├── .gitattributes ├── .gitignore ├── LICENSE ├── README.md ├── datagan.py ├── esrgan ├── __init__.py ├── architecture.py ├── block.py ├── esrgan.py └── models │ └── RRDB_ESRGAN_x4.pth ├── requirements.txt └── screenshot.png /.gitattributes: -------------------------------------------------------------------------------- 1 | *.pth filter=lfs diff=lfs merge=lfs -text 2 | *.pt filter=lfs diff=lfs merge=lfs -text 3 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | share/python-wheels/ 24 | *.egg-info/ 25 | .installed.cfg 26 | *.egg 27 | MANIFEST 28 | 29 | # PyInstaller 30 | # Usually these files are written by a python script from a template 31 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 32 | *.manifest 33 | *.spec 34 | 35 | # Installer logs 36 | pip-log.txt 37 | pip-delete-this-directory.txt 38 | 39 | # Unit test / coverage reports 40 | htmlcov/ 41 | .tox/ 42 | .nox/ 43 | .coverage 44 | .coverage.* 45 | .cache 46 | nosetests.xml 47 | coverage.xml 48 | *.cover 49 | .hypothesis/ 50 | .pytest_cache/ 51 | 52 | # Translations 53 | *.mo 54 | *.pot 55 | 56 | # Django stuff: 57 | *.log 58 | local_settings.py 59 | db.sqlite3 60 | 61 | # Flask stuff: 62 | instance/ 63 | .webassets-cache 64 | 65 | # Scrapy stuff: 66 | .scrapy 67 | 68 | # Sphinx documentation 69 | docs/_build/ 70 | 71 | # PyBuilder 72 | target/ 73 | 74 | # Jupyter Notebook 75 | .ipynb_checkpoints 76 | 77 | # IPython 78 | profile_default/ 79 | ipython_config.py 80 | 81 | # pyenv 82 | .python-version 83 | 84 | # celery beat schedule file 85 | celerybeat-schedule 86 | 87 | # SageMath parsed files 88 | *.sage.py 89 | 90 | # Environments 91 | .env 92 | .venv 93 | env/ 94 | venv/ 95 | ENV/ 96 | env.bak/ 97 | venv.bak/ 98 | 99 | # Spyder project settings 100 | .spyderproject 101 | .spyproject 102 | 103 | # Rope project settings 104 | .ropeproject 105 | 106 | # mkdocs documentation 107 | /site 108 | 109 | # mypy 110 | .mypy_cache/ 111 | .dmypy.json 112 | dmypy.json 113 | 114 | # Pyre type checker 115 | .pyre/ 116 | 117 | # PyCharm 118 | .idea/* 119 | -------------------------------------------------------------------------------- /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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It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. 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 | . -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # use cupscale instead 2 | https://github.com/n00mkrad/cupscale 3 | 4 | # old: 5 | 6 | # dataGAN 7 | 8 | A PyQt GUI wrapper for [ESRGAN](https://github.com/xinntao/ESRGAN). Based on the work of Xintao Wang, et al. 9 | 10 | ![screenshot](screenshot.png) 11 | 12 | ## Setup 13 | 14 | * Install [Python 3.7](https://www.python.org/). 15 | * Install [PyTorch](https://pytorch.org/get-started/locally/). 16 | * Install the things in the `requirements.txt` file. 17 | ``` 18 | pip install -r requirements.txt 19 | ``` 20 | * Run dataGAN 21 | ``` 22 | python3 datagan.py 23 | ``` 24 | 25 | ## Disclaimer 26 | 27 | I don't actually know how any of the neural network stuff works. I just made a GUI around what it was already doing. 28 | Please don't ask me if some error comes up and it says something about a model. I probably won't be able to help you. 29 | You can make an issue on this repository if you want, but no guarantees that it'll get answered. 