semvec/pypi-packages huggingface dataset:
ont-seqdist-cuda114 0.0.4
Probability distributions over sequences in pytorch and cupy
License: MIT
Requires Python: >=3.5
Keywords: CTC

# 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](docs/images/output_11_0.png)
ont-seqdist-cuda113 0.0.4
Probability distributions over sequences in pytorch and cupy
License: MIT
Requires Python: >=3.5
Keywords: CTC

# 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](docs/images/output_11_0.png)
ont-seqdist-cuda112 0.0.4
Probability distributions over sequences in pytorch and cupy
License: MIT
Requires Python: >=3.5
Keywords: CTC

# 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](docs/images/output_11_0.png)
ont-seqdist-cuda111 0.0.4
Probability distributions over sequences in pytorch and cupy
License: MIT
Requires Python: >=3.5
Keywords: CTC

# 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](docs/images/output_11_0.png)
ont-seqdist-cuda102 0.0.4
Probability distributions over sequences in pytorch and cupy
License: MIT
Requires Python: >=3.5
Keywords: CTC

# 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](docs/images/output_11_0.png)
ont-seqdist-cuda101 0.0.4
Probability distributions over sequences in pytorch and cupy
License: MIT
Requires Python: >=3.5
Keywords: CTC

# 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](docs/images/output_11_0.png)