forked from mindspore-Ecosystem/mindspore
move composite.array_ops.py to ops.array_func.py
This commit is contained in:
changzherui
parent
72f21eb213
commit
0de95930f4
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@ -29,7 +29,7 @@ from mindspore.ops.composite.multitype_ops.ones_like_impl import ones_like
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from mindspore.ops.composite.multitype_ops.zeros_like_impl import zeros_like
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from mindspore.ops.function.random_func import normal, laplace, uniform, gamma, poisson, multinomial
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from mindspore.ops.composite.math_ops import count_nonzero, tensor_dot, dot, batch_dot, matmul, cummin, mm
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from mindspore.ops.composite.array_ops import repeat_interleave, repeat_elements, sequence_mask
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from mindspore.ops.function.array_func import repeat_interleave, repeat_elements, sequence_mask
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from mindspore.ops.composite.vmap_ops import _VmapGeneralPreprocess, _VmapGeneralRule
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from mindspore.ops.function.clip_func import clip_by_value
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@ -1,237 +0,0 @@
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# Copyright 2020-2021 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""array Operations."""
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from mindspore.ops.composite.multitype_ops import _constexpr_utils as const_utils
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from mindspore.common import dtype as mstype
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from mindspore.common._register_for_tensor import tensor_operator_registry
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from mindspore._checkparam import Validator as validator
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from mindspore._checkparam import Rel
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from mindspore.ops.primitive import constexpr
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from mindspore.ops import functional as F
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from mindspore.ops import operations as P
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#TODO: remove comment
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@constexpr
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def _check_is_int(arg_value, arg_name, op_name):
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arg_value = validator.check_is_int(arg_value, arg_name, op_name)
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return arg_value
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#TODO: remove comment
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@constexpr
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def _check_positive_int(arg_value, arg_name, op_name):
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arg_value = validator.check_positive_int(arg_value, arg_name, op_name)
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return arg_value
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#TODO: remove comment
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@constexpr
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def _check_axis_range(arg_value, limit, arg_name, op_name):
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arg_value = validator.check_int_range(arg_value, -limit, limit, Rel.INC_LEFT, arg_name, op_name)
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return arg_value
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#TODO: remove comment
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@constexpr
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def _cal_repeat_dims(x_rank, rep, expand_axis):
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rep_dims = [1] * (x_rank + 1)
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rep_dims[expand_axis] = rep
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return tuple(rep_dims)
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@constexpr
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def _cal_reshape(x_shape, rep, axis):
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x_reshape = list(x_shape)
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x_reshape[axis] *= rep
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return tuple(x_reshape)
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def repeat_interleave(x, repeats, dim=None):
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"""
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Repeat elements of a tensor along an axis, like `numpy.repeat`.
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Args:
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x (Tensor): The tensor to repeat values for. Must be of type: float16,
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float32, int8, uint8, int16, int32, or int64.
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repeats (int): The number of times to repeat, must be positive.
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dim (int, optional): The axis along which to repeat, if None, defaults to 0.
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Returns:
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One tensor with values repeated along the specified axis. If x has shape
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(s1, s2, ..., sn) and axis is i, the output will have shape (s1, s2, ...,
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si * repeats, ..., sn). The output type will be the same as the type of `x`.
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Supported Platforms:
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``Ascend`` ``GPU`` ``CPU``
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Examples:
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>>> import mindspore
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>>> from mindspore import Tensor, ops
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>>> import numpy as np
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>>> x = Tensor(np.array([[0, 1, 2], [3, 4, 5]]), mindspore.int32)
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>>> output = ops.repeat_interleave(x, repeats=2, dim=0)
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>>> print(output)
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[[0 1 2]
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[0 1 2]
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[3 4 5]
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[3 4 5]]
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"""
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return repeat_elements(x, repeats, dim)
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def repeat_elements(x, rep, axis=0):
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"""
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Repeat elements of a tensor along an axis, like `np.repeat` .
