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models.py
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models.py
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import torch
from torch import nn
import torchvision as tv
def VGG16(pretrained=False):
# Define Model
net = tv.models.vgg16(pretrained=pretrained, progress=True)
net.features[0] = torch.nn.Conv2d(5, 64, 3, stride=(1, 1), padding=(1, 1))
net.classifier[-1] = torch.nn.Linear(4096, 2, bias=True)
return net
def VGG19_BN(pretrained=False):
# Define Model
net = tv.models.vgg19_bn(pretrained=pretrained, progress=True)
net.features[0] = torch.nn.Conv2d(5, 64, 3, stride=(1, 1), padding=(1, 1))
net.classifier[-1] = torch.nn.Linear(4096, 2, bias=True)
return net
def RESNET50(pretrained=False):
# Define Model
net = tv.models.resnet50(pretrained=pretrained, progress=True)
net.fc = torch.nn.Linear(2048, 2, bias=True)
return net
def RESNET101(pretrained=False):
# Define Model
net = tv.models.resnet101(pretrained=pretrained, progress=True)
net.conv1 = torch.nn.Conv2d(5, 64, 7, stride=(2, 2), padding=(3, 3))
net.fc = torch.nn.Linear(2048, 2, bias=True)
return net
def RESNET152(pretrained=False):
# Define Model
net = tv.models.resnet152(pretrained=pretrained, progress=True)
net.conv1 = torch.nn.Conv2d(5, 64, 7, stride=(2, 2), padding=(3, 3))
net.fc = torch.nn.Linear(2048, 2, bias=True)
return net
def DENSENET161(pretrained=False):
# Define Model
net = tv.models.densenet161(pretrained=pretrained, progress=True)
net.conv1 = torch.nn.Conv2d(5, 96, 7, stride=(2, 2), padding=(3, 3))
net.classifier = torch.nn.Linear(2208, 2, bias=True)
return net
def MOBILENETV2(pretrained=False):
pretrained = False
net = tv.models.mobilenet_v2(pretrained=pretrained, progress=True)
net.features[0][0] = torch.nn.Conv2d(
5, 32, 3, stride=2, padding=1, bias=False)
net.classifier[-1] = torch.nn.Linear(1280, 2, bias=True)
return net
class Conv(nn.Module):
def __init__(self, in_channels, out_channels,
kernel_size, stride=1, padding=0):
super(Conv, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size,
stride, padding, bias=False),
nn.BatchNorm2d(out_channels),
nn.Dropout(),
nn.ReLU()
)
def forward(self, x):
return self.conv(x)
class ConvT(nn.Module):
def __init__(self, in_channels, out_channels,
kernel_size, stride=1, padding=0):
super(ConvT, self).__init__()
self.conv = nn.Sequential(
nn.ConvTranspose2d(in_channels, out_channels,
kernel_size, stride, padding,
bias=False),
nn.BatchNorm2d(out_channels),
nn.Dropout(),
nn.ReLU()
)
def forward(self, x):
return self.conv(x)
class Flatten(nn.Module):
def forward(self, input):
return input.view(input.size(0), -1)
class unFlatten(nn.Module):
def forward(self, input):
return input.view(input.size(0), -1, 8, 8)
class VAE(nn.Module):
def __init__(self, n_layers=6, base=16, lf=128, n_channels=5):
super(VAE, self).__init__()
self.encoder = nn.Sequential(
Conv(3, base, 3, stride=2, padding=1), # 256
Conv(base, 2 * base, 5, padding=2),
Conv(2 * base, 2 * base, 3, stride=2, padding=1), # 128
Conv(2 * base, 2 * base, 5, padding=2),
Conv(2 * base, 2 * base, 3, stride=2, padding=1), # 64
Conv(2 * base, 4 * base, 5, padding=2),
Conv(4 * base, 4 * base, 3, stride=2, padding=1), # 32
Conv(4 * base, 4 * base, 5, padding=2),
Conv(4 * base, 4 * base, 3, stride=2, padding=1), # 16
