├── .gitignore ├── LICENSE ├── README.md ├── datasets.py ├── download_dataset ├── models.py ├── output ├── fake_A.png ├── fake_B.png ├── loss_D.png ├── loss_G.png ├── loss_G_GAN.png ├── loss_G_cycle.png ├── loss_G_identity.png ├── real_A.jpg └── real_B.jpg ├── test ├── train └── utils.py /.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 | env/ 12 | build/ 13 | develop-eggs/ 14 | dist/ 15 | downloads/ 16 | eggs/ 17 | .eggs/ 18 | lib/ 19 | lib64/ 20 | parts/ 21 | sdist/ 22 | var/ 23 | wheels/ 24 | *.egg-info/ 25 | .installed.cfg 26 | *.egg 27 | 28 | # PyInstaller 29 | # Usually these files are written by a python script from a template 30 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 31 | *.manifest 32 | *.spec 33 | 34 | # Installer logs 35 | pip-log.txt 36 | pip-delete-this-directory.txt 37 | 38 | # Unit test / coverage reports 39 | htmlcov/ 40 | .tox/ 41 | .coverage 42 | .coverage.* 43 | .cache 44 | nosetests.xml 45 | coverage.xml 46 | *.cover 47 | .hypothesis/ 48 | 49 | # Translations 50 | *.mo 51 | *.pot 52 | 53 | # Django stuff: 54 | *.log 55 | local_settings.py 56 | 57 | # Flask stuff: 58 | instance/ 59 | .webassets-cache 60 | 61 | # Scrapy stuff: 62 | .scrapy 63 | 64 | # Sphinx documentation 65 | docs/_build/ 66 | 67 | # PyBuilder 68 | target/ 69 | 70 | # Jupyter Notebook 71 | .ipynb_checkpoints 72 | 73 | # pyenv 74 | .python-version 75 | 76 | # celery beat schedule file 77 | celerybeat-schedule 78 | 79 | # SageMath parsed files 80 | *.sage.py 81 | 82 | # dotenv 83 | .env 84 | 85 | # virtualenv 86 | .venv 87 | venv/ 88 | ENV/ 89 | 90 | # Spyder project settings 91 | .spyderproject 92 | .spyproject 93 | 94 | # Rope project settings 95 | .ropeproject 96 | 97 | # mkdocs documentation 98 | /site 99 | 100 | # mypy 101 | .mypy_cache/ 102 | 103 | # project directories 104 | datasets/ 105 | output/A/ 106 | output/B/ 107 | 108 | # model checkpoints 109 | *.pth -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Pytorch-CycleGAN 2 | A clean and readable Pytorch implementation of CycleGAN (https://arxiv.org/abs/1703.10593) 3 | 4 | ## Prerequisites 5 | Code is intended to work with ```Python 3.6.x```, it hasn't been tested with previous versions 6 | 7 | ### [PyTorch & torchvision](http://pytorch.org/) 8 | Follow the instructions in [pytorch.org](http://pytorch.org) for your current setup 9 | 10 | ### [Visdom](https://github.com/facebookresearch/visdom) 11 | To plot loss graphs and draw images in a nice web browser view 12 | ``` 13 | pip3 install visdom 14 | ``` 15 | 16 | ## Training 17 | ### 1. Setup the dataset 18 | First, you will need to download and setup a dataset. The easiest way is to use one of the already existing datasets on UC Berkeley's repository: 19 | ``` 20 | ./download_dataset 21 | ``` 22 | Valid are: apple2orange, summer2winter_yosemite, horse2zebra, monet2photo, cezanne2photo, ukiyoe2photo, vangogh2photo, maps, cityscapes, facades, iphone2dslr_flower, ae_photos 23 | 24 | Alternatively you can build your own dataset by setting up the following directory structure: 25 | 26 | . 27 | ├── datasets 28 | | ├── # i.e. brucewayne2batman 29 | | | ├── train # Training 30 | | | | ├── A # Contains domain A images (i.e. Bruce Wayne) 31 | | | | └── B # Contains domain B images (i.e. Batman) 32 | | | └── test # Testing 33 | | | | ├── A # Contains domain A images (i.e. Bruce Wayne) 34 | | | | └── B # Contains domain B images (i.e. Batman) 35 | 36 | ### 2. Train! 37 | ``` 38 | ./train --dataroot datasets// --cuda 39 | ``` 40 | This command will start a training session using the images under the *dataroot/train* directory with the hyperparameters that showed best results according to CycleGAN authors. You are free to change those hyperparameters, see ```./train --help``` for a description of those. 