From b3fcc98610e1be70b89c569b0f3ff5d511c61765 Mon Sep 17 00:00:00 2001
From: gaotingquan <gaotingquan@baidu.com>
Date: Tue, 29 Aug 2023 03:38:42 +0000
Subject: [PATCH] to be compatible with training and evaluation

---
 ppcls/metric/metrics.py | 15 +++++++--------
 1 file changed, 7 insertions(+), 8 deletions(-)

diff --git a/ppcls/metric/metrics.py b/ppcls/metric/metrics.py
index 20282e22..723cff84 100644
--- a/ppcls/metric/metrics.py
+++ b/ppcls/metric/metrics.py
@@ -38,6 +38,7 @@ class TopkAcc(AvgMetrics):
             topk = [topk]
         self.topk = topk
         self.reset()
+        self.warned = False
 
     def reset(self):
         self.avg_meters = {
@@ -54,17 +55,15 @@ class TopkAcc(AvgMetrics):
         metric_dict = dict()
         for idx, k in enumerate(self.topk):
             if output_dims < k:
-                msg = f"The output dims({output_dims}) is less than k({k}), so the Top-{k} metric is meaningless."
-                logger.warning(msg)
+                if not self.warned:
+                    msg = f"The output dims({output_dims}) is less than k({k}), so the Top-{k} metric is meaningless."
+                    logger.warning(msg)
+                    self.warned = True
                 metric_dict[f"top{k}"] = 1
-                self.avg_meters.pop(f"top{k}")
-                continue
-            metric_dict[f"top{k}"] = paddle.metric.accuracy(x, label, k=k)
+            else:
+                metric_dict[f"top{k}"] = paddle.metric.accuracy(x, label, k=k)
             self.avg_meters[f"top{k}"].update(metric_dict[f"top{k}"],
                                               x.shape[0])
-
-        self.topk = list(filter(lambda k: k <= output_dims, self.topk))
-
         return metric_dict
 
 
-- 
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