RuntimeError: The size of tensor a (4144) must match the size of tensor b (256) at non-singleton dimension 3 site:stackoverflow.com
I am training a generator network with an image size (3, 256, 256). The network is as shown in the below
# Number of channels in the training images. For color images this is 3
nc = 3

# Size of z latent vector (i.e. size of generator input)
nz = 3

# Size of feature maps in generator
ngf = 64

class Generator(nn.Module):
    def __init__(self):
        super(Generator, self).__init__()
        self.main = nn.Sequential(
            # input is Z, going into a convolution
            nn.ConvTranspose2d( nz, ngf * 8, 4, 1, 0, bias=False),
            nn.BatchNorm2d(ngf * 8),
            nn.ReLU(True),
            # state size. (ngf*8) x 4 x 4
            nn.ConvTranspose2d(ngf * 8, ngf * 4, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ngf * 4),
            nn.ReLU(True),
            # state size. (ngf*4) x 8 x 8
            nn.ConvTranspose2d( ngf * 4, ngf * 2, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ngf * 2),
            nn.ReLU(True),
            # state size. (ngf*2) x 16 x 16
            nn.ConvTranspose2d( ngf * 2, ngf, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ngf),
            nn.ReLU(True),
            # state size. (ngf) x 32 x 32
            nn.ConvTranspose2d( ngf, nc, 4, 2, 1, bias=False),
            nn.Tanh()
            # state size. (nc) x 64 x 64
        )

    def forward(self, input):
        return self.main(input)

net_input_saved = net_input.detach().clone()
noise = net_input.detach().clone()
out_avg = None
last_net = None
psrn_noisy_last = 0
loss = []
psnr_noise = []
psnr_ground = []

i = 0
def closure():
    
    global i, out_avg, psrn_noisy_last, last_net, net_input, loss
    
    if reg_noise_std &gt; 0:
        net_input = net_input_saved + (noise.normal_() * reg_noise_std) #changing the input to the netwok
    
    out = net(net_input)
    
    # Smoothing
    if out_avg is None:
        out_avg = out.detach()
    else:
        out_avg = out_avg * exp_weight + out.detach() * (1 - exp_weight) # calculating average network output
            
    total_loss = mse(out, img_noisy_torch)
    total_loss.backward()

    loss.append(total_loss.item())
        
    
    # caculating psrn
    psrn_noisy = compare_psnr(img_noisy_np, out.detach().cpu().numpy()[0]) # comparing psnr for the output image and the actual noisy image 
    psrn_gt    = compare_psnr(img_noisy_np, out.detach().cpu().numpy()[0]) # comparing psnr for the output image and the original image 
    psrn_gt_sm = compare_psnr(img_np, out_avg.detach().cpu().numpy()[0]) # comparing psnr for the output average and the original image 
    
    psnr_noise.append(psrn_noisy)
    psnr_ground.append(psrn_gt)

    if  PLOT and i % show_every == 0:
        out_np = torch_to_np(out)
        # plotting the output image along the average image calculated
        print(f'\n\nAfter {i} iterations: ')
        print ('Iteration %05d    Loss %f   PSNR_noisy: %f   PSRN_gt: %f   PSNR_gt_sm: %f' % (i, total_loss.item(), psrn_noisy, psrn_gt, psrn_gt_sm), '\r', end='\n')
        plot_image_grid([np.clip(out_np, 0, 1), 
                         np.clip(torch_to_np(out_avg), 0, 1)], factor=figsize, nrow=1)
        
        
    
    # Backtracking
    if i % show_every:
        if psrn_noisy - psrn_noisy_last &lt; -5: 
            print('Falling back to previous checkpoint.')

            for new_param, net_param in zip(last_net, net.parameters()):
                net_param.data.copy_(new_param.cuda())

            return total_loss*0
        else:
            last_net = [x.detach().cpu() for x in net.parameters()]
            psrn_noisy_last = psrn_noisy
            
    i += 1

    return total_loss

p = get_params(OPT_OVER, net, net_input)
optimize(OPTIMIZER, p, closure, LR, num_iter)

when i try to train i am getting error
/usr/local/lib/python3.7/dist-packages/torch/nn/modules/loss.py:528: UserWarning: Using a target size (torch.Size([1, 3, 256, 256])) that is different to the input size (torch.Size([1, 3, 4144, 4144])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
  return F.mse_loss(input, target, reduction=self.reduction)
---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
&lt;ipython-input-83-ed04cb48a6ab&gt; in &lt;module&gt;()
     67 
     68 p = get_params(OPT_OVER, net, net_input)
---&gt; 69 optimize(OPTIMIZER, p, closure, LR, num_iter)

5 frames
/content/utils/common_utils.py in optimize(optimizer_type, parameters, closure, LR, num_iter)
    227         for j in range(num_iter):
    228             optimizer.zero_grad()
--&gt; 229             closure()
    230             optimizer.step()
    231     else:

&lt;ipython-input-83-ed04cb48a6ab&gt; in closure()
     25         out_avg = out_avg * exp_weight + out.detach() * (1 - exp_weight) # calculating average network output
     26 
---&gt; 27     total_loss = mse(out, img_noisy_torch)
     28     total_loss.backward()
     29 

/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
   1049         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
   1050                 or _global_forward_hooks or _global_forward_pre_hooks):
-&gt; 1051             return forward_call(*input, **kwargs)
   1052         # Do not call functions when jit is used
   1053         full_backward_hooks, non_full_backward_hooks = [], []

/usr/local/lib/python3.7/dist-packages/torch/nn/modules/loss.py in forward(self, input, target)
    526 
    527     def forward(self, input: Tensor, target: Tensor) -&gt; Tensor:
--&gt; 528         return F.mse_loss(input, target, reduction=self.reduction)
    529 
    530 

/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py in mse_loss(input, target, size_average, reduce, reduction)
   3087         reduction = _Reduction.legacy_get_string(size_average, reduce)
   3088 
-&gt; 3089     expanded_input, expanded_target = torch.broadcast_tensors(input, target)
   3090     return torch._C._nn.mse_loss(expanded_input, expanded_target, _Reduction.get_enum(reduction))
   3091 

/usr/local/lib/python3.7/dist-packages/torch/functional.py in broadcast_tensors(*tensors)
     71     if has_torch_function(tensors):
     72         return handle_torch_function(broadcast_tensors, tensors, *tensors)
---&gt; 73     return _VF.broadcast_tensors(tensors)  # type: ignore[attr-defined]
     74 
     75 

RuntimeError: The size of tensor a (4144) must match the size of tensor b (256) at non-singleton dimension 3

I understood this error is coming from mismatch of sizes in tensors, but i was unable to rectify the error.

I am providing noise z of size torch.Size([1, 3, 256, 256]) same as the dimensions of the input image to the network but i am getting error.


I think the problem is the net_input before going through the model is not size=(1,3,256,256) instead (1,3,4144,4144). Try resize net_input.
