├── LICENSE
├── README.md
├── analysis_demo.ipynb
├── data
├── Test
│ ├── .DS_Store
│ ├── Kodak23
│ │ ├── 01.ras
│ │ ├── 02.ras
│ │ ├── 03.ras
│ │ ├── 04.ras
│ │ ├── 05.ras
│ │ ├── 06.ras
│ │ ├── 07.ras
│ │ ├── 08.ras
│ │ ├── 09.ras
│ │ ├── 10.ras
│ │ ├── 11.ras
│ │ ├── 12.ras
│ │ ├── 13.ras
│ │ ├── 14.ras
│ │ ├── 15.ras
│ │ ├── 16.ras
│ │ ├── 17.ras
│ │ ├── 18.ras
│ │ ├── 19.ras
│ │ ├── 20.ras
│ │ ├── 21.ras
│ │ ├── 22.ras
│ │ └── 23.ras
│ ├── Set12
│ │ ├── 01.png
│ │ ├── 02.png
│ │ ├── 03.png
│ │ ├── 04.png
│ │ ├── 05.png
│ │ ├── 06.png
│ │ ├── 07.png
│ │ ├── 08.png
│ │ ├── 09.png
│ │ ├── 10.png
│ │ ├── 11.png
│ │ └── 12.png
│ └── Set68
│ │ ├── test001.png
│ │ ├── test002.png
│ │ ├── test003.png
│ │ ├── test004.png
│ │ ├── test005.png
│ │ ├── test006.png
│ │ ├── test007.png
│ │ ├── test008.png
│ │ ├── test009.png
│ │ ├── test010.png
│ │ ├── test011.png
│ │ ├── test012.png
│ │ ├── test013.png
│ │ ├── test014.png
│ │ ├── test015.png
│ │ ├── test016.png
│ │ ├── test017.png
│ │ ├── test018.png
│ │ ├── test019.png
│ │ ├── test020.png
│ │ ├── test021.png
│ │ ├── test022.png
│ │ ├── test023.png
│ │ ├── test024.png
│ │ ├── test025.png
│ │ ├── test026.png
│ │ ├── test027.png
│ │ ├── test028.png
│ │ ├── test029.png
│ │ ├── test030.png
│ │ ├── test031.png
│ │ ├── test032.png
│ │ ├── test033.png
│ │ ├── test034.png
│ │ ├── test035.png
│ │ ├── test036.png
│ │ ├── test037.png
│ │ ├── test038.png
│ │ ├── test039.png
│ │ ├── test040.png
│ │ ├── test041.png
│ │ ├── test042.png
│ │ ├── test043.png
│ │ ├── test044.png
│ │ ├── test045.png
│ │ ├── test046.png
│ │ ├── test047.png
│ │ ├── test048.png
│ │ ├── test049.png
│ │ ├── test050.png
│ │ ├── test051.png
│ │ ├── test052.png
│ │ ├── test053.png
│ │ ├── test054.png
│ │ ├── test055.png
│ │ ├── test056.png
│ │ ├── test057.png
│ │ ├── test058.png
│ │ ├── test059.png
│ │ ├── test060.png
│ │ ├── test061.png
│ │ ├── test062.png
│ │ ├── test063.png
│ │ ├── test064.png
│ │ ├── test065.png
│ │ ├── test066.png
│ │ ├── test067.png
│ │ └── test068.png
├── Train400
│ ├── test_001.png
│ ├── test_002.png
│ ├── test_003.png
│ ├── test_004.png
│ ├── test_005.png
│ ├── test_006.png
│ ├── test_007.png
│ ├── test_008.png
│ ├── test_009.png
│ ├── test_010.png
│ ├── test_011.png
│ ├── test_012.png
│ ├── test_013.png
│ ├── test_014.png
│ ├── test_015.png
│ ├── test_016.png
│ ├── test_017.png
│ ├── test_018.png
│ ├── test_019.png
│ ├── test_020.png
│ ├── test_021.png
│ ├── test_022.png
│ ├── test_023.png
│ ├── test_024.png
│ ├── test_025.png
│ ├── test_026.png
│ ├── test_027.png
│ ├── test_028.png
│ ├── test_029.png
│ ├── test_030.png
│ ├── test_031.png
│ ├── test_032.png
│ ├── test_033.png
│ ├── test_034.png
│ ├── test_035.png
│ ├── test_036.png
│ ├── test_037.png
│ ├── test_038.png
│ ├── test_039.png
│ ├── test_040.png
│ ├── test_041.png
│ ├── test_042.png
│ ├── test_043.png
│ ├── test_044.png
│ ├── test_045.png
│ ├── test_046.png
│ ├── test_047.png
│ ├── test_048.png
│ ├── test_049.png
│ ├── test_050.png
│ ├── test_051.png
│ ├── test_052.png
│ ├── test_053.png
│ ├── test_054.png
│ ├── test_055.png
│ ├── test_056.png
│ ├── test_057.png
│ ├── test_058.png
│ ├── test_059.png
│ ├── test_060.png
│ ├── test_061.png
│ ├── test_062.png
│ ├── test_063.png
│ ├── test_064.png
│ ├── test_065.png
│ ├── test_066.png
│ ├── test_067.png
│ ├── test_068.png
│ ├── test_069.png
│ ├── test_070.png
│ ├── test_071.png
│ ├── test_072.png
│ ├── test_073.png
│ ├── test_074.png
│ ├── test_075.png
│ ├── test_076.png
│ ├── test_077.png
│ ├── test_078.png
│ ├── test_079.png
│ ├── test_080.png
│ ├── test_081.png
│ ├── test_082.png
│ ├── test_083.png
│ ├── test_084.png
│ ├── test_085.png
│ ├── test_086.png
│ ├── test_087.png
│ ├── test_088.png
│ ├── test_089.png
│ ├── test_090.png
│ ├── test_091.png
│ ├── test_092.png
│ ├── test_093.png
│ ├── test_094.png
│ ├── test_095.png
│ ├── test_096.png
│ ├── test_097.png
│ ├── test_098.png
│ ├── test_099.png
│ ├── test_100.png
│ ├── test_101.png
│ ├── test_102.png
│ ├── test_103.png
│ ├── test_104.png
│ ├── test_105.png
│ ├── test_106.png
│ ├── test_107.png
│ ├── test_108.png
│ ├── test_109.png
│ ├── test_110.png
│ ├── test_111.png
│ ├── test_112.png
│ ├── test_113.png
│ ├── test_114.png
│ ├── test_115.png
│ ├── test_116.png
│ ├── test_117.png
│ ├── test_118.png
│ ├── test_119.png
│ ├── test_120.png
│ ├── test_121.png
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│ ├── test_125.png
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│ ├── test_128.png
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│ ├── test_133.png
│ ├── test_134.png
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│ ├── test_136.png
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│ ├── test_139.png
│ ├── test_140.png
│ ├── test_141.png
│ ├── test_142.png
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│ ├── test_144.png
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│ ├── test_146.png
│ ├── test_147.png
│ ├── test_148.png
│ ├── test_149.png
│ ├── test_150.png
│ ├── test_151.png
│ ├── test_152.png
│ ├── test_153.png
│ ├── test_154.png
│ ├── test_155.png
│ ├── test_156.png
│ ├── test_157.png
│ ├── test_158.png
│ ├── test_159.png
│ ├── test_160.png
│ ├── test_161.png
