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import torch.nn as nn
from torchvision import models
class ResNet50Classifier(nn.Module):
def __init__(self, train_base=False):
super().__init__()
self.base_model = models.resnet50(weights=models.ResNet50_Weights.DEFAULT)
for param in self.base_model.parameters():
param.requires_grad = train_base
in_features = self.base_model.fc.in_features
self.base_model.fc = nn.Sequential(
nn.BatchNorm1d(in_features),
nn.Dropout(0.5),
nn.Linear(in_features, 128),
nn.ReLU(),
nn.BatchNorm1d(128),
nn.Dropout(0.5),
nn.Linear(128, 1),
nn.Sigmoid()
)
def forward(self, x):
return self.base_model(x)
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