Skip to main content

Probability distributions over sequences in pytorch and cupy

Project description

Seqdist

Probability distributions over sequences in pytorch and cupy.

Install

pip install seqdist

How to use

Comparison against builtin pytorch implementation of the standard CTC loss:

sample_inputs = logits, targets, input_lengths, target_lengths = ctc.generate_sample_inputs(T_min=450, T_max=500, N=128, C=20, L_min=80, L_max=100)
print('pytorch loss: {:.4f}'.format(ctc.loss_pytorch(*sample_inputs)))
print('seqdist loss: {:.4f}'.format(ctc.loss_cupy(*sample_inputs)))
pytorch loss: 12.8080
seqdist loss: 12.8080

Speed comparison

Pytorch:

report(benchmark_fwd_bwd(ctc.loss_pytorch, *sample_inputs))
fwd: 4.79ms (4.17-5.33ms)
bwd: 9.69ms (8.33-10.88ms)
tot: 14.47ms (12.67-16.20ms)

Seqdist:

report(benchmark_fwd_bwd(ctc.loss_cupy, *sample_inputs))
fwd: 7.22ms (6.78-7.85ms)
bwd: 6.21ms (5.82-8.57ms)
tot: 13.43ms (12.63-16.41ms)

Alignments

betas = [0.1, 1.0, 10.]
alignments = {'beta={:.1f}'.format(beta): to_np(ctc.soft_alignments(*sample_inputs, beta=beta)) for beta in betas}
alignments['viterbi'] = to_np(ctc.viterbi_alignments(*sample_inputs))
fig, axs = plt.subplots(2, 2, figsize=(15, 8))
for (ax, (title, data)) in zip(np.array(axs).flatten(), alignments.items()):
    ax.imshow(data[:, 0].T, vmax=0.05);
    ax.set_title(title)  

png

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distribution

ont_seqdist_cuda111-0.0.4-py3-none-any.whl (21.7 kB view hashes)

Uploaded Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page