import numpy as np import py_paddle.swig_paddle as api from paddle.proto.ParameterConfig_pb2 import ParameterConfig import topology as v2_topology __all__ = ['Parameters', 'create'] def create(layers): """ Create parameter pool by topology. :param layers: :return: """ topology = v2_topology.Topology(layers) pool = Parameters() for param in topology.proto().parameters: pool.__append_config__(param) return pool class Parameters(object): """ Parameters is a dictionary contains Paddle's parameter. The key of Parameters is the name of parameter. The value of Parameters is a plain :code:`numpy.ndarry` . Basically usage is .. code-block:: python data = paddle.layers.data(...) ... out = paddle.layers.fc(...) parameters = paddle.parameters.create(out) parameter_names = parameters.names() fc_mat = parameters.get('fc') print fc_mat """ def __init__(self): self.__param_conf__ = dict() self.__gradient_machines__ = [] self.__tmp_params__ = [] def __append_config__(self, param_conf): """ Append a parameter configuration. It used to initialize Parameters and should be invoked only in paddle.parameters.create :param param_conf: The parameter configuration in protobuf :type param_conf: ParameterConfig :return: Nothing """ if not isinstance(param_conf, ParameterConfig): raise ValueError("param_conf must be paddle.proto.ParameterConfig") if param_conf.name in self.__param_conf__: raise ValueError("duplicated parameter %s" % param_conf.name) self.__param_conf__[param_conf.name] = param_conf def keys(self): """ keys are the names of each parameter. :return: list of parameter name :rtype: list """ return self.__param_conf__.keys() def names(self): """ names of each parameter. :return: list of parameter name :rtype: list """ return self.keys() def has_key(self, key): """ has_key return true if there are such parameter name == key :param key: Parameter name :type key: basestring :return: True if contains such key """ return key in self.__param_conf__.keys() def __iter__(self): """ Return an iterator of parameter name. It is used by `for loop` or `in` operator. .. code-block:: python parameters = paddle.parameters.create(...) if "fc_param" in parameters: print 'OK' :return: an iterator of parameter name :rtype: iterator """ return iter(self.__param_conf__) def __getitem__(self, key): """ Get parameter by parameter name. It uses Python dict syntax. :note: It will always copy the parameter from C++ side. :param key: Parameter name :type key: basestring :return: parameter value :rtype: np.ndarray """ shape = self.get_shape(key) if len(self.__gradient_machines__) == 0: # create new parameter in python numpy. return np.ndarray(shape=shape, dtype=np.float32) else: for each_gradient_machine in self.__gradient_machines__: param = __get_parameter_in_gradient_machine__( each_gradient_machine, key) # for simplify implementation now, we always copy from C++ assert isinstance(param, api.Parameter) val = param.getBuf(api.PARAMETER_VALUE) assert isinstance(val, api.Vector) val = val.copyToNumpyArray() return val # else continue raise RuntimeError("Unexpected branch") def get_shape(self, key): """ get shape of the parameter. :param key: parameter name :type key: basestring :return: parameter's shape :rtype: tuple """ if not isinstance(key, basestring): raise ValueError("parameter name should be string") if not self.has_key(key): raise ValueError("No such parameter %s" % key) conf = self.__param_conf__[key] return tuple(map(int, conf.dims)) def __setitem__(self, key, value): """ Set parameter by parameter name & value. It use Python dict syntax. :note: It will always copy the parameter to C++ side. :param key: Parameter name :type key: basestring :param value: Parameter matrix. :type value: np.ndarray :return: Nothing """ if not isinstance(value, np.ndarray): raise ValueError("Must return ndarray") value = value.astype(dtype=np.float32) shape = self.get_shape(key) if value.shape != shape: raise ValueError("Value shape mismatch, expect %s, should %s" % (shape, value.shape)) if len(self.__gradient_machines__) == 0: self.__tmp_params__.append((key, value)) else: for each_gradient_machine in self.__gradient_machines__: __copy_parameter_to_gradient_machine__(each_gradient_machine, key, value) def get(self, parameter_name): """ Get parameter by parameter name. :note: It will always copy the parameter from C++ side. :param parameter_name: parameter name :type parameter_name: basestring :return: The parameter matrix. :rtype: np.ndarray """ return self.__getitem__(key=parameter_name) def set(self, parameter_name, value): """ Set parameter by parameter name & matrix. :param parameter_name: parameter name :type parameter_name: basestring :param value: parameter matrix :type value: np.ndarray :return: Nothing. """ self.__setitem__(key=parameter_name, value=value) def append_gradient_machine(self, gradient_machine): """ append gradient machine to parameters. This method is used internally in Trainer.train. :param gradient_machine: Paddle C++ GradientMachine object. :type gradient_machine: api.GradientMachine :return: """ if not isinstance(gradient_machine, api.GradientMachine): raise ValueError("gradient_machine should be api.GradientMachine") if len(self.__tmp_params__) != 0: for name, val in self.__tmp_params__: try: __copy_parameter_to_gradient_machine__(gradient_machine, name, val) except ValueError: # If no such parameter in gradient machine, then don't copy pass self.__gradient_machines__.append(gradient_machine) def __get_parameter_in_gradient_machine__(gradient_machine, name): """ :param gradient_machine: :type gradient_machine: api.GradientMachine :param name: :return: :rtype: api.Parameter """ params = filter(lambda p: p.getName() == name, gradient_machine.getParameters()) if len(params) == 0: raise ValueError("No such parameter") elif len(params) > 1: raise ValueError("Unexpected branch") else: return params[0] def __copy_parameter_to_gradient_machine__(gradient_machine, name, arr): """ Copy a python ndarray into the gradient machine. :param gradient_machine: :type gradient_machine: api.GradientMachine :param name: :param arr: :type arr: np.ndarray :return: :rtype: api.Parameter """ param = __get_parameter_in_gradient_machine__(gradient_machine, name) vec = param.getBuf(api.PARAMETER_VALUE) assert isinstance(vec, api.Vector) vec.copyFromNumpyArray(arr.flatten())