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tensor.py
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tensor.py
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# -*- coding: utf-8 -*-
from functools import lru_cache
from typing import Iterable, List, Optional, Sequence, Tuple, Union
import numpy as np
from ..core._imperative_rt import CompNode
from ..core._imperative_rt.core2 import (
Const,
apply,
broadcast_cpp,
create_complex,
dtype_promotion,
expand_dims_cpp,
get_imag,
get_real,
split_cpp,
squeeze_cpp,
)
from ..core._wrap import as_device
from ..core.ops import builtin
from ..core.ops.builtin import Copy, Identity
from ..core.tensor.utils import astensor1d, convert_inputs, get_device, subgraph_fn
from ..device import get_default_device
from ..tensor import Tensor
from .elemwise import ceil, cos, sin
__all__ = [
"arange",
"broadcast_to",
"concat",
"cond_take",
"non_zero",
"copy",
"cumsum",
"diag",
"expand_dims",
"eye",
"flatten",
"full",
"full_like",
"gather",
"imag",
"linspace",
"meshgrid",
"ones",
"ones_like",
"polar",
"repeat",
"reshape",
"roll",
"scatter",
"split",
"squeeze",
"stack",
"swapaxes",
"tile",
"transpose",
"complex",
"real",
"where",
"zeros",
"zeros_like",
]
# creation functions
def arange(
start: Union[int, float] = 0,
stop: Optional[Union[int, float]] = None,
step: Union[int, float] = 1,
*,
dtype="float32",
device=None,
) -> Tensor:
r"""Returns evenly spaced values within the half-open interval ``[start, stop)`` as a one-dimensional tensor.
Note:
This function cannot guarantee that the interval does not include the stop value in those cases
where step is not an integer and floating-point rounding errors affect the length of the output tensor.
Args:
start(Number): if ``stop`` is specified, the start of interval (inclusive); otherwise,
the end of the interval (exclusive). If ``stop`` is not specified, the default starting value is ``0``.
stop(Number): the end of the interval.
step(Number): the distance between two adjacent elements ( ``out[i+1] - out[i]`` ). Must not be 0 ;
may be negative, this results i an empty tensor if stop >= start .
Keyword args:
dtype(:attr:`.Tensor.dtype`, optional): output tensor data type.
device(:attr:`.Tensor.device`, optional): device on which to place the created tensor.
.. seealso:: :func:`~.functional.linspace`
Returns:
A one-dimensional tensor containing evenly spaced values.
The length of the output tensor must be ``ceil((stop-start)/step)``
if ``stop - start`` and ``step`` have the same sign, and length 0 otherwise.
Examples:
>>> F.arange(5)
Tensor([0. 1. 2. 3. 4.], device=xpux:0)
>>> F.arange(1, 4)
Tensor([1. 2. 3.], device=xpux:0)
"""
if stop is None:
start, stop = 0, start
if not isinstance(start, Tensor):
start = Tensor(start, dtype="float32", device=device)
if not isinstance(stop, Tensor):
stop = Tensor(stop, dtype="float32", device=device)
if not isinstance(step, Tensor):
step = Tensor(step, dtype="float32", device=device)
num = ceil((stop - start) / step)
stop = start + step * (num - 1)
result = linspace(start, stop, num, device=device)
if np.dtype(dtype) != np.float32:
return result.astype(dtype)
return result
def linspace(
start: Union[int, float],
stop: Union[int, float],
num: int,
*,
dtype="float32",
device: Optional[CompNode] = None,
) -> Tensor:
r"""Returns evenly spaced numbers over a specified interval.
Returns ``num`` evenly spaced samples, calculated over the interval ``[start, stop]``.
Args:
start(Number): the start of the interval.
stop(Number): the end of the interval.
num(int): number of values to generate.
Keyword args:
dtype(:attr:`.Tensor.dtype`, optional): output tensor data type.
If ``dtype`` is not given, the data type is inferred from ``start`` and ``stop``.
device(:attr:`.Tensor.device`, optional): device on which to place the created tensor.
Returns:
a one-dimensional tensor containing evenly spaced values.
