/
manipulation.py
1765 lines (1410 loc) · 65.4 KB
/
manipulation.py
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
from ..fluid.layers import core
from ..fluid.layer_helper import LayerHelper
from ..fluid.framework import Variable, OpProtoHolder, in_dygraph_mode, convert_np_dtype_to_dtype_, device_guard, dygraph_only
from ..fluid.data_feeder import convert_dtype, check_variable_and_dtype, check_type, check_dtype
from ..fluid.layers.tensor import fill_constant
from ..fluid.layers import utils
import numpy as np
import six
# TODO: define functions to manipulate a tensor
from ..fluid.layers import cast # noqa: F401
from ..fluid.layers import slice # noqa: F401
from ..fluid.layers import transpose # noqa: F401
from ..fluid.layers import unstack # noqa: F401
from ..fluid.layers import scatter_nd # noqa: F401
from ..fluid.layers import shard_index # noqa: F401
from ..fluid import layers
from ..fluid.dygraph.inplace_utils import inplace_apis_in_dygraph_only
import paddle
__all__ = []
@dygraph_only
def tolist(x):
"""
**Notes**:
**This API is ONLY available in Dygraph mode**
This function translate the paddle.Tensor to python list.
Args:
x(Tensor): ``x`` is the Tensor we want to translate to list
Returns:
list: A list that contain the same value of current Tensor.
Returns type:
list: dtype is same as current Tensor
Examples:
.. code-block:: python
import paddle
t = paddle.to_tensor([0,1,2,3,4])
expectlist = t.tolist()
print(expectlist) #[0, 1, 2, 3, 4]
expectlist = paddle.tolist(t)
print(expectlist) #[0, 1, 2, 3, 4]
"""
return x.numpy().tolist()
setattr(core.VarBase, 'tolist', tolist)
def concat(x, axis=0, name=None):
"""
This OP concatenates the input along the axis.
Args:
x(list|tuple): ``x`` is a Tensor list or Tensor tuple which is with data type bool, float16,
float32, float64, int32, int64, uint8. All the Tensors in ``x`` must have same data type.
axis(int|Tensor, optional): Specify the axis to operate on the input Tensors.
It's a scalar with data type int or a Tensor with shape [1] and data type int32
or int64. The effective range is [-R, R), where R is Rank(x). When ``axis < 0``,
it works the same way as ``axis+R``. Default is 0.
name (str, optional): The default value is None. Normally there is no
need for user to set this property. For more information, please
refer to :ref:`api_guide_Name`.
Returns:
Tensor: A Tensor with the same data type as ``x``.
Examples:
.. code-block:: python
import paddle
x1 = paddle.to_tensor([[1, 2, 3],
[4, 5, 6]])
x2 = paddle.to_tensor([[11, 12, 13],
[14, 15, 16]])
x3 = paddle.to_tensor([[21, 22],
[23, 24]])
zero = paddle.full(shape=[1], dtype='int32', fill_value=0)
# When the axis is negative, the real axis is (axis + Rank(x))
# As follow, axis is -1, Rank(x) is 2, the real axis is 1
out1 = paddle.concat(x=[x1, x2, x3], axis=-1)
out2 = paddle.concat(x=[x1, x2], axis=0)
out3 = paddle.concat(x=[x1, x2], axis=zero)
# out1
# [[ 1 2 3 11 12 13 21 22]
# [ 4 5 6 14 15 16 23 24]]
# out2 out3
# [[ 1 2 3]
# [ 4 5 6]
# [11 12 13]
# [14 15 16]]
"""
return paddle.fluid.layers.concat(input=x, axis=axis, name=name)
def flip(x, axis, name=None):
"""
Reverse the order of a n-D tensor along given axis in axis.
Args:
x (Tensor): A Tensor(or LoDTensor) with shape :math:`[N_1, N_2,..., N_k]` . The data type of the input Tensor x
should be float32, float64, int32, int64, bool.
axis (list|tuple|int): The axis(axes) to flip on. Negative indices for indexing from the end are accepted.
name (str, optional): The default value is None. Normally there is no need for user to set this property.
