Create custom convolutional Loss function that only takes parts of the tensor

Pia_Ludemann: <p>Hi everyone,<br>
I have a convolutional network that gets images, but also a colored border on each image for additional information input to the network. Now I want to calculate the loss, but the usual loss function will also take the predicted border into account. The border is completely random, and is just an input to the system. I don’t want the model to think it has performed badly, when it predicted the wrong color.<br>
How can I get the smaller images out of the stacked tensor list.<br>
In particular, I have 6 images, converted to a tensor ( img_tensor = ToTensor()(img).float() ) and put in a list: imgs.append(img_tensor). So all 6 images as tensor in a list. Then this is going to be a tensor too: imgs = torch.stack(imgs, dim=0)<br>
This happens in the DataLoader.<strong>getitem</strong>:</p>
<pre><code class="lang-auto">def __getitem__(self, index):
        path = self.input_data[index]
        imgs_path = sorted(glob.glob(path + '/*.png'))
        #read light conditions
        lightConditions = []
        with open(path +"/lightConditions.json", 'r') as file:
            lightConditions = json.load(file)
        #shift light conditions
        lightConditions.pop(0)
        lightConditions.append(False)
        frameNumber = 0
        imgs = []
        for img_path in imgs_path:
            img = cv2.imread(img_path)
            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
            im_pil = Image.fromarray(img)
            #img = cv2.resize(img, (256,448))
            if lightConditions[frameNumber] ==False:
                imgBorder = ImageOps.expand(im_pil,border = 6, fill='black')
            else:
                imgBorder = ImageOps.expand(im_pil, border = 6, fill='orange')
            img = np.asarray(imgBorder)
            img = cv2.resize(img, (256,448))
            #img = cv2.resize(img, (0, 0), fx=0.5, fy=0.5, interpolation=cv2.INTER_CUBIC) #has been 0.5 for official data, new is fx = 2.63 and fy = 2.84

            img_tensor = ToTensor()(img).float()
            imgs.append(img_tensor)
            frameNumber +=1
        imgs = torch.stack(imgs, dim=0)
        return imgs
</code></pre>
<p>In the training, the stacked 6 image tensor is read and divided in 3 images to use as an input and 3 as targets.</p>
<pre><code class="lang-auto">for idx_epoch in range(startEpoch, nEpochs):

        #set epoch in dataloader for right shuffle -&gt;set seed really random
        val_loader.sampler.set_epoch(idx_epoch)
        #Remember time for displaying time for epoch
        startTimeEpoch = datetime.now()
        i = 0
        if processGPU==0:
            running_loss = 0
        beenValuated = False
        for index, data_sr in enumerate(train_loader):
            #Transfer Data to GPU but don't block other processes because this only effects this single process
            data_sr = data_sr.cuda(processGPU, non_blocking=True)

            startTimeIteration = time.time()
            #Remove all dimensions of size 1
            data_sr = data_sr.squeeze()
            # calculate the index of the input images and GT images
            num_f = len(data_sr)
            #If model_type is 0 -&gt; only calculate one frame that is marked with gt
            if cfg.model_type == 0:
                idx_start = random.randint(-2, 2)
                idx_all = list(np.arange(idx_start, idx_start + num_f).clip(0, num_f - 1))
                idx_gt = [idx_all.pop(int(num_f / 2))]
                idx_input = idx_all
            #Else when model_type is 1 then input frames 1,2,3 and predict frame 4 to number of cfg.dec_frames. Set all images that will be predicted to 'gt' images
            else:
                idx_all = np.arange(0, num_f)
                idx_input = list(idx_all[0:4])
                idx_gt = list(idx_all[4:4+cfg.dec_frames])
            imgs_input = data_sr[idx_input]
            imgs_gt = data_sr[idx_gt]

            # get predicted result
            imgs_pred = model(imgs_input)
</code></pre>
<p>so, we use cfg.model_type = 1. This model will give me new images with also a colored border. And usually here follows a loss calculation:</p>
<pre><code class="lang-auto">loss = criterion_mse(imgs_pred, imgs_gt)
</code></pre>
<p>But I can no longer use this. How to get only the inside of the images.</p>

ptrblck: <p>By default the loss function will return a reduced loss (<code>mean</code> in the default setup).<br>
You could use <code>reduction='none'</code>, return the unreduced loss, mask or index it, calculate the loss, reduce it, and call <code>backward()</code> of this new loss. This approach would allow you to ignore the “border” pixels.<br>
To do so you would need to keep the border indices around in order to be able to reuse them in the last masking step.</p>

Pia_Ludemann: <p>Thank you for your answer! Is it possible to say:</p>
<pre><code class="lang-auto">loss = criterion_mse(imgs_pred[:,:,21:-21,36:-36], imgs_gt[:,:,21:-21,36:-36])
</code></pre>
<p>if the images are 256 in height and 448 in width and the border is 21 on the top and bottom and 36 on the left and right? I tested, if it is valid syntax, and it seems to be.</p>

ptrblck: <p>Yes, your approach should work and will ignore the unused indices.</p>