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models.py
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models.py
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import torch
import torch.nn as nn
import torch.nn.parallel
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
m.weight.data.normal_(0.0, 0.02)
elif classname.find('BatchNorm') != -1:
m.weight.data.normal_(1.0, 0.02)
m.bias.data.fill_(0)
# DCGAN model, fully convolutional architecture
class _netG_1(nn.Module):
def __init__(self, ngpu, nz, nc , ngf, n_extra_layers_g):
super(_netG_1, self).__init__()
self.ngpu = ngpu
#self.nz = nz
#self.nc = nc
#self.ngf = ngf
main = nn.Sequential(
# input is Z, going into a convolution
# state size. nz x 1 x 1
nn.ConvTranspose2d( nz, ngf * 8, 4, 1, 0, bias=False),
nn.BatchNorm2d(ngf * 8),
nn.LeakyReLU(0.2, inplace=True),
# state size. (ngf*8) x 4 x 4
nn.ConvTranspose2d(ngf * 8, ngf * 4, 4, 2, 1, bias=False),
nn.BatchNorm2d(ngf * 4),
nn.LeakyReLU(0.2, inplace=True),
# state size. (ngf*4) x 8 x 8
nn.ConvTranspose2d(ngf * 4, ngf * 2, 4, 2, 1, bias=False),
nn.BatchNorm2d(ngf * 2),
nn.LeakyReLU(0.2, inplace=True),
# state size. (ngf*2) x 16 x 16
nn.ConvTranspose2d(ngf * 2, ngf, 4, 2, 1, bias=False),
nn.BatchNorm2d(ngf),
nn.LeakyReLU(0.2, inplace=True),
# state size. (ngf) x 32 x 32
)
# Extra layers
for t in range(n_extra_layers_g):
main.add_module('extra-layers-{0}.{1}.conv'.format(t, ngf),
nn.Conv2d(ngf, ngf, 3, 1, 1, bias=False))
main.add_module('extra-layers-{0}.{1}.batchnorm'.format(t, ngf),
nn.BatchNorm2d(ngf))
main.add_module('extra-layers-{0}.{1}.relu'.format(t, ngf),
nn.LeakyReLU(0.2, inplace=True))
main.add_module('final_layer.deconv',
nn.ConvTranspose2d(ngf, nc, 4, 2, 1, bias=False)) # 5,3,1 for 96x96
main.add_module('final_layer.tanh',
nn.Tanh())
# state size. (nc) x 96 x 96
self.main = main
def forward(self, input):
gpu_ids = None
if isinstance(input.data, torch.cuda.FloatTensor) and self.ngpu > 1:
gpu_ids = range(self.ngpu)
return nn.parallel.data_parallel(self.main, input, gpu_ids), 0
class _netD_1(nn.Module):
def __init__(self, ngpu, nz, nc, ndf, n_extra_layers_d):
super(_netD_1, self).__init__()
self.ngpu = ngpu
main = nn.Sequential(
# input is (nc) x 96 x 96
nn.Conv2d(nc, ndf, 4, 2, 1, bias=False), # 5,3,1 for 96x96
nn.LeakyReLU(0.2, inplace=True),
# state size. (ndf) x 32 x 32
nn.Conv2d(ndf, ndf * 2, 4, 2, 1, bias=False),
nn.BatchNorm2d(ndf * 2),
nn.LeakyReLU(0.2, inplace=True),
# state size. (ndf*2) x 16 x 16
nn.Conv2d(ndf * 2, ndf * 4, 4, 2, 1, bias=False),
nn.BatchNorm2d(ndf * 4),
nn.LeakyReLU(0.2, inplace=True),
# state size. (ndf*4) x 8 x 8
nn.Conv2d(ndf * 4, ndf * 8, 4, 2, 1, bias=False),
nn.BatchNorm2d(ndf * 8),
nn.LeakyReLU(0.2, inplace=True),
# state size. (ndf*8) x 4 x 4
)
# Extra layers
for t in range(n_extra_layers_d):
main.add_module('extra-layers-{0}.{1}.conv'.format(t, ndf * 8),
nn.Conv2d(ndf * 8, ndf * 8, 3, 1, 1, bias=False))
main.add_module('extra-layers-{0}.{1}.batchnorm'.format(t, ndf * 8),
nn.BatchNorm2d(ndf * 8))
main.add_module('extra-layers-{0}.{1}.relu'.format(t, ndf * 8),
nn.LeakyReLU(0.2, inplace=True))
main.add_module('final_layers.conv', nn.Conv2d(ndf * 8, 1, 4, 1, 0, bias=False))
main.add_module('final_layers.sigmoid', nn.Sigmoid())
# state size. 1 x 1 x 1
self.main = main
def forward(self, input):
gpu_ids = None
if isinstance(input.data, torch.cuda.FloatTensor) and self.ngpu > 1:
gpu_ids = range(self.ngpu)
output = nn.parallel.data_parallel(self.main, input, gpu_ids)
return output.view(-1, 1)
class _netD_2(nn.Module):
def __init__(self, ngpu, nz, nc , ndf):
super(_netD_2, self).__init__()
self.ngpu = ngpu
self.convs = nn.Sequential(
# input is (nc) x 96 x 96
nn.Conv2d(nc, ndf, 4, 2, 1, bias=False),
nn.LeakyReLU(0.2, inplace=True),
