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提交 200cc5e2 编写于 作者: Z zhupengyang 提交者： Kaipeng Deng
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fix APIs: cast, concat, tensor_array_to_tensor, argmin, argmax, argsort (#20363) 

* fix APIs: cast, concat, tensor_array_to_tensor, argmin, argmax, argsort

test=develop
test=document_fix
上级 5ea2cc67
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Showing with 226 addition and 105 deletion
paddle/fluid/API.spec
浏览文件 @ 200cc5e2
......@@ -317,16 +317,16 @@ paddle.fluid.layers.load (ArgSpec(args=['out', 'file_path', 'load_as_fp16'], var
paddle.fluid.layers.create_tensor (ArgSpec(args=['dtype', 'name', 'persistable'], varargs=None, keywords=None, defaults=(None, False)), ('document', 'fdc2d964488e99fb0743887454c34e36'))
paddle.fluid.layers.create_parameter (ArgSpec(args=['shape', 'dtype', 'name', 'attr', 'is_bias', 'default_initializer'], varargs=None, keywords=None, defaults=(None, None, False, None)), ('document', '727aa63c061919bee38547fb126d9428'))
paddle.fluid.layers.create_global_var (ArgSpec(args=['shape', 'value', 'dtype', 'persistable', 'force_cpu', 'name'], varargs=None, keywords=None, defaults=(False, False, None)), ('document', 'fa7f74cfb940521cc9fdffabc83debbf'))
paddle.fluid.layers.cast (ArgSpec(args=['x', 'dtype'], varargs=None, keywords=None, defaults=None), ('document', '1e44a534cf7d26ab230aa9f5e4e0525a'))
paddle.fluid.layers.tensor_array_to_tensor (ArgSpec(args=['input', 'axis', 'name'], varargs=None, keywords=None, defaults=(1, None)), ('document', '764c095ba4562ae740f979e970152d6e'))
paddle.fluid.layers.concat (ArgSpec(args=['input', 'axis', 'name'], varargs=None, keywords=None, defaults=(0, None)), ('document', 'b3f30feb5dec8f110d7393ffeb30dbd9'))
paddle.fluid.layers.cast (ArgSpec(args=['x', 'dtype'], varargs=None, keywords=None, defaults=None), ('document', '45df178cbd8c302f92c30ebdaaa6fa8a'))
paddle.fluid.layers.tensor_array_to_tensor (ArgSpec(args=['input', 'axis', 'name'], varargs=None, keywords=None, defaults=(1, None)), ('document', 'dd7d2f1e12a8a4225d017209866e5621'))
paddle.fluid.layers.concat (ArgSpec(args=['input', 'axis', 'name'], varargs=None, keywords=None, defaults=(0, None)), ('document', 'ec7d6e716fb29ef1e73e1e3efa5ca46b'))
paddle.fluid.layers.sums (ArgSpec(args=['input', 'out'], varargs=None, keywords=None, defaults=(None,)), ('document', '5df743d578638cd2bbb9369499b44af4'))
paddle.fluid.layers.assign (ArgSpec(args=['input', 'output'], varargs=None, keywords=None, defaults=(None,)), ('document', '8bd94aef4e123986d9a8c29f67b5532b'))
paddle.fluid.layers.fill_constant_batch_size_like (ArgSpec(args=['input', 'shape', 'dtype', 'value', 'input_dim_idx', 'output_dim_idx'], varargs=None, keywords=None, defaults=(0, 0)), ('document', '37a288e4400f6d5510e982827461c11b'))
paddle.fluid.layers.fill_constant (ArgSpec(args=['shape', 'dtype', 'value', 'force_cpu', 'out'], varargs=None, keywords=None, defaults=(False, None)), ('document', '66e1e468666dd47e5b2715226cebeac0'))
paddle.fluid.layers.argmin (ArgSpec(args=['x', 'axis'], varargs=None, keywords=None, defaults=(0,)), ('document', '3dd54487232d05df4d70fba94b7d0b79'))
paddle.fluid.layers.argmax (ArgSpec(args=['x', 'axis'], varargs=None, keywords=None, defaults=(0,)), ('document', '7f47cc9aa7531b6bd37c5c96bc7f0469'))