.. seealso:: :func:`~.functional.arange`
Examples:
>>> F.linspace(1, 10, 10)
Tensor([ 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.], device=xpux:0)
>>> F.linspace(2., 3., 5)
Tensor([2. 2.25 2.5 2.75 3. ], device=xpux:0)
"""
for item in (start, stop, num):
cur_device = getattr(item, "device", None)
if device is None:
device = cur_device
else:
if not (cur_device is None or device == cur_device):
raise ("ambiguous device for linspace opr")
if not isinstance(start, Tensor):
start = Tensor(start, device=device)
if not isinstance(stop, Tensor):
stop = Tensor(stop, device=device)
if not isinstance(num, Tensor):
num = Tensor(num, device=device)
op = builtin.Linspace(comp_node=device)
(result,) = apply(op, start, stop, num)
if np.dtype(dtype) != np.float32:
return result.astype(dtype)
return result
def eye(N: int, M: int = None, *, dtype="float32", device=None) -> Tensor:
r"""Returns a two-dimensional tensor with ones on the diagonal and zeros elsewhere.
Args:
N: number of rows in the output tesnor.
M: number of columns in the output tesnor.
If ``None``, the default number of columns in the output tesnor is equal tos ``N``.
Keyword args:
dtype(:attr:`.Tensor.dtype`, optional): output tesnor data type.
If ``None``, the output tesnor data type must be the default floating-point data type.
device(:attr:`.Tensor.device`, optional): device on which to place the created tensor.
.. seealso:: If you want to create a diagonal matrix, see :func:`~.functional.diag`.
Returns:
a tensor where all elements are equal to zero,
except for the diagonal, whose values are equal to one.
Examples:
>>> F.eye(3)
Tensor([[1. 0. 0.]
[0. 1. 0.]
[0. 0. 1.]], device=xpux:0)
>>> F.eye(4, 6)
Tensor([[1. 0. 0. 0. 0. 0.]
[0. 1. 0. 0. 0. 0.]
[0. 0. 1. 0. 0. 0.]
[0. 0. 0. 1. 0. 0.]], device=xpux:0)
"""
if M is not None:
if isinstance(N, Tensor) or isinstance(M, Tensor):
shape = astensor1d((N, M))
else:
shape = Tensor([N, M], dtype="int32", device=device)
elif isinstance(N, Tensor):
shape = N
else:
shape = Tensor(N, dtype="int32", device=device)
op = builtin.Eye(k=0, dtype=dtype, comp_node=device)
(result,) = apply(op, shape)
return result
def diag(inp, k: int = 0) -> Tensor:
r"""Extract a diagonal or construct a diagonal tensor.
If ``inp`` is a 1D tensor, then returns a 2D tensor with the elements of ``inp`` as the diagonal.
If ``inp`` is a 2D tensor, then returns a 1D tensor with the diagonal elements of ``inp``.
Args:
inp: input tensor.
k: diagonal in consider. Use :math:`k=0` for the main diagonal, :math:`k>0` for diagonals above the
main diagonal, and :math:`k<0` for diagonals below the main diagonal.
.. seealso:: If you want to create a identity matrix, see :func:`~.functional.eye`.
Returns:
the extracted diagonal or constructed diagonal tensor.
Examples:
Input is a 1D tensor:
>>> F.diag(Tensor([1, 2, 3]))
Tensor([[1 0 0]
[0 2 0]
[0 0 3]], dtype=int32, device=xpux:0)
>>> F.diag(Tensor([1, 2, 3]), k=1)
Tensor([[0 1 0 0]
[0 0 2 0]
[0 0 0 3]
[0 0 0 0]], dtype=int32, device=xpux:0)
Input is a 2D tensor:
>>> x = F.arange(9).reshape(3, 3)
>>> x
Tensor([[0. 1. 2.]
[3. 4. 5.]
[6. 7. 8.]], device=xpux:0)
>>> F.diag(x)
Tensor([0. 4. 8.], device=xpux:0)
Get the k-th diagonal of a given matrix:
>>> F.diag(x, k=1)
Tensor([1. 5.], device=xpux:0)
>>> F.diag(x, k=-1)
Tensor([3. 7.], device=xpux:0)
"""
op = builtin.Diag(k=k)
(result,) = apply(op, inp)
return result
def full(
shape: Union[int, Tuple[int, ...]],
value: Union[bool, int, float],
*,
dtype=None,
device=None,
) -> Tensor:
r"""Returns a new tensor having a specified shape and filled with given value.
Args:
shape(int...): output tensor shape.
value(Scalar): fill value.
Keyword args:
dtype(:attr:`.Tensor.dtype`, optional): output tensor data type.
If ``dtype`` is ``None``, the output tensor data type must be inferred from ``value``.
