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import torch
import torch.nn as nn
from timm import create_model
class EfficientNetClassifier(nn.Module):
def __init__(self, model_name='efficientnet_b0', num_classes=5, pretrained=True, dropout=0.2):
super().__init__()
self.backbone = create_model(model_name, pretrained=pretrained, num_classes=0, global_pool='avg')
with torch.no_grad():
dummy = torch.randn(1, 3, 224, 224)
self.feature_dim = self.backbone(dummy).shape[1]
# DR分级头
self.classifier_grading = nn.Sequential(
nn.Dropout(dropout),
nn.Linear(self.feature_dim, 512),
nn.ReLU(inplace=True),
nn.Dropout(dropout * 0.5),
nn.Linear(512, num_classes)
)
# 二分类头
self.classifier_diabetic = nn.Sequential(
nn.Dropout(dropout),
nn.Linear(self.feature_dim, 128),
nn.ReLU(inplace=True),
nn.Dropout(dropout * 0.5),
nn.Linear(128, 1)
)
self._initialize_weights()
def _initialize_weights(self):
for m in list(self.classifier_grading) + list(self.classifier_diabetic):
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)
out_grading = self.classifier_grading(features)
out_diabetic = self.classifier_diabetic(features).squeeze(-1) # shape: (B,)
# 返回dict，兼容旧用法
return {'grading': out_grading, 'diabetic': out_diabetic}
def get_features(self, x):
return self.backbone(x)
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