williamfalcon / pix2pix-keras Goto Github PK

Hi William,

I have concern about the L1 regularization in the paper but I have not see in you code.

Bests,

At the moment, when I try to run the main.py script. I get the following error:

py:222: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
  return f(*args, **kwds)
./pixve/lib/python3.5/importlib/_bootstrap.py:222: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
  return f(*args, **kwds)
Traceback (most recent call last):
  File "pix2pix/main.py", line 47, in <module>
    generator_nn = UNETGenerator(input_img_dim=input_img_dim, num_output_channels=output_channels)
  File "./pix2pix/networks/generator.py", line 154, in UNETGenerator
    de_2 = merge([de_2, en_6], mode=merge_mode, concat_axis=1)
  File "./pixve/lib/python3.5/site-packages/keras/engine/topology.py", line 1680, in merge
    name=name)
  File "./pixve/lib/python3.5/site-packages/keras/engine/topology.py", line 1299, in __init__
    node_indices, tensor_indices)
  File "./pixve/lib/python3.5/site-packages/keras/engine/topology.py", line 1371, in _arguments_validation
    'Layer shapes: %s' % (input_shapes))
ValueError: "concat" mode can only merge layers with matching output shapes except for the concat axis. Layer shapes: [(None, 6, 4, 1024), (None, 1, 4, 512)]

I wonder if this could be caused by using a different version of tensorflow, as the version used was not specified anywhere. I tried tensorflow versions 1.9 and 1.7, with the same results. Perhaps you could state what version you used, and that would rule out this one variable.

Have you tried running your model on more datasets for pix2pix? If yes could you post the results of those datasets on readme. And it would be useful for people if you even add the results of facades dataset on readme as well.

Thank You

Dear all,

I am getting this error when I keep patch size as (64,64) and image size (256, 256).--> ValueError: impossible convolution output dim: expected 1x512x1x1 but received 1x512x2x2.
Can someone tell me what is going wrong here? any help is appreciated.

Hi!

I'm trying to get main.py to run, fresh out the box, after I've set up the conda environment as suggested.

In generator.py UNETGenerator I get errors that the combined net works are not compatible size, so I have to resize each decoder to have the same size as the encoder layer, e.g.
from
de_6 = Convolution2D(nb_filter=512, nb_row=4, nb_col=4, border_mode='same')(de_6)
to
de_6 = Convolution2D(nb_filter=128, nb_row=4, nb_col=4, border_mode='same')(de_6)

otherwise this fails:
de_6 = merge([de_6, en_2], mode=merge_mode, concat_axis=1)

Then once adjusted I get an error with
DCGAN

telling me:
"ValueError: Dimensions must be equal, but are 1 and 64 for 'Conv2D_168' (op: 'Conv2D') with input shapes: [?,510,64,1], [4,4,64,64]."

Could you suggest what I'm doing wrong?

Thanks,

Ben

ValueError: Operands could not be broadcast together with shapes (4, 4, 1024) (1, 4, 512)

Hi,
Impressive work.

I would like to ask you that in testing phase. I only feed the input to Unet generator to get the final input,
am I right?.

Thank you so much for implementing cGAN using keras.

Bests,