├── .DS_Store ├── .idea ├── .gitignore ├── TBSN.iml ├── deployment.xml ├── inspectionProfiles │ ├── Project_Default.xml │ └── profiles_settings.xml ├── misc.xml ├── modules.xml ├── vcs.xml └── webServers.xml ├── LICENSE ├── benchmark ├── dnd.py ├── ensemble_wrapper.py └── sidd.py ├── dataset ├── __init__.py ├── base_function.py ├── dnd.py └── sidd.py ├── environment.yaml ├── exercise.py ├── model ├── __init__.py ├── apbsn.py ├── apbsn_distill.py └── base.py ├── network ├── __init__.py ├── base_function.py ├── calculate_param_flops.py ├── tbsn.py └── unet.py ├── option ├── knowledge_distillation_sidd.json ├── tbsn_dnd.json └── tbsn_sidd.json ├── pretrained_models ├── model_dnd.pth └── model_sidd.pth ├── readme.md ├── slurm.sh ├── train.sh ├── train ├── base.py └── experiment_name.py ├── util ├── build.py ├── io.py └── option.py └── validate ├── base.py ├── base_function.py ├── visualization.py └── visualize_receptive_field.py /.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/nagejacob/TBSN/678e93dd4ab816d5c736179b01f7398373db45ef/.DS_Store -------------------------------------------------------------------------------- /.idea/.gitignore: -------------------------------------------------------------------------------- 1 | # Default ignored files 2 | /shelf/ 3 | /workspace.xml 4 | # Editor-based HTTP Client requests 5 | /httpRequests/ 6 | # Datasource local storage ignored files 7 | /dataSources/ 8 | /dataSources.local.xml 9 | -------------------------------------------------------------------------------- /.idea/TBSN.iml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | -------------------------------------------------------------------------------- /.idea/deployment.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 23 | -------------------------------------------------------------------------------- /.idea/inspectionProfiles/Project_Default.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 28 | -------------------------------------------------------------------------------- /.idea/inspectionProfiles/profiles_settings.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 6 | -------------------------------------------------------------------------------- /.idea/misc.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 6 | 7 | -------------------------------------------------------------------------------- /.idea/modules.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | -------------------------------------------------------------------------------- /.idea/vcs.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | -------------------------------------------------------------------------------- /.idea/webServers.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 16 | 17 | -------------------------------------------------------------------------------- /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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If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /benchmark/dnd.py: -------------------------------------------------------------------------------- 1 | import sys 2 | sys.path.append('..') 3 | import argparse 4 | from benchmark.ensemble_wrapper import EnsembleWrapper 5 | from dataset.dnd import DNDBenchmarkPNGANDataset 6 | import numpy as np 7 | import os 8 | import scipy.io as sio 9 | import shutil 10 | from skimage.metrics import peak_signal_noise_ratio 11 | import torch 12 | from torch.utils.data import DataLoader 13 | from tqdm import tqdm 14 | from util.option import parse, recursive_print 15 | 16 | def bundle_submissions_srgb(submission_folder): 17 | ''' 18 | Bundles submission data for sRGB denoising 19 | 20 | submission_folder Folder where denoised images reside 21 | 22 | Output is written to /bundled/. Please submit 23 | the content of this folder. 24 | ''' 25 | out_folder = os.path.join(submission_folder, "bundled/") 26 | try: 27 | os.mkdir(out_folder) 28 | except: 29 | pass 30 | israw = False 31 | eval_version = "1.0" 32 | 33 | for i in range(50): 34 | Idenoised = np.zeros((20,), dtype=object) 35 | for bb in range(20): 36 | filename = '%04d_%02d.mat' % (i + 1, bb + 1) 37 | s = sio.loadmat(os.path.join(submission_folder, filename)) 38 | Idenoised_crop = s["Idenoised_crop"] 39 | Idenoised[bb] = Idenoised_crop 40 | filename = '%04d.mat' % (i + 1) 41 | sio.savemat(os.path.join(out_folder, filename), 42 | {"Idenoised": Idenoised, 43 | "israw": israw, 44 | "eval_version": eval_version}, 45 | ) 46 | 47 | def main(opt): 48 | test_set = DNDBenchmarkPNGANDataset() 49 | test_loader = DataLoader(test_set, batch_size=1) 50 | 51 | if os.path.exists(opt['mat_dir']): 52 | shutil.rmtree(opt['mat_dir']) 53 | os.makedirs(opt['mat_dir']) 54 | 55 | Model = getattr(__import__('model'), opt['model']) 56 | model = Model(opt) 57 | model.data_parallel() 58 | if 'resume_from' in opt: 59 | model.load_model(opt['resume_from']) 60 | if opt['ensemble']: 61 | model = EnsembleWrapper(model) 62 | 63 | psnrs, count = 0, 0 64 | for data in tqdm(test_loader): 65 | output = model.validation_step(data) 66 | output = torch.clamp(output, 0, 1) 67 | 68 | output = output.permute(0, 2, 3, 1).cpu().numpy() 69 | i = count // 20 70 | k = count % 20 71 | save_file = os.path.join(opt['mat_dir'], '%04d_%02d.mat' % (i + 1, k + 1)) 72 | sio.savemat(save_file, {'Idenoised_crop': output}) 73 | count += 1 74 | 75 | output = output[0] 76 | gt = data['H'].squeeze(0).permute(1, 2, 0).numpy() 77 | psnr = peak_signal_noise_ratio(output, gt, data_range=1) 78 | psnrs += psnr 79 | 80 | print('%s, psnr: %6.4f' % (test_set.__class__.__name__, psnrs / count)) 81 | 82 | if __name__ == '__main__': 83 | parser = argparse.ArgumentParser(description="Train the denoiser") 84 | parser.add_argument("--config_file", type=str, default='../option/tbsn_sidd.json') 85 | argspar = parser.parse_args() 86 | 87 | opt = parse(argspar.config_file) 88 | opt['mat_dir'] = 'dnd_mat' 89 | opt['ensemble'] = True 90 | recursive_print(opt) 91 | 92 | main(opt) 93 | bundle_submissions_srgb(opt['mat_dir']) -------------------------------------------------------------------------------- /benchmark/ensemble_wrapper.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | class EnsembleWrapper(): 4 | def __init__(self, model): 5 | self.model = model 6 | 7 | def validation_step(self, data): 8 | input = data['L'] 9 | outputs = [] 10 | for i in range(8): 11 | aug_input = input.clone() 12 | if i >= 4: 13 | aug_input = torch.flip(aug_input, [2]) 14 | if i % 4 > 1: 15 | aug_input = torch.flip(aug_input, [3]) 16 | if (i % 4) % 2 == 1: 17 | aug_input = torch.rot90(aug_input, 1, [2, 3]) 18 | 19 | aug_output = self.model.validation_step({'L': aug_input}) 20 | 21 | if (i % 4) % 2 == 1: 22 | aug_output = torch.rot90(aug_output, 3, [2, 3]) 23 | if i % 4 > 1: 24 | aug_output = torch.flip(aug_output, [3]) 25 | if i >= 4: 26 | aug_output = torch.flip(aug_output, [2]) 27 | outputs.append(aug_output) 28 | output = torch.stack(outputs, dim=0) 29 | output = torch.mean(output, dim=0, keepdim=False) 30 | return output -------------------------------------------------------------------------------- /benchmark/sidd.py: -------------------------------------------------------------------------------- 1 | import sys 2 | sys.path.append('..') 3 | import argparse 4 | from dataset.sidd import SIDDBenchmarkDataset 5 | import numpy as np 6 | import os 7 | import scipy.io as sio 8 | from benchmark.ensemble_wrapper import EnsembleWrapper 9 | import torch 10 | from torch.utils.data import DataLoader 11 | from tqdm import tqdm 12 | from util.option import parse, recursive_print 13 | 14 | def main(opt): 15 | test_set = SIDDBenchmarkDataset() 16 | test_loader = DataLoader(test_set, batch_size=1) 17 | 18 | if os.path.exists(opt['mat_path']): 19 | os.remove(opt['mat_path']) 20 | 21 | Model = getattr(__import__('model'), opt['model']) 22 | model = Model(opt) 23 | model.data_parallel() 24 | if opt['ensemble']: 25 | model = EnsembleWrapper(model) 26 | 27 | count = 0 28 | denoised_block = np.zeros_like(test_set.noisy_block) 29 | for data in tqdm(test_loader): 30 | output = model.validation_step(data) 31 | output = torch.floor(output * 255. + 0.5) 32 | output = torch.clamp(output, 0, 255) 33 | output = output.cpu().squeeze(0).permute(1, 2, 0).numpy() 34 | 35 | index_n = count // test_set.noisy_block.shape[1] 36 | index_k = count % test_set.noisy_block.shape[1] 37 | output = np.uint8(output) 38 | denoised_block[index_n, index_k] = output 39 | count += 1 40 | 41 | save_dict = {} 42 | save_dict['__header__'] = b'MATLAB 5.0 MAT-file, Platform: PCWIN64, Created on: Thu Jan 10 13:08:11 2019' 43 | save_dict['__version__'] = 1.0 44 | save_dict['__globals__'] = [] 45 | save_dict['DenoisedBlocksSrgb'] = denoised_block 46 | sio.savemat(opt['mat_path'], save_dict) 47 | 48 | 49 | if __name__ == '__main__': 50 | parser = argparse.ArgumentParser(description="Train the denoiser") 51 | parser.add_argument("--config_file", type=str, default='../option/tbsn_sidd.json') 52 | argspar = parser.parse_args() 53 | 54 | opt = parse(argspar.config_file) 55 | opt['mat_path'] = 'SubmitSrgb.mat' 56 | opt['ensemble'] = True 57 | recursive_print(opt) 58 | 59 | main(opt) -------------------------------------------------------------------------------- /dataset/__init__.py: -------------------------------------------------------------------------------- 1 | from dataset.dnd import DNDBenchmarkTrainDataset, DNDBenchmarkPNGANDataset 2 | from dataset.sidd import SIDDMediumTrainDataset, SIDDValidationDataset, SIDDBenchmarkDataset -------------------------------------------------------------------------------- /dataset/base_function.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import random 3 | import socket 4 | import torch 5 | 6 | hostname = socket.gethostname() 7 | if 'ubuntu' == hostname: # ubuntu 8 | dataset_path = '/home/nagejacob/Documents/datasets' 9 | else: # hpc 10 | dataset_path = '/mnt/ssd0/anaconda3/lijunyi/datasets' 11 | 12 | # c, h, w numpy 13 | def _aug_np3(img, flip_h, flip_w, transpose): 14 | if flip_h: 15 | img = img[:, ::-1, :] 16 | if flip_w: 17 | img = img[:, :, ::-1] 18 | if transpose: 19 | img = np.transpose(img, (0, 2, 1)) 20 | return img 21 | 22 | def _aug_torch3(img, flip_h, flip_w, transpose): 23 | if flip_h: 24 | img = torch.flip(img, dims=[1]) 25 | if flip_w: 26 | img = torch.flip(img, dims=[2]) 27 | if transpose: 28 | img = img.permute(0, 2, 1) 29 | return img 30 | 31 | def _aug_3(img, flip_h, flip_w, transpose): 32 | if type(img) is np.ndarray: 33 | img = _aug_np3(img, flip_h, flip_w, transpose) 34 | elif type(img) is torch.Tensor: 35 | img = _aug_torch3(img, flip_h, flip_w, transpose) 36 | else: 37 | raise TypeError('img is neither np.ndarray nor torch.Tensor !') 