# -*- coding: utf-8 -*-
from . import element
import torch.nn as nn
import collections
from itertools import repeat
[docs]class SeqName(object):
"""The name of sequence, to be used by class :class:`~LinearSeq`.
"""
def __init__(self, module_name):
self.moduleName = module_name
[docs] def seq_name(self):
self.moduleName = str(eval(self.moduleName)+1)
return self.moduleName
[docs]class BatchNorm(object):
"""
Batch Normalization, to be used by class :class:`~LinearSeq`.
"""
def _batchnorm1d(self, name, n_output):
self.seq.add_module(name, nn.BatchNorm1d(n_output, eps=self.eps, momentum=self.momentum))
def _batchnorm2d(self, name, out_channel):
self.seq.add_module(name, nn.BatchNorm2d(out_channel, eps=self.eps, momentum=self.momentum))
def _batchnorm3d(self, name, out_channel):
self.seq.add_module(name, nn.BatchNorm3d(out_channel, eps=self.eps, momentum=self.momentum))
[docs]class Activation(object):
"""Activation functions, to be used by class :class:`~LinearSeq`.
"""
def _activation(self, module_name, activation_name):
self.seq.add_module(module_name, element.activation(activation_name=activation_name))
[docs]class Pooling(object):
""" Pooling, to be used by classes LinearSeq & Conv2dSeq """
def _pooling(self, module_name, pool_name, kernel_size=2):
self.seq.add_module(module_name, element.pooling(pool_name=pool_name, kernel_size=kernel_size))
[docs]class Dropout(object):
"""Dropout, to be used by class :class:`~LinearSeq`.
"""
def _dropout(self, module_name, dropout_name):
self.seq.add_module(module_name, element.get_dropout(dropout_name))
[docs]class LinearSeq(SeqName,BatchNorm,Activation,Dropout):
"""Sequence of Linear.
Parameters
----------
nodes : list
A list that contains the number of nodes in each layer.
mainBN : bool, optional
If True, the network will contain batch normalization layer in its main part. Default: True
finalBN : bool, optional
If True, the network will contain batch normalization layer in its last layer. Default: False
mainActive : str, optional
The activation function used in the main part of the network. See :func:`~.element.activation` for the available activation functions. Default: 'rrelu'
finalActive : str, optional
The activation function used in the last layer of the network. See :func:`~.element.activation` for the available activation functions. Default: 'None'
mainDropout : str, optional
The dropout used in the main part of the network, 'None' or 'dropout'. Default: 'None'
finalDropout : str, optional
The dropout used in the final layer of the network, 'None' or 'dropout'. Default: 'None'
"""
def __init__(self, nodes, mainBN=True, finalBN=False, mainActive='rrelu',
finalActive='None', mainDropout='None', finalDropout='None'):
SeqName.__init__(self, '-1') #or super(LinearSeq, self).__init__('-1')
self.nodes = nodes
self.layers = len(nodes) - 1
self.mainBN = mainBN
self.finalBN = finalBN
self.mainActive = mainActive
self.finalActive = finalActive
self.mainDropout = mainDropout
self.finalDropout = finalDropout
self.eps = 1e-05
self.momentum = 0.1
self.seq = nn.Sequential()
def __linear(self, name, n_input, n_output):
self.seq.add_module(name, nn.Linear(n_input, n_output))
[docs] def get_seq(self):
for i in range(self.layers-1):
self.__linear(self.seq_name(), self.nodes[i], self.nodes[i+1])
if self.mainBN:
self._batchnorm1d(self.seq_name(), self.nodes[i+1])
if self.mainActive!='None':
self._activation(self.seq_name(), self.mainActive)
if self.mainDropout!='None':
self._dropout(self.seq_name(), self.mainDropout)
self.__linear(self.seq_name(), self.nodes[-2], self.nodes[-1])
if self.finalBN:
self._batchnorm1d(self.seq_name(), self.nodes[-1])
if self.finalActive!='None':
self._activation(self.seq_name(), self.finalActive)
if self.finalDropout!='None':
self._dropout(self.seq_name(), self.finalDropout)
return self.seq