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nn_models.py
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nn_models.py
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import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class PosELU(nn.Module):
def __init__(self, alpha=1.0, inplace=False):
super(PosELU, self).__init__()
self.alpha = alpha
self.inplace = inplace
def forward(self, input):
if isinstance(input, torch.autograd.variable.Variable):
return F.elu(input, self.alpha, self.inplace) + self.alpha
else:
return (
F.elu(torch.autograd.Variable(input), self.alpha, self.inplace)
+
self.alpha
).data
def inverse(self, x):
return (
(x - self.alpha) * (x >= self.alpha).float()
+
torch.log(x / self.alpha) * (x < self.alpha).float()
)
def __repr__(self):
inplace_str = ', inplace' if self.inplace else ''
return self.__class__.__name__ + ' (' \
+ 'alpha=' + str(self.alpha) \
+ inplace_str + ')'
class LearnableTanh(nn.Module):
def __init__(self, in_features, alpha_bound=1.0):
super(LearnableTanh, self).__init__()
self.alpha = nn.parameter.Parameter(
torch.Tensor(1, in_features)
)
self.in_features = in_features
self.alpha_bound = alpha_bound
self.reset_parameters(self.alpha_bound)
def reset_parameters(self, alpha_bound):
self.alpha.data.uniform_(-alpha_bound, alpha_bound)
def forward(self, x):
# max(0,x) + min(0, alpha * (exp(x) - 1))
result = torch.tanh(x) - self.alpha.expand(x.size(0), self.alpha.size(1))
return result
def __repr__(self):
string = self.__class__.__name__ \
+ ' (' + 'ncol: ' + str(self.in_features) + ', ' \
+ 'alpha_bound: ' + str(self.alpha_bound) + ', ' \
+ 'alpha=['
for idx, item in enumerate(self.alpha.data[0]):
string += str(item) + ','
if idx > 1:
string += '...'
break
string += '])'
return string
class LearnableELU(nn.Module):
def __init__(self, alpha_upper_bound=0.1, inplace=False):
super(LearnableELU, self).__init__()
self.alpha = nn.parameter.Parameter(torch.Tensor(1))
self.inplace = inplace
self.reset_parameters(alpha_upper_bound)
def reset_parameters(self, alpha_upper_bound):
self.alpha.data.uniform_(0.0, alpha_upper_bound)
def forward(self, input):
# max(0,x) + min(0, alpha * (exp(x) - 1))
return (
F.threshold(
input,
0.0,
0.0,
self.inplace
)
-
F.threshold(
-self.alpha.expand_as(input) * (torch.exp(input) - 1),
0.0,
0.0,
self.inplace
)
)
def __repr__(self):
inplace_str = ', inplace' if self.inplace else ''
return self.__class__.__name__ + ' (' \
+ 'alpha=' + str(self.alpha.data[0]) \
+ inplace_str + ')'
class ConstantMask(nn.Module):
def __init__(self, in_features, prob=1.0):
super(ConstantMask, self).__init__()
self.mask = nn.parameter.Parameter(
torch.Tensor(1, in_features),
requires_grad=False
)
self.in_features = in_features
self.prob = prob
self.reset_parameters(self.prob)
def reset_parameters(self, prob):
self.mask.data.uniform_()
self.mask.data.apply_(lambda x: x < prob)
def forward(self, x):
return (
x
*
self.mask.expand(
x.size(0),
self.mask.size(1)
)
)
def __repr__(self):
return self.__class__.__name__ + ' (' \
+ 'ncol: ' + str(self.in_features) + ' ' \
+ 'Prob: ' + str(self.prob) + ')'
class AvePosLinear(nn.Module):
def __init__(self, in_features, out_features, fun=PosELU, normalized_init=False, bias=True):
super(AvePosLinear, self).__init__()
self.in_features = in_features
self.out_features = out_features
self.normalized_init = normalized_init
self.fun = fun()
self.weight = nn.parameter.Parameter(torch.Tensor(out_features, in_features))
if bias:
self.bias = nn.parameter.Parameter(torch.Tensor(out_features))
else:
self.register_parameter('bias', None)
self.reset_parameters(self.normalized_init)
def reset_parameters(self, normalized_init):