38 | return img 39 | 40 | def aug_3(img_L, img_H=None): 41 | flip_h = random.random() > 0.5 42 | flip_w = random.random() > 0.5 43 | transpose = random.random() > 0.5 44 | 45 | img_L = _aug_3(img_L, flip_h, flip_w, transpose) 46 | if img_H is not None: 47 | img_H = _aug_3(img_H, flip_h, flip_w, transpose) 48 | return img_L, img_H 49 | else: 50 | return img_L 51 | 52 | def crop_3(patch_size, img_L, img_H=None): 53 | C, H, W = img_L.shape 54 | position_H = random.randint(0, H - patch_size) 55 | position_W = random.randint(0, W - patch_size) 56 | patch_L = img_L[:, position_H:position_H+patch_size, position_W:position_W+patch_size] 57 | if img_H is not None: 58 | patch_H = img_H[:, position_H:position_H+patch_size, position_W:position_W+patch_size] 59 | return patch_L, patch_H 60 | else: 61 | return patch_L -------------------------------------------------------------------------------- /dataset/dnd.py: -------------------------------------------------------------------------------- 1 | from dataset.base_function import dataset_path, crop_3 2 | import h5py 3 | import glob 4 | import numpy as np 5 | import os 6 | import scipy.io as sio 7 | from torch.utils.data import Dataset 8 | 9 | dnd_path = os.path.join(dataset_path, 'DND') 10 | 11 | class DNDBenchmarkTrainDataset(Dataset): 12 | def __init__(self, patch_size, pin_memory=True): 13 | super().__init__() 14 | self.patch_size = patch_size 15 | self.pin_memory = pin_memory 16 | 17 | self._img_paths = self._get_img_paths() 18 | if self.pin_memory: 19 | self._imgs = self._open_images() 20 | 21 | def __getitem__(self, index): 22 | index = index % len(self._img_paths) 23 | 24 | if self.pin_memory: 25 | img_L = self.imgs[index]['L'] 26 | else: 27 | img_path = self._img_paths[index] 28 | img_L = self._open_image(img_path['L']) 29 | 30 | patch_L = crop_3(self.patch_size, img_L) 31 | 32 | return {'L': patch_L.copy()} 33 | 34 | def __len__(self): 35 | return len(self._img_paths) * 100 36 | 37 | def _get_img_paths(self): 38 | L_pattern = os.path.join(dnd_path, 'images_srgb/00*.mat') 39 | L_paths = sorted(glob.glob(L_pattern)) 40 | 41 | img_paths = [] 42 | for L_path in L_paths: 43 | img_paths.append({'L':L_path}) 44 | return img_paths 45 | 46 | def _open_images(self): 47 | self.imgs = [] 48 | for img_path in self._img_paths: 49 | img_L = self._open_image(img_path['L']) 50 | self.imgs.append({'L': img_L}) 51 | 52 | def _open_image(self, path): 53 | img = h5py.File(path) 54 | img = np.float32(np.array(img['InoisySRGB']).T) 55 | img = np.transpose(img, (2, 0, 1)) 56 | return img 57 | 58 | # use PNGAN results as pseudo GT 59 | class DNDBenchmarkPNGANDataset(Dataset): 60 | def __init__(self, length=None): 61 | super(DNDBenchmarkPNGANDataset, self).__init__() 62 | self.length = length 63 | self.imgs = [] 64 | infos = h5py.File(os.path.join(dnd_path, 'info.mat'), 'r') 65 | info = infos['info'] 66 | bb = info['boundingboxes'] 67 | for i in range(50): 68 | filename = os.path.join(dnd_path, 'images_srgb', '%04d.mat' % (i + 1)) 69 | img = h5py.File(filename, 'r') 70 | Inoisy = np.float32(np.array(img['InoisySRGB']).T) 71 | ref = bb[0][i] 72 | boxes = np.array(info[ref]).T 73 | for k in range(20): 74 | idx = [int(boxes[k, 0] - 1), int(boxes[k, 2]), int(boxes[k, 1] - 1), int(boxes[k, 3])] 75 | Inoisy_crop = Inoisy[idx[0]:idx[1], idx[2]:idx[3], :].copy() 76 | Inoisy_crop = np.transpose(Inoisy_crop, (2, 0, 1)) 77 | Iclean_crop = sio.loadmat(os.path.join(dnd_path, 'PNGAN/dnd_srgb_mat', '%04d_%02d.mat' % (i + 1, k + 1)))['Idenoised_crop'] 78 | Iclean_crop = np.transpose(Iclean_crop[0], (2, 0, 1)) 79 | 80 | self.imgs.append({'L':Inoisy_crop, 'H': Iclean_crop}) 81 | 82 | def __getitem__(self, index): 83 | return self.imgs[index] 84 | 85 | def __len__(self): 86 | if self.length is not None: 87 | return self.length 88 | return 1000 -------------------------------------------------------------------------------- /dataset/sidd.py: -------------------------------------------------------------------------------- 1 | import sys 2 | sys.path.append('..') 3 | from dataset.base_function import dataset_path, crop_3 4 | import glob 5 | import imageio 6 | import numpy as np 7 | import os 8 | from PIL import Image 9 | import scipy.io as sio 10 | import torch 11 | from torch.utils.data import Dataset 12 | from tqdm import tqdm 13 | 14 | sidd_path = os.path.join(dataset_path, 'SIDD') 15 | 16 | def open_image_SIDD(path): 17 | img = Image.open(path) 18 | img_np = np.asarray(img) 19 | img.close() 20 | 21 | img_np = np.transpose(img_np, (2, 0, 1)) 22 | return img_np 23 | 24 | class SIDDMediumTrainDataset(Dataset): 25 | def __init__(self, pin_memory, patch_size): 26 | super().__init__() 27 | self.pin_memory = pin_memory 28 | self.patch_size = patch_size 29 | 30 | self._img_paths = self._get_img_paths() 31 | if self.pin_memory: 32 | self._imgs = self._open_images() 33 | 34 | def __getitem__(self, index): 35 | index = index % len(self._img_paths) 36 | 37 | if self.pin_memory: 38 | img_L = self._imgs[index]['L'] 39 | img_H = self._imgs[index]['H'] 40 | else: 41 | img_path = self._img_paths[index] 42 | img_L = self._open_image(img_path['L']) 43 | img_H = self._open_image(img_path['H']) 44 | 45 | img_L, img_H = crop_3(self.patch_size, img_L, img_H) 46 | img_L, img_H = np.float32(img_L) / 255., np.float32(img_H) / 255. 47 | img_L, img_H = torch.from_numpy(img_L), torch.from_numpy(img_H) 48 | 49 | return {'L': img_L, 'H': img_H} 50 | 51 | def __len__(self): 52 | length = len(self._img_paths) 53 | if length <= 10000: 54 | return (10000 // length) * length 55 | else: 56 | return length 57 | 58 | def _get_img_paths(self): 59 | img_paths = [] 60 | L_pattern = os.path.join(sidd_path, 'SIDD_Medium_Srgb/Data/*/*_NOISY_SRGB_*.PNG') 61 | L_paths = sorted(glob.glob(L_pattern)) 62 | for L_path in L_paths: 63 | img_paths.append({'L': L_path, 'H': L_path.replace('NOISY', 'GT')}) 64 | return img_paths 65 | 66 | def _open_images(self): 67 | imgs = [] 68 | for img_path in self._img_paths: 69 | img_L = self._open_image(img_path['L']) 70 | img_H = self._open_image(img_path['H']) 71 | imgs.append({'L': img_L, 'H': img_H}) 72 | return imgs 73 | 74 | def _open_image(self, path): 75 | return open_image_SIDD(path) 76 | 77 | 78 | class SIDDValidationDataset(Dataset): 79 | def __init__(self): 80 | super().__init__() 81 | self._open_images(sidd_path) 82 | self.n = self.noisy_block.shape[0] 83 | self.k = self.noisy_block.shape[1] 84 | 85 | def __getitem__(self, index): 86 | index_n = index // self.k 87 | index_k = index % self.k 88 | 89 | img_H = self.gt_block[index_n, index_k] 90 | img_H = np.float32(img_H) / 255. 91 | img_H = np.transpose(img_H, (2, 0, 1)) 92 | img_H = img_H 93 | 94 | img_L = self.noisy_block[index_n, index_k] 95 | img_L = np.float32(img_L) / 255. 96 | img_L = np.transpose(img_L, (2, 0, 1)) 97 | img_L = img_L 98 | 99 | return {'H':img_H, 'L':img_L} 100 | 101 | def __len__(self): 102 | return self.n * self.k 103 | 104 | def _open_images(self, path): 105 | mat = sio.loadmat(os.path.join(path, 'SIDD_Validation/ValidationNoisyBlocksSrgb.mat')) 106 | self.noisy_block = mat['ValidationNoisyBlocksSrgb'] 107 | mat = sio.loadmat(os.path.join(path, 'SIDD_Validation/ValidationGtBlocksSrgb.mat')) 108 | self.gt_block = mat['ValidationGtBlocksSrgb'] 109 | 110 | 111 | class SIDDBenchmarkDataset(Dataset): 112 | def __init__(self): 113 | super().__init__() 114 | self._open_images(sidd_path) 115 | self.n = self.noisy_block.shape[0] 116 | self.k = self.noisy_block.shape[1] 117 | 118 | def __getitem__(self, index): 119 | index_n = index // self.k 120 | index_k = index % self.k 121 | 122 | img_L = self.noisy_block[index_n, index_k] 123 | img_L = np.float32(img_L) / 255. 124 | img_L = np.transpose(img_L, (2, 0, 1)) 125 | img_L = img_L 126 | 127 | return {'L': img_L} 128 | 129 | def __len__(self): 130 | return self.n * self.k 131 | 132 | def _open_images(self, path): 133 | mat = sio.loadmat(os.path.join(path, 'SIDD_Benchmark/BenchmarkNoisyBlocksSrgb.mat')) 134 | self.noisy_block = mat['BenchmarkNoisyBlocksSrgb'] 135 | 136 | 137 | # extract sidd validation images 138 | if __name__ == '__main__': 139 | dest_dir = os.path.join(sidd_path, 'SIDD_Validation/ValidationGTImagesSrgb') 140 | os.makedirs(dest_dir, exist_ok=True) 141 | 142 | mat = sio.loadmat(os.path.join(sidd_path, 'SIDD_Validation/ValidationNoisyBlocksSrgb.mat')) 143 | noisy_block = mat['ValidationNoisyBlocksSrgb'] 144 | mat = sio.loadmat(os.path.join(sidd_path, 'SIDD_Validation/ValidationGtBlocksSrgb.mat')) 145 | gt_block = mat['ValidationGtBlocksSrgb'] 146 | n = noisy_block.shape[0] 147 | k = noisy_block.shape[1] 148 | for i in tqdm(range(n)): 149 | for j in range(k): 150 | gt_img = gt_block[i, j] 151 | imageio.imwrite(os.path.join(dest_dir, '%02d_%02d.png' % (i, j)), gt_img) -------------------------------------------------------------------------------- /environment.yaml: -------------------------------------------------------------------------------- 1 | name: tbsn 2 | channels: 3 | - pytorch 4 | - nvidia 5 | - defaults 6 | dependencies: 7 | - python==3.8.18 8 | - pytorch==2.0.1 9 | - pytorch-cuda=11.8 10 | - torchvision==0.15.2 11 | - torchaudio==2.0.2 12 | - h5py 13 | - scikit-image 14 | - tqdm 15 | - pip: 16 | - einops 17 | - opencv-python 18 | 19 | prefix: tbsn -------------------------------------------------------------------------------- /exercise.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import torch 3 | 4 | if __name__ == '__main__': 5 | tbsn_psnrs = torch.zeros((1280)) 6 | unet_psnrs = torch.zeros((1280)) 7 | 8 | with open('validate/tbsn.txt', 'r') as f: 9 | for i in range(1280): 10 | psnr = float(f.readline()) 11 | tbsn_psnrs[i] = psnr 12 | 13 | with open('validate/unet.txt', 'r') as f: 14 | for i in range(1280): 15 | psnr = float(f.readline()) 16 | unet_psnrs[i] = psnr - 0.1 17 | 18 | print('mean: ', torch.mean(tbsn_psnrs), torch.mean(unet_psnrs)) 19 | print('std: ', torch.std(tbsn_psnrs - unet_psnrs)) 20 | 21 | a = torch.abs(tbsn_psnrs - unet_psnrs) 22 | count = 0 23 | for i in range(1280): 24 | if a[i] < 0.2: 25 | count += 1 26 | print(count) 27 | 28 | indices = [(8, 20), (8, 23), (9, 5), (9, 8), (11, 29), (12, 19), (35, 0-21), (36, 0-21)] 29 | indices = [276, 279, 293, 296, 381, 403, 1120, 1121, 1122, 1123, 30 | 1124, 1125, 1126, 1127, 1128, 1129, 1130, 1131, 1132, 1133, 31 | 1134, 1135, 1136, 1137, 1138, 1139, 1140, 1141, 1152, 1153, 32 | 1154, 1155, 1156, 1157, 1158, 1159, 1160, 1161, 1162, 1163, 33 | 1164, 1165, 1166, 1167, 1168, 1169, 1170, 1171, 1172, 1173] -------------------------------------------------------------------------------- /model/__init__.py: -------------------------------------------------------------------------------- 1 | from model.apbsn import APBSNModel 2 | from model.apbsn_distill import APBSNDistillModel -------------------------------------------------------------------------------- /model/apbsn.py: -------------------------------------------------------------------------------- 1 | from model.base import BaseModel 2 | import torch 3 | import torch.nn as nn 4 | import torch.nn.functional as F 5 | 6 | def pixel_shuffle_down_sampling(x:torch.Tensor, f:int, train=False): 7 | b,c,w,h = x.shape 8 | unshuffled = F.pixel_unshuffle(x, f) 9 | unshuffled = unshuffled.view(b, c, f * f, w // f, h // f).permute(0, 2, 1, 3, 4) 10 | if train: 11 | unshuffled = unshuffled[:, 0] 12 | unshuffled = unshuffled.reshape(b, c, w // f, h // f) 13 | else: 14 | unshuffled = unshuffled.reshape(b * f * f, c, w // f, h // f) 15 | return unshuffled 16 | 17 | def pixel_shuffle_up_sampling(x:torch.Tensor, f:int): 18 | b,c,h,w = x.shape 19 | before_shuffle = x.view(b // (f * f), f * f, c, h, w).permute(0, 2, 1, 3, 4) 20 | before_shuffle = before_shuffle.reshape(b // (f * f), c * f * f, h, w) 21 | return F.pixel_shuffle(before_shuffle, f) 22 | 23 | class APBSNModel(BaseModel): 24 | def __init__(self, opt): 25 | super(APBSNModel, self).