Dataset and Database Loaders

class radbm.loaders.base.IRLoader(mode, which, backend, device, rng=None)

A subclass of Loader meant for Information Retrieval. This introduces the notion of mode which gouverns to way batches will be given.

Parameters:mode (str) – should be in IRLoader.get_available_modes()

class radbm.loaders.base.Loader(which, backend, device, rng=None)

An abstract class managing numpy vs torch, cpu vs gpu and train vs valid vs test. This should be subclassed with a particular dataset (i.e. Mnist).

Parameters:

• which (str) – Which datasets version to use. Should be ‘train’, ‘valid’ or ‘test’.
• backend (str) – Which backend to use, should be ‘numpy’ or ‘torch’.
• device (str) – Which device to use, should be ‘cpu’ or ‘cuda’. ‘cuda’ is only available if backend==’torch’.
• rng (numpy.random.RandomState) – A random number generator for reproducibility.

cpu()

Transfers each registered data (using register_switch) to the CPU

cuda()

Transfers each registered data (using register_switch) to the GPU

Raises:ValueError – If backend==’numpy’

dynamic_cast(data)

Cast data according to the current state of the class. E.g. when backend==’torch’, device==’cuda’ and the inputed data is numpy.ndarray, the array will be converted to torch.Tensor and transfered on the GPU.

Parameters:data (numpy.ndarray or torch.Tensor) – The data to cast Returns:casted_data – The casted data Return type:numpy.ndarray or torch.Tensor

get_rng()

Utility method to get the rng.

Returns:rng – The rng used inside the utility class TorchNumpyRNG. Return type:numpy.random.RandomState

numpy()

Converts each registered data (using register_switch) into numpy.ndarray

Raises:ValueError – If device==’cuda’

register_switch(name, data)

This function should only be used when subclassing. This is to register data for when a user will call: numpy(), torch(), cpu() or cuda(). Each value will be transfered to the appropriate format.

Parameters:

• name (str) – The name of the data. setarrt is used so one could later do self.<name> to reach the data.
• data (numpy.ndarray or torch.Tensor) – The data to register.

set_rng(rng)

Utility method to set the rng.

Parameters:rng (numpy.random.RandomState or TorchNumpyRNG) – The rng to use going forward. Returns:self Return type:Loader

test()

Switch to testing dataset.

torch()

Converts each registered data (using register_switch) into torch.Tensor

train()

Switch to training dataset.

valid()

Switch to validation dataset.