RuntimeError: Sizes of tensors must match except in dimension 1. Expected size 18 but got size 17 for tensor number 1 in the list

username: khaw235
cooked: <p>Hi there,</p>
<p>I am new to Pytorch and using the pre-trained model for my Django App which restores broken images. I took the help from the code given in <a href="https://github.com/safwankdb/Deep-Image-Prior/tree/master" rel="noopener nofollow ugc">this repository</a>.</p>
<p>My ML Model Class’s code goes like this:</p>
<pre><code class="lang-auto">class Hourglass(nn.Module):
    def __init__(self):
        super(Hourglass, self).__init__()

        self.leaky_relu = nn.LeakyReLU()

        self.d_conv_1 = nn.Conv2d(2, 8, 5, stride=2, padding=2)
        self.d_bn_1 = nn.BatchNorm2d(8)

        self.d_conv_2 = nn.Conv2d(8, 16, 5, stride=2, padding=2)
        self.d_bn_2 = nn.BatchNorm2d(16)

        self.d_conv_3 = nn.Conv2d(16, 32, 5, stride=2, padding=2)
        self.d_bn_3 = nn.BatchNorm2d(32)
        self.s_conv_3 = nn.Conv2d(32, 4, 5, stride=1, padding=2)

        self.d_conv_4 = nn.Conv2d(32, 64, 5, stride=2, padding=2)
        self.d_bn_4 = nn.BatchNorm2d(64)
        self.s_conv_4 = nn.Conv2d(64, 4, 5, stride=1, padding=2)

        self.d_conv_5 = nn.Conv2d(64, 128, 5, stride=2, padding=2)
        self.d_bn_5 = nn.BatchNorm2d(128)
        self.s_conv_5 = nn.Conv2d(128, 4, 5, stride=1, padding=2)

        self.d_conv_6 = nn.Conv2d(128, 256, 5, stride=2, padding=2)
        self.d_bn_6 = nn.BatchNorm2d(256)

        self.u_deconv_5 = nn.ConvTranspose2d(256, 64, 4, stride=2, padding=1)
        self.u_bn_5 = nn.BatchNorm2d(64)

        self.u_deconv_4 = nn.ConvTranspose2d(128, 32, 4, stride=2, padding=1)
        self.u_bn_4 = nn.BatchNorm2d(32)

        self.u_deconv_3 = nn.ConvTranspose2d(64, 16, 4, stride=2, padding=1)
        self.u_bn_3 = nn.BatchNorm2d(16)

        self.u_deconv_2 = nn.ConvTranspose2d(32, 8, 4, stride=2, padding=1)
        self.u_bn_2 = nn.BatchNorm2d(8)

        self.u_deconv_1 = nn.ConvTranspose2d(16, 4, 4, stride=2, padding=1)
        self.u_bn_1 = nn.BatchNorm2d(4)

        self.out_deconv = nn.ConvTranspose2d(4, 4, 4, stride=2, padding=1)
        self.out_bn = nn.BatchNorm2d(4)

    def forward(self, noise):
        down_1 = self.d_conv_1(noise)
        down_1 = self.d_bn_1(down_1)
        down_1 = self.leaky_relu(down_1)

        down_2 = self.d_conv_2(down_1)
        down_2 = self.d_bn_2(down_2)
        down_2 = self.leaky_relu(down_2)

        down_3 = self.d_conv_3(down_2)
        down_3 = self.d_bn_3(down_3)
        down_3 = self.leaky_relu(down_3)
        skip_3 = self.s_conv_3(down_3)

        down_4 = self.d_conv_4(down_3)
        down_4 = self.d_bn_4(down_4)
        down_4 = self.leaky_relu(down_4)
        skip_4 = self.s_conv_4(down_4)

        down_5 = self.d_conv_5(down_4)
        down_5 = self.d_bn_5(down_5)
        down_5 = self.leaky_relu(down_5)
        skip_5 = self.s_conv_5(down_5)

        down_6 = self.d_conv_6(down_5)
        down_6 = self.d_bn_6(down_6)
        down_6 = self.leaky_relu(down_6)

        up_5 = self.u_deconv_5(down_6)
        up_5 = torch.cat([up_5, skip_5], 1)
        up_5 = self.u_bn_5(up_5)
        up_5 = self.leaky_relu(up_5)

        up_4 = self.u_deconv_4(up_5)
        up_4 = torch.cat([up_4, skip_4], 1)
        up_4 = self.u_bn_4(up_4)
        up_4 = self.leaky_relu(up_4)