41 | 42 | Both generators and discriminators weights will be saved under the output directory. 43 | 44 | If you don't own a GPU remove the --cuda option, although I advise you to get one! 45 | 46 | You can also view the training progress as well as live output images by running ```python3 -m visdom``` in another terminal and opening [http://localhost:8097/](http://localhost:8097/) in your favourite web browser. This should generate training loss progress as shown below (default params, horse2zebra dataset): 47 | 48 | ![Generator loss](https://github.com/ai-tor/PyTorch-CycleGAN/raw/master/output/loss_G.png) 49 | ![Discriminator loss](https://github.com/ai-tor/PyTorch-CycleGAN/raw/master/output/loss_D.png) 50 | ![Generator GAN loss](https://github.com/ai-tor/PyTorch-CycleGAN/raw/master/output/loss_G_GAN.png) 51 | ![Generator identity loss](https://github.com/ai-tor/PyTorch-CycleGAN/raw/master/output/loss_G_identity.png) 52 | ![Generator cycle loss](https://github.com/ai-tor/PyTorch-CycleGAN/raw/master/output/loss_G_cycle.png) 53 | 54 | ## Testing 55 | ``` 56 | ./test --dataroot datasets// --cuda 57 | ``` 58 | This command will take the images under the *dataroot/test* directory, run them through the generators and save the output under the *output/A* and *output/B* directories. As with train, some parameters like the weights to load, can be tweaked, see ```./test --help``` for more information. 59 | 60 | Examples of the generated outputs (default params, horse2zebra dataset): 61 | 62 | ![Real horse](https://github.com/ai-tor/PyTorch-CycleGAN/raw/master/output/real_A.jpg) 63 | ![Fake zebra](https://github.com/ai-tor/PyTorch-CycleGAN/raw/master/output/fake_B.png) 64 | ![Real zebra](https://github.com/ai-tor/PyTorch-CycleGAN/raw/master/output/real_B.jpg) 65 | ![Fake horse](https://github.com/ai-tor/PyTorch-CycleGAN/raw/master/output/fake_A.png) 66 | 67 | ## License 68 | This project is licensed under the GPL v3 License - see the [LICENSE.md](LICENSE.md) file for details 69 | 70 | ## Acknowledgments 71 | Code is basically a cleaner and less obscured implementation of [pytorch-CycleGAN-and-pix2pix](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix). All credit goes to the authors of [CycleGAN](https://arxiv.org/abs/1703.10593), Zhu, Jun-Yan and Park, Taesung and Isola, Phillip and Efros, Alexei A. 72 | -------------------------------------------------------------------------------- /datasets.py: -------------------------------------------------------------------------------- 1 | import glob 2 | import random 3 | import os 4 | 5 | from torch.utils.data import Dataset 6 | from PIL import Image 7 | import torchvision.transforms as transforms 8 | 9 | class ImageDataset(Dataset): 10 | def __init__(self, root, transforms_=None, unaligned=False, mode='train'): 11 | self.transform = transforms.Compose(transforms_) 12 | self.unaligned = unaligned 13 | 14 | self.files_A = sorted(glob.glob(os.path.join(root, '%s/A' % mode) + '/*.*')) 15 | self.files_B = sorted(glob.glob(os.path.join(root, '%s/B' % mode) + '/*.