提交 d9f7e56b 编写于 作者: 万万没想到

fix review opinions in doc/comments

上级 0565e464
...@@ -276,7 +276,7 @@ def initializer(init, shape=None, dtype=mstype.float32): ...@@ -276,7 +276,7 @@ def initializer(init, shape=None, dtype=mstype.float32):
shape (Union[tuple, list, int]): A list of integers, a tuple of integers or an integer as the shape of shape (Union[tuple, list, int]): A list of integers, a tuple of integers or an integer as the shape of
output. Default: None. output. Default: None.
dtype (:class:`mindspore.dtype`): The type of data in initialized tensor. Default: mstype.float32. dtype (:class:`mindspore.dtype`): The type of data in initialized tensor. Default: mindspore.float32.
Returns: Returns:
Tensor, initialized tensor. Tensor, initialized tensor.
......
...@@ -62,7 +62,7 @@ class ExpandDims(PrimitiveWithInfer): ...@@ -62,7 +62,7 @@ class ExpandDims(PrimitiveWithInfer):
Examples: Examples:
>>> input_tensor = Tensor(np.array([[2, 2], [2, 2]]), mindspore.float32) >>> input_tensor = Tensor(np.array([[2, 2], [2, 2]]), mindspore.float32)
>>> expand_dims = ExpandDims() >>> expand_dims = P.ExpandDims()
>>> output = expand_dims(input_tensor, 0) >>> output = expand_dims(input_tensor, 0)
""" """
...@@ -101,7 +101,7 @@ class DType(PrimitiveWithInfer): ...@@ -101,7 +101,7 @@ class DType(PrimitiveWithInfer):
Examples: Examples:
>>> input_tensor = Tensor(np.array([[2, 2], [2, 2]]), mindspore.float32) >>> input_tensor = Tensor(np.array([[2, 2], [2, 2]]), mindspore.float32)
>>> type = DType()(input_tensor) >>> type = P.DType()(input_tensor)
""" """
@prim_attr_register @prim_attr_register
...@@ -134,7 +134,7 @@ class SameTypeShape(PrimitiveWithInfer): ...@@ -134,7 +134,7 @@ class SameTypeShape(PrimitiveWithInfer):
Examples: Examples:
>>> input_x = Tensor(np.array([[2, 2], [2, 2]]), mindspore.float32) >>> input_x = Tensor(np.array([[2, 2], [2, 2]]), mindspore.float32)
>>> input_y = Tensor(np.array([[2, 2], [2, 2]]), mindspore.float32) >>> input_y = Tensor(np.array([[2, 2], [2, 2]]), mindspore.float32)
>>> out = SameTypeShape()(input_x, input_y) >>> out = P.SameTypeShape()(input_x, input_y)
""" """
@prim_attr_register @prim_attr_register
...@@ -175,7 +175,7 @@ class Cast(PrimitiveWithInfer): ...@@ -175,7 +175,7 @@ class Cast(PrimitiveWithInfer):
>>> input_np = np.random.randn(2, 3, 4, 5).astype(np.float32) >>> input_np = np.random.randn(2, 3, 4, 5).astype(np.float32)
>>> input_x = Tensor(input_np) >>> input_x = Tensor(input_np)
>>> type_dst = mindspore.int32 >>> type_dst = mindspore.int32
>>> cast = Cast() >>> cast = P.Cast()
>>> result = cast(input_x, type_dst) >>> result = cast(input_x, type_dst)
>>> expect = input_np.astype(type_dst) >>> expect = input_np.astype(type_dst)
""" """
...@@ -227,7 +227,7 @@ class IsSubClass(PrimitiveWithInfer): ...@@ -227,7 +227,7 @@ class IsSubClass(PrimitiveWithInfer):
bool, the check result. bool, the check result.
Examples: Examples:
>>> result = IsSubClass()(mindspore.int32, mindspore.intc) >>> result = P.IsSubClass()(mindspore.int32, mindspore.intc)
""" """
@prim_attr_register @prim_attr_register
...@@ -262,7 +262,7 @@ class IsInstance(PrimitiveWithInfer): ...@@ -262,7 +262,7 @@ class IsInstance(PrimitiveWithInfer):
Examples: Examples:
>>> a = 1 >>> a = 1
>>> result = IsInstance()(a, mindspore.int32) >>> result = P.IsInstance()(a, mindspore.int32)
""" """
@prim_attr_register @prim_attr_register
...@@ -303,7 +303,7 @@ class Reshape(PrimitiveWithInfer): ...@@ -303,7 +303,7 @@ class Reshape(PrimitiveWithInfer):
Examples: Examples:
>>> input_tensor = Tensor(np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]), mindspore.float32) >>> input_tensor = Tensor(np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]), mindspore.float32)
>>> reshape = Reshape() >>> reshape = P.Reshape()
>>> output = reshape(input_tensor, (3, 2)) >>> output = reshape(input_tensor, (3, 2))
""" """
...@@ -366,7 +366,7 @@ class Shape(Primitive): ...@@ -366,7 +366,7 @@ class Shape(Primitive):
Examples: Examples:
>>> input_tensor = Tensor(np.ones(shape=[3, 2, 1]), mindspore.float32) >>> input_tensor = Tensor(np.ones(shape=[3, 2, 1]), mindspore.float32)
>>> shape = Shape() >>> shape = P.Shape()
>>> output = shape(input_tensor) >>> output = shape(input_tensor)
""" """
...@@ -398,7 +398,7 @@ class Squeeze(PrimitiveWithInfer): ...@@ -398,7 +398,7 @@ class Squeeze(PrimitiveWithInfer):
Examples: Examples:
>>> input_tensor = Tensor(np.ones(shape=[3, 2, 1]), mindspore.float32) >>> input_tensor = Tensor(np.ones(shape=[3, 2, 1]), mindspore.float32)
>>> squeeze = Squeeze(2) >>> squeeze = P.Squeeze(2)
>>> output = squeeze(input_tensor) >>> output = squeeze(input_tensor)
""" """
...@@ -450,7 +450,7 @@ class Transpose(PrimitiveWithInfer): ...@@ -450,7 +450,7 @@ class Transpose(PrimitiveWithInfer):
Examples: Examples:
>>> input_tensor = Tensor(np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]), mindspore.float32) >>> input_tensor = Tensor(np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]), mindspore.float32)
>>> perm = (0, 2, 1) >>> perm = (0, 2, 1)
>>> transpose = Transpose() >>> transpose = P.Transpose()
>>> output = transpose(input_tensor, perm) >>> output = transpose(input_tensor, perm)
""" """
...@@ -504,10 +504,10 @@ class GatherV2(PrimitiveWithInfer): ...@@ -504,10 +504,10 @@ class GatherV2(PrimitiveWithInfer):
Tensor, the shape of tensor is :math:`(z_1, z_2, ..., z_N)`. Tensor, the shape of tensor is :math:`(z_1, z_2, ..., z_N)`.