        up_3 = self.u_deconv_3(up_4)
        up_3 = torch.cat([up_3, skip_3], 1)
        up_3 = self.u_bn_3(up_3)
        up_3 = self.leaky_relu(up_3)

        up_2 = self.u_deconv_2(up_3)
        up_2 = self.u_bn_2(up_2)
        up_2 = self.leaky_relu(up_2)

        up_1 = self.u_deconv_1(up_2)
        up_1 = self.u_bn_1(up_1)
        up_1 = self.leaky_relu(up_1)

        out = self.out_deconv(up_1)
        out = self.out_bn(out)
        out = nn.Sigmoid()(out)

        return out
</code></pre>
<p>and I am using it in my view to restore image which goes like this:</p>
<pre><code class="lang-auto">def restore_image_deep_image_prior(original_image_path):
    lr = 1e-2
    device = 'cpu'
    print('Using {} for computation'.format(device.upper()))

    hg_net = Hourglass()
    hg_net.to(device)
    mse = nn.MSELoss()
    optimizer = optim.Adam(hg_net.parameters(), lr=lr)

    n_iter = 500
    images = []
    losses = []
    to_tensor = tv.transforms.ToTensor()
    z = torch.Tensor(np.mgrid[:542, :347]).unsqueeze(0).to(device) / 512  # Adjust the size according to your requirement

    x = PILImage.open(original_image_path)
    x = to_tensor(x).unsqueeze(0)
    x, mask = pixel_thanos(x, 0.5)
    mask = mask[:, :3, :, :].to(device).float()  # Keep only the first 3 channels if mask has 4 channels
    x = x.to(device)

    for i in range(n_iter):
        optimizer.zero_grad()
        y = hg_net(z)
        loss = mse(x, y*mask)
        losses.append(loss.item())
        loss.backward()
        optimizer.step()
        if i &lt; 1000 and (i+1)%4==0 or i==0:
            with torch.no_grad():
                out = x + y * (1 - mask)
                out = out[0].cpu().detach().permute(1, 2, 0)*255
                out = np.array(out, np.uint8)
                images.append(out)
        if (i+1) % 20 == 0:
            print('Iteration: {} Loss: {:.07f}'.format(i+1, losses[-1]))
    
    restored_image_bytes = image_to_bytes(images)

    return restored_image_bytes

def pixel_thanos(img, p=0.5):
    assert p &gt; 0 and p &lt; 1, 'The probability value should lie in (0, 1)'
    mask = torch.rand(1, 3, 542, 347)
    img[mask &lt; p,] = 0
    mask = mask &gt; p
    mask = mask.repeat(1, 3, 1, 1)
    return img, mask

def image_to_bytes(image):
    buffer = BytesIO()
    image.save(buffer, format="JPEG")
    return buffer.getvalue()
</code></pre>
<p>Now, the problem is I am getting the error listen in the title as well, which in detail, is:</p>
<pre><code class="lang-auto">Traceback (most recent call last):
  File "C:\Users\khubi\AppData\Local\Programs\Python\Python39\lib\site-packages\django\core\handlers\exception.py", line 55, in inner
    response = get_response(request)
  File "C:\Users\khubi\AppData\Local\Programs\Python\Python39\lib\site-packages\django\core\handlers\base.py", line 197, in _get_response
    response = wrapped_callback(request, *callback_args, **callback_kwargs)
  File "C:\Users\khubi\Desktop\Projects\VU\CS619\image restorer\image_restorer\image_app\views.py", line 35, in restore_image
    restored_image = restore_image_deep_image_prior(original_image.image_file.path)
  File "C:\Users\khubi\Desktop\Projects\VU\CS619\image restorer\image_restorer\image_app\views.py", line 76, in restore_image_deep_image_prior
    y = hg_net(z)
  File "C:\Users\khubi\AppData\Local\Programs\Python\Python39\lib\site-packages\torch\nn\modules\module.py", line 1518, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "C:\Users\khubi\AppData\Local\Programs\Python\Python39\lib\site-packages\torch\nn\modules\module.py", line 1527, in _call_impl
    return forward_call(*args, **kwargs)
  File "C:\Users\khubi\Desktop\Projects\VU\CS619\image restorer\image_restorer\image_app\views.py", line 182, in forward
    up_5 = torch.cat([up_5, skip_5], 1)
RuntimeError: Sizes of tensors must match except in dimension 1. Expected size 18 but got size 17 for tensor number 1 in the list.
</code></pre>
<p>I know that I had to set the channels. I set it to 4 as the image which I am using for restoration has 4 channels. But still getting the error. I also took help from ChatGPT but all in vain. Can anybody help me, please?</p>