class Config(object):
'''
wraps all configuration parameters in 'static' variables
'''
##
# network parameters
nz = 3 # num of dim for Z at each field position
zx = 5 # number of spatial dimensions in Z
zx_sample = 10 # size of the spatial dimension in Z for producing the samples
# l = h/r, m = w/r
nc = 1 # number of channels in input X (i.e. r,g,b) #3
gen_ks = ([(3,3)] * 5)[::-1] # kernel sizes on each layer - should be odd numbers for zero-padding stuff
dis_ks = [(3,3)] * 5 # kernel sizes on each layer - should be odd numbers for zero-padding stuff
gen_ls = len(gen_ks) # num of layers in the generative network
dis_ls = len(dis_ks) # num of layers in the discriminative network
gen_fn = [nc]+[2**(n+6) for n in range(gen_ls-1)] # generative number of filters
gen_fn = gen_fn[::-1]
dis_fn = [2**(n+6) for n in range(dis_ls-1)]+[1] # discriminative number of filters
lr = 0.0005 # learning rate of adam
b1 = 0.5 # momentum term of adam
l2_fac = 1e-5 # L2 weight regularization factor #1e-5
batch_size = 64
epoch_iters = batch_size * 100
k = 1 # number of D updates vs G updates
npx = zx_to_npx(zx, gen_ls) # num of pixels width/height of images in X
##
# data input folder
sub_name = 'ti_braided_river'
home = os.path.expanduser("~")
texture_dir = home + "/SGANinv/SGAN/2D/training/%s/" % sub_name
data_iter = get_texture_iter(texture_dir, npx=npx, mirror=False, batch_size=batch_size,n_channel=nc)
save_name = sub_name+ "_filters%d_npx%d_%dgL_%ddL" % (dis_fn[0],npx,gen_ls, dis_ls)
load_name = None # if None, initializing network from scratch
# load_name = "braided_river_filters64_npx129_5gL_5dL_epoch92.sgan"
@classmethod
def print_info(cls):
##
# output some information
print("Learning and generating samples from zx ", cls.zx, ", which yields images of size npx ", zx_to_npx(cls.zx, cls.gen_ls))
print("Producing samples from zx_sample ", cls.zx_sample, ", which yields images of size npx ", zx_to_npx(cls.zx_sample, cls.gen_ls))
print("Saving samples and model data to file ", cls.save_name)
def data_iter(self, texture_path, batch_size, inverse=0, n_samples=20):
return get_texture_iter(texture_path,
npx=self.npx,
batch_size=batch_size,
inverse=inverse,
n_samples=n_samples)
def data_iter(self):
return get_texture_iter(self.texture_dir,
npx=self.npx,
mirror=False,
batch_size=self.batch_size)