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Args:
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x (Tensor): The tensor to repeat values for. Must be of type: float16,
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float32, int8, uint8, int16, int32, or int64.
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rep (int): The number of times to repeat, must be positive.
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axis (int): The axis along which to repeat, default 0.
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Returns:
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One tensor with values repeated along the specified axis. If x has shape
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(s1, s2, ..., sn) and axis is i, the output will have shape (s1, s2, ...,
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si * rep, ..., sn). The output type will be the same as the type of `x`.
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Supported Platforms:
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``Ascend`` ``GPU`` ``CPU``
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Examples:
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>>> from mindspore import Tensor, ops
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>>> import mindspore
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>>> import numpy as np
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>>> # case 1 : repeat on axis 0
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>>> x = Tensor(np.array([[0, 1, 2], [3, 4, 5]]), mindspore.int32)
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>>> output = ops.repeat_elements(x, rep = 2, axis = 0)
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>>> print(output)
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[[0 1 2]
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[0 1 2]
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[3 4 5]
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[3 4 5]]
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>>> # case 2 : repeat on axis 1
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>>> x = Tensor(np.array([[0, 1, 2], [3, 4, 5]]), mindspore.int32)
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>>> output = ops.repeat_elements(x, rep = 2, axis = 1)
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>>> print(output)
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[[0 0 1 1 2 2]
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[3 3 4 4 5 5]]
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"""
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const_utils.check_type_valid(F.dtype(x), mstype.number_type, 'input x')
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rep = _check_positive_int(rep, "rep", "repeat_elements")
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axis = _check_is_int(axis, "axis", "repeat_elements")
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shape_op = P.Shape()
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rank_op = P.Rank()
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tile_op = P.Tile()
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expand_dims_op = P.ExpandDims()
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reshape_op = P.Reshape()
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x_rank = rank_op(x)
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axis = _check_axis_range(axis, x_rank, "axis", "repeat_elements")
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expand_axis = axis + 1
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x_expand = expand_dims_op(x, expand_axis)
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rep_dims = _cal_repeat_dims(x_rank, rep, expand_axis)
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x_expand = tile_op(x_expand, rep_dims)
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x_shape = shape_op(x)
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x_reshape = _cal_reshape(x_shape, rep, axis)
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x_rep = reshape_op(x_expand, x_reshape)
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return x_rep
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tensor_operator_registry.register('repeat_elements', repeat_elements)
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def sequence_mask(lengths, maxlen=None):
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"""
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Returns a mask tensor representing the first N positions of each cell.
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If `lengths` has shape :math:`(d_1, d_2, ..., d_n)`, then the resulting tensor mask has type and shape
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:math:`(d_1, d_2, ..., d_n, maxlen)`, with mask :math:`[i_1, i_2, ..., i_n, j] = (j < lengths[i_1, i_2, ..., i_n])`.
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Args:
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lengths (Tensor): Tensor to calculate the mask for. All values in this tensor should be
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less than or equal to `maxlen`. Values greater than `maxlen` will be treated as `maxlen`.
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maxlen (int): size of the last dimension of returned tensor. Must be positive and same
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type as elements in `lengths`. Default is None.
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Returns:
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One mask tensor of shape `lengths.shape + (maxlen,)` .
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Raises:
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TypeError: If `lengths` is not a Tensor.
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TypeError: If `maxlen` is not an int.
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TypeError: If dtype of `lengths` is neither int32 nor int64.
