from paddle.v2.framework.framework import Variable, OpProtoHolder, g_program, g_init_program import paddle.v2.framework.core as core import copy import itertools def unique_name(prefix): uid = core.unique_integer() # unique during whole process. return "_".join([prefix, str(uid)]) class LayerHelper(object): def __init__(self, layer_type, **kwargs): self.kwargs = kwargs self.layer_type = layer_type name = self.kwargs.get('name', None) if name is None: self.kwargs['name'] = unique_name(self.layer_type) @property def name(self): return self.kwargs['name'] @property def program(self): prog = self.kwargs.get('program', None) if prog is None: return g_program else: return prog @property def init_program(self): prog = self.kwargs.get('init_program', None) if prog is None: return g_init_program else: return prog def append_op(self, *args, **kwargs): return self.program.current_block().append_op(*args, **kwargs) def multiple_input(self, input_param_name='input'): inputs = self.kwargs.get(input_param_name, []) type_error = TypeError( "Input of {0} layer should be Variable or sequence of Variable". format(self.layer_type)) if isinstance(inputs, Variable): inputs = [inputs] elif not isinstance(inputs, list) and not isinstance(inputs, tuple): raise type_error else: for each in inputs: if not isinstance(each, Variable): raise type_error return inputs def input(self, input_param_name='input'): inputs = self.multiple_input(input_param_name) if len(inputs) != 1: raise "{0} layer only takes one input".format(self.layer_type) return inputs[0] @property def param_attr(self): default = { 'name': None, 'init_attr': { 'type': 'uniform_random', 'min': -1.0, 'max': 1.0 } } actual = self.kwargs.get('param_attr', None) return actual if actual is not None else default def bias_attr(self): bias_attr = self.kwargs.get('bias_attr', None) if bias_attr is True: bias_attr = { 'name': None, 'init_attr': { 'type': 'fill_constant', 'value': 0.0 } } return bias_attr def multiple_param_attr(self, length): param_attr = self.param_attr if isinstance(param_attr, dict): param_attr = [param_attr] if len(param_attr) != 1 and len(param_attr) != length: raise ValueError("parameter number mismatch") elif len(param_attr) == 1 and length != 1: tmp = [None] * length for i in xrange(length): tmp[i] = copy.deepcopy(param_attr[0]) param_attr = tmp return param_attr def iter_inputs_and_params(self, input_param_name='input'): inputs = self.multiple_input(input_param_name) param_attrs = self.multiple_param_attr(len(inputs)) for ipt, param_attr in itertools.izip(inputs, param_attrs): yield ipt, param_attr def input_dtype(self, input_param_name='input'): inputs = self.multiple_input(input_param_name) dtype = None for each in inputs: if dtype is None: dtype = each.data_type elif dtype != each.data_type: raise ValueError("Data Type mismatch") return dtype def create_parameter(self, attr, shape, dtype, suffix='w'): if attr['name'] is None: attr['name'] = unique_name(".".join([self.name, suffix])) self.init_program.global_block().create_parameter( name=attr['name'], dtype=dtype, shape=shape, init_attr=attr['init_attr']) return self.program.global_block().create_parameter( name=attr['name'], dtype=dtype, shape=shape) def create_tmp_variable(self, dtype): return self.program.current_block().create_var( name=unique_name(".".join([self.name, 'tmp'])), dtype=dtype, persistable=False) def create_global_variable(self, *args, **kwargs): return self.program.global_block().create_var( *args, persistable=False, **kwargs) def append_bias_op(self, input_var): size = list(input_var.shape[1:]) bias_attr = self.bias_attr() if not bias_attr: return input_var b = self.create_parameter( attr=bias_attr, shape=size, dtype=input_var.data_type, suffix='b') tmp = self.create_tmp_variable(dtype=input_var.data_type) self.append_op( type='elementwise_add', inputs={'X': [input_var], 'Y': [b]}, outputs={'Out': [tmp]}) return tmp def append_activation(self, input_var): act = self.kwargs.get('act', None) if act is None: return input_var if isinstance(act, basestring): act = {'type': act} tmp = self.create_tmp_variable(dtype=input_var.data_type) act_type = act.pop('type') self.append_op( type=act_type, inputs={"X": [input_var]}, outputs={"Y": [tmp]}, attrs=act) return tmp