data_loader.py

import os

import numpy as np
import six.moves.cPickle as pickle
import torch
import torch.nn as nn
import torch.utils.data as data

class PolyphonicDataset(data.Dataset):
    def __init__(self, filepath):
        # 1. Initialize file path or list of file names.
        """read training sequences(list of int array) from a pickle file"""
        print("loading data...")
        f= open(filepath, "rb")
        data = pickle.load(f)
        self.seqs = data['sequences']
        self.seqlens = data['seq_lens']
        self.data_len = len(self.seqs)
        print("{} entries".format(self.data_len))

    def __getitem__(self, offset):
        seq=self.seqs[offset].astype('float32')
        rev_seq= seq.copy()
        rev_seq[0:len(seq), :] = seq[(len(seq)-1)::-1, :]
        seq_len=self.seqlens[offset].astype('int64')
        return seq, rev_seq, seq_len

    def __len__(self):
        return self.data_len
   

class SyntheticDataset(data.Dataset):
    def __init__(self, filepath):
        # 1. Initialize file path or list of file names.
        """read training sequences(list of int array) from a pickle file"""
        print("loading data...")
        f= open(filepath, "rb")
        data = pickle.load(f)
        self.seqs = data['sequences']
        self.seqlens = data['seq_lens']
        self.z = data['z']
        self.data_len = len(self.seqs)
        print("{} entries".format(self.data_len))

    def __getitem__(self, offset):
        seq=self.seqs[offset].astype('float32')
        rev_seq= seq.copy()
        rev_seq[0:len(seq), :] = seq[(len(seq)-1)::-1, :]
        seq_len=self.seqlens[offset].astype('int64')
        z = self.z[offset]
        return seq, rev_seq, seq_len, z

    def __len__(self):
        return self.data_len