30 | -------------------------------------------------------------------------------- /datagan.py: -------------------------------------------------------------------------------- 1 | from pathlib import Path 2 | 3 | from PyQt5 import QtCore 4 | from PyQt5.QtGui import QIntValidator 5 | from PyQt5.QtWidgets import QApplication, QWidget, QPushButton, QFileDialog, QLabel, QVBoxLayout, QProgressBar, \ 6 | QComboBox, QLineEdit, QMessageBox 7 | 8 | from esrgan.esrgan import ESRGAN 9 | 10 | 11 | # noinspection PyArgumentList,PyUnresolvedReferences,PyCallByClass 12 | class MainWidget(QWidget): 13 | DEFAULT_MODEL_PATH = (Path(__file__).parent / 'esrgan/models').absolute() 14 | 15 | def __init__(self): 16 | super().__init__() 17 | 18 | self.setMinimumWidth(400) 19 | 20 | self.inputs = None 21 | self.output_directory = None 22 | self.model = str(self.DEFAULT_MODEL_PATH / 'RRDB_ESRGAN_x4.pth') 23 | 24 | self.setWindowTitle('dataGAN') 25 | 26 | self.layout = QVBoxLayout() 27 | 28 | self.input_select = QPushButton('Select files', self) 29 | self.input_select.clicked.connect(self.select_inputs) 30 | self.input_label = QLabel('Input file(s)') 31 | 32 | self.output_select = QPushButton('Select Directory', self) 33 | self.output_select.clicked.connect(self.select_output) 34 | self.output_label = QLabel('Output Directory') 35 | 36 | self.device = QComboBox(self) 37 | self.device.addItem('CUDA') 38 | self.device.addItem('CPU') 39 | self.device.setCurrentIndex(0) 40 | self.device_label = QLabel('Device') 41 | 42 | self.scale = QLineEdit() 43 | self.scale.setValidator(QIntValidator(bottom=1)) 44 | self.scale.setText('4') 45 | self.scale_label = QLabel('Scale') 46 | 47 | self.model_select = QPushButton(self.model, self) 48 | self.model_select.clicked.connect(self.select_model) 49 | self.model_label = QLabel('Neural Network Model') 50 | 51 | self.process = QPushButton('Process', self) 52 | self.process.clicked.connect(self.upscale) 53 | self.process.setDisabled(True) 54 | self.result_label = QLabel('Select an input and output.') 55 | 56 | self.progress = QProgressBar(self) 57 | self.progress.setValue(0) 58 | 59 | self.error = QMessageBox() 60 | self.error.setText('An error occurred while running.') 61 | self.error.setWindowTitle('Error') 62 | self.error.setIcon(QMessageBox.Critical) 63 | 64 | self.layout.addWidget(self.input_label) 65 | self.layout.addWidget(self.input_select) 66 | self.layout.addWidget(self.output_label) 67 | self.layout.addWidget(self.output_select) 68 | self.layout.addWidget(self.device_label) 69 | self.layout.addWidget(self.device) 70 | self.layout.addWidget(self.scale_label) 71 | self.layout.addWidget(self.scale) 72 | self.layout.addWidget(self.model_label) 73 | self.layout.addWidget(self.model_select) 74 | self.layout.addWidget(self.process) 75 | self.layout.addWidget(self.result_label) 76 | self.layout.addWidget(self.progress) 77 | 78 | self.setLayout(self.layout) 79 | 80 | self.worker = None 81 | self.thread = None 82 | 83 | def select_inputs(self): 84 | self.inputs, _ = QFileDialog.getOpenFileNames(self, 'Open File', str(Path.home())) 85 | if len(self.inputs) == 1: 86 | name = Path(self.inputs[0]).name 87 | self.input_select.setText(name) 88 | elif len(self.inputs) == 0: 89 | self.input_select.setText('Select files') 90 | else: 91 | self.input_select.setText(f'{len(self.inputs)} files') 92 | 93 | self.process.setDisabled(not (self.inputs and self.output_directory)) 94 | 95 | def select_output(self): 96 | self.output_directory = QFileDialog.getExistingDirectory(self, 'Output Directory', str(Path.home())) 97 | self.output_select.setText(self.output_directory) 98 | self.process.setDisabled(not (self.inputs and self.output_directory)) 99 | 100 | def select_model(self): 101 | self.model, _ = QFileDialog.getOpenFileName(self, 'Open File', str(self.DEFAULT_MODEL_PATH), 102 | 'NN Models (*.pth *.pt);;All Files (*)') 103 | self.model_select.setText(self.model) 104 | 105 | def upscale(self): 106 | try: 107 | device = self.device.currentText().lower() 108 | scale = int(self.scale.text()) 109 | self.worker = Worker(self.inputs, self.output_directory, scale, device, self.model) 110 | self.thread = QtCore.QThread() 111 | self.worker.moveToThread(self.thread) 112 | self.worker.finished_one.connect(self.incr_progress) 113 | self.worker.finished.connect(self.thread.quit) 114 | 115 | self.progress.setRange(0, len(self.inputs)) 116 | self.progress.setValue(0) 117 | 118 | self.disable_widgets(True) 119 | self.thread.started.connect(self.worker.process_esrgan) 120 | self.thread.finished.connect(self.enable_widgets) 121 | 122 | self.thread.start() 123 | except Exception as ex: 124 | self.error.setDetailedText(str(ex)) 125 | self.error.show() 126 | 127 | def disable_widgets(self, disable): 128 | for widget in (self.input_select, self.output_select, self.device, self.scale, self.process): 129 | widget.setDisabled(disable) 130 | 131 | @QtCore.pyqtSlot() 132 | def enable_widgets(self): 133 | self.disable_widgets(False) 134 | 135 | @QtCore.pyqtSlot() 136 | def incr_progress(self): 137 | self.progress.setValue(self.progress.value() + 1) 138 | 139 | 140 | # noinspection PyArgumentList 141 | class Worker(QtCore.QObject): 142 | OUTPUT_FORMAT = '{file.stem}-upscaled{file.suffix}' 143 | 144 | finished = QtCore.pyqtSignal() 145 | finished_one = QtCore.pyqtSignal() 146 | 147 | def __init__(self, inputs, output_directory, upscale, device, model_path): 148 | super().__init__() 149 | self.inputs = inputs 150 | self.output_directory = output_directory 151 | self.esrgan = ESRGAN(model_path, device, upscale) 152 | 153 | @QtCore.pyqtSlot() 154 | def process_esrgan(self): 155 | for input in self.inputs: 156 | input_file = Path(input) 157 | output = Path(self.output_directory) / self.OUTPUT_FORMAT.format(file=input_file) 158 | output = str(output.absolute()) 159 | self.esrgan.upscale(input, output) 160 | self.finished_one.emit() 161 | self.finished.emit() 162 | 163 | 164 | if __name__ == '__main__': 165 | app = QApplication([]) 166 | app.setApplicationName('dataGAN') 167 | 168 | main = MainWidget() 169 | main.show() 170 | 171 | app.exec_() 172 | -------------------------------------------------------------------------------- /esrgan/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dsluo/dataGAN/f28fe4285376ba9a1a9da3dd0ea362c39b4babb2/esrgan/__init__.py -------------------------------------------------------------------------------- /esrgan/architecture.py: -------------------------------------------------------------------------------- 1 | import math 2 | import torch.nn as nn 3 | from esrgan import block as B 4 | 5 | 6 | class RRDB_Net(nn.Module): 7 | def __init__(self, in_nc, out_nc, nf, nb, gc=32, upscale=4, norm_type=None, act_type='leakyrelu', \ 8 | mode='CNA', res_scale=1, upsample_mode='upconv'): 9 | super(RRDB_Net, self).