Thanks for your awesome work. When i run the trian.py with multi card, I find the tb_logger have a bug with rank > 0. Here, rank is checked. train.py#L125

            if rank <= 0:
                logger.info('Number of val images in [{:s}]: {:d}'.format(
                    dataset_opt['name'], len(val_set)))

But not check in here. train.py#L215

                # tensorboard logger
                if opt['use_tb_logger'] and 'debug' not in opt['name']:
                    tb_logger.add_scalar('psnr', avg_psnr, current_step)

It should change to

                # tensorboard logger
                if rank <= 0 and opt['use_tb_logger'] and 'debug' not in opt['name']:
                    tb_logger.add_scalar('psnr', avg_psnr, current_step)

the train_ClassSR has same bug.

1. 请问一下，论文里“Then we densely crop 1.59M sub-images with size 32 × 32 from LR images. These sub-images are equally divided into three classes (0.53M for each) according to their PSNR values through MSRResNet. ” 这里的1.59M户和0.53M 指的是文件大小还是其他？

2. 20-04-14 00:47:54.197 - INFO: 0558_s290 - PSNR: 26.473503 dB; SSIM: 0.751480; PSNR_Y: 27.808832 dB; SSIM_Y: 0.769544. 20-04-14 00:47:54.248 - INFO: 0558_s291 - PSNR: 26.580721 dB; SSIM: 0.764139; PSNR_Y: 27.929448 dB; SSIM_Y: 0.785679. 20-04-14 00:47:54.300 - INFO: 0558_s292 - PSNR: 25.955706 dB; SSIM: 0.801685; PSNR_Y: 27.237049 dB; SSIM_Y: 0.822988. divide_train.log 里面scale=1的图名字直接是0558_s290，0558_s291。。。。但是extract_subimages_train.py生成的文件夹DIV2K_scale_sub里面的图名字是1_0558_s290，所以在运行divide_sub_images_train.py时会报错

3. 论文里说“To prepare the training data, we first downsample3 the original images with scaling factors 0.6, 0.7, 0.8, 0.9 to generate the HR images.” 这个意思时scale=1的图不参与训练吗？

Thank for the great work.

I have trained on a custom dataset by using ClassSR repo. But not able to find the trained weights after each epochs . Should i wait to complete the entire epochs to get the trained weights ?

Recently, I am impressed by your brilliant and impressive idea and work in this paper:" ClassSR: A General Framework to Accelerate Super-Resolution Networks by Data Characteristic". It groundbreakingly accelerates SR models by classing sub-images, which attracts me a lot.

Therefore, I download the released code on Github, follow the guidance in README.md, and test it. However, the results are weird. Both PSNR and SSIM are acceptable in branches testing, nevertheless, these indexes drop to ~9dB when it comes to the whole model, which confused me a lot.

And when I try to train the model, it shows that there is a lack of LMDB file to handle validating set "Set5" during the training procedure. 2 affixed pictures demonstrate the situation. I also try to read through the above paper several times, but little things are related to this problem.

So in a word, there are 2 problems I need your valuable help with:

1. Is there something wrong when I test the model?

2. How can I tackle LMDB issues?

Thank you for your reading and priceless help!

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Hello, I have now generated sub-images. I want to use the generated sub-images to train my models of different branches. How can I create a divide_train.log file suitable for my model?

https://github.com/Xiangtaokong/ClassSR/blob/0ebefe75b2d5d08cffd8613c78d6dbb323dbd2c5/codes/models/archs/arch_util.py#L103-L114 I face train problems when train class_carn, finally I located the problem in above code. In CARN official repo, I found there are no bn layer, So may be the CARN model is incorrectly implemented?