*')) 16 | 17 | def __getitem__(self, index): 18 | item_A = self.transform(Image.open(self.files_A[index % len(self.files_A)])) 19 | 20 | if self.unaligned: 21 | item_B = self.transform(Image.open(self.files_B[random.randint(0, len(self.files_B) - 1)])) 22 | else: 23 | item_B = self.transform(Image.open(self.files_B[index % len(self.files_B)])) 24 | 25 | return {'A': item_A, 'B': item_B} 26 | 27 | def __len__(self): 28 | return max(len(self.files_A), len(self.files_B)) -------------------------------------------------------------------------------- /download_dataset: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | FILE=$1 4 | 5 | if [[ $FILE != "ae_photos" && $FILE != "apple2orange" && $FILE != "summer2winter_yosemite" && $FILE != "horse2zebra" && $FILE != "monet2photo" && $FILE != "cezanne2photo" && $FILE != "ukiyoe2photo" && $FILE != "vangogh2photo" && $FILE != "maps" && $FILE != "cityscapes" && $FILE != "facades" && $FILE != "iphone2dslr_flower" && $FILE != "ae_photos" ]]; then 6 | echo "Available datasets are: apple2orange, summer2winter_yosemite, horse2zebra, monet2photo, cezanne2photo, ukiyoe2photo, vangogh2photo, maps, cityscapes, facades, iphone2dslr_flower, ae_photos" 7 | exit 1 8 | fi 9 | 10 | URL=https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/$FILE.zip 11 | ZIP_FILE=./datasets/$FILE.zip 12 | TARGET_DIR=./datasets/$FILE 13 | mkdir -p ./datasets 14 | wget -N $URL -O $ZIP_FILE 15 | unzip $ZIP_FILE -d ./datasets/ 16 | rm $ZIP_FILE 17 | 18 | # Adapt to project expected directory heriarchy 19 | mkdir -p "$TARGET_DIR/train" "$TARGET_DIR/test" 20 | mv "$TARGET_DIR/trainA" "$TARGET_DIR/train/A" 21 | mv "$TARGET_DIR/trainB" "$TARGET_DIR/train/B" 22 | mv "$TARGET_DIR/testA" "$TARGET_DIR/test/A" 23 | mv "$TARGET_DIR/testB" "$TARGET_DIR/test/B" 24 | -------------------------------------------------------------------------------- /models.py: -------------------------------------------------------------------------------- 1 | import torch.nn as nn 2 | import torch.nn.functional as F 3 | 4 | class ResidualBlock(nn.Module): 5 | def __init__(self, in_features): 6 | super(ResidualBlock, self).__init__() 7 | 8 | conv_block = [ nn.ReflectionPad2d(1), 9 | nn.Conv2d(in_features, in_features, 3), 10 | nn.InstanceNorm2d(in_features), 11 | nn.ReLU(inplace=True), 12 | nn.ReflectionPad2d(1), 13 | nn.Conv2d(in_features, in_features, 3), 14 | nn.InstanceNorm2d(in_features) ] 15 | 16 | self.conv_block = nn.Sequential(*conv_block) 17 | 18 | def forward(self, x): 19 | return x + self.conv_block(x) 20 | 21 | class Generator(nn.Module): 22 | def __init__(self, input_nc, output_nc, n_residual_blocks=9): 23 | super(Generator, self).__init__() 24 | 25 | # Initial convolution block 26 | model = [ nn.ReflectionPad2d(3), 27 | nn.Conv2d(input_nc, 64, 7), 28 | nn.InstanceNorm2d(64), 29 | nn.ReLU(inplace=True) ] 30 | 31 | # Downsampling 32 | in_features = 64 33 | out_features = in_features*2 34 | for _ in range(2): 35 | model += [ nn.Conv2d(in_features, out_features, 3, stride=2, padding=1), 36 | nn.InstanceNorm2d(out_features), 37 | nn.ReLU(inplace=True) ] 38 | in_features = out_features 39 | out_features = in_features*2 40 | 41 | # Residual blocks 42 | for _ in range(n_residual_blocks): 43 | model += [ResidualBlock(in_features)] 44 | 45 | # Upsampling 46 | out_features = in_features//2 47 | for _ in range(2): 48 | model += [ nn.ConvTranspose2d(in_features, out_features, 3, stride=2, padding=1, output_padding=1), 49 | nn.InstanceNorm2d(out_features), 50 | nn.ReLU(inplace=True) ] 51 | in_features = out_features 52 | out_features = in_features//2 53 | 54 | # Output layer 55 | model += [ nn.ReflectionPad2d(3), 56 | nn.Conv2d(64, output_nc, 7), 57 | nn.Tanh() ] 58 | 59 | self.model = nn.Sequential(*model) 60 | 61 | def forward(self, x): 62 | return self.model(x) 63 | 64 | class Discriminator(nn.Module): 65 | def __init__(self, input_nc): 66 | super(Discriminator, self).