diff --git a/paddle/py_paddle/dataprovider_converter.py b/paddle/py_paddle/dataprovider_converter.py index 218cb5ec560ed..43614b9779d21 100644 --- a/paddle/py_paddle/dataprovider_converter.py +++ b/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) diff --git a/python/paddle/v2/parameters.py b/python/paddle/v2/parameters.py index ad20241b98302..bbaf8bfa979fb 100644 --- a/python/paddle/v2/parameters.py +++ b/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): """ diff --git a/python/paddle/v2/tests/test_parameters.py b/python/paddle/v2/tests/test_parameters.py index 45372e7dd0ec7..7ba8a939fbd1a 100644 --- a/python/paddle/v2/tests/test_parameters.py +++ b/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()