For more information, please refer to :ref:`api_guide_Name` .
Returns:
Tensor: Tensor or LoDTensor calculated by flip layer. The data type is same with input x.
Examples:
.. code-block:: python
import paddle
import numpy as np
image_shape=(3, 2, 2)
x = np.arange(image_shape[0] * image_shape[1] * image_shape[2]).reshape(image_shape)
x = x.astype('float32')
img = paddle.to_tensor(x)
tmp = paddle.flip(img, [0,1])
print(tmp) # [[[10,11],[8, 9]], [[6, 7],[4, 5]], [[2, 3],[0, 1]]]
out = paddle.flip(tmp,-1)
print(out) # [[[11,10],[9, 8]], [[7, 6],[5, 4]], [[3, 2],[1, 0]]]
"""
if isinstance(axis, int):
axis = [axis]
if in_dygraph_mode():
return core.ops.flip(x, "axis", axis)
helper = LayerHelper("flip", **locals())
check_type(x, 'X', (Variable), 'flip')
dtype = helper.input_dtype('x')
check_dtype(dtype, 'X',
['float16', 'float32', 'float64', 'int32', 'int64', 'bool'],
'flip')
check_type(axis, 'axis', (list, tuple), 'flip')
if name is None:
out = helper.create_variable_for_type_inference(dtype)
else:
out = helper.create_variable(name=name, dtype=dtype, persistable=False)
helper.append_op(
type="flip",
inputs={"X": x},
outputs={"Out": out},
attrs={"axis": axis})
return out
def flatten(x, start_axis=0, stop_axis=-1, name=None):
r"""
**Flatten op**
Flattens a contiguous range of axes in a tensor according to start_axis and stop_axis.
Note that the output Tensor will share data with origin Tensor and doesn't have a
Tensor copy in ``dygraph`` mode. If you want to use the Tensor copy version, please
use `Tensor.clone` like ``flatten_clone_x = x.flatten().clone()``.
For Example:
.. code-block:: text
Case 1:
Given
X.shape = (3, 100, 100, 4)
and
start_axis = 1
end_axis = 2
We get:
Out.shape = (3, 1000 * 100, 2)
Case 2:
Given
X.shape = (3, 100, 100, 4)
and
start_axis = 0
stop_axis = -1
We get:
Out.shape = (3 * 100 * 100 * 4)
Args:
x (Tensor): A tensor of number of dimentions >= axis. A tensor with data type float32,
float64, int8, int32, int64, uint8.
start_axis (int): the start axis to flatten
stop_axis (int): the stop axis to flatten
name(str, Optional): For details, please refer to :ref:`api_guide_Name`.
Generally, no setting is required. Default: None.
Returns:
Tensor: A tensor with the contents of the input tensor, with input \
axes flattened by indicated start axis and end axis. \
A Tensor with data type same as input x.
Raises:
ValueError: If x is not a Tensor.
ValueError: If start_axis or stop_axis is illegal.
Examples:
.. code-block:: python
import paddle
image_shape=(2, 3, 4, 4)
x = paddle.arange(end=image_shape[0] * image_shape[1] * image_shape[2] * image_shape[3])
img = paddle.reshape(x, image_shape)
out = paddle.flatten(img, start_axis=1, stop_axis=2)
# out shape is [2, 12, 4]
# out shares data with img in dygraph mode
img[0, 0, 0, 0] = -1
print(out[0, 0, 0]) # [-1]
"""
if not (isinstance(x, Variable)):
raise ValueError("The input x should be a Tensor")
check_variable_and_dtype(
x, 'x', ['float32', 'float64', 'int8', 'int32', 'int64', 'uint8'],
'flatten')
helper = LayerHelper('flatten', **locals())
x_dim = len(x.shape)
if not (isinstance(start_axis, int)) or (
start_axis > x_dim - 1) or start_axis < -x_dim:
raise ValueError(
"The start_axis should be a int, and in range [-rank(x), rank(x))")
if not (isinstance(stop_axis, int)) or (
stop_axis > x_dim - 1) or stop_axis < -x_dim:
raise ValueError(
"The stop_axis should be a int, and in range [-rank(x), rank(x))")
if start_axis < 0:
start_axis = start_axis + x_dim
if stop_axis < 0:
stop_axis = stop_axis + x_dim
if start_axis > stop_axis:
raise ValueError("The stop_axis should be larger than stat_axis")
if in_dygraph_mode():
dy_out, _ = core.ops.flatten_contiguous_range(
x, 'start_axis', start_axis, 'stop_axis', stop_axis)
return dy_out
out = helper.create_variable_for_type_inference(x.dtype)
x_shape = helper.create_variable_for_type_inference(x.dtype)
helper.append_op(
type='flatten_contiguous_range',
inputs={"X": x},
outputs={'Out': out,
'XShape': x_shape},
attrs={"start_axis": start_axis,
"stop_axis": stop_axis})
return out
@inplace_apis_in_dygraph_only
def flatten_(x, start_axis=0, stop_axis=-1, name=None):
"""
Inplace version of ``flatten`` API, the output Tensor will be inplaced with input ``x``.
