def padSeqs()

in E2E_TOD/utils.py [0:0]

• 43 lines of code
• 15 McCabe index (conditional complexity)

def padSeqs(sequences, maxlen=None, truncated = False, pad_method='post',
                     trunc_method='pre', dtype='int32', value=0.):
    if not hasattr(sequences, '__len__'): 
        raise ValueError('`sequences` must be iterable.')
    lengths = []
    for x in sequences:
        if not hasattr(x, '__len__'):
            raise ValueError('`sequences` must be a list of iterables. '
                             'Found non-iterable: ' + str(x))
        lengths.append(len(x))

    num_samples = len(sequences)
    seq_maxlen = np.max(lengths)

    if maxlen is not None and truncated:
        maxlen = min(seq_maxlen, maxlen)
    else:
        maxlen = seq_maxlen
    sample_shape = tuple()
    for s in sequences:
        if len(s) > 0:
            sample_shape = np.asarray(s).shape[1:]
            break

    x = (np.ones((num_samples, maxlen) + sample_shape) * value).astype(dtype)
    for idx, s in enumerate(sequences):
        if not len(s):
            print('empty list/array was found')
            continue 
        if trunc_method == 'pre':
            trunc = s[-maxlen:]
        elif trunc_method == 'post':
            trunc = s[:maxlen]
        else:
            raise ValueError('Truncating type "%s" not understood' % trunc_method)

        trunc = np.asarray(trunc, dtype=dtype)
        if trunc.shape[1:] != sample_shape:
            raise ValueError('Shape of sample %s of sequence at position %s is different from expected shape %s' %
                             (trunc.shape[1:], idx, sample_shape))

        if pad_method == 'post':
            x[idx, :len(trunc)] = trunc
        elif pad_method == 'pre':
            x[idx, -len(trunc):] = trunc
        else:
            raise ValueError('Padding type "%s" not understood' % pad_method)
    return x