask`, `end_mask`, `ellipsis_mask`, `new_axis_mask` or `shrink_axis_mask` is not an int.
TypeError: If `begin`, `end` or `strides` is not a tuple.
ValueError: If `begin_mask`, `end_mask`, `ellipsis_mask`, `new_axis_mask` or `shrink_axis_mask` is less than 0.

Supported Platforms:
``Ascend`` ``GPU`` ``CPU``

@@ -3041,6 +3177,10 @@ class Eye(PrimitiveWithInfer):
Outputs:
Tensor, a tensor with ones on the diagonal and the rest of elements are zero.

Raises:
TypeError: If `m` or `n` is not an int.
ValueError: If `m` or `n` is less than 1.

Supported Platforms:
``Ascend`` ``GPU`` ``CPU``

@@ -3090,6 +3230,10 @@ class ScatterNd(PrimitiveWithInfer):
Outputs:
Tensor, the new tensor, has the same type as `update` and the same shape as `shape`.

Raises:
TypeError: If `shape` is not a tuple.
ValueError: If any element of `shape` is less than 1.

Supported Platforms:
``Ascend`` ``GPU``

@@ -3146,6 +3290,11 @@ class ResizeNearestNeighbor(PrimitiveWithInfer):
Outputs:
Tensor, the shape of the output tensor is :math:`(N, C, NEW\_H, NEW\_W)`.

Raises:
TypeError: If `size` is neither tuple nor list.
TypeError: If `align_corners` is not a bool.
ValueError: If length of `size` is not equal to 2.

Supported Platforms:
``Ascend`` ``GPU``

@@ -3190,6 +3339,9 @@ class GatherNd(PrimitiveWithInfer):
Outputs:
Tensor, has the same type as `input_x` and the shape is indices_shape[:-1] + x_shape[indices_shape[-1]:].

Raises:
ValueError: If length of shape of `input_x` is less than the last dimension of `indices`.

Supported Platforms:
``Ascend`` ``GPU`` ``CPU``

@@ -3223,13 +3375,17 @@ class TensorScatterUpdate(PrimitiveWithInfer):

Inputs:
- **input_x** (Tensor) - The target tensor. The dimension of input_x must be equal to indices.shape[-1].
- **indices** (Tensor) - The index of input tensor whose data type is int32.
- **indices** (Tensor) - The index of input tensor whose data type is int32 or int64.
- **update** (Tensor) - The tensor to update the input tensor, has the same type as input,
and update.shape = indices.shape[:-1] + input_x.shape[indices.shape[-1]:].

Outputs:
Tensor, has the same shape and type as `input_x`.

Raises:
TypeError: If dtype of `indices` is neither int32 nor int64.
ValueError: If length of shape of `input_x` is less than the last dimension of shape of `indices`.

Supported Platforms:
``Ascend``

@@ -3293,6 +3449,9 @@ class ScatterUpdate(_ScatterOp_Dynamic):
Outputs:
Tensor, has the same shape and type as `input_x`.

Raises:
TypeError: If `use_locking` is not a bool.

Supported Platforms:
``Ascend`` ``GPU``

@@ -3351,6 +3510,9 @@ class ScatterNdUpdate(_ScatterNdOp):
Outputs:
Tensor, has the same shape and type as `input_x`.

Raises:
TypeError: If `use_locking` is not a bool.

Supported Platforms:
``Ascend`` ``CPU``

@@ -3411,6 +3573,9 @@ class ScatterMax(_ScatterOp):
Outputs:
Parameter, the updated `input_x`.

Raises:
TypeError: If `use_locking` is not a bool.

Supported Platforms:
``Ascend``

@@ -3457,6 +3622,9 @@ class ScatterMin(_ScatterOp):
Outputs:
Parameter, the updated `input_x`.

Raises:
TypeError: If `use_locking` is not a bool.

Supported Platforms:
``Ascend``

@@ -3502,6 +3670,9 @@ class ScatterAdd(_ScatterOp_Dynamic):
Outputs:
Parameter, the updated `input_x`.