Please refer to :ref:`api_tensor_flatten`.
"""
if not (isinstance(x, Variable)):
raise ValueError("The input x should be a Tensor")
x_dim = len(x.shape)
if not (isinstance(start_axis, int)) or (
start_axis > x_dim - 1) or start_axis < -x_dim:
raise ValueError(
"The start_axis should be a int, and in range [-rank(x), rank(x))")
if not (isinstance(stop_axis, int)) or (
stop_axis > x_dim - 1) or stop_axis < -x_dim:
raise ValueError(
"The stop_axis should be a int, and in range [-rank(x), rank(x))")
if start_axis < 0:
start_axis = start_axis + x_dim
if stop_axis < 0:
stop_axis = stop_axis + x_dim
if start_axis > stop_axis:
raise ValueError("The stop_axis should be larger than stat_axis")
dy_out, _ = core.ops.flatten_contiguous_range_(x, 'start_axis', start_axis,
'stop_axis', stop_axis)
return dy_out
def roll(x, shifts, axis=None, name=None):
"""
Roll the `x` tensor along the given axis(axes). With specific 'shifts', Elements that
roll beyond the last position are re-introduced at the first according to 'shifts'.
If a axis is not specified,
the tensor will be flattened before rolling and then restored to the original shape.
Args:
x (Tensor): The x tensor as input.
shifts (int|list|tuple): The number of places by which the elements
of the `x` tensor are shifted.
axis (int|list|tuple|None): axis(axes) along which to roll.
Returns:
Tensor: A Tensor with same data type as `x`.
Examples:
.. code-block:: python
import paddle
x = paddle.to_tensor([[1.0, 2.0, 3.0],
[4.0, 5.0, 6.0],
[7.0, 8.0, 9.0]])
out_z1 = paddle.roll(x, shifts=1)
print(out_z1)
#[[9. 1. 2.]
# [3. 4. 5.]
# [6. 7. 8.]]
out_z2 = paddle.roll(x, shifts=1, axis=0)
print(out_z2)
#[[7. 8. 9.]
# [1. 2. 3.]
# [4. 5. 6.]]
"""
helper = LayerHelper("roll", **locals())
origin_shape = x.shape
if type(shifts) == int:
shifts = [shifts]
if type(axis) == int:
axis = [axis]
len_origin_shape = len(origin_shape)
if axis:
for i in range(len(axis)):
if axis[i] >= len_origin_shape or axis[i] < -len_origin_shape:
raise ValueError(
"axis is out of range, it should be in range [{}, {}), but received {}".
format(-len_origin_shape, len_origin_shape, axis))
if axis:
check_type(axis, 'axis', (list, tuple), 'roll')
check_type(shifts, 'shifts', (list, tuple), 'roll')
if in_dygraph_mode():
if axis is None:
x = core.ops.reshape(x, 'shape', [-1, 1])
axis = [0]
out = core.ops.roll(x, 'axis', axis, 'shifts', shifts)
return core.ops.reshape(out, 'shape', origin_shape)
out = helper.create_variable_for_type_inference(x.dtype)
if axis is None:
x = reshape(x, shape=[-1, 1])
axis = [0]
helper.append_op(
type='roll',
inputs={'X': x},
outputs={'Out': out},
attrs={'axis': axis,
'shifts': shifts})
out = layers.reshape(out, shape=origin_shape)
return out
def stack(x, axis=0, name=None):
"""
This OP stacks all the input tensors ``x`` along ``axis`` dimemsion.
