Hi,Congratulations to you have a good job! Although I haved changed the number of GPUs in train.sh and Deraining_Restormer.yml to 4 since I only have 4 GPUs，I can't train the code of Deraining due to my GPU memory limitations. I found the program can run if I change the batch_size_per_gpu smaller. But the batch size can't meet the experimental settings. So what can I do if I want to achieve the settings in your experiment ( i.e. For progressive learning, we start training with patch size 128×128 and batch size 64. The patch size and batch size pairs are updated to [(160^2,40), (192^2,32), (256^2,16), (320^2,8),(384^2,8)] at iterations [92K, 156K, 204K, 240K, 276K].) ?

I am pleased with your work; the level of completeness is really professional! Do you guys have any plan to release the code for Google Colab? Unfortunately, I can't run the code on my local machine due to some poor factors.

Hi, How are the quantitative results calculated for the other methods in Restormer Table 1? Are you quoting their results directly or are you retraining them?

Looking forward to your reply. Thank you!

I was trying to run training Restormer, and succeed to run it with 128x128 size.

However my GPU memory runs out when trying to train the network with 256x256 size and a batch size larger than 2. My GPU is RTX3080 with 10GB memory.

Do you know how much memory we need to train it on 256x256 size patch and batch size >= 8 ?

Hi.Thank you so much for your open source work. When I trained motion_deblur, I found that the effect in the paper could not be achieved.

1. I followed the dependency tutorial mentioned in the repository ,downloaded the gopro dataset, and used the provided crop method to prepare the training set and validation set.
2. And use the Deblurring_Restormer.yml configuration file for training. In the configuration file I modified to use single GPU training.
3. In another experiment, I modified the training strategy to fix the crop size to 128. But the results of both experiments were less than 31db, which was much lower than the results in the paper. I wonder if details are missing and why the results are so different.

How to solve the error of create_dataloader, create_dataset in init.py in the train.py file？ Also what is the difference between training on basicsr documents and training on specific tasks (e.g. Deraining)?

Dear author，I met a problem when input the code "go get github.com/prasmussen/gdrive"

package golang.org/x/oauth2/google: unrecognized import path "golang.org/x/oauth2/google" (https fetch: Get https://golang.org/x/oauth2/google?go-get=1: dial tcp 172.217.163.49:443: i/o timeout)

I want to know how to solve this.THANKS!

Hi, would you be interested in adding Restormer to Hugging Face Hub? The Hub offers free hosting, and it would make your work more accessible and visible to the rest of the ML community. We can setup an organization or a user account under which restormer can be added similar to github.

Example from other organizations: Keras: https://huggingface.co/keras-io Microsoft: https://huggingface.co/microsoft Facebook: https://huggingface.co/facebook

Example spaces with repos: github: https://github.com/salesforce/BLIP Spaces: https://huggingface.co/spaces/akhaliq/BLIP

github: https://github.com/facebookresearch/omnivore Spaces: https://huggingface.co/spaces/akhaliq/omnivore

and here are guides for adding spaces/models/datasets to your org

How to add a Space: https://huggingface.co/blog/gradio-spaces how to add models: https://huggingface.co/docs/hub/adding-a-model uploading a dataset: https://huggingface.co/docs/datasets/upload_dataset.html

Please let us know if you would be interested and if you have any questions, we can also help with the technical implementation.

well done ! But can you tell me about your denoising-working ,what dataset your used? real training dataset and Gaussian Denoising dataset. Thank you very much!

Since no training code is given, I write my own training program to train Restormer. However, at the beginning of the training, I could only set batchsize to 48 due to the limitation of GPUs memory. However, I found that the loss would hardly decrease when the first 10,000 to 20,000 iteration was carried out, which verified that the PSNR remained unchanged at about 26.2. Is the training relatively slow, or what is the problem? And if prob, I would like to know the upward trend of Val PSNR and the downward trend of loss during your training

Hello,

In the ablation study, you compared deeper vs wider Restormer. I'm wondering about the wider Restormer you mentioned, so could you inform me of the details of it?

Hi ! In lr_scheduler.py, from torch.optim.lr_scheduler import _LRScheduler The message Cannot find reference '_LRScheduler' in 'lr_scheduler.pyi' How can I solve this problem？

I followed the instructions and conducted 2 Gaussian color image denoising experiments where sigma=15 and 50. But I can't reproduce the same PSNR value as paper shows. Here are my results: sigma=15 For CBSD68 dataset Noise Level 15 PSNR: 34.398237 For Kodak dataset Noise Level 15 PSNR: 35.439437 For McMaster dataset Noise Level 15 PSNR: 35.556497 For Urban100 dataset Noise Level 15 PSNR: 35.058984 sigma=50 For CBSD68 dataset Noise Level 50 PSNR: 28.586302 For Kodak dataset Noise Level 50 PSNR: 29.967525 For McMaster dataset Noise Level 50 PSNR: 30.237451 For Urban100 dataset Noise Level 50 PSNR: 29.891585

Did I miss some important details?

Hi, Did you retrain the IFAN on DPDD? IFAN only provided the results from 8bit images, which is inconsistent with the results in this paper.
I guess you have retrained IFAN. If convenient, could you please provide the test pictures?

Thank you very much!

RuntimeError: NCCL error in: /opt/conda/conda-bld/pytorch_1616554786529/work/torch/lib/c10d/ProcessGroupNCCL.cpp:33, unhandled cuda error, NCCL version 2.7.8 ncclUnhandledCudaError: Call to CUDA function failed.

How can i fix it????? Plz help!

Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed.

Hi, there is a question about the Gaussian color image denoising results on the Kodak24 dataset. I have downloaded the provided pre-trained models and use them for testing, under the provided code base and environment. However, I can not get the similar results on Kodak24 as you have reported in Table 5 of the main paper. In fact, I get lower PSNR values of testing on Kodak24 (e,g,. -0.12 dB for sigma15, -0.11 dB of sigma25, -0.14 dB of sigma 50). Can you give some explanations or suggestions? Thanks very much.