If the value is an ``int``, the output tensor data type must be the default integer data type.
If the value is a ``float``, the output tensor data type must be the default floating-point data type.
If the value is a ``bool``, the output tensor must have boolean data type.
device(:attr:`.Tensor.device`, optional): device on which to place the created tensor.
Returns:
a tensor where every element is equal to ``value``.
Examples:
>>> F.full((2, 3), 6)
Tensor([[6 6 6]
[6 6 6]], dtype=int32, device=xpux:0)
"""
if isinstance(shape, int):
shape = (shape,)
if device is None:
device = get_default_device()
x = Const(value, dtype, device)
if type(shape) in (list, tuple) and len(shape) == 0:
return x
return broadcast_to(x, shape)
def ones(
shape: Union[int, Tuple[int, ...]],
*,
dtype="float32",
device: Optional[CompNode] = None
) -> Tensor:
r"""Returns a new tensor having a specified shape and filled with ones.
Args:
shape(int...): the shape of the output tensor.
Keyword args:
dtype(:attr:`.Tensor.dtype`, optional): output tensor data type.
device(:attr:`.Tensor.device`, optional): device on which to place the created tensor.
Returns:
a tensor containing ones.
Examples:
>>> F.ones(5)
Tensor([1. 1. 1. 1. 1.], device=xpux:0)
>>> F.ones((5, ), dtype='int32')
Tensor([1 1 1 1 1], dtype=int32, device=xpux:0)
>>> F.ones((2, 2))
Tensor([[1. 1.]
[1. 1.]], device=xpux:0)
"""
if isinstance(shape, int):
shape = (shape,)
if device == None:
device = get_default_device()
op = builtin.Fill(1, dtype)
shape = astensor1d(shape, dtype="int32", device=device)
(x,) = apply(op, shape)
return x
def zeros(
shape: Union[int, Tuple[int, ...]],
*,
dtype="float32",
device: Optional[CompNode] = None
) -> Tensor:
r"""Returns a new tensor having a specified shape and filled with zeros.
Args:
shape(int...): the shape of the output tensor.
Keyword args:
dtype(:attr:`.Tensor.dtype`, optional): output tensor data type.
device(:attr:`.Tensor.device`, optional): device on which to place the created tensor.
Returns:
a tensor containing zeros.
Examples:
>>> F.zeros((2, 3))
Tensor([[0. 0. 0.]
[0. 0. 0.]], device=xpux:0)
"""
if isinstance(shape, int):
shape = (shape,)
if device == None:
device = get_default_device()
op = builtin.Fill(0, dtype)
shape = astensor1d(shape, dtype="int32", device=device)
(x,) = apply(op, shape)
return x
def zeros_like(inp: Tensor) -> Tensor:
r"""Returns a tensor filled with zeros with the same shape and data type as input tensor.
Args:
inp(Tensor): input tensor from which to derive the output tensor shape.
Return:
a tensor having the same shape as input tensor and filled with zeros.
Examples:
>>> x = F.arange(6, dtype='int32').reshape(2, 3)
>>> F.zeros_like(x)
Tensor([[0 0 0]
[0 0 0]], dtype=int32, device=xpux:0)
"""
return full_like(inp, 0.0)
def ones_like(inp: Tensor) -> Tensor:
r"""Returns a tensor filled with ones with the same shape and data type as input tensor.
Args:
inp(Tensor): input tensor from which to derive the output tensor shape.
Return:
a tensor having the same shape as input tensor and filled with ones.
Examples:
>>> x = F.arange(6, dtype='int32').reshape(2, 3)
>>> F.ones_like(x)
Tensor([[1 1 1]
[1 1 1]], dtype=int32, device=xpux:0)
"""
return full_like(inp, 1.0)
def polar(abs: Tensor, angle: Tensor) -> Tensor:
r"""Constructs a complex tensor whose elements are Cartesian coordinates
corresponding to the polar coordinates with absolute value abs and angle angle.
Args:
abs(Tensor): the absolute value the complex tensor. Must be float.
angle(Tensor): the angle of the complex tensor. Must be float.
Returns:
the complex tensor
Examples:
>>> abs = Tensor([1, 2], dtype=np.float32)
>>> angle = Tensor([np.pi / 2, 5 * np.pi / 4], dtype=np.float32)
>>> z = F.polar(abs, angle)
>>> z
Tensor([-4.3711e-08+1.j -1.4142e+00-1.4142j], dtype=complex64, device=xpux:0)
"""
return create_complex(abs * cos(angle), abs * sin(angle))
def complex(real: Tensor, imag: Tensor) -> Tensor:
r"""Constructs a complex tensor with its real part equal to real and its imaginary part equal to imag.
