关于导入vgg16bn预训练模型失败

淡淡的烟草味﹌ 2024-04-18 10:45 118阅读 0赞

pytorch 0.4.1
    mod = models.vgg16_bn(pretrained=True)
    self._initialize_weights()
    #print(len(self.frontend.state_dict().items()))
    #print(len(mod.state_dict().items()))
    for i in xrange(len(self.frontend.state_dict().items())):
        xx = self.frontend.state_dict().items()[i][0]
        if "num_batches_tracked" in xx:
            continue
        self.frontend.state_dict().items()[i][1].data[:] = mod.state_dict().items()[i][1].data[:]

因:pytorch 0.4版本中 在BN引入了num\_batches\_tracked

在`mod.state_dict().items()中，一层conv包含两种参数，一层BN包含4种参数。Pooling层不含参数`

暂时采取这个解决办法

`if "num_batches_tracked" in xx:   continue`

`因为，通过观察得知VGG16_BN网络，BN参数中的`num\_batches\_tracked，为tensor（0），与待转入的网络初始值一样。（都为tensor（0），故我选择跳过该参数传递）。

待补充别人的VGG16\_BN预训练模型导入：

[https://blog.csdn.net/u012494820/article/details/79068625][https_blog.csdn.net_u012494820_article_details_79068625]

model = ...
    model_dict = model.state_dict()
    
    # 1. filter out unnecessary keys
    pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
    # 2. overwrite entries in the existing state dict
    model_dict.update(pretrained_dict)
    # 3. load the new state dict
    model.load_state_dict(model_dict)
    
    改写：
    model = ...
    model_dict = model.state_dict()
    
    # 1. filter out unnecessary keys
    temp = {}
    for k,v in pretrained_dict.items():
        if k in model_dict:
            temp[k]=v
    pretrained_dict = temp
    model_dict.update(pretrained_dict)
    model.load_state_dict(model_dict)

[https_blog.csdn.net_u012494820_article_details_79068625]: https://blog.csdn.net/u012494820/article/details/79068625