│ ├── test_162.png
│ ├── test_163.png
│ ├── test_164.png
│ ├── test_165.png
│ ├── test_166.png
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│ ├── test_168.png
│ ├── test_169.png
│ ├── test_170.png
│ ├── test_171.png
│ ├── test_172.png
│ ├── test_173.png
│ ├── test_174.png
│ ├── test_175.png
│ ├── test_176.png
│ ├── test_177.png
│ ├── test_178.png
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│ ├── test_180.png
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│ ├── test_183.png
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│ ├── test_268.png
│ ├── test_269.png
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│ ├── test_293.png
│ ├── test_294.png
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│ ├── test_296.png
│ ├── test_297.png
│ ├── test_298.png
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│ ├── test_300.png
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│ ├── test_302.png
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│ ├── test_305.png
│ ├── test_306.png
│ ├── test_307.png
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│ ├── test_311.png
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│ ├── test_313.png
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│ ├── test_343.png
│ ├── test_344.png
│ ├── test_345.png
│ ├── test_346.png
│ ├── test_347.png
│ ├── test_348.png
│ ├── test_349.png
│ ├── test_350.png
│ ├── test_351.png
│ ├── test_352.png
│ ├── test_353.png
│ ├── test_354.png
│ ├── test_355.png
│ ├── test_356.png
│ ├── test_357.png
│ ├── test_358.png
│ ├── test_359.png
│ ├── test_360.png
│ ├── test_361.png
│ ├── test_362.png
│ ├── test_363.png
│ ├── test_364.png
│ ├── test_365.png
│ ├── test_366.png
│ ├── test_367.png
│ ├── test_368.png
│ ├── test_369.png
│ ├── test_370.png
│ ├── test_371.png
│ ├── test_372.png
│ ├── test_373.png
│ ├── test_374.png
│ ├── test_375.png
│ ├── test_376.png
│ ├── test_377.png
│ ├── test_378.png
│ ├── test_379.png
│ ├── test_380.png
│ ├── test_381.png
│ ├── test_382.png
│ ├── test_383.png
│ ├── test_384.png
│ ├── test_385.png
│ ├── test_386.png
│ ├── test_387.png
│ ├── test_388.png
│ ├── test_389.png
│ ├── test_390.png
│ ├── test_391.png
│ ├── test_392.png
│ ├── test_393.png
│ ├── test_394.png
│ ├── test_395.png
│ ├── test_396.png
│ ├── test_397.png
│ ├── test_398.png
│ ├── test_399.png
│ └── test_400.png
└── preprocess_bsd400.py
├── generalization_demo.ipynb
├── models
├── BFBatchNorm2d.py
├── __init__.py
├── backbone_cnn.py
├── dncnn.py
├── rcnn.py
├── sdensenet.py
└── unet.py
├── precomputed
├── dncnn
│ ├── bias
│ │ ├── 0-100_metrics.p
│ │ ├── 0-10_metrics.p
│ │ ├── 0-30_metrics.p
│ │ └── 0-55_metrics.p
│ └── bias_free
│ │ ├── 0-100_metrics.p
│ │ ├── 0-10_metrics.p
│ │ ├── 0-30_metrics.p
│ │ ├── 0-55_metrics.p
│ │ ├── Jacobian_10.npy
│ │ ├── Jacobian_100.npy
│ │ ├── Jacobian_30.npy
│ │ ├── im_denoised_10.npy
│ │ ├── im_denoised_100.npy
│ │ ├── im_denoised_30.npy
│ │ ├── noisy_im_10.npy
│ │ ├── noisy_im_100.npy
│ │ ├── noisy_im_30.npy
│ │ ├── norm_b_ave.npy
│ │ ├── norm_noise_ave.npy
│ │ ├── norm_res_ave.npy
│ │ └── subspace_dims.npy
├── rcnn
│ ├── bias
│ │ ├── 0-100_metrics.p
│ │ ├── 0-10_metrics.p
│ │ ├── 0-30_metrics.p
│ │ └── 0-55_metrics.p
│ └── bias_free
│ │ ├── 0-100_metrics.p
│ │ ├── 0-10_metrics.p
│ │ ├── 0-30_metrics.p
│ │ └── 0-55_metrics.p
├── sdensenet
│ ├── bias
│ │ ├── 0-100_metrics.p
│ │ ├── 0-10_metrics.p
│ │ ├── 0-30_metrics.p
│ │ └── 0-55_metrics.p
│ └── bias_free
│ │ ├── 0-100_metrics.p
│ │ ├── 0-10_metrics.p
│ │ ├── 0-30_metrics.p
│ │ └── 0-55_metrics.p
└── unet
│ ├── bias
│ ├── 0-100_metrics.p
│ ├── 0-10_metrics.p
│ ├── 0-30_metrics.p
│ └── 0-55_metrics.p
│ └── bias_free
│ ├── 0-100_metrics.p
│ ├── 0-10_metrics.p
│ ├── 0-30_metrics.p
│ └── 0-55_metrics.p
├── pretrained
├── dncnn
│ ├── bias
│ │ ├── 0-10.pt
│ │ ├── 0-100.pt
│ │ ├── 0-30.pt
│ │ └── 0-55.pt
│ └── bias_free
│ │ ├── 0-10.pt
│ │ ├── 0-100.pt
│ │ ├── 0-30.pt
│ │ └── 0-55.pt
├── rcnn
│ ├── bias
│ │ ├── 0-10.pt
│ │ ├── 0-100.pt
│ │ ├── 0-30.pt
│ │ └── 0-55.pt
│ └── bias_free
│ │ ├── 0-10.pt
│ │ ├── 0-100.pt
│ │ ├── 0-30.pt
│ │ └── 0-55.pt
├── sdensenet
│ ├── bias
│ │ ├── 0-10.pt
│ │ ├── 0-100.pt
│ │ ├── 0-30.pt
│ │ └── 0-55.pt
│ └── bias_free
│ │ ├── 0-10.pt
│ │ ├── 0-100.pt
│ │ ├── 0-30.pt
│ │ └── 0-55.pt
└── unet
│ ├── bias
│ ├── 0-10.pt
│ ├── 0-100.pt
│ ├── 0-30.pt
│ └── 0-55.pt
│ └── bias_free
│ ├── 0-10.pt
│ ├── 0-100.pt
│ ├── 0-30.pt
│ └── 0-55.pt
├── requirements.txt
├── train.py
└── utils
├── __init__.py
├── data
└── __init__.py
├── demo_utils.py
├── meters.py
├── metrics.py
├── noise_model.py
├── progress_bar.py
├── test_metrics.py
└── train_utils.py
/LICENSE:
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3 | Copyright (c) 2022 LCV
4 |
5 | Permission is hereby granted, free of charge, to any person obtaining a copy
6 | of this software and associated documentation files (the "Software"), to deal
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/README.md:
--------------------------------------------------------------------------------
1 | # Denoising with Bias-Free CNNs
2 |
3 | Deep Convolutional Neural Networks have produced state-of-the-art results in the problem of removing noise from images.
