fix: fix the bug that DistributedBatchSampler may sample repeatedly

ppcls/engine/evaluation/retrieval.py

@@ -89,9 +89,6 @@ def retrieval_eval(engine, epoch_id=0):
 
 
 def cal_feature(engine, name='gallery'):
-    all_feas = None
-    all_image_id = None
-    all_unique_id = None
     has_unique_id = False
 
     if name == 'gallery':
@@ -103,6 +100,9 @@ def cal_feature(engine, name='gallery'):
     else:
         raise RuntimeError("Only support gallery or query dataset")
 
+    batch_feas_list = []
+    img_id_list = []
+    unique_id_list = []
     max_iter = len(dataloader) - 1 if platform.system() == "Windows" else len(
         dataloader)
     for idx, batch in enumerate(dataloader):  # load is very time-consuming
@@ -140,32 +140,39 @@ def cal_feature(engine, name='gallery'):
         if engine.config["Global"].get("feature_binarize") == "sign":
             batch_feas = paddle.sign(batch_feas).astype("float32")
 
-        if all_feas is None:
-            all_feas = batch_feas
+        if paddle.distributed.get_world_size() > 1:
+            batch_feas_gather = []
+            img_id_gather = []
+            unique_id_gather = []
+            paddle.distributed.all_gather(batch_feas_gather, batch_feas)
+            paddle.distributed.all_gather(img_id_gather, batch[1])
+            batch_feas_list.append(paddle.concat(batch_feas_gather))
+            img_id_list.append(paddle.concat(img_id_gather))
             if has_unique_id:
-                all_unique_id = batch[2]
-            all_image_id = batch[1]
+                paddle.distributed.all_gather(unique_id_gather, batch[2])
+                unique_id_list.append(paddle.concat(unique_id_gather))
         else:
-            all_feas = paddle.concat([all_feas, batch_feas])
-            all_image_id = paddle.concat([all_image_id, batch[1]])
+            batch_feas_list.append(batch_feas)
+            img_id_list.append(batch[1])
             if has_unique_id:
-                all_unique_id = paddle.concat([all_unique_id, batch[2]])
+                unique_id_list.append(batch[2])
 
     if engine.use_dali:
         dataloader.reset()
 
-    if paddle.distributed.get_world_size() > 1:
-        feat_list = []
-        img_id_list = []
-        unique_id_list = []
-        paddle.distributed.all_gather(feat_list, all_feas)
-        paddle.distributed.all_gather(img_id_list, all_image_id)
-        all_feas = paddle.concat(feat_list, axis=0)
-        all_image_id = paddle.concat(img_id_list, axis=0)
-        if has_unique_id:
-            paddle.distributed.all_gather(unique_id_list, all_unique_id)
-            all_unique_id = paddle.concat(unique_id_list, axis=0)
+    all_feas = paddle.concat(batch_feas_list)
+    all_img_id = paddle.concat(img_id_list)
+    if has_unique_id:
+        all_unique_id = paddle.concat(unique_id_list)
+
+    # just for DistributedBatchSampler issue: repeat sampling
+    total_samples = len(
+        dataloader.dataset) if not engine.use_dali else dataloader.size
+    all_feas = all_feas[:total_samples]
+    all_img_id = all_img_id[:total_samples]
+    if has_unique_id:
+        all_unique_id = all_unique_id[:total_samples]
 
     logger.info("Build {} done, all feat shape: {}, begin to eval..".format(
         name, all_feas.shape))
-    return all_feas, all_image_id, all_unique_id
+    return all_feas, all_img_id, all_unique_id