paddle.fluid.layers.argsort (ArgSpec(args=['input', 'axis', 'name'], varargs=None, keywords=None, defaults=(-1, None)), ('document', '9792371e3b66258531225a5551de8961'))
paddle.fluid.layers.argmin (ArgSpec(args=['x', 'axis'], varargs=None, keywords=None, defaults=(0,)), ('document', '53629e27597e5dfb7020aac5bc639ebb'))
paddle.fluid.layers.argmax (ArgSpec(args=['x', 'axis'], varargs=None, keywords=None, defaults=(0,)), ('document', 'd9a89fbedbaebd5f65897ac75ee636f3'))
paddle.fluid.layers.argsort (ArgSpec(args=['input', 'axis', 'name'], varargs=None, keywords=None, defaults=(-1, None)), ('document', '8c7966eb4b37b2272a16717cac3a876c'))
paddle.fluid.layers.ones (ArgSpec(args=['shape', 'dtype', 'force_cpu'], varargs=None, keywords=None, defaults=(False,)), ('document', '812c623ed52610b9773f9fc05413bc34'))
paddle.fluid.layers.zeros (ArgSpec(args=['shape', 'dtype', 'force_cpu'], varargs=None, keywords=None, defaults=(False,)), ('document', '95379f9288c2d05356ec0e2375c6bc57'))
paddle.fluid.layers.reverse (ArgSpec(args=['x', 'axis'], varargs=None, keywords=None, defaults=None), ('document', '628135603692137d52bcf5a8d8d6816d'))
......
python/paddle/fluid/layers/tensor.py
浏览文件 @ 200cc5e2
......@@ -149,23 +149,45 @@ def create_global_var(shape,
def cast(x, dtype):
"""
This layer takes in the Variable :attr:`x` with :attr:`x.dtype` and casts
it to the output with :attr:`dtype`. It's meaningless if the output
dtype equals the input dtype, but it's fine if you do so.
This OP takes in the Variable :attr:`x` with :attr:`x.dtype` and casts it
to the output with :attr:`dtype`. It's meaningless if the output dtype
equals the input dtype, but it's fine if you do so.
Args:
x (Variable): The input Variable for casting.
dtype(np.dtype|core.VarDesc.VarType|str): Data type of the output Variable.
x(Variable): An input N-D Tensor with data type bool, float16,
float32, float64, int32, int64, uint8.
dtype(np.dtype|core.VarDesc.VarType|str): Data type of the output:
bool, float15, float32, float64, int8, int32, int64, uint8.
Returns:
Variable: The output Variable after casting.
Variable: A Tensor with the same shape as input's.
Examples:
.. code-block:: python
import paddle.fluid as fluid
data = fluid.layers.data(name='x', shape=[13], dtype='float32')
result = fluid.layers.cast(x=data, dtype='float64')
import numpy as np
place = fluid.core.CPUPlace()
x_lod = fluid.data(name="x", shape=[2,2], lod_level=0)
cast_res1 = fluid.layers.cast(x=x_lod, dtype="uint8")
cast_res2 = fluid.layers.cast(x=x_lod, dtype=np.int32)
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
x_i_lod = fluid.core.LoDTensor()
x_i_lod.set(np.array([[1.3,-2.4],[0,4]]).astype("float32"), place)
x_i_lod.set_recursive_sequence_lengths([[0,2]])
res1 = exe.run(fluid.default_main_program(), feed={'x':x_i_lod}, fetch_list=[cast_res1], return_numpy=False)
res2 = exe.run(fluid.default_main_program(), feed={'x':x_i_lod}, fetch_list=[cast_res2], return_numpy=False)
print(np.array(res1[0]), np.array(res1[0]).dtype)
# [[ 1 254]
# [ 0 4]] uint8
print(np.array(res2[0]), np.array(res2[0]).dtype)
# [[ 1 -2]
# [ 0 4]] int32
"""
helper = LayerHelper('cast', **locals())
out = helper.create_variable_for_type_inference(dtype=dtype)
......@@ -182,27 +204,47 @@ def concat(input, axis=0, name=None):
"""
**Concat**
This function concatenates the input along the axis mentioned
and returns that as the output.
