In this code:
network = Network()
network.cuda()    

criterion = nn.MSELoss()
optimizer = optim.Adam(network.parameters(), lr=0.0001)

loss_min = np.inf
num_epochs = 1

start_time = time.time()
for epoch in range(1,num_epochs+1):
    
    loss_train = 0
    loss_test = 0
    running_loss = 0
    
    
    network.train()
    print('size of train loader is: ', len(train_loader))

    for step in range(1, len(train_loader)+1):

        
        batch = next(iter(train_loader))
        images, landmarks = batch['image'], batch['landmarks']
        print(images.shape)
       
        images = images.unsqueeze_(1)

        images = torch.cat((images,images,images),1)
        images = images.cuda()
    
        landmarks = landmarks.view(landmarks.size(0),-1).cuda() 
        norm_image = transforms.Normalize(0.3812, 0.1123) 
        for image in images:
            image = image.float()
            ##image = to_tensor(image) #TypeError: pic should be PIL Image or ndarray. Got <class 'torch.Tensor'>
            image = norm_image(image)
        
        ###removing landmarks normalize because of the following error
        ###ValueError: Expected tensor to be a tensor image of size (C, H, W). Got tensor.size() = torch.Size([8, 8])
       
         
        for i in range(8):
            if(i%2==0):
                landmarks[:,i] = landmarks[:,i]/800
            else:
                landmarks[:,i] = landmarks[:,i]/600
                
        print(landmarks.shape)
        print(landmarks)
        
        

        
        norm_landmarks = transforms.Normalize(0.4949, 0.2165)
        landmarks [landmarks != landmarks] = 0
        landmarks = landmarks.unsqueeze_(0)
        landmarks = norm_landmarks(landmarks)
        
        predictions = network(images)
        
        # clear all the gradients before calculating them
        optimizer.zero_grad()
        
        print('predictions are: ', predictions.float())
        print('landmarks are: ', landmarks.float())
        # find the loss for the current step
        loss_train_step = criterion(predictions.float(), landmarks.float())
        
        
        loss_train_step = loss_train_step.to(torch.float32)
        print("loss_train_step before backward: ", loss_train_step)
        
        # calculate the gradients
        loss_train_step.backward()
        
        # update the parameters
        optimizer.step()
        
        print("loss_train_step after backward: ", loss_train_step)

        
        loss_train += loss_train_step.item()
        
        print("loss_train: ", loss_train)
        running_loss = loss_train/step
        print('step: ', step)
        print('running loss: ', running_loss)
        
        print_overwrite(step, len(train_loader), running_loss, 'train')
        
    network.eval() 
    with torch.no_grad():
        
        for step in range(1,len(test_loader)+1):
            
            batch = next(iter(train_loader))
            images, landmarks = batch['image'], batch['landmarks']
            images = images.cuda()
            landmarks = landmarks.view(landmarks.size(0),-1).cuda()
        
            predictions = network(images)

            # find the loss for the current step
            loss_test_step = criterion(predictions, landmarks)

            loss_test += loss_test_step.item()
            running_loss = loss_test/step

            print_overwrite(step, len(test_loader), running_loss, 'Validation')
    
    loss_train /= len(train_loader)
    loss_test /= len(test_loader)
    
    print('\n--------------------------------------------------')
    print('Epoch: {}  Train Loss: {:.4f} Valid Loss: {:.4f}'.format(epoch, loss_train, loss_test))
    print('--------------------------------------------------')
    
    if loss_test < loss_min:
        loss_min = loss_test
        torch.save(network.state_dict(), '../moth_landmarks.pth') 
        print("\nMinimum Valid Loss of {:.4f} at epoch {}/{}".format(loss_min, epoch, num_epochs))
        print('Model Saved\n')
     
print('Training Complete')
print("Total Elapsed Time : {} s".format(time.time()-start_time))

