Merge branch 'feature/serialize_deserialize_in_parameters' into feature/recommendation_v2_api

demo/mnist/.gitignore

@@ -6,3 +6,5 @@ train.log
 *pyc
 .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)
 
@@ -91,10 +91,18 @@ def main():
 
     def event_handler(event):
         if isinstance(event, paddle.event.EndIteration):
-            if event.batch_id % 100 == 0:
-                print "Pass %d, Batch %d, Cost %f, %s" % (
-                    event.pass_id, event.batch_id, event.cost, event.metrics)
-        if isinstance(event, paddle.event.EndPass):
+            if event.batch_id % 1000 == 0:
+                result = trainer.test(reader=paddle.reader.batched(
+                    paddle.dataset.mnist.test(), batch_size=256))
+
+                print "Pass %d, Batch %d, Cost %f, %s, Testing metrics %s" % (
+                    event.pass_id, event.batch_id, event.cost, event.metrics,
+                    result.metrics)
+
+                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(
                 paddle.dataset.mnist.test(), batch_size=128))
             print "Test with Pass %d, Cost %f, %s\n" % (

python/paddle/v2/parameters.py

 import numpy as np
 import py_paddle.swig_paddle as api
 from paddle.proto.ParameterConfig_pb2 import ParameterConfig
-
+import struct
+import tarfile
+import cStringIO
 from topology import Topology
 
 __all__ = ['Parameters', 'create']
@@ -122,6 +124,12 @@ class Parameters(object):
 
         if len(self.__gradient_machines__) == 0:
             # create new parameter in python numpy.
+            if len(self.__tmp_params__) != 0:
+                ret_list = [
+                    mat for name, mat in self.__tmp_params__ if name == key
+                ]
+                if len(ret_list) == 1:
+                    return ret_list[0]
             return np.ndarray(shape=shape, dtype=np.float32)
         else:
             for each_gradient_machine in self.__gradient_machines__:
@@ -228,32 +236,66 @@ 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):
+        """
 
-    def __setstate__(self, obj):
-        Parameters.__init__(self)
+        :param name:
+        :param f:
+        :type f: file
+        :return:
+        """
+        param = self.get(name)
+        size = reduce(lambda a, b: a * b, param.shape)
+        f.write(struct.pack("IIQ", 0, 4, size))
+        param = param.astype(np.float32)
+        f.write(param.tobytes())
 
-        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))
+    def deserialize(self, name, f):
+        """
 
-        __impl__(**obj)
+        :param name:
+        :param f:
+        :type f: file
+        :return:
+        """
+        f.read(16)  # header
+        arr = np.frombuffer(f.read(), dtype=np.float32)
+        self.set(name, arr.reshape(self.get_shape(name)))
+
+    def to_tar(self, f):
+        tar = tarfile.TarFile(fileobj=f, mode='w')
+        for nm in self.names():
+            buf = cStringIO.StringIO()
+            self.serialize(nm, buf)
+            tarinfo = tarfile.TarInfo(name=nm)
+            buf.seek(0)
+            tarinfo.size = len(buf.getvalue())
+            tar.addfile(tarinfo, buf)
+
+            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):

python/paddle/v2/tests/run_tests.sh

@@ -22,7 +22,7 @@ cd $SCRIPTPATH
 
 $1 -m pip install ../../../../paddle/dist/*.whl
 
-test_list="test_data_feeder.py"
+test_list="test_data_feeder.py test_parameters.py"
 
 export PYTHONPATH=$PWD/../../../../python/
 

python/paddle/v2/tests/test_parameters.py

+import unittest
+import sys
+
+try:
+    import py_paddle
+
+    del py_paddle
+except ImportError:
+    print >> sys.stderr, "It seems swig of Paddle is not installed, this " \
+                         "unittest will not be run."
+    sys.exit(0)
+
+import paddle.v2.parameters as parameters
+from paddle.proto.ParameterConfig_pb2 import ParameterConfig
+import random
+import cStringIO
+import numpy
+
+
+def __rand_param_config__(name):
+    conf = ParameterConfig()
+    conf.name = name
+    size = 1
+    for i in xrange(2):
+        dim = random.randint(1, 1000)
+        conf.dims.append(dim)
+        size *= dim
+    conf.size = size
+    assert conf.IsInitialized()
+    return conf
+
+
+class TestParameters(unittest.TestCase):
+    def test_serialization(self):
+        params = parameters.Parameters()
+        params.__append_config__(__rand_param_config__("param_0"))
+        params.__append_config__(__rand_param_config__("param_1"))
+
+        for name in params.names():
+            param = params.get(name)
+            param[:] = numpy.random.uniform(
+                -1.0, 1.0, size=params.get_shape(name))
+            params.set(name, param)
+
+        tmp_file = cStringIO.StringIO()
+        params.to_tar(tmp_file)
+        tmp_file.seek(0)
+        params_dup = parameters.Parameters.from_tar(tmp_file)
+
+        self.assertEqual(params_dup.names(), params.names())
+
+        for name in params.names():
+            self.assertEqual(params.get_shape(name), params_dup.get_shape(name))
+            p0 = params.get(name)
+            p1 = params_dup.get(name)
+            self.assertTrue(numpy.isclose(p0, p1).all())
+
+
+if __name__ == '__main__':
+    unittest.main()