# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import collections
import contextlib
import sys
import numpy as np
import collections
import six
from . import parallel_helper
from .. import unique_name
from paddle.fluid import core
from .layer_object_helper import LayerObjectHelper
from paddle.fluid import framework
from ..param_attr import ParamAttr
__all__ = ['Layer']
class Layer(core.Layer):
"""Layers composed of operators.
Args:
name_scope: prefix name used by the layer to name parameters.
If prefix is "my_model/layer_1", parameter name in MyLayer
can be "my_model/layer_1/MyLayer/w_n", where w is the parameter
base name and n is an unique suffix auto-generated.
dtype: data type for the variables in the layer.
"""
def __init__(self, name_scope, dtype=core.VarDesc.VarType.FP32):
self._full_name = unique_name.generate(name_scope + "/" +
self.__class__.__name__)
self._built = False
self._dtype = dtype
self._parameters = collections.OrderedDict()
self._sub_layers = collections.OrderedDict()
self._loaddict_holder = collections.OrderedDict()
self._helper = LayerObjectHelper(self._full_name)
def train(self):
framework._dygraph_tracer().train_mode()
def eval(self):
framework._dygraph_tracer().eval_mode()
def full_name(self):
"""Full name for this layers.
Full name is composed by name_scope + "/" + MyLayer.__class__.__name__
Returns full name of this name.
"""
return self._full_name
def create_parameter(self,
attr,
shape,
dtype,
is_bias=False,
default_initializer=None):
"""Create parameters for this layers.
Args:
attr: [ParamAttr] should be the parameter attribute for this parameter
shape: shape of the paramter
dtype: data type of this parameter
is_bias: if this is a bias parameter
default_initializer: set the default initializer for this parameter
Returns created parameter Variable.
"""
if isinstance(attr, ParamAttr) and (attr.name is not None):
attr.name = ".".join([self._full_name, attr.name])
elif isinstance(attr, six.string_types):
attr = ".".join([self._full_name, attr])
return self._helper.create_parameter(attr, shape, dtype, is_bias,
default_initializer)
# TODO: Add more parameter list when we need them
def create_variable(self,
name=None,
persistable=None,
dtype=None,
type=core.VarDesc.VarType.LOD_TENSOR):
"""Create Variable for this layers.
Args:
name: name of the variable
persistable: if set this variable persistable
dtype: data type of data in the variable
type: type of the variable
Returns created Variable.
"""
if name is not None:
var_name = ".".join([self._full_name, name])
else:
var_name = unique_name.generate(".".join(
[self._full_name, "_generated_var"]))
return self._helper.main_program.current_block().create_var(
name=var_name, persistable=persistable, dtype=dtype, type=type)
def parameters(self, include_sublayers=True):
"""Returns a list of Parameters from current and sub-layers.
Args:
include_sublayers: If true, also include the parameters from
sublayers.
Returns a list of Parameters.
"""
ret = [p for p in self._parameters.values()]
if include_sublayers:
for l in self._sub_layers.values():
for p in l.parameters(include_sublayers):
ret.append(p)
return ret
def sublayers(self, include_sublayers=True):
"""Returns a list of sub layers.
Args:
include_sublayers: If true, also include the layers from sublayers.
Returns a list of sub layers.
"""
ret = [l for l in self._sub_layers.values()]
if include_sublayers:
for l in self._sub_layers.values():
for sub_l in l.sublayers(include_sublayers):
ret.append(sub_l)
return ret
def clear_gradients(self):
for p in self.parameters():
if p.trainable:
p.clear_gradient()
def _build_once(self, *args):
pass
def __call__(self, *inputs):
if not self._built:
self._build_once(*inputs)
if parallel_helper._is_data_parallel_mode():
parallel_helper._broadcast_parameters(self._parameters.values())
outputs = self.forward(*inputs)
self._built = True
return outputs
def forward(self, *inputs):
raise NotImplementedError
def backward(self, *inputs):
raise ValueError("Layer shouldn't implement backward")
def add_sublayer(self, name, sublayer):
"""Adds a sub Layer instance.
Added sublayer can be access like self.name.
Args:
name: name of this sublayer.
sublayer: an instance of Layer.
Returns:
the sublayer passed in.
"""
assert isinstance(sublayer, core.Layer)
self._sub_layers[name] = sublayer
return sublayer
def add_parameter(self, name, parameter):
"""Adds a Parameter instance.
Added parameter can be access like self.name.
Args:
name: name of this sublayer.
parameter: an instance of Parameter.
Returns:
the parameter passed in.
"""
assert isinstance(parameter, framework.Parameter)
if parameter.name in self._loaddict_holder:
var = parameter._ivar.value()
tensor = var.get_tensor()
tensor.set(self._loaddict_holder[parameter.name].numpy(),
framework._current_expected_place())
self._parameters[name] = parameter
return parameter
def __getattr__(self, name):
if name in self._parameters:
return self._parameters[name]
elif name in self._sub_layers:
return self._sub_layers[name]
def __setattr__(self, name, value):
if isinstance(value, framework.Parameter):
params = self.__dict__.get('_parameters', None)
if params is None:
raise ValueError(
"super(YourLayer, self).__init__() should be called first")
if value.name in self._loaddict_holder:
var = value._ivar.value()
tensor = var.get_tensor()
tensor.set(self._loaddict_holder[value.name].numpy(),
framework._current_expected_place())
params[name] = value
elif isinstance(value, core.Layer):
layers = self.__dict__.get('_sub_layers', None)
if layers is None:
raise ValueError(
"super(YourLayer, self).__init__() should be called first")
layers[name] = value
else:
object.__setattr__(self, name, value)
def __delattr__(self, name):
if name in self._parameters:
del self._parameters[name]
elif name in self._sub_layers:
del self._sub_layers[name]
else:
object.__delattr__(self, name)
def state_dict(self, destination=None, include_sublayers=True):
if destination is None:
destination = collections.OrderedDict()
for name, data in self._parameters.items():
if data is not None:
destination[data.name] = data
if include_sublayers:
for layer_name, layer_item in self._sub_layers.items():
if layer_item is not None:
destination_temp = destination.copy()
destination_temp.update(
layer_item.state_dict(destination_temp,
include_sublayers))
destination = destination_temp
return destination
def load_dict(self, stat_dict, include_sublayers=True):
self._loaddict_holder = stat_dict
for name, item in self.__dict__.get('_parameters', None).items():
if item.name in stat_dict:
var = item._ivar.value()
tensor = var.get_tensor()
tensor.set(stat_dict[item.name].numpy(),
framework._current_expected_place())
if include_sublayers:
for layer_name, layer_item in self._sub_layers.items():
if layer_item is not None:
layer_item.load_dict(stat_dict)