data_process.py

import numpy as np
import torch 
from torch.autograd import Variable

def make_variable(tensor, volatile=False,requires_grad=False):
    """Convert Tensor to Variable."""
    if torch.cuda.is_available():
        tensor = tensor.cuda()
    return Variable(tensor, volatile=volatile, requires_grad = requires_grad)


def shuffle_aligned_list(data):
    """Shuffle arrays in a list by shuffling each array identically."""
    num = data[0].shape[0]
    p = np.random.permutation(num)
    return [d[p] for d in data]


def batch_generator(data, batch_size, shuffle=False, wrap_around=False):
    """Generate batches of data.

    Given a list of array-like objects, generate batches of a given
    size by yielding a list of array-like objects corresponding to the
    same slice of each input.
    """
    if shuffle:
        data = shuffle_aligned_list(data)

    batch_count = 0
    while True:
        if (batch_count * batch_size + batch_size >= len(data[0])):
            batch_count = 0

            if shuffle:
                data = shuffle_aligned_list(data)

        start = batch_count * batch_size
        end = start + batch_size
        batch_count += 1
        yield [d[start:end] for d in data]

        # stop if reaches the end and the user doesn't want to wrap around
        if (batch_count * batch_size + batch_size >= len(data[0]) and not wrap_around):
            break