4 | These networks do not generalize well to noise levels beyond the range on which they are trained. But removing the additive bias terms from the networks allows robust generalization, even when they are trained only on barely-visible levels of noise. In addition, the removal of bias simplifies analysis of network behavior, which indicates that these denoisers perform projections onto local adaptively-estimated subspaces, whose dimensionality varies inversely with noise level. For further information, visit the [project webpage here](https://labforcomputationalvision.github.io/bias_free_denoising/), or the published paper:
5 |
6 | Robust And Interpretable Blind Image Denoising Via Bias-Free Convolutional Neural Networks
7 | Sreyas Mohan*, Zahra Kadkhodaie*, Eero P. Simoncelli, Carlos Fernandez-Granda
8 | Int'l. Conf. on Learning Representations (ICLR), Apr 2020.
9 |
10 | Paper (and reviews): https://openreview.net/forum?id=HJlSmC4FPS
11 | Local copy: https://www.cns.nyu.edu/~lcv/pubs/makeAbs.php?loc=MohanKadkhodaie19b
12 | Conference presentation (video and slides): https://iclr.cc/virtual/poster_HJlSmC4FPS.html
13 |
14 | ## Pre-trained models
15 |
16 | The directory [`pretrained`](pretrained) contains the pretrained models described in section 5 of the paper:
17 | 1. DnCNN [Zhang et. al., IEEE Trans IP 2017];
18 | 2. Recurrent CNN [Zhang et al., arXiv:1805.07709 2018a];
19 | 3. Unet [Ronneberger et. al., Int'l Conf Medical Image Computing, 2017];
20 | 4. A simplified variant of DenseNet [Huang et. al., CVPR 2017].
21 |
22 | For each architecture, we provide both the original model, and its bias-free counterpart.
23 |
24 | ## Demos
25 |
26 | We provide two Python Notebooks with example code for using pre-trained models:
27 |
28 | * In [`generalization_demo.ipynb`](generalization_demo.ipynb), we show that bias free networks generalize to noise levels outside the training range (Section 5 of the [paper](https://openreview.net/forum?id=HJlSmC4FPS)). (https://nbviewer.jupyter.org/github/LabForComputationalVision/bias_free_denoising/blob/master/generalization_demo.ipynb)
29 |
30 | * In [`analysis_demo.ipynb`](analysis_demo.ipynb), we examine the bias free network, visualizing the adaptive filters, and using SVD to analyze the subspace the network is projecting onto (Section 6 of the [paper](https://openreview.net/forum?id=HJlSmC4FPS)). (https://nbviewer.jupyter.org/github/LabForComputationalVision/bias_free_denoising/blob/master/analysis_demo.ipynb)
31 |
32 | The directory [`precomputed`](precomputed) contains precomputed quantities to generate various plots in the demo notebooks. If required files are not present in [`precomputed`](precomputed) the notebooks will compute them and store them in the directory.
33 |
34 | Please refer to [`requirements.txt`](requirements.txt) for required Python packages.
35 |
36 | ## Training
37 |
38 | [`train.py`](train.py) provides the code for training a model (original model, or its bias-free version) from scratch, on a provided image dataset (the Berkeley Segmentation Dataset 400).
39 | Example usage of the script:
40 |
41 | ```shell
42 | python train.py \
43 | --model dncnn \
44 | --min_noise 0 \
45 | --max_noise 10 \
46 | --data-path ./data/
47 | ```
48 |
49 | Available models are `dncnn`, `rcnn`, `sdensenet` and `unet`. Please refer to the script defining each of these models in [`models`](models) for more options in the architecture.
50 |
51 | Adding `--bias` option to `train.py` trains the original model; otherwise, a bias-free version is trained. Please refer to the `argparse` module in [`train.py`](train.py) and [`train_utils.py`](utils/train_utils.py) for additional training options.
52 |
53 | `--data-path` expects to find `train.h5` and `valid.h5` in the folder to start training. Please refer to [pre-processing for training](#pre-processing-for-training) section for more details.
54 |
55 | ### Pre-processing for training
56 |
57 | Following [DnCNN](https://arxiv.org/abs/1608.03981) we extract patches from BSD400 data to train.
58 | [`preprocess_bsd400.py`](data/preprocess_bsd400.py) provides the code to generate patches, perform data augmentation and save. The preprocessing script and data is taken from [SaoYan/DnCNN](https://github.com/SaoYan/DnCNN-PyTorch). Example usage:
59 |
60 | ```shell
61 | python preprocess_BSD400.py \
62 | --data_path data/ \
63 | --patch_size 50 \
64 | --stride 10 \
65 | --aug_times 2
66 | ```
67 |
68 | ### BFBatchNorm2d
69 |
70 | The traditional BatchNorm layer `nn.BatchNorm2d()` introduces additive constants during the mean subtraction step and addition of learned constant step (the `affine=True` option in PyTorch). We provide PyTorch code for a bias-free BatchNorm layer [`BFBatchNorm2d()`](models/BFBatchNorm2d.py), which eliminates the subtraction/addition steps (i.e., it only divides by standard deviation). This can be used in place of `nn.BatchNorm2d()` to produce a bias-free version of a network. Please refer to [`dncnn.py`](models/dncnn.py) for example usage.
