Skip to content

P
Paddle

机器未来 / Paddle
与 Fork 源项目一致

Fork自 PaddlePaddle / Paddle

通知 1
Star 1
Fork 0
P
Paddle
提交 e87cabb7 编写于 作者: S silingtong123 提交者： Tao Luo
浏览文件
操作

updates document of sequence_softmax, sequence_scatter, sequence_unpad (#20269)

上级 eb5c33cb
隐藏空白更改
内联 并排
Showing with 105 addition and 70 deletion
paddle/fluid/API.spec
浏览文件 @ e87cabb7
......@@ -140,7 +140,7 @@ paddle.fluid.layers.sequence_conv (ArgSpec(args=['input', 'num_filters', 'filter
paddle.fluid.layers.conv2d (ArgSpec(args=['input', 'num_filters', 'filter_size', 'stride', 'padding', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name', 'data_format'], varargs=None, keywords=None, defaults=(1, 0, 1, None, None, None, True, None, None, 'NCHW')), ('document', 'e91c63b8ac8c35982c0ac518537e44bf'))
paddle.fluid.layers.conv3d (ArgSpec(args=['input', 'num_filters', 'filter_size', 'stride', 'padding', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name', 'data_format'], varargs=None, keywords=None, defaults=(1, 0, 1, None, None, None, True, None, None, 'NCDHW')), ('document', 'feff9c8ebb4d4d0be5345f9042f57c8e'))
paddle.fluid.layers.sequence_pool (ArgSpec(args=['input', 'pool_type', 'is_test', 'pad_value'], varargs=None, keywords=None, defaults=(False, 0.0)), ('document', '5a709f7ef3fdb8fc819d09dc4fbada9a'))
paddle.fluid.layers.sequence_softmax (ArgSpec(args=['input', 'use_cudnn', 'name'], varargs=None, keywords=None, defaults=(False, None)), ('document', 'eaa9d0bbd3d4e017c8bc4ecdac483711'))
paddle.fluid.layers.sequence_softmax (ArgSpec(args=['input', 'use_cudnn', 'name'], varargs=None, keywords=None, defaults=(False, None)), ('document', '423d8e89236962a1fe7358548b993c64'))
paddle.fluid.layers.softmax (ArgSpec(args=['input', 'use_cudnn', 'name', 'axis'], varargs=None, keywords=None, defaults=(False, None, -1)), ('document', 'f7d6a5173c92c23f9a25cbc58a0eb577'))
paddle.fluid.layers.pool2d (ArgSpec(args=['input', 'pool_size', 'pool_type', 'pool_stride', 'pool_padding', 'global_pooling', 'use_cudnn', 'ceil_mode', 'name', 'exclusive', 'data_format'], varargs=None, keywords=None, defaults=(-1, 'max', 1, 0, False, True, False, None, True, 'NCHW')), ('document', 'daf9ae55b2d54bd5f35acb397fd1e1b5'))
paddle.fluid.layers.pool3d (ArgSpec(args=['input', 'pool_size', 'pool_type', 'pool_stride', 'pool_padding', 'global_pooling', 'use_cudnn', 'ceil_mode', 'name', 'exclusive', 'data_format'], varargs=None, keywords=None, defaults=(-1, 'max', 1, 0, False, True, False, None, True, 'NCDHW')), ('document', 'df8edcb8dd020fdddf778c9f613dc650'))
......@@ -155,7 +155,7 @@ paddle.fluid.layers.conv3d_transpose (ArgSpec(args=['input', 'num_filters', 'out
paddle.fluid.layers.sequence_expand (ArgSpec(args=['x', 'y', 'ref_level', 'name'], varargs=None, keywords=None, defaults=(-1, None)), ('document', '10e122eb755c2bd1f78ef2332b28f1a0'))
paddle.fluid.layers.sequence_expand_as (ArgSpec(args=['x', 'y', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '858c432e7cbd8bb952cc2eb555457d50'))
paddle.fluid.layers.sequence_pad (ArgSpec(args=['x', 'pad_value', 'maxlen', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', 'df08b9c499ab3a90f95d08ab5b6c6c62'))
paddle.fluid.layers.sequence_unpad (ArgSpec(args=['x', 'length', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'e478180d5bc010a84f35af958cafa62c'))
paddle.fluid.layers.sequence_unpad (ArgSpec(args=['x', 'length', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'af40ffc8474eaec0112c8fb55a88439a'))