Args:
real(Tensor): the real part of the complex tensor. Must be float.
imag(Tensor): the imaginary part of the complex tensor. Must be float.
Returns:
the complex tensor
Examples:
>>> real = Tensor([1, 2], dtype=np.float32)
>>> imag = Tensor([3, 4], dtype=np.float32)
>>> z = F.complex(real, imag)
>>> z
Tensor([1.+3.j 2.+4.j], dtype=complex64, device=xpux:0)
>>> z.dtype
dtype('complex64')
"""
if not isinstance(real, Tensor):
real = Tensor(real)
if not isinstance(imag, Tensor):
imag = Tensor(imag)
return create_complex(real, imag)
def real(complex: Tensor) -> Tensor:
r"""Returns a new tensor containing real values of the complex tensor.
Args:
complex(Tensor) the complex tensor
Returns:
the real part of the complex tensor
Examples:
>>> x=Tensor([0.3100+0.3553j, -0.5445-0.7896j, -1.6492-0.0633j, -0.0638-0.8119j], dtype=np.complex64)
>>> F.real(x)
Tensor([[ 0.31 ]
[-0.5445]
[-1.6492]
[-0.0638]], device=xpux:0)
"""
return get_real(complex)
def imag(complex: Tensor) -> Tensor:
r"""Returns a new tensor containing imaginary values of the complex tensor.
Args:
complex(Tensor) the complex tensor
Returns:
the imaginary part of the complex tensor
Examples:
>>> x=Tensor([0.3100+0.3553j, -0.5445-0.7896j, -1.6492-0.0633j, -0.0638-0.8119j], dtype=np.complex64)
>>> F.imag(x)
Tensor([[ 0.3553]
[-0.7896]
[-0.0633]
[-0.8119]], device=xpux:0)
"""
return get_imag(complex)
def full_like(inp: Tensor, value: Union[int, float]) -> Tensor:
r"""Returns a tensor filled with given value with the same shape as input tensor.
Args:
inp(Tensor): input tensor from which to derive the output tensor shape.
value(Scalar): fill value.
Return:
a tensor having the same shape as input tensor and where every element is equal to fill value.
Examples:
>>> x = F.arange(6, dtype='int32').reshape(2, 3)
>>> F.full_like(x, 2)
Tensor([[2 2 2]
[2 2 2]], dtype=int32, device=xpux:0)
"""
op = builtin.FillLike(value=value)
(rst,) = apply(op, inp)
# rst.format = inp.format
# see jira:MGE-4505
return rst
# manipulation functions
def broadcast_to(inp: Tensor, shape: Union[int, Iterable[int]]) -> Tensor:
r"""Broadcasts a tensor to given shape.
Args:
inp: input tensor.
shape: target shape.
Returns:
output tensor.
Examples:
>>> import numpy as np
>>> data = Tensor(np.arange(0, 3, dtype=np.float32).reshape(3))
>>> out = F.broadcast_to(data, (2, 3))
>>> out.numpy()
array([[0., 1., 2.],
[0., 1., 2.]], dtype=float32)
"""
return broadcast_cpp(inp, shape)
def concat(inps: Iterable[Tensor], axis: int = 0, device=None) -> Tensor:
r"""Concat some tensors
Args:
inps: input tensors to concat.
axis: over which dimension the tensors are concatenated. Default: 0
device: which device output will be. Default: None
Returns:
output tensor.
Examples:
>>> import numpy as np
>>> data1 = Tensor(np.arange(0, 6, dtype=np.float32).reshape((2, 3)))
>>> data2 = Tensor(np.arange(6, 12, dtype=np.float32).reshape((2, 3)))
>>> out = F.concat([data1, data2])
>>> out.numpy()
array([[ 0., 1., 2.],
[ 3., 4., 5.],
[ 6., 7., 8.],
[ 9., 10., 11.]], dtype=float32)
"""
if len(inps) == 1:
# if we return inps[0] directly, then the grad manager capture nothing
return copy(inps[0], device)
if device is None:
device = get_device(inps)
device = as_device(device)
(result,) = apply(builtin.Concat(axis=axis, comp_node=device.to_c()), *inps)
return result
def stack(inps, axis=0, device=None):
r"""Concats a sequence of tensors along a new axis.