71 |
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/data/preprocess_bsd400.py:
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1 | # reference implementation: https://github.com/SaoYan/DnCNN-PyTorch/blob/master/dataset.py
2 |
3 | import argparse
4 | import os
5 | import os.path
6 | import numpy as np
7 | import h5py
8 | import cv2
9 | import glob
10 | import tqdm
11 |
12 |
13 | def main(args):
14 | print("Processing training data")
15 | scales = [1, 0.9, 0.8, 0.7]
16 | files = sorted(glob.glob(os.path.join(args.data_path, "Train400", "*.png")))
17 | with h5py.File(os.path.join(args.data_path, "train.h5"), "w") as h5f:
18 | train_size = 0
19 | for file in tqdm.tqdm(files):
20 | img = cv2.imread(file)
21 | h, w, c = img.shape
22 | for k in range(len(scales)):
23 | Img = cv2.resize(img, (int(h * scales[k]), int(w * scales[k])), interpolation=cv2.INTER_CUBIC)
24 | Img = np.expand_dims(Img[:, :, 0].copy(), 0) / 255.0
25 | patches = Im2Patch(Img, win=args.patch_size, stride=args.stride)
26 | for n in range(patches.shape[3]):
27 | data = patches[:, :, :, n].copy()
28 | h5f.create_dataset(str(train_size), data=data)
29 | train_size += 1
30 | for m in range(args.aug_times - 1):
31 | data_aug = data_augmentation(data, np.random.randint(1, 8))
32 | h5f.create_dataset(str(train_size) + "_aug_%d" % (m + 1), data=data_aug)
33 | train_size += 1
34 |
35 | print("Processing validation data")
36 | files = sorted(glob.glob(os.path.join(args.data_path, "Set12", "*.png")))
37 | with h5py.File(os.path.join(args.data_path, "valid.h5"), "w") as h5f:
38 | valid_size = 0
39 | for file in tqdm.tqdm(files):
40 | img = cv2.imread(file)
41 | img = np.expand_dims(img[:, :, 0], 0) / 255.0
42 | h5f.create_dataset(str(valid_size), data=img)
43 | valid_size += 1
44 |
45 | print(f"Training size {train_size}, validation size {valid_size}")
46 |
47 |
48 | def Im2Patch(img, win, stride=1):
49 | k = 0
50 | endc = img.shape[0]
51 | endw = img.shape[1]
52 | endh = img.shape[2]
53 | patch = img[:, 0 : endw - win + 0 + 1 : stride, 0 : endh - win + 0 + 1 : stride]
54 | TotalPatNum = patch.shape[1] * patch.shape[2]
55 | Y = np.zeros([endc, win * win, TotalPatNum], np.float32)
56 | for i in range(win):
57 | for j in range(win):
58 | patch = img[:, i : endw - win + i + 1 : stride, j : endh - win + j + 1 : stride]
59 | Y[:, k, :] = np.array(patch[:]).reshape(endc, TotalPatNum)
60 | k = k + 1
61 | return Y.reshape([endc, win, win, TotalPatNum])
62 |
63 |
64 | def data_augmentation(image, mode):
65 | out = np.transpose(image, (1, 2, 0))
66 | if mode == 0:
67 | # original
68 | out = out
69 | elif mode == 1:
70 | # flip up and down
71 | out = np.flipud(out)
72 | elif mode == 2:
73 | # rotate counterwise 90 degree
74 | out = np.rot90(out)
75 | elif mode == 3:
76 | # rotate 90 degree and flip up and down
77 | out = np.rot90(out)
78 | out = np.flipud(out)
79 | elif mode == 4:
80 | # rotate 180 degree
81 | out = np.rot90(out, k=2)
82 | elif mode == 5:
83 | # rotate 180 degree and flip
84 | out = np.rot90(out, k=2)
85 | out = np.flipud(out)
86 | elif mode == 6:
87 | # rotate 270 degree
88 | out = np.rot90(out, k=3)
89 | elif mode == 7:
90 | # rotate 270 degree and flip
91 | out = np.rot90(out, k=3)
92 | out = np.flipud(out)
93 | return np.transpose(out, (2, 0, 1))
94 |
95 |
96 | def get_args():
97 | parser = argparse.ArgumentParser(allow_abbrev=False)
98 | parser.add_argument("--data-path", default=".", help="path to data directory")
99 | parser.add_argument("--patch-size", default=50, help="patch size")
100 | parser.add_argument("--stride", default=10, help="stride")
101 | parser.add_argument("--aug-times", default=2, help="number of augmentations")
102 | args = parser.parse_args()
103 | return args
104 |
105 |
106 | if __name__ == "__main__":
107 | args = get_args()
108 | main(args)
109 |
--------------------------------------------------------------------------------
/models/BFBatchNorm2d.py:
--------------------------------------------------------------------------------
1 | import torch.nn as nn
2 | import torch
3 |
4 | class BFBatchNorm2d(nn.BatchNorm2d):
5 | def __init__(self, num_features, eps=1e-5, momentum=0.1, use_bias = False, affine=True):
6 | super(BFBatchNorm2d, self).__init__(num_features, eps, momentum)
7 |
8 | self.use_bias = use_bias;
9 |
10 | def forward(self, x):
11 | self._check_input_dim(x)
12 | y = x.transpose(0,1)
13 | return_shape = y.shape
14 | y = y.contiguous().view(x.size(1), -1)
15 | if self.use_bias:
16 | mu = y.mean(dim=1)
17 | sigma2 = y.var(dim=1)
18 |
19 | if self.training is not True:
20 | if self.use_bias:
21 | y = y - self.running_mean.view(-1, 1)
22 | y = y / ( self.running_var.view(-1, 1)**0.5 + self.eps)
23 | else:
24 | if self.track_running_stats is True:
25 | with torch.no_grad():
26 | if self.use_bias:
27 | self.running_mean = (1-self.momentum)*self.running_mean + self.momentum * mu
28 | self.running_var = (1-self.momentum)*self.running_var + self.momentum * sigma2
29 | if self.use_bias:
30 | y = y - mu.view(-1,1)
31 | y = y / (sigma2.view(-1,1)**.5 + self.eps)
32 |
33 | if self.affine:
34 | y = self.weight.view(-1, 1) * y;
35 | if self.use_bias:
36 | y += self.bias.view(-1, 1)
37 |
38 | return y.view(return_shape).transpose(0,1)
39 |
40 |
41 | def unit_test():
42 |
43 | def print_bn_details(bn):
44 | print(bn.running_mean)
45 | print(bn.running_var)
46 |
47 | bn_bf = BFBatchNorm2d(5, use_bias = False);
48 | bn_bias = BFBatchNorm2d(5, use_bias = True);
49 |
50 | print('train mode');
51 | bn_bf.train()
52 | bn_bias.train()
53 |
54 | for _ in range(25):
55 | temp_inp = torch.randn(100, 5, 128, 128)*10 + 10;
56 | bias_out = bn_bias(temp_inp);
57 | print('bias: variance %f, mean %f'%(torch.var(bias_out), torch.mean(bias_out)));
58 | print_bn_details(bn_bias)
59 |
60 | bf_out = bn_bf(temp_inp);
61 | print('bf: variance %f, mean %f'%(torch.var(bf_out), torch.mean(bf_out)))
62 | print_bn_details(bn_bf)
63 |
64 | print('eval mode')
65 | bn_bf.eval()
66 | bn_bias.eval()
67 |
68 | for _ in range(10):
69 | temp_inp = torch.randn(100, 5, 128, 128)*10 + 10;
70 | bias_out = bn_bias(temp_inp);
71 | print('bias: variance %f, mean %f'%(torch.var(bias_out), torch.mean(bias_out)));
72 | print('eval')
73 | print_bn_details(bn_bias)
74 |
75 | bf_out = bn_bf(temp_inp);
76 | print('bf: variance %f, mean %f'%(torch.var(bf_out), torch.mean(bf_out)))
77 | print('eval')
78 | print_bn_details(bn_bf)
79 |
80 |
81 |
82 |
--------------------------------------------------------------------------------
/models/__init__.py:
--------------------------------------------------------------------------------
