Source code for paddle.fluid.dygraph.layers

# 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# 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']

[docs]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()
[docs] 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
[docs] 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 ( is not None): = ".".join([self._full_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
[docs] 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)
[docs] 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
[docs] 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")
[docs] def add_sublayer(self, name, sublayer): """Adds a sub Layer instance. Added sublayer can be access like 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
[docs] def add_parameter(self, name, parameter): """Adds a Parameter instance. Added parameter can be access like Args: name: name of this sublayer. parameter: an instance of Parameter. Returns: the parameter passed in. """ assert isinstance(parameter, framework.Parameter) if in self._loaddict_holder: var = parameter._ivar.value() tensor = var.get_tensor() tensor.set(self._loaddict_holder[].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 in self._loaddict_holder: var = value._ivar.value() tensor = var.get_tensor() tensor.set(self._loaddict_holder[].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 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 in stat_dict: var = item._ivar.value() tensor = var.get_tensor() tensor.set(stat_dict[].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)