I have the following code snippet in the train cell:

network = Network()
network.cuda()    

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

loss_min = np.inf
num_epochs = 10

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()
            ##[8, 600, 800] --> [8,3,600,800]
            images = images.unsqueeze(1)
            images = torch.cat((images, images, images), 1)
            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))

And I have the following code in the evaluation cell:
start_time = time.time()

with torch.no_grad():

    best_network = Network()
    best_network.cuda()
    best_network.load_state_dict(torch.load('../moth_landmarks.pth')) 
    best_network.eval()
    
    batch = next(iter(train_loader))
    images, landmarks = batch['image'], batch['landmarks']
        
    images = images.cuda()
    landmarks = (landmarks + 0.5) * 224
    
    ##[8, 600, 800] --> [8,3,600,800]
    images = images.unsqueeze(1)
    images = torch.cat((images, images, images), 1)

    predictions = (best_network(images).cpu() + 0.5) * 224
    predictions = predictions.view(-1,4,2)
    
    plt.figure(figsize=(10,40))
    
    for img_num in range(8):
        plt.subplot(8,1,img_num+1)
        plt.imshow(images[img_num].cpu().numpy().transpose(1,2,0).squeeze(), cmap='gray')
        plt.scatter(predictions[img_num,:,0], predictions[img_num,:,1], c = 'r')
        plt.scatter(landmarks[img_num,:,0], landmarks[img_num,:,1], c = 'g')

print('Total number of test images: {}'.format(len(test_dataset)))

end_time = time.time()
print("Elapsed Time : {}".format(end_time - start_time))

These are the predictions I am getting (the green ones are the ground truth):
predicted_landmarks2034×1010 72.1 KB
I am not sure what is the main reason these predictions are so off and how to fix them.
The line:
    landmarks = (landmarks + 0.5) * 224

is taken from https://thecleverprogrammer.com/2020/07/22/face-landmarks-detection/ 2
This is what I have in the network section:
num_classes = 4 * 2 #4 coordinates X and Y flattened --> 4 of 2D keypoints or landmarks

class Network(nn.Module):
    def __init__(self,num_classes=8):
        super().__init__()
        self.model_name = 'resnet18'
        self.model = models.resnet18()
        self.model.fc = nn.Linear(self.model.fc.in_features, num_classes)
        
    def forward(self, x):
        x = x.float()
        out = self.model(x)
        return out

Additionally, do you know how to apply:
        for i in range(8):
            if(i%2==0):
                landmarks[:,i] = landmarks[:,i]/800
            else:
                landmarks[:,i] = landmarks[:,i]/600

That I have in train to evaluation cell?