add module_desc.proto and fix senta example usage issues

Senta/sentiment_classify.py

@@ -152,10 +152,12 @@ def train_net(train_reader,
               (pass_id, avg_acc, avg_cost))
 
     # save the model
-    module_path = os.path.join(save_dirname, network_name)
-    hub.ModuleDesc.save_module_dict(
-        module_path=module_path, word_dict=word_dict)
-    fluid.io.save_inference_model(module_path, ["words"], emb, exe)
+    module_dir = os.path.join(save_dirname, network_name)
+    fluid.io.save_inference_model(module_dir, ["words"], emb, exe)
+
+    config = hub.ModuleConfig(module_dir)
+    config.save_dict(word_dict=word_dict)
+    config.dump()
 
 
 def retrain_net(train_reader,
@@ -209,10 +211,7 @@ def retrain_net(train_reader,
     #TODO(ZeyuChen): how to get output paramter according to proto config
     emb = module.get_module_output()
 
-    print(
-        "adfjkajdlfjoqi jqiorejlmsfdlkjoi jqwierjoajsdklfjoi qjerijoajdfiqwjeor adfkalsf"
-    )
-    # # # embedding layer
+    # # embedding layer
     # emb = fluid.layers.embedding(input=data, size=[dict_dim, emb_dim])
     # #input=data, size=[dict_dim, emb_dim], param_attr="bow_embedding")
     # # bow layer
@@ -264,8 +263,8 @@ def retrain_net(train_reader,
         # print("senta_load_module", fluid.default_main_program())
 
     # save the model
-    module_path = os.path.join(save_dirname, network_name + "_retrain")
-    fluid.io.save_inference_model(module_path, ["words"], emb, exe)
+    module_dir = os.path.join(save_dirname, network_name + "_retrain")
+    fluid.io.save_inference_model(module_dir, ["words"], emb, exe)
 
 
 def eval_net(test_reader, use_gpu, model_path=None):

paddle_hub/__init__.py

@@ -6,4 +6,5 @@ import paddle.fluid as fluid
 
 from paddle_hub.module import Module
 from paddle_hub.module import ModuleConfig
+from paddle_hub.module import ModuleUtils
 from paddle_hub.downloader import download_and_uncompress

paddle_hub/module.py

@@ -46,15 +46,16 @@ class Module(object):
                 module_url)
         else:
             # otherwise it's local path, no need to deal with it
+            print("Module.__init__", module_url)
             self.module_dir = module_url
-            self.module_name = module_url.split()[-1]
+            self.module_name = module_url.split("/")[-1]
 
         # load paddle inference model
         place = fluid.CPUPlace()
         self.exe = fluid.Executor(fluid.CPUPlace())
         [self.inference_program, self.feed_target_names,
          self.fetch_targets] = fluid.io.load_inference_model(
-             dirname=module_dir, executor=self.exe)
+             dirname=self.module_dir, executor=self.exe)
 
         print("inference_program")
         print(self.inference_program)
@@ -63,8 +64,8 @@ class Module(object):
         print("fetch_targets")
         print(self.fetch_targets)
 
-        config = ModuleConfig()
-        config.load(self.module_dir)
+        config = ModuleConfig(self.module_dir)
+        config.load()
         # load assets
         # self.dict = defaultdict(int)
         # self.dict.setdefault(0)
@@ -188,11 +189,12 @@ class ModuleConfig(object):
         self.dict = defaultdict(int)
         self.dict.setdefault(0)
 
-    def load(self, module_dir):
+    def load(self):
         """load module config from module dir
         """
         #TODO(ZeyuChen): check module_desc.pb exsitance
-        with open(pb_file_path, "rb") as fi:
+        pb_path = os.path.join(self.module_dir, "module_desc.pb")
+        with open(pb_path, "rb") as fi:
             self.desc.ParseFromString(fi.read())
 
         if self.desc.contain_assets: