dbcollection for Matlab

This is a simple Matlab wrapper for the Python's dbcollection module. The functionality is almost the same, appart from some few minor differences related to Lua, namely regarding setting up ranges when fetching data.

Internally it calls the Python's dbcollection module for data download/process/management. The, Matlab interacts solely with the metadata hdf5 file to fetch data from disk.

Package installation

Requirements

This package requires:

• dbcollection (Python).
• Matlab >= 2014a or octave >= 4.0.0
• JSONlab

Note: This code may work on other previous versions of Matlab but, since it was developed and tested only on Matlab 2014a and octave >= 4.0.0, I cannot provide any garantees concerning older version compatabilities.

Installation

To install the dbcollection's Matlab API you must first have the Python's version installed in your system. If you do not have it already installed, then you can install it either via pip, conda or from source. Here we'll use pip to install this package:

$ pip install dbcollection==0.2.6

After you have the Python's version installed in your system, get the Matlab's API via the following repository:

$ git clone https://github.com/dbcollection/dbcollection-matlab

Then, add dbcollection-matlab/ to your Matlab's path:

addpath('<path>/dbcollection-matlab/');

Also, this package requires the JSONlab json encoder/decoder to work. To install this package just download the repo to disk:

$ git clone https://github.com/fangq/jsonlab

and add it your Matlab's path:

addpath('/path/to/jsonlab');

Getting started

Usage

This package follows the same API as the Python version. Once installed, to use the package simply call the class dbcollection:

dbc = dbcollection();

Then, just like in Python, to load a dataset you simply do:

mnist = dbc.load('mnist');

You can also select a specific task for any dataset by using the task option.

mnist = dbc.load(struct('name', 'mnist', 'task', 'classification'));

This API lets you download+extract most dataset's data directly from its source to the disk. For that, you can use the download() method to fetch data online from the dataset's source repository:

dbc.download(struct('name', 'cifar10', 'data_dir', 'some/dir/path'));

Data fetching

Once a dataset has been loaded, in order to retrieve data you can either use Matlab's HDF5 API or use the provided methods to retrive data from the .h5 metadata file.

For example, to retrieve an image and its label from the MNIST dataset using the Matlab's HDF5 API you can do the following:

imgs = h5read(mnist.cache_path, 'default/train/images');  % fetch data
imgs = permute(imgs, ndims(imgs):-1:1);  % permute dimensions to the original format
img = imgs(1,:,:,:);  % slice array

labels = h5read(mnist.cache_path, 'default/train/labels');  % fetch data
labels = permute(labels, ndims(labels):-1:1);  % permute dimensions to the original format
label = labels(1,:);  % slice array

or you can use the API provided by this package:

img = mnist.get('train', 'images', 1)
label = mnist.get('train', 'labels', 1)

Documentation

For a more detailed view of the Matlab's API documentation see here.

License

MIT license