This is a Pytorch implementation of Deep Convolutional Generative Adversarial Networks (DCGAN) [2] for humanface datasets,which can genarate some beautiful girls' face.
This project is inspired by this blog :https://zhuanlan.zhihu.com/p/26639135
- you can download the dataset from the url below:
- HumanFace dataset: https://pan.baidu.com/share/init?surl=dERYUmH
- with the password: 09h9
- This dataset contains about 8000 girls‘ face images
The main code structure is forked from https://github.com/pytorch/examples/tree/master/dcgan
- The input and output image size of the neural network is 64 x 64 ,
- The learning rate is initialized as 0.0002，and reduces a half every 50 epoch.
- The dimension of random noise is set to 1000.
- For each iteration, we train the netD first for once and then train the netG for twice.
- We trained this network for 300 epoch and got the final result below.
- Generate using fixed noise (fixed_z_)
| Training image | Result image |
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- Real image vs Generated images
| Real image | DCGAN after 50 epochs | DCGAN after 150 epochs |
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Learning Time
- Humanface DCGAN - about 10 sec per epoch, 300 epoch takes nearly 50 minutes ( If you want to reduce the training time, set the dimension of the noise to 100，then the epoch time is about 7 sec,in my development environment.)
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Results
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Model: You can find the final trained model in the "checkpoints " folder
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Logs: You can get the training log in the "logs " folder
- Data
You should download the dataset showed above, and unzip the dataset to the same folder witch contains the main.py file
- Run
usage: main.py [-h] [--dataset DATASET] [--dataroot DATAROOT]
[--train_image_list TRAIN_IMAGE_LIST] [--workers WORKERS]
[--batchSize BATCHSIZE] [--imageSize IMAGESIZE] [--nz NZ]
[--ngf NGF] [--ndf NDF] [--niter NITER] [--lr LR]
[--decay_round DECAY_ROUND] [--beta1 BETA1] [--cuda CUDA]
[--ngpu NGPU] [--netG NETG] [--netD NETD] [--outpics OUTPICS]
[--outckpts OUTCKPTS] [--outlogs OUTLOGS]
[--manualSeed MANUALSEED]
optional arguments:
-h, --help show this help message and exit
--dataset DATASET cifar10 | lsun | imagenet | folder | lfw | fake
--dataroot DATAROOT path to dataset
--train_image_list TRAIN_IMAGE_LIST
pics path lists
--workers WORKERS number of data loading workers
--batchSize BATCHSIZE
input batch size
--imageSize IMAGESIZE
the height / width of the input image to network
--nz NZ size of the latent z vector
--ngf NGF number of the filter of generate network
--ndf NDF number of the filter of descriminator network
--niter NITER number of epochs to train for
--lr LR learning rate, default=0.0002
--decay_round DECAY_ROUND
learning rate decay 0.5 each decay_round
--beta1 BETA1 beta1 for adam. default=0.5
--cuda CUDA enables cuda
--ngpu NGPU number of GPUs to use
--netG NETG path to netG (to continue training)
--netD NETD path to netD (to continue training)
--outpics OUTPICS folder to output images
--outckpts OUTCKPTS folder to output checkpoints
--outlogs OUTLOGS folder to output images
--manualSeed MANUALSEED manual seed
- Ubuntu 14.04 LTS
- NVIDIA GTX 1080 ti
- cuda 8.0
- Python 3.5
- pytorch 0.3.0
- torchvision 0.2.0
[1] Goodfellow, Ian, et al. "Generative adversarial nets." Advances in neural information processing systems. 2014.
(Full paper: http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf)
[2] Radford, Alec, Luke Metz, and Soumith Chintala. "Unsupervised representation learning with deep convolutional generative adversarial networks." arXiv preprint arXiv:1511.06434 (2015).
(Full paper: https://arxiv.org/pdf/1511.06434.pdf)
[3] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. "Gradient-based learning applied to document recognition." Proceedings of the IEEE, 86(11):2278-2324, November 1998.
[4] Liu, Ziwei, et al. "Deep learning face attributes in the wild." Proceedings of the IEEE International Conference on Computer Vision. 2015.