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model.py
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model.py
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import torch.nn as nn
import torch
import torch.nn.functional as F
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Linear') != -1:
m.weight.data.normal_(0.0, 0.02)
m.bias.data.fill_(0)
elif classname.find('BatchNorm') != -1:
m.weight.data.normal_(1.0, 0.02)
m.bias.data.fill_(0)
class MLP_AC_D(nn.Module):
def __init__(self, opt):
super(MLP_AC_D, self).__init__()
self.fc1 = nn.Linear(opt.resSize, opt.ndh)
self.disc_linear = nn.Linear(opt.ndh, 1)
self.aux_linear = nn.Linear(opt.ndh, opt.attSize)
self.lrelu = nn.LeakyReLU(0.2, True)
self.sigmoid = nn.Sigmoid()
self.apply(weights_init)
def forward(self, x):
h = self.lrelu(self.fc1(x))
s = self.sigmoid(self.disc_linear(h))
a = self.aux_linear(h)
return s,a
class MLP_AC_2HL_D(nn.Module):
def __init__(self, opt):
super(MLP_AC_2HL_D, self).__init__()
self.fc1 = nn.Linear(opt.resSize, opt.ndh)
self.fc2 = nn.Linear(opt.ndh, opt.ndh)
self.disc_linear = nn.Linear(opt.ndh, 1)
self.aux_linear = nn.Linear(opt.ndh, opt.attSize)
self.lrelu = nn.LeakyReLU(0.2, True)
self.sigmoid = nn.Sigmoid()
self.dropout = nn.Dropout(p=0.5)
self.apply(weights_init)
def forward(self, x):
h = self.dropout(self.lrelu(self.fc1(x)))
h = self.dropout(self.lrelu(self.fc2(h)))
s = self.sigmoid(self.disc_linear(h))
a = self.aux_linear(h)
return s,a
class MLP_3HL_CRITIC(nn.Module):
def __init__(self, opt):
super(MLP_3HL_CRITIC, self).__init__()
self.fc1 = nn.Linear(opt.resSize + opt.attSize, opt.ndh)
self.fc2 = nn.Linear(opt.ndh, opt.ndh)
self.fc3 = nn.Linear(opt.ndh, opt.ndh)
self.fc4 = nn.Linear(opt.ndh, 1)
self.lrelu = nn.LeakyReLU(0.2, True)
self.apply(weights_init)
def forward(self, x, att):
h = torch.cat((x, att), 1)
h = self.lrelu(self.fc1(h))
h = self.lrelu(self.fc2(h))
h = self.lrelu(self.fc3(h))
h = self.fc4(h)
return h
class MLP_2HL_CRITIC(nn.Module):
def __init__(self, opt):
super(MLP_2HL_CRITIC, self).__init__()
self.fc1 = nn.Linear(opt.resSize + opt.attSize, opt.ndh)
self.fc2 = nn.Linear(opt.ndh, opt.ndh)
self.fc3 = nn.Linear(opt.ndh, 1)
self.lrelu = nn.LeakyReLU(0.2, True)
self.apply(weights_init)
def forward(self, x, att):
h = torch.cat((x, att), 1)
h = self.lrelu(self.fc1(h))
h = self.lrelu(self.fc2(h))
h = self.fc3(h)
return h
class MLP_2HL_Dropout_CRITIC(nn.Module):
def __init__(self, opt):
super(MLP_2HL_Dropout_CRITIC, self).__init__()
self.fc1 = nn.Linear(opt.resSize + opt.attSize, opt.ndh)
self.fc2 = nn.Linear(opt.ndh, opt.ndh)
self.fc3 = nn.Linear(opt.ndh, 1)
self.lrelu = nn.LeakyReLU(0.2, True)
self.dropout = nn.Dropout(p=0.5)
self.apply(weights_init)
def forward(self, x, att):
h = torch.cat((x, att), 1)
h = self.dropout(self.lrelu(self.fc1(h)))
h = self.dropout(self.lrelu(self.fc2(h)))
h = self.fc3(h)
return h
class MLP_CRITIC(nn.Module):
def __init__(self, opt):
super(MLP_CRITIC, self).__init__()
self.fc1 = nn.Linear(opt.resSize + opt.attSize, opt.ndh)
