when i use the nn.DataParallel(model), i got the problem as follows Traceback (most recent call last): File "main.py", line 67, in main(args) File "main.py", line 31, in main _train(model, args) File "MIMO-UNet/train.py", line 53, in _train pred_img = model(input_img) File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 722, in _call_impl result = self.forward(*input, **kwargs) File "/usr/local/lib/python3.6/dist-packages/torch/nn/parallel/data_parallel.py", line 154, in forward replicas = self.replicate(self.module, self.device_ids[:len(inputs)]) File "/usr/local/lib/python3.6/dist-packages/torch/nn/parallel/data_parallel.py", line 159, in replicate return replicate(module, device_ids, not torch.is_grad_enabled()) File "/usr/local/lib/python3.6/dist-packages/torch/nn/parallel/replicate.py", line 88, in replicate param_copies = _broadcast_coalesced_reshape(params, devices, detach) File "/usr/local/lib/python3.6/dist-packages/torch/nn/parallel/replicate.py", line 71, in _broadcast_coalesced_reshape tensor_copies = Broadcast.apply(devices, *tensors) File "/usr/local/lib/python3.6/dist-packages/torch/nn/parallel/_functions.py", line 21, in forward outputs = comm.broadcast_coalesced(inputs, ctx.target_gpus) File "/usr/local/lib/python3.6/dist-packages/torch/cuda/comm.py", line 39, in broadcast_coalesced return torch._C._broadcast_coalesced(tensors, devices, buffer_size) RuntimeError: inputs must be on unique devices

Hello, when I was training MIMO-UNet model, I needed a verification set after 100 training epochs. Could you tell me how to make the verification set? The error is as follows: No such file or directory: 'dataset/GOPRO\valid\blur\'

I encountered some problems when training the Go_Pro dataset on MIMO-UNet++. May I ask the author during the training process, will there be some unknown color blocks in the image generated at certain positions of the image?

Thank you for your great works. I want to evaluate the images using your model with geometric self-ensemble. Is there a testing code with self-ensemble?

Thanks

안녕하십니까,

화질 개선 연구를 진행하고 있는 석사과정 학생입니다. 선배님의 훌륭한 연구에 감사 드립니다. 올려주신 MIMO-UNet 모델을 Realblur-J데이터셋에서 PSNR을 확인한 결과 28.99가 나왔습니다. 제가 성능 평가 방식에 문제가 있는지 확인 해 주실 수 있으십니까? dataset함수는 제가 직접 구현했으며, Reablur-J 데이터셋의 Test_list 파일을 통해 데이터를 불러오는 방식으로 구현했습니다. PSNR은 올려주신 방식과 동일하게 구했으며, 이미지 사이즈가 8의 배수가 될 수 있도록 zero padding후 forward할 수 있도록했습니다. 이후 출력 이미지를 잘라서 Ground truth와 비교하여 평가했습니다.

감사합니다.

Hi,

Thanks for sharing this great work~ I found your implementation of loss here is different from the paper, where the "1/t_k" normalizationweight is not used in your code. Could you help clarify this?

Hello. Thanks for your great work and code. I train it using the Tesla V100 16G, and it takes about 4 hours for 50 epochs. Could you please tell me how long it took you to train for 3000 epochs? Thanks in advance.

In the Page 5 in paper (Figure 5. Several examples on the GoPro test dataset. ) I found the data in the Gopro dataset, the image is “test/blur/GOPR0854_11_00/001653_3.png” right? I don't see the original image as blurry as the image shown in the paper. Were you testing the image with the original Gopro dataset?

In line 53 of eval.py(show as below), the output tensor is turned to PIL image after adding 0.5/255, then is saved. Why is this? This tensor has already been clipped to 0-1. I can't find the same processing way in the official document of torchvision

if args.save_image:
    save_name = os.path.join(args.result_dir, name[0])
    pred_clip += 0.5 / 255
    pred = F.to_pil_image(pred_clip.squeeze(0).cpu(), 'RGB')
    pred.save(save_name)

Hello,

How do you train the model for RealBlur? Do you fine-tune on the GoPro pre-trained weights or train form scratch with GoPro+BSD+RealBlurR datasets like MPRNet? And, do you plan to release the deblurring results of RealBlur_J and RealBlur_R?

When you test SSIM ，did you use the function from skimage.metrics import structural_similarity？And set structural_similarity(p_numpy, label_numpy, data_range=1，multichannel=True)

Hi~ @chosj95 Thanks for sharing your nice work and conducting careful comparison of the runtime performance. I want to compare my algorithm with the existing algorithms shown in the figure, could you please share the code and data for drawing this figure? Thanks a lot!

Hello, author, is your model suitable for mixed image restoration of motion blur and focus blur? Moreover, after training with motion blur data only, I tested the normal and focus blur images, and the results were extremely poor.

Hello, thanks for your excellent work. I have some confusions about the dataset you used for RealBlur. Could you please tell me which dataset(s) did you use to obtain the results in Table 2, both GoPro and RealBlur or just only RealBlur itself. MPRNET only used RealBlur to get the RealBlur results.

Thanks.

Thank you for your great work!

I have a question on evaluation code. In line 53 of eval.py: pred_clip += 0.5 / 255 0.5/255 is added. What does that mean? and the code calculates PSNR with unadded one(pred_numpy). Why is it?

Thank you.

Thank you for sharing your great code. :smiley_cat:

What is the license for this model? I'd like to cite it to the repository I'm working on if possible, but I want to post the license correctly. https://github.com/PINTO0309/PINTO_model_zoo

Thank you.