__init__(opt) 26 | self.pd_a = opt['pd_a'] 27 | self.pd_b = opt['pd_b'] 28 | self.R3 = opt['R3'] 29 | self.R3_T = opt['R3_T'] 30 | self.R3_p = opt['R3_p'] 31 | self.criteron = nn.L1Loss(reduction='mean') 32 | self.optimizer = torch.optim.AdamW(self.networks['bsn'].parameters(), lr=opt['lr']) 33 | 34 | milestones = [int(opt['num_iters'] * 0.4), int(opt['num_iters'] * 0.8)] 35 | self.scheduler = torch.optim.lr_scheduler.MultiStepLR(self.optimizer, milestones=milestones, gamma=0.1) 36 | 37 | def train_step(self, data): 38 | input = data['L'].to(self.device) * 255. 39 | 40 | self.networks['bsn'].train() 41 | input = pixel_shuffle_down_sampling(input, f=self.pd_a, train=True) 42 | output = self.networks['bsn'](input) 43 | 44 | self.loss = self.criteron(output, input) 45 | self.optimizer.zero_grad() 46 | self.loss.backward() 47 | self.optimizer.step() 48 | self.scheduler.step() 49 | self.iter += 1 50 | 51 | def validation_step(self, data): 52 | input = data['L'].to(self.device) * 255. 53 | b, c, h, w = input.shape 54 | input_pd = pixel_shuffle_down_sampling(input, f=self.pd_b) 55 | 56 | self.networks['bsn'].eval() 57 | with torch.no_grad(): 58 | output_pd = self.networks['bsn'](input_pd) 59 | 60 | output = pixel_shuffle_up_sampling(output_pd, f=self.pd_b) 61 | if not self.R3: 62 | ''' Directly return the result (w/o R3) ''' 63 | denoised = output[:, :, :h, :w] 64 | else: 65 | denoised = torch.empty(*(input.shape), self.R3_T, device=input.device) 66 | # torch.manual_seed(0) 67 | for t in range(self.R3_T): 68 | indice = torch.rand_like(input) 69 | mask = indice < self.R3_p 70 | 71 | tmp_input = torch.clone(output).detach() 72 | tmp_input[mask] = input[mask] 73 | with torch.no_grad(): 74 | tmp_output = self.networks['bsn'](tmp_input) 75 | denoised[..., t] = tmp_output 76 | 77 | denoised = torch.mean(denoised, dim=-1) 78 | 79 | return denoised / 255. 80 | -------------------------------------------------------------------------------- /model/apbsn_distill.py: -------------------------------------------------------------------------------- 1 | from model.base import BaseModel 2 | from model.apbsn import pixel_shuffle_down_sampling, pixel_shuffle_up_sampling 3 | import torch 4 | import torch.nn as nn 5 | 6 | 7 | class APBSNDistillModel(BaseModel): 8 | def __init__(self, opt): 9 | super(APBSNDistillModel, self).__init__(opt) 10 | self.pd_a = opt['pd_a'] 11 | self.pd_b = opt['pd_b'] 12 | self.R3 = opt['R3'] 13 | self.R3_T = opt['R3_T'] 14 | self.R3_p = opt['R3_p'] 15 | self.criteron = nn.L1Loss(reduction='mean') 16 | self.optimizer = torch.optim.AdamW(self.networks['network'].parameters(), lr=opt['lr']) 17 | 18 | milestones = [int(opt['num_iters'] * 0.4), int(opt['num_iters'] * 0.8)] 19 | self.scheduler = torch.optim.lr_scheduler.MultiStepLR(self.optimizer, milestones=milestones, gamma=0.1) 20 | 21 | 22 | def train_step(self, data): 23 | 24 | input = data['L'].to(self.device) * 255. 25 | b, c, h, w = input.shape 26 | input_pd = pixel_shuffle_down_sampling(input, f=self.pd_b) 27 | 28 | self.networks['bsn'].eval() 29 | with torch.no_grad(): 30 | output_pd = self.networks['bsn'](input_pd) 31 | 32 | output = pixel_shuffle_up_sampling(output_pd, f=self.pd_b) 33 | if not self.R3: 34 | ''' Directly return the result (w/o R3) ''' 35 | denoised = output[:, :, :h, :w] 36 | else: 37 | denoised = torch.empty(*(input.shape), self.R3_T, device=input.device) 38 | # torch.manual_seed(0) 39 | for t in range(self.R3_T): 40 | indice = torch.rand_like(input) 41 | mask = indice < self.R3_p 42 | 43 | tmp_input = torch.clone(output).detach() 44 | tmp_input[mask] = input[mask] 45 | with torch.no_grad(): 46 | tmp_output = self.networks['bsn'](tmp_input) 47 | denoised[..., t] = tmp_output 48 | 49 | denoised = torch.mean(denoised, dim=-1) 50 | 51 | target = denoised / 255. 52 | 53 | input = data['L'].to(self.device) 54 | self.networks['network'].train() 55 | output = self.networks['network'](input) 56 | 57 | self.loss = self.criteron(output, target) 58 | self.optimizer.zero_grad() 59 | self.loss.backward() 60 | self.optimizer.step() 61 | self.scheduler.step() 62 | self.iter += 1 63 | 64 | 65 | def validation_step(self, data): 66 | input = data['L'].to(self.device) 67 | 68 | self.networks['network'].eval() 69 | with torch.no_grad(): 70 | output = self.networks['network'](input) 71 | 72 | return output -------------------------------------------------------------------------------- /model/base.py: -------------------------------------------------------------------------------- 1 | from abc import abstractmethod 2 | import os 3 | import torch 4 | from torch.nn.parallel import DataParallel, DistributedDataParallel 5 | from util.build import build 6 | from util.io import log 7 | 8 | class BaseModel(): 9 | def __init__(self, opt): 10 | self.opt = opt 11 | self.iter = 0 if 'iter' not in opt else opt['iter'] 12 | self.networks = {} 13 | for network_opt in opt['networks']: 14 | Net = getattr(__import__('network'), network_opt['type']) 15 | net = build(Net, network_opt['args']) 16 | if 'path' in network_opt.keys(): 17 | self.load_net(net, network_opt['path']) 18 | self.networks[network_opt['name']] = net 19 | 20 | @abstractmethod 21 | def train_step(self, data): 22 | pass 23 | 24 | @abstractmethod 25 | def validation_step(self, data): 26 | pass 27 | 28 | def data_parallel(self): 29 | self.device = torch.device('cuda') 30 | for name in self.networks.keys(): 31 | net = self.networks[name] 32 | net = net.cuda() 33 | net = DataParallel(net) 34 | self.networks[name] = net 35 | 36 | def distributed_parallel(self, rank): 37 | self.device = torch.device('cuda:%d' % rank) 38 | torch.cuda.set_device(rank) 39 | for name in self.networks.keys(): 40 | net = self.networks[name] 41 | net = net.to(torch.device('cuda', rank)) 42 | net = DistributedDataParallel(net, device_ids=[rank], output_device=rank) 43 | self.networks[name] = net 44 | 45 | def save_net(self): 46 | for name, net in self.networks.items(): 47 | if isinstance(net, (DataParallel, DistributedDataParallel)): 48 | net = net.module 49 | torch.save(net.state_dict(), os.path.join(self.opt['log_dir'], '%s_iter_%08d.pth' % (name, self.iter))) 50 | 51 | def load_net(self, net, path): 52 | state_dict = torch.load(path, map_location='cpu') 53 | if 'model_weight' in state_dict: 54 | state_dict = state_dict['model_weight']['denoiser'] 55 | if 'bsn' in list(state_dict.keys())[0]: 56 | for key in list(state_dict.keys()): 57 | state_dict[key.replace('bsn.', '')] = state_dict.pop(key) 58 | net.load_state_dict(state_dict) 59 | 60 | 61 | def log(self): 62 | with torch.no_grad(): 63 | log(self.opt['log_file'], 'iter: %d, loss: %f\n' % (self.iter, self.loss.item())) 64 | -------------------------------------------------------------------------------- /network/__init__.py: -------------------------------------------------------------------------------- 1 | from network.tbsn import TBSN 2 | from network.unet import Unet -------------------------------------------------------------------------------- /network/base_function.py: -------------------------------------------------------------------------------- 1 | from einops import rearrange 2 | import numbers 3 | import torch 4 | from torch import einsum 5 | import torch.nn as nn 6 | 7 | 8 | def to(x): 9 | return {'device': x.device, 'dtype': x.dtype} 10 | 11 | def pair(x): 12 | return (x, x) if not isinstance(x, tuple) else x 13 | 14 | def expand_dim(t, dim, k): 15 | t = t.unsqueeze(dim = dim) 16 | expand_shape = [-1] * len(t.shape) 17 | expand_shape[dim] = k 18 | return t.expand(*expand_shape) 19 | 20 | def rel_to_abs(x): 21 | b, l, m = x.shape 22 | r = (m + 1) // 2 23 | 24 | col_pad = torch.zeros((b, l, 1), **to(x)) 25 | x = torch.cat((x, col_pad), dim = 2) 26 | flat_x = rearrange(x, 'b l c -> b (l c)') 27 | flat_pad = torch.zeros((b, m - l), **to(x)) 28 | flat_x_padded = torch.cat((flat_x, flat_pad), dim = 1) 29 | final_x = flat_x_padded.reshape(b, l + 1, m) 30 | final_x = final_x[:, :l, -r:] 31 | return final_x 32 | 33 | def relative_logits_1d(q, rel_k): 34 | b, h, w, _ = q.shape 35 | r = (rel_k.shape[0] + 1) // 2 36 | 37 | logits = einsum('b x y d, r d -> b x y r', q, rel_k) 38 | logits = rearrange(logits, 'b x y r -> (b x) y r') 39 | logits = rel_to_abs(logits) 40 | 41 | logits = logits.reshape(b, h, w, r) 42 | logits = expand_dim(logits, dim = 2, k = r) 43 | return logits 44 | 45 | class RelPosEmb(nn.Module): 46 | def __init__( 47 | self, 48 | block_size, 49 | rel_size, 50 | dim_head 51 | ): 52 | super().__init__() 53 | height = width = rel_size 54 | scale = dim_head ** -0.5 55 | 56 | self.block_size = block_size 57 | self.rel_height = nn.Parameter(torch.randn(height * 2 - 1, dim_head) * scale) 58 | self.rel_width = nn.Parameter(torch.randn(width * 2 - 1, dim_head) * scale) 59 | 60 | def forward(self, q): 61 | block = self.block_size 62 | 63 | q = rearrange(q, 'b (x y) c -> b x y c', x = block) 64 | rel_logits_w = relative_logits_1d(q, self.rel_width) 65 | rel_logits_w = rearrange(rel_logits_w, 'b x i y j-> b (x y) (i j)') 66 | 67 | q = rearrange(q, 'b x y d -> b y x d') 68 | rel_logits_h = relative_logits_1d(q, self.rel_height) 69 | rel_logits_h = rearrange(rel_logits_h, 'b x i y j -> b (y x) (j i)') 70 | return rel_logits_w + rel_logits_h 71 | 72 | 73 | class FixedPosEmb(nn.Module): 74 | def __init__(self, window_size, overlap_window_size): 75 | super().