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https://github.com/leocvml/CycleGAN-gluon-mxnet
this repo attemps to reproduce Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks(CycleGAN) use gluon reimplementation
https://github.com/leocvml/CycleGAN-gluon-mxnet
computer-graphics computer-vision cyclegan gan generative-adversarial-network gluon iccv-2017 mxnet
Last synced: about 2 months ago
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this repo attemps to reproduce Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks(CycleGAN) use gluon reimplementation
- Host: GitHub
- URL: https://github.com/leocvml/CycleGAN-gluon-mxnet
- Owner: leocvml
- Created: 2018-08-19T05:59:56.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2018-08-19T07:51:27.000Z (about 6 years ago)
- Last Synced: 2024-06-12T05:08:00.190Z (3 months ago)
- Topics: computer-graphics, computer-vision, cyclegan, gan, generative-adversarial-network, gluon, iccv-2017, mxnet
- Language: Python
- Homepage:
- Size: 33.4 MB
- Stars: 32
- Watchers: 2
- Forks: 5
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- Awesome-MXNet - **CycleGAN**
README
# CycleGAN-gluon-mxnet #
this repo attemps to reproduce [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks(CycleGAN)](https://arxiv.org/pdf/1703.10593.pdf) use gluon reimplementation
## Quick start ##
1. download dataset (my sample is samll set apple <-> orange(each domain have 45 images))
you can download complete dataset from this link
[dataset website](https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/)
2. train
3. inference ( weighting is trained by complete apple2orange dataset)
## Requirements ##
mxnet 1.1.0
## Abstract ##
**Image to image translation :** learn the mapping between an input image and an output image using a training set of aligned image pair.However for many tasks, paired training data will not be available.
**present an approach for learning translate an image from a source domain X to a target domain Y in the absence of paired examples.**
## Network architecture ##
### generate ##
```
The network consists of:
c7s1-32,d64,d128,R128,R128,R128,
R128,R128,R128,R128,R128,R128,u64,u32,c7s1-3
```
```python
class Generator_256(gluon.nn.HybridBlock):
def __init__(self):
super(Generator_256, self).__init__()
self.net = nn.HybridSequential()
with self.net.name_scope():
self.net.add(
nn.ReflectionPad2D(3),
nn.Conv2D(32, kernel_size=7, strides=1),
nn.InstanceNorm(),
nn.Activation('relu'), #c7s1-32
conv_inst_relu(64),
conv_inst_relu(128),
)
for _ in range(9):
self.net.add(
ResBlock(128)
)
self.net.add(
upconv_inst_relu(64),
upconv_inst_relu(32),
nn.ReflectionPad2D(3),
nn.Conv2D(3,kernel_size=7,strides=1),
nn.Activation('sigmoid')
)
def hybrid_forward(self, F, x):
return self.net(x)
```
### discriminator ###
```
use kernel size = 3
```
```python
class Discriminator(gluon.nn.HybridBlock):
def __init__(self):
super(Discriminator, self).__init__()
self.net = nn.HybridSequential()
with self.net.name_scope():
self.net.add(
nn.Conv2D(64, kernel_size=3,strides=2,padding=1),
nn.LeakyReLU(0.2),
nn.Conv2D(128, kernel_size=3,strides=2,padding=1),
nn.InstanceNorm(),
nn.LeakyReLU(0.2),
nn.Conv2D(256, kernel_size=3,strides=2,padding=1),
nn.InstanceNorm(),
nn.LeakyReLU(0.2),
nn.Conv2D(512, kernel_size=3,strides=2,padding=1),
nn.InstanceNorm(),
nn.LeakyReLU(0.2),
nn.Conv2D(1,kernel_size=1,strides=1),
)
def hybrid_forward(self, F, x):
return self.net(x)
```
## training step ##
**train Discriminator**
1. Da aims to distinguish between translated samples G(B) and real smaples A
```python
with autograd.record(): # train A
real_A = D_A(A) # distinguish real image A
fake_BA = G_BA(B) #generate fake A image from B
fake_A = D_A(fake_BA)# distinguish fake image
real_label = nd.ones_like(real_A,ctx=ctx)
fake_label = nd.zeros_like(fake_A,ctx=ctx)
errA_real = L2_loss(real_A, real_label)
errA_fake = L2_loss(fake_A, fake_label)
errDA = (errA_real + errA_fake) * 0.5
errDA.backward()
DA_trainer.step(A.shape[0])
```
**train generate**
1. generate fake_B from domain A
2. generate reconstruct A from fake B
3. calculate cycle consistency loss(recA,A) (lamda =10)
4. use fake_B image fool disciminator B
```python
with autograd.record():
fake_AB = G_AB(A)
fake_A = G_BA(fake_AB)
cycA_loss = cyc_loss(fake_A,A)
fake_B = D_B(fake_AB)
errG_AB = L2_loss(fake_B,real_label) + lamba * cycA_loss
errG_AB.backward()
GAB_trainer.step(A.shape[0])
```
# Result #
**apple2Orange**
**Orange2apple**
## Reference ##
https://github.com/junyanz/CycleGAN thanks author propose this new method
and thanks gluon, mxnet team give us this wonderful tool, we can reimplement project very quickly
thanks