├── LICENSE ├── README.md ├── data ├── 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 └── train │ ├── 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 │ ├── test_122.png │ ├── test_123.png │ ├── test_124.png │ ├── test_125.png │ ├── test_126.png │ ├── test_127.png │ ├── test_128.png │ ├── test_129.png │ ├── test_130.png │ ├── test_131.png │ ├── test_132.png │ ├── test_133.png │ ├── test_134.png │ ├── test_135.png │ ├── test_136.png │ ├── test_137.png │ ├── test_138.png │ ├── test_139.png │ ├── test_140.png │ ├── test_141.png │ ├── test_142.png │ ├── test_143.png │ ├── test_144.png │ ├── test_145.png │ ├── 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 │ ├── test_167.png │ ├── 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 │ ├── test_179.png │ ├── test_180.png │ ├── test_181.png │ ├── test_182.png │ ├── test_183.png │ ├── test_184.png │ ├── test_185.png │ ├── test_186.png │ ├── test_187.png │ ├── test_188.png │ ├── test_189.png │ ├── test_190.png │ ├── test_191.png │ ├── test_192.png │ ├── test_193.png │ ├── test_194.png │ ├── test_195.png │ ├── test_196.png │ ├── test_197.png │ ├── test_198.png │ ├── test_199.png │ ├── test_200.png │ ├── test_201.png │ ├── test_202.png │ ├── test_203.png │ ├── test_204.png │ ├── test_205.png │ ├── test_206.png │ ├── test_207.png │ ├── test_208.png │ ├── test_209.png │ ├── test_210.png │ ├── test_211.png │ ├── test_212.png │ ├── test_213.png │ ├── test_214.png │ ├── test_215.png │ ├── test_216.png │ ├── test_217.png │ ├── test_218.png │ ├── test_219.png │ ├── test_220.png │ ├── test_221.png │ ├── test_222.png │ ├── test_223.png │ ├── test_224.png │ ├── test_225.png │ ├── test_226.png │ ├── test_227.png │ ├── test_228.png │ ├── test_229.png │ ├── test_230.png │ ├── test_231.png │ ├── test_232.png │ ├── test_233.png │ ├── test_234.png │ ├── test_235.png │ ├── test_236.png │ ├── test_237.png │ ├── test_238.png │ ├── test_239.png │ ├── test_240.png │ ├── test_241.png │ ├── test_242.png │ ├── test_243.png │ ├── test_244.png │ ├── test_245.png │ ├── test_246.png │ ├── test_247.png │ ├── test_248.png │ ├── test_249.png │ ├── test_250.png │ ├── test_251.png │ ├── test_252.png │ ├── test_253.png │ ├── test_254.png │ ├── test_255.png │ ├── test_256.png │ ├── test_257.png │ ├── test_258.png │ ├── test_259.png │ ├── test_260.png │ ├── test_261.png │ ├── test_262.png │ ├── test_263.png │ ├── test_264.png │ ├── test_265.png │ ├── test_266.png │ ├── test_267.png │ ├── test_268.png │ ├── test_269.png │ ├── test_270.png │ ├── test_271.png │ ├── test_272.png │ ├── test_273.png │ ├── test_274.png │ ├── test_275.png │ ├── test_276.png │ ├── test_277.png │ ├── test_278.png │ ├── test_279.png │ ├── test_280.png │ ├── test_281.png │ ├── test_282.png │ ├── test_283.png │ ├── test_284.png │ ├── test_285.png │ ├── test_286.png │ ├── test_287.png │ ├── test_288.png │ ├── test_289.png │ ├── test_290.png │ ├── test_291.png │ ├── test_292.png │ ├── test_293.png │ ├── test_294.png │ ├── test_295.png │ ├── test_296.png │ ├── test_297.png │ ├── test_298.png │ ├── test_299.png │ ├── test_300.png │ ├── test_301.png │ ├── test_302.png │ ├── test_303.png │ ├── test_304.png │ ├── test_305.png │ ├── test_306.png │ ├── test_307.png │ ├── test_308.png │ ├── test_309.png │ ├── test_310.png │ ├── test_311.png │ ├── test_312.png │ ├── test_313.png │ ├── test_314.png │ ├── test_315.png │ ├── test_316.png │ ├── test_317.png │ ├── test_318.png │ ├── test_319.png │ ├── test_320.png │ ├── test_321.png │ ├── test_322.png │ ├── test_323.png │ ├── test_324.png │ ├── test_325.png │ ├── test_326.png │ ├── test_327.png │ ├── test_328.png │ ├── test_329.png │ ├── test_330.png │ ├── test_331.png │ ├── test_332.png │ ├── test_333.png │ ├── test_334.png │ ├── test_335.png │ ├── test_336.png │ ├── test_337.png │ ├── test_338.png │ ├── test_339.png │ ├── test_340.png │ ├── test_341.png │ ├── test_342.png │ ├── 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 ├── dataset.py ├── logs ├── DnCNN-B │ └── net.pth ├── DnCNN-S-15 │ └── net.pth ├── DnCNN-S-25 │ └── net.pth └── DnCNN-S-50 │ └── net.pth ├── models.py ├── test.py ├── train.py └── utils.py /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # DnCNN-PyTorch 2 | 3 | This is a PyTorch implementation of the TIP2017 paper [*Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising*](http://ieeexplore.ieee.org/document/7839189/). The author's [MATLAB implementation is here](https://github.com/cszn/DnCNN). 4 | 5 | **** 6 | This code was written with PyTorch<0.4, but most people must be using PyTorch>=0.4 today. Migrating the code is easy. Please refer to [PyTorch 0.4.0 Migration Guide](https://pytorch.org/blog/pytorch-0_4_0-migration-guide/). 7 | 8 | **** 9 | 10 | ## How to run 11 | 12 | ### 1. Dependences 13 | * [PyTorch](http://pytorch.org/)(<0.4) 14 | * [torchvision](https://github.com/pytorch/vision) 15 | * OpenCV for Python 16 | * [HDF5 for Python](http://www.h5py.org/) 17 | * [tensorboardX](https://github.com/lanpa/tensorboard-pytorch) (TensorBoard for PyTorch) 18 | 19 | ### 2. Train DnCNN-S (DnCNN with known noise level) 20 | ``` 21 | python train.py \ 22 | --preprocess True \ 23 | --num_of_layers 17 \ 24 | --mode S \ 25 | --noiseL 25 \ 26 | --val_noiseL 25 27 | ``` 28 | **NOTE** 29 | * If you've already built the training and validation dataset (i.e. train.h5 & val.h5 files), set *preprocess* to be False. 30 | * According to the paper, DnCNN-S has 17 layers. 31 | * *noiseL* is used for training and *val_noiseL* is used for validation. They should be set to the same value for unbiased validation. You can set whatever noise level you need. 32 | 33 | ### 3. Train DnCNN-B (DnCNN with blind noise level) 34 | ``` 35 | python train.py \ 36 | --preprocess True \ 37 | --num_of_layers 20 \ 38 | --mode B \ 39 | --val_noiseL 25 40 | ``` 41 | **NOTE** 42 | * If you've already built the training and validation dataset (i.e. train.h5 & val.h5 files), set *preprocess* to be False. 43 | * According to the paper, DnCNN-B has 20 layers. 44 | * *noiseL* is ingnored when training DnCNN-B. You can set *val_noiseL* to whatever you need. 45 | 46 | ### 4. Test 47 | ``` 48 | python test.py \ 49 | --num_of_layers 17 \ 50 | --logdir logs/DnCNN-S-15 \ 51 | --test_data Set12 \ 52 | --test_noiseL 15 53 | ``` 54 | **NOTE** 55 | * Set *num_of_layers* to be 17 when testing DnCNN-S models. Set *num_of_layers* to be 20 when testing DnCNN-B model. 56 | * *test_data* can be *Set12* or *Set68*. 57 | * *test_noiseL* is used for testing. This should be set according to which model your want to test (i.e. *logdir*). 