Raises:
TypeError: If `use_locking` is not a bool.

Supported Platforms:
``Ascend`` ``GPU``

@@ -3555,6 +3726,9 @@ class ScatterSub(_ScatterOp):
Outputs:
Parameter, the updated `input_x`.

Raises:
TypeError: If `use_locking` is not a bool.

Supported Platforms:
``Ascend``

@@ -3600,6 +3774,9 @@ class ScatterMul(_ScatterOp):
Outputs:
Parameter, the updated `input_x`.

Raises:
TypeError: If `use_locking` is not a bool.

Supported Platforms:
``Ascend``

@@ -3645,6 +3822,9 @@ class ScatterDiv(_ScatterOp):
Outputs:
Parameter, the updated `input_x`.

Raises:
TypeError: If `use_locking` is not a bool.

Supported Platforms:
``Ascend``

@@ -3694,6 +3874,9 @@ class ScatterNdAdd(_ScatterNdOp):
Outputs:
Parameter, the updated `input_x`.

Raises:
TypeError: If `use_locking` is not a bool.

Supported Platforms:
``Ascend``

@@ -3742,6 +3925,9 @@ class ScatterNdSub(_ScatterNdOp):
Outputs:
Parameter, the updated `input_x`.

Raises:
TypeError: If `use_locking` is not a bool.

Supported Platforms:
``Ascend``

@@ -3777,6 +3963,10 @@ class ScatterNonAliasingAdd(_ScatterNdOp):
Outputs:
Parameter, the updated `input_x`.

Raises:
TypeError: If dtype of `indices` is not int32.
TypeError: If dtype of `input_x` is not one of float16, float32, int32.

Supported Platforms:
``Ascend``

@@ -3825,6 +4015,11 @@ class SpaceToDepth(PrimitiveWithInfer):
Outputs:
Tensor, the same data type as `x`. It must be a 4-D tensor.

Raises:
TypeError: If `block_size` is not an int.
ValueError: If `block_size` is less than 2.
ValueError: If length of shape of `x` is not equal to 4.

Supported Platforms:
``Ascend``

@@ -3887,6 +4082,11 @@ class DepthToSpace(PrimitiveWithInfer):
Outputs:
Tensor of shape :math:`(N, C_{in} / \text{block_size}, H_{in} * \text{block_size}, W_{in} * \text{block_size})`.

Raises:
TypeError: If `block_size` is not an int.
ValueError: If `block_size` is less than 2.
ValueError: If length of shape of `x` is not equal to 4.

Supported Platforms:
``Ascend``

@@ -3934,7 +4134,7 @@ class SpaceToBatch(PrimitiveWithInfer):
of the input are zero padded according to paddings if necessary.

Args:
block_size (int): The block size of dividing blocks with value greater than 2.
block_size (int): The block size of dividing blocks with value greater than or euqual to 2.
paddings (Union[tuple, list]): The padding values for H and W dimension, containing 2 subtraction lists.
Each subtraction list contains 2 integer value. All values must be greater than 0.
paddings[i] specifies the paddings for the spatial dimension i, which corresponds to the
@@ -3957,6 +4157,10 @@ class SpaceToBatch(PrimitiveWithInfer):

:math:`w' = (w+paddings[1][0]+paddings[1][1])//block\_size`

Raises:
TypeError: If `block_size` is not an int.
ValueError: If `block_size` is less than 2.

Supported Platforms:
``Ascend``

@@ -4036,6 +4240,11 @@ class BatchToSpace(PrimitiveWithInfer):

:math:`w' = w*block\_size-crops[1][0]-crops[1][1]`

Raises:
TypeError: If `block_size` or element of `crops` is not an int.
TypeError: If `crops` is neither list nor tuple.
ValueError: If `block_size` is less than 2.