Examples: Examples:
>>> params = Tensor(np.array([[1, 2, 7, 42], [3, 4, 54, 22], [2, 2, 55, 3]]), mindspore.float32) >>> input_params = Tensor(np.array([[1, 2, 7, 42], [3, 4, 54, 22], [2, 2, 55, 3]]), mindspore.float32)
>>> indices = Tensor(np.array([1, 2]), mindspore.int32) >>> input_indices = Tensor(np.array([1, 2]), mindspore.int32)
>>> axis = 1 >>> axis = 1
>>> out = GatherV2()(params, indices, axis) >>> out = P.GatherV2()(input_params, input_indices, axis)
""" """
@prim_attr_register @prim_attr_register
...@@ -556,7 +556,7 @@ class Split(PrimitiveWithInfer): ...@@ -556,7 +556,7 @@ class Split(PrimitiveWithInfer):
:math:`(y_1, y_2, ..., y_S)`. :math:`(y_1, y_2, ..., y_S)`.
Examples: Examples:
>>> split = Split(1, 2) >>> split = P.Split(1, 2)
>>> x = Tensor(np.array([[1, 1, 1, 1], [2, 2, 2, 2]])) >>> x = Tensor(np.array([[1, 1, 1, 1], [2, 2, 2, 2]]))
>>> output = split(x) >>> output = split(x)
""" """
...@@ -606,7 +606,7 @@ class Rank(PrimitiveWithInfer): ...@@ -606,7 +606,7 @@ class Rank(PrimitiveWithInfer):
Examples: Examples:
>>> input_tensor = Tensor(np.array([[2, 2], [2, 2]]), mindspore.float32) >>> input_tensor = Tensor(np.array([[2, 2], [2, 2]]), mindspore.float32)
>>> rank = Rank() >>> rank = P.Rank()
>>> rank(input_tensor) >>> rank(input_tensor)
""" """
...@@ -640,7 +640,7 @@ class TruncatedNormal(PrimitiveWithInfer): ...@@ -640,7 +640,7 @@ class TruncatedNormal(PrimitiveWithInfer):
Examples: Examples:
>>> input_shape = Tensor(np.array([1, 2, 3])) >>> input_shape = Tensor(np.array([1, 2, 3]))
>>> truncated_normal = TruncatedNormal() >>> truncated_normal = P.TruncatedNormal()
>>> output = truncated_normal(input_shape) >>> output = truncated_normal(input_shape)
""" """
...@@ -681,7 +681,7 @@ class Size(PrimitiveWithInfer): ...@@ -681,7 +681,7 @@ class Size(PrimitiveWithInfer):
Examples: Examples:
>>> input_tensor = Tensor(np.array([[2, 2], [2, 2]]), mindspore.float32) >>> input_tensor = Tensor(np.array([[2, 2], [2, 2]]), mindspore.float32)
>>> size = Size() >>> size = P.Size()
>>> output = size(input_tensor) >>> output = size(input_tensor)
""" """
...@@ -826,7 +826,7 @@ class TupleToArray(PrimitiveWithInfer): ...@@ -826,7 +826,7 @@ class TupleToArray(PrimitiveWithInfer):
Tensor, if the input tuple contain `N` numbers, then the output tensor shape is (N,). Tensor, if the input tuple contain `N` numbers, then the output tensor shape is (N,).
Examples: Examples:
>>> type = TupleToArray()((1,2,3)) >>> type = P.TupleToArray()((1,2,3))
""" """
@prim_attr_register @prim_attr_register
...@@ -861,7 +861,7 @@ class ScalarToArray(PrimitiveWithInfer): ...@@ -861,7 +861,7 @@ class ScalarToArray(PrimitiveWithInfer):
Tensor. 0-D Tensor and the content is the input. Tensor. 0-D Tensor and the content is the input.
Examples: Examples:
>>> op = ScalarToArray() >>> op = P.ScalarToArray()
>>> data = 1.0 >>> data = 1.0
>>> output = op(data) >>> output = op(data)
""" """
...@@ -893,7 +893,7 @@ class ScalarToTensor(PrimitiveWithInfer): ...@@ -893,7 +893,7 @@ class ScalarToTensor(PrimitiveWithInfer):
Tensor. 0-D Tensor and the content is the input. Tensor. 0-D Tensor and the content is the input.
Examples: Examples:
>>> op = ScalarToTensor() >>> op = P.ScalarToTensor()
>>> data = 1 >>> data = 1
>>> output = op(data, mindspore.float32) >>> output = op(data, mindspore.float32)
""" """
...@@ -934,7 +934,7 @@ class InvertPermutation(PrimitiveWithInfer): ...@@ -934,7 +934,7 @@ class InvertPermutation(PrimitiveWithInfer):
tuple[int]. the lenth is same as input. tuple[int]. the lenth is same as input.
Examples: Examples:
>>> invert = InvertPermutation() >>> invert = P.InvertPermutation()
>>> input_data = (3, 4, 0, 2, 1) >>> input_data = (3, 4, 0, 2, 1)
>>> output = invert(input_data) >>> output = invert(input_data)
>>> output == (2, 4, 3, 0, 1) >>> output == (2, 4, 3, 0, 1)
...@@ -982,8 +982,8 @@ class Argmax(PrimitiveWithInfer): ...@@ -982,8 +982,8 @@ class Argmax(PrimitiveWithInfer):
Tensor, indices of the max value of input tensor across the axis. Tensor, indices of the max value of input tensor across the axis.
Examples: Examples:
>>> input = Tensor(np.array([2.0, 3.1, 1.2])) >>> input_x = Tensor(np.array([2.0, 3.1, 1.2]))
>>> index = Argmax()(input) >>> index = P.Argmax()(input_x)
>>> assert index == Tensor(1, mindspore.int64) >>> assert index == Tensor(1, mindspore.int64)
""" """
...@@ -1030,8 +1030,8 @@ class Argmin(PrimitiveWithInfer): ...@@ -1030,8 +1030,8 @@ class Argmin(PrimitiveWithInfer):
Tensor, indices of the min value of input tensor across the axis. Tensor, indices of the min value of input tensor across the axis.