__init__() 76 | self.window_size = window_size 77 | self.overlap_window_size = overlap_window_size 78 | 79 | attention_mask_table = torch.zeros((window_size + overlap_window_size - 1), (window_size + overlap_window_size - 1)) 80 | attention_mask_table[0::2, :] = float('-inf') 81 | attention_mask_table[:, 0::2] = float('-inf') 82 | attention_mask_table = attention_mask_table.view((window_size + overlap_window_size - 1) * (window_size + overlap_window_size - 1)) 83 | 84 | # get pair-wise relative position index for each token inside the window 85 | coords_h = torch.arange(self.window_size) 86 | coords_w = torch.arange(self.window_size) 87 | coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww 88 | coords_flatten_1 = torch.flatten(coords, 1) # 2, Wh*Ww 89 | coords_h = torch.arange(self.overlap_window_size) 90 | coords_w = torch.arange(self.overlap_window_size) 91 | coords = torch.stack(torch.meshgrid([coords_h, coords_w])) 92 | coords_flatten_2 = torch.flatten(coords, 1) 93 | 94 | relative_coords = coords_flatten_1[:, :, None] - coords_flatten_2[:, None, :] # 2, Wh*Ww, Wh*Ww 95 | relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 96 | relative_coords[:, :, 0] += self.overlap_window_size - 1 # shift to start from 0 97 | relative_coords[:, :, 1] += self.overlap_window_size - 1 98 | relative_coords[:, :, 0] *= self.window_size + self.overlap_window_size - 1 99 | relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww 100 | self.attention_mask = nn.Parameter(attention_mask_table[relative_position_index.view(-1)].view( 101 | 1, self.window_size ** 2, self.overlap_window_size ** 2 102 | ), requires_grad=False) 103 | def forward(self): 104 | return self.attention_mask 105 | 106 | 107 | ########################################################################## 108 | ## Layer Norm 109 | 110 | def to_3d(x): 111 | return rearrange(x, 'b c h w -> b (h w) c') 112 | 113 | def to_4d(x,h,w): 114 | return rearrange(x, 'b (h w) c -> b c h w',h=h,w=w) 115 | 116 | class BiasFree_LayerNorm(nn.Module): 117 | def __init__(self, normalized_shape): 118 | super(BiasFree_LayerNorm, self).__init__() 119 | if isinstance(normalized_shape, numbers.Integral): 120 | normalized_shape = (normalized_shape,) 121 | normalized_shape = torch.Size(normalized_shape) 122 | 123 | assert len(normalized_shape) == 1 124 | 125 | self.weight = nn.Parameter(torch.ones(normalized_shape)) 126 | self.normalized_shape = normalized_shape 127 | 128 | def forward(self, x): 129 | sigma = x.var(-1, keepdim=True, unbiased=False) 130 | return x / torch.sqrt(sigma+1e-5) * self.weight 131 | 132 | class WithBias_LayerNorm(nn.Module): 133 | def __init__(self, normalized_shape): 134 | super(WithBias_LayerNorm, self).__init__() 135 | if isinstance(normalized_shape, numbers.Integral): 136 | normalized_shape = (normalized_shape,) 137 | normalized_shape = torch.Size(normalized_shape) 138 | 139 | assert len(normalized_shape) == 1 140 | 141 | self.weight = nn.Parameter(torch.ones(normalized_shape)) 142 | self.bias = nn.Parameter(torch.zeros(normalized_shape)) 143 | self.normalized_shape = normalized_shape 144 | 145 | def forward(self, x): 146 | mu = x.mean(-1, keepdim=True) 147 | sigma = x.var(-1, keepdim=True, unbiased=False) 148 | return (x - mu) / torch.sqrt(sigma+1e-5) * self.weight + self.bias 149 | 150 | class LayerNorm(nn.Module): 151 | def __init__(self, dim, LayerNorm_type): 152 | super(LayerNorm, self).__init__() 153 | if LayerNorm_type =='BiasFree': 154 | self.body = BiasFree_LayerNorm(dim) 155 | else: 156 | self.body = WithBias_LayerNorm(dim) 157 | 158 | def forward(self, x): 159 | h, w = x.shape[-2:] 160 | return to_4d(self.body(to_3d(x)), h, w) -------------------------------------------------------------------------------- /network/calculate_param_flops.py: -------------------------------------------------------------------------------- 1 | import sys 2 | sys.path.append('..') 3 | from deepspeed.accelerator import get_accelerator 4 | from deepspeed.profiling.flops_profiler import get_model_profile 5 | import time 6 | import torch 7 | 8 | 9 | if __name__ == '__main__': 10 | with get_accelerator().device(0): 11 | Model = getattr(__import__('network'), 'TBSN') 12 | model = Model().cuda() 13 | 14 | # t = time.time() 15 | # input = torch.zeros(1, 3, 256, 256).cuda() 16 | # for i in range(1000): 17 | # with torch.no_grad(): 18 | # output = model(input) 19 | # print(time.time() - t), exit() 20 | 21 | 22 | flops, macs, params = get_model_profile(model=model, # model 23 | input_shape=(1, 3, 256, 256), 24 | # input shape to the model. If specified, the model takes a tensor with this shape as the only positional argument. 25 | args=None, # list of positional arguments to the model. 26 | kwargs=None, # dictionary of keyword arguments to the model. 27 | print_profile=False, 28 | # prints the model graph with the measured profile attached to each module 29 | detailed=False, # print the detailed profile 30 | module_depth=-1, 31 | # depth into the nested modules, with -1 being the inner most modules 32 | top_modules=1, # the number of top modules to print aggregated profile 33 | warm_up=0, 34 | # the number of warm-ups before measuring the time of each module 35 | as_string=False, 36 | # print raw numbers (e.g. 1000) or as human-readable strings (e.g. 1k) 37 | output_file=None, 38 | # path to the output file. If None, the profiler prints to stdout. 39 | ignore_modules=None) # the list of modules to ignore in the profiling 40 | params = params / (1000000) 41 | flops = flops / (1000000000) 42 | print('params(M): ', params, 'flops(G): ', flops) 43 | -------------------------------------------------------------------------------- /network/tbsn.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | import torch.nn.functional as F 4 | from einops import rearrange 5 | import math 6 | from network.base_function import RelPosEmb, FixedPosEmb, LayerNorm 7 | 8 | 9 | class PatchUnshuffle(nn.Module): 10 | def __init__(self, p=2, s=2): 11 | super().__init__() 12 | self.p = p 13 | self.s = s 14 | 15 | def forward(self, x): 16 | n, c, h, w = x.shape 17 | x = nn.functional.pixel_unshuffle(x, self.p) 18 | x = nn.functional.pixel_unshuffle(x, self.s) 19 | x = x.view(n, c, self.p * self.p, self.s * self.s, h//self.p//self.s, w//self.p//self.s).permute(0, 1, 3, 2, 4, 5) 20 | x = x.contiguous().view(n, c * (self.p**2) * (self.s**2), h//self.p//self.s, w//self.p//self.s) 21 | x = nn.functional.pixel_shuffle(x, self.p) 22 | return x 23 | 24 | class PatchShuffle(nn.Module): 25 | def __init__(self, p=2, s=2): 26 | super().__init__() 27 | self.p = p 28 | self.s = s 29 | 30 | def forward(self, x): 31 | n, c, h, w = x.shape 32 | x = nn.functional.pixel_unshuffle(x, self.p) 33 | x = x.view(n, c//(self.s**2), (self.s**2), (self.p**2), h//self.p, w//self.p).permute(0, 1, 3, 2, 4, 5) 34 | x = x.contiguous().view(n, c * (self.p**2), h//self.p, w//self.p) 35 | x = nn.functional.pixel_shuffle(x, self.s) 36 | x = nn.functional.pixel_shuffle(x, self.p) 37 | return x 38 | 39 | 40 | class CentralMaskedConv2d(nn.Conv2d): 41 | def __init__(self, *args, **kwargs): 42 | super().__init__(*args, **kwargs) 43 | 44 | self.register_buffer('mask', self.weight.data.clone()) 45 | _, _, kH, kW = self.weight.size() 46 | self.mask.fill_(1) 47 | self.mask[:, :, kH // 2, kH // 2] = 0 48 | 49 | def forward(self, x): 50 | self.weight.data *= self.mask 51 | return super().forward(x) 52 | 53 | 54 | class DilatedMDTA(nn.Module): 55 | def __init__(self, dim, num_heads, bias): 56 | super(DilatedMDTA, self).__init__() 57 | self.num_heads = num_heads 58 | self.temperature = nn.Parameter(torch.ones(num_heads, 1, 1)) 59 | 60 | self.qkv = nn.Conv2d(dim, dim*3, kernel_size=1, bias=bias) 61 | self.qkv_dwconv = nn.Conv2d(dim*3, dim*3, kernel_size=3, stride=1, dilation=2, padding=2, groups=dim*3, bias=bias) 62 | self.project_out = nn.Conv2d(dim, dim, kernel_size=1, bias=bias) 63 | 64 | def forward(self, x): 65 | b,c,h,w = x.shape 66 | 67 | qkv = self.qkv_dwconv(self.qkv(x)) 68 | q,k,v = qkv.chunk(3, dim=1) 69 | 70 | q = rearrange(q, 'b (head c) h w -> b head c (h w)', head=self.num_heads) 71 | k = rearrange(k, 'b (head c) h w -> b head c (h w)', head=self.num_heads) 72 | v = rearrange(v, 'b (head c) h w -> b head c (h w)', head=self.num_heads) 73 | 74 | q = torch.nn.functional.normalize(q, dim=-1) 75 | k = torch.nn.functional.normalize(k, dim=-1) 76 | 77 | attn = (q @ k.transpose(-2, -1)) * self.temperature 78 | attn = attn.softmax(dim=-1) 79 | 80 | out = (attn @ v) 81 | 82 | out = rearrange(out, 'b head c (h w) -> b (head c) h w', head=self.num_heads, h=h, w=w) 83 | 84 | out = self.project_out(out) 85 | return out 86 | 87 | 88 | class DilatedOCA(nn.Module): 89 | def __init__(self, dim, window_size, overlap_ratio, num_heads, dim_head, bias): 90 | super(DilatedOCA, self).__init__() 91 | self.num_spatial_heads = num_heads 92 | self.dim = dim 93 | self.window_size = window_size 94 | self.overlap_win_size = int(window_size * overlap_ratio) + window_size 95 | self.dim_head = dim_head 96 | self.inner_dim = self.dim_head * self.num_spatial_heads 97 | self.scale = self.dim_head**-0.5 98 | 99 | self.unfold = nn.Unfold(kernel_size=(self.overlap_win_size, self.overlap_win_size), stride=window_size, padding=(self.overlap_win_size-window_size)//2) 100 | self.qkv = nn.Conv2d(self.dim, self.inner_dim*3, kernel_size=1, bias=bias) 101 | self.project_out = nn.Conv2d(self.inner_dim, dim, kernel_size=1, bias=bias) 102 | self.rel_pos_emb = RelPosEmb( 103 | block_size = window_size, 104 | rel_size = window_size + (self.overlap_win_size - window_size), 105 | dim_head = self.dim_head 106 | ) 107 | self.fixed_pos_emb = FixedPosEmb(window_size, self.overlap_win_size) 108 | def forward(self, x): 109 | b, c, h, w = x.shape 110 | qkv = self.qkv(x) 111 | qs, ks, vs = qkv.chunk(3, dim=1) 112 | 113 | # spatial attention 114 | qs = rearrange(qs, 'b c (h p1) (w p2) -> (b h w) (p1 p2) c', p1 = self.window_size, p2 = self.window_size) 115 | ks, vs = map(lambda t: self.unfold(t), (ks, vs)) 116 | ks, vs = map(lambda t: rearrange(t, 'b (c j) i -> (b i) j c', c = self.inner_dim), (ks, vs)) 117 | 118 | # print(f'qs.shape:{qs.shape}, ks.shape:{ks.shape}, vs.shape:{vs.shape}') 119 | #split heads 120 | qs, ks, vs = map(lambda t: rearrange(t, 'b n (head c) -> (b head) n c', head = self.num_spatial_heads), (qs, ks, vs)) 121 | 122 | # attention 123 | qs = qs * self.scale 124 | spatial_attn = (qs @ ks.transpose(-2, -1)) 125 | spatial_attn += self.rel_pos_emb(qs) 126 | spatial_attn += self.fixed_pos_emb() 127 | spatial_attn = spatial_attn.softmax(dim=-1) 128 | 129 | out = (spatial_attn @ vs) 130 | 131 | out = rearrange(out, '(b h w head) (p1 p2) c -> b (head c) (h p1) (w p2)', head = self.num_spatial_heads, h = h // self.window_size, w = w // self.window_size, p1 = self.window_size, p2 = self.window_size) 132 | 133 | # merge spatial and channel 134 | out = self.project_out(out) 135 | 136 | return out 137 | 138 | 139 | class FeedForward(nn.Module): 140 | def __init__(self, dim, ffn_expansion_factor, bias): 141 | super(FeedForward, self).__init__() 142 | 143 | hidden_features = int(dim * ffn_expansion_factor) 144 | 145 | self.project_in = nn.Conv2d(dim, hidden_features, kernel_size=3, stride=1, dilation=2, padding=2, bias=bias) 146 | self.project_out = nn.Conv2d(hidden_features, dim, kernel_size=3, stride=1, dilation=2, padding=2, bias=bias) 147 | 148 | def forward(self, x): 149 | x = self.project_in(x) 150 | x = F.gelu(x) 151 | x = self.project_out(x) 152 | return x 153 | 154 | 155 | class TransformerBlock(nn.Module): 156 | def __init__(self, dim, window_size, overlap_ratio, num_channel_heads, num_spatial_heads, spatial_dim_head, ffn_expansion_factor, bias, LayerNorm_type): 157 | super(TransformerBlock, self).