Supported Platforms:
``Ascend``

@@ -4121,6 +4330,12 @@ class SpaceToBatchND(PrimitiveWithInfer):

:math:`w' = (w+paddings[1][0]+paddings[1][1])//block\_shape[1]`

Raises:
TypeError: If `block_shape` is not one of list, tuple, int.
TypeError: If `paddings` is neither list nor tuple.
ValueError: If length of shape of `block_shape` is not equal to 1.
ValueError: If length of `block_shape` or `paddings` is not equal to 2.

Supported Platforms:
``Ascend``

@@ -4217,6 +4432,11 @@ class BatchToSpaceND(PrimitiveWithInfer):

:math:`w' = w*block\_shape[1]-crops[1][0]-crops[1][1]`

Raises:
TypeError: If `block_shape` is not one of list, tuple, int.
TypeError: If `crops` is neither list nor tuple.
ValueError: If length of `block_shape` or `crops` is not equal to 2.

Supported Platforms:
``Ascend``

@@ -4302,6 +4522,7 @@ class BroadcastTo(PrimitiveWithInfer):
Tensor, with the given `shape` and the same data type as `input_x`.

Raises:
TypeError: If `shape` is not a tuple.
ValueError: Given a shape tuple, if it has several -1; or if the -1 is in an invalid position
such as one that does not have a opposing dimension in an input tensor; or if the target and
input shapes are incompatible.
@@ -4391,6 +4612,10 @@ class Meshgrid(PrimitiveWithInfer):
Outputs:
Tensors, A Tuple of N N-D Tensor objects.

Raises:
TypeError: If `indexing` is not a str or `input` is not a tuple.
ValueError: If `indexing` is neither 'xy' nor 'ij'.

Supported Platforms:
``Ascend``

@@ -4489,6 +4714,10 @@ class InplaceUpdate(PrimitiveWithInfer):
Outputs:
Tensor, with the same type and shape as the input `x`.

Raises:
TypeError: If `indices` is neither int nor tuple.
TypeError: If `indices` is a tuple and its element is not an int.

Supported Platforms:
``Ascend``

@@ -4549,6 +4778,9 @@ class ReverseSequence(PrimitiveWithInfer):
Outputs:
Reversed tensor with the same shape and data type as input.

Raises:
TypeError: If `seq_dim` or `batch_dim` is not an int.

Supported Platforms:
``Ascend``

@@ -4612,6 +4844,9 @@ class EditDistance(PrimitiveWithInfer):
Outputs:
Tensor, a dense tensor with rank `R-1` and float32 data type.

Raises:
TypeError: If `normalize` is not a bool.

Supported Platforms:
``Ascend``

@@ -4729,6 +4964,11 @@ class Sort(PrimitiveWithInfer):
- **y1** (Tensor) - A tensor whose values are the sorted values, with the same shape and data type as input.
- **y2** (Tensor) - The indices of the elements in the original input tensor. Data type is int32.

Raises:
TypeError: If `axis` is not an int.
TypeError: If `descending` is not a bool.
TypeError: If dtype of `x` is neither float16 nor float32.

Supported Platforms:
``Ascend``

@@ -4780,6 +5020,10 @@ class EmbeddingLookup(PrimitiveWithInfer):
Outputs:
Tensor, the shape of tensor is :math:`(z_1, z_2, ..., z_N)`.

Raises:
TypeError: If dtype of `input_indices` is not int.
ValueError: If length of shape of `input_params` is greater than 2.

Supported Platforms:
``Ascend`` ``CPU``

@@ -4843,13 +5087,17 @@ class GatherD(PrimitiveWithInfer):

Inputs:
- **x** (Tensor) - The source tensor.
- **dim** (int) - The axis along which to index. It must be int32. Only constant value is allowed.
- **dim** (int) - The axis along which to index. It must be int32 or int64. Only constant value is allowed.
- **index** (Tensor) - The indices of elements to gather. It can be one of the following data types:
int32, int64. The value range of each index element is [-x_rank[dim], x_rank[dim]).

Outputs:
Tensor, the shape of tensor is :math:`(z_1, z_2, ..., z_N)`.