Examples: Examples:
>>> input = Tensor(np.array([2.0, 3.1, 1.2])) >>> input_x = Tensor(np.array([2.0, 3.1, 1.2]))
>>> index = Argmin()(input) >>> index = P.Argmin()(input_x)
>>> assert index == Tensor(2, mindspore.int64) >>> assert index == Tensor(2, mindspore.int64)
""" """
...@@ -1082,8 +1082,8 @@ class ArgMaxWithValue(PrimitiveWithInfer): ...@@ -1082,8 +1082,8 @@ class ArgMaxWithValue(PrimitiveWithInfer):
:math:`(x_1, x_2, ..., x_{axis-1}, x_{axis+1}, ..., x_N)`. :math:`(x_1, x_2, ..., x_{axis-1}, x_{axis+1}, ..., x_N)`.
Examples: Examples:
>>> input = Tensor(np.random.rand(5)) >>> input_x = Tensor(np.random.rand(5))
>>> index, output = ArgMaxWithValue()(input) >>> index, output = P.ArgMaxWithValue()(input_x)
""" """
@prim_attr_register @prim_attr_register
...@@ -1129,8 +1129,8 @@ class ArgMinWithValue(PrimitiveWithInfer): ...@@ -1129,8 +1129,8 @@ class ArgMinWithValue(PrimitiveWithInfer):
:math:`(x_1, x_2, ..., x_{axis-1}, x_{axis+1}, ..., x_N)`. :math:`(x_1, x_2, ..., x_{axis-1}, x_{axis+1}, ..., x_N)`.
Examples: Examples:
>>> input = Tensor(np.random.rand(5)) >>> input_x = Tensor(np.random.rand(5))
>>> index, output = ArgMinWithValue()(input) >>> index, output = P.ArgMinWithValue()(input_x)
""" """
@prim_attr_register @prim_attr_register
def __init__(self, axis=0, keep_dims=False): def __init__(self, axis=0, keep_dims=False):
...@@ -1325,7 +1325,7 @@ class Concat(PrimitiveWithInfer): ...@@ -1325,7 +1325,7 @@ class Concat(PrimitiveWithInfer):
Examples: Examples:
>>> data1 = Tensor(np.array([[0, 1], [2, 1]]).astype(np.int32)) >>> data1 = Tensor(np.array([[0, 1], [2, 1]]).astype(np.int32))
>>> data2 = Tensor(np.array([[0, 1], [2, 1]]).astype(np.int32)) >>> data2 = Tensor(np.array([[0, 1], [2, 1]]).astype(np.int32))
>>> op = Concat() >>> op = P.Concat()
>>> output = op((data1, data2)) >>> output = op((data1, data2))
""" """
...@@ -1607,7 +1607,7 @@ class Select(PrimitiveWithInfer): ...@@ -1607,7 +1607,7 @@ class Select(PrimitiveWithInfer):
Tensor, has the same shape as input_y. The shape is :math:`(x_1, x_2, ..., x_N, ..., x_R)`. Tensor, has the same shape as input_y. The shape is :math:`(x_1, x_2, ..., x_N, ..., x_R)`.
Examples: Examples:
>>> select = Select() >>> select = P.Select()
>>> input_x = Tensor([True, False]) >>> input_x = Tensor([True, False])
>>> input_y = Tensor([2,3], mindspore.float32) >>> input_y = Tensor([2,3], mindspore.float32)
>>> input_z = Tensor([1,2], mindspore.float32) >>> input_z = Tensor([1,2], mindspore.float32)
...@@ -1681,7 +1681,7 @@ class StridedSlice(PrimitiveWithInfer): ...@@ -1681,7 +1681,7 @@ class StridedSlice(PrimitiveWithInfer):
Examples Examples
>>> input_x = Tensor([[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]], >>> input_x = Tensor([[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]],
>>> [[5, 5, 5], [6, 6, 6]]], mindspore.float32) >>> [[5, 5, 5], [6, 6, 6]]], mindspore.float32)
>>> slice = StridedSlice() >>> slice = P.StridedSlice()
>>> output = slice(input_x, (1, 0, 0), (2, 1, 3), (1, 1, 1)) >>> output = slice(input_x, (1, 0, 0), (2, 1, 3), (1, 1, 1))
>>> output.shape() >>> output.shape()
(1, 1, 3) (1, 1, 3)
...@@ -1913,9 +1913,9 @@ class ScatterNd(PrimitiveWithInfer): ...@@ -1913,9 +1913,9 @@ class ScatterNd(PrimitiveWithInfer):
Tensor, the new tensor, has the same type as `update` and the same shape as `shape`. Tensor, the new tensor, has the same type as `update` and the same shape as `shape`.
Examples: Examples:
>>> op = ScatterNd() >>> op = P.ScatterNd()
>>> update = Tensor(np.array([3.2, 1.1]), mindspore.float32)
>>> indices = Tensor(np.array([[0, 1], [1, 1]]), mindspore.int32) >>> indices = Tensor(np.array([[0, 1], [1, 1]]), mindspore.int32)
>>> update = Tensor(np.array([3.2, 1.1]), mindspore.float32)
>>> shape = (3, 3) >>> shape = (3, 3)
>>> output = op(indices, update, shape) >>> output = op(indices, update, shape)
""" """
...@@ -1964,7 +1964,7 @@ class ResizeNearestNeighbor(PrimitiveWithInfer): ...@@ -1964,7 +1964,7 @@ class ResizeNearestNeighbor(PrimitiveWithInfer):
Examples: Examples:
>>> input_tensor = Tensor(np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]), mindspore.float32) >>> input_tensor = Tensor(np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]), mindspore.float32)
>>> resize = ResizeNearestNeighbor((2, 2)) >>> resize = P.ResizeNearestNeighbor((2, 2))
>>> output = resize(input_tensor) >>> output = resize(input_tensor)
""" """
...@@ -1997,7 +1997,7 @@ class GatherNd(PrimitiveWithInfer): ...@@ -1997,7 +1997,7 @@ class GatherNd(PrimitiveWithInfer):
Examples: Examples:
>>> input_x = Tensor(np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]), mindspore.float32) >>> input_x = Tensor(np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]), mindspore.float32)
>>> indices = Tensor(np.array([[0, 0], [1, 1]]), mindspore.int32) >>> indices = Tensor(np.array([[0, 0], [1, 1]]), mindspore.int32)
>>> op = GatherNd() >>> op = P.GatherNd()
>>> output = op(input_x, indices) >>> output = op(input_x, indices)
""" """
...@@ -2039,7 +2039,7 @@ class ScatterNdUpdate(PrimitiveWithInfer): ...@@ -2039,7 +2039,7 @@ class ScatterNdUpdate(PrimitiveWithInfer):