This OP concatenates the input along the axis.
Args:
input(list): List of tensors to be concatenated
axis(int): Integer axis along which the tensors will be concatenated
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
input(list): List of input Tensors with data type float32, float64, int32,
int64.
axis(int, optional): Axis to compute indices along. 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:
Variable: Output variable of the concatenation
Variable: A Tensor with the same data type as input's.
Examples:
.. code-block:: python
import paddle.fluid as fluid
a = fluid.layers.data(name='a', shape=[2, 13], dtype='float32')
b = fluid.layers.data(name='b', shape=[2, 3], dtype='float32')
c = fluid.layers.data(name='c', shape=[2, 2], dtype='float32')
d = fluid.layers.data(name='d', shape=[2, 5], dtype='float32')
out = fluid.layers.concat(input=[a, b, c, d], axis=2)
import numpy as np
in1 = np.array([[1,2,3],
[4,5,6]])
in2 = np.array([[11,12,13],
[14,15,16]])
in3 = np.array([[21,22],
[23,24]])
with fluid.dygraph.guard():
x1 = fluid.dygraph.to_variable(in1)
x2 = fluid.dygraph.to_variable(in2)
x3 = fluid.dygraph.to_variable(in3)
out1 = fluid.layers.concat(input=[x1,x2,x3], axis=-1)
out2 = fluid.layers.concat(input=[x1,x2], axis=0)
print(out1.numpy())
# [[ 1 2 3 11 12 13 21 22]
# [ 4 5 6 14 15 16 23 24]]
print(out2.numpy())
# [[ 1 2 3]
# [ 4 5 6]
# [11 12 13]
# [14 15 16]]
"""
helper = LayerHelper('concat', **locals())
out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
......@@ -216,47 +258,48 @@ def concat(input, axis=0, name=None):
def tensor_array_to_tensor(input, axis=1, name=None):
"""
This function concatenates the input LodTensorArray along the axis mentioned
and returns that as the output.
A simple example as below:
.. code-block:: text
Given:
input.data = {[[0.6, 0.1, 0.3],
[0.5, 0.3, 0.2]],
[[1.3],
[1.8]],
[[2.3, 2.1],
[2.5, 2.4]]}
axis = 1
Then:
output.data = [[0.6, 0.1, 0.3, 1.3, 2.3, 2.1],
[0.5, 0.3, 0.2, 1.8, 2.5, 2.4]]
output_index.data = [3, 1, 2]
This OP concatenates the input LodTensorArray along the axis.
Args:
input(list): Input LodTensorArray
axis(int): Integer axis along which the tensors will be concatenated
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
input(Variable): A LodTensorArray with data type float32, float64, int32,
int64.
axis(int, optional): Axis to compute indices along. The effective range
is [-R, R), where R is Rank(x). when axis<0, it works the same way
as axis+R. Default is 1.
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:
Variable: Output variable of the concatenation
Variable: The input LodTensorArray items' dims along the axis
Variable: A LoDTensor with the same data type as input's
Variable: The input LodTensorArray items' dims along the axis.