Raises:
TypeError: If dtype of `dim` or `index` is neither int32 nor int64.
ValueError: If length of shape of `x` is not equal to length of shape of `index`.

Supported Platforms:
``Ascend`` ``GPU`` ``CPU``

@@ -4903,6 +5151,9 @@ class Identity(PrimitiveWithInfer):
Outputs:
Tensor, the shape of tensor is the same as `input_x`, :math:`(x_1, x_2, ..., x_R)`.

Raises:
TypeError: If `x` is not a Tensor.

Supported Platforms:
``Ascend``



+ 6
- 1
mindspore/ops/operations/image_ops.py View File

@@ -1,4 +1,4 @@
# Copyright 2020 Huawei Technologies Co., Ltd
# Copyright 2020-2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -53,6 +53,11 @@ class CropAndResize(PrimitiveWithInfer):
Outputs:
A 4-D tensor of shape [num_boxes, crop_height, crop_width, depth] with type: float32.

Raises:
TypeError: If `method` is not a str.
TypeError: If `extrapolation_value` is not a float.
ValueError: If `method` is not one of 'bilinear', 'nearest', 'bilinear_v2'.

Supported Platforms:
``Ascend``



+ 176
- 3
mindspore/ops/operations/math_ops.py View File

@@ -200,6 +200,9 @@ class AssignAdd(PrimitiveWithInfer):
- **value** (Union[numbers.Number, Tensor]) - The value to be added to the `variable`.
It must have the same shape as `variable` if it is a Tensor.

Raises:
TypeError: If `value` is neither Number nor Tensor.

Supported Platforms:
``Ascend`` ``GPU`` ``CPU``

@@ -256,6 +259,9 @@ class AssignSub(PrimitiveWithInfer):
- **value** (Union[numbers.Number, Tensor]) - The value to be subtracted from the `variable`.
It must have the same shape as `variable` if it is a Tensor.

Raises:
TypeError: If `value` is neither Number nor Tensor.

Supported Platforms:
``Ascend``

@@ -673,6 +679,10 @@ class CumProd(PrimitiveWithInfer):
Outputs:
Tensor, has the same shape and dtype as the `input_x`.

Raises:
TypeError: If `exclusive` or `reverse` is not a bool.
ValueError: If `axis` is None.

Supported Platforms:
``Ascend``

@@ -815,6 +825,10 @@ class BatchMatMul(MatMul):
Outputs:
Tensor, the shape of the output tensor is :math:`(*B, N, M)`.

Raises:
TypeError: If `transpose_a` or `transpose_b` is not a bool.
ValueError: If length of shape of `input_x` is less than 3 or not equal to length of shape of `input_y`.

Supported Platforms:
``Ascend`` ``GPU`` ``CPU``

@@ -881,6 +895,10 @@ class CumSum(PrimitiveWithInfer):
Outputs:
Tensor, the shape of the output tensor is consistent with the input tensor's.

Raises:
TypeError: If `exclusive` or `reverse` is not a bool.
TypeError: If `axis` is not an int.

Supported Platforms:
``Ascend`` ``GPU`` ``CPU``

@@ -929,6 +947,9 @@ class AddN(PrimitiveWithInfer):
Outputs:
Tensor, has the same shape and dtype as each entry of the `input_x`.

Raises:
TypeError: If `input_x` is neither tuple nor list.

Supported Platforms:
``Ascend`` ``GPU`` ``CPU``

@@ -1015,6 +1036,9 @@ class AccumulateNV2(PrimitiveWithInfer):
Outputs:
Tensor, has the same shape and dtype as each entry of the `input_x`.

Raises:
TypeError: If `input_x` is neither tuple nor list.

Supported Platforms:
``Ascend``

@@ -1125,6 +1149,11 @@ class InplaceAdd(PrimitiveWithInfer):
Outputs:
Tensor, has the same shape and dtype as input_x.

