remove reader/metrics in attention_lstm.py

fluid/PaddleCV/video/models/attention_lstm/attention_lstm.py

@@ -31,7 +31,7 @@ class AttentionLSTM(ModelBase):
         self.feature_num = self.cfg.MODEL.feature_num
         self.feature_names = self.cfg.MODEL.feature_names
         self.feature_dims = self.cfg.MODEL.feature_dims
-        self.class_num = self.cfg.MODEL.class_num
+        self.num_classes = self.cfg.MODEL.num_classes
         self.embedding_size = self.cfg.MODEL.embedding_size
         self.lstm_size = self.cfg.MODEL.lstm_size
 
@@ -59,7 +59,7 @@ class AttentionLSTM(ModelBase):
             shapes = []
             for dim in self.feature_dims:
                 shapes.append([-1, dim])
-            shapes.append([-1, self.class_num])  # label
+            shapes.append([-1, self.num_classes])  # label
             self.py_reader = fluid.layers.py_reader(
                 capacity=1024,
                 shapes=shapes,
@@ -81,7 +81,7 @@ class AttentionLSTM(ModelBase):
                 self.label_input = None
             else:
                 self.label_input = fluid.layers.data(
-                    shape=[self.class_num], dtype='float32', name='label')
+                    shape=[self.num_classes], dtype='float32', name='label')
 
     def build_model(self):
         att_outs = []
@@ -108,7 +108,7 @@ class AttentionLSTM(ModelBase):
                 regularizer=fluid.regularizer.L2Decay(0.0),
                 initializer=fluid.initializer.NormalInitializer(scale=0.0)))
 
-        self.logit = fluid.layers.fc(input=fc2, size=self.class_num, act=None, \
+        self.logit = fluid.layers.fc(input=fc2, size=self.num_classes, act=None, \
                               bias_attr=ParamAttr(regularizer=fluid.regularizer.L2Decay(0.0),
                                                   initializer=fluid.initializer.NormalInitializer(scale=0.0)))
 
@@ -149,20 +149,3 @@ class AttentionLSTM(ModelBase):
     def weights_info(self):
         return (None, None)
 
-    def create_dataset_args(self):
-        dataset_args = {}
-        dataset_args['num_classes'] = self.class_num
-        dataset_args['list'] = self.get_config_from_sec(self.mode, 'filelist')
-
-        if self.use_gpu and self.py_reader:
-            dataset_args['batch_size'] = int(self.batch_size / self.gpu_num)
-        else:
-            dataset_args['batch_size'] = self.batch_size
-
-        return dataset_args
-
-    def create_metrics_args(self):
-        metrics_args = {}
-        metrics_args['num_classes'] = self.class_num
-        metrics_args['topk'] = 20
-        return metrics_args