58 | 59 | ## Test Results 60 | 61 | ### BSD68 Average RSNR 62 | 63 | | Noise Level | DnCNN-S | DnCNN-B | DnCNN-S-PyTorch | DnCNN-B-PyTorch | 64 | |:-----------:|:-------:|:-------:|:---------------:|:---------------:| 65 | | 15 | 31.73 | 31.61 | 31.71 | 31.60 | 66 | | 25 | 29.23 | 29.16 | 29.21 | 29.15 | 67 | | 50 | 26.23 | 26.23 | 26.22 | 26.20 | 68 | 69 | ### Set12 Average PSNR 70 | 71 | | Noise Level | DnCNN-S | DnCNN-B | DnCNN-S-PyTorch | DnCNN-B-PyTorch | 72 | |:-----------:|:-------:|:-------:|:---------------:|:---------------:| 73 | | 15 | 32.859 | 32.680 | 32.837 | 32.725 | 74 | | 25 | 30.436 | 30.362 | 30.404 | 30.344 | 75 | | 50 | 27.178 | 27.206 | 27.165 | 27.138 | 76 | 77 | ## Tricks useful for boosting performance 78 | * Parameter initialization: 79 | Use *kaiming_normal* initialization for *Conv*; Pay attention to the initialization of *BatchNorm* 80 | ``` 81 | def weights_init_kaiming(m): 82 | classname = m.__class__.__name__ 83 | if classname.find('Conv') != -1: 84 | nn.init.kaiming_normal(m.weight.data, a=0, mode='fan_in') 85 | elif classname.find('Linear') != -1: 86 | nn.init.kaiming_normal(m.weight.data, a=0, mode='fan_in') 87 | elif classname.find('BatchNorm') != -1: 88 | m.weight.data.normal_(mean=0, std=math.sqrt(2./9./64.)).clamp_(-0.025,0.025) 89 | nn.init.constant(m.bias.data, 0.0) 90 | ``` 91 | * The definition of loss function 92 | Set *size_average* to be False when defining the loss function. When *size_average=True*, the **pixel-wise average** will be computed, but what we need is **sample-wise average**. 93 | ``` 94 | criterion = nn.MSELoss(size_average=False) 95 | ``` 96 | The computation of loss will be like: 97 | ``` 98 | loss = criterion(out_train, noise) / (imgn_train.size()[0]*2) 99 | ``` 100 | where we divide the sum over one batch of samples by *2N*, with *N* being # samples. 101 | -------------------------------------------------------------------------------- /data/Set12/01.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SaoYan/DnCNN-PyTorch/6b0804951484eadb7f1ea24e8e5c9ede9bea485b/data/Set12/01.png -------------------------------------------------------------------------------- /data/Set12/02.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SaoYan/DnCNN-PyTorch/6b0804951484eadb7f1ea24e8e5c9ede9bea485b/data/Set12/02.png 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https://raw.githubusercontent.com/SaoYan/DnCNN-PyTorch/6b0804951484eadb7f1ea24e8e5c9ede9bea485b/data/train/test_400.png -------------------------------------------------------------------------------- /dataset.py: -------------------------------------------------------------------------------- 1 | import os 2 | import os.path 3 | import numpy as np 4 | import random 5 | import h5py 6 | import torch 7 | import cv2 8 | import glob 9 | import torch.utils.data as udata 10 | from utils import data_augmentation 11 | 12 | def normalize(data): 13 | return data/255. 14 | 15 | def Im2Patch(img, win, stride=1): 16 | k = 0 17 | endc = img.shape[0] 18 | endw = img.shape[1] 19 | endh = img.shape[2] 20 | patch = img[:, 0:endw-win+0+1:stride, 0:endh-win+0+1:stride] 21 | TotalPatNum = patch.shape[1] * patch.shape[2] 22 | Y = np.zeros([endc, win*win,TotalPatNum], np.float32) 23 | for i in range(win): 24 | for j in range(win): 25 | patch = img[:,i:endw-win+i+1:stride,j:endh-win+j+1:stride] 26 | Y[:,k,:] = np.array(patch[:]).reshape(endc, TotalPatNum) 27 | k = k + 1 28 | return Y.reshape([endc, win, win, TotalPatNum]) 29 | 30 | def prepare_data(data_path, patch_size, stride, aug_times=1): 31 | # train 32 | print('process training data') 33 | scales = [1, 0.9, 0.8, 0.7] 34 | files = glob.glob(os.path.join(data_path, 'train', '*.png')) 35 | files.sort() 36 | h5f = h5py.File('train.h5', 'w') 37 | train_num = 0 38 | for i in range(len(files)): 39 | img = cv2.imread(files[i]) 40 | h, w, c = img.shape 41 | for k in range(len(scales)): 42 | Img = cv2.resize(img, (int(h*scales[k]), int(w*scales[k])), interpolation=cv2.INTER_CUBIC) 43 | Img = np.expand_dims(Img[:,:,0].copy(), 0) 44 | Img = np.float32(normalize(Img)) 45 | patches = Im2Patch(Img, win=patch_size, stride=stride) 46 | print("file: %s scale %.1f # samples: %d" % (files[i], scales[k], patches.shape[3]*aug_times)) 47 | for n in range(patches.shape[3]): 48 | data = patches[:,:,:,n].copy() 49 | h5f.create_dataset(str(train_num), data=data) 50 | train_num += 1 51 | for m in range(aug_times-1): 52 | data_aug = data_augmentation(data, np.random.randint(1,8)) 53 | h5f.create_dataset(str(train_num)+"_aug_%d" % (m+1), data=data_aug) 54 | train_num += 1 55 | h5f.close() 56 | # val 57 | print('\nprocess validation data') 58 | files.clear() 59 | files = glob.glob(os.path.join(data_path, 'Set12', '*.png')) 60 | files.sort() 61 | h5f = h5py.File('val.h5', 'w') 62 | val_num = 0 63 | for i in range(len(files)): 64 | print("file: %s" % files[i]) 65 | img = cv2.imread(files[i]) 66 | img = np.expand_dims(img[:,:,0], 0) 67 | img = np.float32(normalize(img)) 68 | h5f.create_dataset(str(val_num), data=img) 69 | val_num += 1 70 | h5f.close() 71 | print('training set, # samples %d\n' % train_num) 72 | print('val set, # samples %d\n' % val_num) 73 | 74 | class Dataset(udata.Dataset): 75 | def __init__(self, train=True): 76 | super(Dataset, self).__init__() 77 | self.train = train 78 | if self.train: 79 | h5f = h5py.File('train.h5', 'r') 80 | else: 81 | h5f = h5py.File('val.h5', 'r') 82 | self.keys = list(h5f.keys()) 83 | random.shuffle(self.keys) 84 | h5f.close() 85 | def __len__(self): 86 | return len(self.keys) 87 | def __getitem__(self, index): 88 | if self.train: 89 | h5f = h5py.File('train.h5', 'r') 90 | else: 91 | h5f = h5py.File('val.h5', 'r') 92 | key = self.keys[index] 93 | data = np.array(h5f[key]) 94 | h5f.close() 95 | return torch.Tensor(data) 96 | -------------------------------------------------------------------------------- /logs/DnCNN-B/net.pth: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SaoYan/DnCNN-PyTorch/6b0804951484eadb7f1ea24e8e5c9ede9bea485b/logs/DnCNN-B/net.pth -------------------------------------------------------------------------------- /logs/DnCNN-S-15/net.pth: -------------------------------------------------------------------------------- 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__init__(self, channels, num_of_layers=17): 6 | super(DnCNN, self).__init__() 7 | kernel_size = 3 8 | padding = 1 9 | features = 64 10 | layers = [] 11 | layers.append(nn.Conv2d(in_channels=channels, out_channels=features, kernel_size=kernel_size, padding=padding, bias=False)) 12 | layers.append(nn.ReLU(inplace=True)) 13 | for _ in range(num_of_layers-2): 14 | layers.append(nn.Conv2d(in_channels=features, out_channels=features, kernel_size=kernel_size, padding=padding, bias=False)) 15 | layers.append(nn.BatchNorm2d(features)) 16 | layers.append(nn.ReLU(inplace=True)) 17 | layers.append(nn.Conv2d(in_channels=features, out_channels=channels, kernel_size=kernel_size, padding=padding, bias=False)) 18 | self.dncnn = nn.Sequential(*layers) 19 | def forward(self, x): 20 | out = self.dncnn(x) 21 | return out 22 | -------------------------------------------------------------------------------- /test.py: -------------------------------------------------------------------------------- 1 | import cv2 