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import numpy as np
import torch
import torchvision.transforms as transforms
import torch.nn as nn
from torch.utils.data import DataLoader, Dataset
from torchvision import transforms, datasets, models
# Define model
class RetinaNet(nn.Module):
def __init__(self, num_classes=2):
super(RetinaNet, self).__init__()
self.backbone = models.resnet50(weights=models.ResNet50_Weights.IMAGENET1K_V1)
# Freeze backbone parameters
for param in self.backbone.parameters():
param.requires_grad = False
# Unfreeze later layers
for param in self.backbone.layer3.parameters():
param.requires_grad = True
for param in self.backbone.layer4.parameters():
param.requires_grad = False
# Modified classifier head
self.classifier = nn.Sequential(
nn.Linear(2048, 512),
nn.ReLU(),
nn.Dropout(0.5),
nn.Linear(512, num_classes)
# nn.Sigmoid()
)
def forward(self, x):
x = self.backbone.conv1(x)
x = self.backbone.bn1(x)
x = self.backbone.relu(x)
x = self.backbone.maxpool(x)
x = self.backbone.layer1(x)
x = self.backbone.layer2(x)
x = self.backbone.layer3(x)
x = self.backbone.layer4(x)
x = self.backbone.avgpool(x)
x = torch.flatten(x, 1)
x = self.classifier(x)
return x |