编辑：我可以解决它（希望它能做我想做的）。但我不明白为什么它现在工作。我把我的变通办法放在底部。如果有人能启发我，为什么它工作（或者为什么notebook没有它工作），那就太好了。
我使用本教程中的代码：
https://amaarora.github.io/posts/2020-09-13-unet.html#understanding-input-and-output-shapes-in-u-net
它在我的笔记本代码中运行。
现在，我尝试为我的模型修改这段代码。因此，我将其复制到我的model.py中，它只有一个类AENet并相应地修改它。但PyCharm已经发出警告，
对象“Encoder "不可调用对象”Decoder“不可调用
当我从终端调用时程序中断。
使用前：

class AENet(nn.Module):
    def __init__(self,input_dim, block_size):
        super(AENet,self).__init__()
        self.input_dim = input_dim
        self.cov_source = nn.Parameter(torch.zeros(block_size, block_size), requires_grad=False)
        self.cov_target = nn.Parameter(torch.zeros(block_size, block_size), requires_grad=False)
        self.encoder = nn.Sequential(
            nn.Linear(self.input_dim,128),
            nn.BatchNorm1d(128,momentum=0.01, eps=1e-03),
            nn.ReLU(),
            nn.Linear(128,8),
            nn.BatchNorm1d(8,momentum=0.01, eps=1e-03),
            nn.ReLU(),
        )
        self.decoder = nn.Sequential(
            nn.Linear(8,128),
            nn.BatchNorm1d(128,momentum=0.01, eps=1e-03),
            nn.ReLU(),
            nn.Linear(128,self.input_dim)
        )
    def forward(self, x):
        z = self.encoder(x.view(-1,self.input_dim))
        return self.decoder(z), z

字符串
在我复制、粘贴和修改AENet类之后，它看起来像这样：

class Block(nn.Module):
    def __init__(self, in_ch, out_ch):
        super().__init__()
        self.conv1 = nn.Conv2d(in_ch, out_ch, 3)
        self.relu = nn.ReLU()
        self.conv2 = nn.Conv2d(out_ch, out_ch, 3)
    def forward(self, x):
        return self.conv2(self.relu(self.conv1(x)))
class Encoder(nn.Module):
    def __init__(self, chs=(1, 64, 128, 256, 512, 1024)):
        super().__init__()
        self.enc_blocks = nn.ModuleList([Block(chs[i], chs[i + 1]) for i in range(len(chs) - 1)])
        self.pool = nn.MaxPool2d(2)
    def forward(self, x):
        ftrs = []
        for block in self.enc_blocks:
            x = block(x)
            ftrs.append(x)
            x = self.pool(x)
        return ftrs
class Decoder(nn.Module):
    def __init__(self, chs=(1024, 512, 256, 128, 64)):
        super().__init__()
        self.chs = chs
        self.upconvs = nn.ModuleList([nn.ConvTranspose2d(chs[i], chs[i + 1], 2, 2) for i in range(len(chs) - 1)])
        self.dec_blocks = nn.ModuleList([Block(chs[i], chs[i + 1]) for i in range(len(chs) - 1)])
    def forward(self, x, encoder_features):
        for i in range(len(self.chs) - 1):
            x = self.upconvs[i](x)
            enc_ftrs = self.crop(encoder_features[i], x)
            x = torch.cat([x, enc_ftrs], dim=1)
            x = self.dec_blocks[i](x)
        return x
    def crop(self, enc_ftrs, x):
        _, _, H, W = x.shape
        enc_ftrs = torchvision.transforms.CenterCrop([H, W])(enc_ftrs)
        return enc_ftrs
class AENet(nn.Module):
    def __init__(self, input_dim, block_size, enc_chs=(1, 64, 128, 256, 512, 1024), dec_chs=(1024, 512, 256, 128, 64), num_class=1,
                 retain_dim=True, out_sz=(256, 640)):
        super(AENet,self).__init__()
        self.input_dim = input_dim
        self.cov_source = nn.Parameter(torch.zeros(block_size, block_size), requires_grad=False)
        self.cov_target = nn.Parameter(torch.zeros(block_size, block_size), requires_grad=False)
        self.encoder = Encoder(enc_chs)
        self.decoder = Decoder(dec_chs)
        self.head = nn.Conv2d(dec_chs[-1], num_class, 1)
        self.retain_dim = retain_dim
        self.out_sz = out_sz
    def forward(self, x):
        print(f'FOR X: we have the dimensions {x.shape}')
        z = self.encoder(x[None, None, :])
        print(f'FOR Z: we have the dimensions {z.shape}')
        out = self.decoder(z[::-1][0], z[::-1][1:])
        out = self.head(out)
        if self.retain_dim:
            out = F.interpolate(out, self.out_sz)
        print(f'FOR O: we have the dimensions {out.shape}')
        return out, z

型
我的解决方法：

z = self.encoder.forward(x[None, None, :])
out = self.decoder.forward(z[::-1][0], z[::-1][1:])

型
为什么我需要直接调用类的方法，而它在jupyter notebook中直接工作？