From 3e847449adaeaab6963462c32539f5f17daecf6f Mon Sep 17 00:00:00 2001
From: thomwolf <thomwolf@gmail.com>
Date: Tue, 18 Jun 2019 16:53:31 +0200
Subject: [PATCH] fix out_label_ids

---
 examples/run_classifier.py | 13 +++++++++++--
 1 file changed, 11 insertions(+), 2 deletions(-)

diff --git a/examples/run_classifier.py b/examples/run_classifier.py
index d945b0dfc8..b1749ec8be 100644
--- a/examples/run_classifier.py
+++ b/examples/run_classifier.py
@@ -420,6 +420,7 @@ def main():
         eval_loss = 0
         nb_eval_steps = 0
         preds = []
+        out_label_ids = []
 
         for input_ids, input_mask, segment_ids, label_ids in tqdm(eval_dataloader, desc="Evaluating"):
             input_ids = input_ids.to(device)
@@ -442,9 +443,12 @@ def main():
             nb_eval_steps += 1
             if len(preds) == 0:
                 preds.append(logits.detach().cpu().numpy())
+                out_label_ids.append(label_ids.detach().cpu().numpy())
             else:
                 preds[0] = np.append(
                     preds[0], logits.detach().cpu().numpy(), axis=0)
+                out_label_ids[0] = np.append(
+                    out_label_ids[0], label_ids.detach().cpu().numpy(), axis=0)
 
         eval_loss = eval_loss / nb_eval_steps
         preds = preds[0]
@@ -452,7 +456,7 @@ def main():
             preds = np.argmax(preds, axis=1)
         elif output_mode == "regression":
             preds = np.squeeze(preds)
-        result = compute_metrics(task_name, preds, all_label_ids.numpy())
+        result = compute_metrics(task_name, preds, out_label_ids.numpy())
 
         if args.local_rank != -1:
             # Average over distributed nodes if needed
@@ -501,6 +505,7 @@ def main():
             eval_loss = 0
             nb_eval_steps = 0
             preds = []
+            out_label_ids = []
 
             for input_ids, input_mask, segment_ids, label_ids in tqdm(eval_dataloader, desc="Evaluating"):
                 input_ids = input_ids.to(device)
@@ -518,14 +523,18 @@ def main():
                 nb_eval_steps += 1
                 if len(preds) == 0:
                     preds.append(logits.detach().cpu().numpy())
+                    out_label_ids.append(label_ids.detach().cpu().numpy())
                 else:
                     preds[0] = np.append(
                         preds[0], logits.detach().cpu().numpy(), axis=0)
+                    out_label_ids[0] = np.append(
+                        out_label_ids[0], label_ids.detach().cpu().numpy(), axis=0)
+
 
             eval_loss = eval_loss / nb_eval_steps
             preds = preds[0]
             preds = np.argmax(preds, axis=1)
-            result = compute_metrics(task_name, preds, all_label_ids.numpy())
+            result = compute_metrics(task_name, preds, out_label_ids.numpy())
 
             if args.local_rank != -1:
                 # Average over distributed nodes if needed