All tensors must be of the same shape and same dtype.
For example, given N tensors of shape [A, B], if ``axis == 0``, the shape of stacked
tensor is [N, A, B]; if ``axis == 1``, the shape of stacked
tensor is [A, N, B], etc.
.. code-block:: text
Case 1:
Input:
x[0].shape = [1, 2]
x[0].data = [ [1.0 , 2.0 ] ]
x[1].shape = [1, 2]
x[1].data = [ [3.0 , 4.0 ] ]
x[2].shape = [1, 2]
x[2].data = [ [5.0 , 6.0 ] ]
Attrs:
axis = 0
Output:
Out.dims = [3, 1, 2]
Out.data =[ [ [1.0, 2.0] ],
[ [3.0, 4.0] ],
[ [5.0, 6.0] ] ]
Case 2:
Input:
x[0].shape = [1, 2]
x[0].data = [ [1.0 , 2.0 ] ]
x[1].shape = [1, 2]
x[1].data = [ [3.0 , 4.0 ] ]
x[2].shape = [1, 2]
x[2].data = [ [5.0 , 6.0 ] ]
Attrs:
axis = 1 or axis = -2 # If axis = -2, axis = axis+ndim(x[0])+1 = -2+2+1 = 1.
Output:
Out.shape = [1, 3, 2]
Out.data =[ [ [1.0, 2.0]
[3.0, 4.0]
[5.0, 6.0] ] ]
Args:
x (list[Tensor]|tuple[Tensor]): Input ``x`` can be a ``list`` or ``tuple`` of tensors, the Tensors in ``x``
must be of the same shape and dtype. Supported data types: float32, float64, int32, int64.
axis (int, optional): The axis along which all inputs are stacked. ``axis`` range is ``[-(R+1), R+1)``,
where ``R`` is the number of dimensions of the first input tensor ``x[0]``.
If ``axis < 0``, ``axis = axis+R+1``. The default value of axis is 0.
name (str, optional): Please refer to :ref:`api_guide_Name`, Default None.
Returns:
Tensor: The stacked tensor with same data type as input.
Example:
.. code-block:: python
import paddle
x1 = paddle.to_tensor([[1.0, 2.0]])
x2 = paddle.to_tensor([[3.0, 4.0]])
x3 = paddle.to_tensor([[5.0, 6.0]])
out = paddle.stack([x1, x2, x3], axis=0)
print(out.shape) # [3, 1, 2]
print(out)
# [[[1., 2.]],
# [[3., 4.]],
# [[5., 6.]]]
"""
return layers.stack(x, axis, name)
def split(x, num_or_sections, axis=0, name=None):
"""
Split the input tensor into multiple sub-Tensors.
Args:
x (Tensor): A N-D Tensor. The data type is bool, float16, float32, float64, int32 or int64.
num_or_sections (int|list|tuple): If ``num_or_sections`` is an int, then ``num_or_sections``
indicates the number of equal sized sub-Tensors that the ``x`` will be divided into.
If ``num_or_sections`` is a list or tuple, the length of it indicates the number of
sub-Tensors and the elements in it indicate the sizes of sub-Tensors' dimension orderly.
The length of the list must not be larger than the ``x`` 's size of specified ``axis``.
axis (int|Tensor, optional): The axis along which to split, it can be a scalar with type
``int`` or a ``Tensor`` with shape [1] and data type ``int32`` or ``int64``.
If :math::`axis < 0`, the axis to split along is :math:`rank(x) + axis`. Default is 0.
name (str, optional): The default value is None. Normally there is no need for user to set this property.
For more information, please refer to :ref:`api_guide_Name` .
Returns:
list(Tensor): The list of segmented Tensors.