paddle.fluid.layers.lstm_unit (ArgSpec(args=['x_t', 'hidden_t_prev', 'cell_t_prev', 'forget_bias', 'param_attr', 'bias_attr', 'name'], varargs=None, keywords=None, defaults=(0.0, None, None, None)), ('document', 'f5a878b6166f34878376a58d7e6fa95c'))
paddle.fluid.layers.reduce_sum (ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None)), ('document', 'ecb55075fdf89a866bcede85e60aebad'))
paddle.fluid.layers.reduce_mean (ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None)), ('document', '968c9b17affaf714e5021c3dc8d68c73'))
......@@ -213,7 +213,7 @@ paddle.fluid.layers.gather_nd (ArgSpec(args=['input', 'index', 'name'], varargs=
paddle.fluid.layers.scatter (ArgSpec(args=['input', 'index', 'updates', 'name', 'overwrite'], varargs=None, keywords=None, defaults=(None, True)), ('document', '3f94c3348dc79b7b40a839d31a3eaa84'))
paddle.fluid.layers.scatter_nd_add (ArgSpec(args=['ref', 'index', 'updates', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '2607b5c9369fbc52f208de066a80fc25'))
paddle.fluid.layers.scatter_nd (ArgSpec(args=['index', 'updates', 'shape', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'e43f1d3a938b35da246aea3e72a020ec'))
paddle.fluid.layers.sequence_scatter (ArgSpec(args=['input', 'index', 'updates', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'abe3f714120117a5a3d3e639853932bf'))
paddle.fluid.layers.sequence_scatter (ArgSpec(args=['input', 'index', 'updates', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '8e3fb174970251a1840c58ebb12b795f'))
paddle.fluid.layers.random_crop (ArgSpec(args=['x', 'shape', 'seed'], varargs=None, keywords=None, defaults=(None,)), ('document', '44f35002962cf24e14dd2958f6584e3d'))
paddle.fluid.layers.mean_iou (ArgSpec(args=['input', 'label', 'num_classes'], varargs=None, keywords=None, defaults=None), ('document', 'dea29c0c3cdbd5b498afef60e58c9d7c'))
paddle.fluid.layers.relu (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'c46011e3d848bc3dc650772b25fbbf10'))
......
python/paddle/fluid/layers/nn.py
浏览文件 @ e87cabb7
......@@ -2327,10 +2327,13 @@ def sequence_conv(input,
def sequence_softmax(input, use_cudnn=False, name=None):
"""
This function computes the softmax activation among all time-steps for each
sequence. The dimension of each time-step should be 1. Thus, the shape of
input Tensor can be either :math:`[N, 1]` or :math:`[N]`, where :math:`N`
is the sum of the length of all sequences.
**Note**:
**The input type of the OP must be LoDTensor. For Tensor, use:** :ref:`api_fluid_layers_softmax`
A LoD-tensor can be regarded as several sequences, and this op apply softmax algo on each sequence.
The shape of input Tensor can be :math:`[N, 1]` or :math:`[N]`, where :math:`N`
is the sum of the length of all sequences. Recommended usage: :math:`[N]`.
For i-th sequence in a mini-batch:
......@@ -2338,29 +2341,56 @@ def sequence_softmax(input, use_cudnn=False, name=None):
Out(X[lod[i]:lod[i+1]], :) = \\frac{\exp(X[lod[i]:lod[i+1], :])}{\sum(\exp(X[lod[i]:lod[i+1], :]))}
For example, for a mini-batch of 3 sequences with variable-length,
each containing 2, 3, 2 time-steps, the lod of which is [0, 2, 5, 7],
then softmax will be computed among :math:`X[0:2, :]`, :math:`X[2:5, :]`,
:math:`X[5:7, :]`, and :math:`N` turns out to be 7.
For example, for a LoD-Tensor with 6 sequences ([3, 2, 4, 1, 2, 3] - sequence length list in order),
the lod in the runtime is [[0, 3, 5, 9, 10, 12, 15]],
then softmax will be computed among :math:`X[0:3,:],X[3:5,:],X[5:9,:],X[9:10,:],X[10:12,:],X[12:15,:]`,
and :math:`N` turns out to be 15.