1 | import importlib
2 | import os
3 | import torch.nn as nn
4 |
5 |
6 | MODEL_REGISTRY = {}
7 |
8 |
9 | def build_model(args):
10 | return MODEL_REGISTRY[args.model].build_model(args)
11 |
12 |
13 | def register_model(name):
14 | """Decorator to register a new model"""
15 | def register_model_cls(cls):
16 | if name in MODEL_REGISTRY:
17 | raise ValueError('Cannot register duplicate model {}'.format(name))
18 | if not issubclass(cls, nn.Module):
19 | raise ValueError('Model {} must extend {}'.format(name, cls.__name__))
20 | MODEL_REGISTRY[name] = cls
21 | return cls
22 | return register_model_cls
23 |
24 |
25 | # Automatically import any Python files in the models/ directory
26 | for file in os.listdir(os.path.dirname(__file__)):
27 | if file.endswith('.py') and file[0].isalpha():
28 | module = file[:file.find('.py')]
29 | importlib.import_module('models.' + module)
30 |
--------------------------------------------------------------------------------
/models/backbone_cnn.py:
--------------------------------------------------------------------------------
1 | import torch
2 | import torch.nn as nn
3 | import torch.nn.functional as F
4 |
5 |
6 | class denoising_single_stage(nn.Module):
7 |
8 | def __init__(self, kernel_size = 3, input_dimension = 1, output_dimension = 1,
9 | n_hidden = 5, hidden_dim = 64, bias = False, last_layer_bias = False):
10 | super(denoising_single_stage, self).__init__()
11 |
12 | if(kernel_size > 1):
13 | padding = int((kernel_size -1)/2);
14 | else:
15 | padding = 0;
16 |
17 | self.n_hidden = n_hidden;
18 | self.hidden_layer_list = [None] * (self.n_hidden);
19 |
20 | self.first_layer = nn.Conv2d(input_dimension, hidden_dim,
21 | kernel_size, stride = 1, padding = padding,
22 | bias = bias );
23 |
24 | for i in range( self.n_hidden):
25 | self.hidden_layer_list[i] = nn.Conv2d( hidden_dim, hidden_dim,
26 | kernel_size, stride = 1, padding = padding,
27 | bias = bias);
28 | self.hidden_layer_list = nn.ModuleList(self.hidden_layer_list);
29 |
30 | self.last_layer = nn.Conv2d(hidden_dim, output_dimension, kernel_size,
31 | stride = 1, padding = padding, bias = last_layer_bias);
32 |
33 | def forward(self, x):
34 |
35 | out = self.first_layer(x);
36 | out = F.relu(out);
37 | for i in range(self.n_hidden):
38 | out = self.hidden_layer_list[i](out);
39 | out = F.relu(out);
40 |
41 | out = self.last_layer(out);
42 |
43 | return(out)
44 |
45 |
--------------------------------------------------------------------------------
/models/dncnn.py:
--------------------------------------------------------------------------------
1 | import torch.nn as nn
2 | import torch.nn.init as init
3 | import torch.nn.functional as F
4 | from models import register_model
5 | from models import BFBatchNorm2d
6 | import math
7 |
8 | @register_model("dncnn")
9 | class DnCNN(nn.Module):
10 | """DnCNN as defined in https://arxiv.org/abs/1608.03981
11 | reference implementation: https://github.com/SaoYan/DnCNN-PyTorch"""
12 | def __init__(self, depth=20, n_channels=64, image_channels=1, bias=False, kernel_size=3):
13 | super(DnCNN, self).__init__()
14 | kernel_size = 3
15 | padding = 1
16 |
17 | self.bias = bias;
18 | if not bias:
19 | norm_layer = BFBatchNorm2d.BFBatchNorm2d
20 | else:
21 | norm_layer = nn.BatchNorm2d
22 | self.depth = depth;
23 |
24 |
25 | self.first_layer = nn.Conv2d(in_channels=image_channels, out_channels=n_channels, kernel_size=kernel_size, padding=padding, bias=self.bias)
26 |
27 | self.hidden_layer_list = [None] * (self.depth - 2);
28 |
29 | self.bn_layer_list = [None] * (self.depth -2 );
30 |
31 | for i in range(self.depth-2):
32 | self.hidden_layer_list[i] = nn.Conv2d(in_channels=n_channels, out_channels=n_channels, kernel_size=kernel_size, padding=padding, bias=self.bias);
33 | self.bn_layer_list[i] = norm_layer(n_channels)
34 |
35 | self.hidden_layer_list = nn.ModuleList(self.hidden_layer_list);
36 | self.bn_layer_list = nn.ModuleList(self.bn_layer_list);
37 | self.last_layer = nn.Conv2d(in_channels=n_channels, out_channels=image_channels, kernel_size=kernel_size, padding=padding, bias=self.bias)
38 |
39 | self._initialize_weights()
40 |
41 | @staticmethod
42 | def add_args(parser):
43 | """Add model-specific arguments to the parser."""
44 | parser.add_argument("--in-channels", type=int, default=1, help="number of channels")
45 | parser.add_argument("--hidden-size", type=int, default=64, help="hidden dimension")
46 | parser.add_argument("--num-layers", default=20, type=int, help="number of layers")
47 | parser.add_argument("--bias", action='store_true', help="use residual bias")
48 |
49 | @classmethod
50 | def build_model(cls, args):
51 | return cls(image_channels = args.in_channels, n_channels = args.hidden_size, depth = args.num_layers, bias=args.bias)
52 |
53 | def forward(self, x):
54 | y = x
55 | out = self.first_layer(x);
56 | out = F.relu(out);
57 |
58 | for i in range(self.depth-2):
59 | out = self.hidden_layer_list[i](out);
60 | out = self.bn_layer_list[i](out);
61 | out = F.relu(out)
62 |
63 | out = self.last_layer(out);
64 |
65 | return y-out
66 |
67 | def _initialize_weights(self):
68 | for m in self.modules():
69 | if isinstance(m, nn.Conv2d):
70 | init.kaiming_normal_(m.weight, a=0, mode='fan_in')
71 | if m.bias is not None:
72 | init.constant_(m.bias, 0)
73 | elif isinstance(m, nn.BatchNorm2d) or isinstance(m, BFBatchNorm2d.BFBatchNorm2d):
74 | m.weight.data.normal_(mean=0, std=math.sqrt(2./9./64.)).clamp_(-0.025,0.025)
75 | init.constant_(m.bias, 0)
76 |
77 |
--------------------------------------------------------------------------------
/models/rcnn.py:
--------------------------------------------------------------------------------
1 | import torch
2 | import torch.nn as nn
3 | import torch.nn.functional as F
4 | import numpy as np
5 | from models import backbone_cnn
6 | from models import register_model
7 |
8 |
9 | @register_model("rcnn")
10 | class RecurrentCNN(nn.Module):
11 | """Recurrent model inspired by https://arxiv.org/abs/1805.07709"""
12 | def __init__(self, max_stage = 4, kernel_size = 3, n_hidden = 5, hidden_dim = 64,
13 | bias = False, random_n_stages = False):
14 | super(RecurrentCNN, self).__init__()
15 |
16 |
17 | self.max_stage = max_stage;
18 | self.random_n_stages = random_n_stages;
19 |
20 | input_dimension = 2;
21 |
22 | self.cnn = backbone_cnn.denoising_single_stage(kernel_size = kernel_size,
23 | input_dimension = input_dimension,
24 | n_hidden = n_hidden,
25 | hidden_dim = hidden_dim,
26 | bias = bias);
27 |
28 |
29 | @staticmethod
30 | def add_args(parser):
31 | """Add model-specific arguments to the parser."""
