kozistr / awesome-gans Goto Github PK

Hello,

I am trying to run CoGAN, but I get the following error below:

Traceback (most recent call last):
File "cogan_train.py", line 131, in
main()
File "cogan_train.py", line 42, in main
model = cogan.CoGAN(s)
File "/home/maria/Documents/Awesome-GANs/CoGAN/cogan_model.py", line 166, in init
self.build_cogan() # build CoGAN model
File "/home/maria/Documents/Awesome-GANs/CoGAN/cogan_model.py", line 224, in build_cogan
self.g_1 = self.generator(self.z, self.y, share_params=False, reuse=False, name='g1')
File "/home/maria/Documents/Awesome-GANs/CoGAN/cogan_model.py", line 196, in generator
x = tf.layers.dense(x, 7 * 7, reuse=True, name='gen-dense-reshape')
File "/home/maria/anaconda2/envs/anaconda3gpu/lib/python3.6/site-packages/tensorflow/python/layers/core.py", line 250, in dense
return layer.apply(inputs)
File "/home/maria/anaconda2/envs/anaconda3gpu/lib/python3.6/site-packages/tensorflow/python/layers/base.py", line 671, in apply
return self.call(inputs, *args, **kwargs)
File "/home/maria/anaconda2/envs/anaconda3gpu/lib/python3.6/site-packages/tensorflow/python/layers/base.py", line 559, in call
self.build(input_shapes[0])
File "/home/maria/anaconda2/envs/anaconda3gpu/lib/python3.6/site-packages/tensorflow/python/layers/core.py", line 137, in build
trainable=True)
File "/home/maria/anaconda2/envs/anaconda3gpu/lib/python3.6/site-packages/tensorflow/python/layers/base.py", line 458, in add_variable
trainable=trainable and self.trainable)
File "/home/maria/anaconda2/envs/anaconda3gpu/lib/python3.6/site-packages/tensorflow/python/ops/variable_scope.py", line 1203, in get_variable
constraint=constraint)
File "/home/maria/anaconda2/envs/anaconda3gpu/lib/python3.6/site-packages/tensorflow/python/ops/variable_scope.py", line 1092, in get_variable
constraint=constraint)
File "/home/maria/anaconda2/envs/anaconda3gpu/lib/python3.6/site-packages/tensorflow/python/ops/variable_scope.py", line 425, in get_variable
constraint=constraint)
File "/home/maria/anaconda2/envs/anaconda3gpu/lib/python3.6/site-packages/tensorflow/python/ops/variable_scope.py", line 394, in _true_getter
use_resource=use_resource, constraint=constraint)
File "/home/maria/anaconda2/envs/anaconda3gpu/lib/python3.6/site-packages/tensorflow/python/ops/variable_scope.py", line 760, in _get_single_variable
"reuse=tf.AUTO_REUSE in VarScope?" % name)
ValueError: Variable generator-g1/gen-dense-reshape/kernel does not exist, or was not created with tf.get_variable(). Did you mean to set reuse=tf.AUTO_REUSE in VarScope?

Any help would be greatly appreciated! Thank you in advance.

Hi There,

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It's just not worked well... It'll be fixed soon

In run-time, loss became NaN...

now on re-modeling & building for real SR images...

i can not understand the difference between batch_a and batch_b in stargan

In the lapgan_train.py, the 56th line says:
batch_images = batch_images.astype(np.float32) / 225.
Why it is 225 instead of 255?

Image generation has a problem. (maybe, entire model...)

Hello! I've found a performance issue in /awesome_gans/pggan/pggan_train.py: with tf.Session(config=config) as s(here) is repeatedly called in the loop for idx, n_pg in enumerate(pg)(here).

tf.Session being defined repeatedly could lead to incremental overhead. If you define tf.Session out of the loop and pass tf.Session as a parameter to the loop, your program would be much more efficient. Here is the Stack Overflow post to support it.

Looking forward to your reply. Btw, I am very glad to create a PR to fix it if you are too busy.

Currently, using h5py for saving images.
Later, it'll be changed to TFRecord, overall.

And kinda image normalize like dividing into 255.0, it'll be also removed from datasets.py
Or by option :).

Anyway, in short, datasets.py reformed soon!

