Skip to content
体验新版
项目
组织
正在加载...
登录
切换导航
打开侧边栏
wmsofts
Paddle
提交
2360dd20
P
Paddle
项目概览
wmsofts
/
Paddle
与 Fork 源项目一致
Fork自
PaddlePaddle / Paddle
通知
1
Star
0
Fork
0
代码
文件
提交
分支
Tags
贡献者
分支图
Diff
Issue
0
列表
看板
标记
里程碑
合并请求
0
Wiki
0
Wiki
分析
仓库
DevOps
项目成员
Pages
P
Paddle
项目概览
项目概览
详情
发布
仓库
仓库
文件
提交
分支
标签
贡献者
分支图
比较
Issue
0
Issue
0
列表
看板
标记
里程碑
合并请求
0
合并请求
0
Pages
分析
分析
仓库分析
DevOps
Wiki
0
Wiki
成员
成员
收起侧边栏
关闭侧边栏
动态
分支图
创建新Issue
提交
Issue看板
体验新版 GitCode,发现更多精彩内容 >>
未验证
提交
2360dd20
编写于
1月 18, 2018
作者:
W
whs
提交者:
GitHub
1月 18, 2018
浏览文件
操作
浏览文件
下载
差异文件
Merge pull request #7438 from wanghaoshuang/ctc_py
Add python API for Warp-CTC op
上级
c73f00fe
4de6cbd3
变更
1
显示空白变更内容
内联
并排
Showing
1 changed file
with
73 addition
and
43 deletion
+73
-43
python/paddle/v2/fluid/layers/nn.py
python/paddle/v2/fluid/layers/nn.py
+73
-43
未找到文件。
python/paddle/v2/fluid/layers/nn.py
浏览文件 @
2360dd20
...
...
@@ -22,36 +22,13 @@ from ..param_attr import ParamAttr
from
tensor
import
concat
__all__
=
[
'fc'
,
'embedding'
,
'dynamic_lstm'
,
'gru_unit'
,
'linear_chain_crf'
,
'crf_decoding'
,
'cos_sim'
,
'cross_entropy'
,
'square_error_cost'
,
'accuracy'
,
'chunk_eval'
,
'sequence_conv'
,
'conv2d'
,
'sequence_pool'
,
'pool2d'
,
'batch_norm'
,
'beam_search_decode'
,
'conv2d_transpose'
,
'sequence_expand'
,
'lstm_unit'
,
'reduce_sum'
,
'reduce_mean'
,
'reduce_max'
,
'reduce_min'
,
'sequence_first_step'
,
'sequence_last_step'
,
'dropout'
,
'split'
,
'l2_normalize'
,
'matmul'
,
'fc'
,
'embedding'
,
'dynamic_lstm'
,
'gru_unit'
,
'linear_chain_crf'
,
'crf_decoding'
,
'cos_sim'
,
'cross_entropy'
,
'square_error_cost'
,
'accuracy'
,
'chunk_eval'
,
'sequence_conv'
,
'conv2d'
,
'sequence_pool'
,
'pool2d'
,
'batch_norm'
,
'beam_search_decode'
,
'conv2d_transpose'
,
'sequence_expand'
,
'lstm_unit'
,
'reduce_sum'
,
'reduce_mean'
,
'reduce_max'
,
'reduce_min'
,
'sequence_first_step'
,
'sequence_last_step'
,
'dropout'
,
'split'
,
'l2_normalize'
,
'matmul'
,
'warpctc'
]
...
...
@@ -1788,3 +1765,56 @@ def matmul(x, y, transpose_x=False, transpose_y=False, name=None):
attrs
=
{
'transpose_X'
:
transpose_x
,
'transpose_Y'
:
transpose_y
})
return
out
def
warpctc
(
input
,
label
,
blank
=
0
,
norm_by_times
=
False
,
**
kwargs
):
"""
An operator integrating the open source Warp-CTC library
(https://github.com/baidu-research/warp-ctc)
to compute Connectionist Temporal Classification (CTC) loss.
It can be aliased as softmax with CTC, since a native softmax activation is
interated to the Warp-CTC library, to to normlize values for each row of the
input tensor.
Args:
input(Variable): (LodTensor, default: LoDTensor<float>),
the unscaled probabilities of variable-length sequences,
which is a 2-D Tensor with LoD information.
It's shape is [Lp, num_classes + 1], where Lp is the sum of all input
sequences' length and num_classes is the true number of classes.
(not including the blank label).
label(Variable): (LodTensor, default: LoDTensor<int>), the ground truth
of variable-length sequence, which is a 2-D Tensor with LoD
information. It is of the shape [Lg, 1], where Lg is th sum of
all labels' length.
blank: (int, default: 0), the blank label index of Connectionist
Temporal Classification (CTC) loss, which is in the
half-opened interval [0, num_classes + 1).
norm_by_times: (bool, default: false), whether to normalize
the gradients by the number of time-step,which is also the
sequence's length. There is no need to normalize the gradients
if warpctc layer was follewed by a mean_op.
Returns:
Variable: The Connectionist Temporal Classification (CTC) loss,
which is a 2-D Tensor of the shape [batch_size, 1].
Examples:
.. code-block:: python
y = layers.data(name='y', shape=[11, 8], dtype='float32', lod_level=1)
y_predict = layers.data(name='y_predict', shape=[11, 1], dtype='float32')
cost = layers.warpctc(input=y_predict, label=y)
"""
helper
=
LayerHelper
(
'warpctc'
,
**
kwargs
)
loss_out
=
helper
.
create_tmp_variable
(
dtype
=
input
.
dtype
)
grad_out
=
helper
.
create_tmp_variable
(
dtype
=
input
.
dtype
)
helper
.
append_op
(
type
=
'warpctc'
,
inputs
=
{
'Logits'
:
[
input
],
'Label'
:
[
label
]},
outputs
=
{
'WarpCTCGrad'
:
[
grad_out
],
'Loss'
:
[
loss_out
]},
attrs
=
{
'blank'
:
blank
,
'norm_by_times'
:
norm_by_times
})
return
loss_out
编辑
预览
Markdown
is supported
0%
请重试
或
添加新附件
.
添加附件
取消
You are about to add
0
people
to the discussion. Proceed with caution.
先完成此消息的编辑!
取消
想要评论请
注册
或
登录