.. code-block:: text
*Case 1:
Given:
input.data = [0.7, 1, 0.6,
1.5, 1.1,
1.2, 0.2, 0.6, 1.9,
3.1,
2.5, 0.8,
0.1, 2.4, 1.3]
input.lod = [[0, 3, 5, 9, 10, 12, 15]]
then:
output.data = [0.30724832, 0.41474187, 0.2780098,
0.59868765, 0.40131235,
0.2544242, 0.09359743, 0.13963096, 0.5123474,
1.,
0.84553474, 0.15446526,
0.06995796, 0.69777346, 0.23226859]
output.lod = [[0, 3, 5, 9, 10, 12, 15]]
Args:
input (Variable): The input variable which is a LoDTensor.
use_cudnn (bool): Use cudnn kernel or not, it is valid only when the cudnn \
library is installed. Default: False.
name (str|None): A name for this layer(optional). If set None, the layer
will be named automatically. Default: None.
input (Variable):A LoDTensor with shape of :math:`[N, 1]` or :math:`[N]`, Recommended usage: :math:`[N]`.
Supported data types: float32, float64.
use_cudnn (bool, optional): Use cudnn kernel or not. Effective only when the cudnn version of the paddle
library is installed and GPU is used for training or reasoning. Default: False.
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 of sequence_softmax
Variable: A LoD-Tensor which has the same shape and data type with input.
Examples:
.. code-block:: python
import paddle.fluid as fluid
x = fluid.layers.data(name='x', shape=[7, 1],
x = fluid.data(name='x', shape=[7, 1],
dtype='float32', lod_level=1)
x_sequence_softmax = fluid.layers.sequence_softmax(input=x)
x_sequence_softmax_1 = fluid.layers.sequence_softmax(input=x)
y = fluid.data(name='y', shape=[7],
dtype='float32', lod_level=1)
x_sequence_softmax_2 = fluid.layers.sequence_softmax(input=y)
"""
assert not in_dygraph_mode(), (
"sequence layer is not supported in dygraph mode yet.")
......@@ -5592,15 +5622,15 @@ def sequence_pad(x, pad_value, maxlen=None, name=None):
def sequence_unpad(x, length, name=None):
"""
**Sequence Unpad Layer**
This layer removes the padding data in the input sequences and convert
them into sequences with actual length as output, identitied by lod
information.
**Note**:
**The input of the OP is Tensor and the output is LoDTensor. For padding operation, See:** :ref:`api_fluid_layers_sequence_pad`
The OP removes the padding data from the input based on the length information and returns a LoDTensor.
.. code-block:: text
Example:
Case 1:
Given input Variable **x**:
x.data = [[ 1.0, 2.0, 3.0, 4.0, 5.0],
......@@ -5615,18 +5645,18 @@ def sequence_unpad(x, length, name=None):
after unpadding, the output Variable will be:
out.data = [[1.0, 2.0, 6.0, 7.0, 8.0, 11.0, 12.0, 13.0, 14.0]]
out.lod = [[2, 3, 4]]
out.lod = [[0, 2, 5, 9]]
Args:
x(Variable): Input Variable which contains the padded sequences with
equal length.
length(Variable): The Variable that specifies the actual ength of
sequences after unpadding.
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
x(Variable): A Tensor which contains padding data, and its shape size can not be less than 2.
Supported data types: float32, float64, int32, int64.
length(Variable): A 1D Tensor that stores the actual length of each sample, and the Tensor
has the same shape with the 0th dimension of the X . Supported data types: int64.
name(str|None): 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: The Variable contains the unpadded sequences.
Variable: A LoDTensor whose recursive sequence length is consistent with the information of the length parameter and it has the same data type with input.
Examples:
.. code-block:: python
......@@ -5635,11 +5665,11 @@ def sequence_unpad(x, length, name=None):
import numpy
# pad data
x = fluid.layers.data(name='x', shape=[10, 5], dtype='float32', lod_level=1)
x = fluid.data(name='x', shape=[10, 5], dtype='float32', lod_level=1)
pad_value = fluid.layers.assign(input=numpy.array([0.0], dtype=numpy.float32))
pad_data, len = fluid.layers.sequence_pad(x=x, pad_value=pad_value)
# upad data
# unpad data
unpad_data = fluid.layers.sequence_unpad(x=pad_data, length=len)
"""
......@@ -10672,60 +10702,65 @@ def scatter_nd(index, updates, shape, name=None):
def sequence_scatter(input, index, updates, name=None):
"""
**Sequence Scatter Layer**
This operator scatters the Updates tensor to the input X. It uses the LoD
information of Ids to select the rows to update, and use the values in Ids as
the columns to update in each row of X.