Adding visual contents would be good #14

@kozistr When I try to run your code, then I am getting a out of memory error. I looked at it and apparently, when you are loading the data, you are storing all images in memory at once, which is causing this error. Can you help me in this matter?

hi @kozistr thank you for your hard and great work
could you please explain to me how to use your srgan to upscale my own photo
thank you

Currently, all of the codes are based on tensorflow 1.x, which is legacy.

so, rework all codes to pytorch code, instead of tensorflow 2.x

• Select some of the papers which have impacts and important as i think,
• Summary all of the papers within 3 lines!
• Grouping the papers w/ following characteristic

Describe the bug
Trying to follow the README.md and code does nothing.

To Reproduce
Steps to reproduce the behavior:

1. Follow README.md instructions to train GANs

python3 -m awesome_gans.acgan

3. Nothing happens

Expected behavior
GAN should train

Additional context
This isn't entirely unexpected since the code for awesome_gans.acgan (i.e. acgan/__main__.py) is essentially blank as shown below. I'm wondering if this ever worked, or if something is very out of date.

from awesome_gans.config import parse_args
from awesome_gans.utils import set_seed

def main():
    config = parse_args()

    set_seed(config.seed)

    if config.mode == 'train':
        pass
    elif config.mode == 'inference':
        pass
    else:
        raise ValueError()


main()

Describe the bug
In the file "fgan_model.py":
(1) Line 209, the conjugate function of Total-Variation in your code is supposed to be the same as the activation function. Since in your implementation, the conjugate function should be f*(g(x)) where x is the input. Thus, for Total Variation, f*(g(x))=g(x) rather than x;
I think it should be: -torch.mean(torch.tanh(x) / 2.) ?
(2) The conjugate of Jenson-Shannon is also incorrect? Line159;
I think it should be: -torch.mean(x + torch.log(1. + torch.exp(-x))) - torch.log(torch.tensor(2.))?
(3) The conjugate of Hellinger is also incorrect? Line175.
I think it should be: -torch.mean((1. - torch.exp(x)) / (torch.exp(x)))?

Currently, there're GANs that don't work properly with some reasons that i didn't find yet, but not in the past,

• EBGAN

Need to be fixed. Any helps or suggestions are welcome :)

Could you show your "list_attr_celeba.txt" used in the infogan folder? I have made the txt file myself, but it doesn't work well, plz!

Is it possible for you to have an annual review of new GANs that arrives on the scene through https://github.com/nightrome/really-awesome-gan?

P.S. it would be nice if you can draw a diagram for each of the GANs and explain how they differ, and maybe create a generalized GAN interface so that people can hot-swap whatever CNNs they would like to use from https://github.com/CeLuigi/models-comparison.pytorch/wiki/Accuracy-vs-Computational-complexity for more diverse designs.

• For better results
• For better codes
• For checking fully working codes
• etc...

now on going ~

I find PGGAN can not train.
When pg ==1, generator make the fake image which shape is [-1,16,16, 3].
And i find reason: because generator has upsample twice on line of block the pggan_model.py .
repair

x = block(x, nf(i + 1), name="1")
# x = block(x, nf(i + 1), name="2")
x = tf.layers.conv2d(x, filters=get_out_channels(i + 1), kernel_size=3, strides=1, padding='SAME',
                        kernel_initializer=w_init, kernel_regularizer=w_reg, bias_initializer=b_init,
                        name='gen_n_%s_conv2d-%d' % (name, x.get_shape()[1]))
x = tf.nn.leaky_relu(x)
x = pix_norm(x)

Hi @kozistr, Thanks for your great work!

In your CGAN implementation, you feed G and D with the same real label from the dataset as the condition.However, I have a question about this since several other implementations feed G with a sampled label as the condition.

I have seen several CGAN implementations. There are two ways to use the condition labels:

1. feed G with a random label, feed D with the real label
   • Kera-GAN
   • PyTorch-GAN
   • pytorch-MNIST-CelebA-cGAN-cDCGAN
   • tensorflow-MNIST-cGAN-cDCGAN
2. feed both G and D with the same real label
   • GAN
   • tensorflow-generative-model-collections
   • generative-models
   • pytorch-generative-model-collections

Here are my questions:

• Which way is used by the CGAN paper in 2014?
• When training the CGAN, should we use the same condition label for both G and D?

The same issue has been asked here. I hope we can figure out it together.

Currently, there're lots of duplicates of codes. and hard to use and read.

So, modulizing and refactoring the code is needed.