Example:
.. code-block:: python
import paddle
# x is a Tensor of shape [3, 9, 5]
x = paddle.rand([3, 9, 5])
out0, out1, out2 = paddle.split(x, num_or_sections=3, axis=1)
print(out0.shape) # [3, 3, 5]
print(out1.shape) # [3, 3, 5]
print(out2.shape) # [3, 3, 5]
out0, out1, out2 = paddle.split(x, num_or_sections=[2, 3, 4], axis=1)
print(out0.shape) # [3, 2, 5]
print(out1.shape) # [3, 3, 5]
print(out2.shape) # [3, 4, 5]
out0, out1, out2 = paddle.split(x, num_or_sections=[2, 3, -1], axis=1)
print(out0.shape) # [3, 2, 5]
print(out1.shape) # [3, 3, 5]
print(out2.shape) # [3, 4, 5]
# axis is negative, the real axis is (rank(x) + axis)=1
out0, out1, out2 = paddle.split(x, num_or_sections=3, axis=-2)
print(out0.shape) # [3, 3, 5]
print(out1.shape) # [3, 3, 5]
print(out2.shape) # [3, 3, 5]
"""
return paddle.fluid.layers.split(
input=x, num_or_sections=num_or_sections, dim=axis, name=name)
def squeeze(x, axis=None, name=None):
"""
This OP will squeeze the dimension(s) of size 1 of input tensor x's shape.
Note that the output Tensor will share data with origin Tensor and doesn't have a
Tensor copy in ``dygraph`` mode. If you want to use the Tensor copy version,
please use `Tensor.clone` like ``squeeze_clone_x = x.squeeze().clone()``.
If axis is provided, it will remove the dimension(s) by given axis that of size 1.
If the dimension of given axis is not of size 1, the dimension remain unchanged.
If axis is not provided, all dims equal of size 1 will be removed.
.. code-block:: text
Case1:
Input:
x.shape = [1, 3, 1, 5] # If axis is not provided, all dims equal of size 1 will be removed.
axis = None
Output:
out.shape = [3, 5]
Case2:
Input:
x.shape = [1, 3, 1, 5] # If axis is provided, it will remove the dimension(s) by given axis that of size 1.
axis = 0
Output:
out.shape = [3, 1, 5]
Case4:
Input:
x.shape = [1, 3, 1, 5] # If the dimension of one given axis (3) is not of size 1, the dimension remain unchanged.
axis = [0, 2, 3]
Output:
out.shape = [3, 5]
Case4:
Input:
x.shape = [1, 3, 1, 5] # If axis is negative, axis = axis + ndim (number of dimensions in x).
axis = [-2]
Output:
out.shape = [1, 3, 5]
Args:
x (Tensor): The input Tensor. Supported data type: float32, float64, bool, int8, int32, int64.
axis (int|list|tuple, optional): An integer or list/tuple of integers, indicating the dimensions to be squeezed. Default is None.
The range of axis is :math:`[-ndim(x), ndim(x))`.
If axis is negative, :math:`axis = axis + ndim(x)`.
If axis is None, all the dimensions of x of size 1 will be removed.
name (str, optional): Please refer to :ref:`api_guide_Name`, Default None.
Returns:
Tensor: Squeezed Tensor with the same data type as input Tensor.
Examples:
.. code-block:: python
import paddle
x = paddle.rand([5, 1, 10])
output = paddle.squeeze(x, axis=1)
print(x.shape) # [5, 1, 10]
print(output.shape) # [5, 10]
# output shares data with x in dygraph mode
x[0, 0, 0] = 10.
print(output[0, 0]) # [10.]
"""
if axis is None:
axis = []
elif isinstance(axis, int):
axis = [axis]
elif isinstance(axis, tuple):
axis = list(axis)
return layers.squeeze(x, axis, name)
@inplace_apis_in_dygraph_only
def squeeze_(x, axis=None, name=None):
"""
Inplace version of ``squeeze`` API, the output Tensor will be inplaced with input ``x``.
Please refer to :ref:`api_paddle_tensor_squeeze`.