Examples:
.. code-block:: python
import paddle.fluid as fluid
tensor_array = fluid.layers.create_parameter(shape=[784, 200], dtype='float32')
output, output_index = fluid.layers.tensor_array_to_tensor(input=tensor_array)
import numpy as np
place = fluid.CPUPlace()
x1 = fluid.data(name="x", shape=[2,2], lod_level=0)
tmp = fluid.layers.fill_constant(shape=[2,3], dtype="float32", value=1)
x_arr = fluid.layers.create_array(dtype="float32")
c0 = fluid.layers.fill_constant(shape=[1], dtype='int64', value=0)
fluid.layers.array_write(x=tmp, i=c0, array=x_arr)
c1 = fluid.layers.fill_constant(shape=[1], dtype='int64', value=1)
fluid.layers.array_write(x=x1, i=c1, array=x_arr)
output, output_index = fluid.layers.tensor_array_to_tensor(input=x_arr, axis=1)
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
feedx = fluid.LoDTensor()
feedx.set(np.array([[1.3,-2.4],[0,4]]).astype("float32"), place)
res = exe.run(fluid.default_main_program(), feed={'x':feedx}, fetch_list=[output], return_numpy=False)
print(np.array(res[0]))
# [[ 1. 1. 1. 1.3 -2.4]
# [ 1. 1. 1. 0. 4. ]]
"""
helper = LayerHelper('tensor_array_to_tensor', **locals())
out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
......@@ -498,24 +541,50 @@ def argmin(x, axis=0):
"""
**argmin**
This function computes the indices of the min elements
of the input tensor's element along the provided axis.
This OP computes the indices of the min elements of the input tensor's
element along the provided axis.
Args:
x(Variable): The input to compute the indices of
the min elements.
axis(int): Axis to compute indices along.
x(Variable): An input N-D Tensor with type float32, float64, int16,
int32, int64, uint8.
axis(int, optional): Axis to compute indices along. 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.
Returns:
Variable: The tensor variable storing the output
Variable: A Tensor with data type int64.
Examples:
.. code-block:: python
import paddle.fluid as fluid
x = fluid.layers.data(name="x", shape=[3, 4], dtype="float32")
out = fluid.layers.argmin(x, axis=0)
out = fluid.layers.argmin(x, axis=-1)
import numpy as np
in1 = np.array([[[5,8,9,5],
[0,0,1,7],
[6,9,2,4]],
[[5,2,4,2],
[4,7,7,9],
[1,7,0,6]]])
with fluid.dygraph.guard():
x = fluid.dygraph.to_variable(in1)
out1 = fluid.layers.argmin(x=x, axis=-1)
out2 = fluid.layers.argmin(x=x, axis=0)
out3 = fluid.layers.argmin(x=x, axis=1)
out4 = fluid.layers.argmin(x=x, axis=2)
print(out1.numpy())
# [[0 0 2]
# [1 0 2]]
print(out2.numpy())
# [[0 1 1 1]
# [0 0 0 0]
# [1 1 1 0]]
print(out3.numpy())
# [[1 1 1 2]
# [2 0 2 0]]
print(out4.numpy())
# [[0 0 2]
# [1 0 2]]
"""
helper = LayerHelper("arg_min", **locals())
out = helper.create_variable_for_type_inference(VarDesc.VarType.INT64)
......@@ -531,24 +600,50 @@ def argmax(x, axis=0):
"""
**argmax**
This function computes the indices of the max elements
of the input tensor's element along the provided axis.
This OP computes the indices of the max elements of the input tensor's
element along the provided axis.
Args:
x(Variable): The input to compute the indices of
the max elements.
axis(int): Axis to compute indices along.
x(Variable): An input N-D Tensor with type float32, float64, int16,
int32, int64, uint8.
axis(int, optional): Axis to compute indices along. 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.
Returns:
Variable: The tensor variable storing the output
Variable: A Tensor with data type int64.
