fix loading bug and check full conversion of model

pytorch_pretrained_bert/convert_transfo_xl_checkpoint_to_pytorch.py

@@ -180,16 +180,18 @@ def convert_transfo_xl_checkpoint_to_pytorch(tf_checkpoint_path,
                     raise
                 print("Initialize PyTorch weight {}".format(name))
                 pointer.data = torch.from_numpy(array)
-            del tf_weights[name]
+            tf_weights.pop(name, None)
+            tf_weights.pop(name + '/Adam', None)
+            tf_weights.pop(name + '/Adam_1', None)
 
         print("Weights not copied to PyTorch model: {}".format(', '.join(tf_weights.keys())))
 
         # Save pytorch-model
-        pytorch_weights_dump_path = pytorch_dump_folder_path + '/' + WEIGHTS_NAME
-        pytorch_config_dump_path = pytorch_dump_folder_path + '/' + CONFIG_NAME
-        print("Save PyTorch model to {}".format(pytorch_weights_dump_path))
+        pytorch_weights_dump_path = os.path.join(pytorch_dump_folder_path, WEIGHTS_NAME)
+        pytorch_config_dump_path = os.path.join(pytorch_dump_folder_path, CONFIG_NAME)
+        print("Save PyTorch model to {}".format(os.path.abspath(pytorch_weights_dump_path)))
         torch.save(model.state_dict(), pytorch_weights_dump_path)
-        print("Save configuration file to {}".format(pytorch_config_dump_path))
+        print("Save configuration file to {}".format(os.path.abspath(pytorch_config_dump_path)))
         with open(pytorch_config_dump_path, "w", encoding="utf-8") as f:
             f.write(config.to_json_string())
 

pytorch_pretrained_bert/modeling_transfo_xl.py

@@ -818,7 +818,7 @@ class TransfoXLPreTrainedModel(nn.Module):
             for name, child in module._modules.items():
                 if child is not None:
                     load(child, prefix + name + '.')
-        # load(model.transformer if hasattr(model, 'transformer') else model, prefix='')
+        load(model, prefix='')
         if len(missing_keys) > 0:
             logger.info("Weights of {} not initialized from pretrained model: {}".format(
                 model.__class__.__name__, missing_keys))