hello I have downloaded trained model and trying to test it but i am getting following error. can u please suggest what went wrong?

I am testing it on google colab and using only 182000 to 182637 images. TypeError: Input 'filename' of 'ReadFile' Op has type float32 that does not match expected type of string.

Hello. I am using hd - celeba 384 dataset with provided 384_shortcut1_inject1_none_hd model. I am trying to use custom number of images instead of using all 202599 images. I tried to do the following: modify list_attr_celeba.txt file to only include first 20 images and put these 20 images in ./data/img_crop_celeba/*.jpg. However, this is the error I get:

TypeError: Input 'filename' of 'ReadFile' Op has type float32 that does not match expected type of string.

I also tried to train with only 20 images and get the same error. I get no errors when running train/test for all 202599 images.

Hi, I am referring to your DTLCGAN code. According to your last reply, I added an encoder to it and have some questions about your code. In your train.py, the z_sample you choose to sample is generated by: z_ipt_samples = [np.stack([np.random.normal(size=[z_dim])] * len(c_ipt_sample)) for i in range(15)] which is of (15,18,100).

So, right now I used an encoder, and the noise of z (as well as the z_ipt used in training) here should be replaced by the encoder's output, right?

But, what does len(c_ipt_sample) here mean? You generated 18 noises for one testing sample? I counted your sampling training, the lowest layer in your decision tree does have 18 images( 233=18). So why do you generate testing sample from bottom to the top, but not the reverse? How can you be certain that this 18 noises all belong to the same person since you generated from bottom to top?

Besides, should my encoder do the parellel, choosing 18 frontcodes of 18 images and uses them to do the sampling? It seems wrong here because the 18 frontcodes of mine are from 18 different images(or say 18 different persons), the resulting sampling tree was weird(some are ok, and I am confused about them). But if I used the same frontcode of one image(or say the same person) and copy it for 18 times, the training samples are the same, no change of attributes at all.

Hello, @LynnHo Can you tell how we can do facial feature extraction, means if input any image of face and then how we can get the 40 facial attribute from that.

Thanks.

As written in the title, I obtain a row of the same images without any changes regardless to the attribute (column). I use custom data-set organized as CelebA. Could you give an advise, what may cause it?

I'm curious what you used for the attribute classifier to measure the attribute editing accuracy and preservation error. Also do you have any plans to release this trained model? Thanks.

The pre-trained model you provided is not well-performing over the Celeb-A-HQ dataset. So I've got a question that for how many epochs you have trained the pre-trained model and on what data set. Another question is that my use case applies glasses to the face, so I need to know that if I trained a new model from scratch over the Celeb-A-HQ dataset it will help us to achieve my task. can we train the model over a single attribute like eyeglasses or a smile? Thanks in advance.

Hi, thank you for sharing the great project. I found your attribute style manipulation particularly meaningful and useful for my recent research. I saw from previous issue that you have no plan to open source the code for this part. I have the following questions:

1. I found nowhere in your paper as for how you derive your θ and the relationship between θ and the image, so how do you get the θ in an unsupervised way for each input?
2. Is this part's idea (and the way you derived θ) based on the paper 'Generative Attribute Controller with Conditional Filtered Generative Adversarial Networks'? (I found their code is also not open source).
3. If I want to realize this part myself, could you give me some hints of where to start or any papers and sources I could refer to (there is really very few works on accurate or multiple attribute style manipulation)?

Thank you!

Hi there,

Your work is interesting. I have a problem. Could you help figure it out?

I applied your method on CelebA-HQ dataset for a single attribute manipulation. But I cannot get the desired result. The result (the interested attribute is "Smiling") at the 59th training epoch is shown as follows. There is no change in the third column images.

Thanks and Regards,

First, I appreciate your excellent work and have been interested in your work since 2018.

I have a question about the test and training in your work. In advance, I clarify that I consider the case where the value of attributes is binary.

For training, the value of attributes seems to be -1 or 1. (Read 0 or 1 then, *2 - 1 -> [-1, 1]) (https://github.com/LynnHo/AttGAN-Tensorflow/blob/master/train.py#L161)

On the other hand, the range of attributes is [-2, 2] for test. ( Read 0 or 1 then, *2 - 1 -> [-1, 1], finally *2 -> [-2, 2] ) (https://github.com/LynnHo/AttGAN-Tensorflow/blob/master/train.py#L246, test_int = 2.0)

Is it right that you use the different values of the attribute vector in training and test?

I just find that I cannot reproduce the result of attribute classification without this trick. However, I can reproduce the result by using [-2, 2].

Thanks!

@LynnHo Hi, I read your code theses days, and I wonder about why the label of attributes has to map into [-1, 1] instead of [0, 1]. It seems that it is very important and has some technical reason because you commented on three exclamation marks on that code. Could you share some experimental knowledge about this?

I want to apply this code on Celeb-A with fake masked images and i want to remove mask .. so how can i apply this concept .. can you guide me if i can do it or no using your code ?? if yes where should i change in the code just train.py and data.py ??

How do you get a good balance among varied hyper-parameters like different loss weights, learning_rate when style manipulation is adopted? I found the training of the network very unstable.

I can get style manipulation results on bangs and eyeglasses, but the control is unstable and the sharpness of images are also affected. The control on eyeglasses is only on the shade and the model has no control on shape and size.

Except for hyper-parameters, is there any other places where there can be problems like training settings?

Besides, when implementing style manipulation, except for the loss of generated style controller, do you also use the original attribute loss?

Looking forward to your answer. Thank you!