"""
if axis is None:
axis = []
elif isinstance(axis, int):
axis = [axis]
elif isinstance(axis, tuple):
axis = list(axis)
out, _ = core.ops.squeeze2_(x, 'axes', axis)
return out
def unique(x,
return_index=False,
return_inverse=False,
return_counts=False,
axis=None,
dtype="int64",
name=None):
r"""
Returns the unique elements of `x` in ascending order.
Args:
x(Tensor): The input tensor, it's data type should be float32, float64, int32, int64.
return_index(bool, optional): If True, also return the indices of the input tensor that
result in the unique Tensor.
return_inverse(bool, optional): If True, also return the indices for where elements in
the original input ended up in the returned unique tensor.
return_counts(bool, optional): If True, also return the counts for each unique element.
axis(int, optional): The axis to apply unique. If None, the input will be flattened.
Default: None.
dtype(np.dtype|str, optional): The date type of `indices` or `inverse` tensor: int32 or int64.
Default: int64.
name(str, optional): Name for the operation. For more information, please refer to
:ref:`api_guide_Name`. Default: None.
Returns:
tuple: (out, indices, inverse, counts). `out` is the unique tensor for `x`. `indices` is \
provided only if `return_index` is True. `inverse` is provided only if `return_inverse` \
is True. `counts` is provided only if `return_counts` is True.
Examples:
.. code-block:: python
import paddle
x = paddle.to_tensor([2, 3, 3, 1, 5, 3])
unique = paddle.unique(x)
np_unique = unique.numpy() # [1 2 3 5]
_, indices, inverse, counts = paddle.unique(x, return_index=True, return_inverse=True, return_counts=True)
np_indices = indices.numpy() # [3 0 1 4]
np_inverse = inverse.numpy() # [1 2 2 0 3 2]
np_counts = counts.numpy() # [1 1 3 1]
x = paddle.to_tensor([[2, 1, 3], [3, 0, 1], [2, 1, 3]])
unique = paddle.unique(x)
np_unique = unique.numpy() # [0 1 2 3]
unique = paddle.unique(x, axis=0)
np_unique = unique.numpy()
# [[2 1 3]
# [3 0 1]]
"""
if axis is None:
axis = []
else:
axis = [axis]
attr_dtype = convert_np_dtype_to_dtype_(dtype)
if in_dygraph_mode():
out, inverse, indices, counts = core.ops.unique(
x, 'dtype', attr_dtype, 'return_index', return_index,
'return_inverse', return_inverse, 'return_counts', return_counts,
'axis', axis, "is_sorted", True)
outs = [out]
if return_index:
outs.append(indices)
if return_inverse:
outs.append(inverse)
if return_counts:
outs.append(counts)
if len(outs) == 1:
return outs[0]
return tuple(outs)
check_variable_and_dtype(x, "input",
['float32', 'float64', 'int32', 'int64'], 'unique')
check_type(return_index, 'return_index', bool, 'unique')
check_type(return_inverse, 'return_inverse', bool, 'unique')
check_type(return_counts, 'return_counts', bool, 'unique')
check_dtype(dtype, 'dtype', ['int32', 'int64'], 'unique')
if len(axis) != 0:
check_type(axis[0], 'axis', int, 'unique')
helper = LayerHelper('unique', **locals())
attrs = {
'dtype': attr_dtype,
"return_index": return_index,
"return_inverse": return_inverse,
"return_counts": return_counts,
"axis": axis,
"is_sorted": True
}
out = helper.create_variable_for_type_inference(
dtype=x.dtype, stop_gradient=True)
indices = helper.create_variable_for_type_inference(
dtype=attr_dtype, stop_gradient=True)
inverse = helper.create_variable_for_type_inference(
dtype=attr_dtype, stop_gradient=True)
counts = helper.create_variable_for_type_inference(
dtype=attr_dtype, stop_gradient=True)
outputs = {
"Out": out,
"Indices": indices,
"Index": inverse,
"Counts": counts
}
outs = [out]
if return_index:
outs.append(indices)
if return_inverse:
outs.append(inverse)
if return_counts:
outs.append(counts)
helper.append_op(
type="unique", inputs={"X": x}, attrs=attrs, outputs=outputs)
if len(outs) == 1:
return outs[0]
return tuple(outs)
def unsqueeze(x, axis, name=None):
"""
Insert single-dimensional entries to the shape of input Tensor ``x``. Takes one
required argument axis, a dimension or list of dimensions that will be inserted.
