Fix eval function for distributed training (#1228)

demo/auto_compression/semantic_segmentation/run.py

@@ -68,8 +68,9 @@ def parse_args():
 def eval_function(exe, compiled_test_program, test_feed_names, test_fetch_list):
 
     nranks = paddle.distributed.ParallelEnv().local_rank
-
-    batch_sampler = paddle.io.DistributedBatchSampler(
+    if nranks > 1 and paddle.distributed.get_rank() != 0:
+        return
+    batch_sampler = paddle.io.BatchSampler(
         eval_dataset, batch_size=1, shuffle=False, drop_last=False)
     loader = paddle.io.DataLoader(
         eval_dataset,
@@ -116,30 +117,9 @@ def eval_function(exe, compiled_test_program, test_feed_names, test_fetch_list):
             paddle.to_tensor(label),
             eval_dataset.num_classes,
             ignore_index=eval_dataset.ignore_index)
-
-        if nranks > 1:
-            intersect_area_list = []
-            pred_area_list = []
-            label_area_list = []
-            paddle.distributed.all_gather(intersect_area_list, intersect_area)
-            paddle.distributed.all_gather(pred_area_list, pred_area)
-            paddle.distributed.all_gather(label_area_list, label_area)
-
-            # Some image has been evaluated and should be eliminated in last iter
-            if (iter + 1) * nranks > len(eval_dataset):
-                valid = len(eval_dataset) - iter * nranks
-                intersect_area_list = intersect_area_list[:valid]
-                pred_area_list = pred_area_list[:valid]
-                label_area_list = label_area_list[:valid]
-
-            for i in range(len(intersect_area_list)):
-                intersect_area_all = intersect_area_all + intersect_area_list[i]
-                pred_area_all = pred_area_all + pred_area_list[i]
-                label_area_all = label_area_all + label_area_list[i]
-        else:
-            intersect_area_all = intersect_area_all + intersect_area
-            pred_area_all = pred_area_all + pred_area
-            label_area_all = label_area_all + label_area
+        intersect_area_all = intersect_area_all + intersect_area
+        pred_area_all = pred_area_all + pred_area
+        label_area_all = label_area_all + label_area
 
     class_iou, miou = metrics.mean_iou(intersect_area_all, pred_area_all,
                                        label_area_all)