PyTorch bindings for Warp-ctc

branch	status
pytorch-1.11

This is an extension onto the original repo found here.

Installation

Install PyTorch v1.11.

WARP_CTC_PATH should be set to the location of a built WarpCTC (i.e. 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/yuyq96/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.

You can also build the wheel:

cd ../pytorch_binding
python setup.py bdist_wheel

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])
probs_sizes = torch.IntTensor([2])
label_sizes = torch.IntTensor([2])
probs.requires_grad_(True)  # tells autograd to compute gradients for probs
cost = ctc_loss(probs, labels, probs_sizes, label_sizes)
cost.backward()

Documentation

CTCLoss(size_average=False, length_average=False, reduce=True)
    # 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)
    # reduce (bool): average or sum over observation for each minibatch.
        If `False`, returns a loss per batch element instead and ignores `average` options.
        (default: `True`)

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