Raises:
TypeError: If `indices` is neither int nor tuple.
TypeError: If `indices` is a tuple whose elements are not all int.
ValueError: If length of shape of `input_x` is not equal to length of shape of `input_v`.

Supported Platforms:
``Ascend``

@@ -1187,6 +1216,11 @@ class InplaceSub(PrimitiveWithInfer):
Outputs:
Tensor, has the same shape and dtype as input_x.

Raises:
TypeError: If `indices` is neither int nor tuple.
TypeError: If `indices` is a tuple whose elements are not all int.
ValueError: If length of shape of `input_x` is not equal to length of shape of `input_v`.

Supported Platforms:
``Ascend``

@@ -1591,6 +1625,9 @@ class Exp(PrimitiveWithInfer):
Outputs:
Tensor, has the same shape and dtype as the `input_x`.

Raises:
TypeError: If dtype of `input_x` is neither float16 nor float32.

Supported Platforms:
``Ascend`` ``GPU`` ``CPU``

@@ -1611,7 +1648,7 @@ class Exp(PrimitiveWithInfer):
return x_shape

def infer_dtype(self, x_type):
validator.check_subclass("x", x_type, mstype.tensor, self.name)
validator.check_tensor_dtype_valid("x", x_type, [mstype.float16, mstype.float32], self.name)
return x_type

def infer_value(self, x):
@@ -1637,6 +1674,9 @@ class Expm1(PrimitiveWithInfer):
Outputs:
Tensor, has the same shape as the `input_x`.

Raises:
TypeError: If dtype of `input_x` is neither float16 nor float32.

Supported Platforms:
``Ascend`` ``GPU`` ``CPU``

@@ -1678,6 +1718,11 @@ class HistogramFixedWidth(PrimitiveWithInfer):
Outputs:
Tensor, the type is int32.

Raises:
TypeError: If `dtype` is not a str or `nbins` is not an int.
ValueError: If `nbins` is less than 1.
ValueError: If `dtype` is neither 'int32' nor 'int64'.

Supported Platforms:
``Ascend``

@@ -1719,6 +1764,9 @@ class Log(PrimitiveWithInfer):
Outputs:
Tensor, has the same shape as the `input_x`.

Raises:
TypeError: If dtype of `input_x` is neither float16 nor float32.

Supported Platforms:
``Ascend`` ``GPU`` ``CPU``

@@ -1739,6 +1787,7 @@ class Log(PrimitiveWithInfer):

def infer_dtype(self, x):
validator.check_subclass("x", x, mstype.tensor, self.name)
validator.check_tensor_dtype_valid("x", x, [mstype.float16, mstype.float32], self.name)
return x

def infer_value(self, x):
@@ -1760,6 +1809,9 @@ class Log1p(PrimitiveWithInfer):
Outputs:
Tensor, has the same shape as the `input_x`.

Raises:
TypeError: If dtype of `input_x` is neither float16 nor float32.

Supported Platforms:
``Ascend``

@@ -1798,6 +1850,9 @@ class Erf(PrimitiveWithInfer):
Outputs:
Tensor, has the same shape and dtype as the `input_x`.

Raises:
TypeError: If dtype of `input_x` is neither float16 nor float32.

Supported Platforms:
``Ascend``

@@ -1836,6 +1891,9 @@ class Erfc(PrimitiveWithInfer):
Outputs:
Tensor, has the same shape and dtype as the `input_x`.

Raises:
TypeError: If dtype of `input_x` is neither float16 nor float32.

Supported Platforms:
``Ascend``

@@ -2015,6 +2073,9 @@ class Div(_MathBinaryOp):
Tensor, the shape is the same as the one after broadcasting,
and the data type is the one with higher precision or higher digits among the two inputs.

Raises:
TypeError: If neither `input_x` nor `input_y` is a Tensor.

Supported Platforms:
``Ascend`` ``GPU`` ``CPU``

@@ -2057,6 +2118,9 @@ class DivNoNan(_MathBinaryOp):
Tensor, the shape is the same as the one after broadcasting,
and the data type is the one with higher precision or higher digits among the two inputs.