The input tensors must have the same shape.
Args:
inps: input tensors.
axis: which axis will be concatenated.
device: the device output will be. Default: None
Returns:
output concatenated tensor.
Examples:
>>> import numpy as np
>>> x1 = Tensor(np.arange(0, 3, dtype=np.float32).reshape((3)))
>>> x2 = Tensor(np.arange(6, 9, dtype=np.float32).reshape((3)))
>>> out = F.stack([x1, x2], axis=0)
>>> out.numpy()
array([[0., 1., 2.],
[6., 7., 8.]], dtype=float32)
"""
if len(inps) == 1:
ret = expand_dims(inps[0], axis=axis)
if device is None:
return ret
else:
return copy(ret, device)
if device is None:
device = get_device(inps)
device = as_device(device)
(result,) = apply(builtin.Stack(axis=axis, comp_node=device.to_c()), *inps)
return result
def split(inp, nsplits_or_sections, axis=0):
r"""Splits the input tensor into several smaller tensors.
When nsplits_or_sections is int, the last tensor may be smaller than others.
Args:
inp: input tensor.
nsplits_or_sections: number of sub tensors or sections information list.
axis: which axis will be splited.
Returns:
output tensor list.
Examples:
>>> import os
>>> import numpy as np
>>> x = Tensor(np.random.random((10, 20)), dtype=np.float32)
>>> y = F.split(x, 3)
>>> z = F.split(x, [6, 17], axis=1)
>>> print([i.numpy().shape for i in y])
[(4, 20), (3, 20), (3, 20)]
>>> print([i.numpy().shape for i in z])
[(10, 6), (10, 11), (10, 3)]
"""
return split_cpp(inp, nsplits_or_sections, axis)
def _get_idx(index, axis):
index_dims = len(index.shape)
idx = []
if axis < 0:
axis += index_dims
for i in range(index_dims):
if i != axis:
shape = [1] * index_dims
shape[i] = index.shape[i]
arange = linspace(
0, index.shape[i] - 1, index.shape[i], device=index.device,
)
arange = (
broadcast_to(arange.reshape(*shape), index.shape)
.reshape(-1)
.astype(np.int32)
)
idx.append(arange)
else:
idx.append(index.reshape(-1))
return tuple(idx)
def gather(inp: Tensor, axis: int, index: Tensor) -> Tensor:
# TODO: rewrite doc
r"""
Gathers data from input tensor on axis using index.
For a 3-D tensor, the output is specified by:
.. code-block::
out[i][j][k] = inp[index[i][j][k]][j][k] # if axis == 0
out[i][j][k] = inp[i][index[i][j][k]][k] # if axis == 1
out[i][j][k] = inp[i][j][index[i][j][k]] # if axis == 2
if input tensor is a n-dimensional tensor with size
:math:`(x_0,x_1,...,x_{i-1},x_i,x_{i+1},...,x_{n-1})` and axis=i,
then index must be a n-dimensional tensor with size
:math:`(x_0,x_1,...,x_{i-1},y,x_{i+1},...,x_{n-1})` where :math:`y\ge 1` and
output will have the same size as index.
Args:
inp: input tensor.
axis: along which axis to index.
index: indices of elements to gather.
Return:
output tensor.
Examples:
>>> inp = Tensor([
... [1,2], [3,4], [5,6],
... ])
>>> index = Tensor([[0,2], [1,0]])
>>> F.gather(inp, 0, index)
Tensor([[1 6]
[3 2]], dtype=int32, device=xpux:0)
"""
input_shape = inp.shape
index_shape = index.shape
input_dims = len(input_shape)
index_dims = len(index_shape)
if input_dims != index_dims:
raise ValueError(
"The index tensor must have same dimensions as input tensor, "
"But the input dims:{}, the index dims:{}".format(input_dims, index_dims)
)
idx = _get_idx(index, axis)
return inp[idx].reshape(index.shape) # pylint: disable=no-member
def scatter(inp: Tensor, axis: int, index: Tensor, source: Tensor) -> Tensor:
# TODO: rewrite doc
r"""
Writes all values from the tensor source into input tensor
at the indices specified in the index tensor.
For each value in source, its output index is specified by its index
in source for ``axis != dimension`` and by the corresponding value in
index for ``axis = dimension``.
