Unsupervised Image-to-Image Translation

Hi, Mingyu. I'm studying your amazing work about UNIT, and I'm very interested in UNIT's attractive poteintial of IR/RGB conversion. So I tried to repeat the experiment. Since the detailed parameters of training are not provided, I just tried borrowing settings in synthia2cityscape.yaml for the training. But the results were not satisfying. So I'm wondering is it right for me to do so? If it's not right to borrow settings in synthia2cityscape.yaml, is there any possibility that settings for IR/RGB conversion may be released? Thanks for your attention and amazing work:)

Hello, I downloaded the vgg16.t7 manually, when I ran"load_lua(r"D:\Progarm Files\eclipse\preferences\UNIT\vgg16.t7")", I got this error message:"unknown type id 1056626884. The file may be corrupted." Why is this problem? I had downloaded it several times.

Thank you for your answer.

I am running version_01, and I am facing problems with the summary writer.

1. this line throws the following error:

AttributeError: 'module' object has no attribute 'FileWriter'

So, I replaced it with the following line:

train_writer = tf.summary.FileWriter("%s/%s" % (opts.log,os.path.splitext(os.path.basename(opts.config))[0]))

Upon doing so the program runs fine, until this line.

The error it throws is:

in add_summary for value in summary.value: AttributeError: 'Tensor' object has no attribute 'value'

Can anybody please point out the change I need to make.

Hi ! I am reproducing your code with tensorflow. But I do not know the current hyperparameter information. (batch size, input size, dropout rate, etc.) Could you tell me which code I can check?

Hi ! *** Q1 *** When using Image translation, did you not use augmentation? I want to know about this, but I can not find it in your code

*** Q2 *** For example, suppose I want to convert from domain_A (dog) to domain_B (cat). (domainA -> domain_B)

Assume that the number of dog images in domain_A is 1000, and the number of cat images in domain_B is 1500. (That means # domainA < # domainB)

If so, how do I train? I think the learning imbalance will happen because the number of data in two domains is different. Did you make the number of images in both domains the same?

*** Q3 *** Generator_loss = G_A_loss + G_B_loss Discriminator_loss = D_A_loss + D_B_loss

When you train Generator A and B, why did you train with sum of two loss(Generator_loss), instead of training about G_A_loss and G_B_loss, respectively? Likewise, why did the Discriminator do so?

Hi! Thank you very much for sharing your code. I encountered some error when training from scratch with celeba blond hair translation using python 3 according to the usage. The itertool.izip in python2 is changed to the built-in zip in python3:

Traceback (most recent call last): File "cocogan_train.py", line 88, in <module> main(sys.argv) File "cocogan_train.py", line 56, in main for it, (images_a, images_b) in enumerate(zip(train_loader_a,train_loader_b)): File "/home/fox/anaconda3/lib/python3.5/site-packages/torch/utils/data/dataloader.py", line 301, in __iter__ return DataLoaderIter(self) File "/home/fox/anaconda3/lib/python3.5/site-packages/torch/utils/data/dataloader.py", line 171, in __init__ self._put_indices() File "/home/fox/anaconda3/lib/python3.5/site-packages/torch/utils/data/dataloader.py", line 210, in _put_indices indices = next(self.sample_iter, None) File "/home/fox/anaconda3/lib/python3.5/site-packages/torch/utils/data/sampler.py", line 115, in __iter__ for idx in self.sampler: File "/home/fox/anaconda3/lib/python3.5/site-packages/torch/utils/data/sampler.py", line 50, in __iter__ return iter(torch.randperm(len(self.data_source)).long()) RuntimeError: invalid argument 1: must be strictly positive at /opt/conda/conda-bld/pytorch_1503968623488/work/torch/lib/TH/generic/THTensorMath.c:2033 How can I fix this problem?

Hi, there, i'm trying to train dataset and stucked with some learning problems

Example of 10000 iterations day 2 night (train) Example of 10000 iterations night 2 day (train)

I think that it's unit_day2night.yaml configuration problem. Can you share yours config file?

Another option, that it's dataset's split issue. I used 50/50 for train and test. And both divided into two equal domains A and B.

Hi I couldnt find in the paper or understand from the code what you are doing during test time in the next manner:

On train time you insert noise and you compute the mu and sd and represent z as a random vector.