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Supported Platforms:
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``GPU``
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Examples:
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>>> from mindspore import Tensor, ops
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>>> import mindspore
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>>> import numpy as np
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>>> # case 1: When maxlen is assigned
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>>> x = Tensor(np.array([1, 2, 3, 4]))
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>>> output = ops.sequence_mask(x, 5)
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>>> print(output)
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[[ True False False False False]
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[ True True False False False]
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[ True True True False False]
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[ True True True True False]]
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>>> # case 2: When there is 0 in x
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>>> x = Tensor(np.array([[1, 3], [2, 0]]))
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>>> output = ops.sequence_mask(x, 5)
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>>> print(output)
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[[[ True False False False False]
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[ True True True False False]]
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[[ True True False False False]
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[False False False False False]]]
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>>> # case 3: when the maxlen is not assigned
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>>> x = Tensor(np.array([[1, 3], [2, 4]]))
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>>> output = ops.sequence_mask(x)
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>>> print(output)
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[[[ True False False False ]
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[ True True True False ]]
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[[ True True False False ]
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[ True True True True ]]]
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"""
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argmax_op = P.ArgMaxWithValue()
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reshape_op = P.Reshape()
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range_op = P.Range()
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expand_op = P.ExpandDims()
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cast_op = P.Cast()
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to_tensor_op = P.ScalarToTensor()
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const_utils.check_type_valid(F.dtype(lengths), [mstype.int64, mstype.int32], 'lengths')
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if maxlen is None:
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flatten_data = reshape_op(lengths, (-1,))
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flatten_data = cast_op(flatten_data, mstype.float32)
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_, value = argmax_op(flatten_data)
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maxlen = cast_op(value, mstype.int32)
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else:
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maxlen = _check_positive_int(maxlen, "maxlen", "sequence_mask")
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maxlen = to_tensor_op(maxlen, mstype.int32)
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range_vector = range_op(to_tensor_op(0, mstype.int32), maxlen
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, to_tensor_op(1, mstype.int32))
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mask = expand_op(lengths, -1)
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result = range_vector < mask
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return result
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@ -139,6 +139,9 @@ from .array_func import (
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lstsq,
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mvlgamma,
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argsort,
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sequence_mask,
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repeat_elements,
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repeat_interleave,
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)
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from .parameter_func import (
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assign,
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@ -25,6 +25,7 @@ from mindspore.ops.primitive import constexpr
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import mindspore.ops.function as ops
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from mindspore.ops import functional as F
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from mindspore.ops.operations._inner_ops import DynamicBroadcastTo
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from mindspore.ops.composite.multitype_ops import _constexpr_utils as const_utils
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from mindspore.ops.operations.array_ops import (
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UniqueConsecutive,
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@ -5978,6 +5979,198 @@ def count_nonzero(x, dims=None):
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return count_nonzero_(x)
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@constexpr
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def _check_is_int(arg_value, arg_name, op_name):
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arg_value = validator.check_is_int(arg_value, arg_name, op_name)
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return arg_value
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@constexpr
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def _check_positive_int(arg_value, arg_name, op_name):
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arg_value = validator.check_positive_int(arg_value, arg_name, op_name)
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return arg_value
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@constexpr
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def _check_axis_range(arg_value, limit, arg_name, op_name):
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arg_value = validator.check_int_range(arg_value, -limit, limit, Rel.INC_LEFT, arg_name, op_name)
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return arg_value
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@constexpr
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def _cal_repeat_dims(x_rank, rep, expand_axis):
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rep_dims = [1] * (x_rank + 1)
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rep_dims[expand_axis] = rep
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return tuple(rep_dims)
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@constexpr
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def _cal_reshape(x_shape, rep, axis):
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x_reshape = list(x_shape)
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x_reshape[axis] *= rep
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return tuple(x_reshape)
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def repeat_interleave(x, repeats, dim=None):
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"""
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Repeat elements of a tensor along an axis, like `numpy.repeat`.
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Args:
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x (Tensor): The tensor to repeat values for. Must be of type: float16,
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float32, int8, uint8, int16, int32, or int64.
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repeats (int): The number of times to repeat, must be positive.
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dim (int, optional): The axis along which to repeat, if None, defaults to 0.
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Returns:
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One tensor with values repeated along the specified axis. If x has shape
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(s1, s2, ..., sn) and axis is i, the output will have shape (s1, s2, ...,
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si * repeats, ..., sn). The output type will be the same as the type of `x`.