32 | parser.add_argument("--max_stage", type=int, default=4, help="number of stages")
33 | parser.add_argument("--hidden-size", type=int, default=64, help="hidden dimension")
34 | parser.add_argument("--num-layers", default=5, type=int, help="number of layers")
35 | parser.add_argument("--bias", action='store_true', help="use residual bias")
36 |
37 | @classmethod
38 | def build_model(cls, args):
39 | return cls(max_stage = args.max_stage, hidden_dim = args.hidden_size, n_hidden = args.num_layers, bias = args.bias)
40 |
41 | def forward(self, x):
42 |
43 | if self.random_n_stages or self.training:
44 | n_stages = np.random.randint(self.max_stage)+1;
45 | else:
46 | n_stages = self.max_stage
47 |
48 | prev_out = x
49 | for stage in range(n_stages):
50 | temp_input = torch.cat([ x, prev_out], dim=1);
51 | prev_out = self.cnn(temp_input)
52 |
53 | return prev_out
54 |
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/models/sdensenet.py:
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1 | import torch
2 | import torch.nn as nn
3 | import torch.nn.functional as F
4 | from models import register_model
5 | from models import backbone_cnn
6 |
7 |
8 | @register_model("sdensenet")
9 | class SimplifiedDenseNet(nn.Module):
10 | """Model with DenseNet style connections, which is equivalent to unrolling rcnn.py"""
11 |
12 | def __init__(self, max_stage = 4, kernel_size = 3, n_hidden = 5, hidden_dim = 64,
13 | bias = False, random_n_stages = False):
14 | super(SimplifiedDenseNet, self).__init__()
15 |
16 |
17 | self.max_stage = max_stage;
18 | self.random_n_stages = random_n_stages;
19 |
20 |
21 | self.cnn_list = [None]* self.max_stage
22 | for i in range(self.max_stage):
23 | self.cnn_list[i] = backbone_cnn.denoising_single_stage(kernel_size = kernel_size,
24 | input_dimension = 1 if i==0 else n_hidden+1,
25 | output_dimension = 1 if i==self.max_stage-1 else n_hidden,
26 | n_hidden = n_hidden,
27 | hidden_dim = hidden_dim,
28 | bias = bias,
29 | last_layer_bias = (i != self.max_stage-1) and bias );
30 | self.cnn_list = nn.ModuleList(self.cnn_list)
31 |
32 |
33 | @staticmethod
34 | def add_args(parser):
35 | """Add model-specific arguments to the parser."""
36 | parser.add_argument("--max_stage", type=int, default=4, help="number of stages")
37 | parser.add_argument("--hidden-size", type=int, default=64, help="hidden dimension")
38 | parser.add_argument("--num-layers", default=5, type=int, help="number of layers")
39 | parser.add_argument("--bias", action='store_true', help="use residual bias")
40 |
41 | @classmethod
42 | def build_model(cls, args):
43 | return cls(max_stage = args.max_stage, hidden_dim = args.hidden_size, n_hidden = args.num_layers, bias = args.bias)
44 |
45 | def forward(self, x):
46 |
47 | for stage in range(self.max_stage):
48 | if stage==0:
49 | temp_input = x
50 | else:
51 | temp_input = torch.cat([ x, prev_out], dim=1);
52 | prev_out = self.cnn_list[stage](temp_input)
53 |
54 | return prev_out
55 |
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/models/unet.py:
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1 | import torch
2 | import torch.nn as nn
3 | import torch.nn.functional as F
4 | from models import register_model
5 |
6 |
7 |
8 | @register_model("unet")
9 | class UNet(nn.Module):
10 | """UNet as defined in https://arxiv.org/abs/1805.07709"""
11 | def __init__(self, bias, residual_connection = False):
12 | super(UNet, self).__init__()
13 | self.conv1 = nn.Conv2d(1,32,5,padding = 2, bias = bias)
14 | self.conv2 = nn.Conv2d(32,32,3,padding = 1, bias = bias)
15 | self.conv3 = nn.Conv2d(32,64,3,stride=2, padding = 1, bias = bias)
16 | self.conv4 = nn.Conv2d(64,64,3,padding = 1, bias=bias)
17 | self.conv5 = nn.Conv2d(64,64,3,dilation=2, padding = 2, bias = bias)
18 | self.conv6 = nn.Conv2d(64,64,3,dilation = 4,padding = 4, bias = bias)
19 | self.conv7 = nn.ConvTranspose2d(64,64, 4,stride = 2, padding = 1, bias = bias)
20 | self.conv8 = nn.Conv2d(96,32,3,padding=1, bias = bias)
21 | self.conv9 = nn.Conv2d(32,1,5,padding = 2, bias = False)
22 |
23 | self.residual_connection = residual_connection;
24 |
25 | @staticmethod
26 | def add_args(parser):
27 | """Add model-specific arguments to the parser."""
