创建仅采用部分张量的自定义卷积损失函数

Question

我有一个获取图像的卷积网络，而且每个图像上还有一个彩色边框，用于向网络输入额外信息。现在我想计算损失，但通常的损失函数也会考虑预测的边界。边界是完全随机的，只是系统的输入。我不希望模型在预测错误颜色时认为它表现不佳。这发生在 DataLoader.getitem:

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

然后在训练中完成：

for idx_epoch in range(startEpoch, nEpochs):

        #set epoch in dataloader for right shuffle ->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 -> 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)

我使用 cfg.model_type = 1。这个模型会给我新的图像，也有彩色边框。通常这里会进行损失计算：

loss = criterion_mse(imgs_pred, imgs_gt)

但是我不能再使用这个了。有谁知道如何编写仅考虑张量的某些部分或张量中的哪些部分代表哪些图像的自定义损失函数？

Answer 1

您可以像在 numpy 中一样对张量进行切片。图像批次的维度是 NCHW。如果 b 是你的边框大小，并且它从各个方面都是对称的，那么只需 crop 张量：

loss = criterion_mse(imgs_pred[:, :, b:-b, b:-b] , imgs_gt[:, :, b:-b, b:-b])

创建仅采用部分张量的自定义卷积损失函数

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

image-processing

pytorch

tensor

loss

mse