igitugraz / weatherdiffusion Goto Github PK
View Code? Open in Web Editor NEWCode for "Restoring Vision in Adverse Weather Conditions with Patch-Based Denoising Diffusion Models" [TPAMI 2023]
License: MIT License
Code for "Restoring Vision in Adverse Weather Conditions with Patch-Based Denoising Diffusion Models" [TPAMI 2023]
License: MIT License
Hello, can you please provide the image results on the test datasets so that I can do some visual comparsion with other methods?
Thank you for your amazing work. I noticed that you trained all models for about 2, 000, 000 iterations. It may takes too much time to train the model from scratch. So can you release some checkpoints or provide some training details such as hardware and training time?
Thank you for your wonderful work. I would like to study this paper in more detail. Where can I find the supplementary materials?
Dear authors, thank you for sharing your codes for the community, I have a question about the model training, is it nomarl that the model crashes during the training, as I tried to reproduct the model on derain and desnow datasets, but during certain iters, as 800000, the visual results shows like pure noise or pure black. Should I wait more iters or is there any error in my reproduction? Thank you very much
Thanks for your impressive work which is inspiring. I would like to ask when the code will be released.
Many thanks again!
Is there a way to run the derain operation on my own images? The examples show how to evaluate your model, but I don't see how to just run derain on other images.
I'm also not sure where to get the appropriate weights. I found the Allweather 64|128 files, but not the allweather.txt file, despite what it says in #10 .
Thanks!
Hi, I don't see a step in your code about calculating the metrics for validation, so how did you choose the best model?
Also it takes about 2 minutes to recover a single snow image on 3090 and there are 16000+ images in the transweather snow test set which takes a lot of time, I was wondering if you tested on all the data or just selected some of it?
Thanks for your interesting work! I find loss shocks violently,so i would like to know how we can prove that the network has converged and stop training.(maybe some steps perform so poor that some loss is huge?)
I would like to follow your work, and need the checkpoints to test on new datasets. But there are only checkpoints of
In the Outdoor-Rain dataset, There seems to be an overlap between the training dataset and the test dataset in a data?
Are you split the dataset as default?
Best,
Thanks for your interesting and amazing work. I wanna to compare the results of two different imagesize network and use them as the pre-trained weights. But I only find the WeatherDiff64 model weights in the readme. So can you provide the WeatherDiff128 model weights? Thank you!
Hi,
Thanks for sharing your great work! we wonder about some details of the evaluation metrics, i.e., which task is evaluated on Y channel.
Best,
Josh
Thank you so much for your awesome work! I would like to ask when the code will be released.
Best wishes.
I am in the process of training my own dataset and found that after 200000 iterations, the loss does not show a decreasing trend. May I ask if this is normal?
Thanks for your interesting work! I would like to know, how long does it take to train WeatherDiff64?
Thank you for your patch-based DDM. But I have a question. What's the difference between 'mean estimated noise' and 'mean reconstructed image' during sampling? I think they should be the same. Because Eq. (19) in your paper is linear with respect to
thanks
I was trying to test the model but there lacks information about how to set up the test dataset. (especially the allweather.txt
and raindroptesta.txt
file).
Could you please add some instructions on how to set it up. Thanks!
Could you upload the test datasets? Because I can't find them in TransWeather
Thank you for your wonderful work.Could you provide train bash or correct my bash? I find some color differences when I use your code to train other images.Thus, I want to check if this is something wrong with train bash.
my train bash:
CUDA_VISIBLE_DEVICES=1,2 python train_diffusion.py --config "allweather.yml" --image_folder='results/all_weather'
Dear author
I'd like to ask you three questions
Most importantly, the training code seems to be incomplete (for example, the training process does not match the description in the paper, especially regarding patch). Is there a more sound training code? Thank you very much.
Second, are data_transform and inverse_data_transform necessary?
Finally, when I was training 128*128 pictures on Nvidia 3090, a single card could actually run the batch of 24 data. Is this normal?
I'm interest to raindrop removal problem for my bachelor thesis. Could you provide the RainDropDiff128 model weight? Thank you in advance and thanks for this useful work. If you have more time, if you don't mind, could you provide RainDropDiff64 too?
Furthermore, the urgency is, unlike RainHazeDiff and DeSnowDiff that are still enough to use WeatherDiff, in RainDrop case, RainDropDiff is more powerful.
Hi. first,Thank you for releasing the code.
When executing the code in sampling, there is an error, so I ask you a question.
When you enter the model, the size of the tensor is halved. Therefore, the index value does not match.
ex) model(x) ::: tensor[64,6,64,64] ==> tensor[32,3,64,64]
I don't know why 0 dimensions are halved.
Using device: cuda
Note: Currently supports evaluations (restoration) when run only on a single GPU!
=> using dataset 'AllWeather'
=> evaluating snowtest100K-L...
=> creating denoising-diffusion model with wrapper...
scratch/ozan/ckpts/AllWeather_ddpm.pth.tar
=> loaded checkpoint 'scratch/ozan/ckpts/AllWeather_ddpm.pth.tar' (epoch 209, step 470000)
starting processing from image ['beautiful_smile_00003']
Traceback (most recent call last):
File "eval_diffusion.py", line 83, in
main()
File "eval_diffusion.py", line 79, in main
model.restore(val_loader, validation=args.test_set, r=args.grid_r)
File "C:\Users\PIAI\Desktop\WeatherDiffusion\models\restoration.py", line 36, in restore
x_output = self.diffusive_restoration(x_cond, r=r)
File "C:\Users\PIAI\Desktop\WeatherDiffusion\models\restoration.py", line 45, in diffusive_restoration
x_output = self.diffusion.sample_image(x_cond, x, patch_locs=corners, patch_size=p_size)
File "C:\Users\PIAI\Desktop\WeatherDiffusion\models\ddm.py", line 194, in sample_image
xs = utils.sampling.generalized_steps_overlapping(x, x_cond, seq, self.model, self.betas, eta=0.,
File "C:\Users\PIAI\Desktop\WeatherDiffusion\utils\sampling.py", line 74, in generalized_steps_overlapping
et_output[0, :, hi:hi + p_size, wi:wi + p_size] += outputs[idx]
IndexError: index 32 is out of bounds for dimension 0 with size 32
Hello author! Thank you very much for your contribution! I came across your paper and now I would like to dive into the following experiments, but I am a novice, what are the instructions needed if I train your model? Or is there anything I need to prepare in advance? Thanks!
Hi @oozdenizci , thanks for your amazing work!
I would like to ask where I can find the test set Test1 corresponding to Outdoor-Rain, because I did not find the test set in the official github of Outdoor-Rain.
Looking forward to your reply!
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.