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# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
from . import BaseWrapperDataset, data_utils
class AddTargetDataset(BaseWrapperDataset):
def __init__(
self,
dataset,
labels,
pad,
eos,
batch_targets,
process_label=None,
add_to_input=False,
):
super().__init__(dataset)
self.labels = labels
self.batch_targets = batch_targets
self.pad = pad
self.eos = eos
self.process_label = process_label
self.add_to_input = add_to_input
def get_label(self, index):
return (
self.labels[index]
if self.process_label is None
else self.process_label(self.labels[index])
)
def __getitem__(self, index):
item = self.dataset[index]
item["label"] = self.get_label(index)
return item
def size(self, index):
sz = self.dataset.size(index)
own_sz = len(self.get_label(index))
return (sz, own_sz)
def collater(self, samples):
collated = self.dataset.collater(samples)
if len(collated) == 0:
return collated
indices = set(collated["id"].tolist())
target = [s["label"] for s in samples if s["id"] in indices]
if self.batch_targets:
collated["target_lengths"] = torch.LongTensor([len(t) for t in target])
target = data_utils.collate_tokens(target, pad_idx=self.pad, left_pad=False)
collated["ntokens"] = collated["target_lengths"].sum().item()
else:
collated["ntokens"] = sum([len(t) for t in target])
collated["target"] = target
if self.add_to_input:
eos = target.new_full((target.size(0), 1), self.eos)
collated["target"] = torch.cat([target, eos], dim=-1).long()
collated["net_input"]["prev_output_tokens"] = torch.cat(
[eos, target], dim=-1
).long()
collated["ntokens"] += target.size(0)
return collated
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