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architecture.py
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from torch import nn
from torchvision import models
import torch
"""
This file contains all CNN architectures as classes (non-pretrained and pretrained) inheriting from torch.nn.Module.
"""
class LeNet(nn.Module):
def __init__(self):
super(LeNet, self).__init__()
self.ReLu = nn.ReLU(inplace=True)
self.flatten = nn.Flatten()
self.conv2d_0 = nn.Conv2d(3, 6, kernel_size=5, padding=2)
self.pool_1 = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv2d_2 = nn.Conv2d(6, 16, kernel_size=5)
self.pool_3 = nn.MaxPool2d(kernel_size=2, stride=2)
self.linear_4 = nn.Linear(16 * 22 * 22, 120)
self.linear_5 = nn.Linear(120, 10)
# two outputs for softmax in final layer
self.output = nn.Linear(10, 1)
def forward(self, X, **kwargs):
X = X.view(-1, 3, 96, 96).float()
X = self.ReLu(self.conv2d_0(X))
X = self.pool_1(X)
X = self.ReLu(self.conv2d_2(X))
X = self.pool_3(X)
X = self.flatten(X)
X = self.ReLu(self.linear_4(X))
X = self.ReLu(self.linear_5(X))
X = self.output(X)
return X
class DenseNet121(nn.Module):
def __init__(self):
super(DenseNet121, self).__init__()
base_net = models.densenet121(pretrained=False)
self.features = base_net.features
self.dense121_relu = nn.ReLU(inplace=True)
self.dense121_pool = nn.AdaptiveAvgPool2d((1, 1))
self.classifier = nn.Sequential(nn.Linear(1024, 512),
nn.Dropout(p=0.1),
nn.ReLU(),
nn.Linear(512, 1))
del base_net
def forward(self, X):
X = X.view(-1, 3, 96, 96).float()
X = self.features(X)
# Convert output of predifined dense121 layers to a format that can used by the classifier "layers"
X = self.dense121_relu(X)
X = self.dense121_pool(X)
X = torch.flatten(X, 1)
X = self.classifier(X)
return X
class DenseNet121Pretrained(nn.Module):
def __init__(self):
super(DenseNet121Pretrained, self).__init__()
base_net = models.densenet121(pretrained=True)
self.features = base_net.features
self.dense121_relu = nn.ReLU(inplace=True)
self.dense121_pool = nn.AdaptiveAvgPool2d((1, 1))
# we only want to train the last layers
for param in list(self.features.parameters()):
param.requires_grad = False # as default is True for all
for param in list(self.features.denseblock4.denselayer13.parameters()):
param.requires_grad = True
for param in list(self.features.denseblock4.denselayer14.parameters()):
param.requires_grad = True
for param in list(self.features.denseblock4.denselayer15.parameters()):
param.requires_grad = True
for param in list(self.features.denseblock4.denselayer16.parameters()):
param.requires_grad = True
self.classifier = nn.Sequential(nn.Linear(1024, 512),
nn.Dropout(p=0.1),
nn.ReLU(),
nn.Linear(512, 1))
del base_net
def forward(self, X):
X = X.view(-1, 3, 224, 224).float()
X = self.features(X)
X = self.dense121_relu(X)
X = self.dense121_pool(X)
X = torch.flatten(X, 1)
X = self.classifier(X)
return X
class DenseNet201(nn.Module):
def __init__(self):
super(DenseNet201, self).__init__()
base_net = models.densenet201(pretrained=False)
self.features = base_net.features
self.dense201_relu = nn.ReLU(inplace=True)
self.dense201_pool = nn.AdaptiveAvgPool2d((1, 1))
self.classifier = nn.Sequential(nn.Linear(1920, 512),
nn.Dropout(p=0.1),
nn.ReLU(),
nn.Linear(512, 1))
del base_net
def forward(self, X):
X = X.view(-1, 3, 96, 96).float()
X = self.features(X)
X = self.dense201_relu(X)
X = self.dense201_pool(X)
X = torch.flatten(X, 1)
X = self.classifier(X)
return X
class DenseNet201Pretrained(nn.Module):
def __init__(self):
super(DenseNet201Pretrained, self).__init__()
base_net = models.densenet201(pretrained=True)
self.features = base_net.features
self.dense201_relu = nn.ReLU(inplace=True)
self.dense201_pool = nn.AdaptiveAvgPool2d((1, 1))
# we only want to train the last layers
for param in list(self.features.parameters()):
param.requires_grad = False # as default is True for all
for param in list(self.features.denseblock4.denselayer29.parameters()):
param.requires_grad = True
for param in list(self.features.denseblock4.denselayer30.parameters()):
param.requires_grad = True
for param in list(self.features.denseblock4.denselayer31.parameters()):
param.requires_grad = True
for param in list(self.features.denseblock4.denselayer32.parameters()):
param.requires_grad = True