28 | parser.add_argument("--bias", action='store_true', help="use residual bias")
29 | parser.add_argument("--residual", action='store_true', help="use residual connection")
30 |
31 | @classmethod
32 | def build_model(cls, args):
33 | return cls(args.bias, args.residual)
34 |
35 | def forward(self, x):
36 | pad_right = x.shape[-2]%2
37 | pad_bottom = x.shape[-1]%2
38 | padding = nn.ZeroPad2d((0, pad_bottom, 0, pad_right))
39 | x = padding(x)
40 |
41 | out = F.relu(self.conv1(x))
42 |
43 | out_saved = F.relu(self.conv2(out))
44 |
45 | out = F.relu(self.conv3(out_saved))
46 | out = F.relu(self.conv4(out))
47 | out = F.relu(self.conv5(out))
48 | out = F.relu(self.conv6(out))
49 | out = F.relu(self.conv7(out))
50 |
51 | out = torch.cat([out,out_saved],dim = 1)
52 |
53 | out = F.relu(self.conv8(out))
54 | out = self.conv9(out)
55 |
56 | if self.residual_connection:
57 | out = x - out;
58 |
59 | if pad_bottom:
60 | out = out[:, :, :, :-1]
61 | if pad_right:
62 | out = out[:, :, :-1, :]
63 |
64 | return out
65 |
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/requirements.txt:
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1 | pytorch>=1.2
2 | tensorboard>=1.14
3 | skimage=0.16.2
4 | cv2
5 | hdf5
6 | tqdm
7 |
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/utils/__init__.py:
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1 | from .meters import AverageMeter, RunningAverageMeter, TimeMeter
2 | from .progress_bar import ProgressBar
3 | from .noise_model import get_noise
4 | from .train_utils import *
5 | from .metrics import *
6 | from .test_metrics import metrics_avg_on_noise_range
7 | from .demo_utils import *
8 |
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/utils/data/__init__.py:
--------------------------------------------------------------------------------
1 | import os
2 | import os.path
3 | import numpy as np
4 | import h5py
5 | import torch
6 |
7 |
8 | DATASET_REGISTRY = {}
9 |
10 |
11 | def build_dataset(name, *args, **kwargs):
12 | return DATASET_REGISTRY[name](*args, **kwargs)
13 |
14 |
15 | def register_dataset(name):
16 | def register_dataset_fn(fn):
17 | if name in DATASET_REGISTRY:
18 | raise ValueError("Cannot register duplicate dataset ({})".format(name))
19 | DATASET_REGISTRY[name] = fn
20 | return fn
21 |
22 | return register_dataset_fn
23 |
24 |
25 | @register_dataset("bsd400")
26 | def load_bsd400(data, batch_size=100, num_workers=0):
27 | train_dataset = Dataset(filename=os.path.join(data, "train.h5"))
28 | train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, num_workers=1, shuffle=True)
29 |
30 | valid_dataset = Dataset(filename=os.path.join(data, "valid.h5"))
31 | valid_loader = torch.utils.data.DataLoader(valid_dataset, batch_size=1, num_workers=1, shuffle=False)
32 | return train_loader, valid_loader, None
33 |
34 |
35 | class Dataset(torch.utils.data.Dataset):
36 | def __init__(self, filename):
37 | super().__init__()
38 | self.h5f = h5py.File(filename, "r")
39 | self.keys = list(self.h5f.keys())
40 |
41 | def __len__(self):
42 | return len(self.keys)
43 |
44 | def __getitem__(self, index):
45 | key = self.keys[index]
46 | data = np.array(self.h5f[key])
47 | return torch.Tensor(data)
48 |
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/utils/meters.py:
--------------------------------------------------------------------------------
1 | import time
2 | import torch
3 |
4 |
5 | class AverageMeter(object):
6 | def __init__(self):
7 | self.reset()
8 |
9 | def reset(self):
10 | self.val = 0
11 | self.avg = 0
12 | self.sum = 0
13 | self.count = 0
14 |
15 | def update(self, val, n=1):
16 | if isinstance(val, torch.Tensor):
17 | val = val.item()
18 | self.val = val / n
19 | self.sum += val
20 | self.count += n
21 | self.avg = self.sum / self.count
22 |
23 |
24 | class RunningAverageMeter(object):
25 | def __init__(self, momentum=0.98):
26 | self.momentum = momentum
27 | self.reset()
28 |
29 | def reset(self):
30 | self.val = None
31 | self.avg = 0
32 |
33 | def update(self, val):
34 | if isinstance(val, torch.Tensor):
35 | val = val.item()
36 | if self.val is None:
37 | self.avg = val
38 | else:
39 | self.avg = self.avg * self.momentum + val * (1 - self.momentum)
40 | self.val = val
41 |
42 |
43 | class TimeMeter(object):
44 | def __init__(self, init=0):
45 | self.reset(init)
46 |
47 | def reset(self, init=0):
48 | self.init = init
49 | self.start = time.time()
50 | self.n = 0
51 |
52 | def update(self, val=1):
53 | self.n += val
54 |
55 | @property
56 | def avg(self):
57 | return self.n / self.elapsed_time
58 |
59 | @property
60 | def elapsed_time(self):
61 | return self.init + (time.time() - self.start)
62 |
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/utils/metrics.py:
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1 | import numpy as np
2 | from skimage.metrics import peak_signal_noise_ratio, structural_similarity
3 |
4 |
5 | def ssim(clean, noisy, normalized=True):
6 | """Use skimage.meamsure.compare_ssim to calculate SSIM
7 | Args:
8 | clean (Tensor): (B, 1, H, W)
9 | noisy (Tensor): (B, 1, H, W)
10 | normalized (bool): If True, the range of tensors are [0., 1.] else [0, 255]
11 | Returns:
12 | SSIM per image: (B, )
13 | """
14 | if normalized:
15 | clean = clean.mul(255).clamp(0, 255)
16 | noisy = noisy.mul(255).clamp(0, 255)
17 |
18 | clean = clean.cpu().detach().numpy().astype(np.float32)
19 | noisy = noisy.cpu().detach().numpy().astype(np.float32)
20 | return np.array([structural_similarity(c[0], n[0], data_range=255) for c, n in zip(clean, noisy)]).mean()
21 |
22 |
23 | def psnr(clean, noisy, normalized=True):
24 | """Use skimage.meamsure.compare_ssim to calculate SSIM
25 | Args:
26 | clean (Tensor): (B, 1, H, W)
27 | noisy (Tensor): (B, 1, H, W)
28 | normalized (bool): If True, the range of tensors are [0., 1.]
