fix drop last for training process (#713)

ppcls/data/reader.py

@@ -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):
@@ -291,7 +293,7 @@ class Reader:
             dataset,
             batch_size=batch_size,
             shuffle=self.shuffle and is_train,
-            drop_last=is_train)
+            drop_last=False)
         loader = DataLoader(
             dataset,
             batch_sampler=batch_sampler,

tools/eval.py

@@ -72,6 +72,10 @@ def main(args, return_dict={}):
 
     init_model(config, net, optimizer=None)
     valid_dataloader = Reader(config, 'valid', places=place)()
+    if len(valid_dataloader) <= 0:
+        logger.error(
+            "valid dataloader is empty, please check your data config again!")
+        sys.exit(-1)
     net.eval()
     with paddle.no_grad():
         if not multilabel:

tools/train.py

@@ -88,9 +88,18 @@ def main(args):
     init_model(config, net, optimizer)
 
     train_dataloader = Reader(config, 'train', places=place)()
+    if len(train_dataloader) <= 0:
+        logger.error(
+            "train dataloader is empty, please check your data config again!")
+        sys.exit(-1)
 
     if config.validate:
         valid_dataloader = Reader(config, 'valid', places=place)()
+        if len(valid_dataloader) <= 0:
+            logger.error(
+                "valid dataloader is empty, please check your data config again!"
+            )
+            sys.exit(-1)
 
     last_epoch_id = config.get("last_epoch", -1)
     best_top1_acc = 0.0  # best top1 acc record