**Note**:
**The index and updates parameters of the OP must be LoDTensor.**
Plus the updates data to the correspoding input according to the index.
The updated algorithm is as follows: output[instance_index][index [pos]] = input[instance_index][index [pos]] + updates[pos],
where instance_idx is the K sample corresponding to pos in batch.
Here is an example:
The value of output[i][j] depends on whether j can be found in the i+1th interval of the index. If found,
out[i][j] = input[i][j] + update[m] [n], otherwise, out[i][j] = input[i][j].
Given the following input:
For example, in the following example, the lod information for index is divided into three sequences. Among
them, because the element 0 can be found in the first interval of the index, it is updated with the value of
the corresponding position of the updates, out[0][0] = input[0][0]+updates[0][0] . Because element 1 cannot
be found in the third interval of index, out[2][1] = input[2][1].
.. code-block:: text
*Case 1:
input.data = [[1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0]]
input.dims = [3, 6]
index.data = [[0], [1], [2], [5], [4], [3], [2], [1], [3], [2], [5], [4]]
index.lod = [[0, 3, 8, 12]]
updates.data = [[0.3], [0.3], [0.4], [0.1], [0.2], [0.3], [0.4], [0.0], [0.2], [0.3], [0.1], [0.4]]
updates.lod = [[ 0, 3, 8, 12]]
Given:
input.data = [[1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0]]
input.dims = [3, 6]
Then we have the output:
index.data = [[0], [1], [2], [5], [4], [3], [2], [1], [3], [2], [5], [4]]
index.lod = [[0, 3, 8, 12]]
.. code-block:: text
updates.data = [[0.3], [0.3], [0.4], [0.1], [0.2], [0.3], [0.4], [0.0], [0.2], [0.3], [0.1], [0.4]]
updates.lod = [[ 0, 3, 8, 12]]
out.data = [[1.3, 1.3, 1.4, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.4, 1.3, 1.2, 1.1],
[1.0, 1.0, 1.3, 1.2, 1.4, 1.1]]
out.dims = X.dims = [3, 6]
Then:
out.data = [[1.3, 1.3, 1.4, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.4, 1.3, 1.2, 1.1],
[1.0, 1.0, 1.3, 1.2, 1.4, 1.1]]
out.dims = X.dims = [3, 6]
Args:
input (Variable): The source input with rank>=1.
index (Variable): A LoD Tensor. The index input of sequence scatter op
where input will be updated. The index input with rank=1. Its dtype
should be int32 or int64 as it is used as indexes.
updates (Variable): A LoD Tensor. The values to scatter to the input
tensor X, must be a LoDTensor with the same LoD information as index.
name (str|None): The output variable name. Default None.
input (Variable): A Tensor with shape of :math:`[N, k_1... k_n]`. Supported data types: float32, float64, int32, int64.
index (Variable): A LoDTensor contains index information. Its LoD level must be 1 and its data type must be int64.
updates (Variable): A LodTensor contains updates information. It has the same LoD level with the index and has the
same data type with the input. Supported data types: float32, float64, int32, int64.
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: The output is a tensor with the same shape as input.
Variable: A Tensor which has been updated. It has the same shape and data type with input.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import paddle.fluid.layers as layers
input = layers.data( name="x", shape=[3, 6], append_batch_size=False, dtype='float32' )
index = layers.data( name='index', shape=[1], dtype='int32')
updates = layers.data( name='updates', shape=[1], dtype='float32')
input = fluid.data( name="x", shape=[None, 3, 6], dtype='float32' )
index = fluid.data( name='index', shape=[12, 1], dtype='int64', lod_level=1)
updates = fluid.data( name='updates', shape=[12, 1], dtype='float32', lod_level=1)
output = fluid.layers.sequence_scatter(input, index, updates)
"""
......
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或