Raises:
TypeError: If neither `input_x` nor `input_y` is a Tensor.

Supported Platforms:
``Ascend`` ``GPU``

@@ -2158,6 +2222,9 @@ class FloorDiv(_MathBinaryOp):
Tensor, the shape is the same as the one after broadcasting,
and the data type is the one with higher precision or higher digits among the two inputs.

Raises:
TypeError: If neither `input_x` nor `input_y` is a Tensor.

Supported Platforms:
``Ascend`` ``GPU`` ``CPU``

@@ -2296,6 +2363,9 @@ class Floor(PrimitiveWithInfer):
Outputs:
Tensor, has the same shape as `input_x`.

Raises:
TypeError: If dtype of `input_x` is not float.

Supported Platforms:
``Ascend`` ``GPU`` ``CPU``

@@ -2340,6 +2410,9 @@ class FloorMod(_MathBinaryOp):
Tensor, the shape is the same as the one after broadcasting,
and the data type is the one with higher precision or higher digits among the two inputs.

Raises:
TypeError: If neither `input_x` nor `input_y` is a Tensor.

Supported Platforms:
``Ascend``

@@ -2367,6 +2440,9 @@ class Ceil(PrimitiveWithInfer):
Outputs:
Tensor, has the same shape as `input_x`.

Raises:
TypeError: If dtype of `input_x` is neither float16 nor float32.

Supported Platforms:
``Ascend``

@@ -2481,6 +2557,9 @@ class Acosh(PrimitiveWithInfer):
Outputs:
Tensor, has the same shape and type as `input_x`.

Raises:
TypeError: If `input_x` is not a Tensor.

Supported Platforms:
``Ascend`` ``GPU``

@@ -2514,6 +2593,9 @@ class Cosh(PrimitiveWithInfer):
Outputs:
Tensor, has the same shape as `input_x`.

Raises:
TypeError: If `input_x` is not a Tensor.

Supported Platforms:
``Ascend``

@@ -2552,6 +2634,9 @@ class Asinh(PrimitiveWithInfer):
Outputs:
Tensor, has the same shape and type as `input_x`.

Raises:
TypeError: If `input_x` is not a Tensor.

Supported Platforms:
``Ascend`` ``GPU``

@@ -2642,6 +2727,9 @@ class Equal(_LogicBinaryOp):
Outputs:
Tensor, the shape is the same as the one after broadcasting,and the data type is bool.

Raises:
TypeError: If neither `input_x` nor `input_y` is a Tensor.

Supported Platforms:
``Ascend`` ``GPU`` ``CPU``

@@ -2692,6 +2780,9 @@ class ApproximateEqual(_LogicBinaryOp):
Outputs:
Tensor, the shape is the same as the shape of 'x1', and the data type is bool.

Raises:
TypeError: If `tolerance` is not a float.

Supported Platforms:
``Ascend``

@@ -2733,6 +2824,10 @@ class EqualCount(PrimitiveWithInfer):
Outputs:
Tensor, with the type same as input tensor and size as (1,).

Raises:
TypeError: If `input_x` or `input_y` is not a Tensor.
ValueError: If shape of `input_x` is not equal to shape of `input_y`.

Supported Platforms:
``GPU`` ``CPU``

@@ -2819,7 +2914,10 @@ class Greater(_LogicBinaryOp):
a bool when the first input is a tensor or a tensor whose data type is number or bool.

Outputs:
Tensor, the shape is the same as the one after broadcasting,and the data type is bool.
Tensor, the shape is the same as the one after broadcasting, and the data type is bool.

Raises:
TypeError: If neither `input_x` nor `input_y` is a Tensor.

Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
@@ -2860,7 +2958,10 @@ class GreaterEqual(_LogicBinaryOp):
a bool when the first input is a tensor or a tensor whose data type is number or bool.

