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79aeec8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 | import torch.nn as nn
from torchvision.models import resnet50, ResNet50_Weights
class MLPHead(nn.Module):
def __init__(self, in_features, hidden_dim, num_classes):
super().__init__()
self.net = nn.Sequential(
nn.Linear(in_features, hidden_dim),
nn.BatchNorm1d(hidden_dim),
nn.ReLU(inplace=True),
nn.Linear(hidden_dim, hidden_dim),
nn.BatchNorm1d(hidden_dim),
nn.ReLU(inplace=True),
nn.Linear(hidden_dim, num_classes),
)
def forward(self, x):
return self.net(x)
class ResNetMLP(nn.Module):
def __init__(self, num_classes=10, freeze_backbone=True, hidden_dim=512):
super().__init__()
weights = ResNet50_Weights.DEFAULT
self.resnet = resnet50(weights=weights)
if freeze_backbone:
for p in self.resnet.parameters():
p.requires_grad = False
for p in self.resnet.layer3.parameters():
p.requires_grad = True
for p in self.resnet.layer4.parameters():
p.requires_grad = True
num_features = self.resnet.fc.in_features
self.resnet.fc = nn.Identity()
self.mlp_head = MLPHead(
in_features=num_features,
hidden_dim=hidden_dim,
num_classes=num_classes,
)
def forward(self, x):
x = self.resnet(x)
x = x.view(x.size(0), -1)
x = self.mlp_head(x)
return x |