stdv = 1. / math.sqrt(self.weight.size(1))
if normalized_init:
self.weight.data.uniform_(-stdv, stdv)
self.weight.data = self.fun.forward(self.weight.data)
self.weight.data /= torch.sum(self.weight.data)
self.weight.data = self.fun.inverse(self.weight.data) / 10.0
else:
self.weight.data.uniform_(-stdv, stdv)
if self.bias is not None:
self.bias.data.uniform_(-stdv, stdv)
def forward(self, input):
weight = self.fun(self.weight)
# weight = weight / torch.sum(weight).expand_as(weight)
if self.bias is None:
return self._backend.Linear()(input, weight)
else:
return self._backend.Linear()(input, weight, self.bias)
def __repr__(self):
return self.__class__.__name__ + ' (' \
+ self.fun.__repr__() + ', ' \
+ 'NI: ' + str(self.normalized_init) + ', ' \
+ str(self.in_features) + ' -> ' \
+ str(self.out_features) + ')'
class BoostAPLTMFullyCon(nn.Module):
def __init__(self, n_channel_to_nn, first_dim_to_nn, second_dim_to_nn,
dim_of_hidden_layer_list, mask_prob, normalized_apl_init):
super(BoostAPLTMFullyCon, self).__init__()
self.n_channel_from_boosting = n_channel_to_nn
self.first_dim_from_boosting = first_dim_to_nn
self.second_dim_from_boosting = second_dim_to_nn
input_dim_list = (
[
n_channel_to_nn * first_dim_to_nn * second_dim_to_nn
]
+
dim_of_hidden_layer_list
)
output_dim_list = (
dim_of_hidden_layer_list
+
[1]
)
self.layers = nn.Sequential()
for idx, input_output_dim in enumerate(zip(input_dim_list, output_dim_list)):
self.layers.add_module(
'mask_%d' % idx,
ConstantMask(
input_output_dim[0],
prob=mask_prob
)
)
self.layers.add_module(
'ltanh_%d' % idx,
LearnableTanh(
input_output_dim[0]
)
)
self.layers.add_module(
'apl_%d' % idx,
AvePosLinear(
input_output_dim[0],
input_output_dim[1],
normalized_init=normalized_apl_init
)
)
def forward(self, x):
x = x.view(
-1,
self.n_channel_from_boosting *
self.first_dim_from_boosting *
self.second_dim_from_boosting
)
for layer in self.layers:
x = layer(x)
return x
class BoostFC(nn.Module):
def __init__(self, n_channel_to_nn, first_dim_to_nn, second_dim_to_nn, dim_of_hidden_layer,
use_ave_pos_linear, dropout_prob=0.0):
super(BoostFC, self).__init__()
self.n_channel_from_boosting = n_channel_to_nn
self.first_dim_from_boosting = first_dim_to_nn
self.second_dim_from_boosting = second_dim_to_nn
self.dropout_0 = nn.Dropout(dropout_prob)
self.dropout_1 = nn.Dropout(dropout_prob)
if use_ave_pos_linear:
self.fc_0 = AvePosLinear(
self.n_channel_from_boosting
* self.first_dim_from_boosting
* self.second_dim_from_boosting,
dim_of_hidden_layer
)
self.fc_1 = AvePosLinear(
dim_of_hidden_layer,
1
)
else:
self.fc_0 = nn.Linear(
self.n_channel_from_boosting
* self.first_dim_from_boosting
* self.second_dim_from_boosting,
dim_of_hidden_layer
)
self.fc_1 = nn.Linear(
dim_of_hidden_layer,
1
)
def forward(self, x):
x = x.view(-1,
self.n_channel_from_boosting *
self.first_dim_from_boosting *
self.second_dim_from_boosting)
x = F.tanh(x)
x = self.dropout_0(x)
x = self.fc_0(x)
x = F.tanh(x)
x = self.dropout_1(x)
x = self.fc_1(x)
return x
class PureXGB(nn.Module):
def __init__(self):
super(PureXGB, self).__init__()
self.n_channel_from_boosting = 1
self.first_dim_from_boosting = 1
self.second_dim_from_boosting = 1
self.fc = nn.Linear(1, 1)
def forward(self, x):
x = x.view(-1, 1)
return x
class CovNN(nn.Module):
def __init__(self):
super(CovNN, self).__init__()
self.n_channel_from_boosting = 2
self.first_dim_from_boosting = 8
self.second_dim_from_boosting = 8
self.features = nn.Sequential(
nn.Conv2d(2, 8, kernel_size=4, stride=2, padding=2),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=4, stride=1),
)
self.classifier = nn.Sequential(
# nn.Dropout(),
nn.Linear(8 * 2 * 2, 24),