Conv(4 * base, 4 * base, 5, padding=2),
Conv(4 * base, 4 * base, 3, stride=2, padding=1), # 8
Conv(4 * base, 4 * base, 5, padding=2),
Conv(4 * base, 4 * base, 3, stride=2, padding=1), # 4
nn.Conv2d(4 * base, 64 * base, 4),
nn.LeakyReLU()
)
# self.encoder_mu = nn.Linear(lf, lf)
# self.encoder_logvar = nn.Linear(lf, lf)
self.encoder_mu = nn.Conv2d(64 * base, lf, 1)
self.encoder_logvar = nn.Conv2d(64 * base, lf, 1)
self.decoder = nn.Sequential(
Conv(lf, 64 * base, 1),
ConvT(64 * base, 4 * base, 4),
Conv(4 * base, 4 * base, 3, padding=1),
ConvT(4 * base, 4 * base, 4, stride=2, padding=1),
Conv(4 * base, 4 * base, 5, padding=2),
ConvT(4 * base, 4 * base, 4, stride=2, padding=1),
Conv(4 * base, 4 * base, 5, padding=2),
ConvT(4 * base, 4 * base, 4, stride=2, padding=1),
Conv(4 * base, 4 * base, 5, padding=2),
ConvT(4 * base, 4 * base, 4, stride=2, padding=1),
Conv(4 * base, 2 * base, 5, padding=2),
ConvT(2 * base, 2 * base, 4, stride=2, padding=1),
Conv(2 * base, 2 * base, 5, padding=2),
ConvT(2 * base, 2 * base, 4, stride=2, padding=1),
Conv(2 * base, base, 5, padding=2),
ConvT(base, base, 4, stride=2, padding=1),
nn.Conv2d(base, 3, 3, padding=1),
nn.Tanh()
)
def encode(self, x):
x = self.encoder(x)
return self.encoder_mu(x), self.encoder_logvar(x)
def reparameterize(self, mu, logvar):
std = torch.exp(0.5 * logvar)
eps = torch.randn_like(std)
return mu + eps * std
def decode(self, z):
return self.decoder(z)
def forward(self, x):
mu, logvar = self.encode(x)
z = self.reparameterize(mu, logvar)
return self.decode(z), mu, logvar
class VAE_fm(nn.Module):
def __init__(self, n_layers=6, base=16, lf=128, n_channels=5):
super(VAE_fm, self).__init__()
self.encoder = nn.Sequential(
Conv(3, base, 3, stride=2, padding=1), # 256
Conv(base, 2 * base, 5, padding=2),
Conv(2 * base, 2 * base, 3, stride=2, padding=1), # 128
Conv(2 * base, 2 * base, 5, padding=2),
Conv(2 * base, 2 * base, 3, stride=2, padding=1), # 64
Conv(2 * base, 4 * base, 5, padding=2),
Conv(4 * base, 4 * base, 3, stride=2, padding=1), # 32
Conv(4 * base, 4 * base, 5, padding=2),
Conv(4 * base, 4 * base, 3, stride=2, padding=1), # 16
Conv(4 * base, 4 * base, 5, padding=2),
nn.Conv2d(4 * base, 64 * base, 8),
nn.LeakyReLU()
)
# self.encoder_mu = nn.Linear(lf, lf)
# self.encoder_logvar = nn.Linear(lf, lf)
self.encoder_mu = nn.Conv2d(64 * base, lf, 1)
self.encoder_logvar = nn.Conv2d(64 * base, lf, 1)
self.decoder = nn.Sequential(
Conv(lf, 64 * base, 1),
ConvT(64 * base, 4 * base, 8),
Conv(4 * base, 4 * base, 5, padding=2),
ConvT(4 * base, 4 * base, 4, stride=2, padding=1),
Conv(4 * base, 4 * base, 5, padding=2),
ConvT(4 * base, 4 * base, 4, stride=2, padding=1),
Conv(4 * base, 2 * base, 5, padding=2),
ConvT(2 * base, 2 * base, 4, stride=2, padding=1),
Conv(2 * base, 2 * base, 5, padding=2),
ConvT(2 * base, 2 * base, 4, stride=2, padding=1),
Conv(2 * base, base, 5, padding=2),
ConvT(base, base, 4, stride=2, padding=1),
nn.Conv2d(base, 3, 3, padding=1),
nn.Tanh()
)
def encode(self, x):
x = self.encoder(x)
return self.encoder_mu(x), self.encoder_logvar(x)
def reparameterize(self, mu, logvar):
std = torch.exp(0.5 * logvar)
eps = torch.randn_like(std)
return mu + eps * std
def decode(self, z):
return self.decoder(z)
def forward(self, x):
mu, logvar = self.encode(x)
z = self.reparameterize(mu, logvar)
return self.decode(z), mu, logvar