sohyun-l / fifo Goto Github PK
View Code? Open in Web Editor NEW[CVPR 2022 Oral, Best Paper Finalist] Official PyTorch implementation of FIFO
License: Other
[CVPR 2022 Oral, Best Paper Finalist] Official PyTorch implementation of FIFO
License: Other
Hi,
Thank you for sharing the code of your work.
I followed the training process on NVIDIA RTX A6000 GPU as below:
$ python main.py --file-name 'FIFO_model' --restore-from '.pretrained/Cityscapes_pretrained_model.pth' --restore-from-fogpass './pretrained/FogPassFilter_pretrained.pth' --modeltrain 'train'
And I got result of evaluation on Foggy Zurich, Foggy Driving Dense, and Cityscapes Lindau:
pth name: FIFO2000.pth, Foggy Zurich: 39.56 Foggy Driving Dense: 38.16 Foggy Driving: 47.57 Cityscapes lindau: 67.06
pth name: FIFO4000.pth, Foggy Zurich: 41.91 Foggy Driving Dense: 35.25 Foggy Driving: 47.07 Cityscapes lindau: 68.89
pth name: FIFO6000.pth, Foggy Zurich: 42.66 Foggy Driving Dense: 36.4 Foggy Driving: 46.3 Cityscapes lindau: 68.45
pth name: FIFO8000.pth, Foggy Zurich: 44.93 Foggy Driving Dense: 37.8 Foggy Driving: 47.38 Cityscapes lindau: 60.95
pth name: FIFO10000.pth, Foggy Zurich: 42.11 Foggy Driving Dense: 36.93 Foggy Driving: 47.48 Cityscapes lindau: 67.99
pth name: FIFO12000.pth, Foggy Zurich: 44.97 Foggy Driving Dense: 38.88 Foggy Driving: 48.65 Cityscapes lindau: 63.42
pth name: FIFO14000.pth, Foggy Zurich: 43.95 Foggy Driving Dense: 36.22 Foggy Driving: 46.08 Cityscapes lindau: 68.48
pth name: FIFO16000.pth, Foggy Zurich: 43.45 Foggy Driving Dense: 36.41 Foggy Driving: 46.17 Cityscapes lindau: 72.79
pth name: FIFO18000.pth, Foggy Zurich: 45.52 Foggy Driving Dense: 39.41 Foggy Driving: 47.76 Cityscapes lindau: 67.81
pth name: FIFO20000.pth, Foggy Zurich: 43.78 Foggy Driving Dense: 35.51 Foggy Driving: 46.02 Cityscapes lindau: 67.63
pth name: FIFO25000.pth, Foggy Zurich: 40.43 Foggy Driving Dense: 39.66 Foggy Driving: 47.84 Cityscapes lindau: 61.55
pth name: FIFO30000.pth, Foggy Zurich: 40.05 Foggy Driving Dense: 39.42 Foggy Driving: 46.0 Cityscapes lindau: 67.57
pth name: FIFO35000.pth, Foggy Zurich: 42.91 Foggy Driving Dense: 40.02 Foggy Driving: 46.79 Cityscapes lindau: 66.17
pth name: FIFO40000.pth, Foggy Zurich: 46.28 Foggy Driving Dense: 39.63 Foggy Driving: 47.21 Cityscapes lindau: 66.35
pth name: FIFO45000.pth, Foggy Zurich: 46.30 Foggy Driving Dense: 38.57 Foggy Driving: 45.88 Cityscapes lindau: 68.62
pth name: FIFO50000.pth, Foggy Zurich: 42.95 Foggy Driving Dense: 39.42 Foggy Driving: 46.0 Cityscapes lindau: 68.14
pth name: FIFO55000.pth, Foggy Zurich: 47.23 Foggy Driving Dense: 36.35 Foggy Driving: 45.2 Cityscapes lindau: 66.59
pth name: FIFO60000.pth, Foggy Zurich: 44.85 Foggy Driving Dense: 38.54 Foggy Driving: 45.64 Cityscapes lindau: 68.90
pth name: FIFO65000.pth, Foggy Zurich: 47.59 Foggy Driving Dense: 40.71 Foggy Driving: 46.79 Cityscapes lindau: 66.45
pth name: FIFO70000.pth, Foggy Zurich: 45.73 Foggy Driving Dense: 38.58 Foggy Driving: 46.89 Cityscapes lindau: 61.37
pth name: FIFO75000.pth, Foggy Zurich: 41.55 Foggy Driving Dense: 39.5 Foggy Driving: 45.98 Cityscapes lindau: 69.37
pth name: FIFO80000.pth, Foggy Zurich: 43.80 Foggy Driving Dense: 40.92 Foggy Driving: 47.55 Cityscapes lindau: 68.76
pth name: FIFO85000.pth, Foggy Zurich: 41.53 Foggy Driving Dense: 37.28 Foggy Driving: 45.46 Cityscapes lindau: 60.95
pth name: FIFO90000.pth, Foggy Zurich: 41.64 Foggy Driving Dense: 37.11 Foggy Driving: 44.7 Cityscapes lindau: 65.16
pth name: FIFO95000.pth, Foggy Zurich: 43.69 Foggy Driving Dense: 43.97 Foggy Driving: 48.41 Cityscapes lindau: 64.76
However, the results are not good as reported in paper especially on Foggy Driving Dense dataset.
