dataloader_lab3.py

from torch.utils.data import Dataset
from sklearn.preprocessing import LabelEncoder


class LabelTransformer(LabelEncoder):
    def inverse(self, y):
        try:
            return super(LabelTransformer, self).inverse_transform(y)
        except:
            return super(LabelTransformer, self).inverse_transform([y])

    def transform(self, y):
        try:
            return super(LabelTransformer, self).transform(y)
        except:
            return super(LabelTransformer, self).transform([y])


class SpectrogramDataset(Dataset):
    def __init__(self, path, train=True):
        t = 'train' if train else 'test'
        p = os.path.join(path, t)
        self.index = os.path.join(path, "{}_labels.txt".format(t))
        self.files, labels = self.get_files_labels(self.index)
        self.feats = [self.read_spectrogram(os.path.join(p, f)) for f in self.files]
        self.feat_dim = self.feats[0].shape[1]
        self.lengths = [len(i) for i in self.feats]
        self.feats = self.zero_pad_and_stack(self.feats)
        self.label_transformer = LabelTransformer()
        if isinstance(labels, (list, tuple)):
            self.labels = np.array(self.label_transformer.fit_transform(labels)).astype('int64')

    def read_spectrogram(self, spectrogram_file):
        # Return transposed fused spectrogram file
        raise NotImplementedError
        
    def get_files_labels(self, txt):
        # Read the [train|test]_labels.txt and return a list of files and a list of their labels
        raise NotImplementedError
    
    def zero_pad_and_stack(self, x):
        # Pad Features. You can use code from Lab 2
        raise NotImplementedError

    def __getitem__(self, item):
        return self.feats[item], self.labels[item], self.lengths[item]

    def __len__(self):
        return len(self.labels)