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import torch
import torch.nn as nn
import torchvision.models as models
class ResNetClassifier(nn.Module):
def __init__(self, model_name='resnet18', num_classes=5, pretrained=True, dropout=0.2):
super().__init__()
if model_name == 'resnet18':
self.backbone = models.resnet18(pretrained=pretrained)
self.feature_dim = 512
elif model_name == 'resnet34':
self.backbone = models.resnet34(pretrained=pretrained)
self.feature_dim = 512
elif model_name == 'resnet50':
self.backbone = models.resnet50(pretrained=pretrained)
self.feature_dim = 2048
else:
raise ValueError(f'不支持的ResNet模型: {model_name}')
self.backbone = nn.Sequential(*list(self.backbone.children())[:-1])
self.classifier = nn.Sequential(
nn.AdaptiveAvgPool2d(1),
nn.Flatten(),
nn.Dropout(dropout),
nn.Linear(self.feature_dim, 512),
nn.ReLU(inplace=True),
nn.Dropout(dropout * 0.5),
nn.Linear(512, num_classes)
)
self._initialize_weights()
def _initialize_weights(self):
for m in self.classifier:
if isinstance(m, nn.Linear):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
nn.init.constant_(m.bias, 0)
def forward(self, x):
features = self.backbone(x)
return self.classifier(features)
def get_features(self, x):
features = self.backbone(x)
return features.view(features.size(0), -1)
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