提供一个在Mask RCNN 的resnext
时间: 2023-05-27 18:02:55 浏览: 55
中使用的backbone网络的示例代码:
```
import torch.nn as nn
import torchvision.models as models
class ResNeXtBackbone(nn.Module):
def __init__(self):
super(ResNeXtBackbone, self).__init__()
# Load ResNeXt model pre-trained on ImageNet dataset
self.resnext101 = models.resnext101_32x8d(pretrained=True)
# Remove last layer and max pooling layer
self.features = nn.Sequential(*list(self.resnext101.children())[:-2])
# Freeze all layers except the last four blocks
for param in self.features.parameters():
param.requires_grad = False
for param in self.features[-1][-1].parameters():
param.requires_grad = True
for param in self.features[-2].parameters():
param.requires_grad = True
for param in self.features[-3].parameters():
param.requires_grad = True
for param in self.features[-4].parameters():
param.requires_grad = True
def forward(self, x):
x = self.features(x)
return x
```
这个代码中,我们使用了ResNeXt-101-32x8d模型作为backbone,并移除了最后一层全连接层和最大池化层,只保留了卷积层。我们进一步冻结了除了最后四个块之外的所有层,以减少需要更新的参数数量。这是为了加速训练,并防止过拟合。最后,我们定义了forward方法,使其返回经过backbone处理过的特征。