Dimension indices in axis are as seen in the output tensor.
Note that the output Tensor will share data with origin Tensor and doesn't have a
Tensor copy in ``dygraph`` mode. If you want to use the Tensor copy version,
please use `Tensor.clone` like ``unsqueeze_clone_x = x.unsqueeze(-1).clone()``.
Args:
x (Tensor): The input Tensor to be unsqueezed. Supported data type: float32, float64, bool, int8, int32, int64.
axis (int|list|tuple|Tensor): Indicates the dimensions to be inserted. The data type is ``int32`` .
If ``axis`` is a list or tuple, the elements of it should be integers or Tensors with shape [1].
If ``axis`` is a Tensor, it should be an 1-D Tensor .
If ``axis`` is negative, ``axis = axis + ndim(x) + 1``.
name (str|None): Name for this layer. Please refer to :ref:`api_guide_Name`, Default None.
Returns:
Tensor: Unsqueezed Tensor with the same data type as input Tensor.
Examples:
.. code-block:: python
import paddle
x = paddle.rand([5, 10])
print(x.shape) # [5, 10]
out1 = paddle.unsqueeze(x, axis=0)
print(out1.shape) # [1, 5, 10]
out2 = paddle.unsqueeze(x, axis=[0, 2])
print(out2.shape) # [1, 5, 1, 10]
axis = paddle.to_tensor([0, 1, 2])
out3 = paddle.unsqueeze(x, axis=axis)
print(out3.shape) # [1, 1, 1, 5, 10]
# out1, out2, out3 share data with x in dygraph mode
x[0, 0] = 10.
print(out1[0, 0, 0]) # [10.]
print(out2[0, 0, 0, 0]) # [10.]
print(out3[0, 0, 0, 0, 0]) # [10.]
"""
return layers.unsqueeze(x, axis, name)
@inplace_apis_in_dygraph_only
def unsqueeze_(x, axis, name=None):
"""
Inplace version of ``unsqueeze`` API, the output Tensor will be inplaced with input ``x``.
Please refer to :ref:`api_paddle_tensor_unsqueeze`.
"""
if isinstance(axis, int):
axis = [axis]
elif isinstance(axis, Variable):
axis = axis.numpy().tolist()
elif isinstance(axis, (list, tuple)):
axis = [
item.numpy().item(0) if isinstance(item, Variable) else item
for item in axis
]
out, _ = core.ops.unsqueeze2_(x, 'axes', axis)
return out
def gather(x, index, axis=None, name=None):
"""
Output is obtained by gathering entries of ``axis``
of ``x`` indexed by ``index`` and concatenate them together.
.. code-block:: text
Given:
x = [[1, 2],
[3, 4],
[5, 6]]
index = [1, 2]
axis=[0]
Then:
out = [[3, 4],
[5, 6]]
Args:
x (Tensor): The source input tensor with rank>=1. Supported data type is
int32, int64, float32, float64 and uint8 (only for CPU),
float16 (only for GPU).
index (Tensor): The index input tensor with rank=1. Data type is int32 or int64.
axis (Tensor|int, optional): The axis of input to be gathered, it's can be int or a Tensor with data type is int32 or int64. The default value is None, if None, the ``axis`` is 0.
name (str, optional): The default value is None. Normally there is no need for user to set this property.
For more information, please refer to :ref:`api_guide_Name` .
Returns:
output (Tensor): The output is a tensor with the same rank as ``x``.