__init__() 67 | 68 | # A bunch of convolutions one after another 69 | model = [ nn.Conv2d(input_nc, 64, 4, stride=2, padding=1), 70 | nn.LeakyReLU(0.2, inplace=True) ] 71 | 72 | model += [ nn.Conv2d(64, 128, 4, stride=2, padding=1), 73 | nn.InstanceNorm2d(128), 74 | nn.LeakyReLU(0.2, inplace=True) ] 75 | 76 | model += [ nn.Conv2d(128, 256, 4, stride=2, padding=1), 77 | nn.InstanceNorm2d(256), 78 | nn.LeakyReLU(0.2, inplace=True) ] 79 | 80 | model += [ nn.Conv2d(256, 512, 4, padding=1), 81 | nn.InstanceNorm2d(512), 82 | nn.LeakyReLU(0.2, inplace=True) ] 83 | 84 | # FCN classification layer 85 | model += [nn.Conv2d(512, 1, 4, padding=1)] 86 | 87 | self.model = nn.Sequential(*model) 88 | 89 | def forward(self, x): 90 | x = self.model(x) 91 | # Average pooling and flatten 92 | return F.avg_pool2d(x, x.size()[2:]).view(x.size()[0], -1) -------------------------------------------------------------------------------- /output/fake_A.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aitorzip/PyTorch-CycleGAN/67da8f9e2b69bd68763451803c7700aaccc92f18/output/fake_A.png -------------------------------------------------------------------------------- /output/fake_B.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aitorzip/PyTorch-CycleGAN/67da8f9e2b69bd68763451803c7700aaccc92f18/output/fake_B.png -------------------------------------------------------------------------------- /output/loss_D.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aitorzip/PyTorch-CycleGAN/67da8f9e2b69bd68763451803c7700aaccc92f18/output/loss_D.png -------------------------------------------------------------------------------- /output/loss_G.png: -------------------------------------------------------------------------------- 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https://raw.githubusercontent.com/aitorzip/PyTorch-CycleGAN/67da8f9e2b69bd68763451803c7700aaccc92f18/output/loss_G_identity.png -------------------------------------------------------------------------------- /output/real_A.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aitorzip/PyTorch-CycleGAN/67da8f9e2b69bd68763451803c7700aaccc92f18/output/real_A.jpg -------------------------------------------------------------------------------- /output/real_B.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aitorzip/PyTorch-CycleGAN/67da8f9e2b69bd68763451803c7700aaccc92f18/output/real_B.jpg -------------------------------------------------------------------------------- /test: -------------------------------------------------------------------------------- 1 | #!/usr/bin/python3 2 | 3 | import argparse 4 | import sys 5 | import os 6 | 7 | import torchvision.transforms as transforms 8 | from torchvision.utils import save_image 9 | from torch.utils.data import DataLoader 10 | from torch.autograd import Variable 11 | import torch 12 | 13 | from models import Generator 14 | from datasets import ImageDataset 15 | 16 | parser = argparse.ArgumentParser() 17 | parser.add_argument('--batchSize', type=int, default=1, help='size of the batches') 18 | parser.add_argument('--dataroot', type=str, default='datasets/horse2zebra/', help='root directory of the dataset') 19 | parser.add_argument('--input_nc', type=int, default=3, help='number of channels of input data') 20 | parser.add_argument('--output_nc', type=int, default=3, help='number of channels of output data') 21 | parser.add_argument('--size', type=int, default=256, help='size of the data (squared assumed)') 22 | parser.add_argument('--cuda', action='store_true', help='use GPU computation') 23 | parser.add_argument('--n_cpu', type=int, default=8, help='number of cpu threads to use during batch generation') 24 | parser.add_argument('--generator_A2B', type=str, default='output/netG_A2B.pth', help='A2B generator