sys.path.insert(0, './')
from GAN.models import Generator, Discriminator, Autoencoder, GAN, AEGAN
from GAN.utils import vis_grid
from GAN.utils.data import transform_skeleton, inverse_transform_skeleton
from GAN.utils.init import InitNormal
from keras.optimizers import Adam, SGD, RMSprop
if __name__ == '__main__':
nbatch = 128
nmax = nbatch * 100
npxw, npxh = 64, 128
from load import people, load_all
va_data, tr_stream, _ = people(pathfile='protocol/PPPS.txt', size=(npxw, npxh), batch_size=nbatch)
g = Generator(g_size=(8, npxh, npxw), g_nb_filters=128, g_nb_coding=500, g_scales=4, g_init=InitNormal(scale=0.002))#, g_FC=[5000])
d = Discriminator(d_size=g.g_size, d_nb_filters=128, d_scales=4, d_init=InitNormal(scale=0.002))#, d_FC=[5000])
gan = GAN(g, d)
from keras.optimizers import Adam, SGD, RMSprop
gan.fit(tr_stream,
save_dir='./samples/parsing_skeleton/',
k=1,
nbatch=nbatch,
nmax=nmax,
opt=Adam(lr=0.0002, beta_1=0.5, decay=1e-5),
transform=transform_skeleton, #opt=RMSprop(lr=0.01))
inverse_transform=inverse_transform_skeleton)
import numpy as np
from sklearn.datasets import fetch_mldata
from GAN.models import Generator, Discriminator, GAN
from GAN.utils import vis_grid
from GAN.utils.data import transform, inverse_transform
from GAN.utils.init import InitNormal
if __name__ == '__main__':
nbatch = 128
nmax = nbatch * 100
npxw, npxh = 64, 128
from load import people
va_data, tr_stream, _ = people(pathfile='protocol/cuhk01-train.txt',
size=(npxw, npxh),
batch_size=nbatch)
g = Generator(g_size=(3, npxh, npxw),
g_nb_filters=128,
g_nb_coding=200,
g_scales=4,
g_init=InitNormal(scale=0.002))
d = Discriminator(d_size=g.g_size,
d_nb_filters=128,
d_scales=4,
d_init=InitNormal(scale=0.002))
gan = GAN(g, d)
from keras.optimizers import Adam, SGD, RMSprop
gan.fit(tr_stream,