Merge pull request #719 from vslyu/2.1/fix_xpu_eval

[Kunlun]2.1cherry-pick: xpu use one cards for evaluation in multi cards training

ppcls/data/reader.py

@@ -197,7 +197,7 @@ class CommonDataset(Dataset):
 
     def __len__(self):
         return self.num_samples
-    
+
 
 class MultiLabelDataset(Dataset):
     """
@@ -224,9 +224,11 @@ class MultiLabelDataset(Dataset):
             labels = label_str.split(',')
             labels = [int(i) for i in labels]
 
-            return (transform(img, self.ops), np.array(labels).astype("float32"))
+            return (transform(img, self.ops),
+                    np.array(labels).astype("float32"))
         except Exception as e:
-            logger.error("data read failed: {}, exception info: {}".format(line, e))
+            logger.error("data read failed: {}, exception info: {}".format(
+                line, e))
             return self.__getitem__(random.randint(0, len(self)))
 
     def __len__(self):
@@ -263,6 +265,7 @@ class Reader:
             self.collate_fn = self.mix_collate_fn
 
         self.places = places
+        self.use_xpu = config.get("use_xpu", False)
         self.multilabel = config.get("multilabel", False)
 
     def mix_collate_fn(self, batch):
@@ -285,20 +288,29 @@ class Reader:
             dataset = MultiLabelDataset(self.params)
         else:
             dataset = CommonDataset(self.params)
-
-        is_train = self.params['mode'] == "train"
-        batch_sampler = DistributedBatchSampler(
-            dataset,
-            batch_size=batch_size,
-            shuffle=self.shuffle and is_train,
-            drop_last=is_train)
-        loader = DataLoader(
-            dataset,
-            batch_sampler=batch_sampler,
-            collate_fn=self.collate_fn if is_train else None,
-            places=self.places,
-            return_list=True,
-            num_workers=self.params["num_workers"])
+        if (self.params['mode'] != "train") and self.use_xpu:
+            loader = DataLoader(
+                dataset,
+                places=self.places,
+                batch_size=batch_size,
+                drop_last=False,
+                return_list=True,
+                shuffle=False,
+                num_workers=self.params["num_workers"])
+        else:
+            is_train = self.params['mode'] == "train"
+            batch_sampler = DistributedBatchSampler(
+                dataset,
+                batch_size=batch_size,
+                shuffle=self.shuffle and is_train,
+                drop_last=is_train)
+            loader = DataLoader(
+                dataset,
+                batch_sampler=batch_sampler,
+                collate_fn=self.collate_fn if is_train else None,
+                places=self.places,
+                return_list=True,
+                num_workers=self.params["num_workers"])
         return loader
 
 

tools/program.py

@@ -119,7 +119,8 @@ def create_metric(out,
                   classes_num=1000,
                   use_distillation=False,
                   multilabel=False,
-                  mode="train"):
+                  mode="train",
+                  use_xpu=False):
     """
     Create measures of model accuracy, such as top1 and top5
 
@@ -175,11 +176,12 @@ def create_metric(out,
         fetch_list.append(ham_dist)
 
     # multi cards' eval
-    if mode != "train" and paddle.distributed.get_world_size() > 1:
-        for idx, fetch in enumerate(fetch_list):
-            fetch_list[idx] = paddle.distributed.all_reduce(
-                fetch, op=paddle.distributed.ReduceOp.
-                SUM) / paddle.distributed.get_world_size()
+    if not use_xpu:
+        if mode != "train" and paddle.distributed.get_world_size() > 1:
+            for idx, fetch in enumerate(fetch_list):
+                fetch_list[idx] = paddle.distributed.all_reduce(
+                    fetch, op=paddle.distributed.ReduceOp.
+                    SUM) / paddle.distributed.get_world_size()
 
     fetchs = OrderedDict()
     for idx, name in enumerate(metric_names):
@@ -213,6 +215,7 @@ def create_fetchs(feeds, net, config, mode="train"):
     use_mix = config.get('use_mix') and mode == 'train'
     use_distillation = config.get('use_distillation')
     multilabel = config.get('multilabel', False)
+    use_xpu = config.get("use_xpu", False)
 
     out = net(feeds["image"])
 
@@ -229,7 +232,8 @@ def create_fetchs(feeds, net, config, mode="train"):
             classes_num,
             use_distillation,
             multilabel=multilabel,
-            mode=mode)
+            mode=mode,
+            use_xpu=use_xpu)
         fetchs.update(metric)
 
     return fetchs