https://github.com/nmhkahn/CARN-pytorch/blob/master/carn/model/ops.py#L33-L48

E:\Anaconda\envs\torch11\python.exe D:/迅雷下载/ClassSR-main/codes/data_scripts/extract_subimages_test.py process GT... mkdir [D:/迅雷下载/ClassSR-main/data0/xtkong/data/DIV2K_valid_HR_sub/GT] ... [ ] 0/100, elapsed: 0s, ETA: Start... All subprocesses done. process LR... mkdir [D:/迅雷下载/ClassSR-main/data0/xtkong/data/DIV2K_valid_HR_sub/LR] ... [ ] 0/100, elapsed: 0s, ETA: Start... All subprocesses done. Traceback (most recent call last): File "D:/迅雷下载/ClassSR-main/codes/data_scripts/extract_subimages_test.py", line 143, in main() File "D:/迅雷下载/ClassSR-main/codes/data_scripts/extract_subimages_test.py", line 66, in main assert len(data_util._get_paths_from_images(save_GT_folder)) == len( File "D:\迅雷下载\ClassSR-main\codes\data\util.py", line 31, in _get_paths_from_images assert images, '{:s} has no valid image file'.format(path) AssertionError: D:/迅雷下载/ClassSR-main/data0/xtkong/data/DIV2K_valid_HR_sub/GT has no valid image file

NICE WORK！ I train classSR structure to process the denoising task. the branchs adopt small network, which calculation cost is only about 5 to 16 GMAC. There are two problems that bother me： First, the training speed is too slow. I use two 3090 cards to train models and it takes 12 minutes to run 100iter. However, when I increased to four 3090 cards, the speed did not increase, and the utilization rate of each GPU was very low. Could you please give me some suggestions？ Second, because the training is too slow, I used .pth at 30000iter for testing, and the classification is uneven. The image results have a strong sense of demarcation, which is mainly because different patches take different branches.Will this phenomenon be improved in the next training？

您好，Class-Module我已经训练了两万多个Iteration了，但是验证的时候所有的图片还是都只进入一个分支，并没有平均进入三个分支，请问这样可能是什么原因造成的呢，（initial learning rate设置为10-3，the weights w1, w2, w3 are set to 2000,1, 6）

Hi, first of all thanks for your work! I have question about custom data. If I have HR images with resolution 512384 and lowres with 256192, and I want to convert LR->HR, what parameters should I change change in test_ClassSR_RCAN.yml for example apart from scale ( if I change only scale I've got this error:

sr_img[i*step*self.scale:i*step*self.scale+patch_size*self.scale,j*step*self.scale:j*step*self.scale+patch_size*self.scale,:]+=sr_list[index]
ValueError: operands could not be broadcast together with shapes (64,64,3) (128,128,3) (64,64,3) 

Hi, @Xiangtaokong , Thanks very much for your interesting work. Currently I want to reproduce your work. I follow the readme and finish the data processing. while during run training command "python train_ClassSR.py -opt options/train/train_ClassSR_FSRCNN.yml", it seems to be stuck. Can you give me some suggestions?

Hi @Xiangtaokong , a nice work for SR acceleration. I have some questions. Could you help me explain these? Thanks ahead.

• Conflict between the Class loss and Average loss It seems that the Class-loss is designed to teach the Class-Module to predict the probability of branches unevenly, while the Average-loss is trying to teach the Class-Module to predict probability evenly. I think that the Class-Module tries to strike a balance between certain routing and uncertain routing. Could you help me explain this trade off?
• Loss curve during the training Class-Module Besides, during the training, I find it that Class-loss decreases obviously. But Average-loss changes smoothly. Is this obversation normal? Waiting for your reply! Thanks for your time!

您好！我有两个问题需要请教：

1. 请问代码里为什么需要除以sum(num_ress) ?
2. 为什么对FRCNN网络的flops还需要加上7.4 ? 期待您的解答，谢谢！！

def cal_FLOPs(which_model,num_ress):
    if which_model =='classSR_3class_fsrcnn_net':
        flops = ((141 * num_ress[0] + 304 * num_ress[1] + 468 * num_ress[2]) / sum(num_ress))+7.4
        percent=flops/468
    elif which_model=='classSR_3class_carn':
        flops = (0.38 * num_ress[0] + 0.77 * num_ress[1] + 1.15 * num_ress[2]) / sum(num_ress)
        percent = flops / 1.15
    elif which_model=='classSR_3class_srresnet':
        flops = (1.66 * num_ress[0] + 3.44 * num_ress[1] + 5.20 * num_ress[2]) / sum(num_ress)
        percent = flops / 5.20
    elif which_model=='classSR_3class_rcan':
        flops = (10.33 * num_ress[0] + 19.90 * num_ress[1] + 32.60 * num_ress[2]) / sum(num_ress)
        percent = flops / 32.60
    return flops,percent