#self.fc2 = nn.Linear(opt.ndh, opt.ndh)
self.fc2 = nn.Linear(opt.ndh, 1)
self.lrelu = nn.LeakyReLU(0.2, True)
self.apply(weights_init)
def forward(self, x, att):
h = torch.cat((x, att), 1)
h = self.lrelu(self.fc1(h))
h = self.fc2(h)
return h
class MLP_D(nn.Module):
def __init__(self, opt):
super(MLP_D, self).__init__()
self.fc1 = nn.Linear(opt.resSize + opt.attSize, opt.ndh)
self.fc2 = nn.Linear(opt.ndh, 1)
self.lrelu = nn.LeakyReLU(0.2, True)
self.sigmoid = nn.Sigmoid()
self.apply(weights_init)
def forward(self, x, att):
h = torch.cat((x, att), 1)
h = self.lrelu(self.fc1(h))
h = self.sigmoid(self.fc2(h))
return h
class MLP_2HL_Dropout_G(nn.Module):
def __init__(self, opt):
super(MLP_2HL_Dropout_G, self).__init__()
self.fc1 = nn.Linear(opt.attSize + opt.nz, opt.ngh)
self.fc2 = nn.Linear(opt.ngh, opt.ngh)
self.fc3 = nn.Linear(opt.ngh, opt.resSize)
self.lrelu = nn.LeakyReLU(0.2, True)
#self.prelu = nn.PReLU()
self.relu = nn.ReLU(True)
self.dropout = nn.Dropout(p=0.5)
self.apply(weights_init)
def forward(self, noise, att):
h = torch.cat((noise, att), 1)
h = self.dropout(self.lrelu(self.fc1(h)))
h = self.dropout(self.lrelu(self.fc2(h)))
h = self.relu(self.fc3(h))
return h
class MLP_3HL_G(nn.Module):
def __init__(self, opt):
super(MLP_3HL_G, self).__init__()
self.fc1 = nn.Linear(opt.attSize + opt.nz, opt.ngh)
self.fc2 = nn.Linear(opt.ngh, opt.ngh)
self.fc3 = nn.Linear(opt.ngh, opt.ngh)
self.fc4 = nn.Linear(opt.ngh, opt.resSize)
self.lrelu = nn.LeakyReLU(0.2, True)
#self.prelu = nn.PReLU()
self.relu = nn.ReLU(True)
self.apply(weights_init)
def forward(self, noise, att):
h = torch.cat((noise, att), 1)
h = self.lrelu(self.fc1(h))
h = self.lrelu(self.fc2(h))
h = self.lrelu(self.fc3(h))
h = self.relu(self.fc4(h))
return h
class MLP_2HL_G(nn.Module):
def __init__(self, opt):
super(MLP_2HL_G, self).__init__()
self.fc1 = nn.Linear(opt.attSize + opt.nz, opt.ngh)
self.fc2 = nn.Linear(opt.ngh, opt.ngh)
self.fc3 = nn.Linear(opt.ngh, opt.resSize)
self.lrelu = nn.LeakyReLU(0.2, True)
#self.prelu = nn.PReLU()
self.relu = nn.ReLU(True)
self.apply(weights_init)
def forward(self, noise, att):
h = torch.cat((noise, att), 1)
h = self.lrelu(self.fc1(h))
h = self.lrelu(self.fc2(h))
h = self.relu(self.fc3(h))
return h
class MLP_Dropout_G(nn.Module):
def __init__(self, opt):
super(MLP_Dropout_G, self).__init__()
self.fc1 = nn.Linear(opt.attSize + opt.nz, opt.ngh)
self.fc2 = nn.Linear(opt.ngh, opt.resSize)
self.lrelu = nn.LeakyReLU(0.2, True)
self.relu = nn.ReLU(True)
self.dropout = nn.Dropout(p=0.2)
self.apply(weights_init)
def forward(self, noise, att):
h = torch.cat((noise, att), 1)
h = self.dropout(self.lrelu(self.fc1(h)))
h = self.relu(self.fc2(h))
return h
class MLP_G(nn.Module):
def __init__(self, opt):
super(MLP_G, self).__init__()
self.fc1 = nn.Linear(opt.attSize + opt.nz, opt.ngh)
self.fc2 = nn.Linear(opt.ngh, opt.resSize)
self.lrelu = nn.LeakyReLU(0.2, True)
#self.prelu = nn.PReLU()
self.relu = nn.ReLU(True)
self.apply(weights_init)
