PyTorch bindings for Warp-ctc
This is an extension onto the original repo found here.
Install PyTorch v0.4.
WARP_CTC_PATH should be set to the location of a built WarpCTC
libwarpctc.so). This defaults to
../build, so from within a
new warp-ctc clone you could build WarpCTC like this:
git clone https://github.com/SeanNaren/warp-ctc.git cd warp-ctc mkdir build; cd build cmake .. make
Now install the bindings:
cd pytorch_binding python setup.py install
If you try the above and get a dlopen error on OSX with anaconda3 (as recommended by pytorch):
cd ../pytorch_binding python setup.py install cd ../build cp libwarpctc.dylib /Users/$WHOAMI/anaconda3/lib
This will resolve the library not loaded error. This can be easily modified to work with other python installs if needed.
Example to use the bindings below.
import torch from warpctc_pytorch import CTCLoss ctc_loss = CTCLoss() # expected shape of seqLength x batchSize x alphabet_size probs = torch.FloatTensor([[[0.1, 0.6, 0.1, 0.1, 0.1], [0.1, 0.1, 0.6, 0.1, 0.1]]]).transpose(0, 1).contiguous() labels = torch.IntTensor([1, 2]) label_sizes = torch.IntTensor() probs_sizes = torch.IntTensor() probs.requires_grad_(True) # tells autograd to compute gradients for probs cost = ctc_loss(probs, labels, probs_sizes, label_sizes) cost.backward()
CTCLoss(size_average=False, length_average=False) # size_average (bool): normalize the loss by the batch size (default: False) # length_average (bool): normalize the loss by the total number of frames in the batch. If True, supersedes size_average (default: False) forward(acts, labels, act_lens, label_lens) # acts: Tensor of (seqLength x batch x outputDim) containing output activations from network (before softmax) # labels: 1 dimensional Tensor containing all the targets of the batch in one large sequence # act_lens: Tensor of size (batch) containing size of each output sequence from the network # label_lens: Tensor of (batch) containing label length of each example