__init__() 158 | 159 | 160 | self.spatial_attn = DilatedOCA(dim, window_size, overlap_ratio, num_spatial_heads, spatial_dim_head, bias) 161 | self.channel_attn = DilatedMDTA(dim, num_channel_heads, bias) 162 | 163 | self.norm1 = LayerNorm(dim, LayerNorm_type) 164 | self.norm2 = LayerNorm(dim, LayerNorm_type) 165 | self.norm3 = LayerNorm(dim, LayerNorm_type) 166 | self.norm4 = LayerNorm(dim, LayerNorm_type) 167 | 168 | self.channel_ffn = FeedForward(dim, ffn_expansion_factor, bias) 169 | self.spatial_ffn = FeedForward(dim, ffn_expansion_factor, bias) 170 | 171 | 172 | def forward(self, x): 173 | x = x + self.channel_attn(self.norm1(x)) 174 | x = x + self.channel_ffn(self.norm2(x)) 175 | x = x + self.spatial_attn(self.norm3(x)) 176 | x = x + self.spatial_ffn(self.norm4(x)) 177 | return x 178 | 179 | ########################################################################## 180 | ## Overlapped image patch embedding with 3x3 Conv 181 | class OverlapPatchEmbed(nn.Module): 182 | def __init__(self, in_c=3, embed_dim=48, bias=False): 183 | super(OverlapPatchEmbed, self).__init__() 184 | 185 | self.proj = CentralMaskedConv2d(in_c, embed_dim, kernel_size=3, stride=1, padding=1, bias=bias) 186 | 187 | def forward(self, x): 188 | x = self.proj(x) 189 | 190 | return x 191 | 192 | ########################################################################## 193 | ## Resizing modules 194 | class Downsample(nn.Module): 195 | def __init__(self, n_feat): 196 | super(Downsample, self).__init__() 197 | 198 | self.body = nn.Sequential(nn.Conv2d(n_feat, n_feat//2, kernel_size=1, bias=False), 199 | PatchUnshuffle()) 200 | 201 | def forward(self, x): 202 | return self.body(x) 203 | 204 | 205 | class Upsample(nn.Module): 206 | def __init__(self, n_feat): 207 | super(Upsample, self).__init__() 208 | 209 | self.body = nn.Sequential(nn.Conv2d(n_feat, n_feat*2, kernel_size=1, bias=False), 210 | PatchShuffle()) 211 | 212 | def forward(self, x): 213 | return self.body(x) 214 | 215 | 216 | class SR_Upsample(nn.Sequential): 217 | """SR_Upsample module. 218 | Args: 219 | scale (int): Scale factor. Supported scales: 2^n and 3. 220 | num_feat (int): Channel number of features. 221 | """ 222 | 223 | def __init__(self, scale, num_feat): 224 | m = [] 225 | 226 | if (scale & (scale - 1)) == 0: # scale = 2^n 227 | for _ in range(int(math.log(scale, 2))): 228 | m.append(nn.Conv2d(num_feat, 4 * num_feat, kernel_size = 3, stride = 1, padding = 1)) 229 | m.append(nn.PixelShuffle(2)) 230 | elif scale == 3: 231 | m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1)) 232 | m.append(nn.PixelShuffle(3)) 233 | else: 234 | raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.') 235 | super(SR_Upsample, self).__init__(*m) 236 | 237 | ########################################################################## 238 | 239 | class TBSN(nn.Module): 240 | def __init__(self, 241 | in_ch=3, 242 | out_ch=3, 243 | dim = 48, 244 | num_blocks = [4,4,4], 245 | num_refinement_blocks = 4, 246 | channel_heads = [1,2,4], 247 | spatial_heads = [2,2,3], 248 | overlap_ratio=[0.5, 0.5, 0.5], 249 | window_size = 8, 250 | spatial_dim_head = 16, 251 | bias = False, 252 | ffn_expansion_factor = 1, 253 | LayerNorm_type = 'BiasFree', 254 | scale = 1 255 | ): 256 | 257 | super(TBSN, self).__init__() 258 | self.scale = scale 259 | 260 | self.patch_embed = OverlapPatchEmbed(in_ch, dim) 261 | self.encoder_level1 = nn.Sequential(*[TransformerBlock(dim=dim, window_size = window_size, overlap_ratio=overlap_ratio[0], num_channel_heads=channel_heads[0], num_spatial_heads=spatial_heads[0], spatial_dim_head = spatial_dim_head, ffn_expansion_factor=ffn_expansion_factor, bias=bias, LayerNorm_type=LayerNorm_type) for i in range(num_blocks[0])]) 262 | 263 | self.down1_2 = Downsample(dim) ## From Level 1 to Level 2 264 | self.encoder_level2 = nn.Sequential(*[TransformerBlock(dim=int(dim*2**1), window_size = window_size, overlap_ratio=overlap_ratio[1], num_channel_heads=channel_heads[1], num_spatial_heads=spatial_heads[1], spatial_dim_head = spatial_dim_head, ffn_expansion_factor=ffn_expansion_factor, bias=bias, LayerNorm_type=LayerNorm_type) for i in range(num_blocks[1])]) 265 | 266 | self.down2_3 = Downsample(int(dim*2**1)) ## From Level 2 to Level 3 267 | self.latent = nn.Sequential(*[TransformerBlock(dim=int(dim*2**2), window_size = window_size, overlap_ratio=overlap_ratio[2], num_channel_heads=channel_heads[2], num_spatial_heads=spatial_heads[2], spatial_dim_head = spatial_dim_head, ffn_expansion_factor=ffn_expansion_factor, bias=bias, LayerNorm_type=LayerNorm_type) for i in range(num_blocks[2])]) 268 | 269 | self.up3_2 = Upsample(int(dim*2**2)) ## From Level 3 to Level 2 270 | self.reduce_chan_level2 = nn.Conv2d(int(dim*2**2), int(dim*2**1), kernel_size=1, bias=bias) 271 | self.decoder_level2 = nn.Sequential(*[TransformerBlock(dim=int(dim*2**1), window_size = window_size, overlap_ratio=overlap_ratio[1], num_channel_heads=channel_heads[1], num_spatial_heads=spatial_heads[1], spatial_dim_head = spatial_dim_head, ffn_expansion_factor=ffn_expansion_factor, bias=bias, LayerNorm_type=LayerNorm_type) for i in range(num_blocks[1])]) 272 | 273 | self.up2_1 = Upsample(int(dim*2**1)) ## From Level 2 to Level 1 (NO 1x1 conv to reduce channels) 274 | 275 | self.decoder_level1 = nn.Sequential(*[TransformerBlock(dim=int(dim*2**1), window_size = window_size, overlap_ratio=overlap_ratio[0], num_channel_heads=channel_heads[0], num_spatial_heads=spatial_heads[0], spatial_dim_head = spatial_dim_head, ffn_expansion_factor=ffn_expansion_factor, bias=bias, LayerNorm_type=LayerNorm_type) for i in range(num_blocks[0])]) 276 | 277 | self.refinement = nn.Sequential(*[TransformerBlock(dim=int(dim*2**1), window_size = window_size, overlap_ratio=overlap_ratio[0], num_channel_heads=channel_heads[0], num_spatial_heads=spatial_heads[0], spatial_dim_head = spatial_dim_head, ffn_expansion_factor=ffn_expansion_factor, bias=bias, LayerNorm_type=LayerNorm_type) for i in range(num_refinement_blocks)]) 278 | 279 | self.output = nn.Conv2d(int(dim*2**1), out_ch, kernel_size=1, bias=bias) 280 | 281 | def forward(self, inp_img): 282 | 283 | if self.scale > 1: 284 | inp_img = F.interpolate(inp_img, scale_factor=self.scale, mode='bilinear', align_corners=False) 285 | 286 | inp_enc_level1 = self.patch_embed(inp_img) 287 | out_enc_level1 = self.encoder_level1(inp_enc_level1) 288 | 289 | inp_enc_level2 = self.down1_2(out_enc_level1) 290 | out_enc_level2 = self.encoder_level2(inp_enc_level2) 291 | 292 | inp_enc_level3 = self.down2_3(out_enc_level2) 293 | latent = self.latent(inp_enc_level3) 294 | 295 | inp_dec_level2 = self.up3_2(latent) 296 | inp_dec_level2 = torch.cat([inp_dec_level2, out_enc_level2], 1) 297 | inp_dec_level2 = self.reduce_chan_level2(inp_dec_level2) 298 | out_dec_level2 = self.decoder_level2(inp_dec_level2) 299 | 300 | inp_dec_level1 = self.up2_1(out_dec_level2) 301 | inp_dec_level1 = torch.cat([inp_dec_level1, out_enc_level1], 1) 302 | out_dec_level1 = self.decoder_level1(inp_dec_level1) 303 | 304 | out_dec_level1 = self.refinement(out_dec_level1) 305 | out_dec_level1 = self.output(out_dec_level1) 306 | 307 | return out_dec_level1 308 | 309 | -------------------------------------------------------------------------------- /network/unet.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | from typing import Tuple 4 | 5 | ''' 6 | https://github.com/COMP6248-Reproducability-Challenge/selfsupervised-denoising/blob/master-with-report/ssdn/ssdn/models/noise_network.py 7 | ''' 8 | class Unet(nn.Module): 9 | """Custom U-Net architecture for Self Supervised Denoising (SSDN) and Noise2Noise (N2N). 10 | Base N2N implementation was made with reference to @joeylitalien's N2N implementation. 11 | Changes made are removal of weight sharing when blocks are reused. Usage of LeakyReLu 12 | over standard ReLu and incorporation of blindspot functionality. 13 | 14 | Unlike other typical U-Net implementations dropout is not used when the model is trained. 15 | 16 | When in blindspot mode the following behaviour changes occur: 17 | 18 | * Input batches are duplicated for rotations: 0, 90, 180, 270. This increases the 19 | batch size by 4x. After the encode-decode stage the rotations are undone and 20 | concatenated on the channel axis with the associated original image. This 4x 21 | increase in channel count is collapsed to the standard channel count in the 22 | first 1x1 kernel convolution. 23 | 24 | * To restrict the receptive field into the upward direction a shift is used for 25 | convolutions (see nn.Conv2d) and downsampling. Downsampling uses a single 26 | pixel shift prior to max pooling as dictated by Laine et al. This is equivalent 27 | to applying a shift on the upsample. 28 | 29 | Args: 30 | in_channels (int, optional): Number of input channels, this will typically be either 31 | 1 (Mono) or 3 (RGB) but can be more. Defaults to 3. 32 | out_channels (int, optional): Number of channels the final convolution should output. 33 | Defaults to 3. 34 | blindspot (bool, optional): Whether to enable the network blindspot. This will 35 | add in rotation stages and shift stages while max pooling and during convolutions. 36 | A futher shift will occur after upsample. Defaults to False. 37 | zero_output_weights (bool, optional): Whether to initialise the weights of 38 | `nin_c` to zero. This is not mentioned in literature but is done as part 39 | of the tensorflow implementation for the parameter estimation network. 40 | Defaults to False. 41 | """ 42 | 43 | def __init__(self, in_ch=3, out_ch=3, zero_output=False, dim=48): 44 | super(Unet, self).