Merge pull request #1321 from RainFrost1/release/2.3

[cherry pick]fix clas distributed eval bug

ppcls/engine/evaluation/classification.py

@@ -34,6 +34,10 @@ def classification_eval(engine, epoch_id=0):
 
     metric_key = None
     tic = time.time()
+    accum_samples = 0
+    total_samples = len(
+        engine.eval_dataloader.
+        dataset) if not engine.use_dali else engine.eval_dataloader.size
     max_iter = len(engine.eval_dataloader) - 1 if platform.system(
     ) == "Windows" else len(engine.eval_dataloader)
     for iter_id, batch in enumerate(engine.eval_dataloader):
@@ -61,15 +65,31 @@ def classification_eval(engine, epoch_id=0):
                 if key not in output_info:
                     output_info[key] = AverageMeter(key, '7.5f')
                 output_info[key].update(loss_dict[key].numpy()[0], batch_size)
+
+        # just for DistributedBatchSampler issue: repeat sampling
+        current_samples = batch_size * paddle.distributed.get_world_size()
+        accum_samples += current_samples
+
         # calc metric
         if engine.eval_metric_func is not None:
-            metric_dict = engine.eval_metric_func(out, batch[1])
             if paddle.distributed.get_world_size() > 1:
-                for key in metric_dict:
-                    paddle.distributed.all_reduce(
-                        metric_dict[key], op=paddle.distributed.ReduceOp.SUM)
-                    metric_dict[key] = metric_dict[
-                        key] / paddle.distributed.get_world_size()
+                pred_list = []
+                label_list = []
+                if isinstance(out, dict):
+                    out = out["logits"]
+                paddle.distributed.all_gather(pred_list, out)
+                paddle.distributed.all_gather(label_list, batch[1])
+                pred = paddle.concat(pred_list, 0)
+                labels = paddle.concat(label_list, 0)
+                if accum_samples > total_samples:
+                    pred = pred[:total_samples + current_samples -
+                                accum_samples]
+                    labels = labels[:total_samples + current_samples -
+                                    accum_samples]
+                    current_samples = total_samples + current_samples - accum_samples
+                metric_dict = engine.eval_metric_func(pred, labels)
+            else:
+                metric_dict = engine.eval_metric_func(out, batch[1])
             for key in metric_dict:
                 if metric_key is None:
                     metric_key = key
@@ -77,7 +97,7 @@ def classification_eval(engine, epoch_id=0):
                     output_info[key] = AverageMeter(key, '7.5f')
 
                 output_info[key].update(metric_dict[key].numpy()[0],
-                                        batch_size)
+                                        current_samples)
 
         time_info["batch_cost"].update(time.time() - tic)