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models.py
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import torch
import torch.nn as nn
import torch.nn.functional as nf
class fc_part(nn.Module):
def __init__(self):
super().__init__()
# self.fc1 = nn.Linear(512,512)
self.fc1 = nn.Linear(512, 256)
self.fc2 = nn.Linear(256, 120)
self.fc3 = nn.Linear(120,5)
def forward(self, x):
x = nf.relu(self.fc1(x))
x = nf.relu(self.fc2(x))
x = nf.relu(self.fc3(x))
# x = self.fc1(x)
return x
class FeatureExtractorResNet(nn.Module):
def __init__(self, original_model):
super(FeatureExtractorResNet, self).__init__()
self.conv_features = None
for name, layer in original_model.named_children():
if name == 'fc': # Stop before the classifier
break
setattr(self, name, layer)
# print(f"{name}")
def forward(self, x):
for name, layer in self.named_children():
x = layer(x)
if name == 'avgpool': # Save the output of the last convolutional layer
self.conv_features = x
features = self.conv_features.view(self.conv_features.size(0), -1)
return features
class FeatureExtractorResNet18(nn.Module):
def __init__(self, original_model):
super(FeatureExtractorResNet18, self).__init__()
self.conv1 = original_model.conv1
self.bn1 = original_model.bn1
self.relu = original_model.relu
self.maxpool = original_model.maxpool
self.layer1 = original_model.layer1
self.layer2 = original_model.layer2
self.layer3 = original_model.layer3
self.layer4 = original_model.layer4
self.avgpool = original_model.avgpool
# self.features = nn.Sequential(
# original_model.conv1,
# original_model.bn1,
# original_model.relu,
# original_model.maxpool,
# original_model.layer1,
# original_model.layer2,
# original_model.layer3,
# original_model.layer4,
# original_model.avgpool
# )
def forward(self, x):
# x = self.features(x)
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
return x
class ClassifierResNet(nn.Module):
def __init__(self, original_model):
super(ClassifierResNet, self).__init__()
self.fc = original_model.fc
def forward(self, x):
x = self.fc(x)
return x
from torchvision import models
def load_resnet18_by_featureExtractor_classifier(feature_extractor_model,classifier_model,resnet18=None):
if resnet18 is None:
resnet18 = models.resnet18(pretrained=True)
resnet18.conv1 = feature_extractor_model.conv1
resnet18.bn1 = feature_extractor_model.bn1
resnet18.relu = feature_extractor_model.relu
resnet18.maxpool = feature_extractor_model.maxpool
resnet18.layer1 = feature_extractor_model.layer1
resnet18.layer2 = feature_extractor_model.layer2
resnet18.layer3 = feature_extractor_model.layer3
resnet18.layer4 = feature_extractor_model.layer4
resnet18.avgpool = feature_extractor_model.avgpool
resnet18.fc = classifier_model.fc
return resnet18
def load_resnet_by_featureExtractor_classifier(feature_extractor_model,classifier_model,resnet):
for name, layer in feature_extractor_model.named_children():
setattr(resnet,name,layer)
resnet.fc = classifier_model.fc
return resnet
class Discriminator(nn.Module):
def __init__(self, input_size=512, hidden_size1=200, hidden_size2=20):
super(Discriminator, self).__init__()
self.layer1 = nn.Linear(input_size, hidden_size1)
self.relu1 = nn.ReLU()
self.layer2 = nn.Linear(hidden_size1, hidden_size2)
self.relu2 = nn.ReLU()
self.output_layer = nn.Linear(hidden_size2, 1)
def forward(self, x):
x = self.layer1(x)
x = self.relu1(x)
x = self.layer2(x)
x = self.relu2(x)
x = self.output_layer(x)
return x
if __name__ == "__main__":
from torchvision import models
MODEL_FILE = './model1/best_resnet18_model.pth' # pretrained model using simulated dataset
model = models.resnet18(pretrained=True)
model.fc = fc_part()
model.load_state_dict(torch.load(MODEL_FILE))
feature_extractor_model = FeatureExtractorResNet18(model)
classifier_model = ClassifierResNet(model)
print(feature_extractor_model)
print(classifier_model)
batch_size = 10
channels = 3 # Assuming RGB images
height = 60
width = 80
random_data = torch.rand((batch_size, channels, height, width))
features = feature_extractor_model(random_data)
output = classifier_model(features)
output2 = model.forward(random_data)
print("Input Data Shape:", random_data.shape)
print("Extracted Features Shape:", features.shape)
print("Classifier Output Shape:", output.shape)
print(torch.equal(output,output2))
new_model = models.resnet18(pretrained=True)
# new_model = load_resnet_by_featureExtractor_classifier(feature_extractor_model,classifier_model, new_model)
# new_model = load_resnet18_by_featureExtractor_classifier(feature_extractor_model,classifier_model, new_model)
new_model = load_resnet18_by_featureExtractor_classifier(feature_extractor_model,classifier_model)
output3 = new_model.forward(random_data)
print(torch.equal(output2,output3))
# model2 = models.resnet34(pretrained=True)
# print(model2)
# feature_extractor_model2 = FeatureExtractorResNet(model2)
# classifier_model2 = ClassifierResNet(model2)
# print(feature_extractor_model2)
# print(classifier_model2)