>>> input_x = Tensor(np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]), mindspore.float32) >>> input_x = Tensor(np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]), mindspore.float32)
>>> indices = Tensor(np.array([[0, 0], [1, 1]]), mindspore.int32) >>> indices = Tensor(np.array([[0, 0], [1, 1]]), mindspore.int32)
>>> update = Tensor(np.array([1.0, 2.2]), mindspore.float32) >>> update = Tensor(np.array([1.0, 2.2]), mindspore.float32)
>>> op = ScatterNdUpdate() >>> op = P.ScatterNdUpdate()
>>> output = op(input_x, indices, update) >>> output = op(input_x, indices, update)
""" """
...@@ -2090,7 +2090,7 @@ class SpaceToDepth(PrimitiveWithInfer): ...@@ -2090,7 +2090,7 @@ class SpaceToDepth(PrimitiveWithInfer):
Examples: Examples:
>>> x = Tensor(np.random.rand(1,3,2,2), mindspore.float32) >>> x = Tensor(np.random.rand(1,3,2,2), mindspore.float32)
>>> block_size = 2 >>> block_size = 2
>>> op = SpaceToDepth(block_size) >>> op = P.SpaceToDepth(block_size)
>>> output = op(x) >>> output = op(x)
>>> output.asnumpy().shape == (1,12,1,1) >>> output.asnumpy().shape == (1,12,1,1)
""" """
...@@ -2148,7 +2148,7 @@ class DepthToSpace(PrimitiveWithInfer): ...@@ -2148,7 +2148,7 @@ class DepthToSpace(PrimitiveWithInfer):
Examples: Examples:
>>> x = Tensor(np.random.rand(1,12,1,1), mindspore.float32) >>> x = Tensor(np.random.rand(1,12,1,1), mindspore.float32)
>>> block_size = 2 >>> block_size = 2
>>> op = DepthToSpace(block_size) >>> op = P.DepthToSpace(block_size)
>>> output = op(x) >>> output = op(x)
>>> output.asnumpy().shape == (1,3,2,2) >>> output.asnumpy().shape == (1,3,2,2)
""" """
...@@ -2212,8 +2212,8 @@ class SpaceToBatch(PrimitiveWithInfer): ...@@ -2212,8 +2212,8 @@ class SpaceToBatch(PrimitiveWithInfer):
>>> block_size = 2 >>> block_size = 2
>>> paddings = [[0, 0], [0, 0]] >>> paddings = [[0, 0], [0, 0]]
>>> space_to_batch = P.SpaceToBatch(block_size, paddings) >>> space_to_batch = P.SpaceToBatch(block_size, paddings)
>>> x = Tensor(np.array([[[[1, 2], [3, 4]]]]), mindspore.float32) >>> input_x = Tensor(np.array([[[[1, 2], [3, 4]]]]), mindspore.float32)
>>> space_to_batch(x) >>> space_to_batch(input_x)
[[[[1.]]], [[[2.]]], [[[3.]]], [[[4.]]]] [[[[1.]]], [[[2.]]], [[[3.]]], [[[4.]]]]
""" """
...@@ -2280,8 +2280,8 @@ class BatchToSpace(PrimitiveWithInfer): ...@@ -2280,8 +2280,8 @@ class BatchToSpace(PrimitiveWithInfer):
>>> block_size = 2 >>> block_size = 2
>>> crops = [[0, 0], [0, 0]] >>> crops = [[0, 0], [0, 0]]
>>> op = P.BatchToSpace(block_size, crops) >>> op = P.BatchToSpace(block_size, crops)
>>> x = Tensor(np.array([[[[1]]], [[[2]]], [[[3]]], [[[4]]]]), mindspore.float32) >>> input_x = Tensor(np.array([[[[1]]], [[[2]]], [[[3]]], [[[4]]]]), mindspore.float32)
>>> output = op(x) >>> output = op(input_x)
[[[[1., 2.], [3., 4.]]]] [[[[1., 2.], [3., 4.]]]]
""" """
......
...@@ -112,9 +112,9 @@ class TensorAdd(_MathBinaryOp): ...@@ -112,9 +112,9 @@ class TensorAdd(_MathBinaryOp):
Examples: Examples:
>>> add = P.TensorAdd() >>> add = P.TensorAdd()
>>> x = Tensor(np.array([1,2,3]).astype(np.float32)) >>> input_x = Tensor(np.array([1,2,3]).astype(np.float32))
>>> y = Tensor(np.array([4,5,6]).astype(np.float32)) >>> input_y = Tensor(np.array([4,5,6]).astype(np.float32))
>>> add(x, y) >>> add(input_x, input_y)
[5,7,9] [5,7,9]
""" """
...@@ -124,23 +124,24 @@ class AssignAdd(PrimitiveWithInfer): ...@@ -124,23 +124,24 @@ class AssignAdd(PrimitiveWithInfer):
Updates a `Parameter` by adding a value to it. Updates a `Parameter` by adding a value to it.
Inputs: Inputs:
- **input_x** (Parameter) - The `Parameter`. - **variable** (Parameter) - The `Parameter`.
- **input_y** (Union[scalar, Tensor]) - Has the same shape as `input_x`. - **value** (Union[numbers.Number, Tensor]) - The value to be added to the `variable`.
It should have the same shape as `variable` if it is a Tensor.
Examples: Examples:
>>> class Net(Cell): >>> class Net(Cell):
>>> def __init__(self): >>> def __init__(self):
>>> super(Net, self).__init__() >>> super(Net, self).__init__()
>>> self.AssignAdd = P.AssignAdd() >>> self.AssignAdd = P.AssignAdd()
>>> self.inputdata = Parameter(initializer(1, [1], mindspore.int64), name="global_step") >>> self.variable = Parameter(initializer(1, [1], mindspore.int64), name="global_step")
>>> >>>
>>> def construct(self, x): >>> def construct(self, x):
>>> self.AssignAdd(self.inputdata, x) >>> self.AssignAdd(self.variable, x)
>>> return self.inputdata >>> return self.variable
>>> >>>
>>> net = Net() >>> net = Net()
>>> x = Tensor(np.ones([1]).astype(np.int64)*100) >>> value = Tensor(np.ones([1]).astype(np.int64)*100)
>>> net(x) >>> net(value)
""" """
__mindspore_signature__ = ( __mindspore_signature__ = (
('variable', sig_rw.RW_WRITE, sig_kind.KIND_POSITIONAL_KEYWORD), ('variable', sig_rw.RW_WRITE, sig_kind.KIND_POSITIONAL_KEYWORD),
...@@ -166,22 +167,24 @@ class AssignSub(PrimitiveWithInfer): ...@@ -166,22 +167,24 @@ class AssignSub(PrimitiveWithInfer):
Updates a `Parameter` by subtracting a value from it. Updates a `Parameter` by subtracting a value from it.
Inputs: Inputs:
- **input_x** (Parameter) - The `Parameter`. - **variable** (Parameter) - The `Parameter`.
- **input_y** (Union[scalar, Tensor]) - Has the same shape as `input_x`. - **value** (Union[numbers.Number, Tensor]) - The value to be subtracted from the `variable`.
