OOP的新功能混淆了声明变量

如何解决OOP的新功能混淆了声明变量

在pytorch上构建一个神经网络，并尝试使完全连接的层接受我调用过的扁平层所产生的尺寸。

到目前为止，我正在执行以下操作：

from torch import nn
from torch.nn import functional as F

class my_model(nn.Module):
    def __init__(self,channel=16,out_class=1,flat_dim=24):
        super(my_model,self).__init__()

        self.channel = channel
        n_class = out_class

        self.conv1 = nn.Conv3d(1,channel // 8,kernel_size=5,stride=2,padding=1)
        self.pool1 = nn.AvgPool3d(2)
        self.bn1 = nn.BatchNorm3d(channel //8)
        self.conv2 = nn.Conv3d(channel // 8,channel // 4,kernel_size=4,padding=1)
        self.bn2 = nn.BatchNorm3d(channel // 4)
        self.pool2 = nn.AvgPool3d(2)
        self.fc1 = nn.Linear(flat_dim,500)
        self.fc2 = nn.Linear(500,100)

    def forward(self,x):
        batch_size,D,W,H,T = x.shape
        print('input to model',x.shape)
        x = x.permute(0,4,1,2,3)
        x = x.reshape(batch_size * T,H)
        print('after reshape',x.shape)
        h1 = F.relu(self.bn1(self.pool1(self.conv1(x))))
        print('after first layer sequence',h1.shape)
        h2 = F.relu(self.bn2(self.pool2(self.conv2(h1))))
        print('output to model',h2.shape)
        h2,flat_dim = self.flatten_cube(h2)
        h3 = self.fc1(h2)
        h4 = self.fc2(h3)
        print(h4.shape)

        return h4

    def flatten_cube(self,x):
        super_batch,C,H = x.shape
        flat_dim = C * D * W * H
        y = x.view(super_batch,flat_dim)
        return y,flat_dim

这是这样做的正确方法吗？