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updating run_classif

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thomwolf 2019-06-18 22:23:10 +02:00
parent 29b7b30eaa
commit 3359955622
1 changed files with 3 additions and 20 deletions
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examples/run_classifier.py
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@ -50,15 +50,6 @@ else:
logger = logging.getLogger(__name__)
def average_distributed_scalar(scalar, args):
""" Average a scalar over the nodes if we are in distributed training. We use this for distributed evaluation. """
if args.local_rank == -1:
return scalar
scalar_t = torch.tensor(scalar, dtype=torch.float, device=args.device) / torch.distributed.get_world_size()
torch.distributed.all_reduce(scalar_t, op=torch.distributed.ReduceOp.SUM)
return scalar_t.item()
def main():
parser = argparse.ArgumentParser()
@ -368,7 +359,7 @@ def main():
model = BertForSequenceClassification.from_pretrained(args.output_dir, num_labels=num_labels)
tokenizer = BertTokenizer.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
else:
model = BertForQuestionAnswering.from_pretrained(args.bert_model)
model = BertForSequenceClassification.from_pretrained(args.bert_model)
model.to(device)
@ -453,10 +444,6 @@ def main():
preds = np.squeeze(preds)
result = compute_metrics(task_name, preds, out_label_ids)
if args.local_rank != -1:
# Average over distributed nodes if needed
result = {key: average_distributed_scalar(value, args) for key, value in result.items()}
loss = tr_loss/global_step if args.do_train else None
result['eval_loss'] = eval_loss
@ -510,10 +497,10 @@ def main():
with torch.no_grad():
logits = model(input_ids, segment_ids, input_mask, labels=None)
loss_fct = CrossEntropyLoss()
tmp_eval_loss = loss_fct(logits.view(-1, num_labels), label_ids.view(-1))
eval_loss += tmp_eval_loss.mean().item()
nb_eval_steps += 1
if len(preds) == 0:
@ -530,10 +517,6 @@ def main():
preds = np.argmax(preds, axis=1)
result = compute_metrics(task_name, preds, out_label_ids)
if args.local_rank != -1:
# Average over distributed nodes if needed
result = {key: average_distributed_scalar(value, args) for key, value in result.items()}
loss = tr_loss/global_step if args.do_train else None
result['eval_loss'] = eval_loss