megengine / nbnet

NBNet: Noise Basis Learning for Image Denoising with Subspace Projection

MegEngine

Documentation | 中文文档

MegEngine is a fast, scalable, and user friendly deep learning framework with 3 key features.

• Unified framework for both training and inference
  • Quantization, dynamic shape/image pre-processing, and even derivation with a single model.
  • After training, put everything into your model to inference on any platform with speed and precision. Check here for a quick guide.
• The lowest hardware requirements
  • The memory usage of the GPU can be reduced to one-third of the original memory usage when DTR algorithm is enabled.
  • Inference models with the lowest memory usage by leveraging our Pushdown memory planner.
• Inference efficiently on all platforms
  • Inference with speed and high-precision on x86, Arm, CUDA, and RoCM.
  • Supports Linux, Windows, iOS, Android, TEE, etc.
  • Optimize performance and memory usage by leveraging our advanced features.

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Installation

NOTE: MegEngine now supports Python installation on Linux-64bit/Windows-64bit/MacOS(CPU-Only)-10.14+/Android 7+(CPU-Only) platforms with Python from 3.6 to 3.9. On Windows 10 you can either install the Linux distribution through Windows Subsystem for Linux (WSL) or install the Windows distribution directly. Many other platforms are supported for inference.

Binaries

To install the pre-built binaries via pip wheels:

python3 -m pip install --upgrade pip
python3 -m pip install megengine -f https://megengine.org.cn/whl/mge.html

Building from Source

• CMake build details. please refer to BUILD_README.md
• Python binding build details, Please refer to BUILD_PYTHON_WHL_README.md

How to Contribute

• MegEngine adopts Contributor Covenant as a guideline to run our community. Please read the Code of Conduct.
• Every contributor of MegEngine must sign a Contributor License Agreement (CLA) to clarify the intellectual property license granted with the contributions.
• You can help to improve MegEngine in many ways:
  • Write code.
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  • Answer questions on MegEngine Forum, or Stack Overflow.
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  • Try a new idea on MegStudio.
  • Report or investigate bugs and issues.
  • Review Pull Requests.
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  • Cite MegEngine in your papers and articles.
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  • Any other form of contribution is welcomed.

We strive to build an open and friendly community. We aim to power humanity with AI.

How to Contact Us

• Issue: github.com/MegEngine/MegEngine/issues
• Email: [email protected]
• Forum: discuss.megengine.org.cn
• QQ Group: 1029741705

Resources

• MegEngine
• MegStudio
• mirror repo
  • OPENI: openi.org.cn/MegEngine
  • Gitee: gitee.com/MegEngine/MegEngine

License

MegEngine is licensed under the Apache License, Version 2.0

Citation

If you use MegEngine in your publication,please cite it by using the following BibTeX entry.

@Misc{MegEngine,
  institution = {megvii},
  title =  {MegEngine:A fast, scalable and easy-to-use deep learning framework},
  howpublished = {\url{https://github.com/MegEngine/MegEngine}},
  year = {2020}
}

Copyright (c) 2014-2021 Megvii Inc. All rights reserved.

nbnet's Issues

about test data

when run ‘python test.py -d ./data -c ./NBNet_mge.pkl’，occur error ‘FileNotFoundError: [Errno 2] No such file or directory: './data/ValidationNoisyBlocksSrgb.mat'’,does the test dataset need a .mat file？

Question about AWGN removal?

Would you release the pre-trained checkpoint for AWGN removal? I want to evaluate the NBNet on some other dataset, such as Urban100. @Sumching

psnr is

how many about train pictures number？

Hello, how many pictures did you use in your training set

When test img size is not 128, how to get a complete result,

Thank you for your work. NBNet requires size 128 as its input, but when testing img size > 128, if we clip the input to size 128 and then merge the patches, there will be obvious blocky phenomena. How do you solve them?

A question about train

Hello, when I try to run this code, an error occured:FileNotFoundError: [Errno 2] No such file or directory: 'data/sidd/ValidationNoisyBlocksSrgb.mat'. Then I find ValidationNoisyBlocksSrgb.mat and ValidationGtBlocksSrgb.mat are not generated afterI run prepare_data.py

val_data_dict = loadmat(os.path.join(path, 'ValidationNoisyBlocksSrgb.mat'))
val_data_noisy = val_data_dict['ValidationNoisyBlocksSrgb']
val_data_dict = loadmat(os.path.join(path,'ValidationGtBlocksSrgb.mat'))
val_data_gt = val_data_dict['ValidationGtBlocksSrgb']

A question about network structure

A question about test result

Hello, after I read your test code, I found that your test code seemed to only compute the PSNR value but cannot produce the denoised images. Therefore, I want to know how to modify the test code to realize my demand. Could you tell me the solution? thx.

How did you get the psnr of ROI in DND

How did you get the psnr of ROI in DND

Why is the process stuck?

Why does this step get stuck before the test loads the model？Unable to run further down。

why in linux loss blow up?