2 | import os 3 | import argparse 4 | import glob 5 | import numpy as np 6 | import torch 7 | import torch.nn as nn 8 | from torch.autograd import Variable 9 | from models import DnCNN 10 | from utils import * 11 | 12 | os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" 13 | os.environ["CUDA_VISIBLE_DEVICES"] = "0" 14 | 15 | parser = argparse.ArgumentParser(description="DnCNN_Test") 16 | parser.add_argument("--num_of_layers", type=int, default=17, help="Number of total layers") 17 | parser.add_argument("--logdir", type=str, default="logs", help='path of log files') 18 | parser.add_argument("--test_data", type=str, default='Set12', help='test on Set12 or Set68') 19 | parser.add_argument("--test_noiseL", type=float, default=25, help='noise level used on test set') 20 | opt = parser.parse_args() 21 | 22 | def normalize(data): 23 | return data/255. 24 | 25 | def main(): 26 | # Build model 27 | print('Loading model ...\n') 28 | net = DnCNN(channels=1, num_of_layers=opt.num_of_layers) 29 | device_ids = [0] 30 | model = nn.DataParallel(net, device_ids=device_ids).cuda() 31 | model.load_state_dict(torch.load(os.path.join(opt.logdir, 'net.pth'))) 32 | model.eval() 33 | # load data info 34 | print('Loading data info ...\n') 35 | files_source = glob.glob(os.path.join('data', opt.test_data, '*.png')) 36 | files_source.sort() 37 | # process data 38 | psnr_test = 0 39 | for f in files_source: 40 | # image 41 | Img = cv2.imread(f) 42 | Img = normalize(np.float32(Img[:,:,0])) 43 | Img = np.expand_dims(Img, 0) 44 | Img = np.expand_dims(Img, 1) 45 | ISource = torch.Tensor(Img) 46 | # noise 47 | noise = torch.FloatTensor(ISource.size()).normal_(mean=0, std=opt.test_noiseL/255.) 48 | # noisy image 49 | INoisy = ISource + noise 50 | ISource, INoisy = Variable(ISource.cuda()), Variable(INoisy.cuda()) 51 | with torch.no_grad(): # this can save much memory 52 | Out = torch.clamp(INoisy-model(INoisy), 0., 1.) 53 | ## if you are using older version of PyTorch, torch.no_grad() may not be supported 54 | # ISource, INoisy = Variable(ISource.cuda(),volatile=True), Variable(INoisy.cuda(),volatile=True) 55 | # Out = torch.clamp(INoisy-model(INoisy), 0., 1.) 56 | psnr = batch_PSNR(Out, ISource, 1.) 57 | psnr_test += psnr 58 | print("%s PSNR %f" % (f, psnr)) 59 | psnr_test /= len(files_source) 60 | print("\nPSNR on test data %f" % psnr_test) 61 | 62 | if __name__ == "__main__": 63 | main() 64 | -------------------------------------------------------------------------------- /train.py: -------------------------------------------------------------------------------- 1 | import os 2 | import argparse 3 | import numpy as np 4 | import torch 5 | import torch.nn as nn 6 | import torch.optim as optim 7 | import torchvision.utils as utils 8 | from torch.autograd import Variable 9 | from torch.utils.data import DataLoader 10 | from tensorboardX import SummaryWriter 11 | from models import DnCNN 12 | from dataset import prepare_data, Dataset 13 | from utils import * 14 | 15 | os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" 16 | os.environ["CUDA_VISIBLE_DEVICES"] = "0" 17 | 18 | parser = argparse.ArgumentParser(description="DnCNN") 19 | parser.add_argument("--preprocess", type=bool, default=False, help='run prepare_data or not') 20 | parser.add_argument("--batchSize", type=int, default=128, help="Training batch size") 21 | parser.add_argument("--num_of_layers", type=int, default=17, help="Number of total layers") 22 | parser.add_argument("--epochs", type=int, default=50, help="Number of training epochs") 23 | parser.add_argument("--milestone", type=int, default=30, help="When to decay learning rate; should be less than epochs") 24 | parser.add_argument("--lr", type=float, default=1e-3, help="Initial learning rate") 25 | parser.add_argument("--outf", type=str, default="logs", help='path of log files') 26 | parser.add_argument("--mode", type=str, default="S", help='with known noise level (S) or blind training (B)') 27 | parser.add_argument("--noiseL", type=float, default=25, help='noise level; ignored when mode=B') 28 | parser.add_argument("--val_noiseL", type=float, default=25, help='noise level used on validation set') 29 | opt = parser.parse_args() 30 | 31 | def main(): 32 | # Load dataset 33 | print('Loading dataset ...\n') 34 | dataset_train = Dataset(train=True) 35 | dataset_val = Dataset(train=False) 36 | loader_train = DataLoader(dataset=dataset_train, num_workers=4, batch_size=opt.batchSize, shuffle=True) 37 | print("# of training samples: %d\n" % int(len(dataset_train))) 38 | # Build model 39 | net = DnCNN(channels=1, num_of_layers=opt.num_of_layers) 40 | net.apply(weights_init_kaiming) 41 | criterion = nn.MSELoss(size_average=False) 42 | # Move to GPU 43 | device_ids = [0] 44 | model = nn.DataParallel(net, device_ids=device_ids).cuda() 45 | criterion.cuda() 46 | # Optimizer 47 | optimizer = optim.Adam(model.parameters(), lr=opt.lr) 48 | # training 49 | writer = SummaryWriter(opt.outf) 50 | step = 0 51 | noiseL_B=[0,55] # ingnored when opt.mode=='S' 52 | for epoch in range(opt.epochs): 53 | if epoch < opt.milestone: 54 | current_lr = opt.lr 55 | else: 56 | current_lr = opt.lr / 10. 57 | # set learning rate 58 | for param_group in optimizer.param_groups: 59 | param_group["lr"] = current_lr 60 | print('learning rate %f' % current_lr) 61 | # train 62 | for i, data in enumerate(loader_train, 0): 63 | # training step 64 | model.train() 65 | model.zero_grad() 66 | optimizer.zero_grad() 67 | img_train = data 68 | if opt.mode == 'S': 69 | noise = torch.FloatTensor(img_train.size()).normal_(mean=0, std=opt.noiseL/255.) 70 | if opt.mode == 'B': 71 | noise = torch.zeros(img_train.size()) 72 | stdN = np.random.uniform(noiseL_B[0], noiseL_B[1], size=noise.size()[0]) 73 | for n in range(noise.size()[0]): 74 | sizeN = noise[0,:,:,:].size() 75 | noise[n,:,:,:] = torch.FloatTensor(sizeN).normal_(mean=0, std=stdN[n]/255.) 76 | imgn_train = img_train + noise 77 | img_train, imgn_train = Variable(img_train.cuda()), Variable(imgn_train.cuda()) 78 | noise = Variable(noise.cuda()) 79 | out_train = model(imgn_train) 80 | loss = criterion(out_train, noise) / (imgn_train.size()[0]*2) 81 | loss.backward() 82 | optimizer.step() 83 | # results 84 | model.eval() 85 | out_train = torch.clamp(imgn_train-model(imgn_train), 0., 1.) 86 | psnr_train = batch_PSNR(out_train, img_train, 1.) 87 | print("[epoch %d][%d/%d] loss: %.4f PSNR_train: %.4f" % 88 | (epoch+1, i+1, len(loader_train), loss.item(), psnr_train)) 89 | # if you are using older version of PyTorch, you may need to change loss.item() to loss.data[0] 90 | if step % 10 == 0: 91 | # Log the scalar values 92 | writer.add_scalar('loss', loss.item(), step) 93 | writer.add_scalar('PSNR on training data', psnr_train, step) 94 | step += 1 95 | ## the end of each epoch 96 | model.eval() 97 | # validate 98 | psnr_val = 0 99 | for k in range(len(dataset_val)): 100 | img_val = torch.unsqueeze(dataset_val[k], 0) 101 | noise = torch.FloatTensor(img_val.size()).normal_(mean=0, std=opt.val_noiseL/255.) 