checkpoint file') 25 | parser.add_argument('--generator_B2A', type=str, default='output/netG_B2A.pth', help='B2A generator checkpoint file') 26 | opt = parser.parse_args() 27 | print(opt) 28 | 29 | if torch.cuda.is_available() and not opt.cuda: 30 | print("WARNING: You have a CUDA device, so you should probably run with --cuda") 31 | 32 | ###### Definition of variables ###### 33 | # Networks 34 | netG_A2B = Generator(opt.input_nc, opt.output_nc) 35 | netG_B2A = Generator(opt.output_nc, opt.input_nc) 36 | 37 | if opt.cuda: 38 | netG_A2B.cuda() 39 | netG_B2A.cuda() 40 | 41 | # Load state dicts 42 | netG_A2B.load_state_dict(torch.load(opt.generator_A2B)) 43 | netG_B2A.load_state_dict(torch.load(opt.generator_B2A)) 44 | 45 | # Set model's test mode 46 | netG_A2B.eval() 47 | netG_B2A.eval() 48 | 49 | # Inputs & targets memory allocation 50 | Tensor = torch.cuda.FloatTensor if opt.cuda else torch.Tensor 51 | input_A = Tensor(opt.batchSize, opt.input_nc, opt.size, opt.size) 52 | input_B = Tensor(opt.batchSize, opt.output_nc, opt.size, opt.size) 53 | 54 | # Dataset loader 55 | transforms_ = [ transforms.ToTensor(), 56 | transforms.Normalize((0.5,0.5,0.5), (0.5,0.5,0.5)) ] 57 | dataloader = DataLoader(ImageDataset(opt.dataroot, transforms_=transforms_, mode='test'), 58 | batch_size=opt.batchSize, shuffle=False, num_workers=opt.n_cpu) 59 | ################################### 60 | 61 | ###### Testing###### 62 | 63 | # Create output dirs if they don't exist 64 | if not os.path.exists('output/A'): 65 | os.makedirs('output/A') 66 | if not os.path.exists('output/B'): 67 | os.makedirs('output/B') 68 | 69 | for i, batch in enumerate(dataloader): 70 | # Set model input 71 | real_A = Variable(input_A.copy_(batch['A'])) 72 | real_B = Variable(input_B.copy_(batch['B'])) 73 | 74 | # Generate output 75 | fake_B = 0.5*(netG_A2B(real_A).data + 1.0) 76 | fake_A = 0.5*(netG_B2A(real_B).data + 1.0) 77 | 78 | # Save image files 79 | save_image(fake_A, 'output/A/%04d.png' % (i+1)) 80 | save_image(fake_B, 'output/B/%04d.png' % (i+1)) 81 | 82 | sys.stdout.write('\rGenerated images %04d of %04d' % (i+1, len(dataloader))) 83 | 84 | sys.stdout.write('\n') 85 | ################################### 86 | -------------------------------------------------------------------------------- /train: -------------------------------------------------------------------------------- 1 | #!/usr/bin/python3 2 | 3 | import argparse 4 | import itertools 5 | 6 | import torchvision.transforms as transforms 7 | from torch.utils.data import DataLoader 8 | from torch.autograd import Variable 9 | from PIL import Image 10 | import torch 11 | 12 | from models import Generator 13 | from models import Discriminator 14 | from utils import ReplayBuffer 15 | from utils import LambdaLR 16 | from utils import Logger 17 | from utils import weights_init_normal 18 | from datasets import ImageDataset 19 | 20 | parser = argparse.ArgumentParser() 21 | parser.add_argument('--epoch', type=int, default=0, help='starting epoch') 22 | parser.add_argument('--n_epochs', type=int, default=200, help='number of epochs of training') 23 | parser.add_argument('--batchSize', type=int, default=1, help='size of the batches') 24 | parser.add_argument('--dataroot', type=str, default='datasets/horse2zebra/', help='root directory of the dataset') 25 | parser.add_argument('--lr', type=float, default=0.0002, help='initial learning rate') 26 | parser.add_argument('--decay_epoch', type=int, default=100, help='epoch to start linearly decaying the learning rate to 0') 27 | parser.add_argument('--size', type=int, default=256, help='size of the data crop (squared