Fix V2 API

paddle/parameter/Parameter.h

@@ -324,6 +324,7 @@ class Parameter {
   std::vector<std::shared_ptr<IParameterUpdaterHook>> updaterHooks_;
 
 public:
+  void setSharedCount(int cnt) { sharedCount_ = cnt; }
   int getSharedCount() { return sharedCount_; }
 
   bool isSparse() { return config_.is_sparse(); }

python/paddle/trainer/config_parser.py

@@ -3371,7 +3371,7 @@ def Import(config_file, local_args={}):
     return Import
 
 
-settings = dict(
+default_settings = dict(
     batch_size=None,
     mini_batch_size=None,
     algorithm='async_sgd',
@@ -3404,6 +3404,8 @@ def Import(config_file, local_args={}):
     adam_beta2=0.999,
     adam_epsilon=1e-8, )
 
+settings = copy.deepcopy(default_settings)
+
 settings_deprecated = dict(usage_ratio=1., )
 
 trainer_settings = dict(
@@ -3544,23 +3546,32 @@ def update_g_config():
     return g_config
 
 
-def parse_config(trainer_config, config_arg_str):
+def begin_parse(config_arg_str=''):
     '''
-    @param trainer_config: can be a string of config file name or a function name
-    with config logic
     @param config_arg_str: a string of the form var1=val1,var2=val2. It will be
     passed to config script as a dictionary CONFIG_ARGS
     '''
     init_config_environment()
     for hook in _parse_config_hooks:
         hook()
 
-    config_args = {}
-
     logger.findCaller = find_caller
     logger.fatal = my_fatal
 
     g_config.model_config.type = "nn"
+
+    global g_current_submodel, g_root_submodel
+    g_root_submodel = g_config.model_config.sub_models.add()
+    g_root_submodel.name = 'root'
+    g_root_submodel.is_recurrent_layer_group = False
+    g_current_submodel = g_root_submodel
+
+
+def parse_config(trainer_config, config_arg_str):
+    begin_parse(config_arg_str)
+
+    config_args = {}
+
     if config_arg_str:
         config_args = dict([f.split('=') for f in config_arg_str.split(',')])
 
@@ -3573,14 +3584,6 @@ def parse_config(trainer_config, config_arg_str):
         extension_module = importlib(extension_module_name)
         g_extended_config_funcs = extension_module.get_config_funcs(g_config)
 
-    g_config.model_config.type = 'nn'
-
-    global g_current_submodel, g_root_submodel
-    g_root_submodel = g_config.model_config.sub_models.add()
-    g_root_submodel.name = 'root'
-    g_root_submodel.is_recurrent_layer_group = False
-    g_current_submodel = g_root_submodel
-
     if hasattr(trainer_config, '__call__'):
         trainer_config.func_globals.update(
             make_config_environment("", config_args))

python/paddle/trainer_config_helpers/config_parser_utils.py

@@ -12,15 +12,19 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import copy
 import paddle.trainer.config_parser as config_parser
+from paddle.proto.TrainerConfig_pb2 import OptimizationConfig
+
 '''
-This file is a wrapper of formal config_parser. The main idea of this file is to 
+This file is a wrapper of formal config_parser. The main idea of this file is to
 separete different config logic into different function, such as network configuration
  and optimizer configuration.
 '''
 
 __all__ = [
-    "parse_trainer_config", "parse_network_config", "parse_optimizer_config"
+    "parse_trainer_config", "parse_network_config", "parse_optimizer_config",
+    "reset_parser"
 ]
 
 
@@ -34,5 +38,15 @@ def parse_network_config(network_conf, config_arg_str=''):
 
 
 def parse_optimizer_config(optimizer_conf, config_arg_str=''):
-    config = config_parser.parse_config(optimizer_conf, config_arg_str)
-    return config.opt_config
+    config_parser.settings = copy.deepcopy(config_parser.default_settings)
+    optimizer_conf()
+    opt_config = OptimizationConfig()
+    for k, v in config_parser.settings.iteritems():
+        if v is None:
+            continue
+        opt_config.__setattr__(k, v)
+    return opt_config
+
+
+def reset_parser():
+    config_parser.begin_parse()

python/paddle/trainer_config_helpers/layers.py

@@ -285,6 +285,7 @@ def __init__(self,
         assert size is not None
         assert LayerType.is_layer_type(layer_type)
         self.name = name
+        self.full_name = MakeLayerNameInSubmodel(name)
         self.layer_type = layer_type
         if parents is not None and type(parents) != list:
             parents = [parents]
@@ -3489,6 +3490,11 @@ def map_in_links(x):
 
     RecurrentLayerGroupEnd(name=name)
 
+    for layer_out in layer_outs:
+        # Thee previous full_name is the name is the rnn group
+        # We need a full_name outside the rnn group
+        layer_out.full_name = MakeLayerNameInSubmodel(layer_out.name)
+
     if len(layer_outs) == 1:
         return layer_outs[0]
     else:

python/paddle/v2/evaluator.py

@@ -25,21 +25,9 @@ def convert_to_new_name(nm):
 
     for __ev_name__ in filter(lambda x: x.endswith('_evaluator'), evs.__all__):
         __ev__ = getattr(evs, __ev_name__)
-        if hasattr(__ev__, 'argspec'):
-            argspec = __ev__.argspec
-        else:
-            argspec = inspect.getargspec(__ev__)
-        parent_names = filter(lambda x: x in ['input', 'label', 'weight'],
-                              argspec.args)
-        v2_ev = __convert_to_v2__(
-            __ev_name__,
-            parent_names=parent_names,
-            is_default_name='name' in argspec.args,
-            attach_parent=True)
-
         __new_name__ = convert_to_new_name(__ev_name__)
 
-        globals()[__new_name__] = v2_ev
+        globals()[__new_name__] = __ev__
         globals()[__new_name__].__name__ = __new_name__
         __all__.append(__new_name__)