F.elu(),
# nn.Dropout(),
nn.Linear(24, 8),
nn.Softmax()
)
# required
def forward(self, x):
x = self.features(x)
x = x.view(-1, 8 * 2 * 2)
x = self.classifier(x)
return x
class BasicBlock(nn.Module):
def __init__(self, input_size, intermedia_size, dropout_prob=0.0):
super(BasicBlock, self).__init__()
self.input_size = input_size
self.intermedia_size = intermedia_size
self.bn1 = nn.BatchNorm1d(input_size)
self.fc1 = nn.Linear(input_size, intermedia_size)
self.bn2 = nn.BatchNorm1d(intermedia_size)
self.fc2 = nn.Linear(intermedia_size, input_size)
self.dropout = nn.Dropout(dropout_prob)
self.dropout_prob = dropout_prob
def forward(self, x):
x = x.view(-1, self.input_size)
residual = x
out = x
if self.dropout_prob > 0.0:
out = self.dropout(out)
out = self.bn1(out)
out = F.elu(out)
out = self.fc1(out)
# if self.dropout_prob > 0.0:
# out = self.dropout(out)
out = self.bn2(out)
out = F.elu(out)
out = self.fc2(out)
out += residual
return out
class ResRegNet(nn.Module):
def __init__(self,
n_channel_from_boosting, first_dim_from_boosting, second_dim_from_boosting,
n_block, intermediate_size_in_block,
dropout_prob=0.0):
"""
intermedia_size_in_block: array like, element[i] represents the size of
intermedia node in i-th block
"""
super(ResRegNet, self).__init__()
self.dropout = nn.Dropout(dropout_prob)
# Basic blocks, each block contains several channels
self.blocks = nn.Sequential()
for i in range(n_block):
self.blocks.add_module(
"block_%d" % i,
BasicBlock(
n_channel_from_boosting
* first_dim_from_boosting
* second_dim_from_boosting,
intermediate_size_in_block[i],
dropout_prob=dropout_prob
)
)
self.fc = nn.Linear(
n_channel_from_boosting * first_dim_from_boosting * second_dim_from_boosting,
1
)
# save parameters for further use
self.n_channel_from_boosting = n_channel_from_boosting
self.first_dim_from_boosting = first_dim_from_boosting
self.second_dim_from_boosting = second_dim_from_boosting
self.n_block = n_block
self.intermediate_size_in_block = intermediate_size_in_block
def forward(self, x):
x = self.dropout(x)
x = x.view(-1,
self.n_channel_from_boosting
* self.first_dim_from_boosting
* self.second_dim_from_boosting)
for block in self.blocks:
x = block(x)
x = self.fc(x)
return x
class TwoLayerAE(nn.Module):
def __init__(self, input_size, hidden_size_0, hidden_size_1):
super(TwoLayerAE, self).__init__()
self.middle_layer = nn.Linear(hidden_size_0, hidden_size_1)
self.output_layer = nn.Linear(hidden_size_1, input_size)
self.n_channel_to_boosting = 1
self.first_dim_to_boosting = 1
self.second_dim_to_boosting = input_size
self.n_channel_from_boosting = 1
self.first_dim_from_boosting = 1
self.second_dim_from_boosting = hidden_size_0
def forward(self, x):
x = x.view(-1,
self.n_channel_from_boosting
* self.first_dim_from_boosting
* self.second_dim_from_boosting
)
x = F.elu(x)
x = self.middle_layer(x)
x = F.elu(x)
x = self.output_layer(x)
x = x.view(-1,
self.n_channel_to_boosting,
self.first_dim_to_boosting,
self.second_dim_to_boosting
)
return x
class OneLayerAE(nn.Module):
def __init__(self, input_size, hidden_size_0):
super(OneLayerAE, self).__init__()
self.output_layer = nn.Linear(hidden_size_0, input_size)
self.n_channel_to_boosting = 1
self.first_dim_to_boosting = 1
self.second_dim_to_boosting = input_size
self.n_channel_from_boosting = 1
self.first_dim_from_boosting = 1
self.second_dim_from_boosting = hidden_size_0
def forward(self, x):
x = x.view(-1,
self.n_channel_from_boosting
* self.first_dim_from_boosting
* self.second_dim_from_boosting
)
x = F.elu(x)
x = self.output_layer(x)
x = x.view(-1,
self.n_channel_to_boosting,
self.first_dim_to_boosting,
self.second_dim_to_boosting
)
return x