def __init__(self, config, ranker, logger, train_ds, vocab, random):
super().__init__(config, ranker, vocab, train_ds, logger, random)
self.loss_fn = {
'softmax': self.softmax,
'cross_entropy': self.cross_entropy,
'nogueira_cross_entropy': self.nogueira_cross_entropy,
'hinge': self.hinge
}[config['lossfn']]
self.dataset = train_ds
self.input_spec = ranker.input_spec()
self.iter_fields = self.input_spec['fields'] | {'runscore'}
self.train_iter_core = onir.datasets.pair_iter(
train_ds,
fields=self.iter_fields,
pos_source=self.config['pos_source'],
neg_source=self.config['neg_source'],
sampling=self.config['sampling'],
pos_minrel=self.config['pos_minrel'],
unjudged_rel=self.config['unjudged_rel'],
num_neg=self.config['num_neg'],
random=self.random,
inf=True)
self.train_iter = util.background(
self.iter_batches(self.train_iter_core))
self.numneg = config['num_neg']
self._ewc = False
def iter_scores(self, ranker, datasource, device):
if isinstance(
ranker, Trivial
) and not ranker.neg and not ranker.qsum and not ranker.max:
for qid, values in self.dataset.run().items():
for did, score in values.items():
yield qid, did, score
return
if isinstance(
ranker,
Trivial) and not ranker.neg and not ranker.qsum and ranker.max:
qrels = self.dataset.qrels()
for qid, values in self.dataset.run().items():
q_qrels = qrels.get(qid, {})
for did in values:
yield qid, did, q_qrels.get(did, -1)
return
with torch.no_grad():
ranker.eval()
ds = next(datasource, None)
total = None
if isinstance(ds, list):
total = sum(len(d['query_id']) for d in ds)
elif self.source == 'run':
if self.run_threshold > 0:
total = sum(
min(len(v), self.run_threshold)
for v in self.dataset.run().values())
else:
total = sum(len(v) for v in self.dataset.run().values())
elif self.source == 'qrels':
total = sum(len(v) for v in self.dataset.qrels().values())
with self.logger.pbar_raw(total=total, desc='pred',
quiet=True) as pbar:
for batch in util.background(ds):
batch = {
k: (v.to(device) if torch.is_tensor(v) else v)
for k, v in batch.items()
}
rel_scores = self.ranker(**batch).cpu()
if len(rel_scores.shape) == 2:
rel_scores = rel_scores[:, 0]
triples = list(
zip(batch['query_id'], batch['doc_id'], rel_scores))
for qid, did, score in triples:
yield qid, did, score.item()
pbar.update(len(batch['query_id']))