Update multilabel

ppcls/configs/quick_start/professional/MobileNetV1_multilabel.yaml

@@ -46,8 +46,8 @@ DataLoader:
   Train:
     dataset:
       name: MultiLabelDataset
-      image_root: ./dataset/NUS-SCENE-dataset/images/
-      cls_label_path: ./dataset/NUS-SCENE-dataset/multilabel_train_list.txt
+      image_root: ./dataset/NUS-WIDE-SCENE/NUS-SCENE-dataset/images/
+      cls_label_path: ./dataset/NUS-WIDE-SCENE/NUS-SCENE-dataset/multilabel_train_list.txt
       transform_ops:
         - DecodeImage:
             to_rgb: True
@@ -74,8 +74,8 @@ DataLoader:
   Eval:
     dataset: 
       name: MultiLabelDataset
-      image_root: ./dataset/NUS-SCENE-dataset/images/
-      cls_label_path: ./dataset/NUS-SCENE-dataset/multilabel_test_list.txt
+      image_root: ./dataset/NUS-WIDE-SCENE/NUS-SCENE-dataset/images/
+      cls_label_path: ./dataset/NUS-WIDE-SCENE/NUS-SCENE-dataset/multilabel_test_list.txt
       transform_ops:
         - DecodeImage:
             to_rgb: True

ppcls/engine/evaluation/classification.py

@@ -50,7 +50,7 @@ def classification_eval(engine, epoch_id=0):
         time_info["reader_cost"].update(time.time() - tic)
         batch_size = batch[0].shape[0]
         batch[0] = paddle.to_tensor(batch[0]).astype("float32")
-        if not evaler.config["Global"].get("use_multilabel", False):
+        if not engine.config["Global"].get("use_multilabel", False):
             batch[1] = batch[1].reshape([-1, 1]).astype("int64")
         # image input
         out = engine.model(batch[0])

ppcls/engine/train/train.py

@@ -76,8 +76,8 @@ def train_epoch(engine, epoch_id, print_batch_step):
         tic = time.time()
 
 
-def forward(trainer, batch):
-    if not trainer.is_rec:
-        return trainer.model(batch[0])
+def forward(engine, batch):
+    if not engine.is_rec:
+        return engine.model(batch[0])
     else:
-        return trainer.model(batch[0], batch[1])
+        return engine.model(batch[0], batch[1])