vagan.hidden_dim = FLAGS.hidden_dim
vagan.dataname_a = FLAGS.dataname_a
vagan.dataname_b = FLAGS.dataname_b
vagan.test_vae = FLAGS.test_vae
vagan.test_gan = FLAGS.test_gan
vagan.tb = FLAGS.tb
vagan.lambda_2 = FLAGS.lambda_2
vagan.vae_ablity = FLAGS.vae_ablity
vagan.logfolder = FLAGS.logfolder
os.environ["CUDA_VISIBLE_DEVICES"] = str(FLAGS.gpu)
train_model = vagan.convMESH()
with tf.Session(config=train_model.config) as train_model.sess:
train_model.train_pre()
train_VAE(train_model)
train_metric(train_model)
'''
#_model.train_VAE()
_model.test_vae(1)
# _model.test_vae_itlp(1)
# _model.test_metric(1)
# _model.recons_error_a()
# _model.recons_error_b()
'''
vcgan.hidden_dim = FLAGS.hidden_dim
vcgan.dataname_a = FLAGS.dataname_a
#vcgan.dataname_b = FLAGS.dataname_b
#vcgan.test_vae = FLAGS.test_vae
#vcgan.test_gan = FLAGS.test_gan
vcgan.tb = FLAGS.tb
vcgan.lambda_2 = FLAGS.lambda_2
vcgan.vae_ablity = FLAGS.vae_ablity
vcgan.logfolder = FLAGS.logfolder
vcgan.featurefile_a = './Features0412.mat' # for faster initialization
os.environ["CUDA_VISIBLE_DEVICES"] = str(FLAGS.gpu)
#os.environ["CUDA_VISIBLE_DEVICES"] = "9"
train_model = vcgan.convMESH()
feature_a, neighbour1_a, degree1_a, logrmin_a, logrmax_a, smin_a, smax_a, modelnum_a, \
pointnum1_a, maxdegree1_a, L1_a, cotw1_a = utils.load_data(vcgan.featurefile_a, vcgan.resultmin, vcgan.resultmax, useS=vcgan.useS)
with tf.Session(config=train_model.config) as train_model.sess:
# train_model.train_pre()
train_model.load_test('VAE_Features0412_16_128/vae_a.model-200')
# train_vae.train_VAE(train_model)
# train_metric(train_model)
test_utils.recons_error(train_model,feature_a)
'''
#_model.train_VAE()
_model.test_vae(1)
# _model.test_vae_itlp(1)
# _model.test_metric(1)
parser.add_argument('-l', '--layers', default=1, type=int)
# parser.add_argument('-m', '--maxepoch', default=2000, type = str)
parser.add_argument('--modelfile', default='convmesh-model-2000', type=str)
args = parser.parse_args()
hidden_dim = args.hiddendim
featurefile = args.featurefile
neighbourfile = args.neighbourfile
neighbourvariable = args.neighbourvariable
# distancefile = args.distancefile
# distancevariable = args.distancevariable
lambda1 = args.l1
lambda2 = args.l2
lr = args.lr
finaldim = args.finaldim
layers = args.layers
modelfile = args.modelfile
# maxepoch = args.maxepoch
feature, logrmin, logrmax, smin, smax, pointnum = load_data(featurefile)
neighbour, degrees, maxdegree = load_neighbour(neighbourfile,
neighbourvariable, pointnum)
# geodesic_weight = load_geodesic_weight(distancefile, distancevariable, pointnum)
model = model.convMESH(pointnum, neighbour, degrees, maxdegree, hidden_dim,
finaldim, layers, lambda1, lambda2, lr)
model.individual_dimension(modelfile, feature, logrmin, logrmax, smin, smax)
model.hidden_dim = FLAGS.hidden_dim
model.dataname_a = FLAGS.dataname_a
model.dataname_b = FLAGS.dataname_b
model.test_vae = FLAGS.test_vae
model.test_gan = FLAGS.test_gan
model.tb = FLAGS.tb
model.lambda_2 = FLAGS.lambda_2
model.vae_ablity = FLAGS.vae_ablity
model.logfolder = FLAGS.logfolder
os.environ["CUDA_VISIBLE_DEVICES"] = str(FLAGS.gpu)
print(model.dataname_a)
train_model = model.convMESH()
with tf.Session(config=train_model.config) as train_model.sess:
#'''
train_model.train_pre()
train_VAE(train_model)
train_metric(train_model)
train_GAN(train_model)
'''
train_model.train_pre()
test_vae(train_model,30000)
test_metric(train_model,22000)
test_gan(train_model,30000)
'''