__init__() 10 | n_upscale = int(math.log(upscale, 2)) 11 | if upscale == 3: 12 | n_upscale = 1 13 | 14 | fea_conv = B.conv_block(in_nc, nf, kernel_size=3, norm_type=None, act_type=None) 15 | rb_blocks = [B.RRDB(nf, kernel_size=3, gc=32, stride=1, bias=True, pad_type='zero', \ 16 | norm_type=norm_type, act_type=act_type, mode='CNA') for _ in range(nb)] 17 | LR_conv = B.conv_block(nf, nf, kernel_size=3, norm_type=norm_type, act_type=None, mode=mode) 18 | 19 | if upsample_mode == 'upconv': 20 | upsample_block = B.upconv_blcok 21 | elif upsample_mode == 'pixelshuffle': 22 | upsample_block = B.pixelshuffle_block 23 | else: 24 | raise NotImplementedError('upsample mode [%s] is not found' % upsample_mode) 25 | if upscale == 3: 26 | upsampler = upsample_block(nf, nf, 3, act_type=act_type) 27 | else: 28 | upsampler = [upsample_block(nf, nf, act_type=act_type) for _ in range(n_upscale)] 29 | HR_conv0 = B.conv_block(nf, nf, kernel_size=3, norm_type=None, act_type=act_type) 30 | HR_conv1 = B.conv_block(nf, out_nc, kernel_size=3, norm_type=None, act_type=None) 31 | 32 | self.model = B.sequential(fea_conv, B.ShortcutBlock(B.sequential(*rb_blocks, LR_conv)),\ 33 | *upsampler, HR_conv0, HR_conv1) 34 | 35 | def forward(self, x): 36 | x = self.model(x) 37 | return x 38 | -------------------------------------------------------------------------------- /esrgan/block.py: -------------------------------------------------------------------------------- 1 | from collections import OrderedDict 2 | import torch 3 | import torch.nn as nn 4 | 5 | #################### 6 | # Basic blocks 7 | #################### 8 | 9 | 10 | def act(act_type, inplace=True, neg_slope=0.2, n_prelu=1): 11 | # helper selecting activation 12 | # neg_slope: for leakyrelu and init of prelu 13 | # n_prelu: for p_relu num_parameters 14 | act_type = act_type.lower() 15 | if act_type == 'relu': 16 | layer = nn.ReLU(inplace) 17 | elif act_type == 'leakyrelu': 18 | layer = nn.LeakyReLU(neg_slope, inplace) 19 | elif act_type == 'prelu': 20 | layer = nn.PReLU(num_parameters=n_prelu, init=neg_slope) 21 | else: 22 | raise NotImplementedError('activation layer [%s] is not found' % act_type) 23 | return layer 24 | 25 | 26 | def norm(norm_type, nc): 27 | # helper selecting normalization layer 28 | norm_type = norm_type.lower() 29 | if norm_type == 'batch': 30 | layer = nn.BatchNorm2d(nc, affine=True) 31 | elif norm_type == 'instance': 32 | layer = nn.InstanceNorm2d(nc, affine=False) 33 | else: 34 | raise NotImplementedError('normalization layer [%s] is not found' % norm_type) 35 | return layer 36 | 37 | 38 | def pad(pad_type, padding): 39 | # helper selecting padding layer 40 | # if padding is 'zero', do by conv layers 41 | pad_type = pad_type.lower() 42 | if padding == 0: 43 | return None 44 | if pad_type == 'reflect': 45 | layer = nn.ReflectionPad2d(padding) 46 | elif pad_type == 'replicate': 47 | layer = nn.ReplicationPad2d(padding) 48 | else: 49 | raise NotImplementedError('padding layer [%s] is not implemented' % pad_type) 50 | return layer 51 | 52 | 53 | def get_valid_padding(kernel_size, dilation): 54 | kernel_size = kernel_size + (kernel_size - 1) * (dilation - 1) 55 | padding = (kernel_size - 1) // 2 56 | return padding 57 | 58 | 59 | class ConcatBlock(nn.Module): 60 | # Concat the output of a submodule to its input 61 | def __init__(self, submodule): 62 | super(ConcatBlock, self).