Unsupervised Image to Image Translation with Generative Adversarial Networks

I’m adaping your code

Now I’m tring to replacement faces

Is “obama_hillary” is custom dataset? Or public dataset

Let me know where can I get “obama_hillary”

Thanks.

Hi,donghao, I am running this project but I find there are so many errors at the beginning of my training, e.g. Traceback (most recent call last): File "train.py", line 362, in tf.app.run() File "/home/zzw/Program/anaconda2/lib/python2.7/site-packages/tensorflow/python/platform/app.py", line 48, in run _sys.exit(main(_sys.argv[:1] + flags_passthrough)) File "train.py", line 355, in main train_ac_gan() File "train.py", line 98, in train_ac_gan g_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(d_logits_fake, tf.ones_like(d_logits_fake))) File "/home/zzw/Program/anaconda2/lib/python2.7/site-packages/tensorflow/python/ops/nn_impl.py", line 149, in sigmoid_cross_entropy_with_logits labels, logits) File "/home/zzw/Program/anaconda2/lib/python2.7/site-packages/tensorflow/python/ops/nn_ops.py", line 1512, in _ensure_xent_args "named arguments (labels=..., logits=..., ...)" % name) ValueError: Only call sigmoid_cross_entropy_with_logits with named arguments (labels=..., logits=..., ...)

I guess these errors are due to differences between mine and yours,so could you please tell me what is your version of tensorflow?

Hey it's me again, browsing through your other repos i found this gem – seems fun! A few months ago i've tested another gender swap network written in TF, but the output resolution was hardcoded and i couldn't figure out how to change it (with my limited knowledge of TF). Your version again seems a lot easier to read – due to the usage of the Tensorlayer library?

I'm using the celebA dataset and have left all thetf.flags by default. So the default image size is 64 x 64px but i've seen that you've also written quite a few lines in train.py and model.py for a 256 x 256px option.

if FLAGS.image_size == 64:
    generator = model.generator
    discriminator = model.discriminator
    imageEncoder = model.imageEncoder
# elif FLAGS.image_size == 256:
#     generator = model.generator_256
#     discriminator = model.discriminator_256
#     imageEncoder = model.imageEncoder_256
else:
    raise Exception("image_size should be 64 or 256")

################## 256x256x3
def generator_256(inputs, is_train=True, reuse=False):
(...)
def discriminator_256(inputs, is_train=True, reuse=False):
(...)

Since the second if-statement (elif FLAGS.image_size == 256:) is commented out and never changes the default 64x64px model generator and encoder, setting flags.DEFINE_integer("image_size", ...) in train.py to 256 doesn't really change the size - is this correct?

I've tried to uncomment the code and enable the elif line but then ran into this error: ValueError: Shapes (64, 64, 64, 256) and (64, 32, 32, 256) are not compatible

You've added generator_256, discriminator_256 and imageEncoder_256 to model.py so i'm wondering if you just have just experimented with this image size and then discarded the option (and just left the 64x64 image_size option) or if i'm missing something here...

There is also a commented out flag for output_size – but this variable doesn't show up anywhere else so i guess it's from a previous version of your code: # flags.DEFINE_integer("output_size", 64, "The size of the output images to produce [64]")

And this one is also non-functional: # flags.DEFINE_integer("train_size", np.inf, "The size of train images [np.inf]")

I just wondered if it's possible to crank up the training and output resolution to 256x256px (and maybe finish the training process this year – when i get my 1080 Ti 😎).

Will try to finish the 64x64px first and save the model-.npz files for later, but it would be interesting to know if the mentioned portions of your code are still functional.

Thanks!