I am trying for more than 4 hours to make the very simple thing of parametrising the wgan of lesson 7 (for bedrooms) so that it uses only the 1 channel of my images (as they are grayscale) and 1 channel input noise.
I have a folder of png images and I am running it inside a script and have version 1.0.60
My get_data function look like this:
def get_data(path, bs, size, padding_size): def _rgb_to_gs(x): return x[1, :, :] rgb_to_gs = TfmPixel(_rgb_to_gs, order=1) return (GANItemList.from_folder(path, noise_sz=100, convert_mode='L') .split_none().label_from_func(noop) .transform(ds_tfms=rgb_to_gs, tfms=[[pad(padding=padding_size, mode='border')], ], size=size) .databunch(bs=bs).normalize())
and my model is defined as
generator = basic_generator(in_size=int(args.size), n_channels=1, n_extra_layers=3) critic = basic_critic(in_size=int(args.size), n_channels=1, n_extra_layers=3) learn = GANLearner.wgan(data, generator, critic, switch_eval=False, opt_func = partial(optim.Adam, betas = (0.,0.99)), wd=0.)
I get the error:
RuntimeError: Given groups=1, weight of size 64 1 4 4, expected input[1, 3, 214, 155] to have 1 channels, but got 3 channels instead
I have tried it all (together):
- defaults.cmap = ‘binary’
- convert_mode = ‘L’
- and I even made a TfmPixel accurately placed at ds_tfms (which I think is not at all executed as I had a print function inside)
Can you please help?
Thanks in advance