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https://github.com/jpuigcerver/prob-phoc
Probabilistic relevance scores from PHOC embeddings
https://github.com/jpuigcerver/prob-phoc
cuda keyword-spotting kws phoc pytorch
Last synced: 1 day ago
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Probabilistic relevance scores from PHOC embeddings
- Host: GitHub
- URL: https://github.com/jpuigcerver/prob-phoc
- Owner: jpuigcerver
- License: mit
- Created: 2018-05-14T16:22:02.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2019-02-08T00:13:38.000Z (almost 6 years ago)
- Last Synced: 2024-10-12T23:07:48.092Z (about 1 month ago)
- Topics: cuda, keyword-spotting, kws, phoc, pytorch
- Language: Shell
- Homepage:
- Size: 50.8 KB
- Stars: 0
- Watchers: 2
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# prob-phoc
[](https://travis-ci.com/jpuigcerver/prob-phoc)
PyTorch functions to compute meaningful probabilistic relevance scores from
PHOC (Pyramid of Histograms of Characters) embeddings.
Although they are called Pyramid of Histograms of Characters, in practice
they are a Pyramid of Bag of Characters. At the end, each word is
represented by a high-dimensional binary vector.
See the [wiki](https://github.com/jpuigcerver/prob-phoc/wiki)
for additional details.
## Usage
The library provides two functions: `cphoc` and `pphoc`, which are
similar to SciPy's `cdist` and `pdist`:
Both functions can operate with PHOC embeddings in the probability space (where
each dimension is a real number in the range [0, 1]), or in the log-probability
space (where each dimension is the logarithm of a probability). These are also
sometimes refered to as the Real and Log semirings.
```python
import torch
from prob_phoc import cphoc, pphoc
x = torch.Tensor(...)
y = torch.Tensor(...)
# Compute the log-relevance scores between all pairs of rows in x, y.
# Note: x and y must have the PHOC log-probabilities.
logprob = cphoc(x, y)
# This is equivalent to:
logprob = cphoc(x, y, method="sum_prod_log")
# If your matrices have probabilities instead of log-probabilities, use:
prob = cphoc(x, y, method="sum_prob_real")
# Compute the log-relevance scores between all pairs of distinct rows in x.
# Note: The output is a vector with N * (N - 1) / 2 elements.
logprob = pphoc(x)
```
## Installation
The easiest way is to install the package from PyPI:
```bash
pip install prob-phoc
```
If you want to install the latest version from the repository, clone it
and use the setup.py script to compile and install the library.
```bash
python setup.py install
```
You will need a C++11 compiler (tested with GCC 4.9).
If you want to compile with CUDA support, you will also need to install
the CUDA Toolkit (tested with versions 8.0, 9.0 and 10.0)
## Tests and benchmarks
After the installation, you can run the tests to ensure that everything is
working fine.
```bash
python -m prob_phoc.test
```
I have also some benchmarks to compare CPU vs. CUDA, for different matrix
sizes and float precision. These take quite a long to run, so don't hold
your breath.
```bash
python -m prob_phoc.benchmark
```