It is a strange issue that the loss will blow up to Nan in linux ubuntu 20.04 after I install the cuda, without cuda the loss decrease normally.

environment:
megengine == 1.3.0
torch == 1.9.1

more strange is the loss will fall back :

Epoch 0 Step 0, Speed=1.7 mb/s, dp_cost=0.0098, Loss=4.02e-02, lr=2.00e-04
Epoch 0 Step 10, Speed=14 mb/s, dp_cost=0.25, Loss=1.56e+00, lr=2.00e-04
Epoch 0 Step 20, Speed=19 mb/s, dp_cost=0.034, Loss= nan, lr=2.00e-04
Epoch 0 Step 30, Speed=17 mb/s, dp_cost=0.054, Loss=3.62e-02, lr=2.00e-04
Epoch 0 Step 40, Speed=20 mb/s, dp_cost=0.037, Loss=2.22e-01, lr=2.00e-04
Epoch 0 Step 50, Speed=15 mb/s, dp_cost=0.16, Loss= nan, lr=2.00e-04
Epoch 0 Step 60, Speed=16 mb/s, dp_cost=0.21, Loss= nan, lr=2.00e-04
Epoch 0 Step 70, Speed=18 mb/s, dp_cost=0.033, Loss= nan, lr=2.00e-04
Epoch 0 Step 80, Speed=18 mb/s, dp_cost=0.11, Loss= nan, lr=2.00e-04
Epoch 0 Step 90, Speed=15 mb/s, dp_cost=0.2, Loss= nan, lr=2.00e-04
Epoch 0 Step 100, Speed=12 mb/s, dp_cost=0.037, Loss= nan, lr=2.00e-04
Epoch 0 Step 110, Speed=15 mb/s, dp_cost=0.22, Loss= nan, lr=2.00e-04
Epoch 0 Step 120, Speed=17 mb/s, dp_cost=0.035, Loss= nan, lr=2.00e-04
Epoch 0 Step 130, Speed=17 mb/s, dp_cost=0.028, Loss= nan, lr=2.00e-04
Epoch 0 Step 140, Speed=13 mb/s, dp_cost=0.1, Loss= nan, lr=2.00e-04
Epoch 0 Step 150, Speed=17 mb/s, dp_cost=0.24, Loss= nan, lr=2.00e-04
Epoch 0 Step 160, Speed=15 mb/s, dp_cost=0.23, Loss= nan, lr=2.00e-04
Epoch 0 Step 170, Speed=8.5 mb/s, dp_cost=0.18, Loss= nan, lr=2.00e-04
Epoch 0 Step 180, Speed=16 mb/s, dp_cost=0.078, Loss= nan, lr=2.00e-04
Epoch 0 Step 190, Speed=15 mb/s, dp_cost=0.21, Loss= nan, lr=2.00e-04
Epoch 0 Step 200, Speed=15 mb/s, dp_cost=0.06, Loss= nan, lr=2.00e-04
Epoch 0 Step 210, Speed=19 mb/s, dp_cost=0.034, Loss=6.01e-02, lr=2.00e-04
Epoch 0 Step 220, Speed=16 mb/s, dp_cost=0.058, Loss= nan, lr=2.00e-04
Epoch 0 Step 230, Speed=19 mb/s, dp_cost=0.037, Loss= nan, lr=2.00e-04
Epoch 0 Step 240, Speed=17 mb/s, dp_cost=0.11, Loss= nan, lr=2.00e-04
Epoch 0 Step 250, Speed=10 mb/s, dp_cost=0.016, Loss= nan, lr=2.00e-04
Epoch 0 Step 260, Speed=16 mb/s, dp_cost=0.028, Loss= nan, lr=2.00e-04
Epoch 0 Step 270, Speed=16 mb/s, dp_cost=0.031, Loss= nan, lr=2.00e-04
Epoch 0 Step 280, Speed=10 mb/s, dp_cost=0.14, Loss= nan, lr=2.00e-04
Epoch 0 Step 290, Speed=17 mb/s, dp_cost=0.034, Loss= nan, lr=2.00e-04
Epoch 0 Step 300, Speed=15 mb/s, dp_cost=0.029, Loss= nan, lr=2.00e-04
Epoch 0 Step 310, Speed=19 mb/s, dp_cost=0.032, Loss=2.82e-01, lr=2.00e-04
Epoch 0 Step 320, Speed=17 mb/s, dp_cost=0.036, Loss=6.47e-02, lr=2.00e-04
Epoch 0 Step 330, Speed=15 mb/s, dp_cost=0.031, Loss= nan, lr=2.00e-04
Epoch 0 Step 340, Speed=13 mb/s, dp_cost=0.082, Loss= nan, lr=2.00e-04
Epoch 0 Step 350, Speed=13 mb/s, dp_cost=0.021, Loss=3.08e-01, lr=2.00e-04
Epoch 0 Step 360, Speed=14 mb/s, dp_cost=0.022, Loss= nan, lr=2.00e-04
Epoch 0 Step 370, Speed=16 mb/s, dp_cost=0.051, Loss= nan, lr=2.00e-04
Epoch 0 Step 380, Speed=11 mb/s, dp_cost=0.19, Loss= nan, lr=2.00e-04
Epoch 0 Step 390, Speed=15 mb/s, dp_cost=0.069, Loss= nan, lr=2.00e-04
Epoch 0 Step 400, Speed=16 mb/s, dp_cost=0.037, Loss= nan, lr=2.00e-04
Epoch 0 Step 410, Speed=17 mb/s, dp_cost=0.032, Loss= nan, lr=2.00e-04
Epoch 0 Step 420, Speed=19 mb/s, dp_cost=0.038, Loss= nan, lr=2.00e-04
Epoch 0 Step 430, Speed=17 mb/s, dp_cost=0.035, Loss= nan, lr=2.00e-04
Epoch 0 Step 440, Speed=11 mb/s, dp_cost=0.021, Loss=9.56e-02, lr=2.00e-04
Epoch 0 Step 450, Speed=16 mb/s, dp_cost=0.057, Loss= nan, lr=2.00e-04
Epoch 0 Step 460, Speed=15 mb/s, dp_cost=0.22, Loss= nan, lr=2.00e-04

How to denoise my own picture?

I can run the whole program completely, but how to use my own pictures and display them?
Thank you very much for your reply.

why test psnr is nan

Problem about TrainSet

Is the training set full data, medium or small data of SIDD data set used？

RuntimeError: The cuda env is set before the forked thread starts. Please do not use any cuda function or variable before forking.