It should have the same shape as `variable` if it is a Tensor.
Examples: Examples:
>>> class Net(Cell): >>> class Net(Cell):
>>> def __init__(self): >>> def __init__(self):
>>> super(Net, self).__init__()
>>> self.AssignSub = P.AssignSub() >>> self.AssignSub = P.AssignSub()
>>> self.inputdata = Parameter(initializer(1, [1], mindspore.int64), name="global_step") >>> self.variable = Parameter(initializer(1, [1], mindspore.int64), name="global_step")
>>> >>>
>>> def construct(self, x): >>> def construct(self, x):
>>> self.AssignSub(self.inputdata, x) >>> self.AssignSub(self.variable, x)
>>> return self.inputdata >>> return self.variable
>>> >>>
>>> net = Net() >>> net = Net()
>>> x = Tensor(np.ones([1]).astype(np.int64)*100) >>> value = Tensor(np.ones([1]).astype(np.int64)*100)
>>> net(x) >>> net(value)
""" """
__mindspore_signature__ = ( __mindspore_signature__ = (
...@@ -263,9 +266,9 @@ class ReduceMean(_Reduce): ...@@ -263,9 +266,9 @@ class ReduceMean(_Reduce):
the shape of output is :math:`(x_1, x_4, ..., x_R)`. the shape of output is :math:`(x_1, x_4, ..., x_R)`.
Examples: Examples:
>>> data = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32)) >>> input_x = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32))
>>> op = P.ReduceMean(keep_dims=True) >>> op = P.ReduceMean(keep_dims=True)
>>> output = op(data, 1) >>> output = op(input_x, 1)
""" """
...@@ -295,9 +298,9 @@ class ReduceSum(_Reduce): ...@@ -295,9 +298,9 @@ class ReduceSum(_Reduce):
the shape of output is :math:`(x_1, x_4, ..., x_R)`. the shape of output is :math:`(x_1, x_4, ..., x_R)`.
Examples: Examples:
>>> data = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32)) >>> input_x = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32))
>>> op = P.ReduceSum(keep_dims=True) >>> op = P.ReduceSum(keep_dims=True)
>>> output = op(data, 1) >>> output = op(input_x, 1)
""" """
...@@ -328,9 +331,9 @@ class ReduceAll(_Reduce): ...@@ -328,9 +331,9 @@ class ReduceAll(_Reduce):
the shape of output is :math:`(x_1, x_4, ..., x_R)`. the shape of output is :math:`(x_1, x_4, ..., x_R)`.
Examples: Examples:
>>> data = Tensor(np.array([[True, False], [True, True]])) >>> input_x = Tensor(np.array([[True, False], [True, True]]))
>>> op = P.ReduceAll(keep_dims=True) >>> op = P.ReduceAll(keep_dims=True)
>>> output = op(data, 1) >>> output = op(input_x, 1)
""" """
def __infer__(self, input_x, axis): def __infer__(self, input_x, axis):
...@@ -364,9 +367,9 @@ class ReduceMax(_Reduce): ...@@ -364,9 +367,9 @@ class ReduceMax(_Reduce):
the shape of output is :math:`(x_1, x_4, ..., x_R)`. the shape of output is :math:`(x_1, x_4, ..., x_R)`.
Examples: Examples:
>>> data = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32)) >>> input_x = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32))
>>> op = P.ReduceMax(keep_dims=True) >>> op = P.ReduceMax(keep_dims=True)
>>> output = op(data, 1) >>> output = op(input_x, 1)
""" """
...@@ -397,9 +400,9 @@ class ReduceMin(_Reduce): ...@@ -397,9 +400,9 @@ class ReduceMin(_Reduce):
the shape of output is :math:`(x_1, x_4, ..., x_R)`. the shape of output is :math:`(x_1, x_4, ..., x_R)`.
Examples: Examples:
>>> data = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32)) >>> input_x = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32))
>>> op = P.ReduceMin(keep_dims=True) >>> op = P.ReduceMin(keep_dims=True)
>>> output = op(data, 1) >>> output = op(input_x, 1)
""" """
...@@ -429,9 +432,9 @@ class ReduceProd(_Reduce): ...@@ -429,9 +432,9 @@ class ReduceProd(_Reduce):
the shape of output is :math:`(x_1, x_4, ..., x_R)`. the shape of output is :math:`(x_1, x_4, ..., x_R)`.
Examples: Examples:
>>> data = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32)) >>> input_x = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32))
>>> op = P.ReduceProd(keep_dims=True) >>> op = P.ReduceProd(keep_dims=True)
>>> output = op(data, 1) >>> output = op(input_x, 1)
""" """
...@@ -451,15 +454,15 @@ class CumProd(PrimitiveWithInfer): ...@@ -451,15 +454,15 @@ class CumProd(PrimitiveWithInfer):
Tensor, has the same shape and dtype as the 'input_x'. Tensor, has the same shape and dtype as the 'input_x'.
Examples: Examples:
>>> data = Tensor(np.array([a, b, c]).astype(np.float32)) >>> input_x = Tensor(np.array([a, b, c]).astype(np.float32))
>>> op0 = P.CumProd() >>> op0 = P.CumProd()
>>> output = op0(data, 0) # output=[a, a * b, a * b * c] >>> output = op0(input_x, 0) # output=[a, a * b, a * b * c]
>>> op1 = P.CumProd(exclusive=True) >>> op1 = P.CumProd(exclusive=True)
>>> output = op1(data, 0) # output=[1, a, a * b] >>> output = op1(input_x, 0) # output=[1, a, a * b]
>>> op2 = P.CumProd(reverse=True) >>> op2 = P.CumProd(reverse=True)
>>> output = op2(data, 0) # output=[a * b * c, b * c, c] >>> output = op2(input_x, 0) # output=[a * b * c, b * c, c]
>>> op3 = P.CumProd(exclusive=True, reverse=True) >>> op3 = P.CumProd(exclusive=True, reverse=True)
>>> output = op3(data, 0) # output=[b * c, c, 1] >>> output = op3(input_x, 0) # output=[b * c, c, 1]
""" """
@prim_attr_register @prim_attr_register
def __init__(self, exclusive=False, reverse=False): def __init__(self, exclusive=False, reverse=False):
...@@ -1190,7 +1193,7 @@ class FloorMod(_MathBinaryOp): ...@@ -1190,7 +1193,7 @@ class FloorMod(_MathBinaryOp):
Examples: Examples:
>>> input_x = Tensor(np.array([2, 4, -1]), mindspore.int32) >>> input_x = Tensor(np.array([2, 4, -1]), mindspore.int32)
>>> input_y = Tensor(np.array([3, 3, 3]), mindspore.int32) >>> input_y = Tensor(np.array([3, 3, 3]), mindspore.int32)
>>> floor_mod = FloorMod() >>> floor_mod = P.FloorMod()
>>> floor_mod(input_x, input_y) >>> floor_mod(input_x, input_y)
[2, 1, 2] [2, 1, 2]
""" """
...@@ -1207,9 +1210,9 @@ class Acosh(PrimitiveWithInfer): ...@@ -1207,9 +1210,9 @@ class Acosh(PrimitiveWithInfer):
Tensor, has the same shape as `input_x`. Tensor, has the same shape as `input_x`.