__init__() 63 | self.sub = submodule 64 | 65 | def forward(self, x): 66 | output = torch.cat((x, self.sub(x)), dim=1) 67 | return output 68 | 69 | def __repr__(self): 70 | tmpstr = 'Identity .. \n|' 71 | modstr = self.sub.__repr__().replace('\n', '\n|') 72 | tmpstr = tmpstr + modstr 73 | return tmpstr 74 | 75 | 76 | class ShortcutBlock(nn.Module): 77 | #Elementwise sum the output of a submodule to its input 78 | def __init__(self, submodule): 79 | super(ShortcutBlock, self).__init__() 80 | self.sub = submodule 81 | 82 | def forward(self, x): 83 | output = x + self.sub(x) 84 | return output 85 | 86 | def __repr__(self): 87 | tmpstr = 'Identity + \n|' 88 | modstr = self.sub.__repr__().replace('\n', '\n|') 89 | tmpstr = tmpstr + modstr 90 | return tmpstr 91 | 92 | 93 | def sequential(*args): 94 | # Flatten Sequential. It unwraps nn.Sequential. 95 | if len(args) == 1: 96 | if isinstance(args[0], OrderedDict): 97 | raise NotImplementedError('sequential does not support OrderedDict input.') 98 | return args[0] # No sequential is needed. 99 | modules = [] 100 | for module in args: 101 | if isinstance(module, nn.Sequential): 102 | for submodule in module.children(): 103 | modules.append(submodule) 104 | elif isinstance(module, nn.Module): 105 | modules.append(module) 106 | return nn.Sequential(*modules) 107 | 108 | 109 | def conv_block(in_nc, out_nc, kernel_size, stride=1, dilation=1, groups=1, bias=True, 110 | pad_type='zero', norm_type=None, act_type='relu', mode='CNA'): 111 | """ 112 | Conv layer with padding, normalization, activation 113 | mode: CNA --> Conv -> Norm -> Act 114 | NAC --> Norm -> Act --> Conv (Identity Mappings in Deep Residual Networks, ECCV16) 115 | """ 116 | assert mode in ['CNA', 'NAC', 'CNAC'], 'Wong conv mode [%s]' % mode 117 | padding = get_valid_padding(kernel_size, dilation) 118 | p = pad(pad_type, padding) if pad_type and pad_type != 'zero' else None 119 | padding = padding if pad_type == 'zero' else 0 120 | 121 | c = nn.Conv2d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding, \ 122 | dilation=dilation, bias=bias, groups=groups) 123 | a = act(act_type) if act_type else None 124 | if 'CNA' in mode: 125 | n = norm(norm_type, out_nc) if norm_type else None 126 | return sequential(p, c, n, a) 127 | elif mode == 'NAC': 128 | if norm_type is None and act_type is not None: 129 | a = act(act_type, inplace=False) 130 | # Important! 131 | # input----ReLU(inplace)----Conv--+----output 132 | # |________________________| 133 | # inplace ReLU will modify the input, therefore wrong output 134 | n = norm(norm_type, in_nc) if norm_type else None 135 | return sequential(n, a, p, c) 136 | 137 | 138 | #################### 139 | # Useful blocks 140 | #################### 141 | 142 | 143 | class ResNetBlock(nn.Module): 144 | """ 145 | ResNet Block, 3-3 style 146 | with extra residual scaling used in EDSR 147 | (Enhanced Deep Residual Networks for Single Image Super-Resolution, CVPRW 17) 148 | """ 149 | 150 | def __init__(self, in_nc, mid_nc, out_nc, kernel_size=3, stride=1, dilation=1, groups=1, \ 151 | bias=True, pad_type='zero', norm_type=None, act_type='relu', mode='CNA', res_scale=1): 152 | super(ResNetBlock, self).