For a 3-D tensor, input tensor is updated as:
.. code-block::
inp[index[i][j][k]][j][k] = source[i][j][k] # if axis == 0
inp[i][index[i][j][k]][k] = source[i][j][k] # if axis == 1
inp[i][j][index[i][j][k]] = source[i][j][k] # if axis == 2
``inp``, ``index`` and ``source`` should have same number of dimensions.
It is also required that ``source.shape(d) <= inp.shape(d)`` and ``index.shape(d) == source.shape(d)``
for all dimensions ``d``.
Moreover, the values of index must be between ``0`` and ``inp.shape(axis) - 1`` inclusive.
Note:
Please notice that, due to performance issues, the result is uncertain on the GPU device
if scattering different positions from source to the same destination position
regard to index tensor.
Check the following examples, the oup[0][2] is maybe
from source[0][2] which value is 0.2256 or source[1][2] which value is 0.5339
if set the index[1][2] from 1 to 0.
Args:
inp: inp tensor which to be scattered.
axis: axis along which to index.
index: indices of elements to scatter.
source: source element(s) to scatter.
Return:
output tensor.
Examples:
>>> import numpy as np
>>> inp = Tensor(np.zeros(shape=(3,5),dtype=np.float32))
>>> source = Tensor([[0.9935,0.9465,0.2256,0.8926,0.4396],[0.7723,0.0718,0.5939,0.357,0.4576]])
>>> index = Tensor([[0,2,0,2,1],[2,0,1,1,2]])
>>> oup = F.scatter(inp, 0, index, source)
>>> oup.numpy()
array([[0.9935, 0.0718, 0.2256, 0. , 0. ],
[0. , 0. , 0.5939, 0.357 , 0.4396],
[0.7723, 0.9465, 0. , 0.8926, 0.4576]], dtype=float32)
"""
input_shape = inp.shape
index_shape = index.shape
source_shape = source.shape
input_dims = len(input_shape)
index_dims = len(index_shape)
source_dims = len(source_shape)
if input_dims != index_dims or input_dims != source_dims:
raise ValueError("The input, source and index tensor must have same dimensions")
for i in range(source_dims):
if source_shape[i] > input_shape[i]:
raise ValueError(
"The each shape size for source {} must be less than or equal to input {} ".format(
source_shape, input_shape
)
)
for i in range(index_dims):
if index_shape[i] != source_shape[i]:
raise ValueError(
"The each shape size for index {} must be equal to source {} ".format(
index_shape, source_shape
)
)
for i in range(index_dims):
if i != axis and index_shape[i] > input_shape[i]:
raise ValueError(
"The index {} must be less than or equal to input {} size apart from axis {}".format(
index_shape, input_shape, axis
)
)
idx = _get_idx(index, axis)
inp[idx] = source.flatten()
return inp
def where(mask: Tensor, x: Tensor = None, y: Tensor = None) -> Tensor:
r"""Selects elements either from Tensor x or Tensor y, according to mask.
.. math::
\textrm{out}_i = x_i \textrm{ if } \textrm{mask}_i \textrm{ is True else } y_i
Args:
mask: a mask used for choosing ``x`` or ``y``.
x: first choice.
y: second choice.
Returns:
output tensor.
Examples:
>>> import numpy as np
>>> mask = Tensor(np.array([[True, False], [False, True]], dtype=np.bool_))
>>> x = Tensor(np.array([[1, np.inf], [np.nan, 4]],
... dtype=np.float32))
>>> y = Tensor(np.array([[5, 6], [7, 8]], dtype=np.float32))
>>> out = F.where(mask, x, y)
>>> out.numpy()
array([[1., 6.],
[7., 4.]], dtype=float32)
"""
if x is None and y is None:
return non_zero(mask, as_tuple=True)
if not isinstance(x, Tensor):
raise TypeError("input x must be a tensor")
if not isinstance(y, Tensor):
raise TypeError("input y must be a tensor")
if not isinstance(mask, Tensor):
raise TypeError("mask must be a tensor")
if mask.dtype != np.bool_:
raise ValueError("mask must be bool")
if x.device != mask.device:
raise ValueError("ambiguous device: {} vs {}".format(x.device, mask.device))
where = builtin.Where()
return apply(where, mask, x, y)[0]
def cond_take(mask: Tensor, x: Tensor) -> Tensor:
r"""Takes elements from data if specific condition is satisfied on mask.