Examples: Examples:
>>> acosh = Acosh() >>> acosh = P.Acosh()
>>> X = Tensor(np.array([1.0, 1.5, 3.0, 100.0]), mindspore.float32) >>> input_x = Tensor(np.array([1.0, 1.5, 3.0, 100.0]), mindspore.float32)
>>> output = acosh(X) >>> output = acosh(input_x)
""" """
@prim_attr_register @prim_attr_register
...@@ -1286,7 +1289,7 @@ class EqualCount(PrimitiveWithInfer): ...@@ -1286,7 +1289,7 @@ class EqualCount(PrimitiveWithInfer):
- **input_y** (Tensor) - The second input tensor. - **input_y** (Tensor) - The second input tensor.
Outputs: Outputs:
Tensor, has the same shape as the `input_x`. Tensor, with the type as `mindspore.int32` and size as (1,).
Examples: Examples:
>>> input_x = Tensor(np.array([1, 2, 3]), mindspore.int32) >>> input_x = Tensor(np.array([1, 2, 3]), mindspore.int32)
...@@ -1324,7 +1327,7 @@ class NotEqual(_LogicBinaryOp): ...@@ -1324,7 +1327,7 @@ class NotEqual(_LogicBinaryOp):
Inputs: Inputs:
- **input_x** (Union[Tensor, Number, bool]) - The first input is a tensor whose data type is number or bool, or - **input_x** (Union[Tensor, Number, bool]) - The first input is a tensor whose data type is number or bool, or
a number or a bool object. a number or a bool object.
- **input_y** (Union[Tensor, Number, bool]) - The second input tensor whose data type is same as 'input_x' or - **input_y** (Union[Tensor, Number, bool]) - The second input tensor whose data type is same as `input_x` or
a number or a bool object. a number or a bool object.
Outputs: Outputs:
...@@ -1359,11 +1362,11 @@ class Greater(_LogicBinaryOp): ...@@ -1359,11 +1362,11 @@ class Greater(_LogicBinaryOp):
Inputs: Inputs:
- **input_x** (Union[Tensor, Number]) - The first input is a tensor whose data type is number or a number. - **input_x** (Union[Tensor, Number]) - The first input is a tensor whose data type is number or a number.
- **input_y** (Union[Tensor, Number]) - The second input is a tensor whose data type is same as 'input_x' or - **input_y** (Union[Tensor, Number]) - The second input is a tensor whose data type is same as `input_x` or
a number. a number.
Outputs: Outputs:
Tensor, the shape is same as the shape after broadcasting, and the data type is same as 'input_x'. Tensor, the shape is same as the shape after broadcasting, and the data type is bool.
Examples: Examples:
>>> input_x = Tensor(np.array([1, 2, 3]), mindspore.int32) >>> input_x = Tensor(np.array([1, 2, 3]), mindspore.int32)
...@@ -1386,11 +1389,11 @@ class GreaterEqual(_LogicBinaryOp): ...@@ -1386,11 +1389,11 @@ class GreaterEqual(_LogicBinaryOp):
Inputs: Inputs:
- **input_x** (Union[Tensor, Number]) - The first input is a tensor whose data type is number or a number. - **input_x** (Union[Tensor, Number]) - The first input is a tensor whose data type is number or a number.
- **input_y** (Union[Tensor, Number]) - The second input is a tensor whose data type is same as 'input_x' or - **input_y** (Union[Tensor, Number]) - The second input is a tensor whose data type is same as `input_x` or
a number. a number.
Outputs: Outputs:
Tensor, the shape is same as the shape after broadcasting, and the data type is bool'. Tensor, the shape is same as the shape after broadcasting, and the data type is bool.
Examples: Examples:
>>> input_x = Tensor(np.array([1, 2, 3]), mindspore.int32) >>> input_x = Tensor(np.array([1, 2, 3]), mindspore.int32)
...@@ -1413,7 +1416,7 @@ class Less(_LogicBinaryOp): ...@@ -1413,7 +1416,7 @@ class Less(_LogicBinaryOp):
Inputs: Inputs:
- **input_x** (Union[Tensor, Number]) - The first input is a tensor whose data type is number or a number. - **input_x** (Union[Tensor, Number]) - The first input is a tensor whose data type is number or a number.
- **input_y** (Union[Tensor, Number]) - The second input is a tensor whose data type is same as 'input_x' or - **input_y** (Union[Tensor, Number]) - The second input is a tensor whose data type is same as `input_x` or
a number. a number.
Outputs: Outputs:
...@@ -1440,7 +1443,7 @@ class LessEqual(_LogicBinaryOp): ...@@ -1440,7 +1443,7 @@ class LessEqual(_LogicBinaryOp):
Inputs: Inputs:
- **input_x** (Union[Tensor, Number]) - The first input is a tensor whose data type is number or a number. - **input_x** (Union[Tensor, Number]) - The first input is a tensor whose data type is number or a number.
- **input_y** (Union[Tensor, Number]) - The second input is a tensor whose data type is same as 'input_x' or - **input_y** (Union[Tensor, Number]) - The second input is a tensor whose data type is same as `input_x` or
a number. a number.
Outputs: Outputs:
...@@ -1752,8 +1755,8 @@ class Cos(PrimitiveWithInfer): ...@@ -1752,8 +1755,8 @@ class Cos(PrimitiveWithInfer):
Examples: Examples:
>>> cos = P.Cos() >>> cos = P.Cos()
>>> X = Tensor(np.array([0.24, 0.83, 0.31, 0.09]), mindspore.float32) >>> input_x = Tensor(np.array([0.24, 0.83, 0.31, 0.09]), mindspore.float32)
>>> output = cos(X) >>> output = cos(input_x)
""" """
@prim_attr_register @prim_attr_register
...@@ -1780,8 +1783,8 @@ class ACos(PrimitiveWithInfer): ...@@ -1780,8 +1783,8 @@ class ACos(PrimitiveWithInfer):
Examples: Examples:
>>> acos = P.ACos() >>> acos = P.ACos()
>>> X = Tensor(np.array([0.74, 0.04, 0.30, 0.56]), mindspore.float32) >>> input_x = Tensor(np.array([0.74, 0.04, 0.30, 0.56]), mindspore.float32)
>>> output = acos(X) >>> output = acos(input_x)
""" """
@prim_attr_register @prim_attr_register
...@@ -1993,7 +1996,7 @@ class Atan2(_MathBinaryOp): ...@@ -1993,7 +1996,7 @@ class Atan2(_MathBinaryOp):
- **input_y** (Tensor) - The input tensor. - **input_y** (Tensor) - The input tensor.