__init__() 153 | conv0 = conv_block(in_nc, mid_nc, kernel_size, stride, dilation, groups, bias, pad_type, \ 154 | norm_type, act_type, mode) 155 | if mode == 'CNA': 156 | act_type = None 157 | if mode == 'CNAC': # Residual path: |-CNAC-| 158 | act_type = None 159 | norm_type = None 160 | conv1 = conv_block(mid_nc, out_nc, kernel_size, stride, dilation, groups, bias, pad_type, \ 161 | norm_type, act_type, mode) 162 | # if in_nc != out_nc: 163 | # self.project = conv_block(in_nc, out_nc, 1, stride, dilation, 1, bias, pad_type, \ 164 | # None, None) 165 | # print('Need a projecter in ResNetBlock.') 166 | # else: 167 | # self.project = lambda x:x 168 | self.res = sequential(conv0, conv1) 169 | self.res_scale = res_scale 170 | 171 | def forward(self, x): 172 | res = self.res(x).mul(self.res_scale) 173 | return x + res 174 | 175 | 176 | class ResidualDenseBlock_5C(nn.Module): 177 | """ 178 | Residual Dense Block 179 | style: 5 convs 180 | The core module of paper: (Residual Dense Network for Image Super-Resolution, CVPR 18) 181 | """ 182 | 183 | def __init__(self, nc, kernel_size=3, gc=32, stride=1, bias=True, pad_type='zero', \ 184 | norm_type=None, act_type='leakyrelu', mode='CNA'): 185 | super(ResidualDenseBlock_5C, self).__init__() 186 | # gc: growth channel, i.e. intermediate channels 187 | self.conv1 = conv_block(nc, gc, kernel_size, stride, bias=bias, pad_type=pad_type, \ 188 | norm_type=norm_type, act_type=act_type, mode=mode) 189 | self.conv2 = conv_block(nc+gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type, \ 190 | norm_type=norm_type, act_type=act_type, mode=mode) 191 | self.conv3 = conv_block(nc+2*gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type, \ 192 | norm_type=norm_type, act_type=act_type, mode=mode) 193 | self.conv4 = conv_block(nc+3*gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type, \ 194 | norm_type=norm_type, act_type=act_type, mode=mode) 195 | if mode == 'CNA': 196 | last_act = None 197 | else: 198 | last_act = act_type 199 | self.conv5 = conv_block(nc+4*gc, nc, 3, stride, bias=bias, pad_type=pad_type, \ 200 | norm_type=norm_type, act_type=last_act, mode=mode) 201 | 202 | def forward(self, x): 203 | x1 = self.conv1(x) 204 | x2 = self.conv2(torch.cat((x, x1), 1)) 205 | x3 = self.conv3(torch.cat((x, x1, x2), 1)) 206 | x4 = self.conv4(torch.cat((x, x1, x2, x3), 1)) 207 | x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1)) 208 | return x5.mul(0.2) + x 209 | 210 | 211 | class RRDB(nn.Module): 212 | """ 213 | Residual in Residual Dense Block 214 | """ 215 | 216 | def __init__(self, nc, kernel_size=3, gc=32, stride=1, bias=True, pad_type='zero', \ 217 | norm_type=None, act_type='leakyrelu', mode='CNA'): 218 | super(RRDB, self).__init__() 219 | self.RDB1 = ResidualDenseBlock_5C(nc, kernel_size, gc, stride, bias, pad_type, \ 220 | norm_type, act_type, mode) 221 | self.RDB2 = ResidualDenseBlock_5C(nc, kernel_size, gc, stride, bias, pad_type, \ 222 | norm_type, act_type, mode) 223 | self.RDB3 = ResidualDenseBlock_5C(nc, kernel_size, gc, stride, bias, pad_type, \ 224 | norm_type, act_type, mode) 225 | 226 | def forward(self, x): 227 | out = self.RDB1(x) 228 | out = self.RDB2(out) 229 | out = self.RDB3(out) 230 | return out.mul(0.2) + x 231 | 232 | 233 | #################### 