I'm using the Chainer DCGAN example file found at https://github.com/chainer/chainer/blob/master/examples/dcgan/train_dcgan.py . It works fine for 32x32 images, but for other resolutions, the README.md instructs to modify the network architecture in net.py.
As I understand it from reading the documentation, the size of the training images is sent as a parameter to the constructor to the Generator class, as bottom_width, and ch. Here is the code for a 32x32.
class Generator(chainer.Chain):
def __init__(self, n_hidden, bottom_width=4, ch=512, wscale=0.02):
I'm confused as to how this translates to 32x32, and how to modify this to other resolutions. Any help would be greatly appreciated.
You can calculate it by understanding the behavior of Deconvolution2D.
In net.py, 3 Deconvolution2D layer (self.dc1, self.dc2, self.dc3) is defined with stride=2 (4-th argument of L.Deconvolution2D), which doubles the input height/width.
As a result, output size will be bottom_size * 2^3, which results to 32 when bottom_size=4.
So for example, if you want to get 64x64 images you can set bottom_size=8 for both generator & discriminator (but you need 64x64 images as real data, instead of cifar-100 which is 32x32 images).
Please refer official document for the details of input-output size relationship.