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refine paddle.stack (#25305)

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* refine stack in dygraph mode, test=develop

* update stack, test=develop

* rename Variable to Tensor, test=develop

* add name args to fluid.layers.stack, test=develop

* follow comments

* update doc

* update doc

* follow comments
revert-24895-update_cub
Leo Chen 5 years ago committed by GitHub
parent 224620071b
commit 6e7f0bb284
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GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 41 additions and 61 deletions
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13
python/paddle/fluid/layers/nn.py
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@ -9911,7 +9911,7 @@ def flatten(x, axis=1, name=None):
return out
def stack(x, axis=0):
def stack(x, axis=0, name=None):
"""
This OP stacks all the inputs :code:`x` along axis.
@ -9991,15 +9991,16 @@ def stack(x, axis=0):
data = layers.stack(x1) # stack according to axis 0, data.shape=[1, None, 1, 2]
"""
helper = LayerHelper('stack', **locals())
axis = 0 if axis is None else axis
if not isinstance(x, list) and not isinstance(x, tuple):
x = [x]
if in_dygraph_mode():
return core.ops.stack(x, 'axis', axis)
helper = LayerHelper('stack', **locals())
out = helper.create_variable_for_type_inference(x[0].dtype)
if not in_dygraph_mode() and \
x[0].desc.type() == core.VarDesc.VarType.LOD_TENSOR_ARRAY:
if x[0].desc.type() == core.VarDesc.VarType.LOD_TENSOR_ARRAY:
assert len(x) == 1, "If the elements of 'x' in stack are Variable(LoDTensorArray), " \
"number of the elements must be 1, but received %s." % len(x)
out_index = helper.create_variable_for_type_inference(dtype="int32")

89
python/paddle/tensor/manipulation.py
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@ -359,13 +359,18 @@ def roll(x, shifts, axis=None, name=None):
return out
def stack(x, axis=0, out=None, name=None):
def stack(x, axis=0, name=None):
"""
:alias_main: paddle.stack
:alias: paddle.stack,paddle.tensor.stack,paddle.tensor.manipulation.stack
:alias: paddle.stack, paddle.tensor.stack, paddle.tensor.manipulation.stack
This OP stacks all the inputs :code:`x` along axis.
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
@ -391,7 +396,6 @@ def stack(x, axis=0, out=None, name=None):
Case 2:
Input:
x[0].shape = [1, 2]
x[0].data = [ [1.0 , 2.0 ] ]
@ -402,7 +406,7 @@ def stack(x, axis=0, out=None, name=None):
Attrs:
axis = 1 or axis = -2
axis = 1 or axis = -2 # If axis = -2, axis = axis+ndim(x[0])+1 = -2+2+1 = 1.
Output:
Out.shape = [1, 3, 2]
@ -411,65 +415,40 @@ def stack(x, axis=0, out=None, name=None):
[5.0, 6.0] ] ]
Args:
x (Variable|list(Variable)): Input :code:`x` can be a single Tensor, a :code:`list` of Tensors.
If :code:`x` is a :code:`list`, the shapes of all these Tensors
must be the same. Supposing input is N dims
Tensors :math:`[d_0, d_1, ..., d_{n-1}]`, the output is N+1 dims
Tensor :math:`[d_0, d_1, d_{axis-1}, len(x), d_{axis}, ..., d_{n-1}]`.
Support data types: float32, float64, int32, int64.
axis (int, optional): The axis along which all inputs are stacked. ``axis`` range is :math:`[-(R+1), R+1)`.
R is the first tensor of inputs. If ``axis`` < 0, :math:`axis=axis+rank(x[0])+1`.
The default value of axis is 0.
x (Tensor|list[Tensor]): Input ``x`` can be a single tensor, or a ``list`` of tensors.
If ``x`` is a ``list``, the Tensors in ``x``
must be of the same shape and dtype. Support 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:
Variable: The stacked Tensor, has same data type with input Tensors. Output dim is :math:`rank(x[0])+1`.
Tensor: The stacked tensor with same data type as input.
Example:
.. code-block:: python
import numpy as np
import paddle
import paddle.fluid as fluid
import numpy as np
data1 = np.array([[1.0, 2.0]])
data2 = np.array([[3.0, 4.0]])
data3 = np.array([[5.0, 6.0]])
with fluid.dygraph.guard():
x1 = fluid.dygraph.to_variable(data1)
x2 = fluid.dygraph.to_variable(data2)
x3 = fluid.dygraph.to_variable(data3)
result = paddle.stack([x1, x2, x3], axis=0)
# result shape: [3, 1, 2]
# result value: [[[1.0, 2.0]],
# [[3.0, 4.0]],
# [[5.0, 6.0]]]
"""
helper = LayerHelper('stack', **locals())
axis = 0 if axis is None else axis
if not isinstance(x, list) and not isinstance(x, tuple):
x = [x]
out = helper.create_variable_for_type_inference(x[0].dtype)
if not in_dygraph_mode() and \
x[0].desc.type() == core.VarDesc.VarType.LOD_TENSOR_ARRAY:
assert len(x) == 1, "If the elements of 'x' in stack are Variable(LoDTensorArray), " \
"number of the elements must be 1, but received %s." % len(x)
out_index = helper.create_variable_for_type_inference(dtype="int32")
helper.append_op(
type='tensor_array_to_tensor',
inputs={'X': x[0]},
outputs={'Out': [out],
'OutIndex': [out_index]},
attrs={'axis': axis,
'use_stack': True})
else:
helper.append_op(
type='stack',
inputs={'X': x},
outputs={'Y': out},
attrs={'axis': axis})
return out
paddle.enable_imperative()
x1 = paddle.imperative.to_variable(data1)
x2 = paddle.imperative.to_variable(data2)
x3 = paddle.imperative.to_variable(data3)
out = paddle.stack([x1, x2, x3], axis=0)
print(out.shape) # [3, 1, 2]
print(out.numpy())
# [[[1., 2.]],
# [[3., 4.]],
# [[5., 6.]]]
"""
return layers.stack(x, axis, name)
def split(x, num_or_sections, axis=0, name=None):

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