234 | # Upsampler 235 | #################### 236 | 237 | 238 | def pixelshuffle_block(in_nc, out_nc, upscale_factor=2, kernel_size=3, stride=1, bias=True, 239 | pad_type='zero', norm_type=None, act_type='relu'): 240 | """ 241 | Pixel shuffle layer 242 | (Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional 243 | Neural Network, CVPR17) 244 | """ 245 | conv = conv_block(in_nc, out_nc * (upscale_factor ** 2), kernel_size, stride, bias=bias, 246 | pad_type=pad_type, norm_type=None, act_type=None) 247 | pixel_shuffle = nn.PixelShuffle(upscale_factor) 248 | 249 | n = norm(norm_type, out_nc) if norm_type else None 250 | a = act(act_type) if act_type else None 251 | return sequential(conv, pixel_shuffle, n, a) 252 | 253 | 254 | def upconv_blcok(in_nc, out_nc, upscale_factor=2, kernel_size=3, stride=1, bias=True, 255 | pad_type='zero', norm_type=None, act_type='relu', mode='nearest'): 256 | # Up conv 257 | # described in https://distill.pub/2016/deconv-checkerboard/ 258 | upsample = nn.Upsample(scale_factor=upscale_factor, mode=mode) 259 | conv = conv_block(in_nc, out_nc, kernel_size, stride, bias=bias, 260 | pad_type=pad_type, norm_type=norm_type, act_type=act_type) 261 | return sequential(upsample, conv) 262 | -------------------------------------------------------------------------------- /esrgan/esrgan.py: -------------------------------------------------------------------------------- 1 | import cv2 2 | import numpy as np 3 | import torch 4 | 5 | from esrgan import architecture as arch 6 | 7 | 8 | class ESRGAN: 9 | 10 | def __init__(self, model_path, device='cpu', upscale=4): 11 | self.device = torch.device(device) 12 | model = arch.RRDB_Net(3, 3, 64, 23, gc=32, upscale=upscale, norm_type=None, act_type='leakyrelu', 13 | mode='CNA', res_scale=1, upsample_mode='upconv') 14 | model.load_state_dict(torch.load(model_path), strict=True) 15 | model.eval() 16 | for k, v in model.named_parameters(): 17 | v.requires_grad = False 18 | self.model = model.to(device) 19 | 20 | def upscale(self, input, output): 21 | # read image and convert to 0-1 scale 22 | img = cv2.imread(input, cv2.IMREAD_COLOR) 23 | img = img * 1.0 / 255 24 | img = torch.from_numpy(np.transpose(img[:, :, [2, 1, 0]], (2, 0, 1))).float() 25 | img_LR = img.unsqueeze(0) 26 | img_LR = img_LR.to(self.device) 27 | 28 | # neural network magic 29 | result = self.model(img_LR).data.squeeze().float().cpu().clamp_(0, 1).numpy() 30 | result = np.transpose(result[[2, 1, 0], :, :], (1, 2, 0)) 31 | result = (result * 255.0).round() 32 | cv2.imwrite(output, result) 33 | -------------------------------------------------------------------------------- /esrgan/models/RRDB_ESRGAN_x4.pth: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dsluo/dataGAN/f28fe4285376ba9a1a9da3dd0ea362c39b4babb2/esrgan/models/RRDB_ESRGAN_x4.pth -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | numpy==1.16.0 2 | opencv-python==4.0.0.21 3 | Pillow==5.4.1 4 | PyQt5==5.11.3 5 | PyQt5-sip==4.19.13 6 | six==1.12.0 7 | #torch==1.0.0 8 | #torchvision==0.2.1 9 | -------------------------------------------------------------------------------- /screenshot.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dsluo/dataGAN/f28fe4285376ba9a1a9da3dd0ea362c39b4babb2/screenshot.png --------------------------------------------------------------------------------