"""array Operations."""
from mindspore.ops.composite.multitype_ops import _constexpr_utils as const_utils
from mindspore.common import dtype as mstype
from mindspore.common._register_for_tensor import tensor_operator_registry
from mindspore._checkparam import Validator as validator
from mindspore._checkparam import Rel
from mindspore.ops.primitive import constexpr
from mindspore.ops import functional as F
from .. import operations as P
@constexpr
def _check_is_int(arg_value, arg_name, op_name):
arg_value = validator.check_is_int(arg_value, arg_name, op_name)
return arg_value
@constexpr
def _check_positive_int(arg_value, arg_name, op_name):
arg_value = validator.check_positive_int(arg_value, arg_name, op_name)
return arg_value
@constexpr
def _check_axis_range(arg_value, limit, arg_name, op_name):
arg_value = validator.check_int_range(arg_value, -limit, limit, Rel.INC_LEFT, arg_name, op_name)
return arg_value
@constexpr
def _cal_repeat_dims(x_rank, rep, expand_axis):
rep_dims = [1] * (x_rank + 1)
rep_dims[expand_axis] = rep
return tuple(rep_dims)
@constexpr
def _cal_reshape(x_shape, rep, axis):
x_reshape = list(x_shape)
x_reshape[axis] *= rep
return tuple(x_reshape)
def repeat_elements(x, rep, axis=0):
"""
Repeat elements of a tensor along an axis, like np.repeat.
Args:
x (Tensor): The tensor to repeat values for. Must be of type: float16,
float32, int8, uint8, int16, int32, or int64.
rep (int): The number of times to repeat, must be positive, required.
axis (int): The axis along which to repeat, default 0.
Outputs:
One tensor with values repeated along the specified axis. If x has shape
(s1, s2, ..., sn) and axis is i, the output will have shape (s1, s2, ...,
si * rep, ..., sn). The output type will be the same as the type of `x`.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> # case 1 : repeat on axis 0
>>> x = Tensor(np.array([[0, 1, 2], [3, 4, 5]]), mindspore.int32)
>>> output = ops.repeat_elements(x, rep = 2, axis = 0)
>>> print(output)
[[0 1 2]
[0 1 2]
[3 4 5]
[3 4 5]]
>>> # case 2 : repeat on axis 1
>>> x = Tensor(np.array([[0, 1, 2], [3, 4, 5]]), mindspore.int32)
>>> output = ops.repeat_elements(x, rep = 2, axis = 1)
>>> print(output)
[[0 0 1 1 2 2]
[3 3 4 4 5 5]]
"""
const_utils.check_type_valid(F.dtype(x), mstype.number_type, 'input x')
rep = _check_positive_int(rep, "rep", "repeat_elements")
axis = _check_is_int(axis, "axis", "repeat_elements")
shape_op = P.Shape()
rank_op = P.Rank()
tile_op = P.Tile()
expand_dims_op = P.ExpandDims()
reshape_op = P.Reshape()
x_rank = rank_op(x)
axis = _check_axis_range(axis, x_rank, "axis", "repeat_elements")
expand_axis = axis + 1
x_expand = expand_dims_op(x, expand_axis)
rep_dims = _cal_repeat_dims(x_rank, rep, expand_axis)
x_expand = tile_op(x_expand, rep_dims)
x_shape = shape_op(x)
x_reshape = _cal_reshape(x_shape, rep, axis)
x_rep = reshape_op(x_expand, x_reshape)
return x_rep
tensor_operator_registry.register('repeat_elements', repeat_elements)
@constexpr
def _check_sequence_mask_input_len(input_shape, prim_name=None):
msg_prefix = f"For '{prim_name}', the" if prim_name else "The"
if not input_shape:
raise ValueError(f"{msg_prefix} input_shape should be greater than 0, but got {input_shape}.")
shape_size = len(input_shape)
if shape_size >= 7:
raise ValueError(f"{msg_prefix} dimension of input_shape should be less than 7, but got {shape_size}d.")
def sequence_mask(lengths, maxlen=None, prim_name='sequence_mask'):
"""
Returns a mask tensor representing the first N positions of each cell.
If lengths has shape [d_1, d_2, ..., d_n], then the resulting tensor mask has type dtype and shape
[d_1, d_2, ..., d_n, maxlen], with mask[i_1, i_2, ..., i_n, j] = (j < lengths[i_1, i_2, ..., i_n])
Inputs:
- **lengths** (Tensor) - Tensor to calculate the mask for. All values in this tensor should be
less than or equal to `maxlen`. Values greater than `maxlen` will be treated as `maxlen`.
Must be type int32 or int64.
- **maxlen** (int) - size of the last dimension of returned tensor. Must be positive and same
type as elements in `lengths`. Default is None.
Outputs:
One mask tensor of shape lengths.shape + (maxlen,).
Raises:
TypeError: If `lengths` is not a Tensor.
TypeError: If `maxlen` is not an int.
TypeError: If dtype of `lengths` is neither int32 nor int64.
Supported Platforms:
``GPU``
Examples:
>>> # case 1: When maxlen is assigned
>>> x = Tensor(np.array([1, 2, 3, 4]))
>>> output = ops.sequence_mask(x, 5)
>>> print(output)
[[ True False False False False]
[ True True False False False]
[ True True True False False]
[ True True True True False]]
>>> # case 2: When there is 0 in x
>>> x = Tensor(np.array([[1, 3], [2, 0]]))
>>> output = ops.sequence_mask(x, 5)
>>> print(output)
[[[ True False False False False]
[ True True True False False]]
[[ True True False False False]
[False False False False False]]]
>>> # case 3: when the maxlen is not assigned
>>> x = Tensor(np.array([[1, 3], [2, 4]]))
>>> output = ops.sequence_mask(x)
>>> print(output)
[[[ True False False False ]
[ 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()
shape_op = P.Shape()
to_tensor_op = P.ScalarToArray()
const_utils.check_type_valid(F.dtype(lengths), [mstype.int64, mstype.int32], 'lengths')
_check_sequence_mask_input_len(shape_op(lengths), prim_name)
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)
range_vector = range_op(to_tensor_op(0), maxlen
, to_tensor_op(1))
mask = expand_op(lengths, -1)
result = range_vector < mask
return result