update text classifer demo

demo/text_classification/text_classifier_dygraph.py

@@ -20,10 +20,39 @@ parser.add_argument("--max_seq_len",        type=int,               default=512,
 # yapf: enable.
 
 
+class TransformerClassifier(fluid.dygraph.Layer):
+    def __init__(self, num_classes, transformer):
+        super(TransformerClassifier, self).__init__()
+        self.num_classes = num_classes
+        self.transformer = transformer
+        self.fc = Linear(input_dim=768, output_dim=num_classes)
+
+    def forward(self, input_ids, position_ids, segment_ids, input_mask):
+        pooled_output, sequence_output = self.transformer(
+            input_ids, position_ids, segment_ids, input_mask)
+        cls_feats = fluid.layers.dropout(
+            pooled_output,
+            dropout_prob=0.1,
+            dropout_implementation="upscale_in_train")
+        cls_feats = fluid.layers.reshape(cls_feats, shape=[-1, 768])
+        pred = self.fc(cls_feats)
+        return fluid.layers.softmax(pred)
+
+
 def finetune(args):
     with fluid.dygraph.guard():
         ernie = hub.Module(name="ernie")
         dataset = hub.dataset.ChnSentiCorp()
+        tc = TransformerClassifier(
+            num_classes=dataset.num_labels, transformer=ernie)
+        adam = AdamOptimizer(
+            learning_rate=0.001, parameter_list=tc.parameters())
+        print(len(tc.parameters()))
+        state_dict_path = os.path.join(args.checkpoint_dir,
+                                       'dygraph_state_dict')
+        if os.path.exists(state_dict_path + '.pdparams'):
+            state_dict, _ = fluid.load_dygraph(state_dict_path)
+            tc.load_dict(state_dict)
 
         reader = hub.reader.ClassifyReader(
             dataset=dataset,
@@ -34,14 +63,36 @@ def finetune(args):
         train_reader = reader.data_generator(
             batch_size=args.batch_size, phase='train')
 
-        for data_id, data in enumerate(train_reader()):
-            input_ids = np.array(data[0][0]).astype(np.int64)
-            position_ids = np.array(data[0][1]).astype(np.int64)
-            segment_ids = np.array(data[0][2]).astype(np.int64)
-            input_mask = np.array(data[0][3]).astype(np.float32)
-            labels = np.array(data[0][4]).astype(np.int64)
-            pooled_output, sequence_output = ernie(position_ids, input_mask,
-                                                   input_ids, segment_ids)
+        loss_sum = acc_sum = cnt = 0
+        # 执行epoch_num次训练
+        for epoch in range(args.num_epoch):
+            # 读取训练数据进行训练
+            for batch_id, data in enumerate(train_reader()):
+                input_ids = np.array(data[0][0]).astype(np.int64)
+                position_ids = np.array(data[0][1]).astype(np.int64)
+                segment_ids = np.array(data[0][2]).astype(np.int64)
+                input_mask = np.array(data[0][3]).astype(np.float32)
+                labels = np.array(data[0][4]).astype(np.int64)
+                pred = tc(input_ids, position_ids, segment_ids, input_mask)
+
+                acc = fluid.layers.accuracy(pred, to_variable(labels))
+                loss = fluid.layers.cross_entropy(pred, to_variable(labels))
+                avg_loss = fluid.layers.mean(loss)
+                avg_loss.backward()
+                # 参数更新
+                adam.minimize(avg_loss)
+
+                loss_sum += avg_loss.numpy() * labels.shape[0]
+                acc_sum += acc.numpy() * labels.shape[0]
+                cnt += labels.shape[0]
+                if batch_id % args.log_interval == 0:
+                    print('epoch {}: loss {}, acc {}'.format(
+                        epoch, loss_sum / cnt, acc_sum / cnt))
+                    loss_sum = acc_sum = cnt = 0
+
+                if batch_id % args.save_interval == 0:
+                    state_dict = tc.state_dict()
+                    fluid.save_dygraph(state_dict, state_dict_path)
 
 
 if __name__ == "__main__":

paddlehub/module/module.py

@@ -131,11 +131,11 @@ class Module(fluid.dygraph.Layer):
                  module_dir=None,
                  version=None,
                  **kwargs):
-        super(Module, self).__init__()
         # Avoid module being initialized multiple times
         if "_is_initialize" in self.__dict__ and self._is_initialize:
             return
 
+        super(Module, self).__init__()
         _run_func_name = self._get_func_name(self.__class__,
                                              _module_runnable_func)
         self._run_func = getattr(self,
@@ -146,14 +146,12 @@ class Module(fluid.dygraph.Layer):
         self._initialize(**kwargs)
         self._is_initialize = True
         self._code_version = "v2"
-        self._model_runner = None
+        self.model_runner = fluid.dygraph.StaticModelRunner(
+            self.pretrained_model_path)
 
     @property
-    def model_runner(self):
-        if not self._model_runner:
-            self._model_runner = fluid.dygraph.StaticModelRunner(
-                self.default_pretrained_model_path)
-        return self._model_runner
+    def pretrained_model_path(self):
+        return self.default_pretrained_model_path
 
     def _get_func_name(self, current_cls, module_func_dict):
         mod = current_cls.__module__ + "." + current_cls.__name__

paddlehub/module/nlp_module.py

@@ -246,6 +246,10 @@ class TransformerModule(NLPBaseModule):
     Tranformer Module base class can be used by BERT, ERNIE, RoBERTa and so on.
     """
 
+    @property
+    def pretrained_model_path(self):
+        return self.params_path
+
     def init_pretraining_params(self, exe, pretraining_params_path,
                                 main_program):
         assert os.path.exists(
@@ -353,12 +357,13 @@ class TransformerModule(NLPBaseModule):
 
         return inputs, outputs, module_program
 
-    @property
-    def model_runner(self):
-        if not self._model_runner:
-            self._model_runner = fluid.dygraph.StaticModelRunner(
-                self.params_path)
-        return self._model_runner
+
+#     @property
+#     def model_runner(self):
+#         if not self._model_runner:
+#             self._model_runner = fluid.dygraph.StaticModelRunner(
+#                 self.params_path)
+#         return self._model_runner
 
     def get_embedding(self, texts, use_gpu=False, batch_size=1):
         """