Deep learning 语义分割中的Pytorch运行时错误

我试图在博客文章中重现语义分段。我做了一些小的调整，但仍然无法让模型进行训练。我总是得到运行时错误：给定的组=1，大小的权重[512，1024，1，1]，期望输入[4，512，188，188]有1024个通道，但得到了512个通道。我已尝试取消冻结输入，但没有更改

这是网络

class ConvRelu(nn.Module):

    def __init__(self, in_depth, out_depth):
        super(ConvRelu, self).__init__()
        self.conv = torch.nn.Conv2d(in_depth, out_depth, kernel_size=3, stride=1, padding=1)
        self.activation = nn.ReLU(inplace=True)

    def forward(self, x):
        x = self.conv(x)
        x = self.activation(x)
        return x


class DecoderBlock(nn.Module, ABC):
    def __init__(self, in_depth, middle_depth, out_depth):
        super(DecoderBlock, self).__init__()
        self.conv_relu = ConvRelu(in_depth, middle_depth)
        self.conv_transpose = nn.ConvTranspose2d(middle_depth, out_depth, kernel_size=4, stride=2, padding=1)
        self.activation = nn.ReLU(inplace=True)

    def forward(self, x):
        x = self.conv_relu(x)
        x = self.conv_transpose(x)
        x = self.activation(x)
        return x


class UNetResNet(nn.Module):
    def __init__(self, num_classes):
        super(UNetResNet, self).__init__()
        self.encoder = torchvision.models.resnet101(pretrained=True)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv1 = nn.Sequential(self.encoder.conv1, self.encoder.bn1, self.encoder.relu, self.pool)
        self.conv2 = self.encoder.layer1
        self.conv3 = self.encoder.layer2
        self.conv4 = self.encoder.layer3
        self.conv4 = self.encoder.layer4

        self.pool = nn.MaxPool2d(2, 2)
        self.center = DecoderBlock(2048, 512, 256)

        self.dec5 = DecoderBlock(2048 + 256, 512, 256)
        self.dec4 = DecoderBlock(1024 + 256, 512, 256)
        self.dec3 = DecoderBlock(512 + 256, 256, 64)
        self.dec2 = DecoderBlock(256 + 64, 128, 128)
        self.dec1 = DecoderBlock(128, 128, 32)
        self.dec0 = ConvRelu(32, 32)
        self.final = nn.Conv2d(32, num_classes, kernel_size=1)

    def forward(self, x):
        conv1 = self.conv1(x)
        conv2 = self.conv2(conv1)
        conv3 = self.conv3(conv2)
        conv4 = self.conv4(conv3)
        conv5 = self.conv5(conv4)

        pool = self.pool(conv5)
        center = self.center(pool)

        dec5 = self.dec5(torch.cat([center, conv5], 1))
        dec4 = self.dec4(torch.cat([dec5, conv4], 1))
        dec3 = self.dec4(torch.cat([dec4, conv3], 1))
        dec2 = self.dec4(torch.cat([dec3, conv2], 1))
        dec1 = self.dec1(dec2)
        dec0 = self.dec0(dec1)

        return self.final(dec0)```   
 
and here's the training loop

求你了，我需要帮助。根据错误消息，网络中的错误在conv4上。我需要更改什么，或者如何调整输入？ 输入图像为PIL图像

for epoch_idx in range(2):
    loss_batches = []
    for batch_idx, data in enumerate(tqdm(train_dataloader, desc = "training")):

        imgs = torch.autograd.Variable(data['sat_img'].cuda())
        #imgs = imgs.view(1, -1)
        #imgs.unsqueeze(0)
        #imgs.squeeze()
        masks = torch.autograd.Variable(data['map_img'].cuda())
        masks.unsqueeze_(0)
        #print(imgs.size())


        y = unet_resnet(imgs)
        loss = cross_entropy_loss(y, masks)

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        loss_batches.append(loss.data.cpu().numpy())
    print('epoch: ' + str(epoch_idx) + ' training loss: ' + str(np.sum(loss_batches)))

model_file = './unet-' + str(epoch_idx)
unet_resnet = unet_resnet.cpu()
torch.save(unet_resnet.state_dict(), model_file)
unet_resnet = unet_resnet.cuda()
print('model saved') ```