Outputs:
Tensor, the shape is the same as the one after broadcasting,and the data type is bool.
Tensor, the shape is the same as the one after broadcasting, and the data type is bool.

Raises:
TypeError: If neither `input_x` nor `input_y` is a Tensor.

Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
@@ -2903,6 +3004,9 @@ class Less(_LogicBinaryOp):
Outputs:
Tensor, the shape is the same as the one after broadcasting,and the data type is bool.

Raises:
TypeError: If neither `input_x` nor `input_y` is a Tensor.

Supported Platforms:
``Ascend`` ``GPU`` ``CPU``

@@ -2944,6 +3048,9 @@ class LessEqual(_LogicBinaryOp):
Outputs:
Tensor, the shape is the same as the one after broadcasting,and the data type is bool.

Raises:
TypeError: If neither `input_x` nor `input_y` is a Tensor.

Supported Platforms:
``Ascend`` ``GPU`` ``CPU``

@@ -2975,6 +3082,9 @@ class LogicalNot(PrimitiveWithInfer):
Outputs:
Tensor, the shape is the same as the `input_x`, and the dtype is bool.

Raises:
TypeError: If `input_x` is not a Tensor.

Supported Platforms:
``Ascend`` ``GPU`` ``CPU``

@@ -3018,6 +3128,9 @@ class LogicalAnd(_LogicBinaryOp):
Outputs:
Tensor, the shape is the same as the one after broadcasting, and the data type is bool.

Raises:
TypeError: If neither `input_x` nor `input_y` is a Tensor.

Supported Platforms:
``Ascend`` ``GPU`` ``CPU``

@@ -3053,6 +3166,9 @@ class LogicalOr(_LogicBinaryOp):
Outputs:
Tensor, the shape is the same as the one after broadcasting,and the data type is bool.

Raises:
TypeError: If neither `input_x` nor `input_y` is a Tensor.

Supported Platforms:
``Ascend`` ``GPU`` ``CPU``

@@ -3079,6 +3195,9 @@ class IsNan(PrimitiveWithInfer):
Outputs:
Tensor, has the same shape of input, and the dtype is bool.

Raises:
TypeError: If `input_x` is not a Tensor.

Supported Platforms:
``GPU``

@@ -3112,6 +3231,9 @@ class IsInf(PrimitiveWithInfer):
Outputs:
Tensor, has the same shape of input, and the dtype is bool.

Raises:
TypeError: If `input_x` is not a Tensor.

Supported Platforms:
``GPU``

@@ -3145,6 +3267,9 @@ class IsFinite(PrimitiveWithInfer):
Outputs:
Tensor, has the same shape of input, and the dtype is bool.

Raises:
TypeError: If `input_x` is not a Tensor.

Supported Platforms:
``Ascend`` ``GPU`` ``CPU``

@@ -3179,6 +3304,9 @@ class FloatStatus(PrimitiveWithInfer):
Outputs:
Tensor, has the shape of `(1,)`, and the dtype is `mindspore.dtype.float32`.

Raises:
TypeError: If dtype of `input_x` is neither float16 nor float32.

Supported Platforms:
``GPU``

@@ -3341,6 +3469,9 @@ class Cos(PrimitiveWithInfer):
Outputs:
Tensor, has the same shape as `input_x`.

Raises:
TypeError: If `input_x` is not a Tensor.

Supported Platforms:
``Ascend`` ``GPU`` ``CPU``

@@ -3378,6 +3509,9 @@ class ACos(PrimitiveWithInfer):
Outputs:
Tensor, has the same shape as `input_x`.

Raises:
TypeError: If `input_x` is not a Tensor.

Supported Platforms:
``Ascend`` ``GPU`` ``CPU``

@@ -3448,6 +3582,9 @@ class Asin(PrimitiveWithInfer):
Outputs:
Tensor, has the same shape as `input_x`.

Raises:
TypeError: If `input_x` is not a Tensor.