I get an output like this (only 1 epoch and batch size 8):
predictions are:  tensor([[ 0.7045, -0.3278,  1.5776, -0.4021, -0.4360, -0.3449,  0.3101,  0.1729],
        [-0.1054, -0.8457,  1.1455, -1.4383, -0.2255, -1.5432,  0.4840,  0.2633],
        [ 0.6063, -0.3380,  1.5276, -0.1688, -0.4002, -0.4386,  0.2235,  0.2763],
        [ 0.7645, -0.3076,  1.5969, -0.0705, -0.4203, -0.1109,  0.3278,  0.2545],
        [ 0.0870, -0.7487,  1.3689, -0.9824, -0.5139, -1.0490,  0.1499,  0.2277],
        [ 0.3001, -0.5763,  1.4895, -0.5979, -0.5189, -0.7502,  0.1075,  0.1689],
        [ 0.5470, -0.4144,  1.4193, -0.7669, -0.3635, -0.8172,  0.4247,  0.2369],
        [ 0.5765, -0.4259,  1.7669, -0.2599, -0.3736, -0.4769,  0.4189,  0.1796]],
       device='cuda:0', grad_fn=<AddmmBackward>)
landmarks are:  tensor([[[ 0.5227, -0.4615,  1.6575, -0.1304, -0.5076, -0.0149,  0.1815,
           0.0021],
         [ 0.6125, -0.4273,  1.2807, -1.3253, -0.2574, -1.2542,  0.6864,
           0.1575],
         [ 0.5452, -0.4067,  1.7557,  0.0543, -0.4961, -0.3306,  0.1323,
           0.4306],
         [ 0.5908, -0.4366,  1.7557,  0.1390, -0.5192,  0.1313,  0.6529,
           0.0236],
         [ 0.5366, -0.4232,  1.5478, -0.7771, -0.6289, -0.7463,  0.2288,
           0.3177],
         [ 0.5598, -0.4129,  1.7210, -0.4999, -0.5711, -0.4229,  0.1136,
           0.0983],
         [ 0.5255, -0.4495,  1.5651, -0.4999, -0.5711, -0.8463,  0.4566,
           0.1621],
         [ 0.6070, -0.4085,  1.8885, -0.2921, -0.6289, -0.1843,  0.6356,
           0.1390]]], device='cuda:0')
loss_train_step before backward:  tensor(0.0436, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward:  tensor(0.0436, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train:  12.310782719403505
step:  90
running loss:  0.13678647466003896
Train Steps: 90/90  Loss: 0.1368 

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-31-c95badccd7c5> in <module>
    101             landmarks = landmarks.view(landmarks.size(0),-1).cuda()
    102 
--> 103             predictions = network(images)
    104 
    105             # find the loss for the current step

~/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
    720             result = self._slow_forward(*input, **kwargs)
    721         else:
--> 722             result = self.forward(*input, **kwargs)
    723         for hook in itertools.chain(
    724                 _global_forward_hooks.values(),

<ipython-input-10-46116d2a7101> in forward(self, x)
     10     def forward(self, x):
     11         x = x.float()
---> 12         out = self.model(x)
     13         return out

~/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
    720             result = self._slow_forward(*input, **kwargs)
    721         else:
--> 722             result = self.forward(*input, **kwargs)
    723         for hook in itertools.chain(
    724                 _global_forward_hooks.values(),

~/anaconda3/lib/python3.7/site-packages/torchvision/models/resnet.py in forward(self, x)
    218 
    219     def forward(self, x):
--> 220         return self._forward_impl(x)
    221 
    222 

~/anaconda3/lib/python3.7/site-packages/torchvision/models/resnet.py in _forward_impl(self, x)
    201     def _forward_impl(self, x):
    202         # See note [TorchScript super()]
--> 203         x = self.conv1(x)
    204         x = self.bn1(x)
    205         x = self.relu(x)

~/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
    720             result = self._slow_forward(*input, **kwargs)
    721         else:
--> 722             result = self.forward(*input, **kwargs)
    723         for hook in itertools.chain(
    724                 _global_forward_hooks.values(),

~/anaconda3/lib/python3.7/site-packages/torch/nn/modules/conv.py in forward(self, input)
    417 
    418     def forward(self, input: Tensor) -> Tensor:
--> 419         return self._conv_forward(input, self.weight)
    420 
    421 class Conv3d(_ConvNd):

~/anaconda3/lib/python3.7/site-packages/torch/nn/modules/conv.py in _conv_forward(self, input, weight)
    414                             _pair(0), self.dilation, self.groups)
    415         return F.conv2d(input, weight, self.bias, self.stride,
--> 416                         self.padding, self.dilation, self.groups)
    417 
    418     def forward(self, input: Tensor) -> Tensor:

RuntimeError: Expected 4-dimensional input for 4-dimensional weight [64, 3, 7, 7], but got 3-dimensional input of size [8, 600, 800] instead

1

start_time = time.time()

2

​

Should I do something like RuntimeError: Given groups=1, weight of size [64, 3, 7, 7], expected input[1, 8, 600, 800] to have 3 channels, but got 8 channels instead here too?