complete serialize

* Test gzip

demo/mnist/.gitignore

@@ -7,3 +7,4 @@ train.log
 .ipynb_checkpoints
 params.pkl
 params.tar
+params.tar.gz

demo/mnist/api_train_v2.py

 import paddle.v2 as paddle
-import cPickle
+import gzip
 
 
 def softmax_regression(img):
@@ -73,8 +73,8 @@ def main():
     cost = paddle.layer.classification_cost(input=predict, label=label)
 
     try:
-        with open('params.pkl', 'r') as f:
-            parameters = cPickle.load(f)
+        with gzip.open('params.tar.gz', 'r') as f:
+            parameters = paddle.parameters.Parameters.from_tar(f)
     except IOError:
         parameters = paddle.parameters.create(cost)
 
@@ -99,12 +99,8 @@ def main():
                     event.pass_id, event.batch_id, event.cost, event.metrics,
                     result.metrics)
 
-                with open('params.pkl', 'w') as f:
-                    cPickle.dump(
-                        parameters, f, protocol=cPickle.HIGHEST_PROTOCOL)
-
-                with open('params.tar', 'w') as f:
-                    parameters.serialize_to_tar(f)
+                with gzip.open('params.tar.gz', 'w') as f:
+                    parameters.to_tar(f)
 
         elif isinstance(event, paddle.event.EndPass):
             result = trainer.test(reader=paddle.reader.batched(

python/paddle/v2/parameters.py

@@ -224,19 +224,6 @@ class Parameters(object):
 
         self.__gradient_machines__.append(gradient_machine)
 
-    def __getstate__(self):
-        params = {}
-        for name in self.names():
-            params[name] = self.get(name)
-
-        param_conf = {}
-        for name in self.__param_conf__:
-            conf = self.__param_conf__[name]
-            assert isinstance(conf, ParameterConfig)
-            param_conf[name] = conf.SerializeToString()
-
-        return {'conf': param_conf, 'params': params}
-
     def serialize(self, name, f):
         """
 
@@ -260,10 +247,10 @@ class Parameters(object):
         :return:
         """
         f.read(16)  # header
-        arr = np.fromfile(f, dtype=np.float32)
+        arr = np.frombuffer(f.read(), dtype=np.float32)
         self.set(name, arr.reshape(self.get_shape(name)))
 
-    def serialize_to_tar(self, f):
+    def to_tar(self, f):
         tar = tarfile.TarFile(fileobj=f, mode='w')
         for nm in self.names():
             buf = cStringIO.StringIO()
@@ -273,19 +260,30 @@ class Parameters(object):
             tarinfo.size = len(buf.getvalue())
             tar.addfile(tarinfo, buf)
 
-    def __setstate__(self, obj):
-        Parameters.__init__(self)
-
-        def __impl__(conf, params):
-            for name in conf:
-                p = ParameterConfig()
-                p.ParseFromString(conf[name])
-                self.__append_config__(p)
-            for name in params:
-                shape = self.get_shape(name)
-                self.set(name, params[name].reshape(shape))
-
-        __impl__(**obj)
+            conf = self.__param_conf__[nm]
+            confStr = conf.SerializeToString()
+            tarinfo = tarfile.TarInfo(name="%s.protobuf" % nm)
+            tarinfo.size = len(confStr)
+            buf = cStringIO.StringIO(confStr)
+            buf.seek(0)
+            tar.addfile(tarinfo, fileobj=buf)
+
+    @staticmethod
+    def from_tar(f):
+        params = Parameters()
+        tar = tarfile.TarFile(fileobj=f, mode='r')
+        for finfo in tar:
+            assert isinstance(finfo, tarfile.TarInfo)
+            if finfo.name.endswith('.protobuf'):
+                f = tar.extractfile(finfo)
+                conf = ParameterConfig()
+                conf.ParseFromString(f.read())
+                params.__append_config__(conf)
+
+        for param_name in params.names():
+            f = tar.extractfile(param_name)
+            params.deserialize(param_name, f)
+        return params
 
 
 def __get_parameter_in_gradient_machine__(gradient_machine, name):