# state size. (ndf) x 32 x 32
nn.Conv2d(ndf, ndf * 2, 4, 2, 1, bias=False),
nn.BatchNorm2d(ndf * 2),
nn.LeakyReLU(0.2, inplace=True),
# state size. (ndf*2) x 16 x 16
nn.Conv2d(ndf * 2, ndf * 4, 4, 2, 1, bias=False),
nn.BatchNorm2d(ndf * 4),
nn.LeakyReLU(0.2, inplace=True),
# state size. (ndf*4) x 8 x 8
nn.Conv2d(ndf * 4, ndf * 8, 4, 2, 1, bias=False),
nn.BatchNorm2d(ndf * 8),
nn.LeakyReLU(0.2, inplace=True),
# state size. (ndf*8) x 4 x 4
nn.Conv2d(ndf * 8, 1024, 4, 1, 0, bias=False),
nn.LeakyReLU(inplace=True),
nn.Dropout(0.5),
# state size. 1024 x 1 x 1
)
self.fcs = nn.Sequential(
nn.Linear(1024, 1024),
nn.LeakyReLU(inplace=True),
nn.Dropout(0.5),
nn.Linear(1024, 1),
nn.Sigmoid()
)
def forward(self, input):
gpu_ids = None
if isinstance(input.data, torch.cuda.FloatTensor) and self.ngpu > 1:
gpu_ids = range(self.ngpu)
output = nn.parallel.data_parallel(self.convs, input, gpu_ids)
output = self.fcs(output.view(-1,1024))
return output.view(-1, 1)
# with z decoder and fc layers
class _netG_2(nn.Module):
def __init__(self, ngpu, nz, nc , ngf):
super(_netG_2, self).__init__()
self.ngpu = ngpu
self.nz = nz
self.fcs = nn.Sequential(
# input is Z, going into a convolution
# state size. nz x 1 x 1
nn.Linear(nz, 1024),
nn.ReLU(inplace=True),
nn.Dropout(0.5),
nn.Linear(1024, 1024),
nn.ReLU(inplace=True),
nn.Dropout(0.5),
)
self.decode_fcs = nn.Sequential(
nn.Linear(1024, 1024),
nn.ReLU(inplace=True),
nn.Dropout(0.5),
nn.Linear(1024, nz),
)
self.convs = nn.Sequential(
# 1024x1x1
nn.ConvTranspose2d(1024, ngf * 8, 4, 1, 0, bias=False),
nn.BatchNorm2d(ngf * 8),
nn.ReLU(inplace=True),
# state size. (ngf*8) x 4 x 4
nn.ConvTranspose2d(ngf * 8, ngf * 4, 4, 2, 1, bias=False),
nn.BatchNorm2d(ngf * 4),
nn.ReLU(inplace=True),
# state size. (ngf*4) x 8 x 8
nn.ConvTranspose2d(ngf * 4, ngf * 2, 4, 2, 1, bias=False),
nn.BatchNorm2d(ngf * 2),
nn.ReLU(inplace=True),
# state size. (ngf*2) x 16 x 16
nn.ConvTranspose2d(ngf * 2, ngf, 4, 2, 1, bias=False),
nn.BatchNorm2d(ngf),
nn.ReLU(inplace=True),
# state size. (ngf) x 32 x 32
nn.ConvTranspose2d( ngf, nc, 4, 2, 1, bias=False),
nn.Tanh()
# state size. (nc) x 96 x 96
)
def forward(self, input):
input = self.fcs(input.view(-1,self.nz))
gpu_ids = None
if isinstance(input.data, torch.cuda.FloatTensor) and self.ngpu > 1:
gpu_ids = range(self.ngpu)
z_prediction = self.decode_fcs(input)
input = input.view(-1,1024,1,1)
output = nn.parallel.data_parallel(self.convs, input, gpu_ids)
return output, z_prediction
# DCGAN model with fc layers
class _netG_3(nn.Module):
def __init__(self, ngpu, nz, nc , ngf):
super(_netG_3, self).__init__()
self.ngpu = ngpu
self.fcs = nn.Sequential(
# input is Z, going into a convolution
# state size. nz x 1 x 1
nn.Linear(nz, 1024),
nn.ReLU(inplace=True),
nn.Dropout(0.5),
nn.Linear(1024, 1024),
nn.ReLU(inplace=True),
nn.Dropout(0.5),
)
self.convs = nn.Sequential(
# 1024x1x1
nn.ConvTranspose2d(1024, ngf * 8, 4, 1, 0, bias=False),
nn.BatchNorm2d(ngf * 8),
nn.ReLU(inplace=True),
# state size. (ngf*8) x 4 x 4
nn.ConvTranspose2d(ngf * 8, ngf * 4, 4, 2, 1, bias=False),
nn.BatchNorm2d(ngf * 4),
nn.ReLU(inplace=True),
# state size. (ngf*4) x 8 x 8
nn.ConvTranspose2d(ngf * 4, ngf * 2, 4, 2, 1, bias=False),
nn.BatchNorm2d(ngf * 2),
nn.ReLU(inplace=True),
# state size. (ngf*2) x 16 x 16
nn.ConvTranspose2d(ngf * 2, ngf, 4, 2, 1, bias=False),
nn.BatchNorm2d(ngf),
nn.ReLU(inplace=True),
# state size. (ngf) x 32 x 32
nn.ConvTranspose2d( ngf, nc, 4, 2, 1, bias=False),
nn.Tanh()
# state size. (nc) x 96 x 96
)
def forward(self, input):
input = self.fcs(input.view(-1,nz))
gpu_ids = None
if isinstance(input.data, torch.cuda.FloatTensor) and self.ngpu > 1:
gpu_ids = range(self.ngpu)
input = input.view(-1,1024,1,1)
return nn.parallel.data_parallel(self.convs, input, gpu_ids)