assumed)') 28 | parser.add_argument('--input_nc', type=int, default=3, help='number of channels of input data') 29 | parser.add_argument('--output_nc', type=int, default=3, help='number of channels of output data') 30 | parser.add_argument('--cuda', action='store_true', help='use GPU computation') 31 | parser.add_argument('--n_cpu', type=int, default=8, help='number of cpu threads to use during batch generation') 32 | opt = parser.parse_args() 33 | print(opt) 34 | 35 | if torch.cuda.is_available() and not opt.cuda: 36 | print("WARNING: You have a CUDA device, so you should probably run with --cuda") 37 | 38 | ###### Definition of variables ###### 39 | # Networks 40 | netG_A2B = Generator(opt.input_nc, opt.output_nc) 41 | netG_B2A = Generator(opt.output_nc, opt.input_nc) 42 | netD_A = Discriminator(opt.input_nc) 43 | netD_B = Discriminator(opt.output_nc) 44 | 45 | if opt.cuda: 46 | netG_A2B.cuda() 47 | netG_B2A.cuda() 48 | netD_A.cuda() 49 | netD_B.cuda() 50 | 51 | netG_A2B.apply(weights_init_normal) 52 | netG_B2A.apply(weights_init_normal) 53 | netD_A.apply(weights_init_normal) 54 | netD_B.apply(weights_init_normal) 55 | 56 | # Lossess 57 | criterion_GAN = torch.nn.MSELoss() 58 | criterion_cycle = torch.nn.L1Loss() 59 | criterion_identity = torch.nn.L1Loss() 60 | 61 | # Optimizers & LR schedulers 62 | optimizer_G = torch.optim.Adam(itertools.chain(netG_A2B.parameters(), netG_B2A.parameters()), 63 | lr=opt.lr, betas=(0.5, 0.999)) 64 | optimizer_D_A = torch.optim.Adam(netD_A.parameters(), lr=opt.lr, betas=(0.5, 0.999)) 65 | optimizer_D_B = torch.optim.Adam(netD_B.parameters(), lr=opt.lr, betas=(0.5, 0.999)) 66 | 67 | lr_scheduler_G = torch.optim.lr_scheduler.LambdaLR(optimizer_G, lr_lambda=LambdaLR(opt.n_epochs, opt.epoch, opt.decay_epoch).step) 68 | lr_scheduler_D_A = torch.optim.lr_scheduler.LambdaLR(optimizer_D_A, lr_lambda=LambdaLR(opt.n_epochs, opt.epoch, opt.decay_epoch).step) 69 | lr_scheduler_D_B = torch.optim.lr_scheduler.LambdaLR(optimizer_D_B, lr_lambda=LambdaLR(opt.n_epochs, opt.epoch, opt.decay_epoch).step) 70 | 71 | # Inputs & targets memory allocation 72 | Tensor = torch.cuda.FloatTensor if opt.cuda else torch.Tensor 73 | input_A = Tensor(opt.batchSize, opt.input_nc, opt.size, opt.size) 74 | input_B = Tensor(opt.batchSize, opt.output_nc, opt.size, opt.size) 75 | target_real = Variable(Tensor(opt.batchSize).fill_(1.0), requires_grad=False) 76 | target_fake = Variable(Tensor(opt.batchSize).fill_(0.0), requires_grad=False) 77 | 78 | fake_A_buffer = ReplayBuffer() 79 | fake_B_buffer = ReplayBuffer() 80 | 81 | # Dataset loader 82 | transforms_ = [ transforms.Resize(int(opt.size*1.12), Image.BICUBIC), 83 | transforms.RandomCrop(opt.size), 84 | transforms.RandomHorizontalFlip(), 85 | transforms.ToTensor(), 86 | transforms.Normalize((0.5,0.5,0.5), (0.5,0.5,0.5)) ] 87 | dataloader = DataLoader(ImageDataset(opt.dataroot, transforms_=transforms_, unaligned=True), 88 | batch_size=opt.batchSize, shuffle=True, num_workers=opt.n_cpu) 89 | 90 | # Loss plot 91 | logger = Logger(opt.n_epochs, len(dataloader)) 92 | ################################### 93 | 94 | ###### Training ###### 95 | for epoch in range(opt.epoch, opt.n_epochs): 96 | for i, batch in enumerate(dataloader): 97 | # Set model input 98 | real_A = Variable(input_A.copy_(batch['A'])) 99 | real_B = Variable(input_B.copy_(batch['B'])) 100 | 101 | ###### Generators A2B and B2A ###### 102 | optimizer_G.zero_grad() 103 | 104 | # Identity loss 105 | # G_A2B(B) should equal B if real B is fed 106 | same_B = netG_A2B(real_B) 107 | loss_identity_B = criterion_identity(same_B, real_B)*5.0 108 | # G_B2A(A) should