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# Copyright 2022 The OFA-Sys Team.
# All rights reserved.
# This source code is licensed under the Apache 2.0 license
# found in the LICENSE file in the root directory.
import logging
import warnings
import torch
import numpy as np
from data import data_utils
from data.ofa_dataset import OFADataset
logger = logging.getLogger(__name__)
warnings.filterwarnings("ignore", "(Possibly )?corrupt EXIF data", UserWarning)
def collate(samples, pad_idx, eos_idx):
if len(samples) == 0:
return {}
def merge(key):
return data_utils.collate_tokens(
[s[key] for s in samples],
pad_idx,
eos_idx=eos_idx,
)
src_tokens = merge("source")
src_lengths = torch.LongTensor([s["source"].ne(pad_idx).long().sum() for s in samples])
prev_output_tokens = None
target = None
if samples[0].get("target", None) is not None:
target = merge("target")
tgt_lengths = torch.LongTensor(
[s["target"].ne(pad_idx).long().sum() for s in samples]
)
ntokens = tgt_lengths.sum().item()
if samples[0].get("prev_output_tokens", None) is not None:
prev_output_tokens = merge("prev_output_tokens")
else:
ntokens = src_lengths.sum().item()
target_strs = np.array([s["target_str"] for s in samples])
batch = {
"nsentences": len(samples),
"ntokens": ntokens,
"net_input": {
"src_tokens": src_tokens,
"src_lengths": src_lengths,
"prev_output_tokens": prev_output_tokens
},
"target": target,
"target_strs": target_strs
}
return batch
class SummaryDataset(OFADataset):
def __init__(
self,
split,
dataset,
bpe,
src_dict,
tgt_dict=None,
code_dict_size=8192,
num_bins=1000,
max_src_length=512,
max_tgt_length=128,
noise_ratio=0.0
):
super().__init__(split, dataset, bpe, src_dict, tgt_dict)
self.max_src_length = max_src_length
self.max_tgt_length = max_tgt_length
self.code_dict_size = code_dict_size
self.num_bins = num_bins
self.noise_ratio = noise_ratio
if type(bpe).__name__ == 'GPT2BPE':
self.prompt = ' what is the summary of article " {} "?'
elif type(bpe).__name__ == 'BertBPE':
self.prompt = "{} 请用一个句子简单总结上文："
def __getitem__(self, index):
source, target = self.dataset[index]
target_str = target.lower()
source = self.pre_caption(source, max_words=self.max_src_length)
target = self.pre_caption(target, max_words=self.max_tgt_length)
source = source.replace('<unk>', 'unk')
target = target.replace('<unk>', 'unk')
src_item = self.encode_text(
self.prompt.format(source),
length=self.max_src_length
)
tgt_item = self.encode_text('{}'.format(target))
noise_tgt_item = self.add_noise_to_tgt(tgt_item.clone(), self.noise_ratio)
src_item = torch.cat([self.bos_item, src_item, self.eos_item])
target_item = torch.cat([tgt_item, self.eos_item])
prev_output_item = torch.cat([self.bos_item, noise_tgt_item])
example = {
"source": src_item,
"target": target_item,
"prev_output_tokens": prev_output_item,
"target_str": target_str
}
return example
def add_noise_to_tgt(self, target, p):
noise_indices = torch.FloatTensor(target.size(0)).uniform_() < p
target[noise_indices] = torch.randint(
4, len(self.src_dict) - self.code_dict_size - self.num_bins, size=(noise_indices.sum(),)
)
return target
def collater(self, samples, pad_to_length=None):
"""Merge a list of samples to form a mini-batch.
Args:
samples (List[dict]): samples to collate
Returns:
dict: a mini-batch containing the data of the task
"""
return collate(samples, pad_idx=self.pad, eos_idx=self.eos) |