| branch | status |
|---|---|
pytorch_bindings |
 |
pytorch-0.4 |
 |
pytorch-1.0 |
 |
This is an extension onto the original repo found here.
Install PyTorch first.
warpctc-pytorch wheel uses local version identifiers,
which has a restriction that users have to specify the version explicitly.
$ pip install warpctc-pytorch==X.X.X+torchYY.cudaZZThe latest version is 0.2.1 and if you work with PyTorch 1.6 and CUDA 10.2, you can run:
$ pip install warpctc-pytorch==0.2.1+torch16.cuda102warpctc-pytorch wheels are provided for Python 3.8, 3.7, 3.6 and CUDA 10.2, 10.1, 10.0, 9.2.
warpctc-pytorch wheels are provided for Python 3.7, 3.6 and CUDA 10.2, 10.1, 10.0, 9.2.
warpctc-pytorch10-cudaYY wheels are provided for Python 3.7, 3.6 and CUDA 10.1, 10.0, 9.2, 9.1, 9.0, 8.0.
If you work with CUDA 10.1, you can run:
$ pip install warpctc-pytorch10-cuda101Wheels for PyTorch 0.4.1 are not provided so users have to build from source manually.
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/espnet/warp-ctc.git
$ cd warp-ctc; git checkout -b pytorch-0.4 remotes/origin/pytorch-0.4
$ mkdir build; cd build
$ cmake ..
$ makeNow install the bindings:
$ cd ../pytorch_binding
$ pip install numpy cffi
$ python setup.py installExample 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([2])
probs_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()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
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