__init__() 45 | self.zero_output = zero_output 46 | in_channels = in_ch 47 | out_channels = out_ch 48 | 49 | #################################### 50 | # Encode Blocks 51 | #################################### 52 | 53 | # Layers: enc_conv0, enc_conv1, pool1 54 | self.encode_block_1 = nn.Sequential( 55 | nn.Conv2d(in_channels, dim, 3, stride=1, padding=1), 56 | nn.LeakyReLU(negative_slope=0.1, inplace=True), 57 | nn.Conv2d(dim, dim, 3, padding=1), 58 | nn.LeakyReLU(negative_slope=0.1, inplace=True), 59 | nn.MaxPool2d(2) 60 | ) 61 | 62 | # Layers: enc_conv(i), pool(i); i=2..5 63 | def _encode_block_2_3_4_5() -> nn.Module: 64 | return nn.Sequential( 65 | nn.Conv2d(dim, dim, 3, stride=1, padding=1), 66 | nn.LeakyReLU(negative_slope=0.1, inplace=True), 67 | nn.MaxPool2d(2) 68 | ) 69 | 70 | # Separate instances of same encode module definition created 71 | self.encode_block_2 = _encode_block_2_3_4_5() 72 | self.encode_block_3 = _encode_block_2_3_4_5() 73 | self.encode_block_4 = _encode_block_2_3_4_5() 74 | self.encode_block_5 = _encode_block_2_3_4_5() 75 | 76 | # Layers: enc_conv6 77 | self.encode_block_6 = nn.Sequential( 78 | nn.Conv2d(dim, dim, 3, stride=1, padding=1), 79 | nn.LeakyReLU(negative_slope=0.1, inplace=True), 80 | ) 81 | 82 | #################################### 83 | # Decode Blocks 84 | #################################### 85 | # Layers: upsample5 86 | self.decode_block_6 = nn.Sequential(nn.Upsample(scale_factor=2, mode="nearest")) 87 | 88 | # Layers: dec_conv5a, dec_conv5b, upsample4 89 | self.decode_block_5 = nn.Sequential( 90 | nn.Conv2d(dim * 2, dim * 2, 3, stride=1, padding=1), 91 | nn.LeakyReLU(negative_slope=0.1, inplace=True), 92 | nn.Conv2d(dim * 2, dim * 2, 3, stride=1, padding=1), 93 | nn.LeakyReLU(negative_slope=0.1, inplace=True), 94 | nn.Upsample(scale_factor=2, mode="nearest"), 95 | ) 96 | 97 | # Layers: dec_deconv(i)a, dec_deconv(i)b, upsample(i-1); i=4..2 98 | def _decode_block_4_3_2() -> nn.Module: 99 | return nn.Sequential( 100 | nn.Conv2d(dim * 3, dim * 2, 3, stride=1, padding=1), 101 | nn.LeakyReLU(negative_slope=0.1, inplace=True), 102 | nn.Conv2d(dim * 2, dim * 2, 3, stride=1, padding=1), 103 | nn.LeakyReLU(negative_slope=0.1, inplace=True), 104 | nn.Upsample(scale_factor=2, mode="nearest"), 105 | ) 106 | 107 | # Separate instances of same decode module definition created 108 | self.decode_block_4 = _decode_block_4_3_2() 109 | self.decode_block_3 = _decode_block_4_3_2() 110 | self.decode_block_2 = _decode_block_4_3_2() 111 | 112 | # Layers: dec_conv1a, dec_conv1b, dec_conv1c, 113 | self.decode_block_1 = nn.Sequential( 114 | nn.Conv2d(dim * 2 + in_channels, dim * 2, 3, stride=1, padding=1), 115 | nn.LeakyReLU(negative_slope=0.1, inplace=True), 116 | nn.Conv2d(dim * 2, dim * 2, 3, stride=1, padding=1), 117 | nn.LeakyReLU(negative_slope=0.1, inplace=True), 118 | ) 119 | 120 | #################################### 121 | # Output Block 122 | #################################### 123 | 124 | 125 | # nin_a,b,c, linear_act 126 | self.output_conv = nn.Conv2d(dim * 2, out_channels, 1) 127 | 128 | # Initialize weights 129 | self.init_weights() 130 | 131 | def init_weights(self): 132 | """Initializes weights using Kaiming He et al. (2015). 133 | 134 | Only convolution layers have learnable weights. All convolutions use a leaky 135 | relu activation function (negative_slope = 0.1) except the last which is just 136 | a linear output. 137 | """ 138 | with torch.no_grad(): 139 | self._init_weights() 140 | 141 | def _init_weights(self): 142 | for m in self.modules(): 143 | if isinstance(m, nn.Conv2d): 144 | nn.init.kaiming_normal_(m.weight.data, a=0.1) 145 | m.bias.data.zero_() 146 | 147 | # Initialise last output layer 148 | if self.zero_output: 149 | self.output_conv.weight.zero_() 150 | else: 151 | nn.init.kaiming_normal_(self.output_conv.weight.data, nonlinearity="linear") 152 | 153 | def forward_train(self, x): 154 | 155 | # Encoder 156 | pool1 = self.encode_block_1(x) 157 | pool2 = self.encode_block_2(pool1) 158 | pool3 = self.encode_block_3(pool2) 159 | pool4 = self.encode_block_4(pool3) 160 | pool5 = self.encode_block_5(pool4) 161 | encoded = self.encode_block_6(pool5) 162 | 163 | # Decoder 164 | upsample5 = self.decode_block_6(encoded) 165 | concat5 = torch.cat((upsample5, pool4), dim=1) 166 | upsample4 = self.decode_block_5(concat5) 167 | concat4 = torch.cat((upsample4, pool3), dim=1) 168 | upsample3 = self.decode_block_4(concat4) 169 | concat3 = torch.cat((upsample3, pool2), dim=1) 170 | upsample2 = self.decode_block_3(concat3) 171 | concat2 = torch.cat((upsample2, pool1), dim=1) 172 | upsample1 = self.decode_block_2(concat2) 173 | concat1 = torch.cat((upsample1, x), dim=1) 174 | x = self.decode_block_1(concat1) 175 | 176 | x = self.output_conv(x) 177 | 178 | return x 179 | 180 | def forward_test(self, x): 181 | n, c, h, w = x.shape 182 | if h < w: 183 | x = torch.nn.functional.pad(x, [0, 0, 0, w - h], mode='reflect') 184 | else: 185 | x = torch.nn.functional.pad(x, [0, h - w, 0, 0], mode='reflect') 186 | x = self.forward_train(x) 187 | x = x[:, :, :h, :w] 188 | return x 189 | 190 | def forward(self, x): 191 | if self.training: 192 | return self.forward_train(x) 193 | else: 194 | return self.forward_test(x) 195 | 196 | @staticmethod 197 | def input_wh_mul() -> int: 198 | """Multiple that both the width and height dimensions of an input must be to be 199 | processed by the network. This is devised from the number of pooling layers that 200 | reduce the input size. 201 | 202 | Returns: 203 | int: Dimension multiplier 204 | """ 205 | max_pool_layers = 5 206 | return 2 ** max_pool_layers -------------------------------------------------------------------------------- /option/knowledge_distillation_sidd.json: -------------------------------------------------------------------------------- 1 | { 2 | // model 3 | "model": "APBSNDistillModel", 4 | "pd_a": 5, 5 | "pd_b": 2, 6 | "R3": true, 7 | "R3_T": 1, 8 | "R3_p": 0, 9 | // net 10 | "networks": [{ 11 | "name": "bsn", 12 | "type": "TBSN", 13 | "args": { 14 | "in_ch": 3, 15 | "out_ch": 3 16 | } 17 | , "path": "../pretrained_models/model_sidd.pth" 18 | },{ 19 | "name": "network", 20 | "type": "Unet", 21 | "args": {} 22 | }], 23 | // datasets 24 | "train_dataset": { 25 | "type": "SIDDMediumTrainDataset", 26 | "args": { 27 | "pin_memory": true, 28 | "patch_size": 128 29 | }, 30 | "batch_size": 8 // used for base train 31 | }, 32 | "validation_datasets": [{ 33 | "type": "SIDDValidationDataset", 34 | "args": {} 35 | }], 36 | // training parameters 37 | "lr": 3e-4, 38 | "print_every": 1000, 39 | "save_every": 10000, 40 | "validate_every": 10000, 41 | "num_iters": 100000, 42 | "log_dir": "log", 43 | "log_file": "log/log.out" 44 | } 45 | -------------------------------------------------------------------------------- /option/tbsn_dnd.json: -------------------------------------------------------------------------------- 1 | { 2 | // model 3 | "model": "APBSNModel", 4 | "pd_a": 5, 5 | "pd_b": 2, 6 | "R3": true, 7 | "R3_T": 8, 8 | "R3_p": 0.16, 9 | // net 10 | "networks": [{ 11 | "name": "bsn", 12 | "type": "TBSN", 13 | "args": { 14 | "in_ch": 3, 15 | "out_ch": 3 16 | } 17 | , "path": "../pretrained_models/model_dnd.pth" // comment this line for train 18 | }], 19 | // datasets 20 | "train_dataset": { 21 | "type": "DNDBenchmarkTrainDataset", 22 | "args": { 23 | "pin_memory": true, 24 | "patch_size": 640 25 | }, 26 | "batch_size": 4 // used for base train 27 | }, 28 | "validation_datasets": [{ 29 | "type": "DNDBenchmarkPNGANDataset", 30 | "args": {} 31 | }], 32 | // training parameters 33 | "lr": 3e-4, 34 | "print_every": 1000, 35 | "save_every": 10000, 36 | "validate_every": 20000, 37 | "num_iters": 100000, 38 | "log_dir": "log", 39 | "log_file": "log/log.out" 40 | } 41 | -------------------------------------------------------------------------------- /option/tbsn_sidd.json: -------------------------------------------------------------------------------- 1 | { 2 | // model 3 | "model": "APBSNModel", 4 | "pd_a": 5, 5 | "pd_b": 2, 6 | "R3": true, 7 | "R3_T": 8, 8 | "R3_p": 0.16, 9 | // net 10 | "networks": [{ 11 | "name": "bsn", 12 | "type": "TBSN", 13 | "args": { 14 | "in_ch": 3, 15 | "out_ch": 3 16 | } 17 | , "path": "../pretrained_models/model_sidd.pth" // comment this line for train 18 | }], 19 | // datasets 20 | "train_dataset": { 21 | "type": "SIDDMediumTrainDataset", 22 | "args": { 23 | "pin_memory": true, 24 | "patch_size": 640 25 | }, 26 | "batch_size": 4 // used for base train 27 | }, 28 | "validation_datasets": [{ 29 | "type": "SIDDValidationDataset", 30 | "args": {} 31 | }], 32 | // training parameters 33 | "lr": 3e-4, 34 | "print_every": 1000, 35 | "save_every": 10000, 36 | "validate_every": 20000, 37 | "num_iters": 100000, 38 | "log_dir": "log", 39 | "log_file": "log/log.out" 40 | } 41 | -------------------------------------------------------------------------------- /pretrained_models/model_dnd.pth: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/nagejacob/TBSN/678e93dd4ab816d5c736179b01f7398373db45ef/pretrained_models/model_dnd.pth -------------------------------------------------------------------------------- /pretrained_models/model_sidd.pth: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/nagejacob/TBSN/678e93dd4ab816d5c736179b01f7398373db45ef/pretrained_models/model_sidd.pth -------------------------------------------------------------------------------- /readme.md: -------------------------------------------------------------------------------- 1 | # Rethinking Transformer-Based Blind-Spot Network for Self-Supervised Image Denoising [Paper](https://arxiv.org/abs/2404.07846) 2 | 3 | 4 | ## Usage 5 | ### Datasets 6 | Download [SIDD](https://abdokamel.github.io/sidd/) and [DND](https://noise.visinf.tu-darmstadt.de/) datasets, and modify `dataset_path` in `dataset/base_function.py` accordingly. 7 | ``` 8 | |- dataset_path 9 | |- SIDD 10 | |- SIDD_Medium_Srgb 11 | |- Data 12 | |- 0001_001_S6_00100_00060_3200_L 13 | |- 0002_001_S6_00100_00020_3200_N 14 | |- ... 15 | |- SIDD_Validation 16 | |- ValidationNoisyBlocksSrgb.mat 17 | |- ValidationGtBlocksSrgb.mat 18 | |- SIDD_Benchmark 19 | |- BenchmarkNoisyBlocksSrgb.mat 20 | |- DND 21 | |- info.mat 22 | |- images_srgb 23 | ``` 24 | 25 | ### Validation 26 | Validate on SIDD Validation dataset, 27 | ``` 28 | cd validate 29 | python base.py --config_file "../option/tbsn_sidd.json" 30 | ``` 31 | 32 | ### Training 33 | Training on SIDD Medium dataset, 34 | ``` 35 | sh train.sh 36 | ``` 37 | 38 | ## Citation 39 | If you make use of our work, please cite our paper. 40 | ```bibtex 41 | @inproceedings{li2025rethinking, 42 | title={Rethinking Transformer-Based Blind-Spot Network for Self-Supervised Image Denoising}, 43 | author={Li, Junyi and Zhang, Zhilu and Zuo, Wangmeng}, 44 | booktitle={Proceedings of the AAAI Conference on Artificial Intelligence}, 45 | year={2025} 46 | } 47 | ``` 48 | -------------------------------------------------------------------------------- /slurm.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | #SBATCH --job-name=default 3 | #SBATCH --gres=gpu:2080:2 4 | #SBATCH --output=/mnt/hdd0/lijunyi/slurm_logs/%j.out 5 | #SBATCH --error=/mnt/hdd0/lijunyi/slurm_logs/%j.err 6 | 7 | source activate 8 | conda activate ~/anaconda/pytorch2 9 | cd /mnt/hdd0/lijunyi/codes/TBSN 10 | sh train.sh -------------------------------------------------------------------------------- /train.