How to use CTC loss function? about paddle

[qingqing01]

You can use ctc_layer and ctc_error_evaluator as follows. Note, in consideration of the ‘blank’ label is needed by CTC, the size of ctc_layer and its input layer should be 134 (133 + 1). The 'blank' is the last category index by default, namely 133. We also integrate warp-CTC before, which may be merged later.

   ... # omit the previous content

   output = fc_layer(input=inputs, size=134, act=SoftmaxActivation())

   label = data_layer('label', class_dim)
   ctc = ctc_layer(input=output, label=label, 134)
   eval = ctc_error_evaluator(input=output, label=label)
   outputs(ctc)

[F0REacH]

@qingqing01 Many thanks.

[qingqing01]

@F0REacH I forget to remind you to update the code.

[F0REacH]

@qingqing01 I saw commit 9f3cbed fixed ctc issues. Nice work

[F0REacH]

Hi, @qingqing01
I've pulled the latest code and changed the model as you suggested:

def stacked_gru_net_with_ctc(input_dim=24,
                     class_dim=133,
                     hid_dim=129,
                     stacked_num=3,
                     is_predict=False):
    """
    """
    assert stacked_num % 2 == 1

    linear = LinearActivation()

    data = data_layer("word", input_dim)
    fc1 = fc_layer(input=data, size=hid_dim, act=linear)
    gru1 = grumemory(input=fc1)

    inputs = [fc1, gru1]
    for i in range(2, stacked_num + 1):
        fc = fc_layer(input=inputs, size=hid_dim, act=linear)
        gru = grumemory(input=fc, reverse=(i % 2) == 0)
        inputs = [fc, gru]

    output = fc_layer(input=inputs, size=class_dim+1, act=SoftmaxActivation())
    label = data_layer('label', class_dim)
    ctc = ctc_layer(input=output, label=label, size=class_dim+1)

    if is_predict:
        outputs(output)
    else:
        eval = ctc_error_evaluator(input=output, label=label)
        outputs(ctc)

But somehow started getting segmentation fault error:

Program terminated with signal SIGSEGV, Segmentation fault.
#0  0x0000000000b1ed66 in __gnu_cxx::__ops::_Iter_less_iter::operator()<float*, float*> (this=0x7ffcb7b25980, __it1=0x21cc21000, __it2=0x21cc21004) at /opt/gcc/include/c++/4.9.4/bits/predefined_ops.h:42
42            { return *__it1 < *__it2; }
[Current thread is 1 (Thread 0x7f5fe144f800 (LWP 8836))]
(gdb) bt
#0  0x0000000000b1ed66 in __gnu_cxx::__ops::_Iter_less_iter::operator()<float*, float*> (this=0x7ffcb7b25980, __it1=0x21cc21000, __it2=0x21cc21004) at /opt/gcc/include/c++/4.9.4/bits/predefined_ops.h:42
#1  0x0000000000b1e9ed in std::__max_element<float*, __gnu_cxx::__ops::_Iter_less_iter> (__first=0x21cc21004, __last=0x21cc21218, __comp=...) at /opt/gcc/include/c++/4.9.4/bits/stl_algo.h:5474
#2  0x0000000000b1e84c in std::max_element<float*> (__first=0x21cc21000, __last=0x21cc21218) at /opt/gcc/include/c++/4.9.4/bits/stl_algo.h:5497
#3  0x0000000000b1d1dc in paddle::CTCErrorEvaluator::bestLabelSeq (this=0x13a573f0) at /home/foreach/SOFT/BAIDU/PADDLE/Paddle/paddle/gserver/evaluators/CTCErrorEvaluator.cpp:52
#4  0x0000000000b1dc29 in paddle::CTCErrorEvaluator::editDistance (this=0x13a573f0, output=0x219046ce8, numTimes=120314, numClasses=134, labels=0x2049e5928, labelsLen=61) at /home/foreach/SOFT/BAIDU/PADDLE/Paddle/paddle/gserver/evaluators/CTCErrorEvaluator.cpp:175
#5  0x0000000000b1e152 in paddle::CTCErrorEvaluator::evalImp (this=0x13a573f0, arguments=std::vector of length 2, capacity 2 = {...}) at /home/foreach/SOFT/BAIDU/PADDLE/Paddle/paddle/gserver/evaluators/CTCErrorEvaluator.cpp:212
#6  0x0000000000affcae in paddle::Evaluator::eval (this=0x13a573f0, nn=...) at /home/foreach/SOFT/BAIDU/PADDLE/Paddle/paddle/gserver/evaluators/Evaluator.cpp:32
#7  0x0000000000b1e23e in paddle::CTCErrorEvaluator::eval (this=0x13a573f0, nn=...) at /home/foreach/SOFT/BAIDU/PADDLE/Paddle/paddle/gserver/evaluators/CTCErrorEvaluator.cpp:221
#8  0x0000000000ad6e4e in paddle::CombinedEvaluator::eval (this=0x13a57340, nn=...) at /home/foreach/SOFT/BAIDU/PADDLE/Paddle/paddle/gserver/gradientmachines/NeuralNetwork.cpp:321
#9  0x0000000000ad4f44 in paddle::NeuralNetwork::eval (this=0x136f0e70, evaluator=0x13a57340) at /home/foreach/SOFT/BAIDU/PADDLE/Paddle/paddle/gserver/gradientmachines/NeuralNetwork.cpp:379
#10 0x0000000000bc461e in paddle::TrainerInternal::trainOneBatch (this=0x7ffcb7b26a30, batchId=0, dataBatch=...) at /home/foreach/SOFT/BAIDU/PADDLE/Paddle/paddle/trainer/TrainerInternal.cpp:143
#11 0x0000000000bbe532 in paddle::Trainer::trainOnePass (this=0x7ffcb7b26980, passId=0) at /home/foreach/SOFT/BAIDU/PADDLE/Paddle/paddle/trainer/Trainer.cpp:434
#12 0x0000000000bbd1e0 in paddle::Trainer::train (this=0x7ffcb7b26980, numPasses=100000) at /home/foreach/SOFT/BAIDU/PADDLE/Paddle/paddle/trainer/Trainer.cpp:280
#13 0x00000000009f2761 in main (argc=12, argv=0x7ffcb7b27518) at /home/foreach/SOFT/BAIDU/PADDLE/Paddle/paddle/trainer/TrainerMain.cpp:100
(gdb) frame 3
#3  0x0000000000b1d1dc in paddle::CTCErrorEvaluator::bestLabelSeq (this=0x13a573f0) at /home/foreach/SOFT/BAIDU/PADDLE/Paddle/paddle/gserver/evaluators/CTCErrorEvaluator.cpp:52
52                                            acts + (i + 1) * numClasses_) -
(gdb) info locals
i = 117089
path = std::vector of length 117089, capacity 131072 = {118, 60, 7, 7, 97, 40, 53, 40, 53, 53, 53, 7, 7, 7, 7, 7, 44, 44, 132, 7, 7, 53, 53, 53, 53, 53, 53, 53, 53, 97, 97, 7, 53, 53, 40, 53, 53, 17, 7, 7, 44, 44, 53, 53, 53, 22, 7, 44, 44, 17, 7, 87, 87, 53, 53, 53, 53, 53, 53, 53, 
  53, 53, 53, 53, 53, 53, 7, 51, 53, 53, 53, 129, 129, 48, 48, 48, 48, 48, 112, 112, 2, 47, 31, 60, 48, 48, 48, 20, 60, 60, 60, 60, 60, 6, 48, 48, 48, 48, 39, 39, 60, 127, 48, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 6, 127, 127, 127, 127, 127, 127, 60, 
  127, 127, 60, 6, 38, 118, 38, 84, 84, 84, 84, 39, 39, 127, 127, 127, 127, 127, 2, 54, 54, 47, 47, 47, 48, 48, 48, 48, 48, 15, 48, 48, 48, 47, 47, 47, 48, 47, 48, 48, 48, 48, 48, 47, 48, 48, 48, 112, 97, 97, 6, 39, 6, 21, 6, 21, 39, 127, 48, 48, 54, 54, 48, 48, 127, 127, 127, 127, 
  127, 127, 127, 127, 127, 127, 127...}
acts = 0x219046ce8
(gdb) p numClasses_
$1 = 134
(gdb) p numTimes_
$2 = 120314

[qingqing01]

Hi, please refer to the changes in paddle/gserver/evaluators/CTCErrorEvaluator.cpp at this pull https://github.com/baidu/Paddle/pull/82/files

[F0REacH]

Thanks, @qingqing01 with pull #82 all is OK.