What happens during test time, cause i dont see you do the same, do you use the average mu and sd or something else? i would love to know.

thanks, Gal.

Hi, I tried to run the training with CelebA dataset, and got the following error when I started train :

Traceback (most recent call last): File "cocogan_train.py", line 6, in from tools import * File "/home/paperspace/Downloads/UNIT/src/tools/init.py", line 6, in from net_config import * ModuleNotFoundError: No module named 'net_config'

Do you have any hints I could solve it? thanks!

Dear M. Liu,

I am studying your paper that I found very interesting, thank you for sharing your research ! I read the other issues/questions asked, where you pointed that some explanations were not given in the paper, about that I would like to ask you more details about the domain adaptation experiment.

Could you explain the discriminator update rule and more precisely when you compute that please ? feature_loss_a = self._compute_ll_loss(fake_feat_ab - fake_feat_aa, dummy_variable) feature_loss_b = self._compute_ll_loss(fake_feat_ba - fake_feat_bb, dummy_variable) (cocogan_trainer_da.py lines 102-103)

Also, my experiments focus more on domain translation than domain adaptation. For that purpose, is this update rule still relevant ?

Thank you for your answer. Adrien

I am getting low accuracy. May I know why ?

$python cocogan_train_domain_adaptation.py --config ../exps/unit/svhn2mnist.yaml --log ../logs

Iteration: 00000010/00200000 Iteration: 00000020/00200000 Iteration: 00000030/00200000 Iteration: 00000040/00200000 Iteration: 00000050/00200000 Iteration: 00000060/00200000 Iteration: 00000070/00200000 Iteration: 00000080/00200000 Iteration: 00000090/00200000 Iteration: 00000100/00200000 Classification accuracy for Test_B dataset: 0.1296 Iteration: 00000110/00200000 Iteration: 00000120/00200000 Iteration: 00000130/00200000 Iteration: 00000140/00200000 Iteration: 00000150/00200000 Iteration: 00000160/00200000 Iteration: 00000170/00200000 Iteration: 00000180/00200000 Iteration: 00000190/00200000 Iteration: 00000200/00200000 Classification accuracy for Test_B dataset: 0.1032 Iteration: 00000210/00200000 Iteration: 00000220/00200000 Iteration: 00000230/00200000 Iteration: 00000240/00200000 Iteration: 00000250/00200000 Iteration: 00000260/00200000 Iteration: 00000270/00200000 Iteration: 00000280/00200000 Iteration: 00000290/00200000 Iteration: 00000300/00200000 Classification accuracy for Test_B dataset: 0.1084 Iteration: 00000310/00200000 Iteration: 00000320/00200000 Iteration: 00000330/00200000 Iteration: 00000340/00200000 Iteration: 00000350/00200000 Iteration: 00000360/00200000 Iteration: 00000370/00200000 Iteration: 00000380/00200000 Iteration: 00000390/00200000 Iteration: 00000400/00200000 Classification accuracy for Test_B dataset: 0.0826 Iteration: 00000410/00200000 Iteration: 00000420/00200000 Iteration: 00000430/00200000 Iteration: 00000440/00200000 Iteration: 00000450/00200000 Iteration: 00000460/00200000 Iteration: 00000470/00200000 Iteration: 00000480/00200000 Iteration: 00000490/00200000 Iteration: 00000500/00200000 Classification accuracy for Test_B dataset: 0.0984 Iteration: 00000510/00200000 Iteration: 00000520/00200000 Iteration: 00000530/00200000 Iteration: 00000540/00200000 Iteration: 00000550/00200000 Iteration: 00000560/00200000 Iteration: 00000570/00200000 Iteration: 00000580/00200000 Iteration: 00000590/00200000 Iteration: 00000600/00200000 Classification accuracy for Test_B dataset: 0.0912 Iteration: 00000610/00200000 Iteration: 00000620/00200000 Iteration: 00000630/00200000 Iteration: 00000640/00200000 Iteration: 00000650/00200000 Iteration: 00000660/00200000 Iteration: 00000670/00200000 Iteration: 00000680/00200000 Iteration: 00000690/00200000 Iteration: 00000700/00200000 Classification accuracy for Test_B dataset: 0.0819