未验证 提交 6612068e 编写于 作者: W whs 提交者: GitHub

Merge pull request #8114 from wanghaoshuang/fix_ctc_align

Make CTC align op support for empty output
......@@ -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);
}
}
};
......
......@@ -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;
}
}
};
......
......@@ -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
......
......@@ -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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