PyTorch bindings for Baidu Warp-CTC
Pytorch bindings for Baidu's Warp-CTC. These bindings were inspired by SeanNaren's but these include some bug fixes, and offer some additional features.
import torch from torch_baidu_ctc import ctc_loss, CTCLoss # Activations. Shape T x N x D. # T -> max number of frames/timesteps # N -> minibatch size # D -> number of output labels (including the CTC blank) x = torch.rand(10, 3, 6) # Target labels y = torch.tensor( [ # 1st sample 1, 1, 2, 5, 2, # 2nd 1, 5, 2, # 3rd 4, 4, 2, 3, ], dtype=torch.int, ) # Activations lengths xs = torch.tensor([10, 6, 9], dtype=torch.int) # Target lengths ys = torch.tensor([5, 3, 4], dtype=torch.int) # By default, the costs (negative log-likelihood) of all samples are summed. # This is equivalent to: # ctc_loss(x, y, xs, ys, average_frames=False, reduction="sum") loss1 = ctc_loss(x, y, xs, ys) # You can also average the cost of each sample among the number of frames. # The averaged costs are then summed. loss2 = ctc_loss(x, y, xs, ys, average_frames=True) # Instead of summing the costs of each sample, you can perform # other `reductions`: "none", "sum", or "mean" # # Return an array with the loss of each individual sample losses = ctc_loss(x, y, xs, ys, reduction="none") # # Compute the mean of the individual losses loss3 = ctc_loss(x, y, xs, ys, reduction="mean") # # First, normalize loss by number of frames, later average losses loss4 = ctc_loss(x, y, xs, ys, average_frames=True, reduction="mean") # Finally, there's also a nn.Module to use this loss. ctc = CTCLoss(average_frames=True, reduction="mean", blank=0) loss4_2 = ctc(x, y, xs, ys) # Note: the `blank` option is also available for `ctc_loss`. # By default it is 0.
- C++11 compiler (tested with GCC 4.9).
- Python: 2.7, 3.5, 3.6, 3.7 (tested with versions 2.7, 3.5 and 3.6).
- PyTorch >= 0.4.1 (tested with version 0.4.1).
- For GPU support: CUDA Toolkit.
The installation process should be pretty straightforward assuming that you have correctly installed the required libraries and tools.
The setup process compiles the package from source, and will compile with CUDA support if this is available for PyTorch.
From Pypi (recommended)
pip install torch-baidu-ctc
git clone --recursive https://github.com/jpuigcerver/pytorch-baidu-ctc.git cd pytorch-baidu-ctc python setup.py build python setup.py install
AVX512 related issues
Some compiling problems may arise when using CUDA and newer host compilers
with AVX512 instructions. Please, install GCC 4.9 and use it as the host
compiler for NVCC. You can simply set the
CXX environment variables
before the build/install commands:
CC=gcc-4.9 CXX=g++-4.9 pip install torch-baidu-ctc
or (if you are using the GitHub source code):
CC=gcc-4.9 CXX=g++-4.9 python setup.py build
You can test the library once installed using
unittest. In particular,
run the following commands:
python -m unittest torch_baidu_ctc.test
All tests should pass (CUDA tests are only executed if supported).
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|torch_baidu_ctc-0.1.1-cp27-cp27mu-manylinux1_x86_64.whl (2.0 MB) Copy SHA256 hash SHA256||Wheel||cp27|
|torch_baidu_ctc-0.1.1-cp35-cp35m-manylinux1_x86_64.whl (2.0 MB) Copy SHA256 hash SHA256||Wheel||cp35|
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|torch-baidu-ctc-0.1.1.tar.gz (53.5 kB) Copy SHA256 hash SHA256||Source||None|