I am wondering (1) if you have any advice for training and (2) the way you selected the 'FIFO_final_model' ?
Thank you in advance.
Hello, when I read the paper I noticed that in equation 1 after calculating the difference between the cosine distance and m you also need to square the difference. But your code does not square the difference, how do you explain this difference between the code and equation 1 of the paper?
Hi authors, thanks for your good work!
How could I obtain the frosty version of Cityscapes? If possible, could you share it with me.
Looking forward to your reply. Thanks.
Hi.
When I tried to implement your code according to your instruction, I encountered some issues.
In ./configs/train_config.py, DATA_LIST_RF is set to './data/Foggy_Zurich/lists_file_names/RGB_sum_filenames.txt'.
However, when I downloaded the Foggy Zurich dataset, there was no 'lists_file_names/RGB_sum_filenames.txt'.
Did you manually merge 'RGB_medium_filenames.txt' and 'RGB_light_filenames.txt' in the Foggy Zurich dataset into 'RGB_sum_filenames.txt'?
I'm looking forward to hearing your reply.
Thank you!
The Fog Style Matching Loss in the paper:
\mathcal{L}{\mathrm{fsm}}^{l}\left(\mathbf{f}^{a, l}, \mathbf{f}^{b, l}\right)=\frac{1}{4 d{l}^{2} n_{l}^{2}} \sum_{i=1}^{d_{l}}\left(\mathbf{f}{i}^{a, l}-\mathbf{f}{i}^{b, l}\right)^{2}
The code of Fog Style Matching Loss:
In main.py:
... ...
393 fog_factor_b = fogpassfilter(vector_b_gram)
394 fog_factor_a = fogpassfilter(vector_a_gram)
395 half = int(fog_factor_b.shape[0]/2)
397 layer_fsm_loss += fsm_weights[layer]torch.mean((fog_factor_b/(hbwb) - fog_factor_a/(ha*wa))**2)/half/ b_feature.size(0)
399 loss_fsm += layer_fsm_loss / 4
The code is inconsistent with the paper, which one is correct?
Hi, Thanks for sharing the code.
I trained the FIFO and reproduced it on FZ, FDD, FD, CW Lindau datasets.
But I'm unable to reproduce on the ACDC dataset. The performance is lower.
(fog : 51.61, rain : 46.34, night : 14.26, snow : 44.3)
I'm wondering if there are any additional changes needed to achieve the reported performance on the ACDC dataset.
Thanks.
Thank you for sharing the code of your excellent work.
I have a question about your implementation.
Could you provide a rough estimate of how much time was required for each model to train?
I'm looking forward to hearing your reply.
Thank you.
Hi,
Thanks for sharing the code of this valuable work.
I was using the following command to train the FIFO model.
python3 main.py --file-name 'FIFO_model' --restore-from './Cityscapes_pretrained_model.pth' --restore-from-fogpass './FogPassFilter_pretrained.pth' --modeltrain 'train'
But I found the performance is not that good. I only get the following results.
Evaluation on Foggy Zurich
===> mIoU: 40.99
Evaluation on Foggy Driving Dense
===> mIoU: 37.74
Evaluation on Foggy Driving
===> mIoU: 47.55
Evaluation on Cityscapes lindau 40
===> mIoU: 64.85
Are there any other tricks for training the model or selecting the model?
Best
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.