Supported Platforms:
``Ascend`` ``GPU`` ``CPU``

@@ -3550,6 +3687,9 @@ class Abs(PrimitiveWithInfer):
Outputs:
Tensor, has the same shape as the `input_x`.

Raises:
TypeError: If `input_x` is not a Tensor.

Supported Platforms:
``Ascend`` ``GPU`` ``CPU``

@@ -3665,6 +3805,9 @@ class Tan(PrimitiveWithInfer):
Outputs:
Tensor, has the same shape as `input_x`.

Raises:
TypeError: If dtype of `input_x` is not one of float16, float32, int32.

Supported Platforms:
``Ascend`` ``CPU``

@@ -3703,6 +3846,9 @@ class Atan(PrimitiveWithInfer):
Outputs:
A Tensor, has the same type as the input.

Raises:
TypeError: If `input_x` is not a Tensor.

Supported Platforms:
``Ascend`` ``GPU`` ``CPU``

@@ -3736,6 +3882,9 @@ class Atanh(PrimitiveWithInfer):
Outputs:
A Tensor, has the same type as the input.

Raises:
TypeError: If `input_x` is not a Tensor.

Supported Platforms:
``Ascend``

@@ -3778,6 +3927,9 @@ class Atan2(_MathBinaryOp):
Outputs:
Tensor, the shape is the same as the one after broadcasting,and the data type is same as `input_x`.

Raises:
TypeError: If `input_x` or `input_y` is not a Tensor.

Supported Platforms:
``Ascend``

@@ -3851,6 +4003,9 @@ class BitwiseAnd(_BitwiseBinaryOp):
Outputs:
Tensor, has the same type as the `input_x1`.

Raises:
TypeError: If `input_x1` or `input_x2` is not a Tensor.

Supported Platforms:
``Ascend``

@@ -3881,6 +4036,9 @@ class BitwiseOr(_BitwiseBinaryOp):
Outputs:
Tensor, has the same type as the `input_x1`.

Raises:
TypeError: If `input_x1` or `input_x2` is not a Tensor.

Supported Platforms:
``Ascend``

@@ -3911,6 +4069,9 @@ class BitwiseXor(_BitwiseBinaryOp):
Outputs:
Tensor, has the same type as the `input_x1`.

Raises:
TypeError: If `input_x1` or `input_x2` is not a Tensor.

Supported Platforms:
``Ascend``

@@ -3935,6 +4096,9 @@ class BesselI0e(PrimitiveWithInfer):
Outputs:
Tensor, has the same shape as `input_x`.

Raises:
TypeError: If `input_x` is not a Tensor.

Supported Platforms:
``Ascend``

@@ -3969,6 +4133,9 @@ class BesselI1e(PrimitiveWithInfer):
Outputs:
Tensor, has the same shape as `input_x`.

Raises:
TypeError: If `input_x` is not a Tensor.

Supported Platforms:
``Ascend``

@@ -4003,6 +4170,9 @@ class Inv(PrimitiveWithInfer):
Outputs:
Tensor, has the same shape and data type as `input_x`.

Raises:
TypeError: If dtype of `input_x` is not one of float16, float32, int32.

Supported Platforms:
``Ascend``

@@ -4037,6 +4207,9 @@ class Invert(PrimitiveWithInfer):
Outputs:
Tensor, has the same shape as `input_x`.

Raises:
TypeError: If dtype of `input_x` is neither int16 nor uint16.

Supported Platforms:
``Ascend``



+ 2
- 2
tests/ut/python/ops/test_math_ops.py View File

@@ -111,10 +111,10 @@ def test_pow():

def test_exp():
""" test_exp """
input_tensor = Tensor(np.array([[2, 2], [3, 3]]))
input_tensor = Tensor(np.array([[2, 2], [3, 3]], np.float32))
testexp = P.Exp()
result = testexp(input_tensor)
expect = np.exp(np.array([[2, 2], [3, 3]]))
expect = np.exp(np.array([[2, 2], [3, 3]], np.float32))
assert np.all(result.asnumpy() == expect)




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