- 👋 Hi, I’m @z-bingo
- 👀 I’m interested in computer vision especially its applications on autonomous driving
z-bingo / fastdvdnet Goto Github PK
View Code? Open in Web Editor NEWAn unoffical implement of FastDVDNet by PyTorch
License: MIT License
An unoffical implement of FastDVDNet by PyTorch
License: MIT License
Hi, first of all, thank you for sharing your codes.
I have run them and I have an issue.
I have found some artifacts in the result images as you have mentioned.
Have you found out why these artifacts are occurred?
and can you tell me the average evaluation PSNR (with vimeo 90k) please?
It would be great if you give me some help.
thanks.
C:\Users\Win10\Desktop\unet-master\venv\Scripts\python.exe C:/Users/Win10/Desktop/unet-master/FastDVD-master/train_eval.py
Namespace(batch_size=64, cuda=False, dataset_path='F:/JPY/vimeo_triplet/sequences', eval=False, frames=2, im_size=96, learning_rate=0.0001, max_epoch=100, num_worker=4, restart=False, txt_path='F:/JPY/vimeo_triplet')
There is no any model to load, restart the train process.
Traceback (most recent call last):
File "C:/Users/Win10/Desktop/unet-master/FastDVD-master/train_eval.py", line 200, in
train(args)
File "C:/Users/Win10/Desktop/unet-master/FastDVD-master/train_eval.py", line 92, in train
for iter, (data, gt) in enumerate(data_loader):
File "C:\Users\Win10\Desktop\unet-master\venv\lib\site-packages\torch\utils\data\dataloader.py", line 521, in next
data = self._next_data()
File "C:\Users\Win10\Desktop\unet-master\venv\lib\site-packages\torch\utils\data\dataloader.py", line 1203, in _next_data
return self._process_data(data)
File "C:\Users\Win10\Desktop\unet-master\venv\lib\site-packages\torch\utils\data\dataloader.py", line 1229, in _process_data
data.reraise()
File "C:\Users\Win10\Desktop\unet-master\venv\lib\site-packages\torch_utils.py", line 425, in reraise
raise self.exc_type(msg)
FileNotFoundError: Caught FileNotFoundError in DataLoader worker process 0.
Original Traceback (most recent call last):
File "C:\Users\Win10\Desktop\unet-master\venv\lib\site-packages\torch\utils\data_utils\worker.py", line 287, in _worker_loop
data = fetcher.fetch(index)
File "C:\Users\Win10\Desktop\unet-master\venv\lib\site-packages\torch\utils\data_utils\fetch.py", line 44, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "C:\Users\Win10\Desktop\unet-master\venv\lib\site-packages\torch\utils\data_utils\fetch.py", line 44, in
data = [self.dataset[idx] for idx in possibly_batched_index]
File "C:\Users\Win10\Desktop\unet-master\FastDVD-master\data_provider.py", line 50, in getitem
img = Image.open(file)
File "C:\Users\Win10\Desktop\unet-master\venv\lib\site-packages\PIL\Image.py", line 2912, in open
fp = builtins.open(filename, "rb")
FileNotFoundError: [Errno 2] No such file or directory: 'F:/JPY/vimeo_triplet/sequences\00068/0821\im4.png'
Process finished with exit code 1
Well, the denoising results seems good but you may wonder why the artefact in you results?
In contrast to the original paper implementation of original author (github), you simply did not have nn.PixelShuffle
.
What does nn.PixelShuffle
do you ask? In short it which helps reducing gridding artefacts stated by Krizhevsky et al..
I can push a fix but with original author (github)
already available and accurate, I won't bother to push. But it's good in case you didn't know
Hi, z-bingo, have you compared the psnr results between the model you trained and the model provided by author? As I see lots of artifacts in your results.
excuse me, can you tell how to denoise my own video?I am a little confused, thanks
Hi, don't you think your setting of feature maps number have some problems?
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.