102 | imgn_val = img_val + noise 103 | img_val, imgn_val = Variable(img_val.cuda(), volatile=True), Variable(imgn_val.cuda(), volatile=True) 104 | out_val = torch.clamp(imgn_val-model(imgn_val), 0., 1.) 105 | psnr_val += batch_PSNR(out_val, img_val, 1.) 106 | psnr_val /= len(dataset_val) 107 | print("\n[epoch %d] PSNR_val: %.4f" % (epoch+1, psnr_val)) 108 | writer.add_scalar('PSNR on validation data', psnr_val, epoch) 109 | # log the images 110 | out_train = torch.clamp(imgn_train-model(imgn_train), 0., 1.) 111 | Img = utils.make_grid(img_train.data, nrow=8, normalize=True, scale_each=True) 112 | Imgn = utils.make_grid(imgn_train.data, nrow=8, normalize=True, scale_each=True) 113 | Irecon = utils.make_grid(out_train.data, nrow=8, normalize=True, scale_each=True) 114 | writer.add_image('clean image', Img, epoch) 115 | writer.add_image('noisy image', Imgn, epoch) 116 | writer.add_image('reconstructed image', Irecon, epoch) 117 | # save model 118 | torch.save(model.state_dict(), os.path.join(opt.outf, 'net.pth')) 119 | 120 | if __name__ == "__main__": 121 | if opt.preprocess: 122 | if opt.mode == 'S': 123 | prepare_data(data_path='data', patch_size=40, stride=10, aug_times=1) 124 | if opt.mode == 'B': 125 | prepare_data(data_path='data', patch_size=50, stride=10, aug_times=2) 126 | main() 127 | -------------------------------------------------------------------------------- /utils.py: -------------------------------------------------------------------------------- 1 | import math 2 | import torch 3 | import torch.nn as nn 4 | import numpy as np 5 | from skimage.measure.simple_metrics import compare_psnr 6 | 7 | def weights_init_kaiming(m): 8 | classname = m.__class__.__name__ 9 | if classname.find('Conv') != -1: 10 | nn.init.kaiming_normal(m.weight.data, a=0, mode='fan_in') 11 | elif classname.find('Linear') != -1: 12 | nn.init.kaiming_normal(m.weight.data, a=0, mode='fan_in') 13 | elif classname.find('BatchNorm') != -1: 14 | # nn.init.uniform(m.weight.data, 1.0, 0.02) 15 | m.weight.data.normal_(mean=0, std=math.sqrt(2./9./64.)).clamp_(-0.025,0.025) 16 | nn.init.constant(m.bias.data, 0.0) 17 | 18 | def batch_PSNR(img, imclean, data_range): 19 | Img = img.data.cpu().numpy().astype(np.float32) 20 | Iclean = imclean.data.cpu().numpy().astype(np.float32) 21 | PSNR = 0 22 | for i in range(Img.shape[0]): 23 | PSNR += compare_psnr(Iclean[i,:,:,:], Img[i,:,:,:], data_range=data_range) 24 | return (PSNR/Img.shape[0]) 25 | 26 | def data_augmentation(image, mode): 27 | out = np.transpose(image, (1,2,0)) 28 | if mode == 0: 29 | # original 30 | out = out 31 | elif mode == 1: 32 | # flip up and down 33 | out = np.flipud(out) 34 | elif mode == 2: 35 | # rotate counterwise 90 degree 36 | out = np.rot90(out) 37 | elif mode == 3: 38 | # rotate 90 degree and flip up and down 39 | out = np.rot90(out) 40 | out = np.flipud(out) 41 | elif mode == 4: 42 | # rotate 180 degree 43 | out = np.rot90(out, k=2) 44 | elif mode == 5: 45 | # rotate 180 degree and flip 46 | out = np.rot90(out, k=2) 47 | out = np.flipud(out) 48 | elif mode == 6: 49 | # rotate 270 degree 50 | out = np.rot90(out, k=3) 51 | elif mode == 7: 52 | # rotate 270 degree and flip 53 | out = np.rot90(out, k=3) 54 | out = np.flipud(out) 55 | return np.transpose(out, (2,0,1)) 56 | --------------------------------------------------------------------------------