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updating evaluation on a single gpu

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thomwolf 2019-06-18 22:20:21 +02:00
parent 7d2001aa44
commit 29b7b30eaa
1 changed files with 6 additions and 14 deletions
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20
examples/run_classifier.py
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@ -306,10 +306,10 @@ def main():
logger.info(" Num steps = %d", num_train_optimization_steps)
model.train()
for _ in trange(int(args.num_train_epochs), desc="Epoch"):
for _ in trange(int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0]):
tr_loss = 0
nb_tr_examples, nb_tr_steps = 0, 0
for step, batch in enumerate(tqdm(train_dataloader, desc="Iteration")):
for step, batch in enumerate(tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])):
batch = tuple(t.to(device) for t in batch)
input_ids, input_mask, segment_ids, label_ids = batch
@ -367,21 +367,13 @@ def main():
# Load a trained model and vocabulary that you have fine-tuned
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)
# Distributed/fp16/parallel settings (optional)
model.to(device)
if args.fp16:
model.half()
if args.local_rank != -1:
model = torch.nn.parallel.DistributedDataParallel(model,
device_ids=[args.local_rank],
output_device=args.local_rank,
find_unused_parameters=True)
elif n_gpu > 1:
model = torch.nn.DataParallel(model)
model.to(device)
### Evaluation
if args.do_eval:
if args.do_eval and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
eval_examples = processor.get_dev_examples(args.data_dir)
cached_eval_features_file = os.path.join(args.data_dir, 'dev_{0}_{1}_{2}'.format(
list(filter(None, args.bert_model.split('/'))).pop(),