self.classifier = nn.Sequential(nn.Linear(1920, 512),
nn.Dropout(p=0.1),
nn.ReLU(),
nn.Linear(512, 1))
del base_net
def forward(self, X):
X = X.view(-1, 3, 224, 224).float()
X = self.features(X)
X = self.dense201_relu(X)
X = self.dense201_pool(X)
X = torch.flatten(X, 1)
X = self.classifier(X)
return X
class ResNet18_96(nn.Module):
def __init__(self):
super(ResNet18_96, self).__init__()
self.model = models.resnet18(pretrained=False)
# change last layer (fc) to adjust for binary classification
n_features_in = self.model.fc.in_features
self.model.fc = nn.Sequential(
nn.Dropout(0.5),
nn.Linear(n_features_in, 1)
)
def forward(self, X):
X = X.view(-1, 3, 96, 96).float()
X = self.model(X)
return X
class ResNet152_96(nn.Module):
def __init__(self, pretrained=False):
super(ResNet152_96, self).__init__()
self.model = models.resnet152(pretrained=pretrained)
print("pretrained=", pretrained)
if pretrained:
# we only want to train the last 2 multilayers (i.e., layer 3 and 4)
for param in list(self.model.parameters()):
param.requires_grad = False # as default is True for all
for param in list(self.model.layer3.parameters()):
param.requires_grad = True
for param in list(self.model.layer4.parameters()):
param.requires_grad = True
# change last layer (fc) to adjust for binary classification
n_features_in = self.model.fc.in_features
self.model.fc = nn.Sequential(
nn.Dropout(0.5),
nn.Linear(n_features_in, 1)
)
def forward(self, X):
X = X.view(-1, 3, 96, 96).float()
X = self.model(X)
return X
class ResNet34Pretrained(nn.Module):
def __init__(self):
super(ResNet34Pretrained, self).__init__()
self.model = models.resnet34(pretrained=True)
# we only want to train the last 2 multilayers (i.e., layer 3 and 4)
for param in list(self.model.parameters()):
param.requires_grad = False # as default is True for all
for param in list(self.model.layer3.parameters()):
param.requires_grad = True
for param in list(self.model.layer4.parameters()):
param.requires_grad = True
# change last layer (fc) to adjust for binary classification
n_features_in = self.model.fc.in_features
self.model.fc = nn.Sequential(
nn.Dropout(0.5),
nn.Linear(n_features_in, 1)
)
def forward(self, X):
X = X.view(-1, 3, 224, 224).float()
X = self.model(X)
return X
class ResNet152Pretrained(nn.Module):
def __init__(self):
super(ResNet152Pretrained, self).__init__()
self.model = models.resnet152(pretrained=True)
# we only want to train the last 2 multilayers (i.e., layer 3 and 4)
for param in list(self.model.parameters()):
param.requires_grad = False # as default is True for all
for param in list(self.model.layer3.parameters()):
param.requires_grad = True
for param in list(self.model.layer4.parameters()):
param.requires_grad = True
# change last layer (fc) to adjust for binary classification
n_features_in = self.model.fc.in_features
self.model.fc = nn.Sequential(
nn.Dropout(0.5),
nn.Linear(n_features_in, 1)
)
def forward(self, X):
X = X.view(-1, 3, 224, 224).float()
X = self.model(X)
return X
class VGG11(nn.Module):
def __init__(self):
super(VGG11, self).__init__()
base_net = models.vgg11(pretrained=False)
self.features = base_net.features
self.avgpool = base_net.avgpool
self.classifier = nn.Sequential(
nn.Linear(in_features=25088, out_features=4096, bias=True),
nn.ReLU(inplace=True),
nn.Dropout(p=0.5, inplace=False),
nn.Linear(in_features=4096, out_features=4096, bias=True),
nn.ReLU(inplace=True),
nn.Dropout(p=0.5, inplace=False),
nn.Linear(in_features=4096, out_features=1, bias=True)
)
del base_net
def forward(self, X):
X = X.view(-1, 3, 96, 96).float()
X = self.features(X)
X = self.avgpool(X)
X = torch.flatten(X, 1)
X = self.classifier(X)
return X
class VGG19(nn.Module):
def __init__(self):
super(VGG19, self).__init__()
base_net = models.vgg19(pretrained=False)
self.features = base_net.features
self.avgpool = base_net.avgpool
self.classifier = nn.Sequential(
nn.Linear(in_features=25088, out_features=4096, bias=True),
nn.ReLU(inplace=True),
nn.Dropout(p=0.5, inplace=False),
nn.Linear(in_features=4096, out_features=4096, bias=True),
nn.ReLU(inplace=True),
nn.Dropout(p=0.5, inplace=False),
nn.Linear(in_features=4096, out_features=1, bias=True)
)
del base_net
def forward(self, X):
X = X.view(-1, 3, 96, 96).float()
X = self.features(X)
X = self.avgpool(X)
X = torch.flatten(X, 1)
X = self.classifier(X)
return X