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ALL_model.py
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from torchvision import models
import torch.nn as nn
class ResNet18_pretrained(nn.Module):
def __init__(self, pretrained=True):
super(ResNet18_pretrained, self).__init__()
self.classify = nn.Linear(512, 5)
pretrained_model = models.__dict__['resnet{}'.format(18)](pretrained=True)
self.conv1 = pretrained_model._modules['conv1']
self.bn1 = pretrained_model._modules['bn1']
self.relu = pretrained_model._modules['relu']
self.maxpool = pretrained_model._modules['maxpool']
self.layer1 = pretrained_model._modules['layer1']
self.layer2 = pretrained_model._modules['layer2']
self.layer3 = pretrained_model._modules['layer3']
self.layer4 = pretrained_model._modules['layer4']
self.avgpool = nn.AdaptiveAvgPool2d(1)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
# x = nn.Dropout(0.35)(x)
x = self.layer1(x)
x = self.layer2(x)
# x = nn.Dropout(0.35)(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
# print(x.shape)
x = x.view(x.size(0), -1)
x = self.classify(x)
return x
class ResNet50_pretrained(nn.Module):
def __init__(self, pretrained=True):
super(ResNet50_pretrained, self).__init__()
self.classify = nn.Linear(2048, 5)
pretrained_model = models.__dict__['resnet{}'.format(50)](pretrained=True)
self.conv1 = pretrained_model._modules['conv1']
self.bn1 = pretrained_model._modules['bn1']
self.relu = pretrained_model._modules['relu']
self.maxpool = pretrained_model._modules['maxpool']
self.layer1 = pretrained_model._modules['layer1']
self.layer2 = pretrained_model._modules['layer2']
self.layer3 = pretrained_model._modules['layer3']
self.layer4 = pretrained_model._modules['layer4']
self.avgpool = nn.AdaptiveAvgPool2d(1)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
# x = nn.Dropout(0.35)(x)
x = self.layer1(x)
x = self.layer2(x)
# x = nn.Dropout(0.35)(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
# print(x.shape)
x = x.view(x.size(0), -1)
x = self.classify(x)
return x
class ResNet18_nonpretrained(nn.Module):
def __init__(self, pretrained=True):
super(ResNet18_nonpretrained, self).__init__()
self.classify = nn.Linear(512, 5)
pretrained_model = models.__dict__['resnet{}'.format(18)](pretrained=False)
self.conv1 = pretrained_model._modules['conv1']
self.bn1 = pretrained_model._modules['bn1']
self.relu = pretrained_model._modules['relu']
self.maxpool = pretrained_model._modules['maxpool']
self.layer1 = pretrained_model._modules['layer1']
self.layer2 = pretrained_model._modules['layer2']
self.layer3 = pretrained_model._modules['layer3']
self.layer4 = pretrained_model._modules['layer4']
self.avgpool = nn.AdaptiveAvgPool2d(1)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
# x = nn.Dropout(0.35)(x)
x = self.layer1(x)
x = self.layer2(x)
# x = nn.Dropout(0.35)(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
# print(x.shape)
x = x.view(x.size(0), -1)
x = self.classify(x)
return x
class ResNet50_nonpretrained(nn.Module):
def __init__(self, pretrained=True):
super(ResNet50_nonpretrained, self).__init__()
self.classify = nn.Linear(2048, 5)
pretrained_model = models.__dict__['resnet{}'.format(50)](pretrained=False)
self.conv1 = pretrained_model._modules['conv1']
self.bn1 = pretrained_model._modules['bn1']
self.relu = pretrained_model._modules['relu']
self.maxpool = pretrained_model._modules['maxpool']
self.layer1 = pretrained_model._modules['layer1']
self.layer2 = pretrained_model._modules['layer2']
self.layer3 = pretrained_model._modules['layer3']
self.layer4 = pretrained_model._modules['layer4']
self.avgpool = nn.AdaptiveAvgPool2d(1)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
# x = nn.Dropout(0.35)(x)
x = self.layer1(x)
x = self.layer2(x)
# x = nn.Dropout(0.35)(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
# print(x.shape)
x = x.view(x.size(0), -1)
x = self.classify(x)
return x
def ResNet18_pretrained_return_model():
model = ResNet18_pretrained()
for name,child in model.named_children():
if name in ['layer4','fc']:
#print(name + 'is unfrozen')
for param in child.parameters():
param.requires_grad = True
else:
#print(name + 'is frozen')
for param in child.parameters():
param.requires_grad = False
return model
def ResNet50_pretrained_return_model():
model = ResNet50_pretrained()
for name,child in model.named_children():
if name in ['layer4','fc']:
#print(name + 'is unfrozen')
for param in child.parameters():
param.requires_grad = True
else:
#print(name + 'is frozen')
for param in child.parameters():
param.requires_grad = False
return model
def ResNet18_nonpretrained_return_model():
model = ResNet18_nonpretrained()
model_layer = [model.layer1,model.layer2,model.layer3,model.layer4]
for layer in model_layer:
for number in range(len(layer)):
layer[number].relu = nn.LeakyReLU(negative_slope=0.01,inplace=True)
model.relu = nn.LeakyReLU(negative_slope=0.01,inplace=True)
return model
def ResNet50_nonpretrained_return_model():
model = ResNet50_nonpretrained()
model_layer = [model.layer1,model.layer2,model.layer3,model.layer4]
for layer in model_layer:
for number in range(len(layer)):
layer[number].relu = nn.LeakyReLU(negative_slope=0.01,inplace=True)
model.relu = nn.LeakyReLU(negative_slope=0.01,inplace=True)
return model