Outputs: Outputs:
Tensor, the shape is same as the shape after broadcasting, and the data type is same as 'input_x'. Tensor, the shape is same as the shape after broadcasting, and the data type is same as `input_x`.
Examples: Examples:
>>> input_x = Tensor(np.array([[0, 1]]), mindspore.float32) >>> input_x = Tensor(np.array([[0, 1]]), mindspore.float32)
......
...@@ -41,7 +41,7 @@ class Flatten(PrimitiveWithInfer): ...@@ -41,7 +41,7 @@ class Flatten(PrimitiveWithInfer):
Examples: Examples:
>>> input_tensor = Tensor(np.ones(shape=[1, 2, 3, 4]), mindspore.float32) >>> input_tensor = Tensor(np.ones(shape=[1, 2, 3, 4]), mindspore.float32)
>>> flatten = Flatten() >>> flatten = P.Flatten()
>>> output = flatten(input_tensor) >>> output = flatten(input_tensor)
>>> assert output.shape() == (1, 24) >>> assert output.shape() == (1, 24)
""" """
...@@ -155,7 +155,7 @@ class ReLU(PrimitiveWithInfer): ...@@ -155,7 +155,7 @@ class ReLU(PrimitiveWithInfer):
Examples: Examples:
>>> input_x = Tensor(np.array([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]], np.float32)) >>> input_x = Tensor(np.array([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]], np.float32))
>>> relu = ReLU() >>> relu = P.ReLU()
>>> result = relu(input_x) >>> result = relu(input_x)
[[0, 4.0, 0.0], [2.0, 0.0, 9.0]] [[0, 4.0, 0.0], [2.0, 0.0, 9.0]]
""" """
...@@ -188,7 +188,7 @@ class ReLU6(PrimitiveWithInfer): ...@@ -188,7 +188,7 @@ class ReLU6(PrimitiveWithInfer):
Examples: Examples:
>>> input_x = Tensor(np.array([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]], np.float32)) >>> input_x = Tensor(np.array([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]], np.float32))
>>> relu6 = ReLU6() >>> relu6 = P.ReLU6()
>>> result = relu6(input_x) >>> result = relu6(input_x)
""" """
...@@ -222,10 +222,10 @@ class Elu(PrimitiveWithInfer): ...@@ -222,10 +222,10 @@ class Elu(PrimitiveWithInfer):
Examples: Examples:
>>> input_x = Tensor(np.array([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]], np.float32)) >>> input_x = Tensor(np.array([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]], np.float32))
>>> elu = Elu() >>> elu = P.Elu()
>>> result = elu(input_x) >>> result = elu(input_x)
Tensor([[-0.632 4.0 -0.999] Tensor([[-0.632 4.0 -0.999]
[2.0 -0.993 9.0 ]], shape=(2, 3), dtype=ms.float32) [2.0 -0.993 9.0 ]], shape=(2, 3), dtype=mindspore.float32)
""" """
@prim_attr_register @prim_attr_register
...@@ -1082,7 +1082,7 @@ class TopK(PrimitiveWithInfer): ...@@ -1082,7 +1082,7 @@ class TopK(PrimitiveWithInfer):
Examples: Examples:
>>> topk = P.TopK(sorted=True) >>> topk = P.TopK(sorted=True)
>>> input_x = Tensor([1, 2, 3, 4, 5], mindspore.float16)) >>> input_x = Tensor([1, 2, 3, 4, 5], mindspore.float16)
>>> k = 3 >>> k = 3
>>> values, indices = topk(input_x, k) >>> values, indices = topk(input_x, k)
>>> assert values == Tensor(np.array([5, 4, 3])) >>> assert values == Tensor(np.array([5, 4, 3]))
...@@ -1223,8 +1223,8 @@ class ApplyMomentum(PrimitiveWithInfer): ...@@ -1223,8 +1223,8 @@ class ApplyMomentum(PrimitiveWithInfer):
Examples: Examples:
>>> net = ResNet50() >>> net = ResNet50()
>>> loss = SoftmaxCrossEntropyWithLogits() >>> loss = nn.SoftmaxCrossEntropyWithLogits()
>>> opt = ApplyMomentum(Tensor(np.array([0.001])), Tensor(np.array([0.9])), >>> opt = P.ApplyMomentum(Tensor(np.array([0.001])), Tensor(np.array([0.9])),
filter(lambda x: x.requires_grad, net.get_parameters())) filter(lambda x: x.requires_grad, net.get_parameters()))
>>> model = Model(net, loss, opt) >>> model = Model(net, loss, opt)
""" """
...@@ -1351,6 +1351,7 @@ class SGD(PrimitiveWithInfer): ...@@ -1351,6 +1351,7 @@ class SGD(PrimitiveWithInfer):
class ApplyRMSProp(PrimitiveWithInfer): class ApplyRMSProp(PrimitiveWithInfer):
""" """
Optimizer that implements the Root Mean Square prop(RMSProp) algorithm. Optimizer that implements the Root Mean Square prop(RMSProp) algorithm.
Please refer to the usage in source code of `nn.RMSProp`.
Note: Note:
Update `var` according to the RMSProp algorithm. Update `var` according to the RMSProp algorithm.
...@@ -1386,12 +1387,6 @@ class ApplyRMSProp(PrimitiveWithInfer): ...@@ -1386,12 +1387,6 @@ class ApplyRMSProp(PrimitiveWithInfer):
Outputs: Outputs:
Tensor, parameters to be update. Tensor, parameters to be update.
Examples:
>>> net = Net()
>>> loss = nn.SoftmaxCrossEntropyWithLogits()
>>> opt = RMSProp(params=net.trainable_params(), learning_rate=learning_rate)
>>> model = Model(net, loss, opt)
""" """
@prim_attr_register @prim_attr_register
...@@ -1424,6 +1419,7 @@ class ApplyRMSProp(PrimitiveWithInfer): ...@@ -1424,6 +1419,7 @@ class ApplyRMSProp(PrimitiveWithInfer):
class ApplyCenteredRMSProp(PrimitiveWithInfer): class ApplyCenteredRMSProp(PrimitiveWithInfer):
""" """
Optimizer that implements the centered RMSProp algorithm. Optimizer that implements the centered RMSProp algorithm.