Examples:
.. code-block:: python
import paddle
input = paddle.to_tensor([[1,2],[3,4],[5,6]])
index = paddle.to_tensor([0,1])
output = paddle.gather(input, index, axis=0)
# expected output: [[1,2],[3,4]]
"""
if axis is None:
axis = 0
if in_dygraph_mode():
axis = axis.item() if isinstance(axis, paddle.Tensor) else axis
return core.ops.gather(x, index, None, "axis", axis, "overwrite", False)
check_variable_and_dtype(
x, 'x', ['float16', 'float32', 'float64', 'int32', 'int64', 'uint8'],
'gather')
check_variable_and_dtype(index, 'index', ['int32', 'int64'], 'gather')
if isinstance(axis, Variable):
check_variable_and_dtype(axis, 'axis', ['int32', 'int64'], 'gather')
helper = LayerHelper('gather', **locals())
dtype = helper.input_dtype('x')
out = helper.create_variable_for_type_inference(dtype)
if not isinstance(axis, Variable):
helper.append_op(
type="gather",
inputs={"X": x,
"Index": index},
attrs={'axis': axis,
'overwrite': False},
outputs={"Out": out})
else:
helper.append_op(
type="gather",
inputs={"X": x,
"Index": index,
"Axis": axis},
attrs={"overwrite": False},
outputs={"Out": out})
return out
def unbind(input, axis=0):
"""
Removes a tensor dimension, then split the input tensor into multiple sub-Tensors.
Args:
input (Tensor): The input variable which is an N-D Tensor, data type being float32, float64, int32 or int64.
axis (int32|int64, optional): A scalar with type ``int32|int64`` shape [1]. The dimension along which to unbind.
If :math:`axis < 0`, the dimension to unbind along is :math:`rank(input) + axis`. Default is 0.
Returns:
list(Tensor): The list of segmented Tensor variables.
Example:
.. code-block:: python
import paddle
import numpy as np
# input is a variable which shape is [3, 4, 5]
np_input = np.random.rand(3, 4, 5).astype('float32')
input = paddle.to_tensor(np_input)
[x0, x1, x2] = paddle.unbind(input, axis=0)
# x0.shape [4, 5]
# x1.shape [4, 5]
# x2.shape [4, 5]
[x0, x1, x2, x3] = paddle.unbind(input, axis=1)
# x0.shape [3, 5]
# x1.shape [3, 5]
# x2.shape [3, 5]
# x3.shape [3, 5]
"""
helper = LayerHelper("unbind", **locals())
check_type(input, 'input', (Variable), 'unbind')
dtype = helper.input_dtype()
check_dtype(dtype, 'unbind', ['float32', 'float64', 'int32', 'int64'],
'unbind')
if not isinstance(axis, (int)):
raise TypeError("The type of 'axis' must be int, but received %s." %
(type(axis)))
if isinstance(axis, np.generic):
axis = np.asscalar(axis)
input_shape = input.shape
axis_ = axis if axis >= 0 else len(input_shape) + axis
num = input_shape[axis_]
outs = [
helper.create_variable_for_type_inference(dtype=helper.input_dtype())
for i in range(num)
]
if in_dygraph_mode():
return core.ops.unbind(input, num, 'axis', axis)
helper.append_op(
type="unbind",
inputs={"X": input},
outputs={"Out": outs},
attrs={"axis": axis})
return outs
def scatter(x, index, updates, overwrite=True, name=None):
"""
**Scatter Layer**
Output is obtained by updating the input on selected indices based on updates.
.. code-block:: python
import numpy as np
#input:
x = np.array([[1, 1], [2, 2], [3, 3]])
index = np.array([2, 1, 0, 1])
# shape of updates should be the same as x
# shape of updates with dim > 1 should be the same as input
updates = np.array([[1, 1], [2, 2], [3, 3], [4, 4]])
overwrite = False
# calculation:
if not overwrite:
for i in range(len(index)):
x[index[i]] = np.zeros((2))
for i in range(len(index)):
if (overwrite):
x[index[i]] = updates[i]
else:
x[index[i]] += updates[i]
# output:
out = np.array([[3, 3], [6, 6], [1, 1]])
out.shape # [3, 2]
**NOTICE**: The order in which updates are applied is nondeterministic,
so the output will be nondeterministic if index contains duplicates.
Args:
x (Tensor): The input N-D Tensor with ndim>=1. Data type can be float32, float64.
index (Tensor): The index 1-D Tensor. Data type can be int32, int64. The length of index cannot exceed updates's length, and the value in index cannot exceed input's length.