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Supported Platforms:
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``Ascend`` ``GPU`` ``CPU``
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Examples:
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>>> x = Tensor(np.array([[0, 1, 2], [3, 4, 5]]), mindspore.int32)
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>>> output = ops.repeat_interleave(x, repeats=2, dim=0)
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>>> print(output)
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[[0 1 2]
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[0 1 2]
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[3 4 5]
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[3 4 5]]
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"""
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return repeat_elements(x, repeats, dim)
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def repeat_elements(x, rep, axis=0):
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"""
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Repeat elements of a tensor along an axis, like `np.repeat` .
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Args:
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x (Tensor): The tensor to repeat values for. Must be of type: float16,
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float32, int8, uint8, int16, int32, or int64.
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rep (int): The number of times to repeat, must be positive.
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axis (int): The axis along which to repeat, default 0.
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Returns:
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One tensor with values repeated along the specified axis. If x has shape
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(s1, s2, ..., sn) and axis is i, the output will have shape (s1, s2, ...,
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si * rep, ..., sn). The output type will be the same as the type of `x`.
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Supported Platforms:
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``Ascend`` ``GPU`` ``CPU``
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Examples:
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>>> # case 1 : repeat on axis 0
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>>> x = Tensor(np.array([[0, 1, 2], [3, 4, 5]]), mindspore.int32)
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>>> output = ops.repeat_elements(x, rep = 2, axis = 0)
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>>> print(output)
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[[0 1 2]
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[0 1 2]
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[3 4 5]
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[3 4 5]]
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>>> # case 2 : repeat on axis 1
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>>> x = Tensor(np.array([[0, 1, 2], [3, 4, 5]]), mindspore.int32)
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>>> output = ops.repeat_elements(x, rep = 2, axis = 1)
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>>> print(output)
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[[0 0 1 1 2 2]
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[3 3 4 4 5 5]]
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"""
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const_utils.check_type_valid(F.dtype(x), mstype.number_type, 'input x')
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rep = _check_positive_int(rep, "rep", "repeat_elements")
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axis = _check_is_int(axis, "axis", "repeat_elements")
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shape_op = P.Shape()
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rank_op = P.Rank()
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tile_op = P.Tile()
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expand_dims_op = P.ExpandDims()
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reshape_op = P.Reshape()
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x_rank = rank_op(x)
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axis = _check_axis_range(axis, x_rank, "axis", "repeat_elements")
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expand_axis = axis + 1
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x_expand = expand_dims_op(x, expand_axis)
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rep_dims = _cal_repeat_dims(x_rank, rep, expand_axis)
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x_expand = tile_op(x_expand, rep_dims)
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x_shape = shape_op(x)
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x_reshape = _cal_reshape(x_shape, rep, axis)
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x_rep = reshape_op(x_expand, x_reshape)
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return x_rep
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def sequence_mask(lengths, maxlen=None):
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"""
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Returns a mask tensor representing the first N positions of each cell.
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If `lengths` has shape :math:`(d_1, d_2, ..., d_n)`, then the resulting tensor mask has type and shape
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:math:`(d_1, d_2, ..., d_n, maxlen)`, with mask :math:`[i_1, i_2, ..., i_n, j] = (j < lengths[i_1, i_2, ..., i_n])`.
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Args:
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lengths (Tensor): Tensor to calculate the mask for. All values in this tensor should be
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less than or equal to `maxlen`. Values greater than `maxlen` will be treated as `maxlen`.
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maxlen (int): size of the last dimension of returned tensor. Must be positive and same
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type as elements in `lengths`. Default is None.
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Returns:
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One mask tensor of shape `lengths.shape + (maxlen,)` .
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Raises:
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TypeError: If `lengths` is not a Tensor.
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TypeError: If `maxlen` is not an int.
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TypeError: If dtype of `lengths` is neither int32 nor int64.
