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6612068e
编写于
2月 08, 2018
作者:
W
whs
提交者:
GitHub
2月 08, 2018
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Merge pull request #8114 from wanghaoshuang/fix_ctc_align
Make CTC align op support for empty output
上级
e80255ce
3aae7815
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
47 addition
and
20 deletion
+47
-20
paddle/operators/ctc_align_op.cu
paddle/operators/ctc_align_op.cu
+8
-0
paddle/operators/ctc_align_op.h
paddle/operators/ctc_align_op.h
+8
-1
python/paddle/v2/fluid/layers/nn.py
python/paddle/v2/fluid/layers/nn.py
+20
-19
python/paddle/v2/fluid/tests/test_ctc_align.py
python/paddle/v2/fluid/tests/test_ctc_align.py
+11
-0
未找到文件。
paddle/operators/ctc_align_op.cu
浏览文件 @
6612068e
...
...
@@ -80,6 +80,14 @@ class CTCAlignOpCUDAKernel : public framework::OpKernel<T> {
// resize output dims
output
->
Resize
({
static_cast
<
int64_t
>
(
host_out_lod0
.
back
()),
1
});
if
(
host_out_lod0
.
back
()
==
0
)
{
output
->
Resize
({
1
,
1
});
output
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
math
::
SetConstant
<
platform
::
CUDADeviceContext
,
T
>
set_constant
;
set_constant
(
ctx
.
template
device_context
<
platform
::
CUDADeviceContext
>(),
output
,
-
1
);
}
}
};
...
...
paddle/operators/ctc_align_op.h
浏览文件 @
6612068e
...
...
@@ -16,6 +16,8 @@ limitations under the License. */
#include <string.h>
#include "paddle/framework/op_registry.h"
#include "paddle/operators/math/math_function.h"
namespace
paddle
{
namespace
operators
{
...
...
@@ -65,9 +67,14 @@ class CTCAlignKernel : public framework::OpKernel<T> {
framework
::
LoD
output_lod
;
output_lod
.
push_back
(
output_lod0
);
output
->
set_lod
(
output_lod
);
// resize output dims
output
->
Resize
({
static_cast
<
int64_t
>
(
output_lod0
.
back
()),
1
});
// for empty sequence
if
(
output_lod0
.
back
()
==
0
)
{
output
->
Resize
({
1
,
1
});
output_data
=
output
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
output_data
[
0
]
=
-
1
;
}
}
};
...
...
python/paddle/v2/fluid/layers/nn.py
浏览文件 @
6612068e
...
...
@@ -410,12 +410,12 @@ def dynamic_lstmp(input,
"""
**Dynamic LSTMP Layer**
LSTMP (LSTM with recurrent projection) layer has a separate projection
layer after the LSTM layer, projecting the original hidden state to a
lower-dimensional one, which is proposed to reduce the number of total
parameters and furthermore computational complexity for the LSTM,
espeacially for the case that the size of output units is relative
large (https://research.google.com/pubs/archive/43905.pdf).
LSTMP (LSTM with recurrent projection) layer has a separate projection
layer after the LSTM layer, projecting the original hidden state to a
lower-dimensional one, which is proposed to reduce the number of total
parameters and furthermore computational complexity for the LSTM,
espeacially for the case that the size of output units is relative
large (https://research.google.com/pubs/archive/43905.pdf).
The formula is as follows:
...
...
@@ -441,27 +441,27 @@ def dynamic_lstmp(input,
the matrix of weights from the input gate to the input).
* :math:`W_{ic}`, :math:`W_{fc}`, :math:`W_{oc}`: Diagonal weight
\
matrices for peephole connections. In our implementation,
\
we use vectors to reprenset these diagonal weight matrices.
we use vectors to reprenset these diagonal weight matrices.
* :math:`b`: Denotes bias vectors (e.g. :math:`b_i` is the input gate
\
bias vector).
bias vector).
* :math:`\sigma`: The activation, such as logistic sigmoid function.