equal A if real A is fed 109 | same_A = netG_B2A(real_A) 110 | loss_identity_A = criterion_identity(same_A, real_A)*5.0 111 | 112 | # GAN loss 113 | fake_B = netG_A2B(real_A) 114 | pred_fake = netD_B(fake_B) 115 | loss_GAN_A2B = criterion_GAN(pred_fake, target_real) 116 | 117 | fake_A = netG_B2A(real_B) 118 | pred_fake = netD_A(fake_A) 119 | loss_GAN_B2A = criterion_GAN(pred_fake, target_real) 120 | 121 | # Cycle loss 122 | recovered_A = netG_B2A(fake_B) 123 | loss_cycle_ABA = criterion_cycle(recovered_A, real_A)*10.0 124 | 125 | recovered_B = netG_A2B(fake_A) 126 | loss_cycle_BAB = criterion_cycle(recovered_B, real_B)*10.0 127 | 128 | # Total loss 129 | loss_G = loss_identity_A + loss_identity_B + loss_GAN_A2B + loss_GAN_B2A + loss_cycle_ABA + loss_cycle_BAB 130 | loss_G.backward() 131 | 132 | optimizer_G.step() 133 | ################################### 134 | 135 | ###### Discriminator A ###### 136 | optimizer_D_A.zero_grad() 137 | 138 | # Real loss 139 | pred_real = netD_A(real_A) 140 | loss_D_real = criterion_GAN(pred_real, target_real) 141 | 142 | # Fake loss 143 | fake_A = fake_A_buffer.push_and_pop(fake_A) 144 | pred_fake = netD_A(fake_A.detach()) 145 | loss_D_fake = criterion_GAN(pred_fake, target_fake) 146 | 147 | # Total loss 148 | loss_D_A = (loss_D_real + loss_D_fake)*0.5 149 | loss_D_A.backward() 150 | 151 | optimizer_D_A.step() 152 | ################################### 153 | 154 | ###### Discriminator B ###### 155 | optimizer_D_B.zero_grad() 156 | 157 | # Real loss 158 | pred_real = netD_B(real_B) 159 | loss_D_real = criterion_GAN(pred_real, target_real) 160 | 161 | # Fake loss 162 | fake_B = fake_B_buffer.push_and_pop(fake_B) 163 | pred_fake = netD_B(fake_B.detach()) 164 | loss_D_fake = criterion_GAN(pred_fake, target_fake) 165 | 166 | # Total loss 167 | loss_D_B = (loss_D_real + loss_D_fake)*0.5 168 | loss_D_B.backward() 169 | 170 | optimizer_D_B.step() 171 | ################################### 172 | 173 | # Progress report (http://localhost:8097) 174 | logger.log({'loss_G': loss_G, 'loss_G_identity': (loss_identity_A + loss_identity_B), 'loss_G_GAN': (loss_GAN_A2B + loss_GAN_B2A), 175 | 'loss_G_cycle': (loss_cycle_ABA + loss_cycle_BAB), 'loss_D': (loss_D_A + loss_D_B)}, 176 | images={'real_A': real_A, 'real_B': real_B, 'fake_A': fake_A, 'fake_B': fake_B}) 177 | 178 | # Update learning rates 179 | lr_scheduler_G.step() 180 | lr_scheduler_D_A.step() 181 | lr_scheduler_D_B.step() 182 | 183 | # Save models checkpoints 184 | torch.save(netG_A2B.state_dict(), 'output/netG_A2B.pth') 185 | torch.save(netG_B2A.state_dict(), 'output/netG_B2A.pth') 186 | torch.save(netD_A.state_dict(), 'output/netD_A.pth') 187 | torch.save(netD_B.state_dict(), 'output/netD_B.pth') 188 | ################################### 189 | -------------------------------------------------------------------------------- /utils.py: -------------------------------------------------------------------------------- 1 | import random 2 | import time 3 | import datetime 4 | import sys 5 | 6 | from torch.autograd import Variable 7 | import torch 8 | from visdom import Visdom 9 | import numpy as np 10 | 11 | def tensor2image(tensor): 12 | image = 127.5*(tensor[0].cpu().float().numpy() + 1.0) 13 | if image.shape[0] == 1: 14 | image = np.tile(image, (3,1,1)) 15 | return image.astype(np.uint8) 16 | 17 | class Logger(): 18 | def __init__(self, n_epochs, batches_epoch): 19 | self.viz = Visdom() 20 | self.n_epochs = n_epochs 21 | self.batches_epoch = batches_epoch 22 | self.epoch = 1 23 | self.batch = 1 24 | self.prev_time = time.time() 25 | self.mean_period = 0 26 | self.losses = {} 27 | self.loss_windows = {} 28 | self.image_windows = {} 29 | 30 | 31 | def log(self, losses=None, images=None): 32 | self.mean_period += (time.time() - self.prev_time) 33 | self.prev_time = time.time() 34 | 35 | sys.stdout.write('\rEpoch %03d/%03d [%04d/%04d] -- ' % (self.epoch, self.n_epochs, self.batch, self.batches_epoch)) 36 | 37 | for i, loss_name in enumerate(losses.keys()): 38 | if loss_name not in self.losses: 39 | self.losses[loss_name] = losses[loss_name].data[0] 40 | else: 41 | self.losses[loss_name] += losses[loss_name].data[0] 42 | 43 | if (i+1) == len(losses.keys()): 44 | sys.stdout.write('%s: %.4f -- ' % (loss_name, self.losses[loss_name]/self.batch)) 45 | else: 46 | sys.stdout.write('%s: %.4f | ' % (loss_name, self.losses[loss_name]/self.batch)) 47 | 48 | batches_done = self.batches_epoch*(self.epoch - 1) + self.batch 49 | batches_left = self.batches_epoch*(self.n_epochs - self.epoch) + self.batches_epoch - self.batch 50 | sys.stdout.write('ETA: %s' % (datetime.timedelta(seconds=batches_left*self.mean_period/batches_done))) 51 | 52 | # Draw images 53 | for image_name, tensor in images.items(): 54 | if image_name not in self.image_windows: 55 | self.image_windows[image_name] = self.viz.image(tensor2image(tensor.data), opts={'title':image_name}) 56 | else: 57 | self.viz.image(tensor2image(tensor.data), win=self.image_windows[image_name], opts={'title':image_name}) 58 | 59 | # End of epoch 60 | if (self.batch % self.batches_epoch) == 0: 61 | # Plot losses 62 | for loss_name, loss in self.losses.items(): 63 | if loss_name not in self.loss_windows: 64 | self.loss_windows[loss_name] = self.viz.line(X=np.array([self.epoch]), Y=np.array([loss/self.batch]), 65 | opts={'xlabel': 'epochs', 'ylabel': loss_name, 'title': loss_name}) 66 | else: 67 | self.viz.line(X=np.array([self.epoch]), Y=np.array([loss/self.batch]), win=self.loss_windows[loss_name], update='append') 68 | # Reset losses for next epoch 69 | self.losses[loss_name] = 0.0 70 | 71 | self.epoch += 1 72 | self.batch = 1 73 | sys.stdout.write('\n') 74 | else: 75 | self.batch += 1 76 | 77 | 78 | 79 | class ReplayBuffer(): 80 | def __init__(self, max_size=50): 81 | assert (max_size > 0), 'Empty buffer or trying to create a black hole. Be careful.' 82 | self.max_size = max_size 83 | self.data = [] 84 | 85 | def push_and_pop(self, data): 86 | to_return = [] 87 | for element in data.data: 88 | element = torch.unsqueeze(element, 0) 89 | if len(self.data) < self.max_size: 90 | self.data.append(element) 91 | to_return.append(element) 92 | else: 93 | if random.uniform(0,1) > 0.5: 94 | i = random.randint(0, self.max_size-1) 95 | to_return.append(self.data[i].clone()) 96 | self.data[i] = element 97 | else: 98 | to_return.append(element) 99 | return Variable(torch.cat(to_return)) 100 | 101 | class LambdaLR(): 102 | def __init__(self, n_epochs, offset, decay_start_epoch): 103 | assert ((n_epochs - decay_start_epoch) > 0), "Decay must start before the training session ends!" 104 | self.n_epochs = n_epochs 105 | self.offset = offset 106 | self.decay_start_epoch = decay_start_epoch 107 | 108 | def step(self, epoch): 109 | return 1.0 - max(0, epoch + self.offset - self.decay_start_epoch)/(self.n_epochs - self.decay_start_epoch) 110 | 111 | def weights_init_normal(m): 112 | classname = m.__class__.__name__ 113 | if classname.find('Conv') != -1: 114 | torch.nn.init.normal(m.weight.data, 0.0, 0.02) 115 | elif classname.find('BatchNorm2d') != -1: 116 | torch.nn.init.normal(m.weight.data, 1.0, 0.02) 117 | torch.nn.init.constant(m.bias.data, 0.0) 118 | 119 | --------------------------------------------------------------------------------