Please refer to the usage in source code of `nn.RMSProp`.
Note: Note:
Update `var` according to the centered RMSProp algorithm. Update `var` according to the centered RMSProp algorithm.
...@@ -1464,12 +1460,6 @@ class ApplyCenteredRMSProp(PrimitiveWithInfer): ...@@ -1464,12 +1460,6 @@ class ApplyCenteredRMSProp(PrimitiveWithInfer):
Outputs: Outputs:
Tensor, parameters to be update. Tensor, parameters to be update.
Examples:
>>> net = Net()
>>> loss = nn.SoftmaxCrossEntropyWithLogits()
>>> opt = RMSProp(params=net.trainable_params(), learning_rate=learning_rate, centered=True)
>>> model = Model(net, loss, opt)
""" """
@prim_attr_register @prim_attr_register
...@@ -1596,7 +1586,7 @@ class DropoutGenMask(Primitive): ...@@ -1596,7 +1586,7 @@ class DropoutGenMask(Primitive):
Tensor, the value of generated mask for input shape. Tensor, the value of generated mask for input shape.
Examples: Examples:
>>> dropout_gen_mask = DropoutGenMask() >>> dropout_gen_mask = P.DropoutGenMask()
>>> shape = (20, 16, 50) >>> shape = (20, 16, 50)
>>> keep_prob = Tensor(0.5, mindspore.float32) >>> keep_prob = Tensor(0.5, mindspore.float32)
>>> mask = dropout_gen_mask(shape, keep_prob) >>> mask = dropout_gen_mask(shape, keep_prob)
...@@ -1631,8 +1621,8 @@ class DropoutDoMask(PrimitiveWithInfer): ...@@ -1631,8 +1621,8 @@ class DropoutDoMask(PrimitiveWithInfer):
>>> x = Tensor(np.ones([20, 16, 50]), mindspore.float32) >>> x = Tensor(np.ones([20, 16, 50]), mindspore.float32)
>>> shape = (20, 16, 50) >>> shape = (20, 16, 50)
>>> keep_prob = Tensor(0.5, mindspore.float32) >>> keep_prob = Tensor(0.5, mindspore.float32)
>>> dropout_gen_mask = DropoutGenMask() >>> dropout_gen_mask = P.DropoutGenMask()
>>> dropout_do_mask = DropoutDoMask() >>> dropout_do_mask = P.DropoutDoMask()
>>> mask = dropout_gen_mask(shape, keep_prob) >>> mask = dropout_gen_mask(shape, keep_prob)
>>> output = dropout_do_mask(x, mask, keep_prob) >>> output = dropout_do_mask(x, mask, keep_prob)
>>> assert output.shape() == (20, 16, 50) >>> assert output.shape() == (20, 16, 50)
...@@ -1737,7 +1727,7 @@ class OneHot(PrimitiveWithInfer): ...@@ -1737,7 +1727,7 @@ class OneHot(PrimitiveWithInfer):
Examples: Examples:
>>> indices = Tensor(np.array([0, 1, 2]), mindspore.int32) >>> indices = Tensor(np.array([0, 1, 2]), mindspore.int32)
>>> depth, on_value, off_value = 3, Tensor(1.0, mindspore.float32), Tensor(0.0, mindspore.float32) >>> depth, on_value, off_value = 3, Tensor(1.0, mindspore.float32), Tensor(0.0, mindspore.float32)
>>> onehot = OneHot() >>> onehot = P.OneHot()
>>> result = onehot(indices, depth, on_value, off_value) >>> result = onehot(indices, depth, on_value, off_value)
[[1, 0, 0], [0, 1, 0], [0, 0, 1]] [[1, 0, 0], [0, 1, 0], [0, 0, 1]]
""" """
...@@ -1793,7 +1783,7 @@ class Gelu(PrimitiveWithInfer): ...@@ -1793,7 +1783,7 @@ class Gelu(PrimitiveWithInfer):
Examples: Examples:
>>> tensor = Tensor(np.array([1.0, 2.0, 3.0]), mindspore.float32) >>> tensor = Tensor(np.array([1.0, 2.0, 3.0]), mindspore.float32)
>>> gelu = Gelu() >>> gelu = P.Gelu()
>>> result = gelu(tensor) >>> result = gelu(tensor)
""" """
...@@ -1834,7 +1824,7 @@ class GetNext(PrimitiveWithInfer): ...@@ -1834,7 +1824,7 @@ class GetNext(PrimitiveWithInfer):
and the type is described is `types`. and the type is described is `types`.
Examples: Examples:
>>> get_next = GetNext([mindspore.float32, mindspore.int32], [[32, 1, 28, 28], [10]], 'shared_name') >>> get_next = P.GetNext([mindspore.float32, mindspore.int32], [[32, 1, 28, 28], [10]], 'shared_name')
>>> feature, label = get_next() >>> feature, label = get_next()
""" """
...@@ -2015,7 +2005,7 @@ class Pad(PrimitiveWithInfer): ...@@ -2015,7 +2005,7 @@ class Pad(PrimitiveWithInfer):
Examples: Examples:
>>> input_tensor = Tensor(np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]), mindspore.float32) >>> input_tensor = Tensor(np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]), mindspore.float32)
>>> pad_op = Pad(((1, 2), (2, 1))) >>> pad_op = P.Pad(((1, 2), (2, 1)))
>>> output_tensor = pad_op(input_tensor) >>> output_tensor = pad_op(input_tensor)
>>> assert output_tensor == Tensor(np.array([[ 0. , 0. , 0. , 0. , 0. , 0. ], >>> assert output_tensor == Tensor(np.array([[ 0. , 0. , 0. , 0. , 0. , 0. ],
>>> [ 0. , 0. , -0.1, 0.3, 3.6, 0. ], >>> [ 0. , 0. , -0.1, 0.3, 3.6, 0. ],
......
...@@ -406,7 +406,7 @@ def export(net, *inputs, file_name, file_format='GEIR'): ...@@ -406,7 +406,7 @@ def export(net, *inputs, file_name, file_format='GEIR'):
file_format (str): MindSpore currently supports 'GEIR', 'ONNX' and 'LITE' format for exported model. file_format (str): MindSpore currently supports 'GEIR', 'ONNX' and 'LITE' format for exported model.
- GEIR: Graph Engine Intermidiate Representation. An intermidiate representation format of - GEIR: Graph Engine Intermidiate Representation. An intermidiate representation format of
Ascend model. Ascend model.
- ONNX: Open Neural Network eXchange. An open format built to represent machine learning models. - ONNX: Open Neural Network eXchange. An open format built to represent machine learning models.
- LITE: Huawei model format for mobile. - LITE: Huawei model format for mobile.
""" """
......
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