* :math:`i, f, o` and :math:`c`: The input gate, forget gate, output
\
gate, and cell activation vectors, respectively, all of which have
\
the same size as the cell output activation vector :math:`h`.
the same size as the cell output activation vector :math:`h`.
* :math:`h`: The hidden state.
* :math:`r`: The recurrent projection of the hidden state.
* :math:`r`: The recurrent projection of the hidden state.
* :math:`
\\
tilde{c_t}`: The candidate hidden state, whose
\
computation is based on the current input and previous hidden state.
* :math:`\odot`: The element-wise product of the vectors.
* :math:`\odot`: The element-wise product of the vectors.
* :math:`act_g` and :math:`act_h`: The cell input and cell output
\
activation functions and `tanh` is usually used for them.
activation functions and `tanh` is usually used for them.
* :math:`\overline{act_h}`: The activation function for the projection
\
output, usually using `identity` or same as :math:`act_h`.
Set `use_peepholes` to `False` to disable peephole connection. The formula
is omitted here, please refer to the paper
http://www.bioinf.jku.at/publications/older/2604.pdf for details.
Note that these :math:`W_{xi}x_{t}, W_{xf}x_{t}, W_{xc}x_{t}, W_{xo}x_{t}`
operations on the input :math:`x_{t}` are NOT included in this operator.
Users can choose to use fully-connected layer before LSTMP layer.
...
...
@@ -479,8 +479,8 @@ def dynamic_lstmp(input,
- Hidden-hidden weight = {:math:`W_{ch}, W_{ih},
\
W_{fh}, W_{oh}`}.
- The shape of hidden-hidden weight is (P x 4D),
where P is the projection size and D the hidden
- The shape of hidden-hidden weight is (P x 4D),
where P is the projection size and D the hidden
size.
- Projection weight = {:math:`W_{rh}`}.
- The shape of projection weight is (D x P).
...
...
@@ -525,9 +525,9 @@ def dynamic_lstmp(input,
hidden_dim, proj_dim = 512, 256
fc_out = fluid.layers.fc(input=input_seq, size=hidden_dim * 4,
act=None, bias_attr=None)
proj_out, _ = fluid.layers.dynamic_lstmp(input=fc_out,
size=hidden_dim * 4,
proj_size=proj_dim,
proj_out, _ = fluid.layers.dynamic_lstmp(input=fc_out,
size=hidden_dim * 4,
proj_size=proj_dim,
use_peepholes=False,
is_reverse=True,
cell_activation="tanh",
...
...
@@ -2525,7 +2525,8 @@ def ctc_greedy_decoder(input, blank, name=None):
interval [0, num_classes + 1).
Returns:
Variable: CTC greedy decode result.
Variable: CTC greedy decode result. If all the sequences in result were
empty, the result LoDTensor will be [-1] with LoD [[0]] and dims [1, 1].
Examples:
.. code-block:: python
...
...
python/paddle/v2/fluid/tests/test_ctc_align.py
浏览文件 @
6612068e
...
...
@@ -31,6 +31,8 @@ def CTCAlign(input, lod, blank, merge_repeated):
result
.
append
(
token
)
prev_token
=
token
result
=
np
.
array
(
result
).
reshape
([
len
(
result
),
1
]).
astype
(
"int32"
)
if
len
(
result
)
==
0
:
result
=
np
.
array
([
-
1
])
return
result
...
...
@@ -72,5 +74,14 @@ class TestCTCAlignOpCase1(TestCTCAlignOp):
[
19
,
1
]).
astype
(
"int32"
)
class
TestCTCAlignOpCase2
(
TestCTCAlignOp
):
def
config
(
self
):
self
.
op_type
=
"ctc_align"
self
.
input_lod
=
[[
0
,
4
]]
self
.
blank
=
0
self
.
merge_repeated
=
True
self
.
input
=
np
.
array
([
0
,
0
,
0
,
0
]).
reshape
([
4
,
1
]).
astype
(
"int32"
)
if
__name__
==
"__main__"
:
unittest
.
main
()
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