This operator has two outputs: the first is the elements taken,
and the second is the indices corresponding to those elements;
they are both 1-dimensional. High-dimension input would first be flattened.
Args:
mask: condition param; must be the same shape with data.
x: input tensor from which to take elements.
Examples:
>>> import numpy as np
>>> mask = Tensor(np.array([[True, False], [False, True]], dtype=np.bool_))
>>> x = Tensor(np.array([[1, np.inf], [np.nan, 4]],
... dtype=np.float32))
>>> v, index = F.cond_take(mask, x)
>>> print(v.numpy(), index.numpy())
[1. 4.] [0 3]
"""
if not isinstance(x, Tensor):
raise TypeError("input must be a tensor")
if not isinstance(mask, Tensor):
raise TypeError("mask must be a tensor")
if mask.dtype != np.bool_:
raise ValueError("mask must be bool")
if x.device != mask.device:
raise ValueError("ambiguous device: {} vs {}".format(x.device, mask.device))
op = builtin.CondTake()
v, index = apply(op, x, mask)
return v, index
def transpose(inp: Tensor, pattern: Iterable[int]) -> Tensor:
r"""Swaps shapes and strides according to given pattern.
Args:
inp: input tensor.
pattern: a list of integers including 0, 1, ... , ``ndim``-1,
and any number of ``'x'`` char in dimensions where this tensor should be broadcasted.
For examples:
* (``'x'``) -> make a 0d (scalar) into a 1d vector
* (0, 1) -> identity for 2d vectors
* (1, 0) -> inverts the first and second dimensions
* (``'x'``, 0) -> make a row out of a 1d vector (N to 1xN)
* (0, ``'x'``) -> make a column out of a 1d vector (N to Nx1)
* (2, 0, 1) -> AxBxC to CxAxB
* (0, ``'x'``, 1) -> AxB to Ax1xB
* (1, ``'x'``, 0) -> AxB to Bx1xA
* (1,) -> this removes dimensions 0. It must be a broadcastable dimension (1xA to A)
Returns:
output tensor.
Examples:
>>> import numpy as np
>>> x = Tensor(np.array([[1, 1], [0, 0]], dtype=np.int32))
>>> F.transpose(x, (1, 0))
Tensor([[1 0]
[1 0]], dtype=int32, device=xpux:0)
"""
return inp.transpose(pattern)
def non_zero(condition: Tensor, as_tuple=False):
r"""When as_tuple is False (default):
Returns a tensor including the indices of all non-zero elements of Tensor condition.
Every row in the result including the indices of a non-zero element in input.
The result is sorted in lexicography order, with the last index changing the fastest (C-style).
When as_tuple is True:
Returns a tuple of 1-D tensors, one for each dimension in input,
each containing the indices (in that dimension) of all non-zero elements of condition.
Args:
condition(Tensor) - the input tensor
Returns:
one tuple of 1-D tensors or one tensor
Examples:
>>> import numpy as np
>>> condition = Tensor(np.array([1,1,0,1]))
>>> index = F.non_zero(condition,as_tuple=True)
>>> print(index)
(Tensor([0 1 3], dtype=int32, device=xpux:0),)
"""
if not isinstance(condition, Tensor):
raise TypeError("input must be a tensor")
op = builtin.NonZero()
(index,) = apply(op, condition)
ret = None
if as_tuple == True:
arr = []
for index_ele in range(0, condition.ndim):
arr.append(index[index_ele, :])
ret = tuple(arr)
else:
ret = transpose(index, (1, 0))
return ret
def swapaxes(inp: Tensor, axis1: int, axis2: int) -> Tensor:
r"""Interchange two axes of a tensor.
Args:
inp: input tensor to swapaxes.
axis1: first axis.
axis2: second axis.
Returns:
a tensor after swapping the two axes of 'inp'.
Examples:
>>> x = Tensor(np.array([[[0,1],[2,3]],[[4,5],[6,7]]], dtype=np.int32))
>>> F.swapaxes(x, 0, 2)
Tensor([[[0 4]
[2 6]]
[[1 5]
[3 7]]], dtype=int32, device=xpux:0)
"""
pattern = list(range(inp.ndim))
tempAxis = pattern[axis1]
pattern[axis1] = pattern[axis2]
pattern[axis2] = tempAxis
return inp.transpose(pattern)
def reshape(inp: Tensor, target_shape: Iterable[int]) -> Tensor:
r"""Reshapes a tensor without changing its data.
Args:
inp: input tensor to reshape.