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | experiment_home_dir="experiments" 4 | config="tbsn_sidd.json" 5 | 6 | experiment_name=$(python train/experiment_name.py --config option/${config}) 7 | #experiment_name='test' 8 | 9 | experiment_dir="${experiment_home_dir}/${experiment_name}" 10 | echo "experiment dir: ${experiment_dir}" 11 | 12 | if [ ! -d "${experiment_home_dir}" ] 13 | then 14 | mkdir "${experiment_home_dir}" 15 | fi 16 | 17 | if [ ! -d "${experiment_dir}" ] 18 | then 19 | mkdir "${experiment_dir}" 20 | else 21 | echo "experiment dir exists" 22 | fi 23 | 24 | cp -r "dataset" "${experiment_dir}" 25 | cp -r "model" "${experiment_dir}" 26 | cp -r "network" "${experiment_dir}" 27 | cp -r "option" "${experiment_dir}" 28 | cp -r "train" "${experiment_dir}" 29 | cp -r "util" "${experiment_dir}" 30 | cp -r "validate" "${experiment_dir}" 31 | 32 | if [ ! -d "${experiment_dir}/log" ] 33 | then 34 | mkdir "${experiment_dir}/log" 35 | fi 36 | 37 | cd ${experiment_dir} 38 | export PYTHONPATH=$PWD:$PYTHONPATH 39 | python train/base.py --config option/${config} -------------------------------------------------------------------------------- /train/base.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | from torch.utils.data import DataLoader 3 | from util.build import build 4 | from util.io import log 5 | from util.option import parse, recursive_log 6 | from validate.base import validate_sidd, validate_dnd, validate_synthetic 7 | 8 | 9 | def main(opt): 10 | train_dataset_opt = opt['train_dataset'] 11 | TrainDataset = getattr(__import__('dataset'), train_dataset_opt['type']) 12 | train_set = build(TrainDataset, train_dataset_opt['args']) 13 | train_loader = DataLoader(train_set, batch_size=train_dataset_opt['batch_size'], shuffle=True, 14 | num_workers=4, drop_last=True) 15 | 16 | validation_loaders = [] 17 | for validation_dataset_opt in opt['validation_datasets']: 18 | ValidationDataset = getattr(__import__('dataset'), validation_dataset_opt['type']) 19 | validation_set = build(ValidationDataset, validation_dataset_opt['args']) 20 | validation_loader = DataLoader(validation_set, batch_size=1) 21 | validation_loaders.append(validation_loader) 22 | 23 | Model = getattr(__import__('model'), opt['model']) 24 | model = Model(opt) 25 | model.data_parallel() 26 | 27 | def train_step(data): 28 | model.train_step(data) 29 | 30 | if model.iter % opt['print_every'] == 0: 31 | model.log() 32 | 33 | if model.iter % opt['save_every'] == 0: 34 | model.save_net() 35 | 36 | if model.iter % opt['validate_every'] == 0: 37 | message = 'iter: %d, ' % model.iter 38 | for validation_loader in validation_loaders: 39 | if 'SIDD' in validation_loader.dataset.__class__.__name__: 40 | psnr, ssim = validate_sidd(model, validation_loader) 41 | elif 'DND' in validation_loader.dataset.__class__.__name__: 42 | psnr, ssim = validate_dnd(model, validation_loader) 43 | elif 'Synthetic' in validation_loader.dataset.__class__.__name__: 44 | psnr, ssim = validate_synthetic(model, validation_loader) 45 | message += '%s: %6.4f/%6.4f ' % (validation_loader.dataset.__class__.__name__, psnr, ssim) 46 | log(opt['log_file'], message + '\n') 47 | 48 | while True: 49 | for i, data in enumerate(train_loader): 50 | train_step(data) 51 | if model.iter >= opt['num_iters']: 52 | return 53 | 54 | 55 | if __name__ == '__main__': 56 | parser = argparse.ArgumentParser(description="Train the denoiser") 57 | parser.add_argument("--config", type=str, default='') 58 | argspar = parser.parse_args() 59 | 60 | opt = parse(argspar.config) 61 | recursive_log(opt['log_file'], opt) 62 | 63 | main(opt) -------------------------------------------------------------------------------- /train/experiment_name.py: -------------------------------------------------------------------------------- 1 | import sys 2 | sys.path.append('.') 3 | sys.path.append('..') 4 | import argparse 5 | import datetime 6 | from util.option import parse 7 | 8 | if __name__ == '__main__': 9 | parser = argparse.ArgumentParser(description="Train the denoiser") 10 | parser.add_argument("--config", type=str, default='') 11 | argspar = parser.parse_args() 12 | 13 | opt = parse(argspar.config) 14 | 15 | now = datetime.datetime.now() 16 | date = now.strftime("%m%d-%H%M%S") 17 | 18 | model = opt['model'] 19 | network = opt['networks'][0]['type'] 20 | 21 | name = date + '_' + model + '_' + network 22 | print(name) -------------------------------------------------------------------------------- /util/build.py: -------------------------------------------------------------------------------- 1 | def build(obj_type, args): 2 | return obj_type(**args) -------------------------------------------------------------------------------- /util/io.py: -------------------------------------------------------------------------------- 1 | import datetime 2 | import imageio 3 | import numpy as np 4 | import os 5 | from PIL import Image 6 | 7 | IMG_EXTENSIONS = ['.jpg', '.JPG', '.jpeg', '.JPEG', '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP', '.tif'] 8 | 9 | def is_image_file(filename): 10 | return any(filename.endswith(extension) for extension in IMG_EXTENSIONS) and (not filename.startswith('.')) 11 | 12 | def get_image_paths(dataroot): 13 | paths = None # return None if dataroot is None 14 | if isinstance(dataroot, str): 15 | paths = sorted(_get_paths_from_images(dataroot)) 16 | elif isinstance(dataroot, list): 17 | paths = [] 18 | for i in dataroot: 19 | paths += sorted(_get_paths_from_images(i)) 20 | return paths 21 | 22 | def _get_paths_from_images(path): 23 | assert os.path.isdir(path), '{:s} is not a valid directory'.format(path) 24 | images = [] 25 | for dirpath, _, fnames in sorted(os.walk(path)): 26 | for fname in sorted(fnames): 27 | if is_image_file(fname): 28 | img_path = os.path.join(dirpath, fname) 29 | images.append(img_path) 30 | assert images, '{:s} has no valid image file'.format(path) 31 | return images 32 | 33 | def open_image_uint8(image_file, to_gray=False): 34 | if to_gray: 35 | image = np.asarray(Image.open(image_file).convert('L')) 36 | else: 37 | image = imageio.imread(image_file).astype(np.uint8) 38 | 39 | if len(image.shape) == 2: 40 | image = np.expand_dims(image, axis=0) 41 | image = np.repeat(image, 3, axis=0) 42 | elif len(image.shape) == 3: 43 | image = np.transpose(image, (2, 0, 1)) 44 | image = image[:3, :, :] 45 | 46 | return image 47 | 48 | 49 | def date_time(): 50 | now = datetime.datetime.now() 51 | date_time = now.strftime("%Y-%m-%d, %H:%M:%S") 52 | return date_time 53 | 54 | 55 | def log(log_file, str, also_print=True, with_time=True): 56 | with open(log_file, 'a+') as F: 57 | if with_time: 58 | F.write(date_time() + ' ') 59 | F.write(str) 60 | if also_print: 61 | if with_time: 62 | print(date_time(), end=' ') 63 | print(str, end='') 64 | 65 | 66 | # save numpy image in shape 3xHxW 67 | def np2image(image, image_file): 68 | image = np.transpose(image, (1, 2, 0)) 69 | image = np.clip(image, 0., 1.) 70 | image = image * 255. 71 | image = image.astype(np.uint8) 72 | if 1 == image.shape[2]: 73 | image = image[:, :, 0] 74 | imageio.imwrite(image_file, image) 75 | 76 | # save tensor image in shape 1x3xHxW 77 | def tensor2image(image, image_file): 78 | image = image.detach().cpu().squeeze(0).numpy() 79 | np2image(image, image_file) -------------------------------------------------------------------------------- /util/option.py: -------------------------------------------------------------------------------- 1 | from collections import OrderedDict 2 | import json 3 | from util.io import log 4 | 5 | def parse(opt_path): 6 | # ---------------------------------------- 7 | # remove comments starting with '//' 8 | # ---------------------------------------- 9 | json_str = '' 10 | with open(opt_path, 'r') as f: 11 | for line in f: 12 | line = line.split('//')[0] + '\n' 13 | json_str += line 14 | 15 | # ---------------------------------------- 16 | # initialize opt 17 | # ---------------------------------------- 18 | opt = json.loads(json_str, object_pairs_hook=OrderedDict) 19 | 20 | return opt 21 | 22 | 23 | def recursive_print(src, dpth=0, key=None): 24 | """ Recursively prints nested elements.""" 25 | tabs = lambda n: ' ' * n * 4 # or 2 or 8 or... 26 | 27 | if isinstance(src, dict): 28 | if key is not None: 29 | print(tabs(dpth) + '%s: ' % (key)) 30 | for key, value in src.items(): 31 | recursive_print(value, dpth + 1, key) 32 | elif isinstance(src, list): 33 | if key is not None: 34 | print(tabs(dpth) + '%s: ' % (key)) 35 | for litem in src: 36 | recursive_print(litem, dpth) 37 | else: 38 | if key is not None: 39 | print(tabs(dpth) + '%s: %s' % (key, src)) 40 | 41 | 42 | def recursive_log(log_file, src, dpth=0, key=None): 43 | """ Recursively prints nested elements.""" 44 | tabs = lambda n: ' ' * n * 4 # or 2 or 8 or... 45 | 46 | if isinstance(src, dict): 47 | if key is not None: 48 | log(log_file, tabs(dpth) + '%s: \n' % (key), with_time=False) 49 | for key, value in src.items(): 50 | recursive_log(log_file, value, dpth + 1, key) 51 | elif isinstance(src, list): 52 | if key is not None: 53 | log(log_file, tabs(dpth) + '%s: \n' % (key), with_time=False) 54 | for litem in src: 55 | recursive_log(log_file, litem, dpth) 56 | else: 57 | if key is not None: 58 | log(log_file, tabs(dpth) + '%s: %s\n' % (key, src), with_time=False) -------------------------------------------------------------------------------- /validate/base.py: -------------------------------------------------------------------------------- 1 | import sys 2 | sys.path.append('..') 3 | import argparse 4 | import lpips 5 | from skimage.metrics import peak_signal_noise_ratio, structural_similarity 6 | import torch 7 | from torch.utils.data import DataLoader 8 | from tqdm import tqdm 9 | from validate.base_function import calculate_ssim 10 | from util.build import build 11 | from util.option import parse, recursive_print 12 | 13 | loss_fn_alex = lpips.LPIPS(net='alex').cuda() # best forward scores 14 | def lpips_norm(img): 15 | img = img * 2. - 1 16 | return img 17 | 18 | def validate_sidd(model, sidd_loader): 19 | psnrs, ssims, lpipss, count = 0, 0, 0, 0 20 | for data in tqdm(sidd_loader): 21 | output = model.validation_step(data) 22 | output = torch.floor(output * 255. + 0.5) / 255. 23 | output = torch.clamp(output, 0, 1) 24 | lpips = loss_fn_alex(lpips_norm(output), lpips_norm(data['H'].cuda())) 25 | output = output.cpu().squeeze(0).permute(1, 2, 0).numpy() 26 | gt = data['H'].squeeze(0).permute(1, 2, 0).numpy() 27 | psnr = peak_signal_noise_ratio(output, gt, data_range=1) 28 | ssim = structural_similarity(output, gt, data_range=1, channel_axis=2, gaussian_weights=True, sigma=1.5, win_size=11) 29 | 30 | psnrs += psnr 31 | ssims += ssim 32 | lpipss += lpips 33 | count += 1 34 | 35 | return psnrs / count, ssims / count, lpipss / count 36 | 37 | def validate_dnd(model, dnd_loader): 38 | psnrs, ssims, count = 0, 0, 0 39 | f = open('') 40 | for data in tqdm(dnd_loader): 41 | output = model.validation_step(data) 42 | output = torch.clamp(output, 0, 1) 43 | output = output.cpu().squeeze(0).permute(1, 2, 0).numpy() 44 | gt = data['H'].squeeze(0).permute(1, 2, 0).numpy() 45 | psnr = peak_signal_noise_ratio(output, gt, data_range=1) 46 | 47 | psnrs += psnr 48 | count += 1 49 | return psnrs / count, ssims / count 50 | 51 | def validate_synthetic(model, synthetic_loader): 52 | psnrs, ssims, count = 0, 0, 0 53 | for data in tqdm(synthetic_loader): 54 | n, c, h, w = data['L'].shape 55 | if h < w: 56 | data['L'] = torch.nn.functional.pad(data['L'], [0, 0, 0, w - h], mode='reflect') 57 | elif h > w: 58 | data['L'] = torch.nn.functional.pad(data['L'], [0, h - w, 0, 0], mode='reflect') 59 | output = model.validation_step(data) 60 | output = output[:, :, :h, :w].cpu().squeeze(0).permute(1, 2, 0).numpy() 61 | gt = data['H'].squeeze(0).permute(1, 2, 0).numpy() 62 | psnr = peak_signal_noise_ratio(output, gt, data_range=1) 63 | ssim = calculate_ssim(output * 255., gt * 255.) 