Init partial network parameters from another saved model.

paddle/py_paddle/dataprovider_converter.py

@@ -144,7 +144,7 @@ def finish_scan(self, argument):
  if len(self.__shape__) > 1:
  # The last-two dimenstions are the frame height and width.
  # For example, the layout is CHW for 3-D feature of image.
- # The H and W are the fram height and width.
+ # The H and W are the frame height and width.
  h, w = self.__shape__[-2:]
  argument.setSlotFrameHeight(self.pos, h)
  argument.setSlotFrameWidth(self.pos, w)

python/paddle/v2/parameters.py

@@ -51,7 +51,7 @@ class Parameters(object):
  def __init__(self):
  self.__param_conf__ = dict()
  self.__gradient_machines__ = []
- self.__tmp_params__ = []
+ self.__tmp_params__ = dict()
 
  def __append_config__(self, param_conf):
  """
@@ -128,13 +128,10 @@ def __getitem__(self, key):
 
  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)
+ if key in self.__tmp_params__:
+ return self.__tmp_params__[key]
+ else:
+ return np.ndarray(shape=shape, dtype=np.float32)
  else:
  for each_gradient_machine in self.__gradient_machines__:
  param = __get_parameter_in_gradient_machine__(
@@ -187,7 +184,7 @@ def __setitem__(self, key, value):
  (shape, value.shape))
 
  if len(self.__gradient_machines__) == 0:
- self.__tmp_params__.append((key, value))
+ self.__tmp_params__[key] = value
  else:
  for each_gradient_machine in self.__gradient_machines__:
  __copy_parameter_to_gradient_machine__(each_gradient_machine,
@@ -231,7 +228,7 @@ def append_gradient_machine(self, gradient_machine):
  raise ValueError("gradient_machine should be api.GradientMachine")
 
  if len(self.__tmp_params__) != 0:
- for name, val in self.__tmp_params__:
+ for name, val in self.__tmp_params__.iteritems():
  try:
  __copy_parameter_to_gradient_machine__(gradient_machine,
  name, val)
@@ -287,6 +284,18 @@ def to_tar(self, f):
 
  @staticmethod
  def from_tar(f):
+ """
+ Create a `Parameters` object from the given file. And
+ the `Parameters` only contains the parameters in this
+ file. It is adapted the parameters are same in the
+ defined network and the given file. For example, it
+ can be used in the inference.
+
+ :param f: the initialized model file.
+ :type f: tar file
+ :return: A Parameters object.
+ :rtype: Parameters.
+ """
  params = Parameters()
  tar = tarfile.TarFile(fileobj=f, mode='r')
  for finfo in tar:
@@ -302,6 +311,21 @@ def from_tar(f):
  params.deserialize(param_name, f)
  return params
 
+ def init_from_tar(self, f):
+ """
+ Different from `from_tar`, this interface can be used to
+ init partial network parameters from another saved model.
+
+ :param f: the initialized model file.
+ :type f: tar file
+ :return: Nothing.
+ """
+
+ tar_param = Parameters.from_tar(f)
+ for pname in tar_param.names():
+ if pname in self.names():
+ self.set(pname, tar_param.get(pname))
+
 
 def __get_parameter_in_gradient_machine__(gradient_machine, name):
  """

python/paddle/v2/tests/test_parameters.py

@@ -20,14 +20,17 @@
 import numpy
 
 
-def __rand_param_config__(name):
+def __rand_param_config__(name, psize=None):
  conf = ParameterConfig()
  conf.name = name
  size = 1
- for i in xrange(2):
- dim = random.randint(1, 1000)
- conf.dims.append(dim)
- size *= dim
+ if psize is None:
+ for i in xrange(2):
+ dim = random.randint(1, 1000)
+ conf.dims.append(dim)
+ size *= dim
+ else:
+ size = psize
  conf.size = size
  assert conf.IsInitialized()
  return conf
@@ -77,6 +80,50 @@ def initializer(name):
  expected = numpy.array([[1, 1], [1, 2], [1, 1]], numpy.float32)
  assert numpy.logical_and.reduce(numpy.reshape(val == expected, 6))
 
+ def test_init_from_tar(self):
+ def get_param(names, size):
+ p = parameters.Parameters()
+ for k, v in zip(names, size):
+ p.__append_config__(__rand_param_config__(k, v))
+ for name in p.names():
+ param = p.get(name)
+ param[:] = numpy.random.uniform(
+ -1.0, 1.0, size=p.get_shape(name))
+ p.set(name, param)
+ return p
+
+ def get_parames():
+ name1 = ['param_0', 'param_1']
+ size1 = [128, 256]
+ p1 = get_param(name1, size1)
+ file1 = cStringIO.StringIO()
+ p1.to_tar(file1)
+ file1.seek(0)
+
+ name2 = ['param_0', 'param_1', 'param_2']
+ size2 = [128, 256, 288]
+ p2 = get_param(name2, size2)
+ file2 = cStringIO.StringIO()
+ p2.to_tar(file2)
+ file2.seek(0)
+ return p1, file1, p2, file2
+
+ p1, file1, p2, file2 = get_parames()
+ p2.init_from_tar(file1)
+ for name in p1.names():
+ self.assertEqual(p1.get_shape(name), p2.get_shape(name))
+ v1 = p1.get(name)
+ v2 = p2.get(name)
+ self.assertTrue(numpy.isclose(v1, v2).all())
+
+ p1, file1, p2, file2 = get_parames()
+ p1.init_from_tar(file2)
+ for name in p1.names():
+ self.assertEqual(p1.get_shape(name), p2.get_shape(name))
+ v1 = p1.get(name)
+ v2 = p2.get(name)
+ self.assertTrue(numpy.isclose(v1, v2).all())
+
 
 if __name__ == '__main__':
  unittest.main()