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)
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