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今天在进行torch模型的初始化的时候，发现报错：

Object has no attribute ‘weight’

回顾模型，发现在模型权重初始化函数，定义的带有conv的层的初始化是这样的。

def weights_init(m):"""init the weight for a network"""classname=m.__class__.__name__# print(classname)if classname.find("conv")!=-1:nn.init.kaiming_normal_(m.weight.data,a=0,mode="fan_out")elif classname.find("BatchNorm")!=-1:m.weight.data.fill_(1)m.bias.data.fill_(0)

然后回去看了一下模型的命名，发现定义的一个层，名字是conv_block,那么匹配到这个名字的时候，就会把conv_block当做卷积层进行初始化。

class conv_block(nn.Module):"""Convolution Block """def __init__(self, input_nc, output_nc):super(conv_block, self).__init__()self.conv = nn.Sequential(nn.Conv2d(input_nc, output_nc, kernel_size=3, stride=1, padding=1, bias=True),nn.BatchNorm2d(output_nc),nn.ReLU(inplace=True),nn.Conv2d(output_nc, output_nc, kernel_size=3, stride=1, padding=1, bias=True),nn.BatchNorm2d(output_nc),nn.ReLU(inplace=True))def forward(self, x):x = self.conv(x)return x

解决方法：，将conv具体制定为conv2d，问题解决。

def weights_init(m):"""init the weight for a network"""classname=m.__class__.__name__# print(classname)if classname.find("conv2d")!=-1:nn.init.kaiming_normal_(m.weight.data,a=0,mode="fan_out")elif classname.find("BatchNorm")!=-1:m.weight.data.fill_(1)m.bias.data.fill_(0)

类似问题参考：

AttributeError: ‘Sequential’ object has no attribute ‘weight’

Object has no attribute ‘weight’