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Supported Platforms:
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``GPU``
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Examples:
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>>> # case 1: When maxlen is assigned
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>>> x = Tensor(np.array([1, 2, 3, 4]))
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>>> output = ops.sequence_mask(x, 5)
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>>> print(output)
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[[ True False False False False]
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[ True True False False False]
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[ True True True False False]
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[ True True True True False]]
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>>> # case 2: When there is 0 in x
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>>> x = Tensor(np.array([[1, 3], [2, 0]]))
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>>> output = ops.sequence_mask(x, 5)
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>>> print(output)
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[[[ True False False False False]
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[ True True True False False]]
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[[ True True False False False]
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[False False False False False]]]
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>>> # case 3: when the maxlen is not assigned
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>>> x = Tensor(np.array([[1, 3], [2, 4]]))
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>>> output = ops.sequence_mask(x)
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>>> print(output)
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[[[ True False False False ]
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||||
[ True True True False ]]
|
||||
[[ True True False False ]
|
||||
[ True True True True ]]]
|
||||
"""
|
||||
|
||||
argmax_op = P.ArgMaxWithValue()
|
||||
reshape_op = P.Reshape()
|
||||
range_op = P.Range()
|
||||
expand_op = P.ExpandDims()
|
||||
cast_op = P.Cast()
|
||||
to_tensor_op = P.ScalarToTensor()
|
||||
|
||||
const_utils.check_type_valid(F.dtype(lengths), [mstype.int64, mstype.int32], 'lengths')
|
||||
|
||||
if maxlen is None:
|
||||
flatten_data = reshape_op(lengths, (-1,))
|
||||
flatten_data = cast_op(flatten_data, mstype.float32)
|
||||
_, value = argmax_op(flatten_data)
|
||||
maxlen = cast_op(value, mstype.int32)
|
||||
else:
|
||||
maxlen = _check_positive_int(maxlen, "maxlen", "sequence_mask")
|
||||
maxlen = to_tensor_op(maxlen, mstype.int32)
|
||||
|
||||
range_vector = range_op(to_tensor_op(0, mstype.int32), maxlen
|
||||
, to_tensor_op(1, mstype.int32))
|
||||
mask = expand_op(lengths, -1)
|
||||
result = range_vector < mask
|
||||
return result
|
||||
|
||||
|
||||
__all__ = [
|
||||
'unique',
|
||||
'unique_with_pad',
|
||||
|
|
@ -6090,6 +6283,9 @@ __all__ = [
|
|||
'diagonal',
|
||||
'lstsq',
|
||||
'mvlgamma',
|
||||
'argsort'
|
||||
'argsort',
|
||||
'sequence_mask',
|
||||
'repeat_elements',
|
||||
'repeat_interleave'
|
||||
]
|
||||
__all__.sort()
|
||||
|
|
|
|||
|
|
@ -27,7 +27,6 @@ from mindspore.ops.operations.math_ops import Median
|
|||
from mindspore.ops.operations.array_ops import UniqueConsecutive, Triu
|
||||
from mindspore.ops.operations.nn_ops import AdaptiveMaxPool2D
|
||||
from mindspore.ops.operations.math_ops import Roll
|
||||
from mindspore.ops.composite.array_ops import repeat_interleave
|
||||
from mindspore.ops.composite.math_ops import mm
|
||||
|
||||
typeof = Primitive('typeof')
|
||||
|
|
@ -398,6 +397,7 @@ tensor_operator_registry.register('deg2rad', deg2rad)
|
|||
tensor_operator_registry.register('copysign', copysign)
|
||||
tensor_operator_registry.register('roll', Roll)
|
||||
tensor_operator_registry.register('rot90', rot90)
|
||||
tensor_operator_registry.register('repeat_elements', repeat_elements)
|
||||
|
||||
__all__ = [name for name in dir() if name[0] != "_"]
|
||||
__all__.remove('Primitive')
|
||||
|
|
|
|||
|
|
@ -64,6 +64,7 @@ approvers:
|
|||
- mengyuanli
|
||||
- liujunzhu
|
||||
- luoyang42
|
||||
- changzherui
|
||||
reviewers:
|
||||
- nicholas_yhr
|
||||
- liubuyu
|
||||
|
|
|
|||
Loading…
Reference in New Issue