def forward(self, noise, att):
h = torch.cat((noise, att), 1)
h = self.lrelu(self.fc1(h))
h = self.relu(self.fc2(h))
return h
class MLP_V2S(nn.Module):
def __init__(self, opt):
super(MLP_V2S, self).__init__()
self.fc1 = nn.Linear(opt.resSize, opt.f_hid)
self.fc2 = nn.Linear(opt.f_hid, opt.attSize)
self.lrelu = nn.LeakyReLU(0.2, True)
# self.lrelu = nn.ReLU(True)
self.relu = nn.ReLU(True)
self.apply(weights_init)
def forward(self, res):
h = self.lrelu(self.fc1(res))
h = self.relu(self.fc2(h))
# h = self.fc2(h)
return h
class MLP_V2S_new(nn.Module):
def __init__(self, opt):
super(MLP_V2S_new, self).__init__()
self.fc1 = nn.Linear(opt.resSize, opt.f_hid)
self.fc2 = nn.Linear(opt.f_hid, opt.attSize)
self.lrelu = nn.LeakyReLU(0.2, True)
# self.lrelu = nn.ReLU(True)
self.relu = nn.ReLU(True)
self.apply(weights_init)
def forward(self, res):
h = self.lrelu(self.fc1(res))
# h = self.relu(self.fc2(h))
h = self.fc2(h)
return h
class DomainClassifier(nn.Module):
def __init__(self,dim):
super(DomainClassifier, self).__init__()
self.fcD = nn.Sequential(
nn.Linear(dim, 2)
)
self.apply(weights_init)
def forward(self, x):
x = self.fcD(F.relu(x))
return x
class MLP_R(nn.Module):
def __init__(self,opt):
super(MLP_R, self).__init__()
self.fc1 = nn.Linear(opt.resSize, opt.attSize)
def forward(self, x):
return self.fc1(x)
class MLP_2048_1024_Dropout_G(nn.Module):
def __init__(self, opt):
super(MLP_2048_1024_Dropout_G, self).__init__()
self.fc1 = nn.Linear(opt.attSize + opt.nz, opt.ngh)
#self.fc2 = nn.Linear(opt.ngh, opt.ngh)
self.fc2 = nn.Linear(opt.ngh, 1024)
self.fc3 = nn.Linear(1024, opt.resSize)
self.lrelu = nn.LeakyReLU(0.2, True)
#self.prelu = nn.PReLU()
#self.relu = nn.ReLU(True)
self.dropout = nn.Dropout(p=0.5)
self.apply(weights_init)
def forward(self, noise, att):
h = torch.cat((noise, att), 1)
h = self.dropout(self.lrelu(self.fc1(h)))
h = self.dropout(self.lrelu(self.fc2(h)))
h = self.fc3(h)
return h
class MLP_SKIP_G(nn.Module):
def __init__(self, opt):
super(MLP_SKIP_G, self).__init__()
self.fc1 = nn.Linear(opt.attSize + opt.nz, opt.ngh)
#self.fc2 = nn.Linear(opt.ngh, opt.ngh)
#self.fc2 = nn.Linear(opt.ngh, 1024)
self.fc2 = nn.Linear(opt.ngh, opt.resSize)
self.fc_skip = nn.Linear(opt.attSize, opt.resSize)
self.lrelu = nn.LeakyReLU(0.2, True)
#self.prelu = nn.PReLU()
self.relu = nn.ReLU(True)
self.apply(weights_init)
def forward(self, noise, att):
h = torch.cat((noise, att), 1)
h = self.lrelu(self.fc1(h))
#h = self.lrelu(self.fc2(h))
h = self.relu(self.fc2(h))
h2 = self.fc_skip(att)
return h+h2
class MLP_SKIP_D(nn.Module):
def __init__(self, opt):
super(MLP_SKIP_D, self).__init__()
self.fc1 = nn.Linear(opt.resSize + opt.attSize, opt.ndh)
self.fc2 = nn.Linear(opt.ndh, 1)
self.fc_skip = nn.Linear(opt.attSize, opt.ndh)
self.lrelu = nn.LeakyReLU(0.2, True)
self.sigmoid = nn.Sigmoid()
self.apply(weights_init)
def forward(self, x, att):
h = torch.cat((x, att), 1)
h = self.lrelu(self.fc1(h))
h2 = self.lrelu(self.fc_skip(att))
h = self.sigmoid(self.fc2(h+h2))
return h