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Commit f55b60b9 authored by thomwolf's avatar thomwolf
Browse files

fixing again

parent 8bd91182
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Inline Side-by-side
Showing with 8 additions and 9 deletions
examples/run_classifier.py
View file @ f55b60b9
...@@ -420,7 +420,7 @@ def main(): ...@@ -420,7 +420,7 @@ def main():
eval_loss = 0 eval_loss = 0
nb_eval_steps = 0 nb_eval_steps = 0
preds = [] preds = []
out_label_ids = [] out_label_ids = None
for input_ids, input_mask, segment_ids, label_ids in tqdm(eval_dataloader, desc="Evaluating"): for input_ids, input_mask, segment_ids, label_ids in tqdm(eval_dataloader, desc="Evaluating"):
input_ids = input_ids.to(device) input_ids = input_ids.to(device)
...@@ -443,12 +443,12 @@ def main(): ...@@ -443,12 +443,12 @@ def main():
nb_eval_steps += 1 nb_eval_steps += 1
if len(preds) == 0: if len(preds) == 0:
preds.append(logits.detach().cpu().numpy()) preds.append(logits.detach().cpu().numpy())
out_label_ids.append(label_ids.detach().cpu().numpy()) out_label_ids = label_ids.detach().cpu().numpy())
else: else:
preds[0] = np.append( preds[0] = np.append(
preds[0], logits.detach().cpu().numpy(), axis=0) preds[0], logits.detach().cpu().numpy(), axis=0)
out_label_ids[0] = np.append( out_label_ids = np.append(
out_label_ids[0], label_ids.detach().cpu().numpy(), axis=0) out_label_ids, label_ids.detach().cpu().numpy(), axis=0)
eval_loss = eval_loss / nb_eval_steps eval_loss = eval_loss / nb_eval_steps
preds = preds[0] preds = preds[0]
...@@ -505,7 +505,7 @@ def main(): ...@@ -505,7 +505,7 @@ def main():
eval_loss = 0 eval_loss = 0
nb_eval_steps = 0 nb_eval_steps = 0
preds = [] preds = []
out_label_ids = [] out_label_ids = None
for input_ids, input_mask, segment_ids, label_ids in tqdm(eval_dataloader, desc="Evaluating"): for input_ids, input_mask, segment_ids, label_ids in tqdm(eval_dataloader, desc="Evaluating"):
input_ids = input_ids.to(device) input_ids = input_ids.to(device)
...@@ -523,13 +523,12 @@ def main(): ...@@ -523,13 +523,12 @@ def main():
nb_eval_steps += 1 nb_eval_steps += 1
if len(preds) == 0: if len(preds) == 0:
preds.append(logits.detach().cpu().numpy()) preds.append(logits.detach().cpu().numpy())
out_label_ids.append(label_ids.detach().cpu().numpy()) out_label_ids = label_ids.detach().cpu().numpy())
else: else:
preds[0] = np.append( preds[0] = np.append(
preds[0], logits.detach().cpu().numpy(), axis=0) preds[0], logits.detach().cpu().numpy(), axis=0)
out_label_ids[0] = np.append( out_label_ids = np.append(
out_label_ids[0], label_ids.detach().cpu().numpy(), axis=0) out_label_ids, label_ids.detach().cpu().numpy(), axis=0)
eval_loss = eval_loss / nb_eval_steps eval_loss = eval_loss / nb_eval_steps
preds = preds[0] preds = preds[0]
......
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