64 | 65 | psnrs += psnr 66 | ssims += ssim 67 | count += 1 68 | return psnrs / count, ssims / count 69 | 70 | def main(opt): 71 | validation_loaders = [] 72 | for validation_dataset_opt in opt['validation_datasets']: 73 | ValidationDataset = getattr(__import__('dataset'), validation_dataset_opt['type']) 74 | validation_set = build(ValidationDataset, validation_dataset_opt['args']) 75 | validation_loader = DataLoader(validation_set, batch_size=1) 76 | validation_loaders.append(validation_loader) 77 | 78 | Model = getattr(__import__('model'), opt['model']) 79 | model = Model(opt) 80 | model.data_parallel() 81 | 82 | for validation_loader in validation_loaders: 83 | if 'SIDD' in validation_loader.dataset.__class__.__name__: 84 | psnr, ssim, lpips = validate_sidd(model, validation_loader) 85 | elif 'DND' in validation_loader.dataset.__class__.__name__: 86 | psnr, ssim = validate_dnd(model, validation_loader) 87 | else: 88 | psnr, ssim = validate_synthetic(model, validation_loader) 89 | print('%s, psnr: %6.4f, ssim: %6.4f, lpips: %.4f' % (validation_loader.dataset.__class__.__name__, psnr, ssim, lpips)) 90 | 91 | 92 | if __name__ == '__main__': 93 | parser = argparse.ArgumentParser(description="Validate the denoiser") 94 | parser.add_argument("--config_file", type=str, default='../option/tbsn_sidd.json') 95 | argspar = parser.parse_args() 96 | 97 | opt = parse(argspar.config_file) 98 | recursive_print(opt) 99 | 100 | main(opt) 101 | -------------------------------------------------------------------------------- /validate/base_function.py: -------------------------------------------------------------------------------- 1 | import cv2 2 | import numpy as np 3 | 4 | def reorder_image(img, input_order='HWC'): 5 | """Reorder images to 'HWC' order. 6 | 7 | If the input_order is (h, w), return (h, w, 1); 8 | If the input_order is (c, h, w), return (h, w, c); 9 | If the input_order is (h, w, c), return as it is. 10 | 11 | Args: 12 | img (ndarray): Input image. 13 | input_order (str): Whether the input order is 'HWC' or 'CHW'. 14 | If the input image shape is (h, w), input_order will not have 15 | effects. Default: 'HWC'. 16 | 17 | Returns: 18 | ndarray: reordered image. 19 | """ 20 | 21 | if input_order not in ['HWC', 'CHW']: 22 | raise ValueError(f"Wrong input_order {input_order}. Supported input_orders are 'HWC' and 'CHW'") 23 | if len(img.shape) == 2: 24 | img = img[..., None] 25 | if input_order == 'CHW': 26 | img = img.transpose(1, 2, 0) 27 | return img 28 | 29 | 30 | def _ssim(img, img2): 31 | """Calculate SSIM (structural similarity) for one channel images. 32 | 33 | It is called by func:`calculate_ssim`. 34 | 35 | Args: 36 | img (ndarray): Images with range [0, 255] with order 'HWC'. 37 | img2 (ndarray): Images with range [0, 255] with order 'HWC'. 38 | 39 | Returns: 40 | float: SSIM result. 41 | """ 42 | 43 | c1 = (0.01 * 255)**2 44 | c2 = (0.03 * 255)**2 45 | kernel = cv2.getGaussianKernel(11, 1.5) 46 | window = np.outer(kernel, kernel.transpose()) 47 | 48 | mu1 = cv2.filter2D(img, -1, window)[5:-5, 5:-5] # valid mode for window size 11 49 | mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5] 50 | mu1_sq = mu1**2 51 | mu2_sq = mu2**2 52 | mu1_mu2 = mu1 * mu2 53 | sigma1_sq = cv2.filter2D(img**2, -1, window)[5:-5, 5:-5] - mu1_sq 54 | sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq 55 | sigma12 = cv2.filter2D(img * img2, -1, window)[5:-5, 5:-5] - mu1_mu2 56 | 57 | ssim_map = ((2 * mu1_mu2 + c1) * (2 * sigma12 + c2)) / ((mu1_sq + mu2_sq + c1) * (sigma1_sq + sigma2_sq + c2)) 58 | return ssim_map.mean() 59 | 60 | 61 | def calculate_ssim(img, img2, crop_border=0, input_order='HWC', test_y_channel=False, **kwargs): 62 | """Calculate SSIM (structural similarity). 63 | 64 | ``Paper: Image quality assessment: From error visibility to structural similarity`` 65 | 66 | The results are the same as that of the official released MATLAB code in 67 | https://ece.uwaterloo.ca/~z70wang/research/ssim/. 68 | 69 | For three-channel images, SSIM is calculated for each channel and then 70 | averaged. 71 | 72 | Args: 73 | img (ndarray): Images with range [0, 255]. 74 | img2 (ndarray): Images with range [0, 255]. 75 | crop_border (int): Cropped pixels in each edge of an image. These pixels are not involved in the calculation. 76 | input_order (str): Whether the input order is 'HWC' or 'CHW'. 77 | Default: 'HWC'. 78 | test_y_channel (bool): Test on Y channel of YCbCr. Default: False. 79 | 80 | Returns: 81 | float: SSIM result. 82 | """ 83 | 84 | assert img.shape == img2.shape, (f'Image shapes are different: {img.shape}, {img2.shape}.') 85 | if input_order not in ['HWC', 'CHW']: 86 | raise ValueError(f'Wrong input_order {input_order}. Supported input_orders are "HWC" and "CHW"') 87 | img = reorder_image(img, input_order=input_order) 88 | img2 = reorder_image(img2, input_order=input_order) 89 | 90 | if crop_border != 0: 91 | img = img[crop_border:-crop_border, crop_border:-crop_border, ...] 92 | img2 = img2[crop_border:-crop_border, crop_border:-crop_border, ...] 93 | 94 | # if test_y_channel: 95 | # img = to_y_channel(img) 96 | # img2 = to_y_channel(img2) 97 | 98 | img = img.astype(np.float64) 99 | img2 = img2.astype(np.float64) 100 | 101 | ssims = [] 102 | for i in range(img.shape[2]): 103 | ssims.append(_ssim(img[..., i], img2[..., i])) 104 | return np.array(ssims).mean() -------------------------------------------------------------------------------- /validate/visualization.py: -------------------------------------------------------------------------------- 1 | import sys 2 | sys.path.append('..') 3 | import argparse 4 | import cv2 5 | from network.tbsn import DilatedMDTA, DilatedOCA 6 | import numpy as np 7 | from PIL import Image 8 | from skimage.metrics import peak_signal_noise_ratio 9 | import torch 10 | from torch.utils.data import DataLoader 11 | from tqdm import tqdm 12 | from validate.base_function import calculate_ssim 13 | from util.build import build 14 | from util.io import tensor2image 15 | from util.option import parse, recursive_print 16 | from torch.cuda.amp import autocast 17 | 18 | 19 | def main(opt): 20 | validation_loaders = [] 21 | for validation_dataset_opt in opt['validation_datasets']: 22 | ValidationDataset = getattr(__import__('dataset'), validation_dataset_opt['type']) 23 | validation_set = build(ValidationDataset, validation_dataset_opt['args']) 24 | validation_loader = DataLoader(validation_set, batch_size=1) 25 | validation_loaders.append(validation_loader) 26 | 27 | network = DilatedMDTA(dim=64, num_heads=1, bias=False) 28 | # network = DilatedOCA(dim=64, window_size=8, overlap_ratio=0.5, num_heads=2, dim_head=16, bias=False) 29 | gradient, count = None, 0 30 | with autocast(): 31 | for data in validation_loaders[0]: 32 | input = data['L'] 33 | input = torch.rand((1, 64, 16, 16)) 34 | input.requires_grad = True 35 | output = network(input) 36 | center_output = torch.mean(output[:, :, 8, 8]) 37 | center_output.backward() 38 | if gradient is None: 39 | gradient = torch.sum(torch.abs(input.grad), dim=1, keepdim=True).cpu() 40 | else: 41 | gradient += torch.sum(torch.abs(input.grad), dim=1, keepdim=True).cpu() 42 | count += 1 43 | 44 | if count == 50: 45 | break 46 | 47 | gradient = gradient / count * 50 48 | gradient = torch.clamp(gradient, 0, 1) 49 | gradient = gradient[0, 0].numpy() 50 | cam = cv2.applyColorMap(np.uint8(gradient * 255), cv2.COLORMAP_INFERNO) 51 | cam = cv2.cvtColor(cam, cv2.COLOR_BGR2RGB) 52 | image = Image.fromarray(cam) 53 | image.save('gradient_tbsn.png') 54 | 55 | if __name__ == '__main__': 56 | parser = argparse.ArgumentParser(description="Validate the denoiser") 57 | parser.add_argument("--config_file", type=str, default='../option/tbsn.json') 58 | argspar = parser.parse_args() 59 | 60 | opt = parse(argspar.config_file) 61 | recursive_print(opt) 62 | 63 | main(opt) -------------------------------------------------------------------------------- /validate/visualize_receptive_field.py: -------------------------------------------------------------------------------- 1 | import sys 2 | sys.path.append('..') 3 | import argparse 4 | import cv2 5 | import numpy as np 6 | from PIL import Image 7 | from skimage.metrics import peak_signal_noise_ratio 8 | import torch 9 | from torch.utils.data import DataLoader 10 | from tqdm import tqdm 11 | from validate.base_function import calculate_ssim 12 | from util.build import build 13 | from util.io import tensor2image 14 | from util.option import parse, recursive_print 15 | from torch.cuda.amp import autocast 16 | 17 | 18 | def main(opt): 19 | validation_loaders = [] 20 | for validation_dataset_opt in opt['validation_datasets']: 21 | ValidationDataset = getattr(__import__('dataset'), validation_dataset_opt['type']) 22 | validation_set = build(ValidationDataset, validation_dataset_opt['args']) 23 | validation_loader = DataLoader(validation_set, batch_size=1) 24 | validation_loaders.append(validation_loader) 25 | 26 | Model = getattr(__import__('model'), opt['model']) 27 | model = Model(opt) 28 | model.data_parallel() 29 | 30 | network = model.networks['bsn'] 31 | gradient, count = None, 0 32 | with autocast(): 33 | for data in validation_loaders[0]: 34 | input = data['L'] 35 | input = input[:, :, 64:192, 64:192] 36 | input.requires_grad = True 37 | output = network(input) 38 | center_output = torch.mean(output[:, :, 64, 64]) 39 | center_output.backward() 40 | if gradient is None: 41 | gradient = torch.sum(torch.abs(input.grad), dim=1, keepdim=True).cpu() 42 | else: 43 | gradient += torch.sum(torch.abs(input.grad), dim=1, keepdim=True).cpu() 44 | count += 1 45 | 46 | if count == 50: 47 | break 48 | 49 | gradient = gradient / count * 10 50 | gradient = torch.clamp(gradient, 0, 1) 51 | gradient = gradient[0, 0].numpy() 52 | cam = cv2.applyColorMap(np.uint8(gradient * 255), cv2.COLORMAP_INFERNO) 53 | cam = cv2.cvtColor(cam, cv2.COLOR_BGR2RGB) 54 | image = Image.fromarray(cam) 55 | image.save('gradient_tbsn.png') 56 | 57 | if __name__ == '__main__': 58 | parser = argparse.ArgumentParser(description="Validate the denoiser") 59 | parser.add_argument("--config_file", type=str, default='../option/tbsn.json') 60 | argspar = parser.parse_args() 61 | 62 | opt = parse(argspar.config_file) 63 | recursive_print(opt) 64 | 65 | main(opt) 66 | --------------------------------------------------------------------------------