Examples:
.. code-block:: python
import paddle.fluid as fluid
x = fluid.layers.data(name="x", shape=[3, 4], dtype="float32")
out = fluid.layers.argmax(x, axis=0)
out = fluid.layers.argmax(x, axis=-1)
import numpy as np
in1 = np.array([[[5,8,9,5],
[0,0,1,7],
[6,9,2,4]],
[[5,2,4,2],
[4,7,7,9],
[1,7,0,6]]])
with fluid.dygraph.guard():
x = fluid.dygraph.to_variable(in1)
out1 = fluid.layers.argmax(x=x, axis=-1)
out2 = fluid.layers.argmax(x=x, axis=0)
out3 = fluid.layers.argmax(x=x, axis=1)
out4 = fluid.layers.argmax(x=x, axis=2)
print(out1.numpy())
# [[2 3 1]
# [0 3 1]]
print(out2.numpy())
# [[0 0 0 0]
# [1 1 1 1]
# [0 0 0 1]]
print(out3.numpy())
# [[2 2 0 1]
# [0 1 1 1]]
print(out4.numpy())
# [[2 3 1]
# [0 3 1]]
"""
helper = LayerHelper("arg_max", **locals())
out = helper.create_variable_for_type_inference(VarDesc.VarType.INT64)
......@@ -562,44 +657,70 @@ def argmax(x, axis=0):
def argsort(input, axis=-1, name=None):
"""
Performs sorting on the input Variable along the given axis, and outputs
sorted data Varibale and its corresponding index Variable with the same
shape as :attr:`input`.
.. code-block:: text
For example, the given axis is -1 and the input Variable
input = [[0.15849551, 0.45865775, 0.8563702 ],
[0.12070083, 0.28766365, 0.18776911]],
after argsort, the sorted Vairable becomes
out = [[0.15849551, 0.45865775, 0.8563702 ],
[0.12070083, 0.18776911, 0.28766365]],
and the sorted indices along the given axis turn outs to be
indices = [[0, 1, 2],
[0, 2, 1]]
This OP sorts the input along the given axis, and returns sorted output
data Varibale and its corresponding index Variable with the same shape as
:attr:`input`.
Args:
input(Variable): The input Variable for sorting.
axis(int): The axis along which to sort the input Variable. When
:attr:`axis` < 0, the actual axis will be :attr:`axis` +
rank(:attr:`input`). Default -1, the last dimension.
name(str|None): (optional) A name for this layer. If set None, the
layer will be named automatically.
input(Variable): An input N-D Tensor with type float32, float64, int16,
int32, int64, uint8.
axis(int, optional): Axis to compute indices along. 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:
tuple: A tuple of sorted data Variable and the sorted indices.
tuple: A tuple of sorted data Variable(with the same shape and data
type as input) and the sorted indices(with the same shape as input's
and with data type int64).
Examples:
.. code-block:: python
import paddle.fluid as fluid
x = fluid.layers.data(name="x", shape=[3, 4], dtype="float32")
out, indices = fluid.layers.argsort(input=x, axis=0)
import numpy as np
in1 = np.array([[[5,8,9,5],
[0,0,1,7],
[6,9,2,4]],
[[5,2,4,2],
[4,7,7,9],
[1,7,0,6]]]).astype(np.float32)
with fluid.dygraph.guard():
x = fluid.dygraph.to_variable(in1)
out1 = fluid.layers.argsort(input=x, axis=-1)
out2 = fluid.layers.argsort(input=x, axis=0)
out3 = fluid.layers.argsort(input=x, axis=1)
print(out1[0].numpy())
# [[[5. 5. 8. 9.]
# [0. 0. 1. 7.]
# [2. 4. 6. 9.]]
# [[2. 2. 4. 5.]
# [4. 7. 7. 9.]
# [0. 1. 6. 7.]]]
print(out1[1].numpy())
# [[[0 3 1 2]
# [0 1 2 3]
# [2 3 0 1]]
# [[1 3 2 0]
# [0 1 2 3]
# [2 0 3 1]]]
print(out2[0].numpy())
# [[[5. 2. 4. 2.]
# [0. 0. 1. 7.]
# [1. 7. 0. 4.]]
# [[5. 8. 9. 5.]
# [4. 7. 7. 9.]
# [6. 9. 2. 6.]]]
print(out3[0].numpy())
# [[[0. 0. 1. 4.]
# [5. 8. 2. 5.]
# [6. 9. 9. 7.]]
# [[1. 2. 0. 2.]
# [4. 7. 4. 6.]
# [5. 7. 7. 9.]]]
"""
helper = LayerHelper("argsort", **locals())
out = helper.create_variable_for_type_inference(
......
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