29 | else [0, 255]
30 | Returns:
31 | SSIM per image: (B, )
32 | """
33 | if normalized:
34 | clean = clean.mul(255).clamp(0, 255)
35 | noisy = noisy.mul(255).clamp(0, 255)
36 |
37 | clean = clean.cpu().detach().numpy().astype(np.float32)
38 | noisy = noisy.cpu().detach().numpy().astype(np.float32)
39 | return np.array([peak_signal_noise_ratio(c[0], n[0], data_range=255) for c, n in zip(clean, noisy)]).mean()
40 |
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/utils/noise_model.py:
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1 | import torch
2 |
3 | def get_noise(data, noise_std = float(25)/255.0, mode='S',
4 | min_noise = float(5)/255., max_noise = float(55)/255.):
5 | noise = torch.randn_like(data);
6 | if mode == 'B':
7 | n = noise.shape[0];
8 | noise_tensor_array = (max_noise - min_noise) * torch.rand(n) + min_noise;
9 | for i in range(n):
10 | noise.data[i] = noise.data[i] * noise_tensor_array[i];
11 | else:
12 | noise.data = noise.data * noise_std;
13 | return noise
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/utils/progress_bar.py:
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1 | from collections import OrderedDict
2 | from numbers import Number
3 | from tqdm import tqdm
4 | from .meters import AverageMeter, RunningAverageMeter, TimeMeter
5 |
6 |
7 | class ProgressBar:
8 | def __init__(self, iterable, epoch=None, prefix=None, quiet=False):
9 | self.epoch = epoch
10 | self.quiet = quiet
11 | self.prefix = prefix + ' | ' if prefix is not None else ''
12 | if epoch is not None:
13 | self.prefix += f"epoch {epoch:02d}"
14 | self.iterable = iterable if self.quiet else tqdm(iterable, self.prefix, leave=False)
15 |
16 | def __iter__(self):
17 | return iter(self.iterable)
18 |
19 | def log(self, stats, verbose=False):
20 | if not self.quiet:
21 | self.iterable.set_postfix(self.format_stats(stats, verbose), refresh=True)
22 |
23 | def format_stats(self, stats, verbose=False):
24 | postfix = OrderedDict(stats)
25 | for key, value in postfix.items():
26 | if isinstance(value, Number):
27 | fmt = "{:.3f}" if value > 0.001 else "{:.1e}"
28 | postfix[key] = fmt.format(value)
29 | elif isinstance(value, AverageMeter) or isinstance(value, RunningAverageMeter):
30 | if verbose:
31 | postfix[key] = f"{value.avg:.3f} ({value.val:.3f})"
32 | else:
33 | postfix[key] = f"{value.avg:.3f}"
34 | elif isinstance(value, TimeMeter):
35 | postfix[key] = f"{value.elapsed_time:.1f}s"
36 | elif not isinstance(postfix[key], str):
37 | postfix[key] = str(value)
38 | return postfix
39 |
40 | def print(self, stats, verbose=False):
41 | postfix = " | ".join(key + " " + value.strip() for key, value in self.format_stats(stats, verbose).items())
42 | return f"{self.prefix + ' | ' if self.epoch is not None else ''}{postfix}"
43 |
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/utils/test_metrics.py:
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1 | import torch
2 | import numpy as np
3 | import matplotlib.pyplot as plt
4 | import glob
5 | import os
6 | import cv2
7 | from utils import get_noise, ssim, psnr
8 |
9 | metrics_key = ['psnr', 'psnr_delta', 'ssim', 'ssim_delta'];
10 |
11 | def tensor_to_image(torch_image, low=0.0, high = 1.0, clamp = True):
12 | if clamp:
13 | torch_image = torch.clamp(torch_image, low, high);
14 | return torch_image[0,0].cpu().data.numpy()
15 |
16 |
17 | def normalize(data):
18 | return data/255.
19 |
20 | def convert_dict_to_string(metrics):
21 | return_string = '';
22 | for x in metrics.keys():
23 | return_string += x+': '+str(round(metrics[x], 3))+' ';
24 | return return_string
25 |
26 |
27 |
28 | def get_all_comparison_metrics(denoised, source, noisy = None, return_title_string = False, clamp = True):
29 |
30 |
31 |
32 | metrics = {};
33 | metrics['psnr'] = np.zeros(len(denoised))
34 | metrics['ssim'] = np.zeros(len(denoised))
35 | if noisy is not None:
36 | metrics['psnr_delta'] = np.zeros(len(denoised))
37 | metrics['ssim_delta'] = np.zeros(len(denoised))
38 |
39 | if clamp:
40 | denoised = torch.clamp(denoised, 0.0, 1.0)
41 |
42 |
43 | metrics['psnr'] = psnr(source, denoised);
44 | metrics['ssim'] = ssim(source, denoised);
45 |
46 | if noisy is not None:
47 | metrics['psnr_delta'] = metrics['psnr'] - psnr(source, noisy);
48 | metrics['ssim_delta'] = metrics['ssim'] - ssim(source, noisy);
49 |
50 |
51 |
52 |
53 | if return_title_string:
54 | return convert_dict_to_string(metrics)
55 | else:
56 | return metrics
57 |
58 |
59 | def average_on_folder(path_to_dataset, net, noise_std,
60 | verbose=True, device = torch.device('cuda') ):
61 |
62 | if(verbose):
63 | print('Loading data info ...\n')
64 | print('Dataset: ', path_to_dataset)
65 |
66 |
67 | files_source = glob.glob(os.path.join(path_to_dataset, '*.png'))
68 | files_source.sort()
69 |
70 | avg_metrics = {};
71 | for x in metrics_key:
72 | avg_metrics[x] = 0.0;
73 |
74 | for f in files_source:
75 | # image
76 | Img = cv2.imread(f)
77 | Img = normalize(np.float32(Img[:,:,0]))
78 | Img = np.expand_dims(Img, 0)
79 | Img = np.expand_dims(Img, 1)
80 | ISource = torch.Tensor(Img)
81 |
82 | # noisy image
83 | INoisy = get_noise(ISource, noise_std = noise_std, mode ='S') + ISource
84 | ISource, INoisy = ISource.to(device), INoisy.to(device)
85 |
86 | out = torch.clamp(net(INoisy), 0., 1.)
87 |
88 | ind_metrics = get_all_comparison_metrics(out, ISource, INoisy, return_title_string = False);
89 | for x in metrics_key:
90 | avg_metrics[x] += ind_metrics[x];
91 |
92 | if(verbose):
93 | print("%s %s" % (f, convert_dict_to_string(ind_metrics)))
94 |
95 | for x in metrics_key:
96 | avg_metrics[x] /= len(files_source);
97 |
98 | if(verbose):
99 | print("\n Average %s" % (convert_dict_to_string(avg_metrics)))
100 |
101 | if(not verbose):
102 | return avg_metrics
103 |
104 |
105 | def metrics_avg_on_noise_range(net, path_to_dataset,noise_std_array, n_average = 1, device = torch.device('cuda')):
106 |
107 | print(path_to_dataset)
108 |
109 | array_metrics = {};
110 | for x in metrics_key:
111 | array_metrics[x] = np.zeros(len(noise_std_array))
112 |
113 | for j, noise_std in enumerate(noise_std_array):
114 | metric_list = [None]*n_average
115 | for i in range(n_average):
116 | metric_list[i] = average_on_folder(path_to_dataset, net,
117 | noise_std = noise_std,
118 | verbose=False, device=device);
119 |
120 | for x in metrics_key:
121 | for i in range(n_average):
122 | array_metrics[x][j] += metric_list[i][x]
123 | array_metrics[x][j] /= n_average
124 | print('noise: ', int(noise_std*255), ' ', x, ': ', str(array_metrics[x][j]))
125 |
126 | return array_metrics
127 |
128 |
129 |
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