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,