685126644ae540be72eb662527269a0395e2c9eb,onmt/IO.py,,make_features,#Any#Any#,59

Before Change

def make_features(batch, fields):
    // TODO: This is bit hacky, add to batch somehow.
    f = ONMTDataset.collect_features(fields)
    cat = [batch.src[0]] + [batch.__dict__[k] for k in f]
    cat = [c.unsqueeze(2) for c in cat]
    return torch.cat(cat, 2)

After Change

    data (Variable): a batch of source or target data
    
    assert side in ["src", "tgt"]
    if isinstance(batch.__dict__[side], tuple):
        data = batch.__dict__[side][0]
    else:
        data = batch.__dict__[side]
    feat_start = side + "_feat_"
    features = sorted(batch.__dict__[k]
                      for k in batch.__dict__ if feat_start in k)
    levels = [data] + features
    return torch.cat([level.unsqueeze(2) for level in levels], 2)


def join_dicts(*args):

Instances