加载resNet预训练模型

最新推荐文章于 2025-11-03 11:32:02 发布

转载最新推荐文章于 2025-11-03 11:32:02 发布 · 3.8k 阅读

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本文介绍了一个基于预训练ResNet101模型的特征提取器类实现，详细解析了输入归一化处理及如何冻结模型参数，避免反向传播。

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# Assume input range is [0, 1]
class ResNet101FeatureExtractor(nn.Module):
    def __init__(self, use_input_norm=True, device=torch.device('cpu')):
        super(ResNet101FeatureExtractor, self).__init__()
        model = torchvision.models.resnet101(pretrained=True)
        self.use_input_norm = use_input_norm
        if self.use_input_norm:
            mean = torch.Tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1).to(device)
            # [0.485-1, 0.456-1, 0.406-1] if input in range [-1,1]
            std = torch.Tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1).to(device)
            # [0.229*2, 0.224*2, 0.225*2] if input in range [-1,1]
            self.register_buffer('mean', mean)
            self.register_buffer('std', std)
        self.features = nn.Sequential(*list(model.children())[:8])
        # No need to BP to variable
        for k, v in self.features.named_parameters():
            v.requires_grad = False

    def forward(self, x):
        if self.use_input_norm:
            x = (x - self.mean) / self.std
        output = self.features(x)
        return output

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